System

Abstract

An object of a system according to an embodiment is to detect a failure of a home appliance at an early stage and notify a user of the failure.SOLUTION: A system according to an embodiment includes an operation data collector, an abnormality detector, and a notifier. The operation data collection unit collects operation data of a home appliance. The abnormality detection unit analyzes the operation data collected by the operation data collection unit to detect an abnormality. The notification unit notifies a user of the abnormality detected by the abnormality detection unit.SELECTED DRAWING: Figure 1

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 have not adequately detected malfunctions in home appliances early and notified users of them, and there is room for improvement.

[0005] The system according to the embodiment aims to detect a malfunction of a home appliance at an early stage and notify the user of the malfunction. [Means for solving the problem]

[0006] The system according to the embodiment includes an operation data collection unit, an anomaly detection unit, and a notification unit. The operation data collection unit collects operation data of the home appliance. The anomaly detection unit analyzes the operation data collected by the operation data collection unit to detect an anomaly. The notification unit notifies a user of the anomaly detected by the anomaly detection unit. [Effects of the Invention]

[0007] The system according to the embodiment can detect a malfunction of a home appliance at an early stage and notify the user. [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) The AI ​​assistant system according to an embodiment of the present invention is a system that detects malfunctions in home appliances and notifies the user. This system analyzes the operation data of the home appliances and notifies the user if an abnormality is detected. This enables the AI ​​assistant system to detect malfunctions in home appliances early and respond quickly.

[0029] An AI assistant system according to an embodiment includes an operation data collection unit, an anomaly detection unit, and a notification unit. The operation data collection unit collects operation data of home appliances. For example, it collects temperature data of a refrigerator, vibration data of a washing machine, and power consumption data of an air conditioner. The operation data collection unit also collects data through sensors built into the home appliances and an internet connection. For example, it acquires data from a refrigerator's temperature sensor and sends it to the cloud via the internet. It can also acquire data from a washing machine's vibration sensor and collect it via a local network. Air conditioner power consumption data can be collected through a smart meter. The anomaly detection unit analyzes the operation data collected by the operation data collection unit to detect anomalies. For example, a sudden rise in the refrigerator temperature or greater than normal vibrations of the washing machine are detected as an anomaly. The anomaly detection unit also analyzes the operation data and detects anomalies using a generation AI (e.g., a text generation AI or a multimodal generation AI). For example, the generation AI analyzes the refrigerator's temperature data and detects an abnormal temperature rise. The generation AI can also analyze the washing machine's vibration data and detect abnormal vibrations. The notification unit notifies the user of the abnormality detected by the abnormality detection unit. For example, the notification may be sent via a smartphone app, email, or voice assistant. For example, the notification may be sent in the form of, "The temperature in the refrigerator is abnormally high. Please check it." The notification unit can also select the notification method depending on the type of abnormality and its urgency. For example, if the urgency is high, the notification may be sent immediately via the voice assistant, and if the urgency is low, the notification may be sent by email. This enables the AI ​​assistant system according to the embodiment to detect malfunctions in home appliances early and respond promptly. For example, early detection of abnormal refrigerator temperatures can prevent food spoilage. Furthermore, detecting abnormal vibrations in a washing machine can prevent major malfunctions. Furthermore, suggesting cleaning of the air conditioner filter can maintain the air conditioner's efficiency and reduce power consumption.

[0030] The operation data collection unit can collect ambient environment data in addition to the operation data of the home appliance. For example, the operation data collection unit uses sensors to collect ambient environment data such as room temperature, humidity, and illuminance in addition to the operation data of the home appliance, and takes this data into consideration when detecting anomalies. For example, the operation data collection unit combines and analyzes the temperature data and room temperature data of a refrigerator. In addition, the operation data collection unit attaches additional sensors to the home appliance to collect ambient environment data, and transmits this data to the cloud for analysis. For example, the operation data collection unit simultaneously collects operation data of an air conditioner and indoor humidity data. In addition, the operation data collection unit collects ambient environment data and incorporates it into anomaly detection algorithms to improve the accuracy of anomaly detection. For example, the operation data collection unit combines and analyzes vibration data from a washing machine and indoor illuminance data. This improves the accuracy of anomaly detection.

[0031] The operation data collection unit collects usage history data of home appliances and can detect abnormalities based on usage frequency and usage time periods. For example, the operation data collection unit collects usage history data of home appliances and analyzes usage frequency and usage time periods. For example, it records the number of times a refrigerator is opened and closed and the usage time periods and uses this data to detect abnormalities. The operation data collection unit also stores usage history data in the cloud, and the AI ​​assistant detects abnormalities based on this data. For example, it detects an abnormality if the frequency of washing machine use increases suddenly. The operation data collection unit also analyzes usage history data of home appliances and incorporates it into an abnormality detection algorithm. For example, it detects an abnormality if an air conditioner is used at a different time period than usual. This makes it possible to detect abnormalities based on usage history.

[0032] The operation data collection unit collects data from other smart devices in addition to operation data from home appliances, enabling comprehensive anomaly detection. For example, the operation data collection unit collects data from smart lighting and smart locks in addition to operation data from home appliances, and uses this data for anomaly detection. For example, it combines and analyzes refrigerator temperature data and smart lighting usage data. The operation data collection unit also sends data from other smart devices to the cloud, and the AI ​​assistant uses this data to detect anomalies. For example, it simultaneously analyzes washing machine vibration data and smart lock usage data. The operation data collection unit also collects smart device data and incorporates it into the anomaly detection algorithm, improving the accuracy of anomaly detection. For example, it combines and analyzes air conditioner power consumption data and smart lighting usage data. This enables comprehensive anomaly detection.

[0033] The operation data collection unit stores operation data of home appliances in the cloud and can share the data among multiple devices to detect anomalies. The operation data collection unit, for example, stores operation data of home appliances in the cloud and shares the data among multiple devices. For example, temperature data from a refrigerator is stored in the cloud and shared with other devices. The operation data collection unit also allows the AI ​​assistant to detect anomalies based on the data stored in the cloud. For example, vibration data from a washing machine is stored in the cloud and used in an anomaly detection algorithm. The operation data collection unit also shares data among multiple devices to improve the accuracy of anomaly detection. For example, power consumption data from an air conditioner is stored in the cloud and shared with other devices for analysis. This makes it possible to share data among multiple devices to detect anomalies.

[0034] The anomaly detection unit can improve detection accuracy by introducing a machine learning model into the anomaly detection algorithm and automatically learning abnormal patterns. The anomaly detection unit, for example, introduces a machine learning model into the anomaly detection algorithm and automatically learns abnormal patterns based on collected operation data. For example, it analyzes refrigerator temperature data and learns abnormal patterns. The anomaly detection unit also improves the accuracy of anomaly detection using the machine learning model. For example, it analyzes vibration data from a washing machine and automatically learns abnormal patterns. The anomaly detection unit also incorporates the machine learning model into the anomaly detection algorithm and continuously learns abnormal patterns. For example, it analyzes power consumption data from an air conditioner and automatically learns abnormal patterns. This can improve the accuracy of anomaly detection.

[0035] When detecting an anomaly, the anomaly detection unit can compare it with past anomaly data to evaluate the severity of the anomaly and set a priority. When detecting an anomaly, the anomaly detection unit, for example, compares it with past anomaly data to evaluate the severity of the anomaly. For example, it compares abnormal temperature data of a refrigerator with past data to evaluate the severity. The anomaly detection unit also sets a priority based on the severity of the anomaly. For example, it compares abnormal vibration data of a washing machine with past data and sets a priority according to the severity. The anomaly detection unit also evaluates the severity of the anomaly based on past anomaly data and sets a priority. For example, it compares abnormal power consumption data of an air conditioner with past data and sets a priority according to the severity. In this way, the severity of the anomaly can be evaluated and a priority can be set.

[0036] The anomaly detection unit cross-references data between different home appliances to detect complex anomalies. For example, the anomaly detection unit cross-references data between different home appliances to detect complex anomalies. For example, it combines and analyzes temperature data from a refrigerator and vibration data from a washing machine. The anomaly detection unit also stores data from different home appliances in the cloud and cross-references it to detect anomalies. For example, it simultaneously analyzes power consumption data from an air conditioner and usage data from smart lighting. The anomaly detection unit also cross-references data between different home appliances and incorporates it into an anomaly detection algorithm. For example, it combines and analyzes temperature data from a refrigerator and power consumption data from an air conditioner. This makes it possible to detect complex anomalies.

[0037] The anomaly detection unit can adapt the anomaly detection algorithm to data from different regions or cultural spheres, thereby achieving global anomaly detection. The anomaly detection unit, for example, adapts the anomaly detection algorithm to data from different regions or cultural spheres. For example, it analyzes refrigerator temperature data for each region and adjusts the anomaly detection algorithm. The anomaly detection unit also stores data from different regions or cultural spheres in the cloud and incorporates it into the anomaly detection algorithm. For example, it analyzes washing machine vibration data for each region and adjusts the anomaly detection algorithm. The anomaly detection unit also adapts the anomaly detection algorithm for each region to achieve global anomaly detection. For example, it analyzes air conditioner power consumption data for each cultural sphere and adjusts the anomaly detection algorithm. This makes global anomaly detection possible.

[0038] The notification unit can customize the notification content based on the user's past response history and suggest the optimal response method. The notification unit, for example, customizes the notification content based on the user's past response history and suggests the optimal response method. For example, when an abnormal temperature occurs in a refrigerator, the optimal response method is suggested based on the past response history. The notification unit also analyzes the user's past response history and customizes the notification content. For example, when an abnormal vibration occurs in a washing machine, the optimal response method is suggested based on the past response history. The notification unit also builds a system that customizes the notification content based on the user's past response history and suggests the optimal response method. For example, when an abnormality in power consumption occurs in an air conditioner, the optimal response method is suggested based on the past response history. This makes it possible to suggest the optimal response method.

[0039] The notification unit can provide a video guide that explains the cause of the abnormality and countermeasures in detail when notifying. The notification unit, for example, provides a video guide that explains the cause of the abnormality and countermeasures in detail when notifying. For example, when an abnormal temperature occurs in a refrigerator, a video guide that explains the cause and countermeasures is provided. Furthermore, a system is constructed in which the notification unit provides a video guide that explains the cause of the abnormality and countermeasures in detail. For example, when an abnormal vibration occurs in a washing machine, a video guide that explains the cause and countermeasures is provided. Furthermore, the notification unit provides a video guide that explains the cause of the abnormality and countermeasures in detail when notifying. For example, when an abnormal power consumption occurs in an air conditioner, a video guide that explains the cause and countermeasures is provided. In this way, a video guide that explains the cause of the abnormality and countermeasures in detail can be provided.

[0040] The notification unit can simultaneously send notifications to the smartphones and smartwatches of all family members to encourage a prompt response. The notification unit, for example, can simultaneously send notifications to the smartphones and smartwatches of all family members to encourage a prompt response. For example, when an abnormal temperature occurs in a refrigerator, a notification is sent to all family members. The notification unit can also build a system that simultaneously sends notifications to the smartphones and smartwatches of all family members. For example, when an abnormal vibration occurs in a washing machine, a notification is sent to all family members. The notification unit can also simultaneously send notifications to the smartphones and smartwatches of all family members to encourage a prompt response. For example, when an abnormality occurs in the power consumption of an air conditioner, a notification is sent to all family members. This allows notifications to be sent to all family members to encourage a prompt response.

[0041] The notification unit can automatically translate the notification content into different languages, making it possible to accommodate international users. The notification unit, for example, automatically translates the notification content into different languages, making it possible to accommodate international users. For example, when an abnormal temperature occurs in a refrigerator, the notification content is automatically translated and sent. The notification unit also builds a system for automatic translation into different languages, making it possible to accommodate international users. For example, when an abnormal vibration occurs in a washing machine, the notification content is automatically translated and sent. The notification unit can also automatically translate the notification content into different languages, making it possible to accommodate international users. For example, when an abnormal power consumption occurs in an air conditioner, the notification content is automatically translated and sent. This makes it possible to accommodate international users.

[0042] The anomaly detection unit introduces deep learning into the failure prediction algorithm, enabling it to predict complex failure patterns with high accuracy. The anomaly detection unit, for example, introduces deep learning into the failure prediction algorithm, enabling it to predict complex failure patterns with high accuracy. For example, it analyzes temperature data from a refrigerator and predicts failure patterns with high accuracy. The anomaly detection unit also uses deep learning to improve the accuracy of failure prediction. For example, it analyzes vibration data from a washing machine and predicts failure patterns with high accuracy. The anomaly detection unit also incorporates deep learning into the failure prediction algorithm, enabling it to continuously learn complex failure patterns. For example, it analyzes power consumption data from an air conditioner and predicts failure patterns with high accuracy. This enables it to predict complex failure patterns with high accuracy.

[0043] The anomaly detection unit can automatically check the replacement timing of parts and the inventory status of consumables when proposing maintenance, and order the necessary parts in advance. For example, the anomaly detection unit can automatically check the replacement timing of parts and the inventory status of consumables when proposing maintenance, and order the necessary parts in advance. For example, the anomaly detection unit can check the replacement timing of refrigerator filters and order the necessary filters in advance. The anomaly detection unit can also build a system that automatically checks the replacement timing of parts and the inventory status of consumables, and order the necessary parts in advance. For example, the anomaly detection unit can check the replacement timing of washing machine parts and order the necessary parts in advance. The anomaly detection unit can also automatically check the replacement timing of parts and the inventory status of consumables when proposing maintenance, and order the necessary parts in advance. For example, the anomaly detection unit can check the replacement timing of air conditioner filters and order the necessary filters in advance. In this way, the necessary parts can be ordered in advance.

[0044] The anomaly detection unit shares the failure prediction data with other home appliance manufacturers, thereby improving the accuracy of failure prediction across the industry. The anomaly detection unit, for example, shares the failure prediction data with other home appliance manufacturers, thereby improving the accuracy of failure prediction across the industry. For example, failure prediction data for refrigerators is shared, thereby improving the accuracy of prediction across the industry. The anomaly detection unit also builds a system for sharing failure prediction data with other home appliance manufacturers, thereby improving the accuracy of prediction across the industry. For example, failure prediction data for washing machines is shared, thereby improving the accuracy of prediction across the industry. The anomaly detection unit also shares the failure prediction data with other home appliance manufacturers, thereby improving the accuracy of failure prediction across the industry. For example, failure prediction data for air conditioners is shared, thereby improving the accuracy of prediction across the industry. This makes it possible to improve the accuracy of failure prediction across the industry.

[0045] The anomaly detection unit can integrate the maintenance suggestions into the maintenance schedule for the entire smart home, thereby realizing efficient maintenance. The anomaly detection unit, for example, integrates the maintenance suggestions into the maintenance schedule for the entire smart home, thereby realizing efficient maintenance. For example, it integrates a suggestion to replace a refrigerator filter with the maintenance schedules for other home appliances. The anomaly detection unit also builds a system that integrates the maintenance suggestions into the maintenance schedule for the entire smart home, thereby realizing efficient maintenance. For example, it integrates a suggestion to replace a washing machine part with the maintenance schedules for other home appliances. The anomaly detection unit also integrates the maintenance suggestions into the maintenance schedule for the entire smart home, thereby realizing efficient maintenance. For example, it integrates a suggestion to replace an air conditioner filter with the maintenance schedules for other home appliances. This makes it possible to realize efficient maintenance.

[0046] The anomaly detection unit can store the maintenance history in the cloud and synchronize and manage it across multiple devices. The anomaly detection unit, for example, stores the maintenance history in the cloud and synchronizes and manages it across multiple devices. For example, the filter replacement history of a refrigerator is stored in the cloud and synchronized with other devices. The anomaly detection unit also builds a system that synchronizes and manages it across multiple devices based on the maintenance history stored in the cloud. For example, the part replacement history of a washing machine is stored in the cloud and synchronized with other devices. The anomaly detection unit also stores the maintenance history in the cloud and synchronizes and manages it across multiple devices. For example, the filter replacement history of an air conditioner is stored in the cloud and synchronized with other devices. This makes it possible to synchronize and manage the maintenance history across multiple devices.

[0047] The anomaly detection unit can analyze the maintenance history and automatically generate an optimal maintenance schedule. The anomaly detection unit, for example, analyzes the maintenance history and automatically generates an optimal maintenance schedule. For example, it analyzes the filter replacement history of a refrigerator and automatically generates the optimal replacement time. The anomaly detection unit also builds a system that automatically generates an optimal maintenance schedule based on the maintenance history. For example, it analyzes the part replacement history of a washing machine and automatically generates the optimal replacement time. The anomaly detection unit also analyzes the maintenance history and automatically generates an optimal maintenance schedule. For example, it analyzes the filter replacement history of an air conditioner and automatically generates the optimal replacement time. In this way, an optimal maintenance schedule can be automatically generated.

[0048] The anomaly detection unit can integrate the maintenance history with the history of other smart devices to perform comprehensive maintenance management. The anomaly detection unit, for example, integrates the maintenance history with the history of other smart devices to perform comprehensive maintenance management. For example, the filter replacement history of a refrigerator is integrated with the maintenance history of a smart light. The anomaly detection unit also builds a system that integrates the history of other smart devices with the maintenance history to perform comprehensive maintenance management. For example, the part replacement history of a washing machine is integrated with the maintenance history of a smart lock. The anomaly detection unit also integrates the maintenance history with the history of other smart devices to perform comprehensive maintenance management. For example, the filter replacement history of an air conditioner is integrated with the maintenance history of a smart speaker. This allows comprehensive maintenance management.

[0049] The anomaly detection unit can compare the maintenance history with data from different home appliance manufacturers and introduce best practices. For example, the anomaly detection unit compares the maintenance history with data from different home appliance manufacturers and introduces best practices. For example, the filter replacement history of a refrigerator is compared with data from other manufacturers and the optimal replacement timing is introduced. The anomaly detection unit also builds a system that compares the maintenance history with data from different home appliance manufacturers and introduces best practices. For example, the part replacement history of a washing machine is compared with data from other manufacturers and the optimal replacement timing is introduced. The anomaly detection unit also compares the maintenance history with data from different home appliance manufacturers and introduces best practices. For example, the filter replacement history of an air conditioner is compared with data from other manufacturers and the optimal replacement timing is introduced. In this way, best practices can be introduced.

[0050] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0051] In addition to the operation data of home appliances, the operation data collection unit can collect user health data and use it for anomaly detection. For example, it combines refrigerator temperature data with the user's body temperature data for analysis. The operation data collection unit also sends the user's health data to the cloud, and the AI ​​assistant uses this data to detect anomalies. For example, it simultaneously analyzes washing machine vibration data and the user's heart rate data. The operation data collection unit also collects health data and incorporates it into the anomaly detection algorithm to improve the accuracy of anomaly detection. For example, it combines and analyzes air conditioner power consumption data with the user's sleep data. This enables comprehensive anomaly detection.

[0052] In addition to the operation data of home appliances, the operation data collection unit can collect user lifestyle data and use it for anomaly detection. For example, it can combine refrigerator temperature data with the user's eating patterns for analysis. The operation data collection unit also sends the user's lifestyle data to the cloud, and the AI ​​assistant uses this data to detect anomalies. For example, it can simultaneously analyze washing machine vibration data and the user's exercise habits. The operation data collection unit also collects lifestyle data and incorporates it into the anomaly detection algorithm to improve the accuracy of anomaly detection. For example, it can combine air conditioner power consumption data with the user's sleep patterns for analysis. This enables comprehensive anomaly detection.

[0053] The operation data collection unit can collect user behavior data in addition to operation data from home appliances and use it for anomaly detection. For example, it combines refrigerator temperature data with user behavior data for analysis. The operation data collection unit also sends user behavior data to the cloud, where the AI ​​assistant uses this data to detect anomalies. For example, it simultaneously analyzes washing machine vibration data and user behavior data. The operation data collection unit also collects behavior data and incorporates it into the anomaly detection algorithm to improve the accuracy of anomaly detection. For example, it combines air conditioner power consumption data with user behavior data for analysis. This enables comprehensive anomaly detection.

[0054] In addition to the operation data of home appliances, the operation data collection unit can collect user health data and use it for anomaly detection. For example, it combines refrigerator temperature data with the user's body temperature data for analysis. The operation data collection unit also sends the user's health data to the cloud, and the AI ​​assistant uses this data to detect anomalies. For example, it simultaneously analyzes washing machine vibration data and the user's heart rate data. The operation data collection unit also collects health data and incorporates it into the anomaly detection algorithm to improve the accuracy of anomaly detection. For example, it combines and analyzes air conditioner power consumption data with the user's sleep data. This enables comprehensive anomaly detection.

[0055] In addition to the operation data of home appliances, the operation data collection unit can collect user lifestyle data and use it for anomaly detection. For example, it can combine refrigerator temperature data with the user's eating patterns for analysis. The operation data collection unit also sends the user's lifestyle data to the cloud, and the AI ​​assistant uses this data to detect anomalies. For example, it can simultaneously analyze washing machine vibration data and the user's exercise habits. The operation data collection unit also collects lifestyle data and incorporates it into the anomaly detection algorithm to improve the accuracy of anomaly detection. For example, it can combine air conditioner power consumption data with the user's sleep patterns for analysis. This enables comprehensive anomaly detection.

[0056] The operation data collection unit can collect user behavior data in addition to operation data from home appliances and use it for anomaly detection. For example, it combines refrigerator temperature data with user behavior data for analysis. The operation data collection unit also sends user behavior data to the cloud, where the AI ​​assistant uses this data to detect anomalies. For example, it simultaneously analyzes washing machine vibration data and user behavior data. The operation data collection unit also collects behavior data and incorporates it into the anomaly detection algorithm to improve the accuracy of anomaly detection. For example, it combines air conditioner power consumption data with user behavior data for analysis. This enables comprehensive anomaly detection.

[0057] The processing flow of the first embodiment will be briefly explained below.

[0058] Step 1: The operation data collection unit collects operation data of the home appliances. For example, it collects temperature data from a refrigerator, vibration data from a washing machine, and power consumption data from an air conditioner. The operation data collection unit also collects data through sensors built into the home appliances and through an internet connection. For example, it obtains data from a refrigerator's temperature sensor and sends it to the cloud via the internet. It can also obtain data from a washing machine's vibration sensor and collect it through a local network. Power consumption data from air conditioners can be collected through a smart meter. Step 2: The anomaly detection unit analyzes the operation data collected by the operation data collection unit and detects anomalies. For example, a sudden rise in refrigerator temperature or a greater-than-normal vibration from a washing machine is detected as an anomaly. The anomaly detection unit also uses a generative AI (e.g., text generation AI or multimodal generation AI) to analyze the operation data and detect anomalies. For example, the generative AI analyzes refrigerator temperature data and detects abnormal temperature rises. The generative AI can also analyze vibration data from a washing machine and detect abnormal vibrations. Step 3: The notification unit notifies the user of the abnormality detected by the abnormality detection unit. For example, the notification may be sent via a smartphone app, email, or voice assistant. For example, the notification may say, "The temperature in the refrigerator is abnormally high. Please check it." The notification unit can also select the notification method depending on the type of abnormality and its level of urgency. For example, if the level of urgency is high, the notification may be sent immediately via the voice assistant, and if the level of urgency is low, the notification may be sent by email.

[0059] (Example 2) The AI ​​assistant system according to an embodiment of the present invention is a system that detects malfunctions in home appliances and notifies the user. This system analyzes the operation data of the home appliances and notifies the user if an abnormality is detected. This enables the AI ​​assistant system to detect malfunctions in home appliances early and respond quickly.

[0060] An AI assistant system according to an embodiment includes an operation data collection unit, an anomaly detection unit, and a notification unit. The operation data collection unit collects operation data of home appliances. For example, it collects temperature data of a refrigerator, vibration data of a washing machine, and power consumption data of an air conditioner. The operation data collection unit also collects data through sensors built into the home appliances and an internet connection. For example, it acquires data from a refrigerator's temperature sensor and sends it to the cloud via the internet. It can also acquire data from a washing machine's vibration sensor and collect it via a local network. Air conditioner power consumption data can be collected through a smart meter. The anomaly detection unit analyzes the operation data collected by the operation data collection unit to detect anomalies. For example, a sudden rise in the refrigerator temperature or greater than normal vibrations of the washing machine are detected as an anomaly. The anomaly detection unit also analyzes the operation data and detects anomalies using a generation AI (e.g., a text generation AI or a multimodal generation AI). For example, the generation AI analyzes the refrigerator's temperature data and detects an abnormal temperature rise. The generation AI can also analyze the washing machine's vibration data and detect abnormal vibrations. The notification unit notifies the user of the abnormality detected by the abnormality detection unit. For example, the notification may be sent via a smartphone app, email, or voice assistant. For example, the notification may be sent in the form of, "The temperature in the refrigerator is abnormally high. Please check it." The notification unit can also select the notification method depending on the type of abnormality and its urgency. For example, if the urgency is high, the notification may be sent immediately via the voice assistant, and if the urgency is low, the notification may be sent by email. This enables the AI ​​assistant system according to the embodiment to detect malfunctions in home appliances early and respond promptly. For example, early detection of abnormal refrigerator temperatures can prevent food spoilage. Furthermore, detecting abnormal vibrations in a washing machine can prevent major malfunctions. Furthermore, suggesting cleaning of the air conditioner filter can maintain the air conditioner's efficiency and reduce power consumption.

[0061] The operation data collection unit can collect ambient environment data in addition to the operation data of the home appliance. For example, the operation data collection unit uses sensors to collect ambient environment data such as room temperature, humidity, and illuminance in addition to the operation data of the home appliance, and takes this data into consideration when detecting anomalies. For example, the operation data collection unit combines and analyzes the temperature data and room temperature data of a refrigerator. In addition, the operation data collection unit attaches additional sensors to the home appliance to collect ambient environment data, and transmits this data to the cloud for analysis. For example, the operation data collection unit simultaneously collects operation data of an air conditioner and indoor humidity data. In addition, the operation data collection unit collects ambient environment data and incorporates it into anomaly detection algorithms to improve the accuracy of anomaly detection. For example, the operation data collection unit combines and analyzes vibration data from a washing machine and indoor illuminance data. This improves the accuracy of anomaly detection.

[0062] The operation data collection unit collects usage history data of home appliances and can detect abnormalities based on usage frequency and usage time periods. For example, the operation data collection unit collects usage history data of home appliances and analyzes usage frequency and usage time periods. For example, it records the number of times a refrigerator is opened and closed and the usage time periods and uses this data to detect abnormalities. The operation data collection unit also stores usage history data in the cloud, and the AI ​​assistant detects abnormalities based on this data. For example, it detects an abnormality if the frequency of washing machine use increases suddenly. The operation data collection unit also analyzes usage history data of home appliances and incorporates it into an abnormality detection algorithm. For example, it detects an abnormality if an air conditioner is used at a different time period than usual. This makes it possible to detect abnormalities based on usage history.

[0063] The operation data collection unit uses the emotion estimation function to collect user emotion data and analyze the association between the usage status of the home appliance and the emotion. The operation data collection unit, for example, uses the emotion estimation function to analyze the user's facial expressions and voice and collect emotion data. For example, the operation data collection unit analyzes the user's facial expressions when the user opens the refrigerator and records the emotion data. The operation data collection unit also analyzes the user's emotion data in association with the usage status of the home appliance. For example, if the user feels stressed while using the washing machine, that data is collected. The operation data collection unit also uses the emotion estimation function to collect the user's emotion data in real time and analyze the association between the usage status of the home appliance and the emotion. For example, it analyzes whether the user is relaxed when using the air conditioner. This makes it possible to analyze the association between the user's emotion and the usage status of the home appliance.

[0064] The operation data collection unit collects data from other smart devices in addition to operation data from home appliances, enabling comprehensive anomaly detection. For example, the operation data collection unit collects data from smart lighting and smart locks in addition to operation data from home appliances, and uses this data for anomaly detection. For example, it combines and analyzes refrigerator temperature data and smart lighting usage data. The operation data collection unit also sends data from other smart devices to the cloud, and the AI ​​assistant uses this data to detect anomalies. For example, it simultaneously analyzes washing machine vibration data and smart lock usage data. The operation data collection unit also collects smart device data and incorporates it into the anomaly detection algorithm, improving the accuracy of anomaly detection. For example, it combines and analyzes air conditioner power consumption data and smart lighting usage data. This enables comprehensive anomaly detection.

[0065] The operation data collection unit stores operation data of home appliances in the cloud and can share the data among multiple devices to detect anomalies. The operation data collection unit, for example, stores operation data of home appliances in the cloud and shares the data among multiple devices. For example, temperature data from a refrigerator is stored in the cloud and shared with other devices. The operation data collection unit also allows the AI ​​assistant to detect anomalies based on the data stored in the cloud. For example, vibration data from a washing machine is stored in the cloud and used in an anomaly detection algorithm. The operation data collection unit also shares data among multiple devices to improve the accuracy of anomaly detection. For example, power consumption data from an air conditioner is stored in the cloud and shared with other devices for analysis. This makes it possible to share data among multiple devices to detect anomalies.

[0066] The operation data collection unit uses the emotion estimation function to collect emotions of a user when operating a home appliance in real time, and can use the collected emotions as a trigger for anomaly detection. The operation data collection unit, for example, uses the emotion estimation function to collect emotions of a user when operating a home appliance in real time. For example, the operation data collection unit analyzes the user's facial expression when opening a refrigerator and records the emotion data. The operation data collection unit also uses the user's emotion data as a trigger for anomaly detection. For example, if the user feels stressed when operating a washing machine, the data is used for anomaly detection. The operation data collection unit also uses the emotion estimation function to collect user's emotion data in real time and incorporates it into an anomaly detection algorithm. For example, the operation data collection unit analyzes whether the user is relaxed when operating an air conditioner and uses the data for anomaly detection. In this way, the user's emotion can be used as a trigger for anomaly detection.

[0067] The anomaly detection unit can improve detection accuracy by introducing a machine learning model into the anomaly detection algorithm and automatically learning abnormal patterns. The anomaly detection unit, for example, introduces a machine learning model into the anomaly detection algorithm and automatically learns abnormal patterns based on collected operation data. For example, it analyzes refrigerator temperature data and learns abnormal patterns. The anomaly detection unit also improves the accuracy of anomaly detection using the machine learning model. For example, it analyzes vibration data from a washing machine and automatically learns abnormal patterns. The anomaly detection unit also incorporates the machine learning model into the anomaly detection algorithm and continuously learns abnormal patterns. For example, it analyzes power consumption data from an air conditioner and automatically learns abnormal patterns. This can improve the accuracy of anomaly detection.

[0068] When detecting an anomaly, the anomaly detection unit can compare it with past anomaly data to evaluate the severity of the anomaly and set a priority. When detecting an anomaly, the anomaly detection unit, for example, compares it with past anomaly data to evaluate the severity of the anomaly. For example, it compares abnormal temperature data of a refrigerator with past data to evaluate the severity. The anomaly detection unit also sets a priority based on the severity of the anomaly. For example, it compares abnormal vibration data of a washing machine with past data and sets a priority according to the severity. The anomaly detection unit also evaluates the severity of the anomaly based on past anomaly data and sets a priority. For example, it compares abnormal power consumption data of an air conditioner with past data and sets a priority according to the severity. In this way, the severity of the anomaly can be evaluated and a priority can be set.

[0069] The anomaly detection unit can analyze the user's emotion data using the emotion estimation function and issue an alert when a change in emotion is detected as a sign of an abnormality. The anomaly detection unit, for example, uses the emotion estimation function to analyze the user's emotion data and issue an alert when a change in emotion is detected as a sign of an abnormality. For example, an alert is issued when the user feels stressed while operating a refrigerator. The anomaly detection unit also analyzes the user's emotion data and issue an alert when a change in emotion is detected as a sign of an abnormality. For example, an alert is issued when the user feels anxious while operating a washing machine. The anomaly detection unit also analyzes the user's emotion data in real time using the emotion estimation function and issue an alert when a change in emotion is detected as a sign of an abnormality. For example, an alert is issued when the user feels surprise while operating an air conditioner. In this way, it is possible to detect a change in emotion as a sign of an abnormality and issue an alert.

[0070] The anomaly detection unit cross-references data between different home appliances to detect complex anomalies. For example, the anomaly detection unit cross-references data between different home appliances to detect complex anomalies. For example, it combines and analyzes temperature data from a refrigerator and vibration data from a washing machine. The anomaly detection unit also stores data from different home appliances in the cloud and cross-references it to detect anomalies. For example, it simultaneously analyzes power consumption data from an air conditioner and usage data from smart lighting. The anomaly detection unit also cross-references data between different home appliances and incorporates it into an anomaly detection algorithm. For example, it combines and analyzes temperature data from a refrigerator and power consumption data from an air conditioner. This makes it possible to detect complex anomalies.

[0071] The anomaly detection unit can adapt the anomaly detection algorithm to data from different regions or cultural spheres, thereby achieving global anomaly detection. The anomaly detection unit, for example, adapts the anomaly detection algorithm to data from different regions or cultural spheres. For example, it analyzes refrigerator temperature data for each region and adjusts the anomaly detection algorithm. The anomaly detection unit also stores data from different regions or cultural spheres in the cloud and incorporates it into the anomaly detection algorithm. For example, it analyzes washing machine vibration data for each region and adjusts the anomaly detection algorithm. The anomaly detection unit also adapts the anomaly detection algorithm for each region to achieve global anomaly detection. For example, it analyzes air conditioner power consumption data for each cultural sphere and adjusts the anomaly detection algorithm. This makes global anomaly detection possible.

[0072] The anomaly detection unit can use the emotion estimation function to incorporate user emotion data into the anomaly detection feedback loop, thereby continuously improving detection accuracy. The anomaly detection unit, for example, uses the emotion estimation function to incorporate user emotion data into the anomaly detection feedback loop, thereby improving detection accuracy. For example, the anomaly detection unit analyzes user emotion data when operating a refrigerator and reflects the data in the anomaly detection algorithm. The anomaly detection unit also incorporates user emotion data into the anomaly detection feedback loop, thereby continuously improving detection accuracy. For example, the anomaly detection unit analyzes user emotion data when operating a washing machine and reflects the data in the anomaly detection algorithm. The anomaly detection unit also uses the emotion estimation function to collect user emotion data in real time and incorporate the data into the anomaly detection algorithm, thereby continuously improving detection accuracy. For example, the anomaly detection unit analyzes user emotion data when operating an air conditioner and reflects the data in the anomaly detection algorithm. This allows for continuously improving detection accuracy.

[0073] The notification unit can customize the notification content based on the user's past response history and suggest the optimal response method. The notification unit, for example, customizes the notification content based on the user's past response history and suggests the optimal response method. For example, when an abnormal temperature occurs in a refrigerator, the optimal response method is suggested based on the past response history. The notification unit also analyzes the user's past response history and customizes the notification content. For example, when an abnormal vibration occurs in a washing machine, the optimal response method is suggested based on the past response history. The notification unit also builds a system that customizes the notification content based on the user's past response history and suggests the optimal response method. For example, when an abnormality in power consumption occurs in an air conditioner, the optimal response method is suggested based on the past response history. This makes it possible to suggest the optimal response method.

[0074] The notification unit can provide a video guide that explains the cause of the abnormality and countermeasures in detail when notifying. The notification unit, for example, provides a video guide that explains the cause of the abnormality and countermeasures in detail when notifying. For example, when an abnormal temperature occurs in a refrigerator, a video guide that explains the cause and countermeasures is provided. Furthermore, a system is constructed in which the notification unit provides a video guide that explains the cause of the abnormality and countermeasures in detail. For example, when an abnormal vibration occurs in a washing machine, a video guide that explains the cause and countermeasures is provided. Furthermore, the notification unit provides a video guide that explains the cause of the abnormality and countermeasures in detail when notifying. For example, when an abnormal power consumption occurs in an air conditioner, a video guide that explains the cause and countermeasures is provided. In this way, a video guide that explains the cause of the abnormality and countermeasures in detail can be provided.

[0075] The notification unit can select a notification method according to the emotional state of the user using the emotion estimation function. The notification unit, for example, uses the emotion estimation function to select a notification method according to the emotional state of the user. For example, when an abnormal temperature occurs in a refrigerator, the notification is made by text, audio, or video depending on the emotional state of the user. The notification unit also builds a system that analyzes the emotional state of the user and selects the optimal notification method. For example, when an abnormal vibration occurs in a washing machine, the notification method is selected according to the emotional state of the user. The notification unit also uses the emotion estimation function to select a notification method according to the emotional state of the user. For example, when an abnormal power consumption occurs in an air conditioner, the notification method is selected according to the emotional state of the user. In this way, it is possible to select a notification method according to the emotional state of the user.

[0076] The notification unit can simultaneously send notifications to the smartphones and smartwatches of all family members to encourage a prompt response. The notification unit, for example, can simultaneously send notifications to the smartphones and smartwatches of all family members to encourage a prompt response. For example, when an abnormal temperature occurs in a refrigerator, a notification is sent to all family members. The notification unit can also build a system that simultaneously sends notifications to the smartphones and smartwatches of all family members. For example, when an abnormal vibration occurs in a washing machine, a notification is sent to all family members. The notification unit can also simultaneously send notifications to the smartphones and smartwatches of all family members to encourage a prompt response. For example, when an abnormality occurs in the power consumption of an air conditioner, a notification is sent to all family members. This allows notifications to be sent to all family members to encourage a prompt response.

[0077] The notification unit can automatically translate the notification content into different languages, making it possible to accommodate international users. The notification unit, for example, automatically translates the notification content into different languages, making it possible to accommodate international users. For example, when an abnormal temperature occurs in a refrigerator, the notification content is automatically translated and sent. The notification unit also builds a system for automatic translation into different languages, making it possible to accommodate international users. For example, when an abnormal vibration occurs in a washing machine, the notification content is automatically translated and sent. The notification unit can also automatically translate the notification content into different languages, making it possible to accommodate international users. For example, when an abnormal power consumption occurs in an air conditioner, the notification content is automatically translated and sent. This makes it possible to accommodate international users.

[0078] The notification unit can use the emotion estimation function to monitor the user's emotional state in real time and suggest a relaxation method to reduce stress. The notification unit, for example, uses the emotion estimation function to monitor the user's emotional state in real time and suggest a relaxation method to reduce stress. For example, when an abnormal temperature occurs in a refrigerator, the notification unit monitors the user's emotional state and suggests a relaxation method. Furthermore, a system is constructed in which the notification unit monitors the user's emotional state in real time and suggests a relaxation method to reduce stress. For example, when an abnormal vibration occurs in a washing machine, the notification unit monitors the user's emotional state and suggests a relaxation method. Furthermore, the notification unit uses the emotion estimation function to monitor the user's emotional state in real time and suggest a relaxation method to reduce stress. For example, when an abnormality occurs in power consumption of an air conditioner, the notification unit monitors the user's emotional state and suggests a relaxation method. In this way, a relaxation method to reduce stress can be suggested.

[0079] The anomaly detection unit introduces deep learning into the failure prediction algorithm, enabling it to predict complex failure patterns with high accuracy. The anomaly detection unit, for example, introduces deep learning into the failure prediction algorithm, enabling it to predict complex failure patterns with high accuracy. For example, it analyzes temperature data from a refrigerator and predicts failure patterns with high accuracy. The anomaly detection unit also uses deep learning to improve the accuracy of failure prediction. For example, it analyzes vibration data from a washing machine and predicts failure patterns with high accuracy. The anomaly detection unit also incorporates deep learning into the failure prediction algorithm, enabling it to continuously learn complex failure patterns. For example, it analyzes power consumption data from an air conditioner and predicts failure patterns with high accuracy. This enables it to predict complex failure patterns with high accuracy.

[0080] The anomaly detection unit can automatically check the replacement timing of parts and the inventory status of consumables when proposing maintenance, and order the necessary parts in advance. For example, the anomaly detection unit can automatically check the replacement timing of parts and the inventory status of consumables when proposing maintenance, and order the necessary parts in advance. For example, the anomaly detection unit can check the replacement timing of refrigerator filters and order the necessary filters in advance. The anomaly detection unit can also build a system that automatically checks the replacement timing of parts and the inventory status of consumables, and order the necessary parts in advance. For example, the anomaly detection unit can check the replacement timing of washing machine parts and order the necessary parts in advance. The anomaly detection unit can also automatically check the replacement timing of parts and the inventory status of consumables when proposing maintenance, and order the necessary parts in advance. For example, the anomaly detection unit can check the replacement timing of air conditioner filters and order the necessary filters in advance. In this way, the necessary parts can be ordered in advance.

[0081] The anomaly detection unit can use the emotion estimation function to analyze the user's emotion data and evaluate the impact that the maintenance suggestion has on the user. For example, the ... evaluate the impact that a suggestion to replace a refrigerator filter has on the user. The anomaly detection unit can also analyze the user's emotion data and build a system to evaluate the impact that the maintenance suggestion has on the user. For example, the anomaly detection unit can evaluate the impact that a suggestion to replace a washing machine part has on the user. The anomaly detection unit can also use the emotion estimation function to analyze the user's emotion data in real time and evaluate the impact that the maintenance suggestion has on the user. For example, the anomaly detection unit can evaluate the impact that a suggestion to replace an air conditioner filter has on the user. This makes it possible to evaluate the impact that the maintenance suggestion has on the user.

[0082] The anomaly detection unit shares the failure prediction data with other home appliance manufacturers, thereby improving the accuracy of failure prediction across the industry. The anomaly detection unit, for example, shares the failure prediction data with other home appliance manufacturers, thereby improving the accuracy of failure prediction across the industry. For example, failure prediction data for refrigerators is shared, thereby improving the accuracy of prediction across the industry. The anomaly detection unit also builds a system for sharing failure prediction data with other home appliance manufacturers, thereby improving the accuracy of prediction across the industry. For example, failure prediction data for washing machines is shared, thereby improving the accuracy of prediction across the industry. The anomaly detection unit also shares the failure prediction data with other home appliance manufacturers, thereby improving the accuracy of failure prediction across the industry. For example, failure prediction data for air conditioners is shared, thereby improving the accuracy of prediction across the industry. This makes it possible to improve the accuracy of failure prediction across the industry.

[0083] The anomaly detection unit can integrate the maintenance suggestions into the maintenance schedule for the entire smart home, thereby realizing efficient maintenance. The anomaly detection unit, for example, integrates the maintenance suggestions into the maintenance schedule for the entire smart home, thereby realizing efficient maintenance. For example, it integrates a suggestion to replace a refrigerator filter with the maintenance schedules for other home appliances. The anomaly detection unit also builds a system that integrates the maintenance suggestions into the maintenance schedule for the entire smart home, thereby realizing efficient maintenance. For example, it integrates a suggestion to replace a washing machine part with the maintenance schedules for other home appliances. The anomaly detection unit also integrates the maintenance suggestions into the maintenance schedule for the entire smart home, thereby realizing efficient maintenance. For example, it integrates a suggestion to replace an air conditioner filter with the maintenance schedules for other home appliances. This makes it possible to realize efficient maintenance.

[0084] The anomaly detection unit uses the emotion estimation function to make maintenance suggestions based on the user's emotional state, thereby improving user satisfaction. The anomaly detection unit, for example, uses the emotion estimation function to make maintenance suggestions based on the user's emotional state, thereby improving user satisfaction. For example, a suggestion to replace a refrigerator filter is made based on the user's emotional state. The anomaly detection unit also analyzes the user's emotional state and builds a system that makes emotion-based maintenance suggestions. For example, a suggestion to replace a washing machine part is made based on the user's emotional state. The anomaly detection unit also uses the emotion estimation function to make maintenance suggestions based on the user's emotional state, thereby improving user satisfaction. For example, a suggestion to replace an air conditioner filter is made based on the user's emotional state. This makes it possible to improve user satisfaction.

[0085] The anomaly detection unit can store the maintenance history in the cloud and synchronize and manage it across multiple devices. The anomaly detection unit, for example, stores the maintenance history in the cloud and synchronizes and manages it across multiple devices. For example, the filter replacement history of a refrigerator is stored in the cloud and synchronized with other devices. The anomaly detection unit also builds a system that synchronizes and manages it across multiple devices based on the maintenance history stored in the cloud. For example, the part replacement history of a washing machine is stored in the cloud and synchronized with other devices. The anomaly detection unit also stores the maintenance history in the cloud and synchronizes and manages it across multiple devices. For example, the filter replacement history of an air conditioner is stored in the cloud and synchronized with other devices. This makes it possible to synchronize and manage the maintenance history across multiple devices.

[0086] The anomaly detection unit can analyze the maintenance history and automatically generate an optimal maintenance schedule. The anomaly detection unit, for example, analyzes the maintenance history and automatically generates an optimal maintenance schedule. For example, it analyzes the filter replacement history of a refrigerator and automatically generates the optimal replacement time. The anomaly detection unit also builds a system that automatically generates an optimal maintenance schedule based on the maintenance history. For example, it analyzes the part replacement history of a washing machine and automatically generates the optimal replacement time. The anomaly detection unit also analyzes the maintenance history and automatically generates an optimal maintenance schedule. For example, it analyzes the filter replacement history of an air conditioner and automatically generates the optimal replacement time. In this way, an optimal maintenance schedule can be automatically generated.

[0087] The anomaly detection unit can use the emotion estimation function to track changes in a user's emotions based on the maintenance history and evaluate the impact of maintenance. The anomaly detection unit, for example, uses the emotion estimation function to track changes in a user's emotions based on the maintenance history and evaluate the impact of maintenance. For example, it tracks changes in a user's emotions after replacing a refrigerator filter. The anomaly detection unit also builds a system that tracks changes in a user's emotions based on the maintenance history and evaluates the impact of maintenance. For example, it tracks changes in a user's emotions after replacing a washing machine part. The anomaly detection unit also uses the emotion estimation function to track changes in a user's emotions based on the maintenance history and evaluate the impact of maintenance. For example, it tracks changes in a user's emotions after replacing an air conditioner filter. This makes it possible to evaluate the impact of maintenance.

[0088] The anomaly detection unit can integrate the maintenance history with the history of other smart devices to perform comprehensive maintenance management. The anomaly detection unit, for example, integrates the maintenance history with the history of other smart devices to perform comprehensive maintenance management. For example, the filter replacement history of a refrigerator is integrated with the maintenance history of a smart light. The anomaly detection unit also builds a system that integrates the history of other smart devices with the maintenance history to perform comprehensive maintenance management. For example, the part replacement history of a washing machine is integrated with the maintenance history of a smart lock. The anomaly detection unit also integrates the maintenance history with the history of other smart devices to perform comprehensive maintenance management. For example, the filter replacement history of an air conditioner is integrated with the maintenance history of a smart speaker. This allows comprehensive maintenance management.

[0089] The anomaly detection unit can compare the maintenance history with data from different home appliance manufacturers and introduce best practices. For example, the anomaly detection unit compares the maintenance history with data from different home appliance manufacturers and introduces best practices. For example, the filter replacement history of a refrigerator is compared with data from other manufacturers and the optimal replacement timing is introduced. The anomaly detection unit also builds a system that compares the maintenance history with data from different home appliance manufacturers and introduces best practices. For example, the part replacement history of a washing machine is compared with data from other manufacturers and the optimal replacement timing is introduced. The anomaly detection unit also compares the maintenance history with data from different home appliance manufacturers and introduces best practices. For example, the filter replacement history of an air conditioner is compared with data from other manufacturers and the optimal replacement timing is introduced. In this way, best practices can be introduced.

[0090] The anomaly detection unit uses the emotion estimation function to analyze user emotion data based on the maintenance history, thereby improving the accuracy of maintenance suggestions. The anomaly detection unit, for example, uses the emotion estimation function to analyze user emotion data based on the maintenance history, thereby improving the accuracy of maintenance suggestions. For example, the anomaly detection unit analyzes user emotion data based on a refrigerator filter replacement history. The anomaly detection unit also analyzes user emotion data based on the maintenance history, thereby building a system that improves the accuracy of maintenance suggestions. For example, the anomaly detection unit analyzes user emotion data based on a washing machine part replacement history. The anomaly detection unit also uses the emotion estimation function to analyze user emotion data based on the maintenance history, thereby improving the accuracy of maintenance suggestions. For example, the anomaly detection unit analyzes user emotion data based on an air conditioner filter replacement history. This improves the accuracy of maintenance suggestions.

[0091] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0092] In addition to the operation data of home appliances, the operation data collection unit can collect user health data and use it for anomaly detection. For example, it combines refrigerator temperature data with the user's body temperature data for analysis. The operation data collection unit also sends the user's health data to the cloud, and the AI ​​assistant uses this data to detect anomalies. For example, it simultaneously analyzes washing machine vibration data and the user's heart rate data. The operation data collection unit also collects health data and incorporates it into the anomaly detection algorithm to improve the accuracy of anomaly detection. For example, it combines and analyzes air conditioner power consumption data with the user's sleep data. This enables comprehensive anomaly detection.

[0093] In addition to the operation data of home appliances, the operation data collection unit can collect user lifestyle data and use it for anomaly detection. For example, it can combine refrigerator temperature data with the user's eating patterns for analysis. The operation data collection unit also sends the user's lifestyle data to the cloud, and the AI ​​assistant uses this data to detect anomalies. For example, it can simultaneously analyze washing machine vibration data and the user's exercise habits. The operation data collection unit also collects lifestyle data and incorporates it into the anomaly detection algorithm to improve the accuracy of anomaly detection. For example, it can combine air conditioner power consumption data with the user's sleep patterns for analysis. This enables comprehensive anomaly detection.

[0094] The operation data collection unit can collect user emotional data in addition to the operation data of home appliances and use it for anomaly detection. For example, it combines refrigerator temperature data with user emotional data for analysis. The operation data collection unit also sends the user emotional data to the cloud, where the AI ​​assistant uses this data to detect anomalies. For example, it simultaneously analyzes vibration data from a washing machine and user emotional data. The operation data collection unit also collects emotional data and incorporates it into the anomaly detection algorithm to improve the accuracy of anomaly detection. For example, it combines and analyzes power consumption data from an air conditioner with user emotional data. This enables comprehensive anomaly detection.

[0095] The operation data collection unit can collect user behavior data in addition to operation data from home appliances and use it for anomaly detection. For example, it combines refrigerator temperature data with user behavior data for analysis. The operation data collection unit also sends user behavior data to the cloud, where the AI ​​assistant uses this data to detect anomalies. For example, it simultaneously analyzes washing machine vibration data and user behavior data. The operation data collection unit also collects behavior data and incorporates it into the anomaly detection algorithm to improve the accuracy of anomaly detection. For example, it combines air conditioner power consumption data with user behavior data for analysis. This enables comprehensive anomaly detection.

[0096] The operation data collection unit can collect user emotion data in addition to the operation data of the home appliances, and analyze the relationship between the usage status of the home appliances and the emotion. For example, the temperature data of the refrigerator is combined with the user emotion data for analysis. The operation data collection unit also analyzes the user emotion data in association with the usage status of the home appliances. For example, if the user feels stressed while using the washing machine, that data is collected. The operation data collection unit also uses an emotion estimation function to collect the user emotion data in real time and analyze the relationship between the usage status of the home appliances and the emotion. For example, it analyzes whether the user is relaxed when using the air conditioner. This makes it possible to analyze the relationship between the user emotion and the usage status of the home appliances.

[0097] In addition to the operation data of home appliances, the operation data collection unit can collect user health data and use it for anomaly detection. For example, it combines refrigerator temperature data with the user's body temperature data for analysis. The operation data collection unit also sends the user's health data to the cloud, and the AI ​​assistant uses this data to detect anomalies. For example, it simultaneously analyzes washing machine vibration data and the user's heart rate data. The operation data collection unit also collects health data and incorporates it into the anomaly detection algorithm to improve the accuracy of anomaly detection. For example, it combines and analyzes air conditioner power consumption data with the user's sleep data. This enables comprehensive anomaly detection.

[0098] In addition to the operation data of home appliances, the operation data collection unit can collect user lifestyle data and use it for anomaly detection. For example, it can combine refrigerator temperature data with the user's eating patterns for analysis. The operation data collection unit also sends the user's lifestyle data to the cloud, and the AI ​​assistant uses this data to detect anomalies. For example, it can simultaneously analyze washing machine vibration data and the user's exercise habits. The operation data collection unit also collects lifestyle data and incorporates it into the anomaly detection algorithm to improve the accuracy of anomaly detection. For example, it can combine air conditioner power consumption data with the user's sleep patterns for analysis. This enables comprehensive anomaly detection.

[0099] The operation data collection unit can collect user emotional data in addition to the operation data of home appliances and use it for anomaly detection. For example, it combines refrigerator temperature data with user emotional data for analysis. The operation data collection unit also sends the user emotional data to the cloud, where the AI ​​assistant uses this data to detect anomalies. For example, it simultaneously analyzes vibration data from a washing machine and user emotional data. The operation data collection unit also collects emotional data and incorporates it into the anomaly detection algorithm to improve the accuracy of anomaly detection. For example, it combines and analyzes power consumption data from an air conditioner with user emotional data. This enables comprehensive anomaly detection.

[0100] The operation data collection unit can collect user behavior data in addition to operation data from home appliances and use it for anomaly detection. For example, it combines refrigerator temperature data with user behavior data for analysis. The operation data collection unit also sends user behavior data to the cloud, where the AI ​​assistant uses this data to detect anomalies. For example, it simultaneously analyzes washing machine vibration data and user behavior data. The operation data collection unit also collects behavior data and incorporates it into the anomaly detection algorithm to improve the accuracy of anomaly detection. For example, it combines air conditioner power consumption data with user behavior data for analysis. This enables comprehensive anomaly detection.

[0101] The operation data collection unit can collect user emotion data in addition to the operation data of the home appliances, and analyze the relationship between the usage status of the home appliances and the emotion. For example, the temperature data of the refrigerator is combined with the user emotion data for analysis. The operation data collection unit also analyzes the user emotion data in association with the usage status of the home appliances. For example, if the user feels stressed while using the washing machine, that data is collected. The operation data collection unit also uses an emotion estimation function to collect the user emotion data in real time and analyze the relationship between the usage status of the home appliances and the emotion. For example, it analyzes whether the user is relaxed when using the air conditioner. This makes it possible to analyze the relationship between the user emotion and the usage status of the home appliances.

[0102] The processing flow of the second embodiment will be briefly explained below.

[0103] Step 1: The operation data collection unit collects operation data of the home appliances. For example, it collects temperature data from a refrigerator, vibration data from a washing machine, and power consumption data from an air conditioner. The operation data collection unit also collects data through sensors built into the home appliances and through an internet connection. For example, it obtains data from a refrigerator's temperature sensor and sends it to the cloud via the internet. It can also obtain data from a washing machine's vibration sensor and collect it through a local network. Power consumption data from air conditioners can be collected through a smart meter. Step 2: The anomaly detection unit analyzes the operation data collected by the operation data collection unit and detects anomalies. For example, a sudden rise in refrigerator temperature or a greater-than-normal vibration from a washing machine is detected as an anomaly. The anomaly detection unit also uses a generative AI (e.g., text generation AI or multimodal generation AI) to analyze the operation data and detect anomalies. For example, the generative AI analyzes refrigerator temperature data and detects abnormal temperature rises. The generative AI can also analyze vibration data from a washing machine and detect abnormal vibrations. Step 3: The notification unit notifies the user of the abnormality detected by the abnormality detection unit. For example, the notification may be sent via a smartphone app, email, or voice assistant. For example, the notification may say, "The temperature in the refrigerator is abnormally high. Please check it." The notification unit can also select the notification method depending on the type of abnormality and its level of urgency. For example, if the level of urgency is high, the notification may be sent immediately via the voice assistant, and if the level of urgency is low, the notification may be sent by email.

[0104] 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.

[0105] 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.

[0106] 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.

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

[0108] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0109] The data processing device 12 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.

[0110] 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.

[0111] 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.

[0112] 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).

[0113] 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.

[0114] 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.

[0115] 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.

[0116] 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.

[0117] 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.

[0118] 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.

[0119] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating 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.

[0120] 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 AI 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.

[0121] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is 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.

[0122] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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).

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] In the headset type terminal 314, 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. 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 specific processing unit 290 using these models.

[0133] 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.

[0134] 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.

[0135] 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 AI 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.

[0136] 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.

[0137] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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).

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] In the robot 414, 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 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 processing similar to that of the specific processing unit 290 using these models.

[0149] 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.

[0150] 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.

[0151] 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 AI 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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).

[0157] 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 "reaction" domain, where sensation is dominant. The right half of the emotion map lists emotions belonging to the "situation" domain, where situational awareness is dominant.

[0158] 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."

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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. [Explanation of symbols]

[0171] 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. an operation data collection unit that collects operation data of the home appliance; an abnormality detection unit that analyzes the operation data collected by the operation data collection unit and detects an abnormality; a notification unit that notifies a user of an abnormality detected by the abnormality detection unit. A system characterized by:

2. The operation data collection unit Collecting ambient environment data in addition to operational data of the home appliances 2. The system of claim 1.

3. The operation data collection unit Collects usage history data for home appliances and detects abnormalities based on frequency and time of use.

2. The system of claim 1.

4. The operation data collection unit Collecting user emotion data and analyzing the relationship between emotions and the usage of home appliances 2. The system of claim 1.

5. The operation data collection unit In addition to the operational data of home appliances, data from other smart devices is also collected to perform comprehensive anomaly detection.

2. The system of claim 1.

6. The operation data collection unit Operational data of home appliances is stored in the cloud and shared among multiple devices to detect anomalies.

2. The system of claim 1.

7. The operation data collection unit Collecting real-time user emotions when operating home appliances and using them as triggers for anomaly detection 2. The system of claim 1.

8. The abnormality detection unit Introduce machine learning models into anomaly detection algorithms to automatically learn abnormal patterns and improve detection accuracy.

2. The system of claim 1.

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