system

Abstract

The system according to this embodiment aims to reduce the burden of housework and childcare and improve the quality of life for families. [Solution] The system according to the embodiment comprises a schedule proposal unit, an execution unit, a list creation unit, and a consultation unit. The schedule proposal unit proposes a household chore schedule. The execution unit performs household chores based on the schedule proposed by the schedule proposal unit. The list creation unit automatically creates a shopping list. The consultation unit responds to childcare consultations.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a persona chatbot control method performed by at least one processor, 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, a comprehensive support system for reducing the burden of housework and childcare has not been sufficiently provided, and there is room for improvement.

[0005] The system according to the embodiment aims to reduce the burden of housework and childcare and improve the quality of life of the family.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a schedule proposal unit, an execution unit, a list creation unit, and a consultation unit. The schedule proposal unit proposes a household chore schedule. The execution unit performs household chores based on the schedule proposed by the schedule proposal unit. The list creation unit automatically creates a shopping list. The consultation unit provides childcare consultations. [Effects of the Invention]

[0007] The system according to this embodiment can reduce the burden of housework and childcare and improve the quality of life for families. [Brief explanation of the drawing]

[0008] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Modes for carrying out the invention]

[0009] Hereinafter, an example of an embodiment of the system relating to the technology of this disclosure will be described with reference to the attached drawings.

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

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

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

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

[0014] In the following embodiments, the numbered communication I / F (Interface) is an interface including a communication processor, an antenna, and the like. The communication I / F manages communication between a plurality of computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B". That is, "A and / or B" means that it may be only A, only B, or a combination of A and B. Also, in this specification, when expressing three or more matters connected by "and / or", the same concept as "A and / or B" is applied.

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

[0017] As shown in FIG. 1, the data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

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

[0019] The smart device 14 comprises a computer 36, a receiving device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The receiving device 38, output device 40, and camera 42 are also connected to the bus 52.

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

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

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

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

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

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

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

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

[0028] (Example of form 1) The AI ​​home manager system according to an embodiment of the present invention is a comprehensive AI service for reducing the burden of housework and childcare and improving the quality of life for families. This AI home manager system works in conjunction with IoT devices in the home and can be operated via smartphones and smart speakers. The AI ​​home manager system learns the family's lifestyle patterns and proposes an optimal housework schedule. It also answers questions about childcare and reduces anxiety about raising children. For example, the AI ​​home manager system works in conjunction with IoT devices in the home. For example, it uses a refrigerator camera to manage food inventory and automatically adds necessary ingredients to the shopping list. The AI ​​home manager system also learns the family's lifestyle patterns and proposes an optimal housework schedule. For example, it suggests times for vacuuming and doing laundry. Furthermore, the AI ​​home manager system answers questions about childcare. For example, it provides advice on questions about child development and concerns about childcare. It also supports children's homework and learning. For example, it manages homework progress and provides necessary learning content. In this way, the AI ​​home manager system can reduce the burden of housework and childcare and improve the quality of life for families. For example, by making housework more efficient and supporting childcare, families can spend more time together. Furthermore, the AI ​​home manager system can learn the family's lifestyle patterns and provide optimal support tailored to individual needs. This allows the AI ​​home manager system to reduce the burden of housework and childcare, improving the family's quality of life.

[0029] The AI ​​home manager system according to this embodiment comprises a schedule proposal unit, an execution unit, a list creation unit, and a consultation unit. The schedule proposal unit proposes a household chore schedule. The schedule proposal unit can, for example, learn the family's lifestyle patterns and propose an optimal household chore schedule. The schedule proposal unit can use AI to analyze the family's lifestyle patterns and generate an optimal household chore schedule. For example, the schedule proposal unit learns the family's wake-up times, meal times, and frequency of household chores, and proposes an optimal household chore schedule based on this. The schedule proposal unit can also estimate the family's emotions and adjust the proposed household chore schedule based on the estimated emotions of the family. For example, if the family is feeling stressed, it will propose a schedule that includes more time for relaxation. The execution unit performs household chores based on the schedule proposed by the schedule proposal unit. For example, the execution unit performs tasks such as vacuuming and laundry according to the proposed schedule. The execution unit can use AI to automate the execution of household chores. For example, the execution unit controls IoT devices such as robotic vacuums and washing machines and performs household chores based on the proposed schedule. The execution unit can also estimate the emotions of family members and adjust the timing of household chores based on those estimates. For example, if family members are feeling stressed, it will perform chores during times when they can relax. The list creation unit automatically creates shopping lists. The list creation unit can manage food inventory using, for example, a refrigerator camera and automatically add necessary ingredients to the shopping list. The list creation unit can use AI to analyze food inventory and automatically add necessary ingredients to the list. For example, it can analyze images of food in the refrigerator, detect ingredients that are running low, and add them to the list. The list creation unit can also estimate the emotions of family members and adjust the contents of the shopping list based on those estimates. For example, if family members are feeling stressed, it will prioritize adding ingredients that promote relaxation to the list. The consultation unit provides childcare advice. The consultation unit can answer questions about childcare and alleviate anxieties about raising children. The consultation unit can use AI to generate answers to questions about childcare.For example, the consultation department analyzes questions about childcare and provides appropriate answers. Furthermore, the consultation department can estimate the family's emotions and adjust the content of the childcare consultation based on those estimated emotions. For instance, if a family is feeling stressed, it can provide relaxing advice. In this way, the AI ​​home manager system according to this embodiment can reduce the burden of housework and childcare and improve the quality of life for the family.

[0030] The schedule suggestion unit proposes a household chore schedule. For example, the schedule suggestion unit learns the family's lifestyle patterns and proposes an optimal household chore schedule. Specifically, the schedule suggestion unit analyzes in detail the family's lifestyle patterns, such as wake-up times, meal times, commute times, return-home times, and bedtimes. This utilizes location information and activity data collected from the family's smartphones and wearable devices. Based on this data, the AI ​​understands the family's daily routine and generates an optimal household chore schedule. For example, it adjusts the schedule so that simple chores are assigned during the busy morning hours, and chores such as cleaning and laundry are performed during the relaxing evening hours. The schedule suggestion unit can also estimate the family's emotions and adjust the proposed household chore schedule based on the estimated emotions. Emotion estimation uses technology that analyzes the family's facial expressions, tone of voice, and social media posts. For example, if the family is feeling stressed, it proposes a schedule that includes more time for relaxation. This can reduce the family's mental burden and provide a more comfortable life. Furthermore, the schedule suggestion unit adjusts the schedule by also considering external factors such as the season, weather, and special events. For example, on rainy days, the system prioritizes indoor chores, while on sunny days, it suggests outdoor chores such as laundry and gardening. This allows the scheduling department to provide a flexible chore schedule tailored to the family's lifestyle.

[0031] The execution unit performs household chores based on the schedule proposed by the schedule proposal unit. For example, the execution unit will perform tasks such as vacuuming and laundry according to the proposed schedule. Specifically, the execution unit controls IoT devices such as robotic vacuums, washing machines, and dishwashers, automating household chores based on the proposed schedule. For example, a robotic vacuum will automatically start cleaning at a specified time, cleaning every corner of the room. A washing machine will start washing at a proposed time and send a notification when the washing is complete. The execution unit can also estimate the emotions of family members and adjust the timing of chore execution based on those emotions. For example, if family members are feeling stressed, chores will be performed during times when they can relax. This ensures that chore execution does not affect the family's daily rhythm. Furthermore, the execution unit monitors the progress of chores in real time and makes adjustments as needed. For example, if a robotic vacuum hits an obstacle or a washing machine detects an abnormality, the execution unit will automatically respond and resolve the problem. The execution unit also records the results of chore execution and provides feedback to the schedule proposal unit. This allows the scheduling function to optimize the schedule based on the progress of household chores, enabling more efficient household management.

[0032] The list creation unit automatically generates shopping lists. For example, it manages food inventory using a refrigerator camera and automatically adds necessary items to the shopping list. Specifically, the list creation unit analyzes images captured by the refrigerator camera to determine the type and quantity of food items. AI uses image recognition technology to identify food items, detecting low stocks and adding them to the list. For example, if the stock of milk, eggs, or vegetables decreases, the list creation unit automatically adds these items to the shopping list. The list creation unit can also estimate family emotions and adjust the shopping list based on these estimations. For example, if family members are stressed, it prioritizes adding relaxing items to the list. This includes considering family preferences and past purchase history. Furthermore, the list creation unit adjusts the shopping list according to the season and special events. For example, it adds special ingredients and decorations to the list for events such as Christmas or birthdays. This allows the list creation unit to provide a flexible shopping list tailored to family needs. Additionally, the list creation unit can integrate with online shopping sites and automatically order the items added to the list. This can reduce the hassle of shopping and make family life more convenient.

[0033] The consultation department provides support for childcare consultations. For example, it answers questions about childcare and helps alleviate anxieties about raising children. Specifically, the consultation department analyzes questions about childcare and provides appropriate answers. AI uses natural language processing technology to understand the content of questions and generates answers based on past databases and expert knowledge. For example, it provides detailed advice on specific childcare concerns such as a baby's nighttime crying or how to introduce solid foods. The consultation department can also estimate the emotions of the family and adjust the content of the childcare consultation based on the estimated emotions of the family. For example, if the family is feeling stressed, it will provide advice to help them relax. This includes suggesting ways to relax during childcare breaks and activities to reduce stress. Furthermore, the consultation department can also provide the latest information and trends related to childcare. For example, it will provide introductions to new childcare products and methods, and interviews with experts, so that families can stay up-to-date. In this way, the consultation department can alleviate anxieties about childcare and provide support to help families raise children with more confidence. In addition, the consultation department can collect feedback from families and continuously improve the accuracy and effectiveness of its answers. This allows the consultation department to always provide the latest information and high-quality support, thereby improving the quality of life for families.

[0034] The schedule suggestion unit can learn the family's lifestyle patterns and propose an optimal household chore schedule. For example, the schedule suggestion unit learns the family's wake-up times, meal times, and frequency of household chores, and proposes an optimal household chore schedule based on this information. The schedule suggestion unit can use AI to analyze the family's lifestyle patterns and generate an optimal household chore schedule. For example, the schedule suggestion unit collects family behavior data to learn the family's lifestyle patterns and inputs it into an AI model. The AI ​​model analyzes the collected data and identifies the family's lifestyle patterns. For example, the schedule suggestion unit learns the family's wake-up times, meal times, and frequency of household chores, and proposes an optimal household chore schedule based on this information. This allows the system to propose an optimal household chore schedule based on the family's lifestyle patterns. Some or all of the above-described processes in the schedule suggestion unit may be performed using AI, for example, or without AI. For example, the schedule suggestion unit can input family behavior data into an AI model, which can then identify the family's lifestyle patterns and generate an optimal household chore schedule.

[0035] The execution unit can perform household chores based on a schedule proposed by the schedule proposal unit. For example, the execution unit can perform tasks such as vacuuming or doing laundry according to the proposed schedule. The execution unit can automate the execution of household chores using AI. For example, the execution unit can control IoT devices such as robotic vacuums or washing machines and perform chores based on a proposed schedule. For example, the execution unit can control a robotic vacuum to start cleaning at a proposed time. It can also control a washing machine to start doing laundry at a proposed time. Furthermore, the execution unit can estimate the emotions of family members and adjust the timing of household chore execution based on the estimated emotions of family members. For example, if family members are feeling stressed, it can perform chores during times when they can relax. This allows the execution of chores to be performed according to the proposed schedule. Some or all of the above processes in the execution unit may be performed using AI, for example, or without AI. For example, the execution unit can connect IoT devices such as robotic vacuums or washing machines to an AI model, and the AI ​​model can perform household chores based on a proposed schedule.

[0036] The list creation unit can manage food inventory using a refrigerator camera and automatically add necessary ingredients to a shopping list. For example, the list creation unit can analyze images of food in the refrigerator, detect ingredients that are running low, and add them to the list. The list creation unit can also use AI to analyze food inventory and automatically add necessary ingredients to the list. For example, the list creation unit can input images of food in the refrigerator into an AI model, which can detect ingredients that are running low and add them to the list. The list creation unit can also estimate the emotions of family members and adjust the contents of the shopping list based on the estimated emotions. For example, if family members are feeling stressed, it can prioritize adding ingredients that promote relaxation to the list. This enables automated food inventory management and shopping list creation. Some or all of the above processes in the list creation unit may be performed using AI, or not. For example, the list creation unit can input images of food in the refrigerator into an AI model, which can detect ingredients that are running low and add them to the list.

[0037] The consultation department can answer questions about childcare and alleviate anxieties about raising children. For example, the consultation department can answer questions about childcare and alleviate anxieties about raising children. The consultation department can use AI to generate answers to questions about childcare. For example, the consultation department can input questions about childcare into an AI model, and the AI ​​model can generate appropriate answers. The consultation department can also estimate the emotions of the family and adjust the content of the childcare consultation answers based on the estimated emotions of the family. For example, if the family is feeling stressed, it can provide relaxing advice. In this way, by answering questions about childcare, anxieties about raising children can be alleviated. Some or all of the above processes in the consultation department may be performed using AI, for example, or not using AI. For example, the consultation department can input questions about childcare into an AI model, and the AI ​​model can generate appropriate answers.

[0038] The support department can provide assistance with children's homework and learning. For example, the support department can manage the progress of children's homework and provide necessary learning content. The support department can also use AI to support children's homework and learning. For example, the support department can input the progress data of children's homework into an AI model, and the AI ​​model can provide appropriate learning content. The support department can also estimate the emotions of the family and adjust the learning support content based on the estimated emotions of the family. For example, if a child is feeling stressed, it can provide learning content that helps them relax. This makes it possible to support children's homework and learning. Some or all of the above processes in the support department may be performed using AI, or not using AI. For example, the support department can input the progress data of children's homework into an AI model, and the AI ​​model can provide appropriate learning content.

[0039] The schedule suggestion unit can analyze a family's past household chore history and select the optimal schedule suggestion method. For example, the schedule suggestion unit can analyze the frequency of household chores performed by the family in the past and propose the optimal schedule. The schedule suggestion unit can use AI to analyze a family's past household chore history and select the optimal schedule suggestion method. For example, the schedule suggestion unit can input the family's past household chore history data into an AI model, which will analyze the frequency and timing of chores and propose the optimal schedule. The schedule suggestion unit can also analyze the timing of household chores performed by the family in the past and propose chores at the optimal time. For example, the schedule suggestion unit can input the family's past household chore history data into an AI model, which will analyze the timing of chores and propose chores at the optimal time. This allows for the selection of the optimal schedule suggestion method based on past household chore history. Some or all of the above processing in the schedule suggestion unit may be performed using AI, for example, or without AI. For example, the schedule suggestion unit can input the family's past household chore history data into an AI model, which will analyze the frequency and timing of chores and propose the optimal schedule.

[0040] The schedule suggestion unit can filter household chore schedules based on the family's current lifestyle and areas of interest. For example, if the family is currently busy, the schedule suggestion unit will prioritize suggesting chores that can be completed quickly. The schedule suggestion unit can use AI to analyze the family's current lifestyle and areas of interest and suggest an optimal household chore schedule. For example, the schedule suggestion unit inputs data on the family's current lifestyle and areas of interest into an AI model, which then analyzes the family's lifestyle and areas of interest and suggests an optimal household chore schedule. Furthermore, if the family is interested in health, the schedule suggestion unit can also suggest healthy chores (e.g., cleaning and cooking). For example, the schedule suggestion unit inputs health data into an AI model, which then suggests healthy chores. This allows the system to suggest household chore schedules based on the family's current lifestyle and areas of interest. Some or all of the above-described processes in the schedule suggestion unit may be performed using AI, or not using AI. For example, the schedule suggestion unit can input data on the family's current living situation and areas of interest into an AI model, which then analyzes the family's living situation and areas of interest to suggest an optimal household chore schedule.

[0041] The schedule suggestion unit can prioritize suggesting highly relevant schedules when proposing household chore schedules, taking into account the geographical location information of the family. For example, the schedule suggestion unit can prioritize suggesting chores that are close to where the family is currently located. The schedule suggestion unit can use AI to analyze the geographical location information of the family and propose an optimal household chore schedule. For example, the schedule suggestion unit can input the family's geographical location data into an AI model, the AI ​​model can analyze the family's current location, and propose an optimal household chore schedule. Furthermore, if the family is on the move, the schedule suggestion unit can prioritize suggesting chores that can be done at their destination. For example, the schedule suggestion unit can input the family's travel route data into an AI model, and the AI ​​model can propose chores that can be done at their destination. This allows for the suggestion of highly relevant schedules based on geographical location information. Some or all of the above processing in the schedule suggestion unit may be performed using AI, or not using AI. For example, the schedule suggestion unit can input the family's geographical location data into an AI model, the AI ​​model can analyze the family's current location, and propose an optimal household chore schedule.

[0042] The schedule suggestion unit can analyze the family's social media activity and suggest relevant schedules when proposing household chore schedules. For example, the schedule suggestion unit can suggest household chore schedules based on events shared by the family on social media. The schedule suggestion unit can use AI to analyze the family's social media activity and suggest the optimal household chore schedule. For example, the schedule suggestion unit can input the family's social media activity data into an AI model, which will analyze the family's interests and suggest the optimal household chore schedule. The schedule suggestion unit can also suggest household chore schedules based on topics the family has shown interest in on social media. For example, the schedule suggestion unit can input the family's social media activity data into an AI model, which will analyze the family's interests and suggest the optimal household chore schedule. This allows for the suggestion of relevant schedules based on social media activity. Some or all of the above processing in the schedule suggestion unit may be performed using AI, for example, or without AI. For example, the schedule suggestion unit can input the family's social media activity data into an AI model, which will analyze the family's interests and suggest the optimal household chore schedule.

[0043] The execution unit can select the optimal execution method when performing household chores by referring to the family's past household chore execution history. For example, the execution unit can refer to the methods used by family members in the past to select the optimal execution method. The execution unit can also use AI to analyze the family's past household chore execution history and select the optimal execution method. For example, the execution unit can input the family's past household chore execution history data into an AI model, which will analyze the methods and timing of the chores and select the optimal execution method. The execution unit can also refer to the timing of chores performed by family members in the past and perform the chores at the optimal time. For example, the execution unit can input the family's past household chore execution history data into an AI model, which will analyze the timing of the chores and perform the chores at the optimal time. This allows the execution unit to select the optimal execution method based on past household chore execution history. Some or all of the above processes in the execution unit may be performed using AI, for example, or without using AI. For example, the execution unit can input data on the family's past household chore performance history into an AI model, which can then analyze the methods and timing of the chores and select the optimal execution method.

[0044] The execution unit can customize the content of household chores based on the family's current living situation. For example, if the family is currently busy, the execution unit will prioritize chores that can be completed quickly. The execution unit can use AI to analyze the family's current living situation and suggest the most suitable chores to perform. For example, the execution unit can input data on the family's current living situation into an AI model, which will analyze the family's situation and suggest the most suitable chores to perform. The execution unit can also prioritize healthy chores (e.g., cleaning and cooking) if the family is health-conscious. For example, the execution unit can input health data on the family into an AI model, which will suggest healthy chores. This allows for customization of household chore content based on the current living situation. Some or all of the above processes in the execution unit may be performed using AI, or not. For example, the execution unit can input data on the family's current living situation into an AI model, which will analyze the family's situation and suggest the most suitable chores to perform.

[0045] The execution unit can select the optimal execution method when performing household chores, taking into account the geographical location information of the family. For example, the execution unit can prioritize performing chores that are close to where the family is currently located. The execution unit can use AI to analyze the geographical location information of the family and propose the optimal method of performing household chores. For example, the execution unit can input the family's geographical location data into an AI model, the AI ​​model can analyze the family's current location, and propose the optimal method of performing household chores. Furthermore, if the family is on the move, the execution unit can prioritize performing chores that can be done at their destination. For example, the execution unit can input the family's travel route data into an AI model, and the AI ​​model can propose chores that can be done at their destination. This allows the execution unit to select the optimal method of execution based on geographical location information. Some or all of the above processing in the execution unit may be performed using AI, for example, or without AI. For example, the execution unit can input the family's geographical location data into an AI model, the AI ​​model can analyze the family's current location, and propose the optimal method of performing household chores.

[0046] The execution unit can analyze the family's social media activity and adjust the execution content when performing household chores. For example, the execution unit can adjust household chore execution based on events shared by the family on social media. The execution unit can use AI to analyze the family's social media activity and suggest the optimal household chore execution. For example, the execution unit can input the family's social media activity data into an AI model, the AI ​​model can analyze the family's interests, and suggest the optimal household chore execution. The execution unit can also adjust household chore execution based on topics that the family has shown interest in on social media. For example, the execution unit can input the family's social media activity data into an AI model, the AI ​​model can analyze the family's interests, and suggest the optimal household chore execution. This allows for adjustment of household chore execution based on social media activity. Some or all of the above processing in the execution unit may be performed using AI, for example, or without AI. For example, the execution unit can input the family's social media activity data into an AI model, the AI ​​model can analyze the family's interests, and suggest the optimal household chore execution.

[0047] The list creation unit can select the optimal list creation method by referring to the family's past purchase history when creating a shopping list. For example, the list creation unit can create an optimal list based on the ingredients the family has purchased in the past. The list creation unit can use AI to analyze the family's past purchase history and select the optimal list creation method. For example, the list creation unit can input the family's past purchase history data into an AI model, the AI ​​model can analyze the purchase history and create an optimal list. The list creation unit can also refer to the frequency of ingredients the family has purchased in the past and add necessary ingredients to the list. For example, the list creation unit can input the family's past purchase history data into an AI model, the AI ​​model can analyze the purchase frequency and add necessary ingredients to the list. This allows the system to select the optimal list creation method based on past purchase history. Some or all of the above processes in the list creation unit may be performed using AI, for example, or without AI. For example, the list creation unit can input the family's past purchase history data into an AI model, the AI ​​model can analyze the purchase history and create an optimal list.

[0048] The list creation unit can customize the contents of a shopping list based on the family's current lifestyle. For example, if the family is currently busy, the list creation unit will prioritize adding easy-to-cook ingredients to the list. The list creation unit can use AI to analyze the family's current lifestyle and suggest an optimal shopping list. For example, the list creation unit inputs the family's current lifestyle data into an AI model, which analyzes the family's lifestyle and suggests an optimal shopping list. The list creation unit can also prioritize adding healthy ingredients to the list if the family is health-conscious. For example, the list creation unit inputs the family's health data into an AI model, which suggests healthy ingredients. This allows the list contents to be customized based on the current lifestyle. Some or all of the above processes in the list creation unit may be performed using AI, or not. For example, the list creation unit can input the family's current lifestyle data into an AI model, which analyzes the family's lifestyle and suggests an optimal shopping list.

[0049] The list creation unit can create an optimal shopping list by considering the geographical location information of the family. For example, the list creation unit can add groceries that can be purchased at stores near the family's current location to the list. The list creation unit can use AI to analyze the family's geographical location information and suggest an optimal shopping list. For example, the list creation unit can input the family's geographical location data into an AI model, which analyzes the family's current location and suggests an optimal shopping list. The list creation unit can also add groceries that can be purchased at the family's destination if the family is on the move. For example, the list creation unit can input the family's travel route data into an AI model, which suggests groceries that can be purchased at the destination. This allows for the creation of an optimal list based on geographical location information. Some or all of the above processes in the list creation unit may be performed using AI, or not using AI. For example, the list creation unit can input the family's geographical location data into an AI model, which analyzes the family's current location and suggests an optimal shopping list.

[0050] The list creation unit can analyze the family's social media activity and adjust the list content when creating a shopping list. For example, the list creation unit can add ingredients to the list based on recipes shared by family members on social media. The list creation unit can use AI to analyze the family's social media activity and suggest an optimal shopping list. For example, the list creation unit can input family social media activity data into an AI model, which analyzes the family's interests and suggests an optimal shopping list. The list creation unit can also add ingredients that family members have shown interest in on social media to the list. For example, the list creation unit can input family social media activity data into an AI model, which analyzes the family's interests and suggests an optimal shopping list. This allows the list content to be adjusted based on social media activity. Some or all of the above processes in the list creation unit may be performed using AI, for example, or without AI. For example, the list creation unit can input family social media activity data into an AI model, which analyzes the family's interests and suggests an optimal shopping list.

[0051] The consultation department can select the most appropriate response method when providing childcare consultations by referring to the family's past consultation history. For example, the consultation department can refer to the content of past consultations the family has had and provide relevant advice. The consultation department can also use AI to analyze the family's past consultation history and select the most appropriate response method. For example, the consultation department can input the family's past consultation history data into an AI model, which will analyze the consultation content and select the most appropriate response method. The consultation department can also refer to the advice the family has received in the past and select the most appropriate response method. For example, the consultation department can input the family's past consultation history data into an AI model, which will analyze past advice and select the most appropriate response method. This allows the department to select the most appropriate response method based on past consultation history. Some or all of the above processes in the consultation department may be performed using AI, for example, or without AI. For example, the consultation department can input the family's past consultation history data into an AI model, which will analyze the consultation content and select the most appropriate response method.

[0052] The consultation department can customize its responses to childcare consultations based on the family's current living situation. For example, if the family is currently busy, the consultation department can provide concise and easy-to-implement advice. The consultation department can use AI to analyze the family's current living situation and propose the most appropriate childcare consultation response. For example, the consultation department can input data on the family's current living situation into an AI model, which will analyze the situation and propose the most appropriate childcare consultation response. The consultation department can also provide health-related advice if the family is interested in health. For example, the consultation department can input health data from the family into an AI model, which will propose health-related advice. This allows for the customization of responses based on the current living situation. Some or all of the above processes in the consultation department may be performed using AI, or not. For example, the consultation department can input data on the family's current living situation into an AI model, which will analyze the situation and propose the most appropriate childcare consultation response.

[0053] The consultation department can provide optimal answers to childcare consultations by considering the family's geographical location. For example, the consultation department can introduce local childcare support services based on the family's current location. The consultation department can use AI to analyze the family's geographical location and propose the most appropriate answers to childcare consultations. For example, the consultation department can input the family's geographical location data into an AI model, which then analyzes the family's current location and proposes the most appropriate answers to childcare consultations. Furthermore, if the family is on the move, the consultation department can also introduce childcare support services available at their destination. For example, the consultation department can input the family's travel route data into an AI model, which then proposes childcare support services available at their destination. This allows the department to provide optimal answers based on geographical location information. Some or all of the above processes in the consultation department may be performed using AI, or not. For example, the consultation department can input the family's geographical location data into an AI model, which then analyzes the family's current location and proposes the most appropriate answers to childcare consultations.

[0054] The consultation department can analyze the family's social media activity and adjust the content of its responses when providing childcare consultations. For example, the consultation department can provide advice based on the childcare concerns that the family has shared on social media. The consultation department can use AI to analyze the family's social media activity and propose the most appropriate responses to childcare consultations. For example, the consultation department can input the family's social media activity data into an AI model, which will analyze the family's interests and propose the most appropriate responses to childcare consultations. The consultation department can also provide advice based on the childcare information that the family has shown interest in on social media. For example, the consultation department can input the family's social media activity data into an AI model, which will analyze the family's interests and propose the most appropriate responses to childcare consultations. This allows the response content to be adjusted based on social media activity. Some or all of the above processes in the consultation department may be performed using AI, for example, or without AI. For example, the consultation department can input the family's social media activity data into an AI model, which will analyze the family's interests and propose the most appropriate responses to childcare consultations.

[0055] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0056] The AI ​​home manager system can also include a health management unit. This unit collects family health data and monitors their health status. For example, it collects data such as family members' body temperature, heart rate, and blood pressure, and issues alerts if abnormalities are detected. The health management unit can also provide dietary and exercise advice based on the family's health status. For instance, it analyzes family health data and suggests balanced meal plans. It can also suggest appropriate exercise plans based on the family's health status. This supports family health management and improves their quality of life.

[0057] The AI ​​home manager system can also be equipped with an energy management unit. This unit monitors household energy consumption and suggests efficient energy use. For example, it collects electricity consumption data from the home and provides advice on reducing wasteful energy consumption. It can also suggest an optimal energy usage schedule based on the family's lifestyle patterns. For instance, it might suggest reducing electricity consumption during times when the family is away. This improves household energy efficiency and reduces the environmental impact.

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

[0059] Step 1: The schedule suggestion unit proposes a household chore schedule. The schedule suggestion unit learns the family's lifestyle patterns and proposes an optimal household chore schedule. For example, it learns the family's wake-up times, meal times, and the frequency of household chores, and proposes an optimal household chore schedule based on that. It can also estimate the family's emotions and adjust the proposed household chore schedule based on the estimated emotions of the family. Step 2: The execution unit performs household chores based on the schedule proposed by the scheduling unit. The execution unit performs tasks such as vacuuming and laundry according to the proposed schedule. For example, it can control IoT devices such as robotic vacuums and washing machines and perform chores based on the proposed schedule. It can also estimate the emotions of family members and adjust the timing of chore execution based on those emotions. Step 3: The list creation unit automatically creates a shopping list. The list creation unit uses a refrigerator camera to manage food inventory and automatically adds necessary ingredients to the shopping list. For example, it analyzes images of food in the refrigerator to detect ingredients that are running low and adds them to the list. It can also estimate the emotions of family members and adjust the contents of the shopping list based on those estimated emotions. Step 4: The consultation department handles childcare consultations. The consultation department answers questions about childcare and alleviates anxieties about raising children. For example, it analyzes questions about childcare and provides appropriate answers. It can also estimate the emotions of the family and adjust the content of the childcare consultation based on the estimated emotions of the family.

[0060] (Example of form 2) The AI ​​home manager system according to an embodiment of the present invention is a comprehensive AI service for reducing the burden of housework and childcare and improving the quality of life for families. This AI home manager system works in conjunction with IoT devices in the home and can be operated via smartphones and smart speakers. The AI ​​home manager system learns the family's lifestyle patterns and proposes an optimal housework schedule. It also answers questions about childcare and reduces anxiety about raising children. For example, the AI ​​home manager system works in conjunction with IoT devices in the home. For example, it uses a refrigerator camera to manage food inventory and automatically adds necessary ingredients to the shopping list. The AI ​​home manager system also learns the family's lifestyle patterns and proposes an optimal housework schedule. For example, it suggests times for vacuuming and doing laundry. Furthermore, the AI ​​home manager system answers questions about childcare. For example, it provides advice on questions about child development and concerns about childcare. It also supports children's homework and learning. For example, it manages homework progress and provides necessary learning content. In this way, the AI ​​home manager system can reduce the burden of housework and childcare and improve the quality of life for families. For example, by making housework more efficient and supporting childcare, families can spend more time together. Furthermore, the AI ​​home manager system can learn the family's lifestyle patterns and provide optimal support tailored to individual needs. This allows the AI ​​home manager system to reduce the burden of housework and childcare, improving the family's quality of life.

[0061] The AI ​​home manager system according to this embodiment comprises a schedule proposal unit, an execution unit, a list creation unit, and a consultation unit. The schedule proposal unit proposes a household chore schedule. The schedule proposal unit can, for example, learn the family's lifestyle patterns and propose an optimal household chore schedule. The schedule proposal unit can use AI to analyze the family's lifestyle patterns and generate an optimal household chore schedule. For example, the schedule proposal unit learns the family's wake-up times, meal times, and frequency of household chores, and proposes an optimal household chore schedule based on this. The schedule proposal unit can also estimate the family's emotions and adjust the proposed household chore schedule based on the estimated emotions of the family. For example, if the family is feeling stressed, it will propose a schedule that includes more time for relaxation. The execution unit performs household chores based on the schedule proposed by the schedule proposal unit. For example, the execution unit performs tasks such as vacuuming and laundry according to the proposed schedule. The execution unit can use AI to automate the execution of household chores. For example, the execution unit controls IoT devices such as robotic vacuums and washing machines and performs household chores based on the proposed schedule. The execution unit can also estimate the emotions of family members and adjust the timing of household chores based on those estimates. For example, if family members are feeling stressed, it will perform chores during times when they can relax. The list creation unit automatically creates shopping lists. The list creation unit can manage food inventory using, for example, a refrigerator camera and automatically add necessary ingredients to the shopping list. The list creation unit can use AI to analyze food inventory and automatically add necessary ingredients to the list. For example, it can analyze images of food in the refrigerator, detect ingredients that are running low, and add them to the list. The list creation unit can also estimate the emotions of family members and adjust the contents of the shopping list based on those estimates. For example, if family members are feeling stressed, it will prioritize adding ingredients that promote relaxation to the list. The consultation unit provides childcare advice. The consultation unit can answer questions about childcare and alleviate anxieties about raising children. The consultation unit can use AI to generate answers to questions about childcare.For example, the consultation department analyzes questions about childcare and provides appropriate answers. Furthermore, the consultation department can estimate the family's emotions and adjust the content of the childcare consultation based on those estimated emotions. For instance, if a family is feeling stressed, it can provide relaxing advice. In this way, the AI ​​home manager system according to this embodiment can reduce the burden of housework and childcare and improve the quality of life for the family.

[0062] The schedule suggestion unit proposes a household chore schedule. For example, the schedule suggestion unit learns the family's lifestyle patterns and proposes an optimal household chore schedule. Specifically, the schedule suggestion unit analyzes in detail the family's lifestyle patterns, such as wake-up times, meal times, commute times, return-home times, and bedtimes. This utilizes location information and activity data collected from the family's smartphones and wearable devices. Based on this data, the AI ​​understands the family's daily routine and generates an optimal household chore schedule. For example, it adjusts the schedule so that simple chores are assigned during the busy morning hours, and chores such as cleaning and laundry are performed during the relaxing evening hours. The schedule suggestion unit can also estimate the family's emotions and adjust the proposed household chore schedule based on the estimated emotions. Emotion estimation uses technology that analyzes the family's facial expressions, tone of voice, and social media posts. For example, if the family is feeling stressed, it proposes a schedule that includes more time for relaxation. This can reduce the family's mental burden and provide a more comfortable life. Furthermore, the schedule suggestion unit adjusts the schedule by also considering external factors such as the season, weather, and special events. For example, on rainy days, the system prioritizes indoor chores, while on sunny days, it suggests outdoor chores such as laundry and gardening. This allows the scheduling department to provide a flexible chore schedule tailored to the family's lifestyle.

[0063] The execution unit performs household chores based on the schedule proposed by the schedule proposal unit. For example, the execution unit will perform tasks such as vacuuming and laundry according to the proposed schedule. Specifically, the execution unit controls IoT devices such as robotic vacuums, washing machines, and dishwashers, automating household chores based on the proposed schedule. For example, a robotic vacuum will automatically start cleaning at a specified time, cleaning every corner of the room. A washing machine will start washing at a proposed time and send a notification when the washing is complete. The execution unit can also estimate the emotions of family members and adjust the timing of chore execution based on those emotions. For example, if family members are feeling stressed, chores will be performed during times when they can relax. This ensures that chore execution does not affect the family's daily rhythm. Furthermore, the execution unit monitors the progress of chores in real time and makes adjustments as needed. For example, if a robotic vacuum hits an obstacle or a washing machine detects an abnormality, the execution unit will automatically respond and resolve the problem. The execution unit also records the results of chore execution and provides feedback to the schedule proposal unit. This allows the scheduling function to optimize the schedule based on the progress of household chores, enabling more efficient household management.

[0064] The list creation unit automatically generates shopping lists. For example, it manages food inventory using a refrigerator camera and automatically adds necessary items to the shopping list. Specifically, the list creation unit analyzes images captured by the refrigerator camera to determine the type and quantity of food items. AI uses image recognition technology to identify food items, detecting low stocks and adding them to the list. For example, if the stock of milk, eggs, or vegetables decreases, the list creation unit automatically adds these items to the shopping list. The list creation unit can also estimate family emotions and adjust the shopping list based on these estimations. For example, if family members are stressed, it prioritizes adding relaxing items to the list. This includes considering family preferences and past purchase history. Furthermore, the list creation unit adjusts the shopping list according to the season and special events. For example, it adds special ingredients and decorations to the list for events such as Christmas or birthdays. This allows the list creation unit to provide a flexible shopping list tailored to family needs. Additionally, the list creation unit can integrate with online shopping sites and automatically order the items added to the list. This can reduce the hassle of shopping and make family life more convenient.

[0065] The consultation department provides support for childcare consultations. For example, it answers questions about childcare and helps alleviate anxieties about raising children. Specifically, the consultation department analyzes questions about childcare and provides appropriate answers. AI uses natural language processing technology to understand the content of questions and generates answers based on past databases and expert knowledge. For example, it provides detailed advice on specific childcare concerns such as a baby's nighttime crying or how to introduce solid foods. The consultation department can also estimate the emotions of the family and adjust the content of the childcare consultation based on the estimated emotions of the family. For example, if the family is feeling stressed, it will provide advice to help them relax. This includes suggesting ways to relax during childcare breaks and activities to reduce stress. Furthermore, the consultation department can also provide the latest information and trends related to childcare. For example, it will provide introductions to new childcare products and methods, and interviews with experts, so that families can stay up-to-date. In this way, the consultation department can alleviate anxieties about childcare and provide support to help families raise children with more confidence. In addition, the consultation department can collect feedback from families and continuously improve the accuracy and effectiveness of its answers. This allows the consultation department to always provide the latest information and high-quality support, thereby improving the quality of life for families.

[0066] The schedule suggestion unit can learn the family's lifestyle patterns and propose an optimal household chore schedule. For example, the schedule suggestion unit learns the family's wake-up times, meal times, and frequency of household chores, and proposes an optimal household chore schedule based on this information. The schedule suggestion unit can use AI to analyze the family's lifestyle patterns and generate an optimal household chore schedule. For example, the schedule suggestion unit collects family behavior data to learn the family's lifestyle patterns and inputs it into an AI model. The AI ​​model analyzes the collected data and identifies the family's lifestyle patterns. For example, the schedule suggestion unit learns the family's wake-up times, meal times, and frequency of household chores, and proposes an optimal household chore schedule based on this information. This allows the system to propose an optimal household chore schedule based on the family's lifestyle patterns. Some or all of the above-described processes in the schedule suggestion unit may be performed using AI, for example, or without AI. For example, the schedule suggestion unit can input family behavior data into an AI model, which can then identify the family's lifestyle patterns and generate an optimal household chore schedule.

[0067] The execution unit can perform household chores based on a schedule proposed by the schedule proposal unit. For example, the execution unit can perform tasks such as vacuuming or doing laundry according to the proposed schedule. The execution unit can automate the execution of household chores using AI. For example, the execution unit can control IoT devices such as robotic vacuums or washing machines and perform chores based on a proposed schedule. For example, the execution unit can control a robotic vacuum to start cleaning at a proposed time. It can also control a washing machine to start doing laundry at a proposed time. Furthermore, the execution unit can estimate the emotions of family members and adjust the timing of household chore execution based on the estimated emotions of family members. For example, if family members are feeling stressed, it can perform chores during times when they can relax. This allows the execution of chores to be performed according to the proposed schedule. Some or all of the above processes in the execution unit may be performed using AI, for example, or without AI. For example, the execution unit can connect IoT devices such as robotic vacuums or washing machines to an AI model, and the AI ​​model can perform household chores based on a proposed schedule.

[0068] The list creation unit can manage food inventory using a refrigerator camera and automatically add necessary ingredients to a shopping list. For example, the list creation unit can analyze images of food in the refrigerator, detect ingredients that are running low, and add them to the list. The list creation unit can also use AI to analyze food inventory and automatically add necessary ingredients to the list. For example, the list creation unit can input images of food in the refrigerator into an AI model, which can detect ingredients that are running low and add them to the list. The list creation unit can also estimate the emotions of family members and adjust the contents of the shopping list based on the estimated emotions. For example, if family members are feeling stressed, it can prioritize adding ingredients that promote relaxation to the list. This enables automated food inventory management and shopping list creation. Some or all of the above processes in the list creation unit may be performed using AI, or not. For example, the list creation unit can input images of food in the refrigerator into an AI model, which can detect ingredients that are running low and add them to the list.

[0069] The consultation department can answer questions about childcare and alleviate anxieties about raising children. For example, the consultation department can answer questions about childcare and alleviate anxieties about raising children. The consultation department can use AI to generate answers to questions about childcare. For example, the consultation department can input questions about childcare into an AI model, and the AI ​​model can generate appropriate answers. The consultation department can also estimate the emotions of the family and adjust the content of the childcare consultation answers based on the estimated emotions of the family. For example, if the family is feeling stressed, it can provide relaxing advice. In this way, by answering questions about childcare, anxieties about raising children can be alleviated. Some or all of the above processes in the consultation department may be performed using AI, for example, or not using AI. For example, the consultation department can input questions about childcare into an AI model, and the AI ​​model can generate appropriate answers.

[0070] The support department can provide assistance with children's homework and learning. For example, the support department can manage the progress of children's homework and provide necessary learning content. The support department can also use AI to support children's homework and learning. For example, the support department can input the progress data of children's homework into an AI model, and the AI ​​model can provide appropriate learning content. The support department can also estimate the emotions of the family and adjust the learning support content based on the estimated emotions of the family. For example, if a child is feeling stressed, it can provide learning content that helps them relax. This makes it possible to support children's homework and learning. Some or all of the above processes in the support department may be performed using AI, or not using AI. For example, the support department can input the progress data of children's homework into an AI model, and the AI ​​model can provide appropriate learning content.

[0071] The schedule suggestion unit can estimate the emotions of family members and adjust the suggested household chore schedule based on those estimated emotions. For example, if family members are feeling stressed, the schedule suggestion unit will suggest a schedule that includes plenty of time for relaxation. The schedule suggestion unit can use AI to estimate family members' emotions and adjust the suggested household chore schedule based on those estimated emotions. For example, the schedule suggestion unit inputs facial expression data and voice data of family members into an AI model, and the AI ​​model estimates the family members' emotions. Based on the estimated emotions, the schedule suggestion unit suggests a schedule that includes plenty of time for relaxation. The schedule suggestion unit can also suggest a schedule that allows family members to complete household chores efficiently in a short amount of time if they are busy. For example, the schedule suggestion unit inputs behavioral data of family members into an AI model, and the AI ​​model analyzes the family's living situation and suggests an optimal household chore schedule. This allows the suggested household chore schedule to be adjusted according to the family members' emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the schedule proposal unit may be performed using AI, for example, or without AI. For example, the schedule proposal unit can input facial expression data and voice data of family members into an AI model, the AI ​​model can estimate the family members' emotions, and adjust the proposed household chore schedule based on the estimated emotions.

[0072] The schedule suggestion unit can analyze a family's past household chore history and select the optimal schedule suggestion method. For example, the schedule suggestion unit can analyze the frequency of household chores performed by the family in the past and propose the optimal schedule. The schedule suggestion unit can use AI to analyze a family's past household chore history and select the optimal schedule suggestion method. For example, the schedule suggestion unit can input the family's past household chore history data into an AI model, which will analyze the frequency and timing of chores and propose the optimal schedule. The schedule suggestion unit can also analyze the timing of household chores performed by the family in the past and propose chores at the optimal time. For example, the schedule suggestion unit can input the family's past household chore history data into an AI model, which will analyze the timing of chores and propose chores at the optimal time. This allows for the selection of the optimal schedule suggestion method based on past household chore history. Some or all of the above processing in the schedule suggestion unit may be performed using AI, for example, or without AI. For example, the schedule suggestion unit can input the family's past household chore history data into an AI model, which will analyze the frequency and timing of chores and propose the optimal schedule.

[0073] The schedule suggestion unit can filter household chore schedules based on the family's current lifestyle and areas of interest. For example, if the family is currently busy, the schedule suggestion unit will prioritize suggesting chores that can be completed quickly. The schedule suggestion unit can use AI to analyze the family's current lifestyle and areas of interest and suggest an optimal household chore schedule. For example, the schedule suggestion unit inputs data on the family's current lifestyle and areas of interest into an AI model, which then analyzes the family's lifestyle and areas of interest and suggests an optimal household chore schedule. Furthermore, if the family is interested in health, the schedule suggestion unit can also suggest healthy chores (e.g., cleaning and cooking). For example, the schedule suggestion unit inputs health data into an AI model, which then suggests healthy chores. This allows the system to suggest household chore schedules based on the family's current lifestyle and areas of interest. Some or all of the above-described processes in the schedule suggestion unit may be performed using AI, or not using AI. For example, the schedule suggestion unit can input data on the family's current living situation and areas of interest into an AI model, which then analyzes the family's living situation and areas of interest to suggest an optimal household chore schedule.

[0074] The schedule suggestion unit can estimate the emotions of family members and determine the priority of schedule suggestions based on those estimated emotions. For example, if family members are feeling stressed, the schedule suggestion unit will prioritize suggesting relaxing household chores. The schedule suggestion unit can use AI to estimate family members' emotions and determine the priority of schedule suggestions based on those estimated emotions. For example, the schedule suggestion unit inputs facial expression data and voice data of family members into an AI model, and the AI ​​model estimates the emotions of the family members. Based on the estimated emotions, the schedule suggestion unit prioritizes suggesting relaxing household chores. The schedule suggestion unit can also prioritize suggesting schedules that allow family members to perform household chores efficiently if they are busy. For example, the schedule suggestion unit inputs family behavior data into an AI model, and the AI ​​model analyzes the family's living situation and proposes an optimal household chore schedule. This allows the schedule suggestion unit to determine the priority of schedule suggestions 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 is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the schedule proposal unit may be performed using AI, for example, or without AI. For example, the schedule proposal unit can input facial expression data and voice data of family members into an AI model, the AI ​​model can estimate the family members' emotions, and the priority of schedule proposals can be determined based on the estimated emotions.

[0075] The schedule suggestion unit can prioritize suggesting highly relevant schedules when proposing household chore schedules, taking into account the geographical location information of the family. For example, the schedule suggestion unit can prioritize suggesting chores that are close to where the family is currently located. The schedule suggestion unit can use AI to analyze the geographical location information of the family and propose an optimal household chore schedule. For example, the schedule suggestion unit can input the family's geographical location data into an AI model, the AI ​​model can analyze the family's current location, and propose an optimal household chore schedule. Furthermore, if the family is on the move, the schedule suggestion unit can prioritize suggesting chores that can be done at their destination. For example, the schedule suggestion unit can input the family's travel route data into an AI model, and the AI ​​model can propose chores that can be done at their destination. This allows for the suggestion of highly relevant schedules based on geographical location information. Some or all of the above processing in the schedule suggestion unit may be performed using AI, or not using AI. For example, the schedule suggestion unit can input the family's geographical location data into an AI model, the AI ​​model can analyze the family's current location, and propose an optimal household chore schedule.

[0076] The schedule suggestion unit can analyze the family's social media activity and suggest relevant schedules when proposing household chore schedules. For example, the schedule suggestion unit can suggest household chore schedules based on events shared by the family on social media. The schedule suggestion unit can use AI to analyze the family's social media activity and suggest the optimal household chore schedule. For example, the schedule suggestion unit can input the family's social media activity data into an AI model, which will analyze the family's interests and suggest the optimal household chore schedule. The schedule suggestion unit can also suggest household chore schedules based on topics the family has shown interest in on social media. For example, the schedule suggestion unit can input the family's social media activity data into an AI model, which will analyze the family's interests and suggest the optimal household chore schedule. This allows for the suggestion of relevant schedules based on social media activity. Some or all of the above processing in the schedule suggestion unit may be performed using AI, for example, or without AI. For example, the schedule suggestion unit can input the family's social media activity data into an AI model, which will analyze the family's interests and suggest the optimal household chore schedule.

[0077] The execution unit can estimate the emotions of family members and adjust the timing of household chores based on the estimated emotions. For example, if family members are feeling stressed, the execution unit will perform household chores during times when they can relax. The execution unit can use AI to estimate family members' emotions and adjust the timing of household chores based on the estimated emotions. For example, the execution unit inputs facial expression data and voice data of family members into an AI model, and the AI ​​model estimates the emotions of family members. Based on the estimated emotions, the execution unit performs household chores during times when they can relax. The execution unit can also perform household chores during times when family members are busy and can do them efficiently. For example, the execution unit inputs behavioral data of family members into an AI model, and the AI ​​model analyzes the family's living situation and proposes the optimal timing for performing household chores. This allows the timing of household chores to be adjusted according to family members' emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the execution unit may be performed using AI, for example, or without AI. For example, the execution unit can input facial expression data and voice data of family members into an AI model, the AI ​​model can estimate the family members' emotions, and adjust the timing of household chores based on the estimated emotions.

[0078] The execution unit can select the optimal execution method when performing household chores by referring to the family's past household chore execution history. For example, the execution unit can refer to the methods used by family members in the past to select the optimal execution method. The execution unit can also use AI to analyze the family's past household chore execution history and select the optimal execution method. For example, the execution unit can input the family's past household chore execution history data into an AI model, which will analyze the methods and timing of the chores and select the optimal execution method. The execution unit can also refer to the timing of chores performed by family members in the past and perform the chores at the optimal time. For example, the execution unit can input the family's past household chore execution history data into an AI model, which will analyze the timing of the chores and perform the chores at the optimal time. This allows the execution unit to select the optimal execution method based on past household chore execution history. Some or all of the above processes in the execution unit may be performed using AI, for example, or without using AI. For example, the execution unit can input data on the family's past household chore performance history into an AI model, which can then analyze the methods and timing of the chores and select the optimal execution method.

[0079] The execution unit can customize the content of household chores based on the family's current living situation. For example, if the family is currently busy, the execution unit will prioritize chores that can be completed quickly. The execution unit can use AI to analyze the family's current living situation and suggest the most suitable chores to perform. For example, the execution unit can input data on the family's current living situation into an AI model, which will analyze the family's situation and suggest the most suitable chores to perform. The execution unit can also prioritize healthy chores (e.g., cleaning and cooking) if the family is health-conscious. For example, the execution unit can input health data on the family into an AI model, which will suggest healthy chores. This allows for customization of household chore content based on the current living situation. Some or all of the above processes in the execution unit may be performed using AI, or not. For example, the execution unit can input data on the family's current living situation into an AI model, which will analyze the family's situation and suggest the most suitable chores to perform.

[0080] The execution unit can estimate the emotions of family members and determine the priority of household chores based on the estimated emotions. For example, if family members are feeling stressed, the execution unit will prioritize household chores that promote relaxation. The execution unit can use AI to estimate family members' emotions and determine the priority of household chores based on the estimated emotions. For example, the execution unit inputs facial expression data and voice data of family members into an AI model, and the AI ​​model estimates the emotions of family members. Based on the estimated emotions, the execution unit prioritizes household chores that promote relaxation. The execution unit can also prioritize household chores that can be done efficiently if family members are busy. For example, the execution unit inputs behavioral data of family members into an AI model, and the AI ​​model analyzes the family's living situation and proposes the optimal priority of household chores. This allows the execution unit to determine the priority of household chores according to family members' emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the execution unit may be performed using AI, for example, or without AI. For example, the execution unit can input facial expression data and voice data of family members into an AI model, the AI ​​model can estimate the emotions of the family members, and based on the estimated emotions, determine the priority of household chores to be performed.

[0081] The execution unit can select the optimal execution method when performing household chores, taking into account the geographical location information of the family. For example, the execution unit can prioritize performing chores that are close to where the family is currently located. The execution unit can use AI to analyze the geographical location information of the family and propose the optimal method of performing household chores. For example, the execution unit can input the family's geographical location data into an AI model, the AI ​​model can analyze the family's current location, and propose the optimal method of performing household chores. Furthermore, if the family is on the move, the execution unit can prioritize performing chores that can be done at their destination. For example, the execution unit can input the family's travel route data into an AI model, and the AI ​​model can propose chores that can be done at their destination. This allows the execution unit to select the optimal method of execution based on geographical location information. Some or all of the above processing in the execution unit may be performed using AI, for example, or without AI. For example, the execution unit can input the family's geographical location data into an AI model, the AI ​​model can analyze the family's current location, and propose the optimal method of performing household chores.

[0082] The execution unit can analyze the family's social media activity and adjust the execution content when performing household chores. For example, the execution unit can adjust household chore execution based on events shared by the family on social media. The execution unit can use AI to analyze the family's social media activity and suggest the optimal household chore execution. For example, the execution unit can input the family's social media activity data into an AI model, the AI ​​model can analyze the family's interests, and suggest the optimal household chore execution. The execution unit can also adjust household chore execution based on topics that the family has shown interest in on social media. For example, the execution unit can input the family's social media activity data into an AI model, the AI ​​model can analyze the family's interests, and suggest the optimal household chore execution. This allows for adjustment of household chore execution based on social media activity. Some or all of the above processing in the execution unit may be performed using AI, for example, or without AI. For example, the execution unit can input the family's social media activity data into an AI model, the AI ​​model can analyze the family's interests, and suggest the optimal household chore execution.

[0083] The list creation unit can estimate the emotions of family members and adjust the contents of the shopping list based on those estimated emotions. For example, if family members are feeling stressed, the list creation unit will prioritize adding relaxing foods to the list. The list creation unit can use AI to estimate family members' emotions and adjust the contents of the shopping list based on those estimated emotions. For example, the list creation unit inputs facial expression data and voice data of family members into an AI model, and the AI ​​model estimates the family members' emotions. Based on the estimated emotions, the list creation unit will prioritize adding relaxing foods to the list. The list creation unit can also prioritize adding easy-to-cook foods to the list if family members are busy. For example, the list creation unit inputs family behavior data into an AI model, and the AI ​​model analyzes the family's living situation and proposes an optimal shopping list. This allows the contents of the shopping list to be adjusted according to family members' emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the list creation unit may be performed using AI, for example, or without AI. For example, the list creation unit can input facial expression data and voice data of family members into an AI model, the AI ​​model can estimate the family members' emotions, and the contents of the shopping list can be adjusted based on the estimated emotions.

[0084] The list creation unit can select the optimal list creation method by referring to the family's past purchase history when creating a shopping list. For example, the list creation unit can create an optimal list based on the ingredients the family has purchased in the past. The list creation unit can use AI to analyze the family's past purchase history and select the optimal list creation method. For example, the list creation unit can input the family's past purchase history data into an AI model, the AI ​​model can analyze the purchase history and create an optimal list. The list creation unit can also refer to the frequency of ingredients the family has purchased in the past and add necessary ingredients to the list. For example, the list creation unit can input the family's past purchase history data into an AI model, the AI ​​model can analyze the purchase frequency and add necessary ingredients to the list. This allows the system to select the optimal list creation method based on past purchase history. Some or all of the above processes in the list creation unit may be performed using AI, for example, or without AI. For example, the list creation unit can input the family's past purchase history data into an AI model, the AI ​​model can analyze the purchase history and create an optimal list.

[0085] The list creation unit can customize the contents of a shopping list based on the family's current lifestyle. For example, if the family is currently busy, the list creation unit will prioritize adding easy-to-cook ingredients to the list. The list creation unit can use AI to analyze the family's current lifestyle and suggest an optimal shopping list. For example, the list creation unit inputs the family's current lifestyle data into an AI model, which analyzes the family's lifestyle and suggests an optimal shopping list. The list creation unit can also prioritize adding healthy ingredients to the list if the family is health-conscious. For example, the list creation unit inputs the family's health data into an AI model, which suggests healthy ingredients. This allows the list contents to be customized based on the current lifestyle. Some or all of the above processes in the list creation unit may be performed using AI, or not. For example, the list creation unit can input the family's current lifestyle data into an AI model, which analyzes the family's lifestyle and suggests an optimal shopping list.

[0086] The list creation unit can estimate the emotions of family members and determine the priority of the shopping list based on those estimated emotions. For example, if family members are feeling stressed, the list creation unit will prioritize adding relaxing ingredients to the list. The list creation unit can use AI to estimate family members' emotions and determine the priority of the shopping list based on those estimated emotions. For example, the list creation unit inputs facial expression data and voice data of family members into an AI model, and the AI ​​model estimates the emotions of family members. Based on the estimated emotions, the list creation unit will prioritize adding relaxing ingredients to the list. The list creation unit can also prioritize adding easy-to-cook ingredients to the list if family members are busy. For example, the list creation unit inputs family behavior data into an AI model, and the AI ​​model analyzes the family's living situation and proposes an optimal shopping list. This allows the priority of the shopping list to be determined according to family members' emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the list creation unit may be performed using AI, for example, or without AI. For example, the list creation unit can input facial expression data and voice data of family members into an AI model, the AI ​​model can estimate the family members' emotions, and the priority of the shopping list can be determined based on the estimated emotions.

[0087] The list creation unit can create an optimal shopping list by considering the geographical location information of the family. For example, the list creation unit can add groceries that can be purchased at stores near the family's current location to the list. The list creation unit can use AI to analyze the family's geographical location information and suggest an optimal shopping list. For example, the list creation unit can input the family's geographical location data into an AI model, which analyzes the family's current location and suggests an optimal shopping list. The list creation unit can also add groceries that can be purchased at the family's destination if the family is on the move. For example, the list creation unit can input the family's travel route data into an AI model, which suggests groceries that can be purchased at the destination. This allows for the creation of an optimal list based on geographical location information. Some or all of the above processes in the list creation unit may be performed using AI, or not using AI. For example, the list creation unit can input the family's geographical location data into an AI model, which analyzes the family's current location and suggests an optimal shopping list.

[0088] The list creation unit can analyze the family's social media activity and adjust the list content when creating a shopping list. For example, the list creation unit can add ingredients to the list based on recipes shared by family members on social media. The list creation unit can use AI to analyze the family's social media activity and suggest an optimal shopping list. For example, the list creation unit can input family social media activity data into an AI model, which analyzes the family's interests and suggests an optimal shopping list. The list creation unit can also add ingredients that family members have shown interest in on social media to the list. For example, the list creation unit can input family social media activity data into an AI model, which analyzes the family's interests and suggests an optimal shopping list. This allows the list content to be adjusted based on social media activity. Some or all of the above processes in the list creation unit may be performed using AI, for example, or without AI. For example, the list creation unit can input family social media activity data into an AI model, which analyzes the family's interests and suggests an optimal shopping list.

[0089] The consultation department can estimate the emotions of families and adjust the content of parenting consultations based on the estimated emotions. For example, if a family is feeling stressed, the consultation department can provide relaxing advice. The consultation department can use AI to estimate the emotions of families and adjust the content of parenting consultations based on the estimated emotions. For example, the consultation department inputs facial expression data and voice data of families into an AI model, and the AI ​​model estimates the emotions of the families. Based on the estimated emotions, the consultation department provides relaxing advice. The consultation department can also provide reassuring advice if a family is feeling anxious. For example, the consultation department inputs behavioral data of families into an AI model, and the AI ​​model analyzes the family's living situation and proposes the most appropriate parenting consultation answer. This allows the content of parenting consultations to be adjusted according to the emotions of the families. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the consultation department may be performed using AI, for example, or without AI. For example, the consultation department can input facial expression data and voice data of the family into an AI model, the AI ​​model can estimate the family's emotions, and adjust the content of the childcare consultation response based on the estimated emotions.

[0090] The consultation department can select the most appropriate response method when providing childcare consultations by referring to the family's past consultation history. For example, the consultation department can refer to the content of past consultations the family has had and provide relevant advice. The consultation department can also use AI to analyze the family's past consultation history and select the most appropriate response method. For example, the consultation department can input the family's past consultation history data into an AI model, which will analyze the consultation content and select the most appropriate response method. The consultation department can also refer to the advice the family has received in the past and select the most appropriate response method. For example, the consultation department can input the family's past consultation history data into an AI model, which will analyze past advice and select the most appropriate response method. This allows the department to select the most appropriate response method based on past consultation history. Some or all of the above processes in the consultation department may be performed using AI, for example, or without AI. For example, the consultation department can input the family's past consultation history data into an AI model, which will analyze the consultation content and select the most appropriate response method.

[0091] The consultation department can customize its responses to childcare consultations based on the family's current living situation. For example, if the family is currently busy, the consultation department can provide concise and easy-to-implement advice. The consultation department can use AI to analyze the family's current living situation and propose the most appropriate childcare consultation response. For example, the consultation department can input data on the family's current living situation into an AI model, which will analyze the situation and propose the most appropriate childcare consultation response. The consultation department can also provide health-related advice if the family is interested in health. For example, the consultation department can input health data from the family into an AI model, which will propose health-related advice. This allows for the customization of responses based on the current living situation. Some or all of the above processes in the consultation department may be performed using AI, or not. For example, the consultation department can input data on the family's current living situation into an AI model, which will analyze the situation and propose the most appropriate childcare consultation response.

[0092] The consultation department can estimate the emotions of families and determine the priority of childcare consultations based on those estimated emotions. For example, if a family is feeling stressed, the consultation department will prioritize providing advice that promotes relaxation. The consultation department can use AI to estimate the emotions of families and determine the priority of childcare consultations based on those estimated emotions. For example, the consultation department inputs facial expression data and voice data of families into an AI model, and the AI ​​model estimates the emotions of the families. Based on the estimated emotions, the consultation department will prioritize providing advice that promotes relaxation. The consultation department can also prioritize providing reassuring advice if a family is feeling anxious. For example, the consultation department inputs behavioral data of families into an AI model, and the AI ​​model analyzes the family's living situation and proposes the optimal priority of childcare consultations. This allows the priority of childcare consultations to be determined according to the emotions of the families. Emotion estimation is achieved using emotion estimation functions, 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. Some or all of the above-described processes in the consultation department may be performed using AI, for example, or without AI. For example, the consultation department can input facial expression data and voice data of family members into an AI model, the AI ​​model can estimate the family's emotions, and based on the estimated emotions, it can determine the priority of childcare consultations.

[0093] The consultation department can provide optimal answers to childcare consultations by considering the family's geographical location. For example, the consultation department can introduce local childcare support services based on the family's current location. The consultation department can use AI to analyze the family's geographical location and propose the most appropriate answers to childcare consultations. For example, the consultation department can input the family's geographical location data into an AI model, which then analyzes the family's current location and proposes the most appropriate answers to childcare consultations. Furthermore, if the family is on the move, the consultation department can also introduce childcare support services available at their destination. For example, the consultation department can input the family's travel route data into an AI model, which then proposes childcare support services available at their destination. This allows the department to provide optimal answers based on geographical location information. Some or all of the above processes in the consultation department may be performed using AI, or not. For example, the consultation department can input the family's geographical location data into an AI model, which then analyzes the family's current location and proposes the most appropriate answers to childcare consultations.

[0094] The consultation department can analyze the family's social media activity and adjust the content of its responses when providing childcare consultations. For example, the consultation department can provide advice based on the childcare concerns that the family has shared on social media. The consultation department can use AI to analyze the family's social media activity and propose the most appropriate responses to childcare consultations. For example, the consultation department can input the family's social media activity data into an AI model, which will analyze the family's interests and propose the most appropriate responses to childcare consultations. The consultation department can also provide advice based on the childcare information that the family has shown interest in on social media. For example, the consultation department can input the family's social media activity data into an AI model, which will analyze the family's interests and propose the most appropriate responses to childcare consultations. This allows the response content to be adjusted based on social media activity. Some or all of the above processes in the consultation department may be performed using AI, for example, or without AI. For example, the consultation department can input the family's social media activity data into an AI model, which will analyze the family's interests and propose the most appropriate responses to childcare consultations.

[0095] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0096] The AI ​​home manager system can also include a health management unit. This unit collects family health data and monitors their health status. For example, it collects data such as family members' body temperature, heart rate, and blood pressure, and issues alerts if abnormalities are detected. The health management unit can also provide dietary and exercise advice based on the family's health status. For instance, it analyzes family health data and suggests balanced meal plans. It can also suggest appropriate exercise plans based on the family's health status. This supports family health management and improves their quality of life.

[0097] The AI ​​home manager system can also be equipped with an entertainment suggestion unit. This unit suggests entertainment content based on the family's hobbies and interests. For example, it can analyze the family's viewing and music playback history to suggest movies and music they might enjoy. Furthermore, it can estimate the family's emotions and suggest relaxing entertainment content based on those emotions. For instance, if a family member is feeling stressed, it can suggest relaxing movies or music. This can improve the family's entertainment experience.

[0098] The AI ​​home manager system can also be equipped with a communication support unit. This unit provides support to facilitate communication among family members. For example, it can share family schedules, ensuring everyone is aware of their plans. It can also estimate family members' emotions and suggest appropriate communication methods based on those estimates. For instance, if a family member is feeling stressed, it can suggest relaxing communication methods. This can improve communication among family members and strengthen family bonds.

[0099] The AI ​​home manager system can also be equipped with an energy management unit. This unit monitors household energy consumption and suggests efficient energy use. For example, it collects electricity consumption data from the home and provides advice on reducing wasteful energy consumption. It can also suggest an optimal energy usage schedule based on the family's lifestyle patterns. For instance, it might suggest reducing electricity consumption during times when the family is away. This improves household energy efficiency and reduces the environmental impact.

[0100] The AI ​​home manager system can also be equipped with a safety management unit. This unit provides support for ensuring safety within the home. For example, it can monitor home security cameras and sensors and issue alerts if an anomaly is detected. Furthermore, the safety management unit can estimate the emotions of family members and suggest safety measures based on those estimates. For instance, if family members are feeling anxious, it can suggest reassuring safety measures. This helps ensure safety within the home and improves the family's sense of security.

[0101] The AI ​​home manager system can also be equipped with a remote work support unit. This unit provides functions to support family members working remotely. For example, it can manage family work schedules and suggest efficient work environments. Furthermore, it can estimate family members' emotions and adjust remote work support based on those emotions. For instance, if a family member is feeling stressed, it can suggest a relaxing work environment. This can improve the efficiency of family members' remote work.

[0102] The AI ​​home manager system can also include a pet care section. This section provides functions for managing the health and well-being of pets in the home. For example, it can manage the pet's feeding and exercise schedules and suggest appropriate care. It can also estimate the pet's emotions and adjust care based on those estimates. For instance, if a pet is stressed, it can suggest relaxing care methods. This helps maintain the pet's health and well-being and improves the quality of life for the entire family.

[0103] The AI ​​home manager system can also be equipped with an environmental monitoring unit. This unit collects environmental data within the home and makes suggestions for maintaining a comfortable living environment. For example, it collects data such as indoor temperature, humidity, and air quality, and suggests optimal environmental settings. Furthermore, it can estimate the emotions of family members and adjust environmental settings based on those emotions. For instance, if family members want to relax, it might suggest a comfortable temperature and humidity. This optimizes the home environment and improves the quality of life for the family.

[0104] The AI ​​home manager system can also be equipped with an educational support unit. This unit provides functions to support children's learning. For example, it can manage a child's learning progress and suggest appropriate learning content. It can also estimate a child's emotions and adjust learning support based on those emotions. For instance, if a child is feeling stressed, it can suggest relaxing learning methods. This can improve a child's learning effectiveness and increase their motivation to learn.

[0105] The AI ​​home manager system can also be equipped with a hobby support section. This section provides functions to support the family's hobby activities. For example, it suggests information and events related to the family's hobbies. Furthermore, it can estimate the family's emotions and adjust the suggested hobby activities based on those emotions. For instance, if a family member wants to relax, it will suggest relaxing hobby activities. This can enrich the family's hobby activities and improve their quality of life.

[0106] The following briefly describes the processing flow for example form 2.

[0107] Step 1: The schedule suggestion unit proposes a household chore schedule. The schedule suggestion unit learns the family's lifestyle patterns and proposes an optimal household chore schedule. For example, it learns the family's wake-up times, meal times, and the frequency of household chores, and proposes an optimal household chore schedule based on that. It can also estimate the family's emotions and adjust the proposed household chore schedule based on the estimated emotions of the family. Step 2: The execution unit performs household chores based on the schedule proposed by the scheduling unit. The execution unit performs tasks such as vacuuming and laundry according to the proposed schedule. For example, it can control IoT devices such as robotic vacuums and washing machines and perform chores based on the proposed schedule. It can also estimate the emotions of family members and adjust the timing of chore execution based on those emotions. Step 3: The list creation unit automatically creates a shopping list. The list creation unit uses a refrigerator camera to manage food inventory and automatically adds necessary ingredients to the shopping list. For example, it analyzes images of food in the refrigerator to detect ingredients that are running low and adds them to the list. It can also estimate the emotions of family members and adjust the contents of the shopping list based on those estimated emotions. Step 4: The consultation department handles childcare consultations. The consultation department answers questions about childcare and alleviates anxieties about raising children. For example, it analyzes questions about childcare and provides appropriate answers. It can also estimate the emotions of the family and adjust the content of the childcare consultation based on the estimated emotions of the family.

[0108] 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.

[0109] 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.

[0110] 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.

[0111] Each of the multiple elements described above, including the schedule proposal unit, execution unit, list creation unit, and consultation unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the schedule proposal unit is implemented by the specific processing unit 290 of the data processing unit 12, which learns the family's lifestyle patterns and proposes an optimal household chore schedule. The execution unit is implemented by the control unit 46A of the smart device 14, which performs household chores based on the proposed schedule. The list creation unit manages the inventory of ingredients using the camera 42 of the smart device 14 and automatically adds necessary ingredients to the shopping list. The consultation unit is implemented by the specific processing unit 290 of the data processing unit 12, which answers questions about childcare and alleviates anxieties about raising children. The correspondence between each unit and the device or control unit is not limited to the examples described above and can be modified in various ways.

[0112] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0113] 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.

[0114] 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.

[0115] 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.

[0116] 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.

[0117] 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).

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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.).

[0124] 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.

[0125] 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.

[0126] 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.

[0127] Each of the multiple elements described above, including the schedule proposal unit, execution unit, list creation unit, and consultation unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the schedule proposal unit is implemented by the specific processing unit 290 of the data processing unit 12, which learns the family's lifestyle patterns and proposes an optimal household chore schedule. The execution unit is implemented by the control unit 46A of the smart glasses 214, which performs household chores based on the proposed schedule. The list creation unit manages the inventory of groceries using the camera 42 of the smart glasses 214 and automatically adds necessary groceries to the shopping list. The consultation unit is implemented by the specific processing unit 290 of the data processing unit 12, which answers questions about childcare and alleviates anxieties about raising children. The correspondence between each unit and the device or control unit is not limited to the examples described above and can be modified in various ways.

[0128] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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).

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.).

[0140] 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.

[0141] 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.

[0142] 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.

[0143] Each of the multiple elements described above, including the schedule proposal unit, execution unit, list creation unit, and consultation unit, is implemented by, for example, at least one of the headset terminal 314 and the data processing unit 12. For example, the schedule proposal unit is implemented by the specific processing unit 290 of the data processing unit 12, which learns the family's lifestyle patterns and proposes an optimal household chore schedule. The execution unit is implemented by, for example, the control unit 46A of the headset terminal 314, which performs household chores based on the proposed schedule. The list creation unit manages the inventory of ingredients using the camera 42 of the headset terminal 314 and automatically adds necessary ingredients to the shopping list. The consultation unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12, which answers questions about childcare and alleviates anxieties about raising children. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0144] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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).

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.).

[0157] 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.

[0158] 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.

[0159] 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.

[0160] Each of the multiple elements described above, including the schedule proposal unit, execution unit, list creation unit, and consultation unit, is implemented by, for example, at least one of the robot 414 and the data processing unit 12. For example, the schedule proposal unit is implemented by the specific processing unit 290 of the data processing unit 12, which learns the family's lifestyle patterns and proposes an optimal household chore schedule. The execution unit is implemented by, for example, the control unit 46A of the robot 414, which performs household chores based on the proposed schedule. The list creation unit manages the inventory of ingredients using, for example, the camera 42 of the robot 414 and automatically adds the necessary ingredients to the shopping list. The consultation unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12, which answers questions about childcare and alleviates anxieties about raising children. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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."

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] (Note 1) The Schedule Proposal Department proposes household chore schedules, An execution unit that performs household chores based on the schedule proposed by the aforementioned schedule proposal unit, A list creation section that automatically generates shopping lists, It includes a consultation department that handles childcare consultations. A system characterized by the following features. (Note 2) The aforementioned schedule proposal unit, Learn your family's lifestyle patterns and suggest the optimal household chore schedule. The system described in Appendix 1, characterized by the features described herein. (Note 3) The execution unit is, Perform household chores based on the schedule proposed by the aforementioned schedule proposal unit. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned list creation unit, Use a refrigerator camera to manage your grocery inventory and automatically add necessary ingredients to your shopping list. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned consultation department, Answering questions about childcare and alleviating anxieties about raising children. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned consultation department, Supporting children with homework and learning. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned schedule proposal unit, The system estimates the emotions of family members and adjusts the suggested household chore schedule based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned schedule proposal unit, Analyze the family's past household chore history to select the most suitable scheduling method. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned schedule proposal unit, When suggesting a household chore schedule, filter the suggestions based on the family's current lifestyle and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned schedule proposal unit, The system estimates the family's emotions and prioritizes schedule suggestions based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned schedule proposal unit, When suggesting household chore schedules, the system prioritizes suggesting schedules that are highly relevant, taking into account the geographical location of family members. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned schedule proposal unit, When suggesting household chore schedules, we analyze the family's social media activity and propose relevant schedules. The system described in Appendix 1, characterized by the features described herein. (Note 13) The execution unit is, It estimates the emotions of family members and adjusts the timing of household chores based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The execution unit is, When performing household chores, refer to the family's past chore performance history to select the most suitable method. The system described in Appendix 1, characterized by the features described herein. (Note 15) The execution unit is, When performing household chores, customize the tasks based on the family's current living situation. The system described in Appendix 1, characterized by the features described herein. (Note 16) The execution unit is, It estimates the emotions of family members and determines the priority of household chores based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The execution unit is, When performing household chores, the optimal method of execution is selected by considering the geographical location information of family members. The system described in Appendix 1, characterized by the features described herein. (Note 18) The execution unit is, When performing household chores, analyze family members' social media activity and adjust the tasks accordingly. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned list creation unit, The system estimates the emotions of family members and adjusts the shopping list based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned list creation unit, When creating a shopping list, refer to your family's past purchase history to select the most suitable list-making method. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned list creation unit, When creating a shopping list, customize the list contents based on your family's current living situation. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned list creation unit, The system estimates family members' emotions and prioritizes the shopping list based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned list creation unit, When creating a shopping list, consider the geographical location of your family members to create an optimal list. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned list creation unit, When creating a shopping list, analyze family members' social media activity to adjust the list contents. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned consultation department, The system estimates the family's emotions and adjusts the content of parenting consultations based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned consultation department, When providing childcare advice, we refer to the family's past consultation history to select the most appropriate response method. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned consultation department, When providing childcare consultations, customize the answers based on the family's current living situation. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned consultation department, The system estimates the family's emotions and determines the priority of childcare consultations based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned consultation department, When providing childcare consultations, we take into account the family's geographical location to provide the most appropriate answers. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned consultation department, When providing parenting advice, we analyze the family's social media activity to tailor our responses. The system described in Appendix 1, characterized by the features described herein. [Explanation of symbols]

[0180] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. The Schedule Proposal Department proposes household chore schedules, An execution unit that performs household chores based on the schedule proposed by the aforementioned schedule proposal unit, A list creation section that automatically generates shopping lists, It includes a consultation department that handles childcare consultations. A system characterized by the following features.

2. The aforementioned schedule proposal unit, Learn your family's lifestyle patterns and suggest the optimal household chore schedule. The system according to feature 1.

3. The execution unit is, Perform household chores based on the schedule proposed by the aforementioned schedule proposal unit. The system according to feature 1.

4. The aforementioned list creation unit, Use a refrigerator camera to manage your grocery inventory and automatically add necessary ingredients to your shopping list. The system according to feature 1.

5. The aforementioned consultation department, Answering questions about childcare and alleviating anxieties about raising children. The system according to feature 1.

6. The aforementioned consultation department, Supporting children with homework and learning. The system according to feature 1.

7. The aforementioned schedule proposal unit, The system estimates the emotions of family members and adjusts the suggested household chore schedule based on those estimated emotions. The system according to feature 1.

8. The aforementioned schedule proposal unit, Analyze the family's past household chore history to select the most suitable scheduling method. The system according to feature 1.

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