system

Abstract

The system according to the embodiment aims to analyze usage data of IoT devices in the home and propose optimal schedules and household chore plans that fit the family's lifestyle. [Solution] A system according to an embodiment includes a collection unit, an analysis unit, and a proposal unit. The collection unit collects usage data of IoT devices in the home. The analysis unit analyzes the family's lifestyle based on the data collected by the collection unit. The proposal unit proposes a schedule and a usage plan for a household robot based on the analysis results obtained by the analysis unit.

Description

[Technical Field]

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

[0002] Patent document 1 discloses a persona chatbot control method performed by at least one processor, the method including the steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to a description of the 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] Japanese Patent Publication No. 2022-180282 Summary of the Invention [Problem to be solved by the invention]

[0004] Conventional technologies do not adequately utilize usage data from IoT devices in the home to propose optimal schedules and household chore plans that suit the family's lifestyle, and there is room for improvement.

[0005] The system according to the embodiment aims to analyze usage data of IoT devices in the home and propose optimal schedules and household chore plans that fit the family's lifestyle. [Means for solving the problem]

[0006] The system according to the embodiment includes a collection unit, an analysis unit, and a proposal unit. The collection unit collects usage data of IoT devices in the home. The analysis unit analyzes the family's lifestyle based on the data collected by the collection unit. The proposal unit proposes a schedule and a usage plan for the household robot based on the analysis results obtained by the analysis unit. [Effects of the Invention]

[0007] The system according to the embodiment can analyze usage data of IoT devices in the home and propose optimal schedules and household chore plans that fit the family's lifestyle. [Brief explanation of the drawings]

[0008] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Figure 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. DETAILED DESCRIPTION OF THE INVENTION

[0009] An example of an embodiment of a system according to the technology of the present disclosure will be described below with reference to the accompanying drawings.

[0010] First, the terms used in the following description will be explained.

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

[0012] In the following embodiments, a coded RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by a processor.

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

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

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "and / or."

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

[0017] 1, a 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, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also 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 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The reception device 38, the output device 40, and the camera 42 are also connected to the bus 52.

[0020] The reception device 38 includes a touch panel 38A and a microphone 38B, and receives user input. The touch panel 38A detects contact with a pointer (for example, a pen or a finger) to receive user input by the touch of the pointer. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 (see FIG. 2) acquires the data indicating the user input.

[0021] Output device 40 includes a display 40A and a speaker 40B, and presents data to a user by outputting the data in a form of expression that the user can perceive (e.g., audio and / or text). Display 40A displays visible information such as text and images in accordance with instructions from processor 46. Speaker 40B outputs audio in accordance with instructions from processor 46. Camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

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

[0024] 2, in the data processing device 12, a specific process 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" according to the technology of the present 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 process is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0025] The storage 32 stores a data generation model 58 and an 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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0026] In the smart device 14, the specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used together 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 the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart device 14 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0027] Note that a device other than the data processing device 12 may 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 a processing result (prediction result, etc.) using the data generation model 58 by communicating with the server device having the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device owned by a user (e.g., a mobile phone, a robot, a home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example 1) A housekeeping support system according to an embodiment of the present invention collects usage data on IoT devices in the home, analyzes the family's lifestyle using a generation AI, and proposes an optimal schedule and a household robot usage plan. The housekeeping support system collects usage data on IoT devices in the home, analyzes the family's lifestyle using a generation AI, and proposes an optimal schedule and a household robot usage plan. For example, the housekeeping support system collects data on the usage status, operating time, power consumption, and other aspects of IoT devices in the home. The generation AI then analyzes the family's lifestyle based on the collected data. The generation AI analyzes the collected data to understand the family's lifestyle patterns and behavioral habits. For example, the generation AI analyzes breakfast and dinner times, cleaning frequency, and laundry timing. The generation AI then proposes an optimal schedule and a household robot usage plan based on the analysis results. For example, the generation AI proposes a meal preparation timeline, an optimal cleaning order, and the timing of laundry. This allows the housekeeping support system to improve the efficiency of housework and make family life more comfortable. This allows the housekeeping support system to propose an optimal schedule and a household robot usage plan tailored to the family's lifestyle. For example, a household assistance system can automatically start the coffee maker at breakfast time, or schedule a cleaning robot to clean while the family is out. It can also suggest the best time to do laundry based on how often the washing machine is used. This will help make household chores more efficient and make family life more comfortable.

[0029] A housework assistance system according to an embodiment includes a collection unit, an analysis unit, and a proposal unit. The collection unit collects usage data of IoT devices in the home. Examples of IoT devices in the home include, but are not limited to, smart speakers, smart lighting, and smart home appliances. The collection unit collects data such as the usage status, operating time, and power consumption of each IoT device. When collecting the usage data of the IoT devices, the collection unit can also add a function to detect device malfunctions or abnormalities. For example, if the refrigerator temperature is abnormally high, the system detects the malfunction and issues an alert. The analysis unit analyzes the family's lifestyle based on the data collected by the collection unit. For example, the analysis unit analyzes the collected data to understand the family's lifestyle patterns and behavioral habits. Examples of lifestyle patterns include repetitive daily activities and weekend activities. Examples of behavioral habits include daily exercise habits and meal timings. The proposal unit proposes an optimal schedule and a plan for using the household robot based on the analysis results obtained by the analysis unit. The suggestion unit suggests, for example, a timeline for preparing meals, an optimal cleaning order, or the timing for doing laundry. For example, the suggestion unit can also suggest a schedule for automatically running a coffee maker to coincide with breakfast time. The suggestion unit can also suggest a schedule for a cleaning robot to clean while family members are out. This allows the housework assistance system according to the embodiment to suggest an optimal schedule or a plan for using the household robot that matches the family's lifestyle. Some or all of the above-described processing by the suggestion unit may be performed using, or without, a generation AI. For example, the suggestion unit can input the analysis results obtained by the analysis unit into the generation AI and cause the generation AI to suggest an optimal schedule or a plan for using the household robot.

[0030] The collection unit can collect data including the usage status, operating time, and power consumption of each IoT device. Examples of usage status include, for example, on / off status and frequency of use. Examples of operating time include, for example, daily operating hours and continuous operating hours. Examples of power consumption include, for example, instantaneous power consumption and cumulative power consumption. The collection unit, for example, collects the usage status, operating time, and power consumption of each IoT device. For example, the collection unit collects data such as the number of times a refrigerator is opened and closed, the operating time of an air conditioner, and the frequency of use of a washing machine. Furthermore, when collecting IoT device usage data, the collection unit can also add a function to detect device malfunctions or abnormalities. For example, if the refrigerator temperature is abnormally high, the malfunction is detected and an alert is issued. By collecting data such as the usage status, operating time, and power consumption of each IoT device, the usage status of each device in the home can be understood. Some or all of the above-described processing by the collection unit may be performed using, or without, a generation AI. For example, the collection unit can input usage data of each IoT device into a generation AI and have the generation AI collect the data.

[0031] The analysis unit analyzes the collected data to understand the family's lifestyle patterns and behavioral habits. Lifestyle patterns include, for example, repetitive daily activities and weekend activities. Behavioral habits include, for example, daily exercise habits and meal timing. The analysis unit analyzes the collected data to understand the family's lifestyle patterns and behavioral habits. For example, the analysis unit analyzes breakfast times, dinner times, cleaning frequency, and laundry timing. The analysis unit can also incorporate an anomaly detection algorithm to detect abnormal patterns when analyzing the collected data. For example, an abnormality is detected when there is an abnormal fluctuation in refrigerator temperature data. This allows the family's lifestyle patterns and behavioral habits to be understood in detail by analyzing the collected data. Some or all of the above-described processing in the analysis unit may be performed using, or without, a generation AI. For example, the analysis unit can input the collected data into a generation AI and have the generation AI analyze the family's lifestyle patterns and behavioral habits.

[0032] The suggestion unit can suggest a meal preparation timeline, a cleaning order, the timing of laundry, etc. based on the analysis results. The meal preparation timeline includes, for example, the start time of cooking and the time it takes to prepare ingredients. The cleaning order includes, for example, the cleaning order for each room and cleaning priorities. The timing of laundry includes, for example, the operating time of a washing machine and timing based on the amount of laundry. The suggestion unit can suggest a meal preparation timeline, a cleaning order, the timing of laundry, etc. based on the analysis results. For example, the suggestion unit can suggest a schedule to automatically run a coffee maker to coincide with breakfast time. The suggestion unit can also suggest a schedule for a cleaning robot to clean while family members are out. This improves the efficiency of housework based on the analysis results, making family life more comfortable. Some or all of the above-mentioned processing by the suggestion unit may be performed using, or without, a generation AI. For example, the suggestion unit can input the analysis results into a generation AI and cause the generation AI to propose an optimal schedule and a usage plan for a household robot.

[0033] The suggestion unit can propose a schedule for automatically operating the coffee maker in accordance with breakfast time. The breakfast time includes, for example, breakfast times for each family member and differences between weekdays and weekends. The schedule for automatically operating the coffee maker includes, for example, an operation start time and an operation frequency. The suggestion unit can propose a schedule for automatically operating the coffee maker in accordance with breakfast time. For example, the suggestion unit can propose a schedule for automatically operating the coffee maker in accordance with breakfast time for each family member. This allows the coffee maker to be automatically operated in accordance with breakfast time, thereby improving the efficiency of housework. Some or all of the above-described processing in the suggestion unit may be performed using, or without, a generation AI. For example, the suggestion unit can cause the generation AI to execute a proposal for a schedule for automatically operating the coffee maker in accordance with breakfast time.

[0034] The suggestion unit can suggest a schedule for the cleaning robot to clean while the family is out. The time when the family is out includes, for example, the time when the family is out and the destination. The cleaning schedule includes, for example, the start time of cleaning and the frequency of cleaning. The suggestion unit, for example, suggests a schedule for the cleaning robot to clean while the family is out. For example, the suggestion unit can suggest a schedule for the cleaning robot to clean based on the time when the family is out. This allows the cleaning robot to clean while the family is out, thereby improving the efficiency of housework. Some or all of the above-mentioned processing in the suggestion unit may be performed using, or without, a generation AI. For example, the suggestion unit can cause the generation AI to suggest a schedule for the cleaning robot to clean while the family is out.

[0035] The collection unit can add a function to detect device failures and abnormalities when collecting usage data from each IoT device. Device failures and abnormalities include, for example, an abnormally high refrigerator temperature or an abnormal sound when an air conditioner is operating. For example, the collection unit detects a failure and issues an alert when the refrigerator temperature is abnormally high. The collection unit can also detect an abnormality when an air conditioner is operating at an abnormal sound and suggest maintenance. The collection unit can also detect a failure and stop use of a washing machine when the vibration is abnormally loud. This enables rapid response by detecting device failures and abnormalities, improving safety in the home. Some or all of the above-described processing by the collection unit can be performed using, or without, a generation AI. For example, the collection unit can input usage data from each IoT device to a generation AI and have the generation AI detect failures and abnormalities.

[0036] When collecting usage data from IoT devices, the collection unit can combine multiple sensors to improve data accuracy. The multiple sensors may include, for example, a temperature sensor, a humidity sensor, and a motion sensor. For example, the collection unit may combine a temperature sensor and a humidity sensor to obtain a detailed understanding of the internal environment of a refrigerator. The collection unit may also combine a vibration sensor and a sound sensor to accurately monitor the operating status of a washing machine. The collection unit may also combine a light sensor and a motion sensor to obtain a detailed understanding of the operating status of an air conditioner. By combining multiple sensors, data accuracy can be improved and more detailed information can be obtained. Some or all of the above-described processing in the collection unit may be performed using, for example, a generation AI, or may be performed without using the generation AI. For example, the collection unit may input data from multiple sensors into the generation AI and have the generation AI improve the accuracy of the data.

[0037] When collecting usage data of IoT devices, the collection unit can improve the efficiency of the data storage method and efficiently manage the data. Data storage methods include, for example, data compression technology and the use of cloud storage. The collection unit can reduce storage capacity by, for example, using data compression technology. The collection unit can also automate data backup by utilizing cloud storage. The collection unit can also set a data storage period and automatically delete old data. In this way, by optimizing the data storage method, storage capacity can be reduced and data can be efficiently managed. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, a generation AI, or may be performed without using the generation AI. For example, the collection unit can cause the generation AI to optimize the data storage method.

[0038] When collecting IoT device usage data, the collection unit can also simultaneously collect household environmental data. Environmental data includes, for example, temperature, humidity, and illuminance. For example, the collection unit collects the kitchen temperature and humidity along with refrigerator usage data. The collection unit can also collect room illuminance along with air conditioner usage data. The collection unit can also collect the laundry room temperature and humidity along with washing machine usage data. In this way, by simultaneously collecting household environmental data, more detailed information can be obtained. Some or all of the above-described processing in the collection unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the collection unit can cause a generation AI to collect environmental data.

[0039] When collecting IoT device usage data, the collection unit can collect data taking into account family schedule information. Family schedule information includes, for example, calendar information and a schedule. For example, the collection unit collects cleaning robot usage data during times when family members are out. The collection unit can also collect air conditioner usage data during times when family members are sleeping. The collection unit can also collect refrigerator usage data during times when family members are eating. In this way, by collecting data taking into account family schedule information, data can be collected at more appropriate times. Some or all of the above-mentioned processing in the collection unit may be performed using, or without, a generation AI. For example, the collection unit can input family schedule information into the generation AI and cause the generation AI to collect data.

[0040] When collecting usage data of IoT devices, the collection unit can introduce encryption technology to enhance data privacy protection. Examples of encryption technologies include AES encryption, RSA encryption, and SSL / TLS encryption. For example, the collection unit protects data using AES encryption technology when collecting data. The collection unit can also protect data using RSA encryption technology when storing data. The collection unit can also protect data using SSL / TLS encryption technology when transferring data. This enhances data privacy protection and improves data security. Some or all of the above-described processing in the collection unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the collection unit can have the generation AI encrypt the data.

[0041] When analyzing the collected data, the analysis unit can introduce an anomaly detection algorithm to detect abnormal patterns. Examples of anomaly detection algorithms include machine learning-based anomaly detection and rule-based anomaly detection. For example, the analysis unit detects an anomaly when there is an abnormal fluctuation in refrigerator temperature data. The analysis unit can also detect an anomaly when the air conditioner is running for longer than usual. The analysis unit can also detect an anomaly when the frequency of washing machine use increases suddenly. In this way, by introducing an anomaly detection algorithm, abnormal patterns can be quickly detected. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, a generation AI, or may be performed without using the generation AI. For example, the analysis unit can input the collected data into the generation AI and have the generation AI detect abnormal patterns.

[0042] When analyzing collected data, the analysis unit can provide analysis results taking into account the health condition of family members. The health condition of family members includes, for example, health checkup results and daily health records. For example, the analysis unit can suggest an optimal temperature setting for an air conditioner based on the health condition of family members. The analysis unit can also suggest an optimal temperature setting for a refrigerator based on the health condition of family members. The analysis unit can also adjust the frequency of washing machine use based on the health condition of family members. This allows for more appropriate suggestions by providing analysis results taking into account the health condition of family members. Some or all of the above-described processing in the analysis unit may be performed using, or without, a generation AI. For example, the analysis unit can input data on the health condition of family members into the generation AI and have the generation AI provide the analysis results.

[0043] When analyzing collected data, the analysis unit can detect changes in lifestyle by comparing it with past data. Changes in lifestyle include, for example, a method for comparing with past data and criteria for change. For example, the analysis unit can detect a change in lifestyle if the frequency of refrigerator use increases when compared with past data. The analysis unit can also detect a change in lifestyle if the operating time of the air conditioner decreases when compared with past data. The analysis unit can also detect a change in lifestyle if the frequency of washing machine use changes when compared with past data. By detecting changes in lifestyle by comparing with past data, it becomes possible to make suggestions that address changes in the family's lifestyle. Some or all of the above-described processing in the analysis unit can be performed using, or without, a generation AI. For example, the analysis unit can input past data into the generation AI and have the generation AI detect changes in lifestyle.

[0044] When analyzing the collected data, the analysis unit can provide analysis results taking into account the family's eating patterns and nutritional balance. Examples of family eating patterns include the number of meals and meal times. Nutritional balance includes, for example, nutrient intake and dietary balance. For example, the analysis unit can suggest optimal food placement in the refrigerator based on the family's eating patterns. The analysis unit can also suggest a meal timeline based on the family's nutritional balance. The analysis unit can also suggest the timing of food purchases based on the family's eating patterns. This allows for support for a healthier lifestyle by providing analysis results that take into account the family's eating patterns and nutritional balance. Some or all of the above-described processing by the analysis unit can be performed using, or without, a generation AI. For example, the analysis unit can input data on the family's eating patterns and nutritional balance into the generation AI and have the generation AI provide the analysis results.

[0045] When analyzing the collected data, the analysis unit can make suggestions for optimizing the family's energy consumption. Energy consumption includes, for example, electricity consumption and gas consumption. For example, the analysis unit can suggest an optimal temperature setting for an air conditioner based on the family's energy consumption. The analysis unit can also suggest an optimal temperature setting for a refrigerator based on the family's energy consumption. The analysis unit can also adjust the frequency of washing machine use based on the family's energy consumption. In this way, suggestions for optimizing the family's energy consumption are made, thereby improving energy efficiency. Some or all of the above-mentioned processing in the analysis unit may be performed using, or without, the generation AI. For example, the analysis unit can input data on the family's energy consumption into the generation AI and cause the generation AI to make suggestions for optimizing energy consumption.

[0046] When analyzing the collected data, the analysis unit can customize the analysis results based on the hobbies and interests of the family members. The hobbies and interests of the family members include, for example, the types of hobbies and the subjects of interest. For example, the analysis unit can suggest how to arrange ingredients in the refrigerator based on the hobbies of the family members. The analysis unit can also suggest air conditioner settings based on the interests of the family members. The analysis unit can also suggest when to use the washing machine based on the hobbies of the family members. This enables more personalized suggestions by customizing the analysis results based on the hobbies and interests of the family members. Some or all of the above-described processing in the analysis unit may be performed using, or without, the generation AI. For example, the analysis unit can input data on the hobbies and interests of the family members into the generation AI and have the generation AI customize the analysis results.

[0047] When providing suggestions, the suggestion unit can improve the accuracy of the suggestions by taking into account the family's past reactions and feedback. Past reactions and feedback include, for example, reactions to past suggestions and the types of feedback. The suggestion unit, for example, makes optimal suggestions based on suggestions that the family has liked in the past. The suggestion unit can also adjust the suggestions based on the family's past feedback. The suggestion unit can also analyze the family's past reactions and improve the accuracy of the suggestions. In this way, the accuracy of the suggestions is improved by taking into account the family's past reactions and feedback. Some or all of the above-mentioned processing in the suggestion unit may be performed using, or without, the generation AI. For example, the suggestion unit can input data on past reactions and feedback into the generation AI and cause the generation AI to improve the accuracy of the suggestions.

[0048] When providing the proposal content, the suggestion unit can make optimal suggestions by taking into account the health condition and lifestyle rhythm of the family members. Life rhythms include, for example, wake-up times and bedtimes. The suggestion unit can, for example, suggest a meal timeline based on the health condition of the family members. The suggestion unit can also suggest an optimal cleaning order based on the lifestyle rhythm of the family members. The suggestion unit can also suggest the timing of laundry based on the health condition of the family members. This enables more appropriate suggestions by taking into account the health condition and lifestyle rhythm of the family members. Some or all of the above-mentioned processing in the suggestion unit can be performed using, or without, the generation AI. For example, the suggestion unit can input data on the health condition and lifestyle rhythm of the family members into the generation AI and cause the generation AI to execute optimal suggestions.

[0049] When providing the proposal content, the suggestion unit can make a proposal to optimize the energy consumption of the family. Energy consumption includes, for example, electricity consumption and gas consumption. For example, the suggestion unit can suggest an optimal temperature setting for an air conditioner based on the energy consumption of the family. The suggestion unit can also suggest an optimal temperature setting for a refrigerator based on the energy consumption of the family. The suggestion unit can also adjust the frequency of washing machine use based on the energy consumption of the family. In this way, by making a proposal to optimize the energy consumption of the family, energy efficiency is improved. Some or all of the above-mentioned processing in the suggestion unit may be performed using, or without, the generation AI. For example, the suggestion unit can input data on the energy consumption of the family into the generation AI and cause the generation AI to execute a proposal to optimize energy consumption.

[0050] When providing the suggestions, the suggestion unit can customize the suggestions based on the hobbies and interests of the family members. The hobbies and interests of the family members include, for example, types of hobbies and subjects of interest. For example, the suggestion unit can suggest a meal timeline based on the hobbies of the family members. The suggestion unit can also suggest an optimal cleaning order based on the interests of the family members. The suggestion unit can also suggest the timing of laundry based on the hobbies of the family members. This enables more personalized suggestions by customizing the suggestions based on the hobbies and interests of the family members. Some or all of the above-described processing in the suggestion unit may be performed using, or without, a generation AI. For example, the suggestion unit can input data on the hobbies and interests of the family members into the generation AI and cause the generation AI to customize the suggestions.

[0051] When providing the proposal content, the suggestion unit can make optimal suggestions by taking into account family schedule information. Family schedule information includes, for example, calendar information and a timetable. For example, the suggestion unit can suggest a meal timeline based on the family schedule. The suggestion unit can also suggest an optimal cleaning order based on the family schedule. The suggestion unit can also suggest the timing of laundry based on the family schedule. In this way, by taking the family schedule information into consideration, suggestions can be made at more appropriate times. Some or all of the above-described processing in the suggestion unit may be performed using, or without, a generation AI. For example, the suggestion unit can input family schedule information into the generation AI and cause the generation AI to execute optimal suggestions.

[0052] When providing the proposal content, the suggestion unit can make a proposal to optimize the energy consumption of the family. Energy consumption includes, for example, electricity consumption and gas consumption. For example, the suggestion unit can suggest an optimal temperature setting for an air conditioner based on the energy consumption of the family. The suggestion unit can also suggest an optimal temperature setting for a refrigerator based on the energy consumption of the family. The suggestion unit can also adjust the frequency of washing machine use based on the energy consumption of the family. In this way, by making a proposal to optimize the energy consumption of the family, energy efficiency is improved. Some or all of the above-mentioned processing in the suggestion unit may be performed using, or without, the generation AI. For example, the suggestion unit can input data on the energy consumption of the family into the generation AI and cause the generation AI to execute a proposal to optimize energy consumption.

[0053] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0054] When analyzing collected data, the analysis unit can provide analysis results taking into account the health condition of family members. For example, it can suggest the optimal temperature setting for an air conditioner based on the health condition of family members. It can also suggest the optimal temperature setting for a refrigerator based on the health condition of family members. It can also adjust the frequency of washing machine use based on the health condition of family members. This allows for more appropriate suggestions by providing analysis results taking into account the health condition of family members. Some or all of the above-mentioned processing in the analysis unit may be performed using or without the generation AI. For example, the analysis unit can input data on the health condition of family members into the generation AI and have the generation AI provide the analysis results.

[0055] When collecting usage data from IoT devices, the collection unit can combine multiple sensors to improve data accuracy. For example, a temperature sensor and a humidity sensor can be combined to obtain a detailed understanding of the internal environment of a refrigerator. A vibration sensor and a sound sensor can be combined to accurately monitor the operating status of a washing machine. Furthermore, a light sensor and a motion sensor can be combined to obtain a detailed understanding of the operating status of an air conditioner. By combining multiple sensors, data accuracy can be improved and more detailed information can be obtained. Some or all of the above-mentioned processing in the collection unit may be performed using or without the generation AI. For example, the collection unit can input data from multiple sensors into the generation AI and have the generation AI improve the accuracy of the data.

[0056] When analyzing collected data, the analysis unit can detect changes in lifestyle by comparing it with past data. For example, if the frequency of refrigerator use increases compared with past data, a change in lifestyle can be detected. Also, if the operating time of the air conditioner decreases compared with past data, a change in lifestyle can be detected. Furthermore, if the frequency of washing machine use changes compared with past data, a change in lifestyle can be detected. In this way, by detecting changes in lifestyle by comparing it with past data, it becomes possible to make suggestions that address changes in the family's lifestyle. Some or all of the above-mentioned processing in the analysis unit may be performed using or without the generation AI. For example, the analysis unit can input past data into the generation AI and have the generation AI detect changes in lifestyle.

[0057] When providing suggestions, the suggestion unit can improve the accuracy of the suggestions by taking into account the family's past reactions and feedback. For example, the suggestion unit can make optimal suggestions based on suggestions that the family has liked in the past. The suggestion unit can also adjust the suggestions based on the family's past feedback. Furthermore, the suggestion unit can analyze the family's past reactions and improve the accuracy of the suggestions. In this way, the accuracy of the suggestions is improved by taking into account the family's past reactions and feedback. Some or all of the above-mentioned processing in the suggestion unit may be performed using or without the generation AI. For example, the suggestion unit can input data on past reactions and feedback into the generation AI and cause the generation AI to improve the accuracy of the suggestions.

[0058] When analyzing the collected data, the analysis unit can provide analysis results taking into account the family's eating patterns and nutritional balance. For example, it can suggest the optimal food ingredient placement in the refrigerator based on the family's eating patterns. It can also suggest a meal timeline based on the family's nutritional balance. It can also suggest the timing of food ingredient purchases based on the family's eating patterns. In this way, by providing analysis results taking into account the family's eating patterns and nutritional balance, it is possible to support a healthier lifestyle. Some or all of the above-mentioned processing in the analysis unit may be performed using or without the generation AI. For example, the analysis unit can input data on the family's eating patterns and nutritional balance into the generation AI and have the generation AI provide the analysis results.

[0059] When collecting IoT device usage data, the collection unit can also simultaneously collect household environmental data. For example, the kitchen temperature and humidity can be collected along with refrigerator usage data. The room illuminance can also be collected along with air conditioner usage data. Furthermore, the laundry room temperature and humidity can also be collected along with washing machine usage data. By simultaneously collecting household environmental data, more detailed information can be obtained. Some or all of the above-described processing in the collection unit may be performed using a generation AI, or may be performed without using a generation AI. For example, the collection unit can cause a generation AI to collect environmental data.

[0060] The processing flow of the first embodiment will be briefly explained below.

[0061] Step 1: The collection unit collects usage data from IoT devices in the home. IoT devices in the home include smart speakers, smart lighting, and smart home appliances. The collection unit collects data such as the usage status, operating time, and power consumption of each IoT device. The collection unit can also add a function to detect device malfunctions and abnormalities. For example, if the temperature in a refrigerator is abnormally high, it will detect the malfunction and issue an alert. Step 2: The analysis unit analyzes the family's lifestyle based on the data collected by the collection unit. The analysis unit analyzes the collected data to understand the family's lifestyle patterns and behavioral habits. Lifestyle patterns include repetitive daily actions and how people spend their weekends, while behavioral habits include daily exercise habits and meal timings. Step 3: The suggestion unit proposes an optimal schedule and a plan for using the household robot based on the analysis results obtained by the analysis unit. The suggestion unit proposes a timeline for meal preparation, the optimal order for cleaning, the timing of laundry, etc. For example, it can propose a schedule to automatically run the coffee maker in time for breakfast, or a schedule for the cleaning robot to clean while the family is out. Some or all of the processing in the suggestion unit may be performed using generative AI.

[0062] (Example 2) A housekeeping support system according to an embodiment of the present invention collects usage data on IoT devices in the home, analyzes the family's lifestyle using a generation AI, and proposes an optimal schedule and a household robot usage plan. The housekeeping support system collects usage data on IoT devices in the home, analyzes the family's lifestyle using a generation AI, and proposes an optimal schedule and a household robot usage plan. For example, the housekeeping support system collects data on the usage status, operating time, power consumption, and other aspects of IoT devices in the home. The generation AI then analyzes the family's lifestyle based on the collected data. The generation AI analyzes the collected data to understand the family's lifestyle patterns and behavioral habits. For example, the generation AI analyzes breakfast and dinner times, cleaning frequency, and laundry timing. The generation AI then proposes an optimal schedule and a household robot usage plan based on the analysis results. For example, the generation AI proposes a meal preparation timeline, an optimal cleaning order, and the timing of laundry. This allows the housekeeping support system to improve the efficiency of housework and make family life more comfortable. This allows the housekeeping support system to propose an optimal schedule and a household robot usage plan tailored to the family's lifestyle. For example, a household assistance system can automatically start the coffee maker at breakfast time, or schedule a cleaning robot to clean while the family is out. It can also suggest the best time to do laundry based on how often the washing machine is used. This will help make household chores more efficient and make family life more comfortable.

[0063] A housework assistance system according to an embodiment includes a collection unit, an analysis unit, and a proposal unit. The collection unit collects usage data of IoT devices in the home. Examples of IoT devices in the home include, but are not limited to, smart speakers, smart lighting, and smart home appliances. The collection unit collects data such as the usage status, operating time, and power consumption of each IoT device. When collecting the usage data of the IoT devices, the collection unit can also add a function to detect device malfunctions or abnormalities. For example, if the refrigerator temperature is abnormally high, the system detects the malfunction and issues an alert. The analysis unit analyzes the family's lifestyle based on the data collected by the collection unit. For example, the analysis unit analyzes the collected data to understand the family's lifestyle patterns and behavioral habits. Examples of lifestyle patterns include repetitive daily activities and weekend activities. Examples of behavioral habits include daily exercise habits and meal timings. The proposal unit proposes an optimal schedule and a plan for using the household robot based on the analysis results obtained by the analysis unit. The suggestion unit suggests, for example, a timeline for preparing meals, an optimal cleaning order, or the timing for doing laundry. For example, the suggestion unit can also suggest a schedule for automatically running a coffee maker to coincide with breakfast time. The suggestion unit can also suggest a schedule for a cleaning robot to clean while family members are out. This allows the housework assistance system according to the embodiment to suggest an optimal schedule or a plan for using the household robot that matches the family's lifestyle. Some or all of the above-described processing by the suggestion unit may be performed using, or without, a generation AI. For example, the suggestion unit can input the analysis results obtained by the analysis unit into the generation AI and cause the generation AI to suggest an optimal schedule or a plan for using the household robot.

[0064] The collection unit can collect data including the usage status, operating time, and power consumption of each IoT device. Examples of usage status include, for example, on / off status and frequency of use. Examples of operating time include, for example, daily operating hours and continuous operating hours. Examples of power consumption include, for example, instantaneous power consumption and cumulative power consumption. The collection unit, for example, collects the usage status, operating time, and power consumption of each IoT device. For example, the collection unit collects data such as the number of times a refrigerator is opened and closed, the operating time of an air conditioner, and the frequency of use of a washing machine. Furthermore, when collecting IoT device usage data, the collection unit can also add a function to detect device malfunctions or abnormalities. For example, if the refrigerator temperature is abnormally high, the malfunction is detected and an alert is issued. By collecting data such as the usage status, operating time, and power consumption of each IoT device, the usage status of each device in the home can be understood. Some or all of the above-described processing by the collection unit may be performed using, or without, a generation AI. For example, the collection unit can input usage data of each IoT device into a generation AI and have the generation AI collect the data.

[0065] The analysis unit analyzes the collected data to understand the family's lifestyle patterns and behavioral habits. Lifestyle patterns include, for example, repetitive daily activities and weekend activities. Behavioral habits include, for example, daily exercise habits and meal timing. The analysis unit analyzes the collected data to understand the family's lifestyle patterns and behavioral habits. For example, the analysis unit analyzes breakfast times, dinner times, cleaning frequency, and laundry timing. The analysis unit can also incorporate an anomaly detection algorithm to detect abnormal patterns when analyzing the collected data. For example, an abnormality is detected when there is an abnormal fluctuation in refrigerator temperature data. This allows the family's lifestyle patterns and behavioral habits to be understood in detail by analyzing the collected data. Some or all of the above-described processing in the analysis unit may be performed using, or without, a generation AI. For example, the analysis unit can input the collected data into a generation AI and have the generation AI analyze the family's lifestyle patterns and behavioral habits.

[0066] The suggestion unit can suggest a meal preparation timeline, a cleaning order, the timing of laundry, etc. based on the analysis results. The meal preparation timeline includes, for example, the start time of cooking and the time it takes to prepare ingredients. The cleaning order includes, for example, the cleaning order for each room and cleaning priorities. The timing of laundry includes, for example, the operating time of a washing machine and timing based on the amount of laundry. The suggestion unit can suggest a meal preparation timeline, a cleaning order, the timing of laundry, etc. based on the analysis results. For example, the suggestion unit can suggest a schedule to automatically run a coffee maker to coincide with breakfast time. The suggestion unit can also suggest a schedule for a cleaning robot to clean while family members are out. This improves the efficiency of housework based on the analysis results, making family life more comfortable. Some or all of the above-mentioned processing by the suggestion unit may be performed using, or without, a generation AI. For example, the suggestion unit can input the analysis results into a generation AI and cause the generation AI to propose an optimal schedule and a usage plan for a household robot.

[0067] The suggestion unit can propose a schedule for automatically operating the coffee maker in accordance with breakfast time. The breakfast time includes, for example, breakfast times for each family member and differences between weekdays and weekends. The schedule for automatically operating the coffee maker includes, for example, an operation start time and an operation frequency. The suggestion unit can propose a schedule for automatically operating the coffee maker in accordance with breakfast time. For example, the suggestion unit can propose a schedule for automatically operating the coffee maker in accordance with breakfast time for each family member. This allows the coffee maker to be automatically operated in accordance with breakfast time, thereby improving the efficiency of housework. Some or all of the above-described processing in the suggestion unit may be performed using, or without, a generation AI. For example, the suggestion unit can cause the generation AI to execute a proposal for a schedule for automatically operating the coffee maker in accordance with breakfast time.

[0068] The suggestion unit can suggest a schedule for the cleaning robot to clean while the family is out. The time when the family is out includes, for example, the time when the family is out and the destination. The cleaning schedule includes, for example, the start time of cleaning and the frequency of cleaning. The suggestion unit, for example, suggests a schedule for the cleaning robot to clean while the family is out. For example, the suggestion unit can suggest a schedule for the cleaning robot to clean based on the time when the family is out. This allows the cleaning robot to clean while the family is out, thereby improving the efficiency of housework. Some or all of the above-mentioned processing in the suggestion unit may be performed using, or without, a generation AI. For example, the suggestion unit can cause the generation AI to suggest a schedule for the cleaning robot to clean while the family is out.

[0069] The collection unit can estimate a user's emotions and adjust the timing of collecting usage data from the IoT device based on the estimated user emotions. Examples of user emotions include states such as stress, relaxation, and hurry. Examples of collection timing include the frequency and start time of data collection. For example, if the user is feeling stressed, the collection unit can delay the collection timing to reduce the user's burden. Furthermore, if the user is relaxed, the collection unit can also advance the collection timing to improve data accuracy. Furthermore, if the user is in a hurry, the collection unit can optimize the collection timing to quickly acquire data. Thus, by adjusting the collection timing according to the user's emotions, the user's burden can be reduced and the data accuracy can be improved. Emotion estimation is realized using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the collection unit can be performed using, for example, the generation AI, or without the generation AI. For example, the collection unit can input the user's emotional data into the generation AI and have the generation AI adjust the collection timing.

[0070] The collection unit can add a function to detect device failures and abnormalities when collecting usage data from each IoT device. Device failures and abnormalities include, for example, an abnormally high refrigerator temperature or an abnormal sound when an air conditioner is operating. For example, the collection unit detects a failure and issues an alert when the refrigerator temperature is abnormally high. The collection unit can also detect an abnormality when an air conditioner is operating at an abnormal sound and suggest maintenance. The collection unit can also detect a failure and stop use of a washing machine when the vibration is abnormally loud. This enables rapid response by detecting device failures and abnormalities, improving safety in the home. Some or all of the above-described processing by the collection unit can be performed using, or without, a generation AI. For example, the collection unit can input usage data from each IoT device to a generation AI and have the generation AI detect failures and abnormalities.

[0071] When collecting usage data from IoT devices, the collection unit can combine multiple sensors to improve data accuracy. The multiple sensors may include, for example, a temperature sensor, a humidity sensor, and a motion sensor. For example, the collection unit may combine a temperature sensor and a humidity sensor to obtain a detailed understanding of the internal environment of a refrigerator. The collection unit may also combine a vibration sensor and a sound sensor to accurately monitor the operating status of a washing machine. The collection unit may also combine a light sensor and a motion sensor to obtain a detailed understanding of the operating status of an air conditioner. By combining multiple sensors, data accuracy can be improved and more detailed information can be obtained. Some or all of the above-described processing in the collection unit may be performed using, for example, a generation AI, or may be performed without using the generation AI. For example, the collection unit may input data from multiple sensors into the generation AI and have the generation AI improve the accuracy of the data.

[0072] When collecting usage data of IoT devices, the collection unit can improve the efficiency of the data storage method and efficiently manage the data. Data storage methods include, for example, data compression technology and the use of cloud storage. The collection unit can reduce storage capacity by, for example, using data compression technology. The collection unit can also automate data backup by utilizing cloud storage. The collection unit can also set a data storage period and automatically delete old data. In this way, by optimizing the data storage method, storage capacity can be reduced and data can be efficiently managed. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, a generation AI, or may be performed without using the generation AI. For example, the collection unit can cause the generation AI to optimize the data storage method.

[0073] The collection unit can estimate the user's emotions and determine the priority of data to be collected based on the estimated user's emotions. Examples of data priorities include highly important data and real-time data. For example, when the user is stressed, the collection unit prioritizes collecting only important data. Furthermore, when the user is relaxed, the collection unit can prioritize collecting detailed data. Furthermore, when the user is in a hurry, the collection unit can prioritize data that can be collected quickly. Thus, by determining the priority of data according to the user's emotions, important data can be collected preferentially. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the collection unit can be performed using, for example, the generation AI. For example, the collection unit can input the user's emotion data into the generation AI and have the generation AI determine the data priority.

[0074] When collecting IoT device usage data, the collection unit can also simultaneously collect household environmental data. Environmental data includes, for example, temperature, humidity, and illuminance. For example, the collection unit collects the kitchen temperature and humidity along with refrigerator usage data. The collection unit can also collect room illuminance along with air conditioner usage data. The collection unit can also collect the laundry room temperature and humidity along with washing machine usage data. In this way, by simultaneously collecting household environmental data, more detailed information can be obtained. Some or all of the above-described processing in the collection unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the collection unit can cause a generation AI to collect environmental data.

[0075] When collecting IoT device usage data, the collection unit can collect data taking into account family schedule information. Family schedule information includes, for example, calendar information and a schedule. For example, the collection unit collects cleaning robot usage data during times when family members are out. The collection unit can also collect air conditioner usage data during times when family members are sleeping. The collection unit can also collect refrigerator usage data during times when family members are eating. In this way, by collecting data taking into account family schedule information, data can be collected at more appropriate times. Some or all of the above-mentioned processing in the collection unit may be performed using, or without, a generation AI. For example, the collection unit can input family schedule information into the generation AI and cause the generation AI to collect data.

[0076] When collecting usage data of IoT devices, the collection unit can introduce encryption technology to enhance data privacy protection. Examples of encryption technologies include AES encryption, RSA encryption, and SSL / TLS encryption. For example, the collection unit protects data using AES encryption technology when collecting data. The collection unit can also protect data using RSA encryption technology when storing data. The collection unit can also protect data using SSL / TLS encryption technology when transferring data. This enhances data privacy protection and improves data security. Some or all of the above-described processing in the collection unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the collection unit can have the generation AI encrypt the data.

[0077] The analysis unit can estimate the user's emotions and adjust the display method of the analysis results based on the estimated user emotions. Examples of display methods for the analysis results include graph display, text display, and the like. For example, if the user is nervous, the analysis unit provides a simple, highly visible display method. Furthermore, if the user is relaxed, the analysis unit can provide a display method that includes detailed information. Furthermore, if the user is in a hurry, the analysis unit can provide a display method that focuses on the main points. This allows the display method of the analysis results to be adjusted according to the user's emotions, making it easier for the user to view. The emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the analysis unit can be performed using, for example, the generation AI, or without the generation AI. For example, the analysis unit can input the user's emotion data into the generation AI and have the generation AI adjust the display method of the analysis results.

[0078] When analyzing the collected data, the analysis unit can introduce an anomaly detection algorithm to detect abnormal patterns. Examples of anomaly detection algorithms include machine learning-based anomaly detection and rule-based anomaly detection. For example, the analysis unit detects an anomaly when there is an abnormal fluctuation in refrigerator temperature data. The analysis unit can also detect an anomaly when the air conditioner is running for longer than usual. The analysis unit can also detect an anomaly when the frequency of washing machine use increases suddenly. In this way, by introducing an anomaly detection algorithm, abnormal patterns can be quickly detected. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, a generation AI, or may be performed without using the generation AI. For example, the analysis unit can input the collected data into the generation AI and have the generation AI detect abnormal patterns.

[0079] When analyzing collected data, the analysis unit can provide analysis results taking into account the health condition of family members. The health condition of family members includes, for example, health checkup results and daily health records. For example, the analysis unit can suggest an optimal temperature setting for an air conditioner based on the health condition of family members. The analysis unit can also suggest an optimal temperature setting for a refrigerator based on the health condition of family members. The analysis unit can also adjust the frequency of washing machine use based on the health condition of family members. This allows for more appropriate suggestions by providing analysis results taking into account the health condition of family members. Some or all of the above-described processing in the analysis unit may be performed using, or without, a generation AI. For example, the analysis unit can input data on the health condition of family members into the generation AI and have the generation AI provide the analysis results.

[0080] When analyzing collected data, the analysis unit can detect changes in lifestyle by comparing it with past data. Changes in lifestyle include, for example, a method for comparing with past data and criteria for change. For example, the analysis unit can detect a change in lifestyle if the frequency of refrigerator use increases when compared with past data. The analysis unit can also detect a change in lifestyle if the operating time of the air conditioner decreases when compared with past data. The analysis unit can also detect a change in lifestyle if the frequency of washing machine use changes when compared with past data. By detecting changes in lifestyle by comparing with past data, it becomes possible to make suggestions that address changes in the family's lifestyle. Some or all of the above-described processing in the analysis unit can be performed using, or without, a generation AI. For example, the analysis unit can input past data into the generation AI and have the generation AI detect changes in lifestyle.

[0081] The analysis unit can estimate the user's emotions and prioritize the analysis results based on the estimated user emotions. The priorities of the analysis results include, for example, analysis results with high importance and real-time analysis results. For example, when the user is stressed, the analysis unit can prioritize displaying important analysis results. Furthermore, when the user is relaxed, the analysis unit can prioritize displaying detailed analysis results. Furthermore, when the user is in a hurry, the analysis unit can prioritize analysis results that can be quickly viewed. Thus, by prioritizing the analysis results according to the user's emotions, important analysis results can be prioritized. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the analysis unit can be performed using, for example, the generation AI. For example, the analysis unit can input the user's emotion data into the generation AI and have the generation AI determine the priority of the analysis results.

[0082] When analyzing the collected data, the analysis unit can provide analysis results taking into account the family's eating patterns and nutritional balance. Examples of family eating patterns include the number of meals and meal times. Nutritional balance includes, for example, nutrient intake and dietary balance. For example, the analysis unit can suggest optimal food placement in the refrigerator based on the family's eating patterns. The analysis unit can also suggest a meal timeline based on the family's nutritional balance. The analysis unit can also suggest the timing of food purchases based on the family's eating patterns. This allows for support for a healthier lifestyle by providing analysis results that take into account the family's eating patterns and nutritional balance. Some or all of the above-described processing by the analysis unit can be performed using, or without, a generation AI. For example, the analysis unit can input data on the family's eating patterns and nutritional balance into the generation AI and have the generation AI provide the analysis results.

[0083] When analyzing the collected data, the analysis unit can make suggestions for optimizing the family's energy consumption. Energy consumption includes, for example, electricity consumption and gas consumption. For example, the analysis unit can suggest an optimal temperature setting for an air conditioner based on the family's energy consumption. The analysis unit can also suggest an optimal temperature setting for a refrigerator based on the family's energy consumption. The analysis unit can also adjust the frequency of washing machine use based on the family's energy consumption. In this way, suggestions for optimizing the family's energy consumption are made, thereby improving energy efficiency. Some or all of the above-mentioned processing in the analysis unit may be performed using, or without, the generation AI. For example, the analysis unit can input data on the family's energy consumption into the generation AI and cause the generation AI to make suggestions for optimizing energy consumption.

[0084] When analyzing the collected data, the analysis unit can customize the analysis results based on the hobbies and interests of the family members. The hobbies and interests of the family members include, for example, the types of hobbies and the subjects of interest. For example, the analysis unit can suggest how to arrange ingredients in the refrigerator based on the hobbies of the family members. The analysis unit can also suggest air conditioner settings based on the interests of the family members. The analysis unit can also suggest when to use the washing machine based on the hobbies of the family members. This enables more personalized suggestions by customizing the analysis results based on the hobbies and interests of the family members. Some or all of the above-described processing in the analysis unit may be performed using, or without, the generation AI. For example, the analysis unit can input data on the hobbies and interests of the family members into the generation AI and have the generation AI customize the analysis results.

[0085] The suggestion unit can estimate the user's emotions and adjust the way the suggestion content is presented based on the estimated user's emotions. Examples of ways to present the suggestion content include text and visual presentation. For example, if the user is nervous, the suggestion unit can provide a simple, highly visible presentation. If the user is relaxed, the suggestion unit can provide a presentation that includes detailed information. If the user is in a hurry, the suggestion unit can provide a presentation that focuses on the main points. This allows the suggestion content to be presented in a way that is easy for the user to read by adjusting the way the suggestion content is presented based on the user's emotions. The emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the suggestion unit can be performed using, for example, the generation AI. For example, the suggestion unit can input the user's emotion data into the generation AI and cause the generation AI to adjust the way the suggestion content is presented.

[0086] When providing suggestions, the suggestion unit can improve the accuracy of the suggestions by taking into account the family's past reactions and feedback. Past reactions and feedback include, for example, reactions to past suggestions and the types of feedback. The suggestion unit, for example, makes optimal suggestions based on suggestions that the family has liked in the past. The suggestion unit can also adjust the suggestions based on the family's past feedback. The suggestion unit can also analyze the family's past reactions and improve the accuracy of the suggestions. In this way, the accuracy of the suggestions is improved by taking into account the family's past reactions and feedback. Some or all of the above-mentioned processing in the suggestion unit may be performed using, or without, the generation AI. For example, the suggestion unit can input data on past reactions and feedback into the generation AI and cause the generation AI to improve the accuracy of the suggestions.

[0087] When providing the proposal content, the suggestion unit can make optimal suggestions by taking into account the health condition and lifestyle rhythm of the family members. Life rhythms include, for example, wake-up times and bedtimes. The suggestion unit can, for example, suggest a meal timeline based on the health condition of the family members. The suggestion unit can also suggest an optimal cleaning order based on the lifestyle rhythm of the family members. The suggestion unit can also suggest the timing of laundry based on the health condition of the family members. This enables more appropriate suggestions by taking into account the health condition and lifestyle rhythm of the family members. Some or all of the above-mentioned processing in the suggestion unit can be performed using, or without, the generation AI. For example, the suggestion unit can input data on the health condition and lifestyle rhythm of the family members into the generation AI and cause the generation AI to execute optimal suggestions.

[0088] When providing the proposal content, the suggestion unit can make a proposal to optimize the energy consumption of the family. Energy consumption includes, for example, electricity consumption and gas consumption. For example, the suggestion unit can suggest an optimal temperature setting for an air conditioner based on the energy consumption of the family. The suggestion unit can also suggest an optimal temperature setting for a refrigerator based on the energy consumption of the family. The suggestion unit can also adjust the frequency of washing machine use based on the energy consumption of the family. In this way, by making a proposal to optimize the energy consumption of the family, energy efficiency is improved. Some or all of the above-mentioned processing in the suggestion unit may be performed using, or without, the generation AI. For example, the suggestion unit can input data on the energy consumption of the family into the generation AI and cause the generation AI to execute a proposal to optimize energy consumption.

[0089] The suggestion unit can estimate the user's emotions and prioritize the suggested content based on the estimated user's emotions. The priorities of the suggested content include, for example, highly important suggested content and real-time suggested content. For example, when the user is stressed, the suggestion unit can prioritize displaying important suggested content. Furthermore, when the user is relaxed, the suggestion unit can prioritize displaying detailed suggested content. Furthermore, when the user is in a hurry, the suggestion unit can prioritize suggested content that can be quickly confirmed. Thus, by prioritizing the suggested content according to the user's emotions, important suggested content can be prioritized and displayed. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the suggestion unit may be performed using, for example, the generation AI. For example, the suggestion unit can input the user's emotion data into the generation AI and cause the generation AI to determine the priority of the suggested content.

[0090] When providing the suggestions, the suggestion unit can customize the suggestions based on the hobbies and interests of the family members. The hobbies and interests of the family members include, for example, types of hobbies and subjects of interest. For example, the suggestion unit can suggest a meal timeline based on the hobbies of the family members. The suggestion unit can also suggest an optimal cleaning order based on the interests of the family members. The suggestion unit can also suggest the timing of laundry based on the hobbies of the family members. This enables more personalized suggestions by customizing the suggestions based on the hobbies and interests of the family members. Some or all of the above-described processing in the suggestion unit may be performed using, or without, a generation AI. For example, the suggestion unit can input data on the hobbies and interests of the family members into the generation AI and cause the generation AI to customize the suggestions.

[0091] When providing the proposal content, the suggestion unit can make optimal suggestions by taking into account family schedule information. Family schedule information includes, for example, calendar information and a timetable. For example, the suggestion unit can suggest a meal timeline based on the family schedule. The suggestion unit can also suggest an optimal cleaning order based on the family schedule. The suggestion unit can also suggest the timing of laundry based on the family schedule. In this way, by taking the family schedule information into consideration, suggestions can be made at more appropriate times. Some or all of the above-described processing in the suggestion unit may be performed using, or without, a generation AI. For example, the suggestion unit can input family schedule information into the generation AI and cause the generation AI to execute optimal suggestions.

[0092] When providing the proposal content, the suggestion unit can make a proposal to optimize the energy consumption of the family. Energy consumption includes, for example, electricity consumption and gas consumption. For example, the suggestion unit can suggest an optimal temperature setting for an air conditioner based on the energy consumption of the family. The suggestion unit can also suggest an optimal temperature setting for a refrigerator based on the energy consumption of the family. The suggestion unit can also adjust the frequency of washing machine use based on the energy consumption of the family. In this way, by making a proposal to optimize the energy consumption of the family, energy efficiency is improved. Some or all of the above-mentioned processing in the suggestion unit may be performed using, or without, the generation AI. For example, the suggestion unit can input data on the energy consumption of the family into the generation AI and cause the generation AI to execute a proposal to optimize energy consumption. === Hard Collateral 1-1 === Each of the multiple elements, including the collection unit, analysis unit, suggestion unit, and emotion estimation function, is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the collection unit collects usage data of IoT devices using the camera 42 and microphone 38B of the smart device 14 and processes the data using the control unit 46A. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the collected data to understand the family's lifestyle. The suggestion unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and suggests an optimal schedule or household robot usage plan based on the analysis results. The emotion estimation function is realized, for example, by the specific processing unit 290 of the data processing device 12 and estimates the user's emotions to adjust the collection timing. === Hard Collateral 1-2 === Each of the multiple elements, including the collection unit, analysis unit, suggestion unit, and emotion estimation function, is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the collection unit collects usage data of IoT devices using the camera 42 and microphone 238 of the smart glasses 214 and processes the data using the control unit 46A. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the collected data to understand the family's lifestyle. The suggestion unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and suggests an optimal schedule or household robot usage plan based on the analysis results. The emotion estimation function is realized, for example, by the specific processing unit 290 of the data processing device 12 and estimates the user's emotion and adjusts the collection timing. === Hard Collateral 1-3 === Each of the multiple elements, including the collection unit, analysis unit, suggestion unit, and emotion estimation function, is realized, for example, by at least one of the headset-type terminal 314 and the data processing device 12. For example, the collection unit collects usage data of IoT devices using the camera 42 and microphone 238 of the headset-type terminal 314 and processes the data using the control unit 46A. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the collected data to understand the family's lifestyle. The suggestion unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and suggests an optimal schedule or a usage plan for the household robot based on the analysis results. The emotion estimation function is realized, for example, by the specific processing unit 290 of the data processing device 12 and estimates the user's emotion and adjusts the collection timing. === Hard Collateral 1-4 === Each of the multiple elements, including the collection unit, analysis unit, suggestion unit, and emotion estimation function, is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the collection unit collects usage data of IoT devices using the camera 42 and microphone 238 of the robot 414 and processes the data using the control unit 46A. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the collected data to understand the family's lifestyle. The suggestion unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and suggests an optimal schedule or a usage plan for the household robot based on the analysis results. The emotion estimation function is realized, for example, by the specific processing unit 290 of the data processing device 12 and estimates the user's emotions to adjust the collection timing.

[0093] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0094] When analyzing collected data, the analysis unit can provide analysis results taking into account the health condition of family members. For example, it can suggest the optimal temperature setting for an air conditioner based on the health condition of family members. It can also suggest the optimal temperature setting for a refrigerator based on the health condition of family members. It can also adjust the frequency of washing machine use based on the health condition of family members. This allows for more appropriate suggestions by providing analysis results taking into account the health condition of family members. Some or all of the above-mentioned processing in the analysis unit may be performed using or without the generation AI. For example, the analysis unit can input data on the health condition of family members into the generation AI and have the generation AI provide the analysis results.

[0095] The suggestion unit can estimate the user's emotions and adjust the way the suggestion content is expressed based on the estimated user's emotions. For example, if the user is nervous, a simple, highly visible expression can be provided. If the user is relaxed, a detailed expression can be provided. If the user is in a hurry, a more concise expression can be provided. This allows the suggestion content to be easily read by adjusting the expression of the suggestion content according to the user's emotions. The emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the suggestion unit may be performed using or without the generation AI. For example, the suggestion unit can input the user's emotion data into the generation AI and have the generation AI adjust the way the suggestion content is expressed.

[0096] When collecting usage data from IoT devices, the collection unit can combine multiple sensors to improve data accuracy. For example, a temperature sensor and a humidity sensor can be combined to obtain a detailed understanding of the internal environment of a refrigerator. A vibration sensor and a sound sensor can be combined to accurately monitor the operating status of a washing machine. Furthermore, a light sensor and a motion sensor can be combined to obtain a detailed understanding of the operating status of an air conditioner. By combining multiple sensors, data accuracy can be improved and more detailed information can be obtained. Some or all of the above-mentioned processing in the collection unit may be performed using or without the generation AI. For example, the collection unit can input data from multiple sensors into the generation AI and have the generation AI improve the accuracy of the data.

[0097] When analyzing collected data, the analysis unit can detect changes in lifestyle by comparing it with past data. For example, if the frequency of refrigerator use increases compared with past data, a change in lifestyle can be detected. Also, if the operating time of the air conditioner decreases compared with past data, a change in lifestyle can be detected. Furthermore, if the frequency of washing machine use changes compared with past data, a change in lifestyle can be detected. In this way, by detecting changes in lifestyle by comparing it with past data, it becomes possible to make suggestions that address changes in the family's lifestyle. Some or all of the above-mentioned processing in the analysis unit may be performed using or without the generation AI. For example, the analysis unit can input past data into the generation AI and have the generation AI detect changes in lifestyle.

[0098] When providing suggestions, the suggestion unit can improve the accuracy of the suggestions by taking into account the family's past reactions and feedback. For example, the suggestion unit can make optimal suggestions based on suggestions that the family has liked in the past. The suggestion unit can also adjust the suggestions based on the family's past feedback. Furthermore, the suggestion unit can analyze the family's past reactions and improve the accuracy of the suggestions. In this way, the accuracy of the suggestions is improved by taking into account the family's past reactions and feedback. Some or all of the above-mentioned processing in the suggestion unit may be performed using or without the generation AI. For example, the suggestion unit can input data on past reactions and feedback into the generation AI and cause the generation AI to improve the accuracy of the suggestions.

[0099] The collection unit can estimate the user's emotions and adjust the timing of collecting usage data from the IoT device based on the estimated user emotions. For example, if the user is stressed, the collection timing can be delayed to reduce the user's burden. Also, if the user is relaxed, the collection timing can be accelerated to improve data accuracy. Furthermore, if the user is in a hurry, the collection timing can be optimized to quickly acquire data. This reduces the user's burden and improves data accuracy by adjusting the collection timing according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the collection unit can be performed using the generation AI, or can be performed without the generation AI. For example, the collection unit can input the user's emotion data into the generation AI and have the generation AI adjust the collection timing.

[0100] When analyzing the collected data, the analysis unit can provide analysis results taking into account the family's eating patterns and nutritional balance. For example, it can suggest the optimal food ingredient placement in the refrigerator based on the family's eating patterns. It can also suggest a meal timeline based on the family's nutritional balance. It can also suggest the timing of food ingredient purchases based on the family's eating patterns. In this way, by providing analysis results taking into account the family's eating patterns and nutritional balance, it is possible to support a healthier lifestyle. Some or all of the above-mentioned processing in the analysis unit may be performed using or without the generation AI. For example, the analysis unit can input data on the family's eating patterns and nutritional balance into the generation AI and have the generation AI provide the analysis results.

[0101] The suggestion unit can estimate the user's emotions and prioritize the suggested content based on the estimated user emotions. For example, if the user is feeling stressed, important suggested content can be displayed with priority. Also, if the user is relaxed, detailed suggested content can be displayed with priority. Furthermore, if the user is in a hurry, suggested content that can be quickly confirmed can be displayed with priority. In this way, by prioritizing the suggested content according to the user's emotions, important suggested content can be displayed with priority. Emotion estimation is realized using an emotion estimation function using an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the suggestion unit can be performed using the generation AI, or can be performed without using the generation AI. For example, the suggestion unit can input the user's emotion data into the generation AI and have the generation AI determine the priority of the suggested content.

[0102] When collecting IoT device usage data, the collection unit can also simultaneously collect household environmental data. For example, the kitchen temperature and humidity can be collected along with refrigerator usage data. The room illuminance can also be collected along with air conditioner usage data. Furthermore, the laundry room temperature and humidity can also be collected along with washing machine usage data. By simultaneously collecting household environmental data, more detailed information can be obtained. Some or all of the above-described processing in the collection unit may be performed using a generation AI, or may be performed without using a generation AI. For example, the collection unit can cause a generation AI to collect environmental data.

[0103] The analysis unit can estimate the user's emotions and prioritize the analysis results based on the estimated user emotions. For example, if the user is stressed, important analysis results can be displayed with priority. Also, if the user is relaxed, detailed analysis results can be displayed with priority. Furthermore, if the user is in a hurry, analysis results that can be quickly viewed can be displayed with priority. Thus, by prioritizing the analysis results according to the user's emotions, important analysis results can be displayed with priority. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the analysis unit can be performed using the generation AI, or can be performed without the generation AI. For example, the analysis unit can input the user's emotion data into the generation AI and have the generation AI determine the priority of the analysis results.

[0104] The processing flow of the second embodiment will be briefly explained below.

[0105] Step 1: The collection unit collects usage data from IoT devices in the home. IoT devices in the home include smart speakers, smart lighting, and smart home appliances. The collection unit collects data such as the usage status, operating time, and power consumption of each IoT device. The collection unit can also add a function to detect device malfunctions and abnormalities. For example, if the temperature in a refrigerator is abnormally high, it will detect the malfunction and issue an alert. Step 2: The analysis unit analyzes the family's lifestyle based on the data collected by the collection unit. The analysis unit analyzes the collected data to understand the family's lifestyle patterns and behavioral habits. Lifestyle patterns include repetitive daily actions and how people spend their weekends, while behavioral habits include daily exercise habits and meal timings. Step 3: The suggestion unit proposes an optimal schedule and a plan for using the household robot based on the analysis results obtained by the analysis unit. The suggestion unit proposes a timeline for meal preparation, the optimal order for cleaning, the timing of laundry, etc. For example, it can propose a schedule to automatically run the coffee maker in time for breakfast, or a schedule for the cleaning robot to clean while the family is out. Some or all of the processing in the suggestion unit may be performed using generative AI.

[0106] 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 a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the 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.

[0107] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0108] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, 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.

[0109] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0110] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0111] 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.

[0112] 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, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0113] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0114] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0115] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0116] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0117] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0118] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0119] The storage 32 stores a data generation model 58 and an 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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0120] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. 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 the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0121] Note that a device other than the data processing device 12 may 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 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0122] 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 a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0123] The data generation model 58 is a so-called generative AI. An example of the 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 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0124] 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 executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0125] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0126] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0127] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0128] 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, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0129] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0130] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0131] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0132] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0133] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0134] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0135] The storage 32 stores a data generation model 58 and an 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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0136] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0137] Note that a device other than the data processing device 12 may 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 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0138] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0139] The data generation model 58 is a so-called generative AI. An example of the 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 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0140] 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 executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0141] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0142] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0143] 7, a 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.

[0144] 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, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0145] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0146] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0147] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0148] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0149] The control 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 emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0150] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0151] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0152] The storage 32 stores a data generation model 58 and an 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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0153] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0154] Note that a device other than the data processing device 12 may 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 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0155] 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 control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0156] The data generation model 58 is a so-called generative AI. An example of the 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 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0157] 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 executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0158] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0159] The emotion identification model 59 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 an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0160] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0161] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0162] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0163] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0164] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs 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 a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0165] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0166] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0167] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0168] 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.

[0169] It is not necessary to store all 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 all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0170] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0171] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with 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). Also, the hardware resource that executes the specific process may be a single processor.

[0172] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0173] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0174] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0175] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0176] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[0177] [Explanation of symbols]

[0178] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a collection unit that collects usage data of IoT devices in the home; an analysis unit that analyzes the lifestyle of the family based on the data collected by the collection unit; a proposal unit that proposes a schedule and a usage plan for the household robot based on the analysis results obtained by the analysis unit; Equipped with A system characterized by:

2. The collecting unit Collect data on the usage status, operating time, and power consumption of each IoT device 2. The system of claim 1.

3. The analysis unit Analyze the collected data to understand the family's lifestyle patterns and behavioral habits 2. The system of claim 1.

4. The proposal unit Based on the analysis results, the system suggests timelines for meal preparation, cleaning orders, and laundry timings.

2. The system of claim 1.

5. The proposal unit Suggest a schedule to automatically run your coffee maker in time for breakfast 2. The system of claim 1.

6. The proposal unit The cleaning robot will suggest a cleaning schedule while the family is out 2. The system of claim 1.

7. The collecting unit Estimate user emotions and adjust the timing of IoT device usage data collection based on the estimated user emotions.

2. The system of claim 1.

8. The collecting unit Add a function to detect device failures and abnormalities when collecting usage data for each IoT device.

2. The system of claim 1.

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