system

Abstract

The system according to this embodiment aims to support working parents in efficiently balancing housework and childcare. [Solution] The system according to the embodiment comprises a collection unit, an integration unit, an allocation unit, a priority determination unit, and a suggestion unit. The collection unit collects the schedules of each household member. The integration unit integrates the schedules collected by the collection unit. The allocation unit allocates household chores and childcare tasks based on the schedules integrated by the integration unit. The priority determination unit determines the priority of the tasks allocated by the allocation unit. The suggestion unit suggests simple recipes based on the priority determined by the priority determination 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 performed by at least one processor, the method including the steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of the chatbot's character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance that responds 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, insufficient support has been provided for working parent couples to efficiently balance housework and childcare, and there is room for improvement.

[0005] The system according to the embodiment aims to support working parent couples to efficiently balance housework and childcare.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a collection unit, an integration unit, an allocation unit, a priority determination unit, and a suggestion unit. The collection unit collects the schedules of each household member. The integration unit integrates the schedules collected by the collection unit. The allocation unit allocates household chores and childcare tasks based on the schedules integrated by the integration unit. The priority determination unit determines the priority of the tasks allocated by the allocation unit. The suggestion unit suggests simple recipes based on the priorities determined by the priority determination unit. [Effects of the Invention]

[0007] The system according to this embodiment can support working parents in efficiently balancing housework and childcare. [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 labeled communication I / F (Interface) is an interface including a communication processor, an antenna, and the like. The communication I / F controls communication between multiple computers. Examples of communication standards applicable to the communication I / F include wireless communication standards such as 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) An AI agent system according to an embodiment of the present invention is a system that supports working parents in efficiently balancing housework and childcare. This AI agent system integrates the schedules of each family member and creates an optimal timetable for housework and childcare. Next, it helps to prioritize tasks and carry them out efficiently. For example, the AI ​​agent system considers the schedules of all family members, decides who is in charge of which task, and suggests simple recipes to streamline meal preparation. This makes it easier for working parents to balance work and family life and to enjoy the time they spend with their families to the fullest. First, the AI ​​agent system collects and integrates the schedules of each family member. Next, it assigns housework and childcare tasks based on the integrated schedule. Furthermore, it helps to prioritize tasks and carry them out efficiently. Finally, it suggests simple recipes to streamline meal preparation. In this way, the AI ​​agent system can support working parents in efficiently balancing housework and childcare.

[0029] The AI ​​agent system according to the embodiment comprises a collection unit, an integration unit, an allocation unit, a priority determination unit, and a suggestion unit. The collection unit collects the schedules of each household member. Each household member's schedule includes, but is not limited to, work appointments, school appointments, personal appointments, etc. The collection unit obtains schedule information from, for example, a calendar application or a schedule management application. The collection unit can also collect schedule information manually entered by household members. Furthermore, the collection unit can also collect schedule information by analyzing voice input or text input from household members. For example, the collection unit obtains schedule information from a calendar application via an API. The collection unit stores schedule information manually entered by household members in a database. The collection unit analyzes voice input or text input using natural language processing technology and extracts schedule information. The integration unit integrates the schedules collected by the collection unit. Integration is performed, for example, by processing schedule overlaps and assigning priorities, but is not limited to such examples. The integration unit detects schedule overlaps and integrates the overlapping schedules. Furthermore, the integration unit can create an efficient schedule by prioritizing schedules. In addition, the integration unit can reflect schedule changes and additions in real time. For example, the integration unit can detect schedule overlaps and merge overlapping schedules. The integration unit creates an efficient schedule by prioritizing schedules. The integration unit reflects schedule changes and additions in real time. The allocation unit allocates household and childcare tasks based on the schedule integrated by the integration unit. Allocation is done, for example, based on each family member's skills and available time, but is not limited to such examples. The allocation unit allocates the most suitable tasks, for example, by considering each family member's skills and available time. The allocation unit can also allocate tasks based on their importance and urgency. Furthermore, the allocation unit can monitor the progress of tasks and adjust task allocation as needed.For example, the assignment unit assigns the most suitable tasks to each household member, taking into account their skills and available time. The assignment unit assigns tasks based on their importance and urgency. The assignment unit monitors the progress of the tasks and adjusts the assignments as needed. The prioritization unit determines the priority of the tasks assigned by the assignment unit. Prioritization is determined, for example, based on the importance and urgency of the tasks, but is not limited to such examples. The prioritization unit determines the optimal priority, for example, taking into account the importance and urgency of the tasks. The prioritization unit can also determine priority by considering the dependencies between tasks. Furthermore, the prioritization unit can monitor the progress of the tasks and adjust the priority as needed. For example, the prioritization unit determines the optimal priority, taking into account the importance and urgency of the tasks. The prioritization unit determines priority by considering the dependencies between tasks. The prioritization unit monitors the progress of the tasks and adjusts the priority as needed. The suggestion unit suggests a simple recipe based on the priority determined by the prioritization unit. The suggestions are based, for example, on cooking time or the type and number of ingredients, but are not limited to such examples. The suggestion unit may, for example, suggest recipes with short cooking times. It may also suggest recipes with a small number or variety of ingredients. Furthermore, the suggestion unit may suggest recipes while considering the dietary preferences and allergy information of family members. For example, the suggestion unit may suggest recipes with short cooking times. The suggestion unit may suggest recipes with a small number or variety of ingredients. The suggestion unit may suggest recipes while considering the dietary preferences and allergy information of family members. In this way, the AI ​​agent system according to the embodiment can support working parents in efficiently balancing housework and childcare.

[0030] The data collection unit collects the schedules of each household member. These schedules may include, but are not limited to, work appointments, school appointments, and personal appointments. The unit obtains schedule information from sources such as calendar applications and schedule management applications. Specifically, it can automatically retrieve schedule information through the API of the calendar application used by each household member, eliminating the need for them to manually enter their schedules. The unit can also collect schedule information manually entered by household members. For example, when a household member enters their schedule using a smartphone or computer, the unit saves that information to the database in real time. Furthermore, the unit can collect schedule information by analyzing household members' voice and text input. For instance, if a household member tells a voice assistant, "I have a meeting at 3 PM tomorrow," the unit analyzes the voice data using natural language processing technology and extracts the schedule information. Similarly, for text input, the unit analyzes the text entered by the household member in a chat or memo application and extracts the schedule information. This allows the unit to efficiently collect schedule information by accommodating various input methods used by household members.

[0031] The integration unit consolidates the schedules collected by the collection unit. This consolidation is performed, for example, by processing and prioritizing schedule overlaps, but is not limited to these examples. Specifically, the integration unit centrally manages the collected schedule information and detects overlapping schedules. For example, if multiple appointments overlap at the same time, the integration unit detects the overlap and notifies the family members. The integration unit can also create efficient schedules by prioritizing them. For example, it can prioritize high-priority appointments such as work or school appointments, and then place personal appointments afterward. Furthermore, the integration unit can reflect schedule changes and additions in real time. For example, if a family member adds a new appointment or changes an existing one, this information is immediately reflected in the integration unit. This ensures that the integration unit always has and provides the family members with the most up-to-date schedule information.

[0032] The allocation unit assigns household and childcare tasks based on the schedule integrated by the integration unit. Allocation is based, for example, on each family member's skills and available time, but is not limited to such examples. Specifically, the allocation unit considers each family member's skills and available time to assign the most suitable tasks. For example, it might assign meal preparation to a member skilled in cooking, or cleaning to a member skilled in cleaning. The allocation unit can also assign tasks based on their importance and urgency. For example, it might prioritize assigning urgent or important tasks, and then assign other tasks later. Furthermore, the allocation unit can monitor task progress and adjust assignments as needed. For example, if a member completes a task earlier than planned, the allocation unit assigns them a new task. If a member is unable to complete a task, the allocation unit reassigns it to another member. This allows the allocation unit to efficiently and flexibly assign tasks, optimizing household task management.

[0033] The priority determination unit determines the priority of tasks assigned by the allocation unit. Priority is determined based on, for example, the importance or urgency of the tasks, but is not limited to such examples. Specifically, the priority determination unit considers the importance and urgency of tasks to determine the optimal priority. For example, it prioritizes tasks with high urgency or high importance, and processes other tasks afterward. The priority determination unit can also determine priority by considering the dependencies between tasks. For example, if one task depends on the completion of another task, it considers these dependencies when determining priority. Furthermore, the priority determination unit can monitor the progress of tasks and adjust priorities as needed. For example, if a task is behind schedule, it raises its priority to ensure early completion. In this way, the priority determination unit can efficiently and flexibly determine task priorities and optimize task management within the home.

[0034] The suggestion department proposes simple recipes based on the priorities determined by the priority determination department. Suggestions are based on, for example, cooking time or the type and number of ingredients, but are not limited to these examples. Specifically, the suggestion department proposes recipes with short cooking times. For example, it might propose a recipe that can be prepared in under 15 minutes for busy family members. The suggestion department can also propose recipes with fewer types and numbers of ingredients. For example, it might propose a recipe that can be easily made using ingredients already in the refrigerator. Furthermore, the suggestion department can propose recipes that take into account the dietary preferences and allergy information of family members. For example, if one family member has an allergy, it will propose a recipe that avoids that allergen. It can also propose recipes that use favorite dishes and ingredients to suit the preferences of family members. In this way, the suggestion department can provide recipes tailored to the needs of family members, supporting efficient and healthy eating.

[0035] The planning department can determine who is responsible for which task by considering the schedules of all family members. For example, the planning department can determine the optimal task assignment by considering the work hours, school hours, and personal appointments of all family members. For example, the planning department can analyze the schedules of all family members and identify each family member's free time. For example, the planning department can fairly assign tasks to family members by considering their schedules. This allows for efficient task allocation while taking into account the schedules of all family members.

[0036] The suggestion department can streamline meal preparation by proposing simple recipes. For example, the suggestion department can propose recipes with short cooking times, recipes with a limited variety or number of ingredients, and recipes that take into account the dietary preferences and allergy information of family members. This makes meal preparation more efficient.

[0037] The priority determination unit can determine the priority of tasks. For example, the priority determination unit determines the optimal priority by considering the importance and urgency of tasks. For example, the priority determination unit determines the priority by considering the dependencies between tasks. For example, the priority determination unit monitors the progress of tasks and adjusts the priority as needed. This allows for efficient determination of task priorities. Some or all of the above processes in the priority determination unit may be performed using, for example, generative AI, or without generative AI. For example, the priority determination unit can determine priorities using a generative AI model that takes the importance and urgency of tasks as input and outputs priorities.

[0038] The integration unit can integrate the schedules of each household member. The integration unit can, for example, detect schedule overlaps and merge overlapping schedules. The integration unit can, for example, create an efficient schedule by prioritizing schedules. The integration unit can, for example, reflect schedule changes and additions in real time. This allows for the efficient integration of each household member's schedule. Some or all of the above processes in the integration unit may be performed using, for example, generative AI, or not using generative AI. For example, the integration unit can integrate schedules using a generative AI model that detects schedule overlaps and merges overlapping schedules.

[0039] The data collection unit can analyze each household member's past schedule history and select the optimal data collection method. For example, the data collection unit can analyze each household member's past schedule history and select the most efficient data collection method. For example, the data collection unit can adjust the data collection frequency based on each household member's past schedule history. For example, the data collection unit can optimize the data collection timing by referring to each household member's past schedule history. This allows the optimal data collection method to be selected based on past schedule history. Some or all of the above processes in the data collection unit may be performed using, for example, a generation AI, or without a generation AI. For example, the data collection unit can input past schedule history data into a generation AI and have the generation AI select the optimal data collection method.

[0040] The collection unit can filter schedules based on the current activities and interests of family members when collecting them. For example, the collection unit considers the current activities of family members and prioritizes collecting schedules that are highly relevant. For example, the collection unit filters the schedules to be collected based on the interests of family members. For example, the collection unit combines the current activities and interests of family members to collect the most suitable schedules. This allows schedules to be filtered based on current activities and interests. Some or all of the above processing in the collection unit may be performed using, for example, a generative AI, or without a generative AI. For example, the collection unit can input data on the current activities of family members into a generative AI and have the generative AI perform schedule filtering.

[0041] The collection unit can prioritize collecting highly relevant schedules by considering the geographical location information of family members when collecting schedules. For example, the collection unit can prioritize collecting events and tasks taking place nearby based on the current location of family members. For example, the collection unit can collect schedules that minimize travel time by considering the geographical location information of family members. For example, the collection unit can filter highly relevant schedules based on the geographical location information of family members. This allows for the collection of highly relevant schedules by considering geographical location information. Some or all of the above processing in the collection unit may be performed using, for example, a generative AI, or without a generative AI. For example, the collection unit can input the geographical location data of family members into a generative AI and have the generative AI perform schedule filtering.

[0042] The collection unit can analyze the social media activities of family members and collect relevant schedules when collecting schedules. For example, the collection unit can analyze the social media activities of family members and collect relevant events and tasks. For example, the collection unit can prioritize collecting schedules of high interest based on the social media activities of family members. For example, the collection unit can collect the most suitable schedules based on the social media activities of family members. This allows for the collection of relevant schedules based on social media activities. Some or all of the above processing in the collection unit may be performed using, for example, a generative AI, or without a generative AI. For example, the collection unit can input the social media data of family members into a generative AI and have the generative AI perform the collection of relevant schedules.

[0043] The integration unit can adjust the level of detail of the integration based on the importance of each household member's schedule during the integration process. For example, the integration unit considers the importance of each household member's schedule and prioritizes integrating important schedules. For example, the integration unit adjusts the level of detail of the integration based on the importance of each household member's schedule. For example, the integration unit selects the optimal integration method based on the importance of each household member's schedule. This allows the level of detail of the integration to be adjusted based on the importance of the schedules. Some or all of the above processes in the integration unit may be performed using, for example, a generating AI, or without using a generating AI. For example, the integration unit can input schedule importance data into a generating AI and have the generating AI perform the adjustment of the level of detail of the integration.

[0044] The integration unit can apply different integration algorithms depending on the schedule category during integration. For example, the integration unit applies different integration algorithms depending on categories such as housework, childcare, and work. For example, the integration unit selects the optimal integration algorithm based on the schedule category. For example, the integration unit applies different integration methods for each schedule category. This allows the optimal integration algorithm to be applied according to the schedule category. Some or all of the above processing in the integration unit may be performed using, for example, a generative AI, or without a generative AI. For example, the integration unit can input schedule category data into a generative AI and have the generative AI execute the application of the integration algorithm.

[0045] The integration unit can determine the priority of integration based on the submission timing of the schedules during the integration process. For example, the integration unit considers the submission timing of the schedules and prioritizes the integration of schedules submitted earlier. For example, the integration unit determines the priority of integration based on the submission timing of the schedules. For example, the integration unit selects the optimal integration method based on the submission timing of the schedules. This allows the integration priority to be determined based on the submission timing of the schedules. Some or all of the above processes in the integration unit may be performed using, for example, a generative AI, or without using a generative AI. For example, the integration unit can input schedule submission timing data into a generative AI and have the generative AI perform the determination of the integration priority.

[0046] The integration unit can adjust the order of integration based on the relevance of the schedules during integration. For example, the integration unit considers the relevance of the schedules and prioritizes integrating schedules with high relevance. For example, the integration unit adjusts the order of integration based on the relevance of the schedules. For example, the integration unit selects the optimal integration method based on the relevance of the schedules. This allows the order of integration to be adjusted based on the relevance of the schedules. Some or all of the above processing in the integration unit may be performed using, for example, a generative AI, or without a generative AI. For example, the integration unit can input schedule relevance data into a generative AI and have the generative AI perform the adjustment of the order of integration.

[0047] The assignment unit can optimally assign tasks based on the skills and abilities of each household member during the assignment process. For example, the assignment unit considers the skills and abilities of each household member and assigns the most suitable tasks. For example, the assignment unit adjusts the task assignment based on the skills and abilities of each household member. For example, the assignment unit selects the optimal assignment method based on the skills and abilities of each household member. This allows for the optimal assignment of tasks based on the skills and abilities of each household member. Some or all of the above-described processes in the assignment unit may be performed using, for example, a generative AI, or without using a generative AI. For example, the assignment unit can input skill data for each household member into a generative AI and have the generative AI perform the task assignment.

[0048] The allocation unit can apply different allocation algorithms depending on the importance of the tasks during allocation. For example, the allocation unit considers the importance of the tasks and prioritizes the allocation of important tasks. For example, the allocation unit adjusts the allocation algorithm based on the importance of the tasks. For example, the allocation unit selects the optimal allocation algorithm based on the importance of the tasks. This allows the optimal allocation algorithm to be applied according to the importance of the tasks. Some or all of the above processes in the allocation unit may be performed using, for example, a generative AI, or without a generative AI. For example, the allocation unit can input task importance data into a generative AI and have the generative AI execute the application of the allocation algorithm.

[0049] The allocation unit can determine the priority of tasks based on their submission timing during the allocation process. For example, the allocation unit may prioritize tasks submitted earlier, taking into account their submission timing. The allocation unit may determine the priority of tasks based on their submission timing. The allocation unit may select the optimal allocation method based on their submission timing. This allows the allocation priority to be determined based on the task submission timing. Some or all of the above-described processes in the allocation unit may be performed using, for example, a generative AI, or without using a generative AI. For example, the allocation unit may input task submission timing data into a generative AI and have the generative AI determine the allocation priority.

[0050] The allocation unit can adjust the allocation order based on the relevance of tasks during allocation. For example, the allocation unit considers the relevance of tasks and prioritizes allocating tasks with high relevance. For example, the allocation unit adjusts the allocation order based on the relevance of tasks. For example, the allocation unit selects the optimal allocation method based on the relevance of tasks. This allows the allocation order to be adjusted based on the relevance of tasks. Some or all of the above processing in the allocation unit may be performed using, for example, a generative AI, or without a generative AI. For example, the allocation unit can input task relevance data into a generative AI and have the generative AI perform the adjustment of the allocation order.

[0051] The priority determination unit can determine the optimal priority by referring to the past task history of each household member when determining priorities. For example, the priority determination unit refers to the past task history of each household member to determine the optimal priority. For example, the priority determination unit adjusts the priority based on the past task history of each household member. For example, the priority determination unit selects the optimal priority determination method by referring to the past task history of each household member. This makes it possible to determine the optimal priority based on past task history. Some or all of the above processing in the priority determination unit may be performed using, for example, a generation AI, or without using a generation AI. For example, the priority determination unit can input past task history data into a generation AI and have the generation AI perform the priority determination.

[0052] The priority determination unit can apply different priority determination algorithms depending on the importance of the tasks when determining priorities. For example, the priority determination unit considers the importance of the tasks and prioritizes important tasks. For example, the priority determination unit adjusts the priority determination algorithm based on the importance of the tasks. For example, the priority determination unit selects the optimal priority determination algorithm based on the importance of the tasks. This allows the optimal priority determination algorithm to be applied according to the importance of the tasks. Some or all of the above processes in the priority determination unit may be performed using, for example, a generative AI, or without using a generative AI. For example, the priority determination unit can input task importance data into a generative AI and have the generative AI execute the application of the priority determination algorithm.

[0053] The priority determination unit can determine priorities based on the task submission timing. For example, the priority determination unit considers the task submission timing and prioritizes tasks submitted earlier. For example, the priority determination unit determines priorities based on the task submission timing. For example, the priority determination unit selects the optimal priority determination method based on the task submission timing. This allows priorities to be determined based on the task submission timing. Some or all of the above processing in the priority determination unit may be performed using, for example, a generation AI, or without a generation AI. For example, the priority determination unit can input task submission timing data into a generation AI and have the generation AI perform the priority determination.

[0054] The priority determination unit can adjust the order of priorities based on the relevance of tasks when determining priorities. For example, the priority determination unit considers the relevance of tasks and prioritizes tasks with high relevance. For example, the priority determination unit adjusts the order of priorities based on the relevance of tasks. For example, the priority determination unit selects the optimal priority determination method based on the relevance of tasks. This allows the order of priorities to be adjusted based on the relevance of tasks. Some or all of the above processing in the priority determination unit may be performed using, for example, a generative AI, or without using a generative AI. For example, the priority determination unit can input task relevance data into a generative AI and have the generative AI perform the adjustment of the order of priorities.

[0055] The suggestion unit can propose the most suitable recipe based on each family member's dietary preferences. For example, the suggestion unit considers each family member's dietary preferences and proposes the most suitable recipe. For example, the suggestion unit adjusts the recipe suggestions based on each family member's dietary preferences. For example, the suggestion unit selects the most suitable recipe based on each family member's dietary preferences. This allows the suggestion unit to propose the most suitable recipe based on each family member's dietary preferences. Some or all of the above processes in the suggestion unit may be performed using, for example, a generative AI, or without using a generative AI. For example, the suggestion unit can input each family member's dietary preference data into a generative AI and have the generative AI execute the recipe suggestion.

[0056] The suggestion unit can propose different recipes depending on the availability of ingredients at the time of proposal. For example, the suggestion unit may consider the availability of ingredients and propose a recipe that uses ingredients that are in short supply. For example, the suggestion unit may adjust the recipe suggestions based on the availability of ingredients. For example, the suggestion unit may select the optimal recipe based on the availability of ingredients. This allows the suggestion unit to propose the optimal recipe according to the availability of ingredients. Some or all of the above processes in the suggestion unit may be performed using, for example, a generative AI, or without using a generative AI. For example, the suggestion unit may input ingredient inventory data into a generative AI and have the generative AI execute the recipe suggestions.

[0057] The proposal department can determine the priority of proposals based on the submission timing of the recipes at the time of proposal. For example, the proposal department will consider the submission timing of the recipes and prioritize the recipes that are submitted earlier. For example, the proposal department will determine the priority of proposals based on the submission timing of the recipes. For example, the proposal department will select the optimal proposal method based on the submission timing of the recipes. This allows the proposal department to determine the priority of proposals based on the submission timing of the recipes. Some or all of the above processes in the proposal department may be performed using, for example, a generative AI, or not using a generative AI. For example, the proposal department can input recipe submission timing data into a generative AI and have the generative AI perform the determination of proposal priority.

[0058] The suggestion unit can adjust the order of suggestions based on the relevance of the recipes when making suggestions. For example, the suggestion unit considers the relevance of the recipes and prioritizes suggesting recipes with high relevance. For example, the suggestion unit adjusts the order of suggestions based on the relevance of the recipes. For example, the suggestion unit selects the optimal suggestion method based on the relevance of the recipes. This allows the suggestion unit to adjust the order of suggestions based on the relevance of the recipes. Some or all of the above processes in the suggestion unit may be performed using, for example, a generative AI, or without using a generative AI. For example, the suggestion unit can input recipe relevance data into a generative AI and have the generative AI perform the adjustment of the suggestion order.

[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 suggestion department can propose recipes that take into account the health status of each family member. For example, it can suggest nutritionally balanced recipes based on the health status of each family member. It can also suggest allergen-free recipes that take into account the allergy information of each family member. Furthermore, it can suggest appropriate recipes according to the health goals of each family member (e.g., weight loss or muscle building).

[0061] The integration unit can utilize a Geographic Information System (GIS) to optimize the travel routes of household members. For example, the integration unit can propose the optimal travel route considering the current location and destination of each household member. The integration unit can also acquire real-time traffic and weather information and adjust the travel route accordingly. Furthermore, the integration unit can propose the optimal mode of transportation by considering public transport operation information.

[0062] The data collection unit can collect vital data from household members and use it for health management. For example, the unit can collect vital data such as heart rate, blood pressure, and body temperature from household members. The unit can also analyze the vital data and issue alerts if abnormalities are detected. Furthermore, based on the vital data, the unit can monitor changes in health status and suggest a visit to a medical institution if necessary.

[0063] The prioritization unit can determine task priorities by considering the long-term goals of family members. For example, it can prioritize tasks related to family members' career goals or learning objectives. It can also prioritize tasks related to health management based on family members' health goals. Furthermore, it can prioritize family activities based on family members' goals of valuing time with family.

[0064] The integration department can coordinate schedules, taking into account the hobbies and interests of family members. For example, it can incorporate relevant events and activities into the schedule based on the hobbies and interests of family members. The integration department can also adjust schedules so that family members can participate in activities with other members who share the same interests. Furthermore, the integration department can plan weekends and holidays based on the hobbies and interests of family members.

[0065] The following briefly describes the processing flow for example form 1.

[0066] Step 1: The collection unit collects the schedules of each household member. Each household member's schedule includes, for example, work appointments, school appointments, and personal appointments. The collection unit obtains schedule information from calendar applications and schedule management applications. It can also collect schedule information manually entered by household members, or by analyzing voice input or text input. Step 2: The integration unit integrates the schedules collected by the collection unit. Integration is performed by processing and prioritizing schedule overlaps. The integration unit detects schedule overlaps and merges the overlapping schedules. It also reflects schedule changes and additions in real time. Step 3: The allocation department assigns household and childcare tasks based on the schedule integrated by the integration department. Assignments are made based on each family member's skills, available time, and the importance and urgency of the tasks. The allocation department monitors the progress of the tasks and adjusts the task assignments as needed. Step 4: The priority determination unit determines the priority of the tasks assigned by the allocation unit. Priority is determined by considering the importance, urgency, and dependencies of the tasks. The priority determination unit monitors the progress of the tasks and adjusts the priority as needed. Step 5: The proposal team proposes simple recipes based on the priorities determined by the priority determination team. The proposals take into account cooking time, types and quantities of ingredients, and the dietary preferences and allergy information of the household members.

[0067] (Example of form 2) An AI agent system according to an embodiment of the present invention is a system that supports working parents in efficiently balancing housework and childcare. This AI agent system integrates the schedules of each family member and creates an optimal timetable for housework and childcare. Next, it helps to prioritize tasks and carry them out efficiently. For example, the AI ​​agent system considers the schedules of all family members, decides who is in charge of which task, and suggests simple recipes to streamline meal preparation. This makes it easier for working parents to balance work and family life and to enjoy the time they spend with their families to the fullest. First, the AI ​​agent system collects and integrates the schedules of each family member. Next, it assigns housework and childcare tasks based on the integrated schedule. Furthermore, it helps to prioritize tasks and carry them out efficiently. Finally, it suggests simple recipes to streamline meal preparation. In this way, the AI ​​agent system can support working parents in efficiently balancing housework and childcare.

[0068] The AI ​​agent system according to the embodiment comprises a collection unit, an integration unit, an allocation unit, a priority determination unit, and a suggestion unit. The collection unit collects the schedules of each household member. Each household member's schedule includes, but is not limited to, work appointments, school appointments, personal appointments, etc. The collection unit obtains schedule information from, for example, a calendar application or a schedule management application. The collection unit can also collect schedule information manually entered by household members. Furthermore, the collection unit can also collect schedule information by analyzing voice input or text input from household members. For example, the collection unit obtains schedule information from a calendar application via an API. The collection unit stores schedule information manually entered by household members in a database. The collection unit analyzes voice input or text input using natural language processing technology and extracts schedule information. The integration unit integrates the schedules collected by the collection unit. Integration is performed, for example, by processing schedule overlaps and assigning priorities, but is not limited to such examples. The integration unit detects schedule overlaps and integrates the overlapping schedules. Furthermore, the integration unit can create an efficient schedule by prioritizing schedules. In addition, the integration unit can reflect schedule changes and additions in real time. For example, the integration unit can detect schedule overlaps and merge overlapping schedules. The integration unit creates an efficient schedule by prioritizing schedules. The integration unit reflects schedule changes and additions in real time. The allocation unit allocates household and childcare tasks based on the schedule integrated by the integration unit. Allocation is done, for example, based on each family member's skills and available time, but is not limited to such examples. The allocation unit allocates the most suitable tasks, for example, by considering each family member's skills and available time. The allocation unit can also allocate tasks based on their importance and urgency. Furthermore, the allocation unit can monitor the progress of tasks and adjust task allocation as needed.For example, the assignment unit assigns the most suitable tasks to each household member, taking into account their skills and available time. The assignment unit assigns tasks based on their importance and urgency. The assignment unit monitors the progress of the tasks and adjusts the assignments as needed. The prioritization unit determines the priority of the tasks assigned by the assignment unit. Prioritization is determined, for example, based on the importance and urgency of the tasks, but is not limited to such examples. The prioritization unit determines the optimal priority, for example, taking into account the importance and urgency of the tasks. The prioritization unit can also determine priority by considering the dependencies between tasks. Furthermore, the prioritization unit can monitor the progress of the tasks and adjust the priority as needed. For example, the prioritization unit determines the optimal priority, taking into account the importance and urgency of the tasks. The prioritization unit determines priority by considering the dependencies between tasks. The prioritization unit monitors the progress of the tasks and adjusts the priority as needed. The suggestion unit suggests a simple recipe based on the priority determined by the prioritization unit. The suggestions are based, for example, on cooking time or the type and number of ingredients, but are not limited to such examples. The suggestion unit may, for example, suggest recipes with short cooking times. It may also suggest recipes with a small number or variety of ingredients. Furthermore, the suggestion unit may suggest recipes while considering the dietary preferences and allergy information of family members. For example, the suggestion unit may suggest recipes with short cooking times. The suggestion unit may suggest recipes with a small number or variety of ingredients. The suggestion unit may suggest recipes while considering the dietary preferences and allergy information of family members. In this way, the AI ​​agent system according to the embodiment can support working parents in efficiently balancing housework and childcare.

[0069] The data collection unit collects the schedules of each household member. These schedules may include, but are not limited to, work appointments, school appointments, and personal appointments. The unit obtains schedule information from sources such as calendar applications and schedule management applications. Specifically, it can automatically retrieve schedule information through the API of the calendar application used by each household member, eliminating the need for them to manually enter their schedules. The unit can also collect schedule information manually entered by household members. For example, when a household member enters their schedule using a smartphone or computer, the unit saves that information to the database in real time. Furthermore, the unit can collect schedule information by analyzing household members' voice and text input. For instance, if a household member tells a voice assistant, "I have a meeting at 3 PM tomorrow," the unit analyzes the voice data using natural language processing technology and extracts the schedule information. Similarly, for text input, the unit analyzes the text entered by the household member in a chat or memo application and extracts the schedule information. This allows the unit to efficiently collect schedule information by accommodating various input methods used by household members.

[0070] The integration unit consolidates the schedules collected by the collection unit. This consolidation is performed, for example, by processing and prioritizing schedule overlaps, but is not limited to these examples. Specifically, the integration unit centrally manages the collected schedule information and detects overlapping schedules. For example, if multiple appointments overlap at the same time, the integration unit detects the overlap and notifies the family members. The integration unit can also create efficient schedules by prioritizing them. For example, it can prioritize high-priority appointments such as work or school appointments, and then place personal appointments afterward. Furthermore, the integration unit can reflect schedule changes and additions in real time. For example, if a family member adds a new appointment or changes an existing one, this information is immediately reflected in the integration unit. This ensures that the integration unit always has and provides the family members with the most up-to-date schedule information.

[0071] The allocation unit assigns household and childcare tasks based on the schedule integrated by the integration unit. Allocation is based, for example, on each family member's skills and available time, but is not limited to such examples. Specifically, the allocation unit considers each family member's skills and available time to assign the most suitable tasks. For example, it might assign meal preparation to a member skilled in cooking, or cleaning to a member skilled in cleaning. The allocation unit can also assign tasks based on their importance and urgency. For example, it might prioritize assigning urgent or important tasks, and then assign other tasks later. Furthermore, the allocation unit can monitor task progress and adjust assignments as needed. For example, if a member completes a task earlier than planned, the allocation unit assigns them a new task. If a member is unable to complete a task, the allocation unit reassigns it to another member. This allows the allocation unit to efficiently and flexibly assign tasks, optimizing household task management.

[0072] The priority determination unit determines the priority of tasks assigned by the allocation unit. Priority is determined based on, for example, the importance or urgency of the tasks, but is not limited to such examples. Specifically, the priority determination unit considers the importance and urgency of tasks to determine the optimal priority. For example, it prioritizes tasks with high urgency or high importance, and processes other tasks afterward. The priority determination unit can also determine priority by considering the dependencies between tasks. For example, if one task depends on the completion of another task, it considers these dependencies when determining priority. Furthermore, the priority determination unit can monitor the progress of tasks and adjust priorities as needed. For example, if a task is behind schedule, it raises its priority to ensure early completion. In this way, the priority determination unit can efficiently and flexibly determine task priorities and optimize task management within the home.

[0073] The suggestion department proposes simple recipes based on the priorities determined by the priority determination department. Suggestions are based on, for example, cooking time or the type and number of ingredients, but are not limited to these examples. Specifically, the suggestion department proposes recipes with short cooking times. For example, it might propose a recipe that can be prepared in under 15 minutes for busy family members. The suggestion department can also propose recipes with fewer types and numbers of ingredients. For example, it might propose a recipe that can be easily made using ingredients already in the refrigerator. Furthermore, the suggestion department can propose recipes that take into account the dietary preferences and allergy information of family members. For example, if one family member has an allergy, it will propose a recipe that avoids that allergen. It can also propose recipes that use favorite dishes and ingredients to suit the preferences of family members. In this way, the suggestion department can provide recipes tailored to the needs of family members, supporting efficient and healthy eating.

[0074] The planning department can determine who is responsible for which task by considering the schedules of all family members. For example, the planning department can determine the optimal task assignment by considering the work hours, school hours, and personal appointments of all family members. For example, the planning department can analyze the schedules of all family members and identify each family member's free time. For example, the planning department can fairly assign tasks to family members by considering their schedules. This allows for efficient task allocation while taking into account the schedules of all family members.

[0075] The suggestion department can streamline meal preparation by proposing simple recipes. For example, the suggestion department can propose recipes with short cooking times, recipes with a limited variety or number of ingredients, and recipes that take into account the dietary preferences and allergy information of family members. This makes meal preparation more efficient.

[0076] The priority determination unit can determine the priority of tasks. For example, the priority determination unit determines the optimal priority by considering the importance and urgency of tasks. For example, the priority determination unit determines the priority by considering the dependencies between tasks. For example, the priority determination unit monitors the progress of tasks and adjusts the priority as needed. This allows for efficient determination of task priorities. Some or all of the above processes in the priority determination unit may be performed using, for example, generative AI, or without generative AI. For example, the priority determination unit can determine priorities using a generative AI model that takes the importance and urgency of tasks as input and outputs priorities.

[0077] The integration unit can integrate the schedules of each household member. The integration unit can, for example, detect schedule overlaps and merge overlapping schedules. The integration unit can, for example, create an efficient schedule by prioritizing schedules. The integration unit can, for example, reflect schedule changes and additions in real time. This allows for the efficient integration of each household member's schedule. Some or all of the above processes in the integration unit may be performed using, for example, generative AI, or not using generative AI. For example, the integration unit can integrate schedules using a generative AI model that detects schedule overlaps and merges overlapping schedules.

[0078] The data collection unit can estimate the user's emotions and adjust the timing of schedule collection based on the estimated emotions. For example, if the user is stressed, the data collection unit can delay the collection timing to collect the schedule when the user is relaxed. For example, if the user is busy, the data collection unit can shorten the collection timing to collect the schedule quickly. For example, if the user is relaxed, the data collection unit can collect the schedule at the normal timing. This allows the timing of schedule collection to be adjusted according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using a generative AI, or not using a generative AI. For example, the data collection unit can input the user's facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0079] The data collection unit can analyze each household member's past schedule history and select the optimal data collection method. For example, the data collection unit can analyze each household member's past schedule history and select the most efficient data collection method. For example, the data collection unit can adjust the data collection frequency based on each household member's past schedule history. For example, the data collection unit can optimize the data collection timing by referring to each household member's past schedule history. This allows the optimal data collection method to be selected based on past schedule history. Some or all of the above processes in the data collection unit may be performed using, for example, a generation AI, or without a generation AI. For example, the data collection unit can input past schedule history data into a generation AI and have the generation AI select the optimal data collection method.

[0080] The collection unit can filter schedules based on the current activities and interests of family members when collecting them. For example, the collection unit considers the current activities of family members and prioritizes collecting schedules that are highly relevant. For example, the collection unit filters the schedules to be collected based on the interests of family members. For example, the collection unit combines the current activities and interests of family members to collect the most suitable schedules. This allows schedules to be filtered based on current activities and interests. Some or all of the above processing in the collection unit may be performed using, for example, a generative AI, or without a generative AI. For example, the collection unit can input data on the current activities of family members into a generative AI and have the generative AI perform schedule filtering.

[0081] The data collection unit can estimate the user's emotions and determine the priority of schedules to collect based on the estimated emotions. For example, if the user is stressed, the data collection unit will postpone less important schedules. For example, if the user is relaxed, the data collection unit will collect schedules with normal priority. For example, if the user is in a hurry, the data collection unit will prioritize collecting high-priority schedules. This allows schedule prioritization to be determined according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using a generative AI, or not using a generative AI. For example, the data collection unit can input user facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0082] The collection unit can prioritize collecting highly relevant schedules by considering the geographical location information of family members when collecting schedules. For example, the collection unit can prioritize collecting events and tasks taking place nearby based on the current location of family members. For example, the collection unit can collect schedules that minimize travel time by considering the geographical location information of family members. For example, the collection unit can filter highly relevant schedules based on the geographical location information of family members. This allows for the collection of highly relevant schedules by considering geographical location information. Some or all of the above processing in the collection unit may be performed using, for example, a generative AI, or without a generative AI. For example, the collection unit can input the geographical location data of family members into a generative AI and have the generative AI perform schedule filtering.

[0083] The collection unit can analyze the social media activities of family members and collect relevant schedules when collecting schedules. For example, the collection unit can analyze the social media activities of family members and collect relevant events and tasks. For example, the collection unit can prioritize collecting schedules of high interest based on the social media activities of family members. For example, the collection unit can collect the most suitable schedules based on the social media activities of family members. This allows for the collection of relevant schedules based on social media activities. Some or all of the above processing in the collection unit may be performed using, for example, a generative AI, or without a generative AI. For example, the collection unit can input the social media data of family members into a generative AI and have the generative AI perform the collection of relevant schedules.

[0084] The integration unit can estimate the user's emotions and adjust the schedule integration method based on the estimated user emotions. For example, if the user is stressed, the integration unit will select a simple integration method. For example, if the user is relaxed, the integration unit will select a detailed integration method. For example, if the user is in a hurry, the integration unit will select a method that allows for quick integration. This allows the schedule integration method to be adjusted according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the integration unit may be performed using a generative AI, or not. For example, the integration unit can input user facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0085] The integration unit can adjust the level of detail of the integration based on the importance of each household member's schedule during the integration process. For example, the integration unit considers the importance of each household member's schedule and prioritizes integrating important schedules. For example, the integration unit adjusts the level of detail of the integration based on the importance of each household member's schedule. For example, the integration unit selects the optimal integration method based on the importance of each household member's schedule. This allows the level of detail of the integration to be adjusted based on the importance of the schedules. Some or all of the above processes in the integration unit may be performed using, for example, a generating AI, or without using a generating AI. For example, the integration unit can input schedule importance data into a generating AI and have the generating AI perform the adjustment of the level of detail of the integration.

[0086] The integration unit can apply different integration algorithms depending on the schedule category during integration. For example, the integration unit applies different integration algorithms depending on categories such as housework, childcare, and work. For example, the integration unit selects the optimal integration algorithm based on the schedule category. For example, the integration unit applies different integration methods for each schedule category. This allows the optimal integration algorithm to be applied according to the schedule category. Some or all of the above processing in the integration unit may be performed using, for example, a generative AI, or without a generative AI. For example, the integration unit can input schedule category data into a generative AI and have the generative AI execute the application of the integration algorithm.

[0087] The integration unit can estimate the user's emotions and adjust the order of the schedule to be integrated based on the estimated user emotions. For example, if the user is stressed, the integration unit will postpone less important schedules. For example, if the user is relaxed, the integration unit will integrate the schedule in the normal order. For example, if the user is in a hurry, the integration unit will prioritize integrating high-priority schedules. This allows the order of the schedule to be adjusted according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the integration unit may be performed using a generative AI, or not using a generative AI. For example, the integration unit can input user facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0088] The integration unit can determine the priority of integration based on the submission timing of the schedules during the integration process. For example, the integration unit considers the submission timing of the schedules and prioritizes the integration of schedules submitted earlier. For example, the integration unit determines the priority of integration based on the submission timing of the schedules. For example, the integration unit selects the optimal integration method based on the submission timing of the schedules. This allows the integration priority to be determined based on the submission timing of the schedules. Some or all of the above processes in the integration unit may be performed using, for example, a generative AI, or without using a generative AI. For example, the integration unit can input schedule submission timing data into a generative AI and have the generative AI perform the determination of the integration priority.

[0089] The integration unit can adjust the order of integration based on the relevance of the schedules during integration. For example, the integration unit considers the relevance of the schedules and prioritizes integrating schedules with high relevance. For example, the integration unit adjusts the order of integration based on the relevance of the schedules. For example, the integration unit selects the optimal integration method based on the relevance of the schedules. This allows the order of integration to be adjusted based on the relevance of the schedules. Some or all of the above processing in the integration unit may be performed using, for example, a generative AI, or without a generative AI. For example, the integration unit can input schedule relevance data into a generative AI and have the generative AI perform the adjustment of the order of integration.

[0090] The task allocation unit can estimate the user's emotions and adjust the task allocation method based on the estimated emotions. For example, if the user is stressed, the allocation unit may prioritize assigning easy tasks. If the user is relaxed, the allocation unit may apply the normal allocation method. If the user is in a hurry, the allocation unit may prioritize assigning important tasks. This allows the task allocation method to be adjusted according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the allocation unit may be performed using a generative AI, or not. For example, the allocation unit may input user facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0091] The assignment unit can optimally assign tasks based on the skills and abilities of each household member during the assignment process. For example, the assignment unit considers the skills and abilities of each household member and assigns the most suitable tasks. For example, the assignment unit adjusts the task assignment based on the skills and abilities of each household member. For example, the assignment unit selects the optimal assignment method based on the skills and abilities of each household member. This allows for the optimal assignment of tasks based on the skills and abilities of each household member. Some or all of the above-described processes in the assignment unit may be performed using, for example, a generative AI, or without using a generative AI. For example, the assignment unit can input skill data for each household member into a generative AI and have the generative AI perform the task assignment.

[0092] The allocation unit can apply different allocation algorithms depending on the importance of the tasks during allocation. For example, the allocation unit considers the importance of the tasks and prioritizes the allocation of important tasks. For example, the allocation unit adjusts the allocation algorithm based on the importance of the tasks. For example, the allocation unit selects the optimal allocation algorithm based on the importance of the tasks. This allows the optimal allocation algorithm to be applied according to the importance of the tasks. Some or all of the above processes in the allocation unit may be performed using, for example, a generative AI, or without a generative AI. For example, the allocation unit can input task importance data into a generative AI and have the generative AI execute the application of the allocation algorithm.

[0093] The task allocation unit can estimate the user's emotions and adjust the task allocation order based on the estimated emotions. For example, if the user is stressed, the allocation unit will prioritize assigning easy tasks. If the user is relaxed, the allocation unit will prioritize assigning tasks in the normal order. If the user is in a hurry, the allocation unit will prioritize assigning important tasks. This allows the task allocation order to be adjusted according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the allocation unit may be performed using a generative AI, or not. For example, the allocation unit can input user facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0094] The allocation unit can determine the priority of tasks based on their submission timing during the allocation process. For example, the allocation unit may prioritize tasks submitted earlier, taking into account their submission timing. The allocation unit may determine the priority of tasks based on their submission timing. The allocation unit may select the optimal allocation method based on their submission timing. This allows the allocation priority to be determined based on the task submission timing. Some or all of the above-described processes in the allocation unit may be performed using, for example, a generative AI, or without using a generative AI. For example, the allocation unit may input task submission timing data into a generative AI and have the generative AI determine the allocation priority.

[0095] The allocation unit can adjust the allocation order based on the relevance of tasks during allocation. For example, the allocation unit considers the relevance of tasks and prioritizes allocating tasks with high relevance. For example, the allocation unit adjusts the allocation order based on the relevance of tasks. For example, the allocation unit selects the optimal allocation method based on the relevance of tasks. This allows the allocation order to be adjusted based on the relevance of tasks. Some or all of the above processing in the allocation unit may be performed using, for example, a generative AI, or without a generative AI. For example, the allocation unit can input task relevance data into a generative AI and have the generative AI perform the adjustment of the allocation order.

[0096] The priority determination unit can estimate the user's emotions and adjust the task priority based on the estimated emotions. For example, if the user is stressed, the priority determination unit will postpone low-priority tasks. For example, if the user is relaxed, the priority determination unit will proceed with tasks at their normal priority. For example, if the user is in a hurry, the priority determination unit will prioritize high-priority tasks. This allows the priority of tasks to be adjusted according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processing in the priority determination unit may be performed using a generative AI, or not using a generative AI. For example, the priority determination unit can input user facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0097] The priority determination unit can determine the optimal priority by referring to the past task history of each household member when determining priorities. For example, the priority determination unit refers to the past task history of each household member to determine the optimal priority. For example, the priority determination unit adjusts the priority based on the past task history of each household member. For example, the priority determination unit selects the optimal priority determination method by referring to the past task history of each household member. This makes it possible to determine the optimal priority based on past task history. Some or all of the above processing in the priority determination unit may be performed using, for example, a generation AI, or without using a generation AI. For example, the priority determination unit can input past task history data into a generation AI and have the generation AI perform the priority determination.

[0098] The priority determination unit can apply different priority determination algorithms depending on the importance of the tasks when determining priorities. For example, the priority determination unit considers the importance of the tasks and prioritizes important tasks. For example, the priority determination unit adjusts the priority determination algorithm based on the importance of the tasks. For example, the priority determination unit selects the optimal priority determination algorithm based on the importance of the tasks. This allows the optimal priority determination algorithm to be applied according to the importance of the tasks. Some or all of the above processes in the priority determination unit may be performed using, for example, a generative AI, or without using a generative AI. For example, the priority determination unit can input task importance data into a generative AI and have the generative AI execute the application of the priority determination algorithm.

[0099] The priority determination unit can estimate the user's emotions and adjust the order in which tasks are displayed based on the estimated emotions. For example, if the user is stressed, the priority determination unit will postpone less important tasks. For example, if the user is relaxed, the priority determination unit will display tasks in the normal order. For example, if the user is in a hurry, the priority determination unit will prioritize displaying more important tasks. This allows the order in which tasks are displayed to be adjusted according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the priority determination unit may be performed using a generative AI, or not. For example, the priority determination unit can input user facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0100] The priority determination unit can determine priorities based on the task submission timing. For example, the priority determination unit considers the task submission timing and prioritizes tasks submitted earlier. For example, the priority determination unit determines priorities based on the task submission timing. For example, the priority determination unit selects the optimal priority determination method based on the task submission timing. This allows priorities to be determined based on the task submission timing. Some or all of the above processing in the priority determination unit may be performed using, for example, a generation AI, or without a generation AI. For example, the priority determination unit can input task submission timing data into a generation AI and have the generation AI perform the priority determination.

[0101] The priority determination unit can adjust the order of priorities based on the relevance of tasks when determining priorities. For example, the priority determination unit considers the relevance of tasks and prioritizes tasks with high relevance. For example, the priority determination unit adjusts the order of priorities based on the relevance of tasks. For example, the priority determination unit selects the optimal priority determination method based on the relevance of tasks. This allows the order of priorities to be adjusted based on the relevance of tasks. Some or all of the above processing in the priority determination unit may be performed using, for example, a generative AI, or without using a generative AI. For example, the priority determination unit can input task relevance data into a generative AI and have the generative AI perform the adjustment of the order of priorities.

[0102] The suggestion unit can estimate the user's emotions and adjust the recipe suggestion method based on the estimated user emotions. For example, if the user is stressed, the suggestion unit will suggest a simple and easy recipe. For example, if the user is relaxed, the suggestion unit will suggest a normal recipe. For example, if the user is in a hurry, the suggestion unit will suggest a recipe that can be cooked quickly. This allows the recipe suggestion method to be adjusted according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the suggestion unit may be performed using a generative AI, or not using a generative AI. For example, the suggestion unit can input user facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0103] The suggestion unit can propose the most suitable recipe based on each family member's dietary preferences. For example, the suggestion unit considers each family member's dietary preferences and proposes the most suitable recipe. For example, the suggestion unit adjusts the recipe suggestions based on each family member's dietary preferences. For example, the suggestion unit selects the most suitable recipe based on each family member's dietary preferences. This allows the suggestion unit to propose the most suitable recipe based on each family member's dietary preferences. Some or all of the above processes in the suggestion unit may be performed using, for example, a generative AI, or without using a generative AI. For example, the suggestion unit can input each family member's dietary preference data into a generative AI and have the generative AI execute the recipe suggestion.

[0104] The suggestion unit can propose different recipes depending on the availability of ingredients at the time of proposal. For example, the suggestion unit may consider the availability of ingredients and propose a recipe that uses ingredients that are in short supply. For example, the suggestion unit may adjust the recipe suggestions based on the availability of ingredients. For example, the suggestion unit may select the optimal recipe based on the availability of ingredients. This allows the suggestion unit to propose the optimal recipe according to the availability of ingredients. Some or all of the above processes in the suggestion unit may be performed using, for example, a generative AI, or without using a generative AI. For example, the suggestion unit may input ingredient inventory data into a generative AI and have the generative AI execute the recipe suggestions.

[0105] The suggestion unit can estimate the user's emotions and adjust the order of suggested recipes based on the estimated emotions. For example, if the user is stressed, the suggestion unit will prioritize suggesting easy and simple recipes. For example, if the user is relaxed, the suggestion unit will suggest recipes in the normal order. For example, if the user is in a hurry, the suggestion unit will prioritize suggesting recipes that can be cooked quickly. This allows the order of suggested recipes to be adjusted according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the suggestion unit may be performed using a generative AI, or not using a generative AI. For example, the suggestion unit can input the user's facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0106] The proposal department can determine the priority of proposals based on the submission timing of the recipes at the time of proposal. For example, the proposal department will consider the submission timing of the recipes and prioritize the recipes that are submitted earlier. For example, the proposal department will determine the priority of proposals based on the submission timing of the recipes. For example, the proposal department will select the optimal proposal method based on the submission timing of the recipes. This allows the proposal department to determine the priority of proposals based on the submission timing of the recipes. Some or all of the above processes in the proposal department may be performed using, for example, a generative AI, or not using a generative AI. For example, the proposal department can input recipe submission timing data into a generative AI and have the generative AI perform the determination of proposal priority.

[0107] The suggestion unit can adjust the order of suggestions based on the relevance of the recipes when making suggestions. For example, the suggestion unit considers the relevance of the recipes and prioritizes suggesting recipes with high relevance. For example, the suggestion unit adjusts the order of suggestions based on the relevance of the recipes. For example, the suggestion unit selects the optimal suggestion method based on the relevance of the recipes. This allows the suggestion unit to adjust the order of suggestions based on the relevance of the recipes. Some or all of the above processes in the suggestion unit may be performed using, for example, a generative AI, or without using a generative AI. For example, the suggestion unit can input recipe relevance data into a generative AI and have the generative AI perform the adjustment of the suggestion order.

[0108] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0109] The suggestion department can propose recipes that take into account the health status of each family member. For example, it can suggest nutritionally balanced recipes based on the health status of each family member. It can also suggest allergen-free recipes that take into account the allergy information of each family member. Furthermore, it can suggest appropriate recipes according to the health goals of each family member (e.g., weight loss or muscle building).

[0110] The task allocation system can incorporate gamification elements into task assignment to increase the motivation of family members. For example, it can award points for each completed task and provide rewards once a certain number of points are reached. The task allocation system can also share task progress among family members and encourage competition. Furthermore, it can award badges or titles based on the level of task completion.

[0111] The integration unit can utilize a Geographic Information System (GIS) to optimize the travel routes of household members. For example, the integration unit can propose the optimal travel route considering the current location and destination of each household member. The integration unit can also acquire real-time traffic and weather information and adjust the travel route accordingly. Furthermore, the integration unit can propose the optimal mode of transportation by considering public transport operation information.

[0112] The data collection unit can collect vital data from household members and use it for health management. For example, the unit can collect vital data such as heart rate, blood pressure, and body temperature from household members. The unit can also analyze the vital data and issue alerts if abnormalities are detected. Furthermore, based on the vital data, the unit can monitor changes in health status and suggest a visit to a medical institution if necessary.

[0113] The suggestion function can estimate the emotions of family members and suggest relaxation methods based on those estimates. For example, if a family member is feeling stressed, the suggestion function can suggest relaxing music or meditation. If a family member is feeling tired, the suggestion function can also suggest refreshing exercise or stretching. Furthermore, if a family member is feeling relaxed, the suggestion function can also suggest hobbies or recreational activities.

[0114] The prioritization unit can determine task priorities by considering the long-term goals of family members. For example, it can prioritize tasks related to family members' career goals or learning objectives. It can also prioritize tasks related to health management based on family members' health goals. Furthermore, it can prioritize family activities based on family members' goals of valuing time with family.

[0115] The data collection unit can estimate the emotions of family members and adjust the timing of communication based on those estimates. For example, if a family member is feeling stressed, the data collection unit will delay communication. If a family member is relaxed, the data collection unit can communicate at the usual time. Furthermore, if a family member is in a hurry, the data collection unit can communicate quickly.

[0116] The integration department can coordinate schedules, taking into account the hobbies and interests of family members. For example, it can incorporate relevant events and activities into the schedule based on the hobbies and interests of family members. The integration department can also adjust schedules so that family members can participate in activities with other members who share the same interests. Furthermore, the integration department can plan weekends and holidays based on the hobbies and interests of family members.

[0117] The task allocation unit can estimate the emotions of family members and adjust task assignments based on those estimates. For example, if a family member is feeling stressed, the allocation unit will prioritize assigning easy tasks. If a family member is relaxed, the allocation unit can also assign normal tasks. Furthermore, if a family member is in a hurry, the allocation unit can prioritize assigning important tasks.

[0118] The suggestion function can estimate the emotions of family members and suggest recipes based on those estimates. For example, if a family member is feeling stressed, the suggestion function can suggest a simple, easy recipe. If a family member is relaxed, the suggestion function can also suggest a regular recipe. Furthermore, if a family member is in a hurry, the suggestion function can suggest a recipe that can be prepared quickly.

[0119] The following briefly describes the processing flow for example form 2.

[0120] Step 1: The collection unit collects the schedules of each household member. Each household member's schedule includes, for example, work appointments, school appointments, and personal appointments. The collection unit obtains schedule information from calendar applications and schedule management applications. It can also collect schedule information manually entered by household members, or by analyzing voice input or text input. Step 2: The integration unit integrates the schedules collected by the collection unit. Integration is performed by processing and prioritizing schedule overlaps. The integration unit detects schedule overlaps and merges the overlapping schedules. It also reflects schedule changes and additions in real time. Step 3: The allocation department assigns household and childcare tasks based on the schedule integrated by the integration department. Assignments are made based on each family member's skills, available time, and the importance and urgency of the tasks. The allocation department monitors the progress of the tasks and adjusts the task assignments as needed. Step 4: The priority determination unit determines the priority of the tasks assigned by the allocation unit. Priority is determined by considering the importance, urgency, and dependencies of the tasks. The priority determination unit monitors the progress of the tasks and adjusts the priority as needed. Step 5: The proposal team proposes simple recipes based on the priorities determined by the priority determination team. The proposals take into account cooking time, types and quantities of ingredients, and the dietary preferences and allergy information of the household members.

[0121] 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.

[0122] 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.

[0123] 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.

[0124] Each of the multiple elements described above, including the collection unit, integration unit, allocation unit, priority determination unit, and suggestion unit, is implemented in at least one of the smart device 14 and the data processing device 12. For example, the collection unit is implemented by the control unit 46A of the smart device 14 and acquires schedule information from a calendar application or schedule management application. The integration unit is implemented by the specific processing unit 290 of the data processing device 12 and integrates the collected schedules. The allocation unit is implemented by the control unit 46A of the smart device 14 and allocates tasks based on the integrated schedules. The priority determination unit is implemented by the specific processing unit 290 of the data processing device 12 and determines the priority of the tasks. The suggestion unit is implemented by the control unit 46A of the smart device 14 and suggests a simple recipe. 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.

[0125] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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).

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.).

[0137] 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.

[0138] 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.

[0139] 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.

[0140] Each of the multiple elements described above, including the collection unit, integration unit, allocation unit, priority determination unit, and suggestion unit, is implemented in at least one of the smart glasses 214 and the data processing device 12. For example, the collection unit is implemented by the control unit 46A of the smart glasses 214 and acquires schedule information from a calendar application or schedule management application. The integration unit is implemented by the specific processing unit 290 of the data processing device 12 and integrates the collected schedules. The allocation unit is implemented by the control unit 46A of the smart glasses 214 and allocates tasks based on the integrated schedules. The priority determination unit is implemented by the specific processing unit 290 of the data processing device 12 and determines the priority of the tasks. The suggestion unit is implemented by the control unit 46A of the smart glasses 214 and suggests a simple recipe. 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.

[0141] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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).

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.).

[0153] 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.

[0154] 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.

[0155] 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.

[0156] Each of the multiple elements described above, including the collection unit, integration unit, allocation unit, priority determination unit, and suggestion unit, is implemented in at least one of the headset terminal 314 and the data processing device 12. For example, the collection unit is implemented by the control unit 46A of the headset terminal 314 and acquires schedule information from a calendar application or schedule management application. The integration unit is implemented by the specific processing unit 290 of the data processing device 12 and integrates the collected schedules. The allocation unit is implemented by the control unit 46A of the headset terminal 314 and allocates tasks based on the integrated schedules. The priority determination unit is implemented by the specific processing unit 290 of the data processing device 12 and determines the priority of tasks. The suggestion unit is implemented by the control unit 46A of the headset terminal 314 and suggests a simple recipe. 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.

[0157] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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).

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.).

[0170] 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.

[0171] 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.

[0172] 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.

[0173] Each of the multiple elements described above, including the collection unit, integration unit, allocation unit, priority determination unit, and suggestion unit, is implemented in at least one of the robot 414 and the data processing unit 12. For example, the collection unit is implemented by the control unit 46A of the robot 414 and acquires schedule information from a calendar application or schedule management application. The integration unit is implemented by the specific processing unit 290 of the data processing unit 12 and integrates the collected schedules. The allocation unit is implemented by the control unit 46A of the robot 414 and allocates tasks based on the integrated schedules. The priority determination unit is implemented by the specific processing unit 290 of the data processing unit 12 and determines the priority of the tasks. The suggestion unit is implemented by the control unit 46A of the robot 414 and suggests a simple recipe. 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.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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."

[0180] 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.

[0181] 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.

[0182] 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.

[0183] 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.

[0184] 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.

[0185] 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.

[0186] 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.

[0187] 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.

[0188] 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.

[0189] 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.

[0190] 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.

[0191] 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.

[0192] (Note 1) The collection department collects the schedules of each household member, An integration unit that integrates the schedules collected by the aforementioned collection unit, A task allocation unit that assigns household chores and childcare tasks based on the schedule integrated by the aforementioned integration unit, A priority determination unit that determines the priority of tasks assigned by the allocation unit, The system includes a suggestion unit that proposes a simple recipe based on the priority determined by the priority determination unit. A system characterized by the following features. (Note 2) The aforementioned proposal section is, Considering everyone's schedule, decide who will be responsible for which task. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned proposal section is, By suggesting simple recipes, meal preparation becomes more efficient. The system described in Appendix 1, characterized by the features described herein. (Note 4) The priority determination unit, Prioritize tasks The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned integration unit is Integrate the schedules of each household member. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned collection unit is It estimates the user's emotions and adjusts the timing of scheduled data collection based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned collection unit is Analyze each household member's past schedule history to select the most suitable collection method. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned collection unit is When collecting schedules, filter them based on the current activities and areas of interest of each family member. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned collection unit is It estimates user sentiment and determines the priority of the collection schedule based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned collection unit is When collecting schedules, the system prioritizes collecting highly relevant schedules by considering the geographical location information of family members. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned collection unit is When collecting schedules, analyze the social media activity of family members and collect relevant schedules. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned integration unit is It estimates the user's emotions and adjusts the scheduling integration method based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned integration unit is During integration, adjust the level of detail based on the importance of each household member's schedule. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned integration unit is During integration, different integration algorithms are applied depending on the schedule category. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned integration unit is It estimates user sentiment and adjusts the scheduling order for integration based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned integration unit is During the integration process, integration priorities will be determined based on the submission deadline for the schedule. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned integration unit is During integration, adjust the integration order based on schedule relevance. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned allocation unit is, It estimates the user's emotions and adjusts the task assignment method based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned allocation unit is, When assigning tasks, the system optimizes task allocation based on each household member's skills and abilities. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned allocation unit is, When allocating tasks, different allocation algorithms are applied depending on the importance of the task. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned allocation unit is, It estimates the user's emotions and adjusts the task assignment order based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned allocation unit is, When assigning tasks, the priority of assignments is determined based on the submission timing of the tasks. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned allocation unit is, When assigning tasks, adjust the assignment order based on the relevance of the tasks. The system described in Appendix 1, characterized by the features described herein. (Note 24) The priority determination unit, It estimates the user's emotions and adjusts task priorities based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 25) The priority determination unit, When determining priorities, we refer to each household member's past task history to determine the optimal priorities. The system described in Appendix 1, characterized by the features described herein. (Note 26) The priority determination unit, When determining priorities, different prioritization algorithms are applied depending on the importance of the task. The system described in Appendix 1, characterized by the features described herein. (Note 27) The priority determination unit, It estimates the user's emotions and adjusts the order in which tasks are displayed based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 28) The priority determination unit, When determining priorities, prioritize tasks based on their submission deadline. The system described in Appendix 1, characterized by the features described herein. (Note 29) The priority determination unit, When determining priorities, adjust the order of priorities based on the relevance of the tasks. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned proposal section is, The system estimates the user's emotions and adjusts the recipe suggestion method based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 31) The aforementioned proposal section is, When making a proposal, we suggest the most suitable recipe based on the food preferences of each household member. The system described in Appendix 1, characterized by the features described herein. (Note 32) The aforementioned proposal section is, When making a proposal, we suggest different recipes depending on the availability of ingredients. The system described in Appendix 1, characterized by the features described herein. (Note 33) The aforementioned proposal section is, It estimates the user's emotions and adjusts the order of recipe suggestions based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 34) The aforementioned proposal section is, When submitting a proposal, prioritize the proposals based on when the recipes were submitted. The system described in Appendix 1, characterized by the features described herein. (Note 35) The aforementioned proposal section is, When making suggestions, adjust the order of suggestions based on the relevance of the recipes. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0193] 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 collection department collects the schedules of each household member, An integration unit that integrates the schedules collected by the aforementioned collection unit, A task allocation unit that assigns household chores and childcare tasks based on the schedule integrated by the aforementioned integration unit, A priority determination unit that determines the priority of tasks assigned by the allocation unit, The system includes a suggestion unit that proposes a simple recipe based on the priority determined by the priority determination unit. A system characterized by the following features.

2. The aforementioned proposal section is, Considering everyone's schedule, decide who will be responsible for which task. The system according to feature 1.

3. The aforementioned proposal section is, By suggesting simple recipes, meal preparation becomes more efficient. The system according to feature 1.

4. The priority determination unit, Prioritize tasks The system according to feature 1.

5. The aforementioned integration unit is Integrate the schedules of each household member. The system according to feature 1.

6. The aforementioned collection unit is It estimates the user's emotions and adjusts the timing of scheduled data collection based on the estimated user emotions. The system according to feature 1.

7. The aforementioned collection unit is Analyze each household member's past schedule history to select the most suitable collection method. The system according to feature 1.

8. The aforementioned collection unit is When collecting schedules, filter them based on the current activities and areas of interest of each family member. The system according to feature 1.

9. The aforementioned collection unit is It estimates user sentiment and determines the priority of the collection schedule based on the estimated user sentiment. The system according to feature 1.

10. The aforementioned collection unit is When collecting schedules, the system prioritizes collecting highly relevant schedules by considering the geographical location information of family members. The system according to feature 1.

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