system

Abstract

The system according to this embodiment aims to suggest the efficient distribution and start timing of household tasks based on the family's schedule. [Solution] The system according to the embodiment comprises a collection unit, a proposal unit, and an execution unit. The collection unit collects the family's schedule. The proposal unit proposes an efficient distribution of household tasks and their start times based on the schedule collected by the collection unit. The execution unit executes the household tasks based on the distribution and timing proposed by the proposal unit.

Description

Technical Field

[0006] , , ,

[0005] , ,

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, efficient sharing of housework tasks based on the schedules of family members and proposing start timings have not been sufficiently carried out, and there is room for improvement.

[0005] The system according to the embodiment aims to propose efficient sharing of housework tasks and start timings based on the schedules of family members.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a collection unit, a proposal unit, and an execution unit. The collection unit collects the family's schedule. The proposal unit proposes an efficient distribution of household tasks and their start times based on the schedule collected by the collection unit. The execution unit executes the household tasks based on the distribution and timing proposed by the proposal unit. [Effects of the Invention]

[0007] The system according to this embodiment can suggest efficient distribution and start times for household tasks based on the family's schedule. [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 reception device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The reception device 38, output device 40, and camera 42 are also connected to the bus 52.

[0020] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, and accepts user input. The touch panel 38A accepts user input via touch by detecting contact with an object (e.g., a pen or finger). The microphone 38B accepts user input via voice by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 (see Figure 2) acquires the data indicating the user input.

[0021] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user by outputting the data in a form perceptible to the user (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

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

[0024] As shown in Figure 2, in the data processing device 12, a specific processing is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" related to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0025] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

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

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

[0028] (Example of form 1) The AI ​​agent system according to an embodiment of the present invention is a system that combines a generating AI (Generating AI) with a smartphone / tablet application. This AI agent system learns the daily schedules of all family members (holidays, overtime, school life, etc.) and proposes efficient division of household tasks and start times. The AI ​​agent system understands the household chores and family schedules in a conversational format, and the generating AI assistant proposes division and adjustments. The AI ​​agent system calculates the time required for cooking and cleaning tasks based on the home appliances, cooking utensils, and cleaning tools owned, and also proposes optimal usage methods and maintenance schedules. The AI ​​agent system links with online shopping sites to propose home appliances and tools suited to each household, and aims to generate revenue by leading to purchases. The AI ​​agent system proposes menus, checks the inventory of ingredients, and also proposes online purchases of necessary ingredients. The AI ​​agent system shares schedules and division of tasks among family members through the application. For example, the AI ​​agent system inputs the family's schedules, such as holidays, overtime, and school life, into the generating AI. The generating AI learns this information and proposes efficient division of household tasks and start times. For example, if the whole family is on holiday, they can divide household tasks such as cleaning and cooking and perform them efficiently. Next, the AI ​​agent system calculates the time required for cooking and cleaning tasks based on the home appliances, cooking utensils, and cleaning supplies owned. For example, it suggests the optimal way to use appliances such as vacuum cleaners, washing machines, and ovens, as well as maintenance schedules. This enables efficient use and maintenance of appliances. Furthermore, the AI ​​agent system links with online shopping sites to suggest appliances and tools that are suitable for each household. For example, by linking with online shopping sites, it suggests appliances and tools that meet the family's needs and generates revenue by leading to purchases. This improves the efficiency of household chores and enhances the quality of life. In addition, the AI ​​agent system suggests menus, checks the inventory of ingredients, and suggests online purchases of necessary ingredients. For example, it checks the inventory of ingredients in the refrigerator and suggests purchasing necessary ingredients online. This reduces food waste and enables efficient meal preparation.Finally, the AI ​​agent system shares schedules and chore assignments among family members through the app. For example, everyone's schedule can be shared via the app, and household chore tasks can be coordinated. This allows everyone to perform household chores efficiently and ensures time for communication and relaxation. In this way, by using an AI agent that combines generative AI with a smartphone / tablet app, a system can be realized that learns the daily schedules of all family members and proposes efficient division of household chores and optimal start times. This improves the efficiency of household chores, enhances the quality of life, and ensures time for communication and relaxation among family members. Thus, the AI ​​agent system can learn the daily schedules of all family members and propose efficient division of household chores and optimal start times.

[0029] The AI ​​agent system according to this embodiment comprises a collection unit, a proposal unit, and an execution unit. The collection unit collects family schedules. Family schedules include, but are not limited to, work schedules, school schedules, and personal appointments. The collection unit collects schedules such as family holidays, overtime, and school life. The collection unit can use a smartphone or tablet app to collect family schedules. For example, the collection unit can collect family schedules by inputting them into the app. The collection unit can also use a generation AI to automatically collect family schedules. For example, the collection unit inputs family schedules into the generation AI, and the generation AI automatically collects the schedules. The proposal unit proposes efficient division and start times for household tasks based on the schedules collected by the collection unit. The proposal unit proposes efficient division and start times for household tasks based on the collected schedules. The proposal unit can use a generation AI to propose efficient division and start times for household tasks. For example, the proposal unit inputs the collected schedule into the generating AI, which then proposes an efficient distribution of household tasks and their start times. The execution unit then executes the household tasks based on the distribution and timing proposed by the proposal unit. The execution unit executes the household tasks based on the proposed distribution and timing. The execution unit can use the generating AI to execute the household tasks. For example, the execution unit inputs the proposed distribution and timing into the generating AI, which then executes the household tasks. Thus, the AI ​​agent system according to the embodiment can propose and execute an efficient distribution of household tasks and their start times based on the family's schedule.

[0030] The data collection unit collects family schedules. Family schedules include, but are not limited to, work schedules, school schedules, and personal appointments. The data collection unit collects schedules such as family holidays, overtime, and school life. The data collection unit can use smartphone or tablet apps to collect family schedules. For example, the data collection unit can collect schedules by inputting them into the app. The data collection unit can also use generative AI to automatically collect family schedules. For example, the data collection unit inputs family schedules into the generative AI, and the generative AI automatically collects the schedules. Specifically, each family member inputs their schedule through a dedicated application installed on a smartphone or tablet. This allows the data collection unit to centrally manage each member's schedule. Furthermore, by utilizing generative AI, it is also possible to automatically extract and collect schedules from, for example, email or calendar apps. The generative AI uses natural language processing technology to analyze the content of emails and calendars and extract schedule information. This reduces manual input work and allows for efficient schedule collection. Furthermore, the data collection unit can update family schedules in real time, ensuring that the latest information is always available. For example, if a family member changes their schedule, that information is reflected in the data collection unit and shared with other members. This allows for smoother schedule management for the entire family and enables efficient task sharing.

[0031] The proposal unit proposes efficient division of household tasks and start times based on schedules collected by the collection unit. For example, the proposal unit can use a generating AI to propose efficient division of household tasks and start times based on the collected schedules. Specifically, the generating AI analyzes the collected schedule data and calculates the optimal division of household tasks and start times, taking into account each member's free time and priorities. Based on past data and statistical information, the generating AI identifies tasks each member excels at and times when they are less burdensome, and makes suggestions accordingly. For example, the generating AI analyzes the history of past household task execution to identify tasks specific members excel at and times when they can perform them efficiently. This allows the proposal unit to propose optimal task divisions tailored to each member's characteristics and schedule. The proposal unit also adjusts start times, taking into account the priority and urgency of household tasks. For example, the suggestion team proposes that high-priority tasks be completed early, while lower-priority tasks be postponed. This enables more efficient execution of household tasks and reduces the overall burden on the family. Furthermore, the suggestion team notifies family members of the proposed tasks and collects feedback. For instance, they use smartphone notification functions to inform each member of the proposed task assignments and start times. Based on the feedback from members, the suggestion team can revise the suggestions and make more appropriate proposals. In this way, the suggestion team can propose efficient task assignments and start times based on the family's schedule, supporting the lives of the entire family.

[0032] The execution unit carries out household tasks based on the division of labor and timing proposed by the proposal unit. For example, the execution unit can use a generating AI to carry out household tasks. For example, the execution unit inputs the proposed division of labor and timing into the generating AI, and the generating AI carries out the household tasks. Specifically, the generating AI instructs each member to carry out the tasks based on the task division of labor and timing provided by the proposal unit. The generating AI sends notifications to each member's smartphone or tablet, informing them of the task start time and execution procedure. For example, the generating AI gives specific instructions for household tasks such as cleaning, laundry, and cooking, and explains the necessary procedures and points to note in detail. This allows each member to carry out the tasks efficiently. In addition, the generating AI monitors the progress of the tasks in real time and provides support as needed. For example, if the progress of the task is behind schedule or a problem occurs, the generating AI provides appropriate advice and support to help the task be carried out smoothly. Furthermore, the execution unit records the results of the task execution and reflects them in the next proposal. For example, by collecting each member's task execution history and feedback, the generating AI analyzes this data to improve the next set of suggestions. This allows the execution unit to support the efficient execution of household tasks and improve the overall quality of life for the family.

[0033] The data collection unit can collect schedules such as family holidays, overtime work, and school life. For example, the data collection unit can collect schedules such as family holidays, overtime work, and school life. The data collection unit can use smartphone or tablet apps to collect family schedules. For example, the data collection unit can collect schedules by inputting them into the app. Alternatively, the data collection unit can use a generation AI to automatically collect family schedules. For example, the data collection unit inputs family schedules into the generation AI, which then automatically collects the schedules. This allows for more accurate suggestions by collecting schedules such as family holidays, overtime work, and school life.

[0034] The suggestion unit can propose efficient division of household tasks and start times based on collected schedules. For example, the suggestion unit can propose efficient division of household tasks and start times based on collected schedules. The suggestion unit can use generative AI to propose efficient division of household tasks and start times. For example, the suggestion unit inputs the collected schedules into the generative AI, and the generative AI proposes efficient division of household tasks and start times. This improves the efficiency of household chores by proposing efficient division of household tasks and start times based on collected schedules.

[0035] The execution unit can perform household tasks based on the proposed division of labor and timing. For example, the execution unit can perform household tasks based on the proposed division of labor and timing. The execution unit can use a generative AI to perform household tasks. For example, the execution unit inputs the proposed division of labor and timing into the generative AI, and the generative AI performs the household tasks. This improves the efficiency of household chores by performing them based on the proposed division of labor and timing.

[0036] The suggestion function can calculate the time required for cooking and cleaning tasks based on the home appliances, cooking utensils, and cleaning tools owned, and propose optimal usage methods and maintenance schedules. For example, the suggestion function can use generative AI to propose optimal usage methods and maintenance schedules for home appliances, cooking utensils, and cleaning tools. For example, the suggestion function inputs information about the home appliances, cooking utensils, and cleaning tools owned into the generative AI, which then proposes optimal usage methods and maintenance schedules. This improves the efficiency of household chores by suggesting optimal usage methods and maintenance schedules based on the home appliances, cooking utensils, and cleaning tools owned.

[0037] The suggestion department can work in conjunction with online shopping sites to propose home appliances and tools tailored to each household. For example, the suggestion department can use generative AI to propose suitable home appliances and tools. For instance, the suggestion department inputs information from online shopping sites into the generative AI, which then proposes suitable home appliances and tools. This allows the department to propose suitable home appliances and tools by linking with online shopping sites, ultimately leading to purchases.

[0038] The suggestion department can propose menus, check ingredient inventory, and suggest online purchases of necessary ingredients. For example, the suggestion department can propose menus, check ingredient inventory, and suggest online purchases of necessary ingredients. The suggestion department can use a generation AI to propose menus, check ingredient inventory, and suggest online purchases. For example, the suggestion department inputs menu information and ingredient inventory information into the generation AI, which then proposes menus, checks ingredient inventory, and suggests online purchases. This reduces food waste and enables efficient meal preparation by proposing menus, checking ingredient inventory, and suggesting online purchases of necessary ingredients.

[0039] The execution unit can share schedules and chores among family members through an app. For example, the execution unit can share schedules and chores among family members through an app. The execution unit can use a generating AI to share schedules and chores. For example, the execution unit inputs family schedule and chore information into the generating AI, and the generating AI shares the schedules and chores. As a result, by sharing schedules and chores among family members through an app, all family members can perform household chores efficiently and secure time for communication and relaxation.

[0040] The data collection unit can analyze the family's past schedule history and select the optimal data collection method. For example, the unit might prioritize suggesting data collection methods frequently used by the family in the past. The unit can also select the most efficient data collection method based on the family's past schedule history. Furthermore, the unit can analyze the family's past schedule history and customize the data collection method. This allows the unit to select the optimal data collection method by analyzing the family's past schedule history.

[0041] The collection unit can filter schedules based on the family's current projects and areas of interest. For example, it can prioritize collecting schedules related to projects the family is currently working on. The collection unit can also filter and collect relevant schedules based on the family's areas of interest. The collection unit can also exclude unnecessary schedules, taking into account the family's current projects and areas of interest. This allows for efficient collection of relevant schedules by filtering based on the family's current projects and areas of interest.

[0042] The data collection unit can prioritize collecting highly relevant schedules by considering the geographical location information of the family members during schedule collection. For example, the data collection unit can prioritize collecting schedules related to the family members' current location. The data collection unit can also filter and collect highly relevant schedules based on the family members' geographical location information. The data collection unit can also exclude unnecessary schedules by considering the family members' geographical location information. This allows for efficient schedule collection by prioritizing the collection of highly relevant schedules while considering the family members' geographical location information.

[0043] The data collection unit can analyze family social media activity and collect relevant schedules during schedule collection. For example, it can collect relevant events and appointments from family social media activity. The data collection unit can also analyze family social media activity and filter to collect relevant schedules. The data collection unit can also exclude unnecessary schedules by considering family social media activity. This allows for efficient collection of relevant schedules by analyzing family social media activity.

[0044] The proposal function can adjust the level of detail in its proposals based on the importance of the household tasks. For example, it can provide detailed proposals for high-importance household tasks, and concise proposals for low-importance tasks. The proposal function can also adjust the level of detail in its proposals according to the importance of the household tasks. This allows for more appropriate proposals by adjusting the level of detail based on the importance of the household tasks.

[0045] The suggestion function can apply different suggestion algorithms depending on the category of household task when making suggestions. For example, for cooking tasks, the suggestion function can apply a recipe suggestion algorithm. For cleaning tasks, it can also apply a cleaning method suggestion algorithm. For laundry tasks, it can also apply a laundry method suggestion algorithm. By applying different suggestion algorithms depending on the category of household task, more appropriate suggestions can be made.

[0046] The proposal department can prioritize proposals based on the submission timing of household tasks. For example, the proposal department will prioritize household tasks with approaching deadlines. The proposal department can also postpone household tasks with later deadlines. The proposal department can also prioritize proposals based on the submission timing of household tasks. This allows for more appropriate proposals by prioritizing proposals based on the submission timing of household tasks.

[0047] The suggestion function can adjust the order of suggestions based on the relevance of household tasks. For example, the suggestion function will prioritize suggesting highly relevant household tasks. It can also postpone suggesting less relevant household tasks. The suggestion function can adjust the order of suggestions based on the relevance of household tasks. This allows for more appropriate suggestions by adjusting the order of suggestions based on the relevance of household tasks.

[0048] The execution unit can analyze the family's past household chore task history during execution to select the optimal execution method. For example, the execution unit may prioritize suggesting execution methods that the family has used in the past. The execution unit can also select the most efficient execution method from the family's past household chore task history. The execution unit can also analyze the family's past household chore task history and customize the execution method. This allows the system to select the optimal execution method by analyzing the family's past household chore task history.

[0049] The execution unit can customize the means of household tasks based on the family's current living situation during execution. For example, the execution unit can suggest the most suitable means of household tasks according to the family's current living situation. The execution unit can also customize the means of household tasks considering the family's current living situation. The execution unit can also select the most efficient means of household tasks based on the family's current living situation. This allows for more appropriate execution by customizing the means of household tasks based on the family's current living situation.

[0050] The execution unit can select the optimal method for performing household tasks while considering the geographical location information of the family members. For example, the execution unit can prioritize performing household tasks related to the family members' current location. The execution unit can also select the optimal method for performing household tasks based on the geographical location information of the family members. The execution unit can also propose efficient methods for performing household tasks while considering the geographical location information of the family members. This allows for more efficient execution by selecting the optimal method for performing household tasks while considering the geographical location information of the family members.

[0051] The execution unit can analyze the family's social media activity during execution and suggest methods for performing household tasks. For example, the execution unit can suggest relevant household task methods based on the family's social media activity. The execution unit can also analyze the family's social media activity and suggest the most suitable method for performing household tasks. The execution unit can also select efficient household task methods considering the family's social media activity. In this way, by analyzing the family's social media activity, it is possible to suggest more appropriate methods for performing household tasks.

[0052] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0053] The data collection unit can analyze the family's past schedule history and select the optimal data collection method. For example, it can prioritize suggesting data collection methods that the family has frequently used in the past. The data collection unit can also select the most efficient data collection method based on the family's past schedule history. Furthermore, the data collection unit can analyze the family's past schedule history and customize the data collection method. This allows for the selection of the optimal data collection method by analyzing the family's past schedule history.

[0054] The suggestion function can adjust the level of detail in its suggestions based on the importance of the household tasks. For example, it can provide detailed suggestions for high-importance tasks, and concise suggestions for low-importance tasks. Furthermore, the suggestion function can adjust the level of detail in its suggestions according to the importance of the household tasks. This allows for more appropriate suggestions by adjusting the level of detail based on the importance of the household tasks.

[0055] The suggestion function can apply different suggestion algorithms depending on the category of household chore task. For example, a recipe suggestion algorithm can be applied to cooking tasks. A cleaning method suggestion algorithm can be applied to cleaning tasks. Furthermore, a laundry method suggestion algorithm can be applied to laundry tasks. By applying different suggestion algorithms depending on the category of household chore task, more appropriate suggestions can be made.

[0056] The execution unit can analyze the family's past household chore task history during execution to select the optimal execution method. For example, it can prioritize suggesting execution methods that the family has used in the past. The execution unit can also select the most efficient execution method from the family's past household chore task history. Furthermore, the execution unit can analyze the family's past household chore task history and customize the execution method. This allows for the selection of the optimal execution method by analyzing the family's past household chore task history.

[0057] The execution unit can customize the means of household tasks based on the family's current living situation during execution. For example, it can suggest the most suitable means of household tasks according to the family's current living situation. The execution unit can also customize the means of household tasks considering the family's current living situation. Furthermore, the execution unit can select the most efficient means of household tasks based on the family's current living situation. By customizing the means of household tasks based on the family's current living situation, more appropriate execution becomes possible.

[0058] The following briefly describes the processing flow for example form 1.

[0059] Step 1: The data collection unit collects family schedules. Family schedules include work schedules, school schedules, and personal appointments. The data collection unit can collect family schedules by having users input them into a smartphone or tablet app. Alternatively, it can automatically collect family schedules using a generation AI. Step 2: The proposal unit proposes an efficient division of household tasks and their start times based on the schedules collected by the collection unit. The proposal unit uses a generation AI to input the collected schedules and proposes an efficient division of household tasks and their start times. Step 3: The execution unit performs household tasks based on the division of labor and timing proposed by the proposal unit. The execution unit inputs the proposed division of labor and timing using the generation AI and performs the household tasks.

[0060] (Example of form 2) The AI ​​agent system according to an embodiment of the present invention is a system that combines a generating AI (Generating AI) with a smartphone / tablet application. This AI agent system learns the daily schedules of all family members (holidays, overtime, school life, etc.) and proposes efficient division of household tasks and start times. The AI ​​agent system understands the household chores and family schedules in a conversational format, and the generating AI assistant proposes division and adjustments. The AI ​​agent system calculates the time required for cooking and cleaning tasks based on the home appliances, cooking utensils, and cleaning tools owned, and also proposes optimal usage methods and maintenance schedules. The AI ​​agent system links with online shopping sites to propose home appliances and tools suited to each household, and aims to generate revenue by leading to purchases. The AI ​​agent system proposes menus, checks the inventory of ingredients, and also proposes online purchases of necessary ingredients. The AI ​​agent system shares schedules and division of tasks among family members through the application. For example, the AI ​​agent system inputs the family's schedules, such as holidays, overtime, and school life, into the generating AI. The generating AI learns this information and proposes efficient division of household tasks and start times. For example, if the whole family is on holiday, they can divide household tasks such as cleaning and cooking and perform them efficiently. Next, the AI ​​agent system calculates the time required for cooking and cleaning tasks based on the home appliances, cooking utensils, and cleaning supplies owned. For example, it suggests the optimal way to use appliances such as vacuum cleaners, washing machines, and ovens, as well as maintenance schedules. This enables efficient use and maintenance of appliances. Furthermore, the AI ​​agent system links with online shopping sites to suggest appliances and tools that are suitable for each household. For example, by linking with online shopping sites, it suggests appliances and tools that meet the family's needs and generates revenue by leading to purchases. This improves the efficiency of household chores and enhances the quality of life. In addition, the AI ​​agent system suggests menus, checks the inventory of ingredients, and suggests online purchases of necessary ingredients. For example, it checks the inventory of ingredients in the refrigerator and suggests purchasing necessary ingredients online. This reduces food waste and enables efficient meal preparation.Finally, the AI ​​agent system shares schedules and chore assignments among family members through the app. For example, everyone's schedule can be shared via the app, and household chore tasks can be coordinated. This allows everyone to perform household chores efficiently and ensures time for communication and relaxation. In this way, by using an AI agent that combines generative AI with a smartphone / tablet app, a system can be realized that learns the daily schedules of all family members and proposes efficient division of household chores and optimal start times. This improves the efficiency of household chores, enhances the quality of life, and ensures time for communication and relaxation among family members. Thus, the AI ​​agent system can learn the daily schedules of all family members and propose efficient division of household chores and optimal start times.

[0061] The AI ​​agent system according to this embodiment comprises a collection unit, a proposal unit, and an execution unit. The collection unit collects family schedules. Family schedules include, but are not limited to, work schedules, school schedules, and personal appointments. The collection unit collects schedules such as family holidays, overtime, and school life. The collection unit can use a smartphone or tablet app to collect family schedules. For example, the collection unit can collect family schedules by inputting them into the app. The collection unit can also use a generation AI to automatically collect family schedules. For example, the collection unit inputs family schedules into the generation AI, and the generation AI automatically collects the schedules. The proposal unit proposes efficient division and start times for household tasks based on the schedules collected by the collection unit. The proposal unit proposes efficient division and start times for household tasks based on the collected schedules. The proposal unit can use a generation AI to propose efficient division and start times for household tasks. For example, the proposal unit inputs the collected schedule into the generating AI, which then proposes an efficient distribution of household tasks and their start times. The execution unit then executes the household tasks based on the distribution and timing proposed by the proposal unit. The execution unit executes the household tasks based on the proposed distribution and timing. The execution unit can use the generating AI to execute the household tasks. For example, the execution unit inputs the proposed distribution and timing into the generating AI, which then executes the household tasks. Thus, the AI ​​agent system according to the embodiment can propose and execute an efficient distribution of household tasks and their start times based on the family's schedule.

[0062] The data collection unit collects family schedules. Family schedules include, but are not limited to, work schedules, school schedules, and personal appointments. The data collection unit collects schedules such as family holidays, overtime, and school life. The data collection unit can use smartphone or tablet apps to collect family schedules. For example, the data collection unit can collect schedules by inputting them into the app. The data collection unit can also use generative AI to automatically collect family schedules. For example, the data collection unit inputs family schedules into the generative AI, and the generative AI automatically collects the schedules. Specifically, each family member inputs their schedule through a dedicated application installed on a smartphone or tablet. This allows the data collection unit to centrally manage each member's schedule. Furthermore, by utilizing generative AI, it is also possible to automatically extract and collect schedules from, for example, email or calendar apps. The generative AI uses natural language processing technology to analyze the content of emails and calendars and extract schedule information. This reduces manual input work and allows for efficient schedule collection. Furthermore, the data collection unit can update family schedules in real time, ensuring that the latest information is always available. For example, if a family member changes their schedule, that information is reflected in the data collection unit and shared with other members. This allows for smoother schedule management for the entire family and enables efficient task sharing.

[0063] The proposal unit proposes efficient division of household tasks and start times based on schedules collected by the collection unit. For example, the proposal unit can use a generating AI to propose efficient division of household tasks and start times based on the collected schedules. Specifically, the generating AI analyzes the collected schedule data and calculates the optimal division of household tasks and start times, taking into account each member's free time and priorities. Based on past data and statistical information, the generating AI identifies tasks each member excels at and times when they are less burdensome, and makes suggestions accordingly. For example, the generating AI analyzes the history of past household task execution to identify tasks specific members excel at and times when they can perform them efficiently. This allows the proposal unit to propose optimal task divisions tailored to each member's characteristics and schedule. The proposal unit also adjusts start times, taking into account the priority and urgency of household tasks. For example, the suggestion team proposes that high-priority tasks be completed early, while lower-priority tasks be postponed. This enables more efficient execution of household tasks and reduces the overall burden on the family. Furthermore, the suggestion team notifies family members of the proposed tasks and collects feedback. For instance, they use smartphone notification functions to inform each member of the proposed task assignments and start times. Based on the feedback from members, the suggestion team can revise the suggestions and make more appropriate proposals. In this way, the suggestion team can propose efficient task assignments and start times based on the family's schedule, supporting the lives of the entire family.

[0064] The execution unit carries out household tasks based on the division of labor and timing proposed by the proposal unit. For example, the execution unit can use a generating AI to carry out household tasks. For example, the execution unit inputs the proposed division of labor and timing into the generating AI, and the generating AI carries out the household tasks. Specifically, the generating AI instructs each member to carry out the tasks based on the task division of labor and timing provided by the proposal unit. The generating AI sends notifications to each member's smartphone or tablet, informing them of the task start time and execution procedure. For example, the generating AI gives specific instructions for household tasks such as cleaning, laundry, and cooking, and explains the necessary procedures and points to note in detail. This allows each member to carry out the tasks efficiently. In addition, the generating AI monitors the progress of the tasks in real time and provides support as needed. For example, if the progress of the task is behind schedule or a problem occurs, the generating AI provides appropriate advice and support to help the task be carried out smoothly. Furthermore, the execution unit records the results of the task execution and reflects them in the next proposal. For example, by collecting each member's task execution history and feedback, the generating AI analyzes this data to improve the next set of suggestions. This allows the execution unit to support the efficient execution of household tasks and improve the overall quality of life for the family.

[0065] The data collection unit can collect schedules such as family holidays, overtime work, and school life. For example, the data collection unit can collect schedules such as family holidays, overtime work, and school life. The data collection unit can use smartphone or tablet apps to collect family schedules. For example, the data collection unit can collect schedules by inputting them into the app. Alternatively, the data collection unit can use a generation AI to automatically collect family schedules. For example, the data collection unit inputs family schedules into the generation AI, which then automatically collects the schedules. This allows for more accurate suggestions by collecting schedules such as family holidays, overtime work, and school life.

[0066] The suggestion unit can propose efficient division of household tasks and start times based on collected schedules. For example, the suggestion unit can propose efficient division of household tasks and start times based on collected schedules. The suggestion unit can use generative AI to propose efficient division of household tasks and start times. For example, the suggestion unit inputs the collected schedules into the generative AI, and the generative AI proposes efficient division of household tasks and start times. This improves the efficiency of household chores by proposing efficient division of household tasks and start times based on collected schedules.

[0067] The execution unit can perform household tasks based on the proposed division of labor and timing. For example, the execution unit can perform household tasks based on the proposed division of labor and timing. The execution unit can use a generative AI to perform household tasks. For example, the execution unit inputs the proposed division of labor and timing into the generative AI, and the generative AI performs the household tasks. This improves the efficiency of household chores by performing them based on the proposed division of labor and timing.

[0068] The suggestion function can calculate the time required for cooking and cleaning tasks based on the home appliances, cooking utensils, and cleaning tools owned, and propose optimal usage methods and maintenance schedules. For example, the suggestion function can use generative AI to propose optimal usage methods and maintenance schedules for home appliances, cooking utensils, and cleaning tools. For example, the suggestion function inputs information about the home appliances, cooking utensils, and cleaning tools owned into the generative AI, which then proposes optimal usage methods and maintenance schedules. This improves the efficiency of household chores by suggesting optimal usage methods and maintenance schedules based on the home appliances, cooking utensils, and cleaning tools owned.

[0069] The suggestion department can work in conjunction with online shopping sites to propose home appliances and tools tailored to each household. For example, the suggestion department can use generative AI to propose suitable home appliances and tools. For instance, the suggestion department inputs information from online shopping sites into the generative AI, which then proposes suitable home appliances and tools. This allows the department to propose suitable home appliances and tools by linking with online shopping sites, ultimately leading to purchases.

[0070] The suggestion department can propose menus, check ingredient inventory, and suggest online purchases of necessary ingredients. For example, the suggestion department can propose menus, check ingredient inventory, and suggest online purchases of necessary ingredients. The suggestion department can use a generation AI to propose menus, check ingredient inventory, and suggest online purchases. For example, the suggestion department inputs menu information and ingredient inventory information into the generation AI, which then proposes menus, checks ingredient inventory, and suggests online purchases. This reduces food waste and enables efficient meal preparation by proposing menus, checking ingredient inventory, and suggesting online purchases of necessary ingredients.

[0071] The execution unit can share schedules and chores among family members through an app. For example, the execution unit can share schedules and chores among family members through an app. The execution unit can use a generating AI to share schedules and chores. For example, the execution unit inputs family schedule and chore information into the generating AI, and the generating AI shares the schedules and chores. As a result, by sharing schedules and chores among family members through an app, all family members can perform household chores efficiently and secure time for communication and relaxation.

[0072] The data collection unit can estimate the emotions of family members and adjust the timing of schedule collection based on the estimated emotions. For example, if family members are feeling stressed, the data collection unit can delay the collection timing to collect information when they are relaxed. If family members are relaxed, the data collection unit can also advance the collection timing to collect schedules more efficiently. If family members are busy, the data collection unit can adjust the collection timing to collect necessary information in a short amount of time. In this way, by adjusting the timing of schedule collection according to family members' emotions, schedules can be collected at a more appropriate time. 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.

[0073] The data collection unit can analyze the family's past schedule history and select the optimal data collection method. For example, the unit might prioritize suggesting data collection methods frequently used by the family in the past. The unit can also select the most efficient data collection method based on the family's past schedule history. Furthermore, the unit can analyze the family's past schedule history and customize the data collection method. This allows the unit to select the optimal data collection method by analyzing the family's past schedule history.

[0074] The collection unit can filter schedules based on the family's current projects and areas of interest. For example, it can prioritize collecting schedules related to projects the family is currently working on. The collection unit can also filter and collect relevant schedules based on the family's areas of interest. The collection unit can also exclude unnecessary schedules, taking into account the family's current projects and areas of interest. This allows for efficient collection of relevant schedules by filtering based on the family's current projects and areas of interest.

[0075] The data collection unit can estimate the emotions of family members and determine the priority of schedules to collect based on those estimated emotions. For example, if family members are stressed, the data collection unit will postpone less important schedules. If family members are relaxed, the data collection unit can also prioritize collecting more important schedules. If family members are busy, the data collection unit can also prioritize collecting more important schedules to efficiently gather information. This allows for the collection of more appropriate schedules by prioritizing schedules according to family members' emotions. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0076] The data collection unit can prioritize collecting highly relevant schedules by considering the geographical location information of the family members during schedule collection. For example, the data collection unit can prioritize collecting schedules related to the family members' current location. The data collection unit can also filter and collect highly relevant schedules based on the family members' geographical location information. The data collection unit can also exclude unnecessary schedules by considering the family members' geographical location information. This allows for efficient schedule collection by prioritizing the collection of highly relevant schedules while considering the family members' geographical location information.

[0077] The data collection unit can analyze family social media activity and collect relevant schedules during schedule collection. For example, it can collect relevant events and appointments from family social media activity. The data collection unit can also analyze family social media activity and filter to collect relevant schedules. The data collection unit can also exclude unnecessary schedules by considering family social media activity. This allows for efficient collection of relevant schedules by analyzing family social media activity.

[0078] The suggestion function can estimate the family's emotions and adjust the way it presents suggestions based on those emotions. For example, if the family is stressed, the suggestion function will provide simple and easy-to-understand suggestions. If the family is relaxed, the suggestion function can also provide detailed suggestions. If the family is busy, the suggestion function can provide concise suggestions. By adjusting the way suggestions are presented according to the family's emotions, more appropriate suggestions can be made. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may include, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0079] The proposal function can adjust the level of detail in its proposals based on the importance of the household tasks. For example, it can provide detailed proposals for high-importance household tasks, and concise proposals for low-importance tasks. The proposal function can also adjust the level of detail in its proposals according to the importance of the household tasks. This allows for more appropriate proposals by adjusting the level of detail based on the importance of the household tasks.

[0080] The suggestion function can apply different suggestion algorithms depending on the category of household task when making suggestions. For example, for cooking tasks, the suggestion function can apply a recipe suggestion algorithm. For cleaning tasks, it can also apply a cleaning method suggestion algorithm. For laundry tasks, it can also apply a laundry method suggestion algorithm. By applying different suggestion algorithms depending on the category of household task, more appropriate suggestions can be made.

[0081] The suggestion unit can estimate the family's emotions and adjust the length of the suggestion based on the estimated emotions. For example, if the family is stressed, the suggestion unit will make a short, to-the-point suggestion. If the family is relaxed, the suggestion unit can also make a detailed suggestion. If the family is busy, the suggestion unit can also make a concise suggestion. This allows for more appropriate suggestions by adjusting the length of the suggestion according to the family's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0082] The proposal department can prioritize proposals based on the submission timing of household tasks. For example, the proposal department will prioritize household tasks with approaching deadlines. The proposal department can also postpone household tasks with later deadlines. The proposal department can also prioritize proposals based on the submission timing of household tasks. This allows for more appropriate proposals by prioritizing proposals based on the submission timing of household tasks.

[0083] The suggestion function can adjust the order of suggestions based on the relevance of household tasks. For example, the suggestion function will prioritize suggesting highly relevant household tasks. It can also postpone suggesting less relevant household tasks. The suggestion function can adjust the order of suggestions based on the relevance of household tasks. This allows for more appropriate suggestions by adjusting the order of suggestions based on the relevance of household tasks.

[0084] The execution unit can estimate the emotions of family members and adjust how household tasks are performed based on the estimated emotions. For example, if family members are stressed, the execution unit will perform household tasks in a simple manner. If family members are relaxed, the execution unit can also perform household tasks with detailed procedures. If family members are busy, the execution unit can also perform household tasks in an efficient manner. This allows for more appropriate execution by adjusting how household tasks are performed according to family members' emotions. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0085] The execution unit can analyze the family's past household chore task history during execution to select the optimal execution method. For example, the execution unit may prioritize suggesting execution methods that the family has used in the past. The execution unit can also select the most efficient execution method from the family's past household chore task history. The execution unit can also analyze the family's past household chore task history and customize the execution method. This allows the system to select the optimal execution method by analyzing the family's past household chore task history.

[0086] The execution unit can customize the means of household tasks based on the family's current living situation during execution. For example, the execution unit can suggest the most suitable means of household tasks according to the family's current living situation. The execution unit can also customize the means of household tasks considering the family's current living situation. The execution unit can also select the most efficient means of household tasks based on the family's current living situation. This allows for more appropriate execution by customizing the means of household tasks based on the family's current living situation.

[0087] The execution unit can estimate the emotions of family members and prioritize household tasks based on those estimated emotions. For example, if family members are stressed, the execution unit will postpone less important household tasks. If family members are relaxed, the execution unit can also prioritize more important household tasks. If family members are busy, the execution unit can also prioritize more important household tasks to perform them efficiently. This allows for more appropriate execution of household tasks by prioritizing them according to family members' emotions. Emotion estimation is achieved using an emotion estimation function, for example, with an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0088] The execution unit can select the optimal method for performing household tasks while considering the geographical location information of the family members. For example, the execution unit can prioritize performing household tasks related to the family members' current location. The execution unit can also select the optimal method for performing household tasks based on the geographical location information of the family members. The execution unit can also propose efficient methods for performing household tasks while considering the geographical location information of the family members. This allows for more efficient execution by selecting the optimal method for performing household tasks while considering the geographical location information of the family members.

[0089] The execution unit can analyze the family's social media activity during execution and suggest methods for performing household tasks. For example, the execution unit can suggest relevant household task methods based on the family's social media activity. The execution unit can also analyze the family's social media activity and suggest the most suitable method for performing household tasks. The execution unit can also select efficient household task methods considering the family's social media activity. In this way, by analyzing the family's social media activity, it is possible to suggest more appropriate methods for performing household tasks.

[0090] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0091] The suggestion unit can estimate the emotions of family members and adjust the distribution of household tasks based on those estimated emotions. For example, if a family is feeling stressed, the suggestion unit can suggest reducing the distribution of household tasks and increasing the time they can relax. If a family is relaxed, the suggestion unit can also suggest increasing the distribution of household tasks and making them more efficient. Furthermore, if a family is busy, the suggestion unit can minimize the distribution of household tasks and adjust them so that they can focus on important tasks. In this way, by adjusting the distribution of household tasks according to the family's emotions, everyone in the family can participate in household chores without undue burden. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0092] The data collection unit can analyze the family's past schedule history and select the optimal data collection method. For example, it can prioritize suggesting data collection methods that the family has frequently used in the past. The data collection unit can also select the most efficient data collection method based on the family's past schedule history. Furthermore, the data collection unit can analyze the family's past schedule history and customize the data collection method. This allows for the selection of the optimal data collection method by analyzing the family's past schedule history.

[0093] The suggestion function can estimate the family's emotions and adjust the way it presents suggestions based on those emotions. For example, if the family is stressed, the suggestion function can offer simple and easy-to-understand suggestions. If the family is relaxed, the suggestion function can offer more detailed suggestions. Furthermore, if the family is busy, the suggestion function can offer concise suggestions. By adjusting the way suggestions are presented according to the family's emotions, more appropriate suggestions can be made. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may include, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0094] The execution unit can estimate the emotions of family members and adjust how household tasks are performed based on those estimated emotions. For example, if family members are stressed, the execution unit can perform household tasks in a simple manner. If family members are relaxed, the execution unit can perform household tasks with detailed procedures. Furthermore, if family members are busy, the execution unit can perform household tasks in an efficient manner. This allows for more appropriate execution by adjusting how household tasks are performed according to family members' emotions. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0095] The suggestion function can adjust the level of detail in its suggestions based on the importance of the household tasks. For example, it can provide detailed suggestions for high-importance tasks, and concise suggestions for low-importance tasks. Furthermore, the suggestion function can adjust the level of detail in its suggestions according to the importance of the household tasks. This allows for more appropriate suggestions by adjusting the level of detail based on the importance of the household tasks.

[0096] The suggestion function can apply different suggestion algorithms depending on the category of household chore task. For example, a recipe suggestion algorithm can be applied to cooking tasks. A cleaning method suggestion algorithm can be applied to cleaning tasks. Furthermore, a laundry method suggestion algorithm can be applied to laundry tasks. By applying different suggestion algorithms depending on the category of household chore task, more appropriate suggestions can be made.

[0097] The suggestion unit can estimate the family's emotions and adjust the length of the suggestion based on the estimated emotions. For example, if the family is stressed, the suggestion unit can make a short, to-the-point suggestion. If the family is relaxed, the suggestion unit can make a detailed suggestion. Furthermore, if the family is busy, the suggestion unit can make a concise suggestion. By adjusting the length of the suggestion according to the family's emotions, more appropriate suggestions can be made. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is not limited to, but may include, text generation AI (e.g., LLM) or multimodal generation AI.

[0098] The execution unit can analyze the family's past household chore task history during execution to select the optimal execution method. For example, it can prioritize suggesting execution methods that the family has used in the past. The execution unit can also select the most efficient execution method from the family's past household chore task history. Furthermore, the execution unit can analyze the family's past household chore task history and customize the execution method. This allows for the selection of the optimal execution method by analyzing the family's past household chore task history.

[0099] The execution unit can customize the means of household tasks based on the family's current living situation during execution. For example, it can suggest the most suitable means of household tasks according to the family's current living situation. The execution unit can also customize the means of household tasks considering the family's current living situation. Furthermore, the execution unit can select the most efficient means of household tasks based on the family's current living situation. By customizing the means of household tasks based on the family's current living situation, more appropriate execution becomes possible.

[0100] The execution unit can estimate the emotions of family members and prioritize household tasks based on those estimated emotions. For example, if family members are stressed, the execution unit can postpone less important household tasks. Conversely, if family members are relaxed, the execution unit can prioritize more important household tasks. Furthermore, if family members are busy, the execution unit can prioritize more important tasks to perform household chores efficiently. This allows for more appropriate execution of household tasks by prioritizing them according to family members' emotions. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0101] The following briefly describes the processing flow for example form 2.

[0102] Step 1: The data collection unit collects family schedules. Family schedules include work schedules, school schedules, and personal appointments. The data collection unit can collect family schedules by having users input them into a smartphone or tablet app. Alternatively, it can automatically collect family schedules using a generation AI. Step 2: The proposal unit proposes an efficient division of household tasks and their start times based on the schedules collected by the collection unit. The proposal unit uses a generation AI to input the collected schedules and proposes an efficient division of household tasks and their start times. Step 3: The execution unit performs household tasks based on the division of labor and timing proposed by the proposal unit. The execution unit inputs the proposed division of labor and timing using the generation AI and performs the household tasks.

[0103] 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.

[0104] 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.

[0105] 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.

[0106] Each of the multiple elements described above, including the collection unit, proposal unit, execution unit, and emotion estimation function, 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 collects the family's schedule. The proposal unit is implemented by the specific processing unit 290 of the data processing device 12 and proposes an efficient distribution and start timing for household tasks based on the collected schedule. The execution unit is implemented by the control unit 46A of the smart device 14 and executes the household tasks based on the proposed distribution and timing. The emotion estimation function is implemented by the specific processing unit 290 of the data processing device 12 and estimates the family's emotions and adjusts the collection timing. 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.

[0107] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0108] 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.

[0109] 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.

[0110] 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.

[0111] 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.

[0112] 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).

[0113] 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.

[0114] 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.

[0115] 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.

[0116] 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.

[0117] 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.

[0118] 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.).

[0119] 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.

[0120] 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.

[0121] 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.

[0122] Each of the multiple elements described above, including the data collection unit, proposal unit, execution unit, and emotion estimation function, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the data collection unit is implemented by the control unit 46A of the smart glasses 214 and collects the family's schedule. The proposal unit is implemented by the identification processing unit 290 of the data processing unit 12 and proposes an efficient distribution and start timing for household tasks based on the collected schedule. The execution unit is implemented by the control unit 46A of the smart glasses 214 and executes the household tasks based on the proposed distribution and timing. The emotion estimation function is implemented by the identification processing unit 290 of the data processing unit 12 and estimates the family's emotions and adjusts the collection timing. 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.

[0123] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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).

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.).

[0135] 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.

[0136] 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.

[0137] 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.

[0138] Each of the multiple elements described above, including the collection unit, proposal unit, execution unit, and emotion estimation function, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the collection unit is implemented by the control unit 46A of the headset terminal 314 and collects the family's schedule. The proposal unit is implemented by the specific processing unit 290 of the data processing unit 12 and proposes an efficient distribution and start timing for household tasks based on the collected schedule. The execution unit is implemented by the control unit 46A of the headset terminal 314 and executes the household tasks based on the proposed distribution and timing. The emotion estimation function is implemented by the specific processing unit 290 of the data processing unit 12 and estimates the family's emotions and adjusts the collection timing. 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.

[0139] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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).

[0145] 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.

[0146] 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.

[0147] 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.

[0148] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0149] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0150] In 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.

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

[0152] The specific processing unit 290 transmits the result of the specific processing to the 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.

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

[0154] The data processing system 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.

[0155] Each of the multiple elements described above, including the collection unit, proposal unit, execution unit, and emotion estimation function, 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 collects the family's schedule. The proposal unit is implemented by the specific processing unit 290 of the data processing unit 12 and proposes an efficient distribution and start timing for household tasks based on the collected schedule. The execution unit is implemented by the control unit 46A of the robot 414 and executes the household tasks based on the proposed distribution and timing. The emotion estimation function is implemented by the specific processing unit 290 of the data processing unit 12 and estimates the family's emotions and adjusts the collection timing. 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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."

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] (Note 1) A collection department that collects family schedules, Based on the schedule collected by the aforementioned collection unit, a proposal unit proposes an efficient distribution and start time for household tasks. The system includes an execution unit that performs household tasks based on the division of labor and timing proposed by the aforementioned proposal unit. A system characterized by the following features. (Note 2) The aforementioned collection unit is Collect family schedules including holidays, overtime, and school life. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned proposal section is, Based on the collected schedules, we propose efficient division and start times for household tasks. The system described in Appendix 1, characterized by the features described herein. (Note 4) The execution unit is, Perform household tasks based on the proposed division of labor and timing. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned proposal section is, Based on your owned home appliances, cooking utensils, and cleaning supplies, we calculate the time required for cooking and cleaning tasks and propose optimal usage methods and maintenance schedules. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned proposal section is, It works in conjunction with online shopping sites to suggest home appliances and tools that are suitable for each household. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned proposal section is, We provide menu suggestions, check ingredient inventory, and suggest online purchases of necessary ingredients. The system described in Appendix 1, characterized by the features described herein. (Note 8) The execution unit is, Share schedules and responsibilities with family members through the app. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned collection unit is It estimates the emotions of family members and adjusts the timing of schedule collection based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned collection unit is Analyze the family's past schedule history and select the optimal data collection method. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned collection unit is When collecting schedules, filter them based on the family's current projects and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned collection unit is Estimate family members' emotions and prioritize the schedule for collecting information based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned collection unit is When collecting schedules, the system prioritizes collecting highly relevant schedules by considering the family's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned collection unit is When collecting schedules, analyze family members' social media activity and collect relevant schedules. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned proposal section is, Estimate the family's emotions and adjust the way the proposal is expressed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned proposal section is, When making suggestions, adjust the level of detail based on the importance of the household tasks. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned proposal section is, When making suggestions, different suggestion algorithms are applied depending on the category of household chore task. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned proposal section is, Estimate the family's emotions and adjust the length of the suggestion based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned proposal section is, When making a proposal, prioritize the proposal based on when household tasks are submitted. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned proposal section is, When making suggestions, adjust the order of suggestions based on the relevance of household tasks. The system described in Appendix 1, characterized by the features described herein. (Note 21) The execution unit is, It estimates the emotions of family members and adjusts how household tasks are performed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 22) The execution unit is, During execution, the system analyzes the family's past history of household chores to select the optimal execution method. The system described in Appendix 1, characterized by the features described herein. (Note 23) The execution unit is, During execution, customize the methods for household tasks based on the family's current living situation. The system described in Appendix 1, characterized by the features described herein. (Note 24) The execution unit is, It estimates the emotions of family members and prioritizes household tasks based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 25) The execution unit is, During execution, the system selects the optimal method for performing household tasks, taking into account the geographical location information of family members. The system described in Appendix 1, characterized by the features described herein. (Note 26) The execution unit is, During execution, analyze family social media activity and suggest ways to perform household tasks. The system described in Appendix 1, characterized by the features described herein. [Explanation of symbols]

[0175] 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. A collection department that collects family schedules, Based on the schedule collected by the aforementioned collection unit, a proposal unit proposes an efficient distribution and start time for household tasks. The system includes an execution unit that performs household tasks based on the division of labor and timing proposed by the aforementioned proposal unit. A system characterized by the following features.

2. The aforementioned collection unit is Collect family schedules including holidays, overtime, and school life. The system according to feature 1.

3. The aforementioned proposal section is, Based on the collected schedules, we propose efficient division and start times for household tasks. The system according to feature 1.

4. The execution unit is, Perform household tasks based on the proposed division of labor and timing. The system according to feature 1.

5. The aforementioned proposal section is, Based on your owned home appliances, cooking utensils, and cleaning supplies, we calculate the time required for cooking and cleaning tasks and propose optimal usage methods and maintenance schedules. The system according to feature 1.

6. The aforementioned proposal section is, It works in conjunction with online shopping sites to suggest home appliances and tools that are suitable for each household. The system according to feature 1.

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

8. The execution unit is, Share schedules and responsibilities with family members through the app. The system according to feature 1.

9. The aforementioned collection unit is It estimates the emotions of family members and adjusts the timing of schedule collection based on those estimated emotions. The system according to feature 1.

10. The aforementioned collection unit is Analyze the family's past schedule history and select the optimal data collection method. The system according to feature 1.

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