system

Abstract

Provide a system. 【Solution means】 Means for obtaining operation information of household appliances in real time, Means for identifying abnormalities based on the obtained information, Means for notifying the user when an abnormality is detected, Means for searching and reserving reliable repairers, Means for controlling other in-house devices according to the abnormality, Means for monitoring energy consumption data of public facilities and detecting abnormalities, Means for notifying the administrator when an abnormality in energy consumption is detected and proposing alternative measures, A system including.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] Conventional household appliances required users to discover their abnormalities by themselves and respond appropriately. Therefore, abnormalities were often not noticed until they developed into serious failures, and it took time and effort to arrange for repairs. Furthermore, there was a problem that the cooperation with other household devices was insufficient, resulting in a decrease in the overall efficiency of household management.

Means for Solving the Problems

[0005] This invention provides a system that monitors the operation data of home appliances in real time and quickly detects abnormalities. When an abnormality is detected, it automatically notifies the user and has a function to search for and book a reliable repair service. Furthermore, when an abnormality occurs, it appropriately controls other household appliances to optimize the efficiency of the entire ecosystem. It also has a function to analyze common problems through community data and advise users on preventative measures and solutions.

[0006] "Home appliances" refer to devices and equipment that use electricity to function within the home.

[0007] "Operation data" refers to various types of information generated when a home appliance is in operation, including usage time, power consumption, and operating noise.

[0008] "Anomaly detection" refers to the process of identifying situations that deviate from normal operating patterns, and is used to determine malfunctions or defects in home appliances at an early stage.

[0009] "User notifications" refer to means of informing users of anomalies or important information, and include push notifications and alerts to their devices.

[0010] A "repair company" refers to a specialized company or technician that repairs malfunctions or problems with home appliances.

[0011] "Reservation" refers to the process of arranging in advance for a repair service to be performed by a repair company, and includes confirming the date, time, and location.

[0012] "Household appliances" refers to all devices and systems used within the home, including home appliances, that can function in conjunction with each other.

[0013] An "ecosystem" refers to an environment in which devices and systems used within a home complement each other, aiming for efficient energy management and optimized functionality.

[0014] "Community data" refers to information collected from multiple users of a system, which is then analyzed to help solve common problems and challenges. [Brief explanation of the drawing]

[0015] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Figure 11] This is a sequence diagram showing the processing flow of the data processing system in Example 1. [Figure 12] This is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13] This is a sequence diagram showing the processing flow of the data processing system in Example 2, which incorporates an emotion engine. [Figure 14]It is a sequence diagram showing the processing flow of a data processing system in Application Example 2 when combined with an emotion engine.

Embodiment for Carrying Out the Invention

[0016] Hereinafter, an example of an embodiment of a system according to the technology of the present disclosure will be described with reference to the accompanying drawings.

[0017] First, the terms used in the following description will be explained.

[0018] In the following embodiments, a labeled processor (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Also, the processor may be a single type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units 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), and the like.

[0019] In the following embodiments, a labeled RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a work memory by the processor.

[0020] In the following embodiments, a labeled storage is one or more non-volatile storage devices that store various programs and various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes, etc.

[0021] In the following embodiments, the signed communication interface (I / F) is an interface that includes a communication processor and an antenna, etc. The communication interface manages communication between multiple computers. Examples of communication standards applicable to the communication interface include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0022] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." That is, "A and / or B" means that it may be A alone, or B alone, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" applies when expressing three or more things linked by "and / or."

[0023] [First Embodiment]

[0024] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0025] As shown in Figure 1, the data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0026] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0027] The smart device 14 comprises a computer 36, a reception device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The reception device 38, output device 40, and camera 42 are also connected to the bus 52.

[0028] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, etc., and receives user input. The touch panel 38A receives user input by detecting contact with an object (e.g., a pen or finger). The microphone 38B receives user input by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

[0029] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form perceptible to the user 20 (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0030] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

[0031] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0032] As shown in Figure 2, in the data processing device 12, a specific processing is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" related to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0033] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0034] In the smart device 14, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The reception output program 60 is used in conjunction with a specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0035] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0036] This invention relates to a system for efficiently managing home appliances and quickly detecting abnormalities. The system acquires operational data from sensors attached to various home appliances and transmits it to a server via a network. The server analyzes this data in real time and monitors for abnormalities in the appliances using an anomaly detection algorithm.

[0037] If the server detects an anomaly, it immediately sends a notification to the user's device. This allows the user to check the nature of the anomaly and take prompt action. The server also refers to a database of trusted repair companies, suggests the best repair date to fit the user's schedule, and automates the booking process. The user can review this suggestion on their device and modify it as needed.

[0038] Furthermore, the server coordinates with other devices in the home and provides alternative solutions in case of malfunctions. For example, if the air conditioner malfunctions, it can activate a smart fan to maintain room temperature control. In this way, the system integrates the entire home ecosystem, managing appliances efficiently and safely.

[0039] For example, if the refrigerator's temperature sensor detects a temperature higher than normal, the server will recognize the anomaly and immediately notify the user. At the same time, the server will determine whether the refrigerator needs repair and, if necessary, arrange for a repair service. It can also revise the usage schedule of other household appliances to temporarily replace the refrigerator's operation.

[0040] This invention aims to improve the quality of life for users by making the monitoring of household appliances more efficient.

[0041] The following describes the processing flow.

[0042] Step 1:

[0043] The server receives operational data from each home appliance via sensors. This data includes parameters such as temperature, power consumption, and vibration. The received data is stored in a database and forms the basis for subsequent processing.

[0044] Step 2:

[0045] The server preprocesses the received operational data. Specifically, it imputes missing data values ​​and filters out abnormal values, adjusting them to normal values. This process improves data consistency and reliability.

[0046] Step 3:

[0047] The server executes an anomaly detection algorithm based on the pre-processed data. Using a machine learning model, it compares the data to normal operating patterns and identifies any deviations as anomalies. This result is then passed on to the next step.

[0048] Step 4:

[0049] If an anomaly is detected, the server will immediately send a notification to the user's device. The notification will include the nature of the detected anomaly, the parts that may be affected, and recommended countermeasures.

[0050] Step 5:

[0051] Users can view detailed information about anomalies detected through their device. Based on this information, they can take necessary actions. They can also select repair service appointment options provided on the device.

[0052] Step 6:

[0053] The server accesses a database of repair providers and automatically generates the optimal repair appointment, taking into account the user's schedule and the provider's availability. Once the user reviews and approves the information, the appointment is confirmed.

[0054] Step 7:

[0055] If an anomaly could affect other devices, the server will control smart devices in the home and take alternative measures. For example, if a problem occurs with the heating system, the server will activate other auxiliary heating devices.

[0056] Step 8:

[0057] The server analyzes all data collected within the home over a long period, accumulating community-based knowledge to suggest future preventative measures. This makes it possible to provide helpful advice to users.

[0058] (Example 1)

[0059] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0060] Modern homes utilize numerous electrical appliances, making efficient management and rapid anomaly detection challenging. Conventional systems struggle to respond immediately to malfunctions, and the effort required to find a suitable repair service and schedule repairs is burdensome for users. Furthermore, adequate measures are not in place to optimize the home environment in the event of a malfunction. Solving these challenges is essential.

[0061] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0062] In this invention, the server includes means for collecting operational information in real time, means for detecting anomalies based on the collected information, and means for searching for and booking a reliable repair service provider. This enables efficient monitoring of household electrical appliances and rapid response to any anomalies that occur. Furthermore, by automatically suggesting the optimal repair schedule in the event of an anomaly and controlling other household devices, the burden on the user can be reduced, and a safe and comfortable living environment can be maintained.

[0063] "Operational information" refers to data about the functions and status of electrical appliances on a daily basis.

[0064] "Real-time" is a term that refers to a process that is performed instantly without delay.

[0065] "An anomaly" refers to an event or state that differs from normal operation or condition, and signifies a potentially problematic situation.

[0066] "Users" refers to individuals or households that use this system.

[0067] "Notification" refers to a means of communicating information to inform users that there is an abnormality.

[0068] A "reliable repair shop" refers to a repair service that provides high-quality service and has established trust with its customers.

[0069] "Household appliances" is a general term for various electrical appliances and equipment used within a home.

[0070] "Analysis" is the process of analyzing data in detail and obtaining meaningful information from it.

[0071] "Proposal" refers to the act of showing users the optimal solution or action.

[0072] This invention is a system for efficiently monitoring household electrical appliances and responding quickly to any abnormalities. This system has the function of acquiring operational information in real time and processing and analyzing that data. Details are described below.

[0073] 1. Data Collection and Communication

[0074] Sensors are attached to each household appliance, which periodically collect operational information about the product. This information is then transmitted to a central server via a wireless network. TLS (Transport Layer Security) is used as the data communication protocol to ensure data security.

[0075] 2. Server analysis function

[0076] The server processes the received operational information using Python®-based data analysis libraries (e.g., Pandas or NumPy). The server analyzes the data using an anomaly detection algorithm and immediately notifies the terminal if an anomaly is detected. The criteria for determining an anomaly are based on comparison with predetermined thresholds and normal ranges. This allows users to immediately notice abnormal situations and take necessary countermeasures.

[0077] 3. Notification and Repair Arrangement

[0078] If an abnormality is detected, the system automatically accesses a database of repair providers and proposes the most suitable repair date. Users can review and adjust this proposal from their device, ensuring a smooth repair process.

[0079] 4. Providing alternative solutions

[0080] In the event of a malfunction, the server will coordinate with other home devices to provide alternative solutions. For example, if the air conditioner breaks down, a smart fan can be automatically activated to maintain a comfortable indoor environment.

[0081] Specific example

[0082] When the refrigerator's temperature sensor detects a temperature higher than normal, the server recognizes it as an anomaly and immediately sends a notification to the user's device. This notification includes details of the anomaly, recommended actions, and information on repair companies. The server also automatically suggests a repair schedule and can adjust the usage plans for other household appliances as an emergency measure.

[0083] Examples of prompts for generative AI models

[0084] "Please describe in detail the anomaly detection system for household electrical appliances and explain specifically how it actually works."

[0085] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0086] Step 1:

[0087] The sensor collects operational information from electrical appliances. Specifically, the sensor measures information such as temperature and power consumption at regular intervals and prepares the obtained data for transmission using radio waves. The input is the operating status of the electrical appliance, and the output is a dataset containing that operational information.

[0088] Step 2:

[0089] The sensor collects motion information and sends it to the server. The server receives the data and stores it in storage in an encrypted state. The input is the encrypted motion information sent from the sensor, and the output is the motion information stored on the server. During this process, the TLS protocol ensures the security of the data communication.

[0090] Step 3:

[0091] The server analyzes the received operational information. The server evaluates the data using a Python-based analysis algorithm and detects anomalies by comparing it to normal values. The input is operational information stored on the server, and the output is the determination of whether it is normal or abnormal. In this process, for example, an abnormal temperature value is calculated using the Z-score method, and if it exceeds a threshold, it is determined to be abnormal.

[0092] Step 4:

[0093] If the server detects an anomaly, it sends a notification to the terminal. The server creates a message detailing the problem and recommended actions, and sends a push notification to the user's mobile device. The input is the result of the anomaly detection, and the output is the notification message sent to the user. The notification includes details of how the anomaly occurred and initial countermeasures.

[0094] Step 5:

[0095] The server proposes repair dates based on repair provider information. The server searches an existing provider database, connects to a suitable provider, and matches dates. Inputs are the user's schedule and the repair provider's availability, and output is the optimal repair date notified to the user. This facilitates quick and effective repair responses.

[0096] Step 6:

[0097] The server controls other household devices to optimize the home environment in the event of a malfunction. Specifically, it issues instructions to temporarily substitute the function of a faulty device with another device. The inputs are the malfunction detection status and the current operating status of household devices, and the output is the control instruction. This control allows, for example, if the air conditioner malfunctions, to automatically turn on a smart fan to maintain the room temperature.

[0098] (Application Example 1)

[0099] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0100] In modern cities, there is a need to efficiently manage the energy consumption of household appliances and public facilities. However, detecting abnormalities in individual devices and facilities in real time and responding quickly is not easy, and systems that provide optimal solutions when abnormalities occur are limited. In this situation, an integrated management system is needed that can quickly detect abnormalities and take appropriate action.

[0101] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0102] In this invention, the server includes means for acquiring operational information of household appliances in real time, means for identifying abnormalities based on the acquired information, and means for notifying the user when an abnormality is detected. This enables rapid detection of abnormalities in household and public facility equipment and the provision of appropriate notifications and countermeasures.

[0103] "Operational information" refers to data on the operating status and energy consumption of various devices in households and public facilities.

[0104] "An anomaly" refers to a state of equipment that deviates from its normal operating pattern, indicating a potential malfunction or failure of home appliances or public facilities.

[0105] "Means of acquisition" refers to methods and devices that use sensors or other equipment to collect motion information in real time.

[0106] "Means of identification" refer to algorithms and processes used to analyze collected operational information and identify anomalies.

[0107] "Means of notification" refer to communication methods or software used to quickly convey information to users and administrators when an anomaly is detected.

[0108] "Public facilities" refer to buildings and equipment within a city that are used for public purposes and are subject to energy management.

[0109] An "administrator" is someone responsible for the operation and maintenance of public facilities or household equipment, or a user of the system.

[0110] "Energy consumption data" refers to information about the amount of energy consumed and usage patterns of equipment and facilities.

[0111] "Means of proposing alternative solutions" refer to system functions or methods that provide other methods or devices that can be used when an anomaly occurs.

[0112] In implementing this invention, a system for managing the energy consumption of household appliances and public facilities is configured. First, a server collects operational information in real time from IoT sensors deployed in homes and public facilities. This includes energy consumption data and operating status of various devices. Examples of sensors used include Bosch environmental sensors.

[0113] Next, the server uses the collected data to apply an anomaly detection algorithm using TENSORFLOW® to identify anomalies. This anomaly detection quickly identifies anomalies that deviate from normal consumption patterns. When an anomaly is detected, the server immediately sends a notification to the administrator's terminal to prompt appropriate action.

[0114] Furthermore, the server utilizes AI to propose countermeasures for anomalies. For example, if energy consumption exceeds normal levels, it can suggest alternative equipment or solutions. Specifically, if the power consumption of a public swimming pool heater becomes abnormally high, the server might resolve the problem by switching the energy supply to solar panels as an alternative.

[0115] An example of a prompt for a generated AI model is: "Write an AI algorithm that monitors real-time data from sensors in public facilities within a city and detects anomalies in energy consumption." This prompt is used to generate an AI model that enables proper anomaly detection.

[0116] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0117] Step 1:

[0118] The server acquires operational information in real time from IoT sensors installed in each household appliance and public facility. This data shows the operating status and energy consumption of the devices and is transmitted from the sensors to a cloud-based database. The input is raw data from the sensors, and the output is organized operational information.

[0119] Step 2:

[0120] The server uses TensorFlow to execute an anomaly detection algorithm to detect abnormalities based on the collected operational information. The data processing performed here involves comparing and analyzing the input information with existing normal pattern data. The output of this process is an indicator of whether or not an anomaly exists. Specifically, it determines whether energy consumption exceeds a certain threshold.

[0121] Step 3:

[0122] If an anomaly is detected, the server sends a push notification to the administrator's terminal. The notification includes details of the anomaly and recommended countermeasures. The input is the data resulting from the anomaly detection, and a notification message is output. This allows the administrator to quickly identify the problem and consider appropriate actions.

[0123] Step 4:

[0124] The server uses AI to propose specific alternative measures for abnormal situations. The input is the type of abnormality and the current equipment status, and the output is the proposed alternative. For example, if an abnormally high temperature is detected, it might instruct the use of a backup energy source.

[0125] Step 5:

[0126] The administrator sends feedback to the server via a terminal and decides whether to implement the proposed countermeasures. The input is the operational instructions based on the administrator's decision, and the output is the result of implementing the countermeasures. In this way, the entire system is integrated and managed smoothly.

[0127] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0128] This invention aims to improve the user experience by incorporating an emotion engine into a home appliance management system. The system is based on a process that monitors operational data acquired from each home appliance in real time and detects abnormalities. Furthermore, the server has an emotion engine on the user's terminal and analyzes the user's emotional state using emotion recognition technology.

[0129] The server adjusts the content and tone of anomaly notifications based on feedback from the emotion engine. When a user is stressed, the system sends notifications in a gentle tone; when the user is relaxed, it provides more detailed information to help resolve the problem. Additionally, when a user's emotions are negative, it offers the option to quickly contact the support center.

[0130] For example, if a refrigerator malfunction is detected, the server uses the user's recent behavioral data to allow the emotion engine to analyze the user's emotional state. If the user is busy, the system will provide a short, concise notification and offer follow-up options such as, "Do you want to address this in your current state?" On the other hand, if the user is relaxed, the system will provide a detailed explanation of the malfunction and a recommended action plan.

[0131] The emotional engine also influences the selection of repair technicians. By considering the user's emotional state, it can prioritize arranging for technicians who can respond more quickly. In this way, the present invention makes home automation more humane and enables responses that are sensitive to the user's emotions.

[0132] The following describes the processing flow.

[0133] Step 1:

[0134] The server receives real-time operational data from each household appliance via sensors. This data includes temperature, power consumption, vibration information, and other information, and is stored in a database built on the server.

[0135] Step 2:

[0136] The server uses the received operational data to execute an anomaly detection algorithm and identify anomalies. In this process, the algorithm compares the data with past data and detects data that falls outside the normal range.

[0137] Step 3:

[0138] If an anomaly is detected, the server sends a request to the emotion engine to analyze the user's current emotional state. The emotion engine operates on the user's device and performs the analysis using accumulated interaction data and sensor data.

[0139] Step 4:

[0140] The device receives the analysis results from the emotion engine and sends them to the server. Based on this emotion information, the server adjusts the content and tone of notifications sent to the user. For example, if the user is feeling stressed, it will send a notification in a gentle tone with concise content.

[0141] Step 5:

[0142] Users check notifications through their devices. They can determine if the information provided is relevant to them and choose the necessary action. For example, they can decide whether to proceed with arranging for a repair service or opt for a temporary solution.

[0143] Step 6:

[0144] If repairs are needed, the server searches a database of repair companies based on information obtained from the emotion engine. It then suggests the most suitable company for the user's emotional state (such as one that can respond quickly) and initiates the booking process.

[0145] Step 7:

[0146] If necessary, the server refers to community-based data and provides users with advice on similar problems and solutions. It can also flexibly customize options and advice based on past experiences and the user's emotional state.

[0147] (Example 2)

[0148] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0149] While modern household appliances have become more diverse and convenient, dealing with malfunctions and problems remains inconvenient. Furthermore, responses that consider the user's emotional state are often insufficient. This leads to challenges such as excessive stress when appliances malfunction and inadequate repair services.

[0150] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0151] In this invention, the server includes means for collecting operational information from home appliances, means for analyzing the received operational information to detect abnormalities, and means for estimating the user's emotional state. This enables flexible and appropriate responses in response to the user's emotions when an abnormality occurs in a home appliance.

[0152] "Home appliances" refer to all electrical devices used in the home that operate automatically or semi-automatically.

[0153] "Operation information" refers to data indicating whether a home appliance is functioning correctly, and includes various measurement values ​​collected by sensors.

[0154] "Anomaly detection" refers to the process of analyzing collected operational information to identify conditions that deviate from normal operational patterns.

[0155] "User emotional state" refers to the psychological situation a user is experiencing and is analyzed using emotion recognition technology.

[0156] "Adjusting the content and tone of notifications" refers to appropriately changing the way information is conveyed and the wording based on the user's emotional state.

[0157] A "repair company" refers to a service provider that specializes in the repair and maintenance of home appliances.

[0158] "Controlling equipment" refers to adjusting or directing the operation of equipment to achieve a desired behavior under specific circumstances.

[0159] This invention is a system for more effectively managing household appliances. Its embodiments are described in detail below.

[0160] The server collects operational information in real time from sensors installed in each household appliance. This includes various measurement data such as temperature, power consumption, and vibration. This information is transmitted to the server via Wi-Fi modules built into the appliances. The server uses Python programs and machine learning libraries such as TensorFlow to process the collected data and perform calculations to detect anomalies.

[0161] Next, the server uses image and audio data collected from the user's device to estimate the user's emotional state. Emotion recognition technology is used in this process. Specific software options include general cloud services that provide facial recognition APIs and audio analysis APIs.

[0162] If an anomaly is detected, the device adjusts the notification content based on the user's emotional state. For example, if the user is busy, it generates a concise message such as "An anomaly has been detected in your home appliance. Do you want to address it now?", while if the user is relaxed, it generates a more detailed message such as "We will provide you with detailed information and solutions regarding the anomaly in your home appliance."

[0163] The system further adjusts the tone of notification messages using a generative AI model. An example of a prompt message in this case would be, "What tone should be used to send a refrigerator malfunction notification when the user is feeling stressed?"

[0164] This invention improves the operational efficiency of household appliances and enables flexible responses that take into account the user's emotional state.

[0165] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0166] Step 1:

[0167] The server collects operational data from each home appliance. Sensors installed in the appliances measure data such as temperature, power consumption, and vibration, and transmit it to the server via Wi-Fi. The input is raw data obtained from the sensors, and the output is time-series data that has been formatted from this data.

[0168] Step 2:

[0169] The server uses collected operational data to detect anomalies. Using Python and TensorFlow, it analyzes the data with a model trained on normal and abnormal values. In this process, time-series data is used as input, and alerts indicating the presence or absence of anomalies are generated as output.

[0170] Step 3:

[0171] The device captures image and audio data collected by the camera and microphone to analyze the user's emotional state. It uses facial recognition APIs and voice analysis APIs to estimate emotions. The input is image and audio data, and the output is numerical data representing the user's emotions.

[0172] Step 4:

[0173] The server combines the results of anomaly detection with the user's emotional state to generate a notification message. A generative AI model uses natural language processing with prompts to create a message with appropriate tone and content. A possible prompt might be, "What information should be included when the user is relaxed?" The input is anomaly alerts and emotional data, and the output is a tailored notification message.

[0174] Step 5:

[0175] The server sends the generated notification message to the user's device. Notifications are prioritized according to the user's situation, enabling immediate action. This results in a system where the input is a notification message, and the output is the user's alert.

[0176] (Application Example 2)

[0177] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as a "server" and the smart device 14 as a "terminal".

[0178] In modern homes, numerous home appliances are installed, and the notification and troubleshooting for each appliance can become a burden. However, conventional systems provide uniform notifications without considering the user's emotional state, resulting in a poor user experience. Furthermore, while a quick and appropriate response is required when an appliance malfunctions, providing personalized services to address this is difficult.

[0179] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0180] In this invention, the server includes means for collecting information on the operating status of home appliances in real time, means for identifying abnormalities based on the collected information, and means for analyzing the user's emotional state and adjusting the content and tone of abnormality notifications. This enables abnormality responses that are sensitive to the user's emotions, making it possible to achieve quick and stress-free home appliance management.

[0181] "Operating status of home appliances" refers to the current functional conditions and behavior of home appliances while they are running or in standby mode.

[0182] "Means of real-time data collection" refers to a system that collects and stores data immediately without time delay via information processing devices and communication networks.

[0183] "Means for identifying anomalies" refers to the process of detecting events that deviate from normal operating patterns and identifying those events as problems.

[0184] "User's emotional state" refers to the user's psychological state at a given time, and is a mental state inferred using various emotion recognition technologies.

[0185] "Means for adjusting the content and tone of abnormality notifications" refers to technologies that optimize the expression and delivery method of abnormality-related messages according to the user's emotions.

[0186] The system of this invention monitors the operating status of home appliances in real time and, when an abnormality is detected, provides appropriate notification considering the user's emotional state. Operating data from each home appliance is collected on a server via sensors and a network. The server analyzes this data, and if an abnormality occurs in an appliance, it uses an emotion engine to analyze the user's emotional state. Depending on the user's emotional state, the server adjusts the content and tone of the notification to convey the most appropriate abnormality information to the user.

[0187] This system requires multiple hardware and software components. Specifically, it necessitates sensors for real-time monitoring of home appliance operation and network modules for data communication. When analyzing emotional states, it uses cameras and microphones to acquire user voice and facial expression data, and employs software with emotion recognition technology (e.g., OpenAI® GPT-3® or Microsoft® Azure® Cognitive Services).

[0188] One concrete example of how this system could be used is a household robot detecting abnormalities in home appliances and notifying the user. For instance, if the robot detects an abnormal temperature in a refrigerator and determines from the user's facial expression and voice that they are relaxed, it can provide detailed information and guide them on how to address the issue.

[0189] Example prompt: "How should you notify the user if a refrigerator malfunction occurs while they are in a relaxed state?"

[0190] In this way, by coordinating the server, terminal, and user elements, a user-friendly home appliance management system can be realized.

[0191] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0192] Step 1:

[0193] The server collects operating status data transmitted from home appliances in real time via sensors. This data indicates the operating patterns and current status of the appliances, and the server performs preprocessing based on this data to evaluate the normality of the operation. Specifically, the data is cleaned and normalized to prepare it for anomaly detection.

[0194] Step 2:

[0195] The server executes an anomaly detection algorithm based on the collected data. The anomaly detection algorithm compares predefined normal patterns with real-time data to detect behavior that is considered abnormal. It uses normal patterns and real-time behavior data as input and generates output indicating whether an anomaly occurred and its details.

[0196] Step 3:

[0197] If an anomaly is detected, the server analyzes the user's emotional state using the user's voice and image data acquired from the camera and microphone installed on the terminal. The emotion recognition model used here (including generative AI models) takes voice tone and facial features as input and provides an estimated emotion as output.

[0198] Step 4:

[0199] The server adjusts the content and tone of anomaly notifications based on the emotion estimation results. Specifically, it provides detailed and gentle explanations to relaxed users and sends simple and gentle notifications to stressed users. This process utilizes a generative AI model to generate prompt sentences and adjusts the output accordingly.

[0200] Step 5:

[0201] The server sends a coordinated notification to the user's device. The user receives the notification and can either follow the instructions to address the anomaly or contact support. The input here is the coordinated notification content, and the output is the information provided to the user and their response options.

[0202] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0203] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (registered trademark) (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0204] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart device 14.

[0205] [Second Embodiment]

[0206] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0207] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0208] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0209] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0210] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0211] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0212] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0213] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0214] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0215] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0216] In the smart glasses 214, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0217] Next, the identification processing performed by the identification processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0218] This invention relates to a system for efficiently managing home appliances and quickly detecting abnormalities. The system acquires operational data from sensors attached to various home appliances and transmits it to a server via a network. The server analyzes this data in real time and monitors for abnormalities in the appliances using an anomaly detection algorithm.

[0219] If the server detects an anomaly, it immediately sends a notification to the user's device. This allows the user to check the nature of the anomaly and take prompt action. The server also refers to a database of trusted repair companies, suggests the best repair date to fit the user's schedule, and automates the booking process. The user can review this suggestion on their device and modify it as needed.

[0220] Furthermore, the server coordinates with other devices in the home and provides alternative solutions in case of malfunctions. For example, if the air conditioner malfunctions, it can activate a smart fan to maintain room temperature control. In this way, the system integrates the entire home ecosystem, managing appliances efficiently and safely.

[0221] For example, if the refrigerator's temperature sensor detects a temperature higher than normal, the server will recognize the anomaly and immediately notify the user. At the same time, the server will determine whether the refrigerator needs repair and, if necessary, arrange for a repair service. It can also revise the usage schedule of other household appliances to temporarily replace the refrigerator's operation.

[0222] This invention aims to improve the quality of life for users by making the monitoring of household appliances more efficient.

[0223] The following describes the processing flow.

[0224] Step 1:

[0225] The server receives operational data from each home appliance via sensors. This data includes parameters such as temperature, power consumption, and vibration. The received data is stored in a database and forms the basis for subsequent processing.

[0226] Step 2:

[0227] The server preprocesses the received operational data. Specifically, it imputes missing data values ​​and filters out abnormal values, adjusting them to normal values. This process improves data consistency and reliability.

[0228] Step 3:

[0229] The server executes an anomaly detection algorithm based on the pre-processed data. Using a machine learning model, it compares the data to normal operating patterns and identifies any deviations as anomalies. This result is then passed on to the next step.

[0230] Step 4:

[0231] If an anomaly is detected, the server will immediately send a notification to the user's device. The notification will include the nature of the detected anomaly, the parts that may be affected, and recommended countermeasures.

[0232] Step 5:

[0233] Users can view detailed information about anomalies detected through their device. Based on this information, they can take necessary actions. They can also select repair service appointment options provided on the device.

[0234] Step 6:

[0235] The server accesses a database of repair providers and automatically generates the optimal repair appointment, taking into account the user's schedule and the provider's availability. Once the user reviews and approves the information, the appointment is confirmed.

[0236] Step 7:

[0237] If an anomaly could affect other devices, the server will control smart devices in the home and take alternative measures. For example, if a problem occurs with the heating system, the server will activate other auxiliary heating devices.

[0238] Step 8:

[0239] The server analyzes all data collected within the home over a long period, accumulating community-based knowledge to suggest future preventative measures. This makes it possible to provide helpful advice to users.

[0240] (Example 1)

[0241] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0242] Modern homes utilize numerous electrical appliances, making efficient management and rapid anomaly detection challenging. Conventional systems struggle to respond immediately to malfunctions, and the effort required to find a suitable repair service and schedule repairs is burdensome for users. Furthermore, adequate measures are not in place to optimize the home environment in the event of a malfunction. Solving these challenges is essential.

[0243] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0244] In this invention, the server includes means for collecting operational information in real time, means for detecting anomalies based on the collected information, and means for searching for and booking a reliable repair service provider. This enables efficient monitoring of household electrical appliances and rapid response to any anomalies that occur. Furthermore, by automatically suggesting the optimal repair schedule in the event of an anomaly and controlling other household devices, the burden on the user can be reduced, and a safe and comfortable living environment can be maintained.

[0245] "Operational information" refers to data about the functions and status of electrical appliances on a daily basis.

[0246] "Real-time" is a term that refers to a process that is performed instantly without delay.

[0247] "An anomaly" refers to an event or state that differs from normal operation or condition, and signifies a potentially problematic situation.

[0248] "Users" refers to individuals or households that use this system.

[0249] "Notification" refers to a means of communicating information to inform users that there is an abnormality.

[0250] A "reliable repair shop" refers to a repair service that provides high-quality service and has established trust with its customers.

[0251] "Household appliances" is a general term for various electrical appliances and equipment used within a home.

[0252] "Analysis" is the process of analyzing data in detail and obtaining meaningful information from it.

[0253] "Proposal" refers to the act of showing users the optimal solution or action.

[0254] This invention is a system for efficiently monitoring household electrical appliances and responding quickly to any abnormalities. This system has the function of acquiring operational information in real time and processing and analyzing that data. Details are described below.

[0255] 1. Data Collection and Communication

[0256] Sensors are attached to each household appliance, which periodically collect operational information about the product. This information is then transmitted to a central server via a wireless network. TLS (Transport Layer Security) is used as the data communication protocol to ensure data security.

[0257] 2. Server analysis function

[0258] The server processes the received operational information using Python-based data analysis libraries (e.g., Pandas or NumPy). The server analyzes the data using an anomaly detection algorithm and immediately notifies the terminal if an anomaly is detected. The criteria for determining an anomaly are based on comparisons with predetermined thresholds and normal ranges. This allows users to immediately recognize abnormal situations and take necessary countermeasures.

[0259] 3. Notification and Repair Arrangement

[0260] If an abnormality is detected, the system automatically accesses a database of repair providers and proposes the most suitable repair date. Users can review and adjust this proposal from their device, ensuring a smooth repair process.

[0261] 4. Providing alternative solutions

[0262] In the event of a malfunction, the server will coordinate with other home devices to provide alternative solutions. For example, if the air conditioner breaks down, a smart fan can be automatically activated to maintain a comfortable indoor environment.

[0263] Specific example

[0264] When the refrigerator's temperature sensor detects a temperature higher than normal, the server recognizes it as an anomaly and immediately sends a notification to the user's device. This notification includes details of the anomaly, recommended actions, and information on repair companies. The server also automatically suggests a repair schedule and can adjust the usage plans for other household appliances as an emergency measure.

[0265] Examples of prompts for generative AI models

[0266] "Please describe in detail the anomaly detection system for household electrical appliances and explain specifically how it actually works."

[0267] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0268] Step 1:

[0269] The sensor collects operational information from electrical appliances. Specifically, the sensor measures information such as temperature and power consumption at regular intervals and prepares the obtained data for transmission using radio waves. The input is the operating status of the electrical appliance, and the output is a dataset containing that operational information.

[0270] Step 2:

[0271] The sensor collects motion information and sends it to the server. The server receives the data and stores it in storage in an encrypted state. The input is the encrypted motion information sent from the sensor, and the output is the motion information stored on the server. During this process, the TLS protocol ensures the security of the data communication.

[0272] Step 3:

[0273] The server analyzes the received operational information. The server evaluates the data using a Python-based analysis algorithm and detects anomalies by comparing it to normal values. The input is operational information stored on the server, and the output is the determination of whether it is normal or abnormal. In this process, for example, an abnormal temperature value is calculated using the Z-score method, and if it exceeds a threshold, it is determined to be abnormal.

[0274] Step 4:

[0275] If the server detects an anomaly, it sends a notification to the terminal. The server creates a message detailing the problem and recommended actions, and sends a push notification to the user's mobile device. The input is the result of the anomaly detection, and the output is the notification message sent to the user. The notification includes details of how the anomaly occurred and initial countermeasures.

[0276] Step 5:

[0277] The server proposes repair dates based on repair provider information. The server searches an existing provider database, connects to a suitable provider, and matches dates. Inputs are the user's schedule and the repair provider's availability, and output is the optimal repair date notified to the user. This facilitates quick and effective repair responses.

[0278] Step 6:

[0279] The server controls other household devices to optimize the home environment in the event of a malfunction. Specifically, it issues instructions to temporarily substitute the function of a faulty device with another device. The inputs are the malfunction detection status and the current operating status of household devices, and the output is the control instruction. This control allows, for example, if the air conditioner malfunctions, to automatically turn on a smart fan to maintain the room temperature.

[0280] (Application Example 1)

[0281] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0282] In modern cities, there is a need to efficiently manage the energy consumption of household appliances and public facilities. However, detecting abnormalities in individual devices and facilities in real time and responding quickly is not easy, and systems that provide optimal solutions when abnormalities occur are limited. In this situation, an integrated management system is needed that can quickly detect abnormalities and take appropriate action.

[0283] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0284] In this invention, the server includes means for acquiring in real time the operation information of household appliances, means for identifying abnormalities based on the acquired information, and means for notifying the user when an abnormality is detected. As a result, it becomes possible to quickly detect abnormalities in household and public facility devices and provide appropriate notifications and countermeasures.

[0285] The "operation information" is data related to the operating status and energy consumption of various devices in household appliances and public facilities.

[0286] "Abnormality" refers to the state of a device that deviates from the normal operation pattern, indicating the possibility of defects or failures in household appliances and public facilities.

[0287] The "means for acquisition" is a method or device for collecting operation information in real time using devices such as sensors.

[0288] The "means for identification" is an algorithm or process for analyzing the collected operation information to identify abnormalities.

[0289] The "means for notification" is a communication means or software for quickly transmitting information to users or administrators when an abnormality is detected.

[0290] "Public facilities" refers to buildings and facilities provided for public use within a city, which are targets that require energy management.

[0291] The "administrator" is a person in charge of the operation and maintenance of public facilities and household appliances or a user of the system.

[0292] "Energy consumption data" is information related to the amount of energy consumed by devices and facilities and their usage patterns.

[0293] The "means for proposing alternative measures" is a system function or technique for providing other available methods or devices when an abnormality occurs.

[0294] In implementing this invention, a system for managing the energy consumption of household appliances and public facilities is configured. First, a server collects operational information in real time from IoT sensors deployed in homes and public facilities. This includes energy consumption data and operating status of various devices. Examples of sensors used include Bosch environmental sensors.

[0295] Next, the server uses the collected data to apply an anomaly detection algorithm using TensorFlow to identify anomalies. This anomaly detection quickly identifies deviations from normal consumption patterns. When an anomaly is detected, the server immediately sends a notification to the administrator's terminal to prompt appropriate action.

[0296] Furthermore, the server utilizes AI to propose countermeasures for anomalies. For example, if energy consumption exceeds normal levels, it can suggest alternative equipment or solutions. Specifically, if the power consumption of a public swimming pool heater becomes abnormally high, the server might resolve the problem by switching the energy supply to solar panels as an alternative.

[0297] An example of a prompt for a generated AI model is: "Write an AI algorithm that monitors real-time data from sensors in public facilities within a city and detects anomalies in energy consumption." This prompt is used to generate an AI model that enables proper anomaly detection.

[0298] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0299] Step 1:

[0300] The server acquires operational information in real time from IoT sensors installed in each household appliance and public facility. This data shows the operating status and energy consumption of the devices and is transmitted from the sensors to a cloud-based database. The input is raw data from the sensors, and the output is organized operational information.

[0301] Step 2:

[0302] The server uses TensorFlow to execute an anomaly detection algorithm to detect anomalies based on the collected operation information. The data processing performed here is to compare and analyze the input information with the existing normal pattern data. The output of this process is an indicator indicating the presence or absence of anomalies. Specifically, it is determined whether the energy consumption exceeds the reference value.

[0303] Step 3:

[0304] If an anomaly is detected, the server sends a push notification to the administrator's terminal. The notification describes the details of the anomaly and the recommended countermeasures. The input is the result data of the anomaly detection, and the notification message is output. This enables the administrator to quickly confirm the problem and consider appropriate countermeasures.

[0305] Step 4:

[0306] The server uses AI to propose specific alternative measures for abnormal situations. The input is the type of anomaly and the current device status, and the output is the proposed alternative measures. As a specific operation, for example, when an abnormal high temperature is detected, it instructs the use of a backup energy source.

[0307] Step 5:

[0308] The administrator sends feedback to the server through the terminal and decides whether to execute the proposed countermeasures. The input is the operation instruction based on the administrator's decision, and the output is the implementation result of the countermeasures. In this way, the entire system is integrated and managed smoothly.

[0309] Furthermore, an emotion engine for estimating the user's emotion may be combined. That is, the specific processing unit 290 may estimate the user's emotion using the emotion identification model 59 and perform specific processing using the user's emotion.

[0310] This invention aims to improve the user experience by incorporating an emotion engine into a home appliance management system. The system is based on a process that monitors operational data acquired from each home appliance in real time and detects abnormalities. Furthermore, the server has an emotion engine on the user's terminal and analyzes the user's emotional state using emotion recognition technology.

[0311] The server adjusts the content and tone of anomaly notifications based on feedback from the emotion engine. When a user is stressed, the system sends notifications in a gentle tone; when the user is relaxed, it provides more detailed information to help resolve the problem. Additionally, when a user's emotions are negative, it offers the option to quickly contact the support center.

[0312] For example, if a refrigerator malfunction is detected, the server uses the user's recent behavioral data to allow the emotion engine to analyze the user's emotional state. If the user is busy, the system will provide a short, concise notification and offer follow-up options such as, "Do you want to address this in your current state?" On the other hand, if the user is relaxed, the system will provide a detailed explanation of the malfunction and a recommended action plan.

[0313] The emotional engine also influences the selection of repair technicians. By considering the user's emotional state, it can prioritize arranging for technicians who can respond more quickly. In this way, the present invention makes home automation more humane and enables responses that are sensitive to the user's emotions.

[0314] The following describes the processing flow.

[0315] Step 1:

[0316] The server receives real-time operational data from each household appliance via sensors. This data includes temperature, power consumption, vibration information, and other information, and is stored in a database built on the server.

[0317] Step 2:

[0318] The server uses the received operational data to execute an anomaly detection algorithm and identify anomalies. In this process, the algorithm compares the data with past data and detects data that falls outside the normal range.

[0319] Step 3:

[0320] If an anomaly is detected, the server sends a request to the emotion engine to analyze the user's current emotional state. The emotion engine operates on the user's device and performs the analysis using accumulated interaction data and sensor data.

[0321] Step 4:

[0322] The device receives the analysis results from the emotion engine and sends them to the server. Based on this emotion information, the server adjusts the content and tone of notifications sent to the user. For example, if the user is feeling stressed, it will send a notification in a gentle tone with concise content.

[0323] Step 5:

[0324] Users check notifications through their devices. They can determine if the information provided is relevant to them and choose the necessary action. For example, they can decide whether to proceed with arranging for a repair service or opt for a temporary solution.

[0325] Step 6:

[0326] If repairs are needed, the server searches a database of repair companies based on information obtained from the emotion engine. It then suggests the most suitable company for the user's emotional state (such as one that can respond quickly) and initiates the booking process.

[0327] Step 7:

[0328] If necessary, the server refers to community-based data and provides users with advice on similar problems and solutions. It can also flexibly customize options and advice based on past experiences and the user's emotional state.

[0329] (Example 2)

[0330] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0331] While modern household appliances have become more diverse and convenient, dealing with malfunctions and problems remains inconvenient. Furthermore, responses that consider the user's emotional state are often insufficient. This leads to challenges such as excessive stress when appliances malfunction and inadequate repair services.

[0332] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0333] In this invention, the server includes means for collecting operational information from home appliances, means for analyzing the received operational information to detect abnormalities, and means for estimating the user's emotional state. This enables flexible and appropriate responses in response to the user's emotions when an abnormality occurs in a home appliance.

[0334] "Home appliances" refer to all electrical devices used in the home that operate automatically or semi-automatically.

[0335] "Operation information" refers to data indicating whether a home appliance is functioning correctly, and includes various measurement values ​​collected by sensors.

[0336] "Anomaly detection" refers to the process of analyzing collected operational information to identify conditions that deviate from normal operational patterns.

[0337] "User emotional state" refers to the psychological situation a user is experiencing and is analyzed using emotion recognition technology.

[0338] "Adjusting the content and tone of notifications" refers to appropriately changing the way information is conveyed and the wording based on the user's emotional state.

[0339] A "repair company" refers to a service provider that specializes in the repair and maintenance of home appliances.

[0340] "Controlling equipment" refers to adjusting or directing the operation of equipment to achieve a desired behavior under specific circumstances.

[0341] This invention is a system for more effectively managing household appliances. Its embodiments are described in detail below.

[0342] The server collects operational information in real time from sensors installed in each household appliance. This includes various measurement data such as temperature, power consumption, and vibration. This information is transmitted to the server via Wi-Fi modules built into the appliances. The server uses Python programs and machine learning libraries such as TensorFlow to process the collected data and perform calculations to detect anomalies.

[0343] Next, the server uses image and audio data collected from the user's device to estimate the user's emotional state. Emotion recognition technology is used in this process. Specific software options include general cloud services that provide facial recognition APIs and audio analysis APIs.

[0344] If an anomaly is detected, the device adjusts the notification content based on the user's emotional state. For example, if the user is busy, it generates a concise message such as "An anomaly has been detected in your home appliance. Do you want to address it now?", while if the user is relaxed, it generates a more detailed message such as "We will provide you with detailed information and solutions regarding the anomaly in your home appliance."

[0345] The system further adjusts the tone of notification messages using a generative AI model. An example of a prompt message in this case would be, "What tone should be used to send a refrigerator malfunction notification when the user is feeling stressed?"

[0346] This invention improves the operational efficiency of household appliances and enables flexible responses that take into account the user's emotional state.

[0347] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0348] Step 1:

[0349] The server collects operational data from each home appliance. Sensors installed in the appliances measure data such as temperature, power consumption, and vibration, and transmit it to the server via Wi-Fi. The input is raw data obtained from the sensors, and the output is time-series data that has been formatted from this data.

[0350] Step 2:

[0351] The server uses collected operational data to detect anomalies. Using Python and TensorFlow, it analyzes the data with a model trained on normal and abnormal values. In this process, time-series data is used as input, and alerts indicating the presence or absence of anomalies are generated as output.

[0352] Step 3:

[0353] The device captures image and audio data collected by the camera and microphone to analyze the user's emotional state. It uses facial recognition APIs and voice analysis APIs to estimate emotions. The input is image and audio data, and the output is numerical data representing the user's emotions.

[0354] Step 4:

[0355] The server combines the results of anomaly detection with the user's emotional state to generate a notification message. A generative AI model uses natural language processing with prompts to create a message with appropriate tone and content. A possible prompt might be, "What information should be included when the user is relaxed?" The input is anomaly alerts and emotional data, and the output is a tailored notification message.

[0356] Step 5:

[0357] The server sends the generated notification message to the user's device. Notifications are prioritized according to the user's situation, enabling immediate action. This results in a system where the input is a notification message, and the output is the user's alert.

[0358] (Application Example 2)

[0359] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0360] In modern homes, numerous home appliances are installed, and the notification and troubleshooting for each appliance can become a burden. However, conventional systems provide uniform notifications without considering the user's emotional state, resulting in a poor user experience. Furthermore, while a quick and appropriate response is required when an appliance malfunctions, providing personalized services to address this is difficult.

[0361] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0362] In this invention, the server includes means for collecting information on the operating status of home appliances in real time, means for identifying abnormalities based on the collected information, and means for analyzing the user's emotional state and adjusting the content and tone of abnormality notifications. This enables abnormality responses that are sensitive to the user's emotions, making it possible to achieve quick and stress-free home appliance management.

[0363] "Operating status of home appliances" refers to the current functional conditions and behavior of home appliances while they are running or in standby mode.

[0364] "Means of real-time data collection" refers to a system that collects and stores data immediately without time delay via information processing devices and communication networks.

[0365] "Means for identifying anomalies" refers to the process of detecting events that deviate from normal operating patterns and identifying those events as problems.

[0366] "User's emotional state" refers to the user's psychological state at a given time, and is a mental state inferred using various emotion recognition technologies.

[0367] "Means for adjusting the content and tone of abnormality notifications" refers to technologies that optimize the expression and delivery method of abnormality-related messages according to the user's emotions.

[0368] The system of this invention monitors the operating status of home appliances in real time and, when an abnormality is detected, provides appropriate notification considering the user's emotional state. Operating data from each home appliance is collected on a server via sensors and a network. The server analyzes this data, and if an abnormality occurs in an appliance, it uses an emotion engine to analyze the user's emotional state. Depending on the user's emotional state, the server adjusts the content and tone of the notification to convey the most appropriate abnormality information to the user.

[0369] This system requires multiple hardware and software components. Specifically, it needs sensors to monitor the operation of home appliances in real time and a network module for data communication. When analyzing emotional states, it uses cameras and microphones to acquire user voice and facial expression data, and employs software with emotion recognition technology (e.g., OpenAI GPT-3 or Microsoft Azure Cognitive Services).

[0370] One concrete example of how this system could be used is a household robot detecting abnormalities in home appliances and notifying the user. For instance, if the robot detects an abnormal temperature in a refrigerator and determines from the user's facial expression and voice that they are relaxed, it can provide detailed information and guide them on how to address the issue.

[0371] Example prompt: "How should you notify the user if a refrigerator malfunction occurs while they are in a relaxed state?"

[0372] In this way, by coordinating the server, terminal, and user elements, a user-friendly home appliance management system can be realized.

[0373] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0374] Step 1:

[0375] The server collects operating status data transmitted from home appliances in real time via sensors. This data indicates the operating patterns and current status of the appliances, and the server performs preprocessing based on this data to evaluate the normality of the operation. Specifically, the data is cleaned and normalized to prepare it for anomaly detection.

[0376] Step 2:

[0377] The server executes an anomaly detection algorithm based on the collected data. The anomaly detection algorithm compares predefined normal patterns with real-time data to detect behavior that is considered abnormal. It uses normal patterns and real-time behavior data as input and generates output indicating whether an anomaly occurred and its details.

[0378] Step 3:

[0379] If an anomaly is detected, the server analyzes the user's emotional state using the user's voice and image data acquired from the camera and microphone installed on the terminal. The emotion recognition model used here (including generative AI models) takes voice tone and facial features as input and provides an estimated emotion as output.

[0380] Step 4:

[0381] The server adjusts the content and tone of anomaly notifications based on the emotion estimation results. Specifically, it provides detailed and gentle explanations to relaxed users and sends simple and gentle notifications to stressed users. This process utilizes a generative AI model to generate prompt sentences and adjusts the output accordingly.

[0382] Step 5:

[0383] The server sends a coordinated notification to the user's device. The user receives the notification and can either follow the instructions to address the anomaly or contact support. The input here is the coordinated notification content, and the output is the information provided to the user and their response options.

[0384] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0385] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0386] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart glasses 214.

[0387] [Third Embodiment]

[0388] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0389] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0390] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0391] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0392] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0393] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0394] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0395] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0396] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0397] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0398] In the headset terminal 314, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0399] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the headset terminal 314 will be referred to as the "terminal".

[0400] This invention relates to a system for efficiently managing home appliances and quickly detecting abnormalities. The system acquires operational data from sensors attached to various home appliances and transmits it to a server via a network. The server analyzes this data in real time and monitors for abnormalities in the appliances using an anomaly detection algorithm.

[0401] If the server detects an anomaly, it immediately sends a notification to the user's device. This allows the user to check the nature of the anomaly and take prompt action. The server also refers to a database of trusted repair companies, suggests the best repair date to fit the user's schedule, and automates the booking process. The user can review this suggestion on their device and modify it as needed.

[0402] Furthermore, the server coordinates with other devices in the home and provides alternative solutions in case of malfunctions. For example, if the air conditioner malfunctions, it can activate a smart fan to maintain room temperature control. In this way, the system integrates the entire home ecosystem, managing appliances efficiently and safely.

[0403] For example, if the refrigerator's temperature sensor detects a temperature higher than normal, the server will recognize the anomaly and immediately notify the user. At the same time, the server will determine whether the refrigerator needs repair and, if necessary, arrange for a repair service. It can also revise the usage schedule of other household appliances to temporarily replace the refrigerator's operation.

[0404] This invention aims to improve the quality of life for users by making the monitoring of household appliances more efficient.

[0405] The following describes the processing flow.

[0406] Step 1:

[0407] The server receives operational data from each home appliance via sensors. This data includes parameters such as temperature, power consumption, and vibration. The received data is stored in a database and forms the basis for subsequent processing.

[0408] Step 2:

[0409] The server preprocesses the received operational data. Specifically, it imputes missing data values ​​and filters out abnormal values, adjusting them to normal values. This process improves data consistency and reliability.

[0410] Step 3:

[0411] The server executes an anomaly detection algorithm based on the pre-processed data. Using a machine learning model, it compares the data to normal operating patterns and identifies any deviations as anomalies. This result is then passed on to the next step.

[0412] Step 4:

[0413] If an anomaly is detected, the server will immediately send a notification to the user's device. The notification will include the nature of the detected anomaly, the parts that may be affected, and recommended countermeasures.

[0414] Step 5:

[0415] Users can view detailed information about anomalies detected through their device. Based on this information, they can take necessary actions. They can also select repair service appointment options provided on the device.

[0416] Step 6:

[0417] The server accesses a database of repair providers and automatically generates the optimal repair appointment, taking into account the user's schedule and the provider's availability. Once the user reviews and approves the information, the appointment is confirmed.

[0418] Step 7:

[0419] If an anomaly could affect other devices, the server will control smart devices in the home and take alternative measures. For example, if a problem occurs with the heating system, the server will activate other auxiliary heating devices.

[0420] Step 8:

[0421] The server analyzes all data collected within the home over a long period, accumulating community-based knowledge to suggest future preventative measures. This makes it possible to provide helpful advice to users.

[0422] (Example 1)

[0423] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0424] Modern homes utilize numerous electrical appliances, making efficient management and rapid anomaly detection challenging. Conventional systems struggle to respond immediately to malfunctions, and the effort required to find a suitable repair service and schedule repairs is burdensome for users. Furthermore, adequate measures are not in place to optimize the home environment in the event of a malfunction. Solving these challenges is essential.

[0425] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0426] In this invention, the server includes means for collecting operational information in real time, means for detecting anomalies based on the collected information, and means for searching for and booking a reliable repair service provider. This enables efficient monitoring of household electrical appliances and rapid response to any anomalies that occur. Furthermore, by automatically suggesting the optimal repair schedule in the event of an anomaly and controlling other household devices, the burden on the user can be reduced, and a safe and comfortable living environment can be maintained.

[0427] "Operational information" refers to data about the functions and status of electrical appliances on a daily basis.

[0428] "Real-time" is a term that refers to a process that is performed instantly without delay.

[0429] "An anomaly" refers to an event or state that differs from normal operation or condition, and signifies a potentially problematic situation.

[0430] "Users" refers to individuals or households that use this system.

[0431] "Notification" refers to a means of communicating information to inform users that there is an abnormality.

[0432] A "reliable repair shop" refers to a repair service that provides high-quality service and has established trust with its customers.

[0433] "Household appliances" is a general term for various electrical appliances and equipment used within a home.

[0434] "Analysis" is the process of analyzing data in detail and obtaining meaningful information from it.

[0435] "Proposal" refers to the act of showing users the optimal solution or action.

[0436] This invention is a system for efficiently monitoring household electrical appliances and responding quickly to any abnormalities. This system has the function of acquiring operational information in real time and processing and analyzing that data. Details are described below.

[0437] 1. Data Collection and Communication

[0438] Sensors are attached to each household appliance, which periodically collect operational information about the product. This information is then transmitted to a central server via a wireless network. TLS (Transport Layer Security) is used as the data communication protocol to ensure data security.

[0439] 2. Server analysis function

[0440] The server processes the received operational information using Python-based data analysis libraries (e.g., Pandas or NumPy). The server analyzes the data using an anomaly detection algorithm and immediately notifies the terminal if an anomaly is detected. The criteria for determining an anomaly are based on comparisons with predetermined thresholds and normal ranges. This allows users to immediately recognize abnormal situations and take necessary countermeasures.

[0441] 3. Notification and Repair Arrangement

[0442] If an abnormality is detected, the system automatically accesses a database of repair providers and proposes the most suitable repair date. Users can review and adjust this proposal from their device, ensuring a smooth repair process.

[0443] 4. Providing alternative solutions

[0444] In the event of a malfunction, the server will coordinate with other home devices to provide alternative solutions. For example, if the air conditioner breaks down, a smart fan can be automatically activated to maintain a comfortable indoor environment.

[0445] Specific example

[0446] When the refrigerator's temperature sensor detects a temperature higher than normal, the server recognizes it as an anomaly and immediately sends a notification to the user's device. This notification includes details of the anomaly, recommended actions, and information on repair companies. The server also automatically suggests a repair schedule and can adjust the usage plans for other household appliances as an emergency measure.

[0447] Examples of prompts for generative AI models

[0448] "Please describe in detail the anomaly detection system for household electrical appliances and explain specifically how it actually works."

[0449] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0450] Step 1:

[0451] The sensor collects operational information from electrical appliances. Specifically, the sensor measures information such as temperature and power consumption at regular intervals and prepares the obtained data for transmission using radio waves. The input is the operating status of the electrical appliance, and the output is a dataset containing that operational information.

[0452] Step 2:

[0453] The sensor collects motion information and sends it to the server. The server receives the data and stores it in storage in an encrypted state. The input is the encrypted motion information sent from the sensor, and the output is the motion information stored on the server. During this process, the TLS protocol ensures the security of the data communication.

[0454] Step 3:

[0455] The server analyzes the received operational information. The server evaluates the data using a Python-based analysis algorithm and detects anomalies by comparing it to normal values. The input is operational information stored on the server, and the output is the determination of whether it is normal or abnormal. In this process, for example, an abnormal temperature value is calculated using the Z-score method, and if it exceeds a threshold, it is determined to be abnormal.

[0456] Step 4:

[0457] If the server detects an anomaly, it sends a notification to the terminal. The server creates a message detailing the problem and recommended actions, and sends a push notification to the user's mobile device. The input is the result of the anomaly detection, and the output is the notification message sent to the user. The notification includes details of how the anomaly occurred and initial countermeasures.

[0458] Step 5:

[0459] The server proposes repair dates based on repair provider information. The server searches an existing provider database, connects to a suitable provider, and matches dates. Inputs are the user's schedule and the repair provider's availability, and output is the optimal repair date notified to the user. This facilitates quick and effective repair responses.

[0460] Step 6:

[0461] The server controls other household devices to optimize the home environment in the event of a malfunction. Specifically, it issues instructions to temporarily substitute the function of a faulty device with another device. The inputs are the malfunction detection status and the current operating status of household devices, and the output is the control instruction. This control allows, for example, if the air conditioner malfunctions, to automatically turn on a smart fan to maintain the room temperature.

[0462] (Application Example 1)

[0463] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0464] In modern cities, there is a need to efficiently manage the energy consumption of household appliances and public facilities. However, detecting abnormalities in individual devices and facilities in real time and responding quickly is not easy, and systems that provide optimal solutions when abnormalities occur are limited. In this situation, an integrated management system is needed that can quickly detect abnormalities and take appropriate action.

[0465] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0466] In this invention, the server includes means for acquiring operational information of household appliances in real time, means for identifying abnormalities based on the acquired information, and means for notifying the user when an abnormality is detected. This enables rapid detection of abnormalities in household and public facility equipment and the provision of appropriate notifications and countermeasures.

[0467] "Operational information" refers to data on the operating status and energy consumption of various devices in households and public facilities.

[0468] "An anomaly" refers to a state of equipment that deviates from its normal operating pattern, indicating a potential malfunction or failure of home appliances or public facilities.

[0469] "Means of acquisition" refers to methods and devices that use sensors or other equipment to collect motion information in real time.

[0470] "Means of identification" refer to algorithms and processes used to analyze collected operational information and identify anomalies.

[0471] "Means of notification" refer to communication methods or software used to quickly convey information to users and administrators when an anomaly is detected.

[0472] "Public facilities" refer to buildings and equipment within a city that are used for public purposes and are subject to energy management.

[0473] An "administrator" is someone responsible for the operation and maintenance of public facilities or household equipment, or a user of the system.

[0474] "Energy consumption data" refers to information about the amount of energy consumed and usage patterns of equipment and facilities.

[0475] "Means of proposing alternative solutions" refer to system functions or methods that provide other methods or devices that can be used when an anomaly occurs.

[0476] In implementing this invention, a system for managing the energy consumption of household appliances and public facilities is configured. First, a server collects operational information in real time from IoT sensors deployed in homes and public facilities. This includes energy consumption data and operating status of various devices. Examples of sensors used include Bosch environmental sensors.

[0477] Next, the server uses the collected data to apply an anomaly detection algorithm using TensorFlow to identify anomalies. This anomaly detection quickly identifies deviations from normal consumption patterns. When an anomaly is detected, the server immediately sends a notification to the administrator's terminal to prompt appropriate action.

[0478] Furthermore, the server utilizes AI to propose countermeasures for anomalies. For example, if energy consumption exceeds normal levels, it can suggest alternative equipment or solutions. Specifically, if the power consumption of a public swimming pool heater becomes abnormally high, the server might resolve the problem by switching the energy supply to solar panels as an alternative.

[0479] An example of a prompt for a generated AI model is: "Write an AI algorithm that monitors real-time data from sensors in public facilities within a city and detects anomalies in energy consumption." This prompt is used to generate an AI model that enables proper anomaly detection.

[0480] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0481] Step 1:

[0482] The server acquires operational information in real time from IoT sensors installed in each household appliance and public facility. This data shows the operating status and energy consumption of the devices and is transmitted from the sensors to a cloud-based database. The input is raw data from the sensors, and the output is organized operational information.

[0483] Step 2:

[0484] The server uses TensorFlow to execute an anomaly detection algorithm to detect abnormalities based on the collected operational information. The data processing performed here involves comparing and analyzing the input information with existing normal pattern data. The output of this process is an indicator of whether or not an anomaly exists. Specifically, it determines whether energy consumption exceeds a certain threshold.

[0485] Step 3:

[0486] If an anomaly is detected, the server sends a push notification to the administrator's terminal. The notification includes details of the anomaly and recommended countermeasures. The input is the data resulting from the anomaly detection, and a notification message is output. This allows the administrator to quickly identify the problem and consider appropriate actions.

[0487] Step 4:

[0488] The server uses AI to propose specific alternative measures for abnormal situations. The input is the type of abnormality and the current equipment status, and the output is the proposed alternative. For example, if an abnormally high temperature is detected, it might instruct the use of a backup energy source.

[0489] Step 5:

[0490] The administrator sends feedback to the server via a terminal and decides whether to implement the proposed countermeasures. The input is the operational instructions based on the administrator's decision, and the output is the result of implementing the countermeasures. In this way, the entire system is integrated and managed smoothly.

[0491] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0492] This invention aims to improve the user experience by incorporating an emotion engine into a home appliance management system. The system is based on a process that monitors operational data acquired from each home appliance in real time and detects abnormalities. Furthermore, the server has an emotion engine on the user's terminal and analyzes the user's emotional state using emotion recognition technology.

[0493] The server adjusts the content and tone of anomaly notifications based on feedback from the emotion engine. When a user is stressed, the system sends notifications in a gentle tone; when the user is relaxed, it provides more detailed information to help resolve the problem. Additionally, when a user's emotions are negative, it offers the option to quickly contact the support center.

[0494] For example, if a refrigerator malfunction is detected, the server uses the user's recent behavioral data to allow the emotion engine to analyze the user's emotional state. If the user is busy, the system will provide a short, concise notification and offer follow-up options such as, "Do you want to address this in your current state?" On the other hand, if the user is relaxed, the system will provide a detailed explanation of the malfunction and a recommended action plan.

[0495] The emotional engine also influences the selection of repair technicians. By considering the user's emotional state, it can prioritize arranging for technicians who can respond more quickly. In this way, the present invention makes home automation more humane and enables responses that are sensitive to the user's emotions.

[0496] The following describes the processing flow.

[0497] Step 1:

[0498] The server receives real-time operational data from each household appliance via sensors. This data includes temperature, power consumption, vibration information, and other information, and is stored in a database built on the server.

[0499] Step 2:

[0500] The server uses the received operational data to execute an anomaly detection algorithm and identify anomalies. In this process, the algorithm compares the data with past data and detects data that falls outside the normal range.

[0501] Step 3:

[0502] If an anomaly is detected, the server sends a request to the emotion engine to analyze the user's current emotional state. The emotion engine operates on the user's device and performs the analysis using accumulated interaction data and sensor data.

[0503] Step 4:

[0504] The device receives the analysis results from the emotion engine and sends them to the server. Based on this emotion information, the server adjusts the content and tone of notifications sent to the user. For example, if the user is feeling stressed, it will send a notification in a gentle tone with concise content.

[0505] Step 5:

[0506] Users check notifications through their devices. They can determine if the information provided is relevant to them and choose the necessary action. For example, they can decide whether to proceed with arranging for a repair service or opt for a temporary solution.

[0507] Step 6:

[0508] If repairs are needed, the server searches a database of repair companies based on information obtained from the emotion engine. It then suggests the most suitable company for the user's emotional state (such as one that can respond quickly) and initiates the booking process.

[0509] Step 7:

[0510] If necessary, the server refers to community-based data and provides users with advice on similar problems and solutions. It can also flexibly customize options and advice based on past experiences and the user's emotional state.

[0511] (Example 2)

[0512] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0513] While modern household appliances have become more diverse and convenient, dealing with malfunctions and problems remains inconvenient. Furthermore, responses that consider the user's emotional state are often insufficient. This leads to challenges such as excessive stress when appliances malfunction and inadequate repair services.

[0514] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0515] In this invention, the server includes means for collecting operational information from home appliances, means for analyzing the received operational information to detect abnormalities, and means for estimating the user's emotional state. This enables flexible and appropriate responses in response to the user's emotions when an abnormality occurs in a home appliance.

[0516] "Home appliances" refer to all electrical devices used in the home that operate automatically or semi-automatically.

[0517] "Operation information" refers to data indicating whether a home appliance is functioning correctly, and includes various measurement values ​​collected by sensors.

[0518] "Anomaly detection" refers to the process of analyzing collected operational information to identify conditions that deviate from normal operational patterns.

[0519] "User emotional state" refers to the psychological situation a user is experiencing and is analyzed using emotion recognition technology.

[0520] "Adjusting the content and tone of notifications" refers to appropriately changing the way information is conveyed and the wording based on the user's emotional state.

[0521] A "repair company" refers to a service provider that specializes in the repair and maintenance of home appliances.

[0522] "Controlling equipment" refers to adjusting or directing the operation of equipment to achieve a desired behavior under specific circumstances.

[0523] This invention is a system for more effectively managing household appliances. Its embodiments are described in detail below.

[0524] The server collects operational information in real time from sensors installed in each household appliance. This includes various measurement data such as temperature, power consumption, and vibration. This information is transmitted to the server via Wi-Fi modules built into the appliances. The server uses Python programs and machine learning libraries such as TensorFlow to process the collected data and perform calculations to detect anomalies.

[0525] Next, the server uses image and audio data collected from the user's device to estimate the user's emotional state. Emotion recognition technology is used in this process. Specific software options include general cloud services that provide facial recognition APIs and audio analysis APIs.

[0526] If an anomaly is detected, the device adjusts the notification content based on the user's emotional state. For example, if the user is busy, it generates a concise message such as "An anomaly has been detected in your home appliance. Do you want to address it now?", while if the user is relaxed, it generates a more detailed message such as "We will provide you with detailed information and solutions regarding the anomaly in your home appliance."

[0527] The system further adjusts the tone of notification messages using a generative AI model. An example of a prompt message in this case would be, "What tone should be used to send a refrigerator malfunction notification when the user is feeling stressed?"

[0528] This invention improves the operational efficiency of household appliances and enables flexible responses that take into account the user's emotional state.

[0529] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0530] Step 1:

[0531] The server collects operational data from each home appliance. Sensors installed in the appliances measure data such as temperature, power consumption, and vibration, and transmit it to the server via Wi-Fi. The input is raw data obtained from the sensors, and the output is time-series data that has been formatted from this data.

[0532] Step 2:

[0533] The server uses collected operational data to detect anomalies. Using Python and TensorFlow, it analyzes the data with a model trained on normal and abnormal values. In this process, time-series data is used as input, and alerts indicating the presence or absence of anomalies are generated as output.

[0534] Step 3:

[0535] The device captures image and audio data collected by the camera and microphone to analyze the user's emotional state. It uses facial recognition APIs and voice analysis APIs to estimate emotions. The input is image and audio data, and the output is numerical data representing the user's emotions.

[0536] Step 4:

[0537] The server combines the results of anomaly detection with the user's emotional state to generate a notification message. A generative AI model uses natural language processing with prompts to create a message with appropriate tone and content. A possible prompt might be, "What information should be included when the user is relaxed?" The input is anomaly alerts and emotional data, and the output is a tailored notification message.

[0538] Step 5:

[0539] The server sends the generated notification message to the user's device. Notifications are prioritized according to the user's situation, enabling immediate action. This results in a system where the input is a notification message, and the output is the user's alert.

[0540] (Application Example 2)

[0541] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0542] In modern homes, numerous home appliances are installed, and the notification and troubleshooting for each appliance can become a burden. However, conventional systems provide uniform notifications without considering the user's emotional state, resulting in a poor user experience. Furthermore, while a quick and appropriate response is required when an appliance malfunctions, providing personalized services to address this is difficult.

[0543] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0544] In this invention, the server includes means for collecting information on the operating status of home appliances in real time, means for identifying abnormalities based on the collected information, and means for analyzing the user's emotional state and adjusting the content and tone of abnormality notifications. This enables abnormality responses that are sensitive to the user's emotions, making it possible to achieve quick and stress-free home appliance management.

[0545] "Operating status of home appliances" refers to the current functional conditions and behavior of home appliances while they are running or in standby mode.

[0546] "Means of real-time data collection" refers to a system that collects and stores data immediately without time delay via information processing devices and communication networks.

[0547] "Means for identifying anomalies" refers to the process of detecting events that deviate from normal operating patterns and identifying those events as problems.

[0548] "User's emotional state" refers to the user's psychological state at a given time, and is a mental state inferred using various emotion recognition technologies.

[0549] "Means for adjusting the content and tone of abnormality notifications" refers to technologies that optimize the expression and delivery method of abnormality-related messages according to the user's emotions.

[0550] The system of this invention monitors the operating status of home appliances in real time and, when an abnormality is detected, provides appropriate notification considering the user's emotional state. Operating data from each home appliance is collected on a server via sensors and a network. The server analyzes this data, and if an abnormality occurs in an appliance, it uses an emotion engine to analyze the user's emotional state. Depending on the user's emotional state, the server adjusts the content and tone of the notification to convey the most appropriate abnormality information to the user.

[0551] This system requires multiple hardware and software components. Specifically, it needs sensors to monitor the operation of home appliances in real time and a network module for data communication. When analyzing emotional states, it uses cameras and microphones to acquire user voice and facial expression data, and employs software with emotion recognition technology (e.g., OpenAI GPT-3 or Microsoft Azure Cognitive Services).

[0552] One concrete example of how this system could be used is a household robot detecting abnormalities in home appliances and notifying the user. For instance, if the robot detects an abnormal temperature in a refrigerator and determines from the user's facial expression and voice that they are relaxed, it can provide detailed information and guide them on how to address the issue.

[0553] Example prompt: "How should you notify the user if a refrigerator malfunction occurs while they are in a relaxed state?"

[0554] In this way, by coordinating the server, terminal, and user elements, a user-friendly home appliance management system can be realized.

[0555] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0556] Step 1:

[0557] The server collects operating status data transmitted from home appliances in real time via sensors. This data indicates the operating patterns and current status of the appliances, and the server performs preprocessing based on this data to evaluate the normality of the operation. Specifically, the data is cleaned and normalized to prepare it for anomaly detection.

[0558] Step 2:

[0559] The server executes an anomaly detection algorithm based on the collected data. The anomaly detection algorithm compares predefined normal patterns with real-time data to detect behavior that is considered abnormal. It uses normal patterns and real-time behavior data as input and generates output indicating whether an anomaly occurred and its details.

[0560] Step 3:

[0561] If an anomaly is detected, the server analyzes the user's emotional state using the user's voice and image data acquired from the camera and microphone installed on the terminal. The emotion recognition model used here (including generative AI models) takes voice tone and facial features as input and provides an estimated emotion as output.

[0562] Step 4:

[0563] The server adjusts the content and tone of anomaly notifications based on the emotion estimation results. Specifically, it provides detailed and gentle explanations to relaxed users and sends simple and gentle notifications to stressed users. This process utilizes a generative AI model to generate prompt sentences and adjusts the output accordingly.

[0564] Step 5:

[0565] The server sends a coordinated notification to the user's device. The user receives the notification and can either follow the instructions to address the anomaly or contact support. The input here is the coordinated notification content, and the output is the information provided to the user and their response options.

[0566] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0567] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0568] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and specific processing may also be performed by the headset terminal 314.

[0569] [Fourth Embodiment]

[0570] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0571] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0572] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0573] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0574] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0575] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0576] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0577] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. Furthermore, the robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0578] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0579] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0580] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0581] In robot 414, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0582] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0583] This invention relates to a system for efficiently managing home appliances and quickly detecting abnormalities. The system acquires operational data from sensors attached to various home appliances and transmits it to a server via a network. The server analyzes this data in real time and monitors for abnormalities in the appliances using an anomaly detection algorithm.

[0584] If the server detects an anomaly, it immediately sends a notification to the user's device. This allows the user to check the nature of the anomaly and take prompt action. The server also refers to a database of trusted repair companies, suggests the best repair date to fit the user's schedule, and automates the booking process. The user can review this suggestion on their device and modify it as needed.

[0585] Furthermore, the server coordinates with other devices in the home and provides alternative solutions in case of malfunctions. For example, if the air conditioner malfunctions, it can activate a smart fan to maintain room temperature control. In this way, the system integrates the entire home ecosystem, managing appliances efficiently and safely.

[0586] For example, if the refrigerator's temperature sensor detects a temperature higher than normal, the server will recognize the anomaly and immediately notify the user. At the same time, the server will determine whether the refrigerator needs repair and, if necessary, arrange for a repair service. It can also revise the usage schedule of other household appliances to temporarily replace the refrigerator's operation.

[0587] This invention aims to improve the quality of life for users by making the monitoring of household appliances more efficient.

[0588] The following describes the processing flow.

[0589] Step 1:

[0590] The server receives operational data from each home appliance via sensors. This data includes parameters such as temperature, power consumption, and vibration. The received data is stored in a database and forms the basis for subsequent processing.

[0591] Step 2:

[0592] The server preprocesses the received operational data. Specifically, it imputes missing data values ​​and filters out abnormal values, adjusting them to normal values. This process improves data consistency and reliability.

[0593] Step 3:

[0594] The server executes an anomaly detection algorithm based on the pre-processed data. Using a machine learning model, it compares the data to normal operating patterns and identifies any deviations as anomalies. This result is then passed on to the next step.

[0595] Step 4:

[0596] If an anomaly is detected, the server will immediately send a notification to the user's device. The notification will include the nature of the detected anomaly, the parts that may be affected, and recommended countermeasures.

[0597] Step 5:

[0598] Users can view detailed information about anomalies detected through their device. Based on this information, they can take necessary actions. They can also select repair service appointment options provided on the device.

[0599] Step 6:

[0600] The server accesses a database of repair providers and automatically generates the optimal repair appointment, taking into account the user's schedule and the provider's availability. Once the user reviews and approves the information, the appointment is confirmed.

[0601] Step 7:

[0602] If an anomaly could affect other devices, the server will control smart devices in the home and take alternative measures. For example, if a problem occurs with the heating system, the server will activate other auxiliary heating devices.

[0603] Step 8:

[0604] The server analyzes all data collected within the home over a long period, accumulating community-based knowledge to suggest future preventative measures. This makes it possible to provide helpful advice to users.

[0605] (Example 1)

[0606] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0607] Modern homes utilize numerous electrical appliances, making efficient management and rapid anomaly detection challenging. Conventional systems struggle to respond immediately to malfunctions, and the effort required to find a suitable repair service and schedule repairs is burdensome for users. Furthermore, adequate measures are not in place to optimize the home environment in the event of a malfunction. Solving these challenges is essential.

[0608] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0609] In this invention, the server includes means for collecting operational information in real time, means for detecting anomalies based on the collected information, and means for searching for and booking a reliable repair service provider. This enables efficient monitoring of household electrical appliances and rapid response to any anomalies that occur. Furthermore, by automatically suggesting the optimal repair schedule in the event of an anomaly and controlling other household devices, the burden on the user can be reduced, and a safe and comfortable living environment can be maintained.

[0610] "Operational information" refers to data about the functions and status of electrical appliances on a daily basis.

[0611] "Real-time" is a term that refers to a process that is performed instantly without delay.

[0612] "An anomaly" refers to an event or state that differs from normal operation or condition, and signifies a potentially problematic situation.

[0613] "Users" refers to individuals or households that use this system.

[0614] "Notification" refers to a means of communicating information to inform users that there is an abnormality.

[0615] A "reliable repair shop" refers to a repair service that provides high-quality service and has established trust with its customers.

[0616] "Household appliances" is a general term for various electrical appliances and equipment used within a home.

[0617] "Analysis" is the process of analyzing data in detail and obtaining meaningful information from it.

[0618] "Proposal" refers to the act of showing users the optimal solution or action.

[0619] This invention is a system for efficiently monitoring household electrical appliances and responding quickly to any abnormalities. This system has the function of acquiring operational information in real time and processing and analyzing that data. Details are described below.

[0620] 1. Data Collection and Communication

[0621] Sensors are attached to each household appliance, which periodically collect operational information about the product. This information is then transmitted to a central server via a wireless network. TLS (Transport Layer Security) is used as the data communication protocol to ensure data security.

[0622] 2. Server analysis function

[0623] The server processes the received operational information using Python-based data analysis libraries (e.g., Pandas or NumPy). The server analyzes the data using an anomaly detection algorithm and immediately notifies the terminal if an anomaly is detected. The criteria for determining an anomaly are based on comparisons with predetermined thresholds and normal ranges. This allows users to immediately recognize abnormal situations and take necessary countermeasures.

[0624] 3. Notification and Repair Arrangement

[0625] If an abnormality is detected, the system automatically accesses a database of repair providers and proposes the most suitable repair date. Users can review and adjust this proposal from their device, ensuring a smooth repair process.

[0626] 4. Providing alternative solutions

[0627] In the event of a malfunction, the server will coordinate with other home devices to provide alternative solutions. For example, if the air conditioner breaks down, a smart fan can be automatically activated to maintain a comfortable indoor environment.

[0628] Specific example

[0629] When the refrigerator's temperature sensor detects a temperature higher than normal, the server recognizes it as an anomaly and immediately sends a notification to the user's device. This notification includes details of the anomaly, recommended actions, and information on repair companies. The server also automatically suggests a repair schedule and can adjust the usage plans for other household appliances as an emergency measure.

[0630] Examples of prompts for generative AI models

[0631] "Please describe in detail the anomaly detection system for household electrical appliances and explain specifically how it actually works."

[0632] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0633] Step 1:

[0634] The sensor collects operational information from electrical appliances. Specifically, the sensor measures information such as temperature and power consumption at regular intervals and prepares the obtained data for transmission using radio waves. The input is the operating status of the electrical appliance, and the output is a dataset containing that operational information.

[0635] Step 2:

[0636] The sensor collects motion information and sends it to the server. The server receives the data and stores it in storage in an encrypted state. The input is the encrypted motion information sent from the sensor, and the output is the motion information stored on the server. During this process, the TLS protocol ensures the security of the data communication.

[0637] Step 3:

[0638] The server analyzes the received operational information. The server evaluates the data using a Python-based analysis algorithm and detects anomalies by comparing it to normal values. The input is operational information stored on the server, and the output is the determination of whether it is normal or abnormal. In this process, for example, an abnormal temperature value is calculated using the Z-score method, and if it exceeds a threshold, it is determined to be abnormal.

[0639] Step 4:

[0640] If the server detects an anomaly, it sends a notification to the terminal. The server creates a message detailing the problem and recommended actions, and sends a push notification to the user's mobile device. The input is the result of the anomaly detection, and the output is the notification message sent to the user. The notification includes details of how the anomaly occurred and initial countermeasures.

[0641] Step 5:

[0642] The server proposes repair dates based on repair provider information. The server searches an existing provider database, connects to a suitable provider, and matches dates. Inputs are the user's schedule and the repair provider's availability, and output is the optimal repair date notified to the user. This facilitates quick and effective repair responses.

[0643] Step 6:

[0644] The server controls other household devices to optimize the home environment in the event of a malfunction. Specifically, it issues instructions to temporarily substitute the function of a faulty device with another device. The inputs are the malfunction detection status and the current operating status of household devices, and the output is the control instruction. This control allows, for example, if the air conditioner malfunctions, to automatically turn on a smart fan to maintain the room temperature.

[0645] (Application Example 1)

[0646] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0647] In modern cities, there is a need to efficiently manage the energy consumption of household appliances and public facilities. However, detecting abnormalities in individual devices and facilities in real time and responding quickly is not easy, and systems that provide optimal solutions when abnormalities occur are limited. In this situation, an integrated management system is needed that can quickly detect abnormalities and take appropriate action.

[0648] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0649] In this invention, the server includes means for acquiring operational information of household appliances in real time, means for identifying abnormalities based on the acquired information, and means for notifying the user when an abnormality is detected. This enables rapid detection of abnormalities in household and public facility equipment and the provision of appropriate notifications and countermeasures.

[0650] "Operational information" refers to data on the operating status and energy consumption of various devices in households and public facilities.

[0651] "An anomaly" refers to a state of equipment that deviates from its normal operating pattern, indicating a potential malfunction or failure of home appliances or public facilities.

[0652] "Means of acquisition" refers to methods and devices that use sensors or other equipment to collect motion information in real time.

[0653] "Means of identification" refer to algorithms and processes used to analyze collected operational information and identify anomalies.

[0654] "Means of notification" refer to communication methods or software used to quickly convey information to users and administrators when an anomaly is detected.

[0655] "Public facilities" refer to buildings and equipment within a city that are used for public purposes and are subject to energy management.

[0656] An "administrator" is someone responsible for the operation and maintenance of public facilities or household equipment, or a user of the system.

[0657] "Energy consumption data" refers to information about the amount of energy consumed and usage patterns of equipment and facilities.

[0658] "Means of proposing alternative solutions" refer to system functions or methods that provide other methods or devices that can be used when an anomaly occurs.

[0659] In implementing this invention, a system for managing the energy consumption of household appliances and public facilities is configured. First, a server collects operational information in real time from IoT sensors deployed in homes and public facilities. This includes energy consumption data and operating status of various devices. Examples of sensors used include Bosch environmental sensors.

[0660] Next, the server uses the collected data to apply an anomaly detection algorithm using TensorFlow to identify anomalies. This anomaly detection quickly identifies deviations from normal consumption patterns. When an anomaly is detected, the server immediately sends a notification to the administrator's terminal to prompt appropriate action.

[0661] Furthermore, the server utilizes AI to propose countermeasures for anomalies. For example, if energy consumption exceeds normal levels, it can suggest alternative equipment or solutions. Specifically, if the power consumption of a public swimming pool heater becomes abnormally high, the server might resolve the problem by switching the energy supply to solar panels as an alternative.

[0662] An example of a prompt for a generated AI model is: "Write an AI algorithm that monitors real-time data from sensors in public facilities within a city and detects anomalies in energy consumption." This prompt is used to generate an AI model that enables proper anomaly detection.

[0663] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0664] Step 1:

[0665] The server acquires operational information in real time from IoT sensors installed in each household appliance and public facility. This data shows the operating status and energy consumption of the devices and is transmitted from the sensors to a cloud-based database. The input is raw data from the sensors, and the output is organized operational information.

[0666] Step 2:

[0667] The server uses TensorFlow to execute an anomaly detection algorithm to detect abnormalities based on the collected operational information. The data processing performed here involves comparing and analyzing the input information with existing normal pattern data. The output of this process is an indicator of whether or not an anomaly exists. Specifically, it determines whether energy consumption exceeds a certain threshold.

[0668] Step 3:

[0669] If an anomaly is detected, the server sends a push notification to the administrator's terminal. The notification includes details of the anomaly and recommended countermeasures. The input is the data resulting from the anomaly detection, and a notification message is output. This allows the administrator to quickly identify the problem and consider appropriate actions.

[0670] Step 4:

[0671] The server uses AI to propose specific alternative measures for abnormal situations. The input is the type of abnormality and the current equipment status, and the output is the proposed alternative. For example, if an abnormally high temperature is detected, it might instruct the use of a backup energy source.

[0672] Step 5:

[0673] The administrator sends feedback to the server via a terminal and decides whether to implement the proposed countermeasures. The input is the operational instructions based on the administrator's decision, and the output is the result of implementing the countermeasures. In this way, the entire system is integrated and managed smoothly.

[0674] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0675] This invention aims to improve the user experience by incorporating an emotion engine into a home appliance management system. The system is based on a process that monitors operational data acquired from each home appliance in real time and detects abnormalities. Furthermore, the server has an emotion engine on the user's terminal and analyzes the user's emotional state using emotion recognition technology.

[0676] The server adjusts the content and tone of anomaly notifications based on feedback from the emotion engine. When a user is stressed, the system sends notifications in a gentle tone; when the user is relaxed, it provides more detailed information to help resolve the problem. Additionally, when a user's emotions are negative, it offers the option to quickly contact the support center.

[0677] For example, if a refrigerator malfunction is detected, the server uses the user's recent behavioral data to allow the emotion engine to analyze the user's emotional state. If the user is busy, the system will provide a short, concise notification and offer follow-up options such as, "Do you want to address this in your current state?" On the other hand, if the user is relaxed, the system will provide a detailed explanation of the malfunction and a recommended action plan.

[0678] The emotional engine also influences the selection of repair technicians. By considering the user's emotional state, it can prioritize arranging for technicians who can respond more quickly. In this way, the present invention makes home automation more humane and enables responses that are sensitive to the user's emotions.

[0679] The following describes the processing flow.

[0680] Step 1:

[0681] The server receives real-time operational data from each household appliance via sensors. This data includes temperature, power consumption, vibration information, and other information, and is stored in a database built on the server.

[0682] Step 2:

[0683] The server uses the received operational data to execute an anomaly detection algorithm and identify anomalies. In this process, the algorithm compares the data with past data and detects data that falls outside the normal range.

[0684] Step 3:

[0685] If an anomaly is detected, the server sends a request to the emotion engine to analyze the user's current emotional state. The emotion engine operates on the user's device and performs the analysis using accumulated interaction data and sensor data.

[0686] Step 4:

[0687] The device receives the analysis results from the emotion engine and sends them to the server. Based on this emotion information, the server adjusts the content and tone of notifications sent to the user. For example, if the user is feeling stressed, it will send a notification in a gentle tone with concise content.

[0688] Step 5:

[0689] Users check notifications through their devices. They can determine if the information provided is relevant to them and choose the necessary action. For example, they can decide whether to proceed with arranging for a repair service or opt for a temporary solution.

[0690] Step 6:

[0691] If repairs are needed, the server searches a database of repair companies based on information obtained from the emotion engine. It then suggests the most suitable company for the user's emotional state (such as one that can respond quickly) and initiates the booking process.

[0692] Step 7:

[0693] If necessary, the server refers to community-based data and provides users with advice on similar problems and solutions. It can also flexibly customize options and advice based on past experiences and the user's emotional state.

[0694] (Example 2)

[0695] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0696] While modern household appliances have become more diverse and convenient, dealing with malfunctions and problems remains inconvenient. Furthermore, responses that consider the user's emotional state are often insufficient. This leads to challenges such as excessive stress when appliances malfunction and inadequate repair services.

[0697] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0698] In this invention, the server includes means for collecting operational information from home appliances, means for analyzing the received operational information to detect abnormalities, and means for estimating the user's emotional state. This enables flexible and appropriate responses in response to the user's emotions when an abnormality occurs in a home appliance.

[0699] "Home appliances" refer to all electrical devices used in the home that operate automatically or semi-automatically.

[0700] "Operation information" refers to data indicating whether a home appliance is functioning correctly, and includes various measurement values ​​collected by sensors.

[0701] "Anomaly detection" refers to the process of analyzing collected operational information to identify conditions that deviate from normal operational patterns.

[0702] "User emotional state" refers to the psychological situation a user is experiencing and is analyzed using emotion recognition technology.

[0703] "Adjusting the content and tone of notifications" refers to appropriately changing the way information is conveyed and the wording based on the user's emotional state.

[0704] A "repair company" refers to a service provider that specializes in the repair and maintenance of home appliances.

[0705] "Controlling equipment" refers to adjusting or directing the operation of equipment to achieve a desired behavior under specific circumstances.

[0706] This invention is a system for more effectively managing household appliances. Its embodiments are described in detail below.

[0707] The server collects operational information in real time from sensors installed in each household appliance. This includes various measurement data such as temperature, power consumption, and vibration. This information is transmitted to the server via Wi-Fi modules built into the appliances. The server uses Python programs and machine learning libraries such as TensorFlow to process the collected data and perform calculations to detect anomalies.

[0708] Next, the server uses image and audio data collected from the user's device to estimate the user's emotional state. Emotion recognition technology is used in this process. Specific software options include general cloud services that provide facial recognition APIs and audio analysis APIs.

[0709] If an anomaly is detected, the device adjusts the notification content based on the user's emotional state. For example, if the user is busy, it generates a concise message such as "An anomaly has been detected in your home appliance. Do you want to address it now?", while if the user is relaxed, it generates a more detailed message such as "We will provide you with detailed information and solutions regarding the anomaly in your home appliance."

[0710] The system further adjusts the tone of notification messages using a generative AI model. An example of a prompt message in this case would be, "What tone should be used to send a refrigerator malfunction notification when the user is feeling stressed?"

[0711] This invention improves the operational efficiency of household appliances and enables flexible responses that take into account the user's emotional state.

[0712] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0713] Step 1:

[0714] The server collects operational data from each home appliance. Sensors installed in the appliances measure data such as temperature, power consumption, and vibration, and transmit it to the server via Wi-Fi. The input is raw data obtained from the sensors, and the output is time-series data that has been formatted from this data.

[0715] Step 2:

[0716] The server uses collected operational data to detect anomalies. Using Python and TensorFlow, it analyzes the data with a model trained on normal and abnormal values. In this process, time-series data is used as input, and alerts indicating the presence or absence of anomalies are generated as output.

[0717] Step 3:

[0718] The device captures image and audio data collected by the camera and microphone to analyze the user's emotional state. It uses facial recognition APIs and voice analysis APIs to estimate emotions. The input is image and audio data, and the output is numerical data representing the user's emotions.

[0719] Step 4:

[0720] The server combines the results of anomaly detection with the user's emotional state to generate a notification message. A generative AI model uses natural language processing with prompts to create a message with appropriate tone and content. A possible prompt might be, "What information should be included when the user is relaxed?" The input is anomaly alerts and emotional data, and the output is a tailored notification message.

[0721] Step 5:

[0722] The server sends the generated notification message to the user's device. Notifications are prioritized according to the user's situation, enabling immediate action. This results in a system where the input is a notification message, and the output is the user's alert.

[0723] (Application Example 2)

[0724] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0725] In modern homes, numerous home appliances are installed, and the notification and troubleshooting for each appliance can become a burden. However, conventional systems provide uniform notifications without considering the user's emotional state, resulting in a poor user experience. Furthermore, while a quick and appropriate response is required when an appliance malfunctions, providing personalized services to address this is difficult.

[0726] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0727] In this invention, the server includes means for collecting information on the operating status of home appliances in real time, means for identifying abnormalities based on the collected information, and means for analyzing the user's emotional state and adjusting the content and tone of abnormality notifications. This enables abnormality responses that are sensitive to the user's emotions, making it possible to achieve quick and stress-free home appliance management.

[0728] "Operating status of home appliances" refers to the current functional conditions and behavior of home appliances while they are running or in standby mode.

[0729] "Means of real-time data collection" refers to a system that collects and stores data immediately without time delay via information processing devices and communication networks.

[0730] "Means for identifying anomalies" refers to the process of detecting events that deviate from normal operating patterns and identifying those events as problems.

[0731] "User's emotional state" refers to the user's psychological state at a given time, and is a mental state inferred using various emotion recognition technologies.

[0732] "Means for adjusting the content and tone of abnormality notifications" refers to technologies that optimize the expression and delivery method of abnormality-related messages according to the user's emotions.

[0733] The system of this invention monitors the operating status of home appliances in real time and, when an abnormality is detected, provides appropriate notification considering the user's emotional state. Operating data from each home appliance is collected on a server via sensors and a network. The server analyzes this data, and if an abnormality occurs in an appliance, it uses an emotion engine to analyze the user's emotional state. Depending on the user's emotional state, the server adjusts the content and tone of the notification to convey the most appropriate abnormality information to the user.

[0734] This system requires multiple hardware and software components. Specifically, it needs sensors to monitor the operation of home appliances in real time and a network module for data communication. When analyzing emotional states, it uses cameras and microphones to acquire user voice and facial expression data, and employs software with emotion recognition technology (e.g., OpenAI GPT-3 or Microsoft Azure Cognitive Services).

[0735] One concrete example of how this system could be used is a household robot detecting abnormalities in home appliances and notifying the user. For instance, if the robot detects an abnormal temperature in a refrigerator and determines from the user's facial expression and voice that they are relaxed, it can provide detailed information and guide them on how to address the issue.

[0736] Example prompt: "How should you notify the user if a refrigerator malfunction occurs while they are in a relaxed state?"

[0737] In this way, by coordinating the server, terminal, and user elements, a user-friendly home appliance management system can be realized.

[0738] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0739] Step 1:

[0740] The server collects operating status data transmitted from home appliances in real time via sensors. This data indicates the operating patterns and current status of the appliances, and the server performs preprocessing based on this data to evaluate the normality of the operation. Specifically, the data is cleaned and normalized to prepare it for anomaly detection.

[0741] Step 2:

[0742] The server executes an anomaly detection algorithm based on the collected data. The anomaly detection algorithm compares predefined normal patterns with real-time data to detect behavior that is considered abnormal. It uses normal patterns and real-time behavior data as input and generates output indicating whether an anomaly occurred and its details.

[0743] Step 3:

[0744] If an anomaly is detected, the server analyzes the user's emotional state using the user's voice and image data acquired from the camera and microphone installed on the terminal. The emotion recognition model used here (including generative AI models) takes voice tone and facial features as input and provides an estimated emotion as output.

[0745] Step 4:

[0746] The server adjusts the content and tone of anomaly notifications based on the emotion estimation results. Specifically, it provides detailed and gentle explanations to relaxed users and sends simple and gentle notifications to stressed users. This process utilizes a generative AI model to generate prompt sentences and adjusts the output accordingly.

[0747] Step 5:

[0748] The server sends a coordinated notification to the user's device. The user receives the notification and can either follow the instructions to address the anomaly or contact support. The input here is the coordinated notification content, and the output is the information provided to the user and their response options.

[0749] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0750] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0751] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the robot 414.

[0752] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0753] Figure 9 shows an emotion map 400 in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0754] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0755] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0756] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, motorcycles, etc., emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0757] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0758] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0759] The above description primarily focuses on the functions of the data processing device 12 in relation to this disclosure. However, the system related to this disclosure is not necessarily implemented on a server. The system related to this disclosure may be implemented as a general information processing system. This disclosure may be implemented, for example, as a software program that runs on a personal computer or as an application that runs on a smartphone. The method related to this disclosure may be provided to users in SaaS (Software as a Service) format.

[0760] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing of the specific process may be performed by multiple computers, including computer 22. For example, a data generation model 58 may be provided in an external device of the data processing device 12, and the external device may generate data according to the input data.

[0761] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0762] 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.

[0763] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0764] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0765] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0766] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0767] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0768] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and the like that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0769] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0770] The following is further disclosed regarding the embodiments described above.

[0771] (Claim 1)

[0772] A means of collecting operating data of home appliances in real time,

[0773] A means of detecting anomalies based on the collected data,

[0774] A means of notifying the user when an anomaly is detected,

[0775] Means for searching for and booking a reliable repair shop,

[0776] A means of controlling other household devices in response to abnormalities,

[0777] A system that includes this.

[0778] (Claim 2)

[0779] The system according to claim 1, further comprising means for analyzing community-based data to learn common problem-solving methods and providing them to users as advice.

[0780] (Claim 3)

[0781] The system according to claim 1, further comprising means for automatically suggesting the optimal repair date and time, taking into account the user's schedule and the repair company's availability.

[0782] "Example 1"

[0783] (Claim 1)

[0784] A means of collecting operational information in real time,

[0785] A means of detecting anomalies based on the collected information,

[0786] A means of notifying users when an anomaly is detected,

[0787] Means for searching for and booking a reliable repair shop,

[0788] Means for controlling other household devices in response to abnormalities,

[0789] A means of analyzing the collected information to provide details of the anomaly,

[0790] A method to automatically suggest the optimal repair date and time,

[0791] A system that includes this.

[0792] (Claim 2)

[0793] The system according to claim 1, further comprising means for analyzing community-based information to learn common problem-solving methods and providing them to users as advice.

[0794] (Claim 3)

[0795] The system according to claim 1, further comprising means for automatically suggesting the optimal repair date and time, taking into account the user's schedule and the repair company's availability.

[0796] "Application Example 1"

[0797] (Claim 1)

[0798] A means of acquiring real-time operating information of household appliances,

[0799] A means of identifying anomalies based on acquired information,

[0800] A means of notifying users when an anomaly is detected,

[0801] Means for searching for and booking a reliable repair shop,

[0802] Means for controlling other household devices in response to abnormalities,

[0803] A means of monitoring energy consumption data of public facilities and detecting anomalies,

[0804] A means of notifying the administrator when an abnormality in energy consumption is detected and proposing alternative measures,

[0805] A system that includes this.

[0806] (Claim 2)

[0807] The system according to claim 1, further comprising means for analyzing community data to learn common problem-solving methods and providing them to users as advice.

[0808] (Claim 3)

[0809] The system according to claim 1, further comprising means for automatically suggesting the optimal repair date and time, taking into account the user's schedule and the repair company's availability.

[0810] "Example 2 of combining an emotion engine"

[0811] (Claim 1)

[0812] A means of collecting operational information from home appliances,

[0813] A means for analyzing received operational information to detect anomalies,

[0814] A means of estimating the user's emotional state,

[0815] A means of adjusting the content and tone of abnormal notifications according to the user's emotional state,

[0816] A means of finding and booking a reliable repair shop,

[0817] Means for controlling other devices in response to abnormalities,

[0818] A system that includes this.

[0819] (Claim 2)

[0820] The system according to claim 1, comprising means for analyzing community data to learn common problem-solving methods and providing them as advice.

[0821] (Claim 3)

[0822] The system according to claim 1, comprising means for automatically suggesting the optimal repair date and time, taking into account the user's schedule and the availability of the repair company.

[0823] "Application example 2 when combining with an emotional engine"

[0824] (Claim 1)

[0825] A means of collecting information on the operating status of home appliances in real time,

[0826] A means of identifying anomalies based on accumulated information,

[0827] A means of notifying the user when an anomaly is identified,

[0828] Means for selecting and booking a reliable maintenance provider,

[0829] Means for operating other in-house devices in response to abnormalities,

[0830] A means of analyzing the user's emotional state and adjusting the content and tone of abnormal notifications,

[0831] A system that includes this.

[0832] (Claim 2)

[0833] The system according to claim 1, further comprising means for analyzing collective data to learn common solutions and providing them to users as advice.

[0834] (Claim 3)

[0835] The system according to claim 1, further comprising means for automatically suggesting the optimal maintenance date and time, taking into account the user's schedule and the maintenance provider's availability. [Explanation of Symbols]

[0836] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots< / url:> < / url:> < / url:> < / url:>

Claims

1. A means of acquiring real-time operating information of household appliances, A means of identifying anomalies based on acquired information, A means of notifying users when an anomaly is detected, Means for searching for and booking a reliable repair shop, Means for controlling other household devices in response to abnormalities, A means of monitoring energy consumption data of public facilities and detecting anomalies, A means of notifying the administrator when an abnormality in energy consumption is detected and proposing alternative measures, A system that includes this.

2. The system according to claim 1, further comprising means for analyzing community data to learn common problem-solving methods and providing them to users as advice.

3. The system according to claim 1, further comprising means for automatically suggesting the optimal repair date and time, taking into account the user's schedule and the repair company's availability.

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