system

Abstract

We provide the system. [Solution] A means of collecting operational data from devices within the home, A means for analyzing collected operational data and detecting anomalies, A means of sending a notification when an anomaly is detected, A method for automatically arranging and booking repair services to address abnormalities, A means of suggesting alternative solutions in conjunction with other electronic devices in the home, A method for analyzing data from multiple households to derive common problems and solutions, A system that includes this.

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, and includes steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In modern households, sudden failures of household appliances can cause significant inconvenience and sometimes unexpected large expenses. In particular, when expensive household appliances that are commonly used malfunction, the impact is severe. To solve such problems early, it is required to monitor the health status of household appliances in real time, immediately detect abnormalities, and take prompt actions through automated arrangements. Also, a system that can take reasonable countermeasures independently without imposing excessive burden on users for such failures of household appliances is needed.

Means for Solving the Problems

[0005] To solve the above problems, the present invention includes means for collecting operational data from household devices and means for analyzing that data to detect abnormalities. When an abnormality is detected, the system promptly notifies the user of the information, automatically arranges for a reliable repair company, and includes means for optimizing the reservation. Furthermore, when an abnormality occurs, it cooperates with other electronic devices in the household to suggest alternative solutions. In addition, by analyzing data collected from multiple households and deriving common problems and solutions, the system reduces the risk of malfunctions in the household and provides continuous support for the user's life.

[0006] "Household appliances" refers to all home appliances and electronic devices used within a home, and which can collect operational data through sensors.

[0007] "Operational data" refers to various types of information such as temperature, power consumption, and operating time that are acquired when household appliances are in operation.

[0008] "Means of detecting anomalies" refers to a system that uses machine learning algorithms or similar methods to identify deviations from normal operating patterns.

[0009] "Means of sending notifications" refers to methods of delivering messages to inform users when an anomaly is detected.

[0010] "An automated method for arranging and booking repair services" refers to a system that selects reliable repair services and automatically secures the optimal repair date in conjunction with the user's schedule.

[0011] "Means of coordinating with other electronic devices to propose alternative solutions" refers to a function that, in the event of an anomaly, uses other electronic devices in the home to suggest and implement measures to mitigate the problem.

[0012] "Methods for analyzing data from multiple households" refers to a system that integrates and analyzes operational data collected from different households to derive common failure patterns and improvement measures. [Brief explanation of the drawing]

[0013] [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] This is a sequence diagram showing the processing flow of the data processing system in Application Example 2, which combines an emotion engine. [Modes for carrying out the invention]

[0014] 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.

[0015] First, the terms used in the following description will be explained.

[0016] 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.

[0017] 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.

[0018] 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.

[0019] 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).

[0020] 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."

[0021] [First Embodiment]

[0022] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0023] 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.

[0024] 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).

[0025] 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.

[0026] 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.

[0027] 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.

[0028] 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.

[0029] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0030] 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.

[0031] 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.

[0032] 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.

[0033] 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".

[0034] This invention provides a system for detecting anomalies and responding quickly by collecting and analyzing operational data from household devices in real time. The system operates with the cooperation of a server, terminals, and users as follows:

[0035] Data collection and anomaly detection

[0036] The device collects operational data such as temperature, power consumption, and operating time from various devices in the home in real time. This data is securely transmitted to a server via the internet.

[0037] The server stores the received data and analyzes it using an anomaly detection algorithm. When an anomaly is detected, it performs an evaluation according to the type and severity of the anomaly.

[0038] Anomaly notification and repair arrangement

[0039] If the server detects an anomaly as a result of its analysis, it generates a notification based on a pre-configured urgency level. This notification is sent to the terminal for verification.

[0040] The device displays a notification from the server to the user, informing them that immediate repair is required.

[0041] The server initiates a process to select the most suitable repair service provider and date / time based on the user's calendar information and a database of repair service providers.

[0042] Collaboration with repair companies

[0043] The device proposes a repair date to the user, and once approved, that information is sent to the server, and a reservation is automatically made with the repair company.

[0044] Proposal of alternative measures

[0045] When an anomaly occurs, the server calculates possible alternative solutions based on information from other household devices. For example, if the refrigerator breaks down, it will present the user with specific alternative solutions via the terminal, such as using the air conditioner to adjust the room temperature to help preserve food while the appliance is unusable.

[0046] Utilization of community data

[0047] The server comprehensively analyzes data collected from multiple households to identify frequently occurring failure patterns. Based on this analysis, the terminal advises the user, "Many problems in this range have been reported with the same model. We recommend regular inspections."

[0048] For example, if abnormal data is reported from the refrigerator's temperature sensor, the server analyzes it and determines that the temperature is outside the acceptable range. This information is immediately notified to the user via their device. A repair technician is automatically arranged to fit the user's schedule, and at the same time, supplementary alternative measures are suggested and implemented according to official recommendations. This allows the user to enjoy a quick and efficient breakdown response.

[0049] The following describes the processing flow.

[0050] Step 1:

[0051] The user activates a device in their home. Sensors connected to the terminal begin operating and continuously measure operational data such as temperature, power consumption, and operating time in real time.

[0052] Step 2:

[0053] The device collects operational data and sends it to the server via the internet. The data is transferred through a secure channel.

[0054] Step 3:

[0055] The server stores the raw data it receives and begins analyzing the data using an anomaly detection algorithm. It performs comparative analysis with past data to check for deviations from normal operating patterns.

[0056] Step 4:

[0057] When the server detects an anomaly, it evaluates the type and severity of the anomaly. Once the anomaly is identified, it generates a notification for the user based on that information.

[0058] Step 5:

[0059] An anomaly notification is sent from the server to the terminal. The notification includes the specific nature of the anomaly and its urgency.

[0060] Step 6:

[0061] The device displays notifications received from the server, immediately informing the user of any anomalies. Based on this information, the user can then consider prompt countermeasures.

[0062] Step 7:

[0063] When a home appliance malfunctions, the server searches its database for a reliable repair company. It then analyzes the user's schedule and the company's availability to prepare to suggest the optimal repair date.

[0064] Step 8:

[0065] The terminal notifies the user of the repair company and proposed schedule based on the server's calculation results. The user can then approve or request changes to the proposal.

[0066] Step 9:

[0067] After the user approves the repair arrangement proposal, that information is sent back to the server. The server automatically makes a reservation with the repair company and completes the arrangement.

[0068] Step 10:

[0069] The server calculates alternative solutions based on data obtained from other electronic devices in the home, depending on the abnormal situation. For example, it considers ways to mitigate the problem by using other devices if a specific device fails.

[0070] Step 11:

[0071] The device presents the user with calculated alternative solutions. The user reviews the proposed alternatives and, if necessary, approves their implementation, after which the alternatives are automatically applied.

[0072] (Example 1)

[0073] 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."

[0074] Conventional systems lacked sufficient mechanisms to quickly respond to malfunctions, even when they collected operational data from household devices. In particular, delays in notification and dispatching repair technicians raised concerns about prolonged problems and further damage. Furthermore, there was a lack of systems that comprehensively utilized data from multiple devices, not just individual ones, to provide rational alternative solutions.

[0075] 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.

[0076] In this invention, the server includes means for acquiring operational information from devices within the living environment, means for identifying abnormalities and distributing notifications, and means for automatically arranging and scheduling repair personnel. This enables residents to quickly identify equipment abnormalities, take prompt action, and arrange for optimal repairs.

[0077] "Devices within the living environment" refers to various electronic and electrical devices installed in the home, and these are the devices from which operational data is collected.

[0078] "Operational information" refers to various data such as temperature, power consumption, and operating time generated when a device is in operation, and is used for system monitoring and analysis.

[0079] An "anomaly" refers to activity that deviates from the normal operating range based on operational information, and is identified using machine learning algorithms or similar methods.

[0080] A "notification" refers to a message or alert sent to a user to inform them of an anomaly detected by the system.

[0081] A "repair technician" refers to a specialist or contractor dispatched to address equipment malfunctions, and is arranged with the user's approval.

[0082] An "alternative solution" refers to a proposal for resolving a problem by coordinating with other devices when one device malfunctions, and is provided to maintain continuity of daily life.

[0083] "Information analysis" refers to the process of using acquired operational information to detect anomalies, identify patterns, and derive appropriate countermeasures based on the situation.

[0084] This invention is a comprehensive system for collecting operational information from multiple devices within a living environment and for detecting and addressing abnormalities. The system is realized through the cooperation of a server, terminals, and users.

[0085] The server first receives operational information sent from the terminal. The hardware used here includes storage devices for storing data and processors for processing the data. The software includes a database system (e.g., MongoDB) and machine learning algorithms for anomaly detection. Machine learning algorithms are typically implemented using the Python language and the Tensorflow® library. This allows the server to analyze the operational information and quickly identify anomalies.

[0086] The terminal collects operational information in real time from various devices within the living environment. This includes temperature sensors, power measurement sensors, and Wi-Fi modules. The collected data is transmitted to a server via secure communication such as the HTTPS protocol. The terminal also receives notifications from the server and informs the user. Notifications are sent to the user visually through smartphone applications, etc.

[0087] When a user receives a notification of an anomaly, they check the situation through the application interface and take appropriate action. The server proposes the optimal repair date, taking into account the user's schedule information and the availability of repair personnel. This proposal is also displayed to the user via their terminal.

[0088] For example, if the refrigerator's temperature sensor reports unusual data, the server analyzes the data and determines that there is a temperature anomaly. The server immediately generates an anomaly notification and sends it to the user's smartphone via a terminal. Subsequently, the server automatically schedules a repair appointment and suggests an alternative to the user, such as using the air conditioner to adjust the room temperature. This allows the user to respond quickly and efficiently.

[0089] An example of a prompt for a generated AI model might be, "Please explain in detail the process of data collection and anomaly detection when a home appliance malfunctions." This would deepen the understanding of the entire process and allow the user to obtain more detailed information about how the system works.

[0090] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0091] Step 1:

[0092] The terminal collects operational data in real time from various devices placed within the home. The input here is raw data from various sensors (temperature sensors, power measurement sensors, etc.) installed in the devices. This data is temporarily stored inside the terminal and sent to the server using the HTTPS protocol. In this process, the data is formatted and compressed and sent to the server in the appropriate format.

[0093] Step 2:

[0094] The server stores the operational data received from the terminal in a database. The input for this step is formatted sensor data. The server stores the data in an unstructured database (e.g., MongoDB) and creates an index to make the data easily accessible when needed. The data is stored in a time-series format and used for subsequent analysis.

[0095] Step 3:

[0096] The server performs anomaly detection using stored data. The input is operational data stored in a database. The server applies machine learning algorithms to identify abnormal patterns. Here, an anomaly detection model using Python and TensorFlow is in operation. If an anomaly is detected as a result of the analysis, that information is generated as an anomaly notification.

[0097] Step 4:

[0098] The server sends the generated anomaly notification to the terminal. The input is the result of the anomaly detection, and the output is the content that the terminal notifies the user of. This notification includes the type of anomaly, its urgency, and recommended actions, which are then communicated to the user by the terminal.

[0099] Step 5:

[0100] The user receives a notification from their device and checks the nature of the anomaly. The input is the notification information from the device. The user views the notification using a smartphone application and considers countermeasures as needed. This includes interface operations on the device.

[0101] Step 6:

[0102] The server selects a repair date based on the user's schedule information and the availability of repair technicians. Inputs include the user's calendar and database information on repair technicians. An algorithm is used to calculate the optimal date, and the result is returned to the terminal. This enables rapid repair arrangements.

[0103] Step 7:

[0104] The server calculates possible alternative solutions based on information from other devices when an anomaly occurs and proposes them to the user via the terminal. Inputs are operational data from other devices and the results of previous anomaly detection. The server calculates the alternative solutions and sends the results as output to the terminal.

[0105] (Application Example 1)

[0106] 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."

[0107] The objectives are to enable early detection and rapid response to malfunctions in household electronic devices, while also suppressing wasteful energy consumption and managing it efficiently. Furthermore, the objectives are to minimize the impact of household malfunctions on other devices and to quickly provide necessary alternative solutions, thereby creating a more comfortable and sustainable living environment.

[0108] 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.

[0109] In this invention, the server includes means for collecting operational data from devices in the home, means for analyzing the collected data to detect anomalies, means for sending notifications regarding anomalies, means for automatically arranging and booking repair services according to the anomaly, means for coordinating with other electronic devices in the home to propose alternative solutions, means for analyzing data from multiple homes to derive common problems and solutions, means for collecting and optimizing energy usage data, and means for detecting anomalies in energy consumption and sending alerts. This enables rapid and effective fault response and energy management in the living environment.

[0110] "Household appliances" refers to electrical, electronic, and mechanical devices installed in a residence, and includes, but is not limited to, refrigerators, air conditioners, washing machines, and lighting.

[0111] "Operational data" refers to information indicating the operating status of a device, such as its temperature, power consumption, and operating time.

[0112] "Analysis" refers to the process of using collected data to identify patterns and anomalies in the data, employing machine learning algorithms and other techniques.

[0113] An "abnormal" state refers to a condition that deviates from the normal operation of a household appliance, which can lead to malfunction or excessive energy consumption.

[0114] "Notification" refers to the means of informing users of anomaly detections, and is carried out via smartphones or other communication devices.

[0115] "Automatic arrangement of repair service providers" refers to the process by which the server automatically schedules the appropriate repair service when an anomaly is detected.

[0116] An "alternative solution" is a method of proposing a temporary solution for a malfunctioning device, aiming to minimize the impact by utilizing other household devices.

[0117] "Energy usage data" refers to information about the energy consumed within a household, and analyzing this data can lead to energy conservation and increased efficiency.

[0118] An "alert" refers to a notification that warns or alerts the user when energy waste or abnormalities are detected.

[0119] This invention is a system that collects operational data in real time from various devices in the home and enables rapid response when an anomaly is detected. The server centrally manages the operational data collected through various sensors and IoT devices, and analyzes the collected data to detect anomalies. Python is used for the analysis, utilizing data analysis libraries such as Pandas and NumPy, and machine learning algorithms using TensorFlow are applied for anomaly detection.

[0120] The device sends a notification to the user when an anomaly is detected, prompting immediate verification and action. The notification is delivered via a smartphone application, which allows users to arrange for a repair service within the application. This includes a feature that suggests the optimal repair date and time in conjunction with the user's schedule.

[0121] Furthermore, the server proposes alternative measures to compensate for the malfunctioning equipment, based on data from other devices. For example, if a refrigerator malfunctions, it will provide the user with specific suggestions via the terminal, such as adjusting the air conditioner temperature to stabilize the indoor environment.

[0122] Energy usage data is also collected simultaneously, enabling efficient energy management. When energy waste is detected based on the data, an alert is sent, and the user receives suggestions on how to optimize their usage.

[0123] This technology can also be enhanced using generative AI models. For example, if the refrigerator is running longer than usual, it can generate a prompt message to advise the user, such as, "Please check that the refrigerator door is properly closed."

[0124] Examples of prompts for generative AI models:

[0125] An abnormal operating pattern has been detected in the refrigerator data. Please create a notification message for the appropriate user.

[0126] Through this system, users can maintain comfort within their homes while being supported in a sustainable and efficient lifestyle.

[0127] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0128] Step 1:

[0129] The server collects operational data from devices within each home via IoT devices. This data includes temperature, power consumption, and operating time. The input is real-time operational data transmitted from each device, and the output is the storage of this data in a centrally managed database. Specifically, it receives data packets from each device, converts the format as needed, and stores them securely.

[0130] Step 2:

[0131] The server analyzes the collected data using data analysis libraries such as Pandas and NumPy, and applies anomaly detection algorithms. The input is behavioral data obtained from the database, and the output is a flag indicating whether an anomaly was detected. Specifically, it compares the data with historical data and searches for patterns that exceed a set threshold.

[0132] Step 3:

[0133] If an anomaly is detected, the server sends a notification to the user's device. The input is the anomaly detection result and the user's contact information, and the output is an alert message sent via the notification system. The anomaly information is displayed as a text message or push notification via the user's smartphone app.

[0134] Step 4:

[0135] The device provides a real-time interface to prompt users who receive notifications to take action. Input is notification data from the server, and output is notification confirmation and option selection via the user interface. Specifically, it supports operations such as displaying a repair provider selection screen by tapping the notification.

[0136] Step 5:

[0137] The server automatically arranges repair technicians in conjunction with the user's schedule. Inputs include the user's preferred date and time and a database of available repair technicians; output is confirmed reservation information. A scheduling algorithm is used to select the optimal date, time, and technician, and automatically confirm the reservation.

[0138] Step 6:

[0139] The terminal presents the user with alternative solutions to compensate for a malfunctioning device. Inputs include operating data from other household appliances and action plan templates, while output is a suggested alternative solution. Specifically, if the refrigerator malfunctions, it creates and sends a message suggesting temperature adjustment using the air conditioner.

[0140] Step 7:

[0141] The server uses a generative AI model to enhance the analysis of anomalies and energy consumption. The input consists of collected data and past anomaly cases, while the output is a prompt message suggesting improvements or actions to take. For example, it might generate a prompt such as, "An anomaly has been detected in the refrigerator data. Please create a notification message for the appropriate user."

[0142] In this way, the entire system achieves efficient and sustainable maintenance of a home environment.

[0143] 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.

[0144] This invention provides optimal responses that take into account the user's emotions by integrating an emotion engine into a system that analyzes operational data acquired from household devices and detects abnormalities. This system is configured as follows.

[0145] Data collection and analysis

[0146] The device collects various data in real time through sensors installed in household appliances. This data includes temperature, power consumption, and appliance operating status. The collected data is transmitted to a server in a secure environment.

[0147] After receiving the data, the server uses machine learning algorithms to analyze the data and detect anomaly patterns. If an anomaly is detected, that information is sent to the terminal.

[0148] User notifications and sentiment analysis

[0149] When the device receives information about an anomaly detection, it displays an appropriate notification to the user. At this time, the emotion engine is activated and analyzes the user's emotions in real time through voice and camera sensors.

[0150] Based on sentiment analysis results, the server adapts notifications and suggestions to the user's emotions. For example, if the user is feeling stressed, a simple and gentle notification message will be used.

[0151] Repair arrangements and alternative solutions proposed

[0152] The server searches for and suggests the most suitable repair service provider based on the user's schedule and emotional state. In this process, an emotional engine considers arrangements to minimize the user's stress.

[0153] The device presents the user with repair arrangement proposals provided by the server, and completes the reservation after receiving approval. It also coordinates with other electronic devices in the home as needed to calculate alternative solutions.

[0154] Utilization of community data

[0155] The server analyzes operational data collected from multiple households to identify frequently occurring anomaly patterns and derive adaptive failure prevention measures.

[0156] The device provides this information to the user and offers advice tailored to their emotional state. For example, it might use gentle language to warn them and alleviate anxiety.

[0157] For example, if an abnormality is detected in the refrigerator, when the user returns home and sentiment analysis begins, the sentiment engine recognizes that the user is experiencing stress. Based on this, the device displays a reassuring notification such as, "A minor abnormality has been found in the refrigerator, but we will address it immediately, so there is no need to worry." Subsequently, it arranges for a repair service and provides a suggestion that best suits the user's schedule and emotions, thereby achieving comprehensive home management.

[0158] The following describes the processing flow.

[0159] Step 1:

[0160] The device collects motion data in real time from sensors connected to devices within the home and transmits that data to a server via the internet.

[0161] Step 2:

[0162] The server stores the operational data it receives in a database and uses machine learning algorithms to analyze patterns that should be detected as anomalies. When an anomaly is detected, detailed information is generated.

[0163] Step 3:

[0164] The server sends the results of the anomaly detection to the terminal. At this time, it creates a notification message according to the type and urgency of the anomaly.

[0165] Step 4:

[0166] The device receives a notification message and displays it to the user. Simultaneously, the device's built-in emotion engine uses the camera and microphone to collect data in order to analyze the user's emotions.

[0167] Step 5:

[0168] The device uses an emotion engine to analyze the user's voice and facial expressions to determine their emotional state. The results are sent to a server and used to optimize notification content and response strategies.

[0169] Step 6:

[0170] The server takes the user's emotional state into account and modifies notification messages and response processes accordingly. For example, if the user is feeling anxious, it generates a more reassuring message.

[0171] Step 7:

[0172] The server searches a database of repair companies and suggests the most suitable repair company and date based on the user's schedule and emotional state. This information is then sent to the terminal.

[0173] Step 8:

[0174] The device displays a list of repair service providers optimized for the user's device and asks for their approval. The user can either approve the proposal or submit a request for modifications.

[0175] Step 9:

[0176] Once the user approves the proposal, the device sends that information to the server. The server then automatically makes a reservation with a selected repair company.

[0177] Step 10:

[0178] The server calculates alternative solutions for devices experiencing malfunctions. It considers ways to coordinate with other home devices to ensure the user experiences no inconvenience.

[0179] Step 11:

[0180] The device presents the user with a calculated alternative solution and requests their approval to implement it. If the user approves, the alternative solution is automatically applied.

[0181] (Example 2)

[0182] 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".

[0183] Modern homes contain many electronic devices, and malfunctions can occur in these devices. However, if these malfunctions are not detected early and dealt with appropriately, they can significantly disrupt daily life. Furthermore, when dealing with malfunctions, it is necessary to not only perform mechanical actions but also to consider the user's feelings and provide appropriate notifications and suggestions. However, current systems face the challenge of making it difficult to provide optimal responses based on emotions.

[0184] 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.

[0185] In this invention, the server includes means for collecting operational information from devices within the home and analyzing the collected information, means for sending notifications when an abnormality is detected, and means for adjusting the content of notifications based on the user's emotions. This enables early detection of abnormalities and rapid response while taking the user's emotions into consideration.

[0186] "Household devices" refers to electronic devices installed in a home that provide various operational information.

[0187] "Operational information" refers to information including various data such as temperature, power consumption, and operating status obtained from the device.

[0188] An "abnormality" refers to a state or pattern that deviates from the normal operating range of a device.

[0189] "Notification" refers to a means of informing users of the occurrence of an anomaly or other important information.

[0190] "User emotions" refers to the psychological state of a user and includes data analyzed from voice, facial expressions, and other factors.

[0191] "Emotion-based notification content adjustment" refers to procedures and methods for conveying information in a more appropriate format and content depending on the user's emotional state.

[0192] "Information from multiple households" refers to operational information collected from devices within multiple households and stored in a database.

[0193] "Deriving general problems and solutions" refers to the process of identifying common problems from a large amount of operational information and finding countermeasures to address them.

[0194] This invention requires a terminal installed in the home and a server that interacts with it. The terminal collects operational information such as temperature, power consumption, and operating status in real time from sensors built into electronic devices in each home. This information is transmitted to the server via a security protocol.

[0195] The server uses programming frameworks such as Python and TensorFlow to analyze the received operational information. Machine learning algorithms identify anomalous patterns and, if necessary, send anomaly notifications to the terminal. A database management system is used to improve the speed and accuracy of the analysis, and the collected data is continuously used to update the learning model.

[0196] The device provides notifications to the user based on the received anomaly information. These notifications are displayed on the screen or announced by voice. Furthermore, the device is equipped with voice and camera sensors, which allow for real-time analysis of the user's emotions, and the analysis results are sent back to the server.

[0197] The server uses the results of sentiment analysis to generate optimal notifications and suggestions tailored to the user's psychological state. This ensures that even when an anomaly occurs, information is provided in a way that minimizes anxiety and stress for the user. For example, if the analysis reveals that the user is stressed when the refrigerator temperature rises, the device will send a notification such as, "There is a slight problem with the refrigerator, but we will address it immediately, so please don't worry."

[0198] Furthermore, the server optimizes repair responses by considering the schedule information of both the user and the repair company. It also enhances user convenience by coordinating with other electronic devices in the home and suggesting alternative solutions to the user.

[0199] As a concrete example, here is an example of a prompt message: "Please describe the design of a system that detects abnormalities in home devices and provides notifications and suggestions tailored to the user's emotional state."

[0200] In this way, it becomes possible to detect abnormalities in various devices within the home early on and to provide prompt and accurate responses that take into consideration the user's feelings.

[0201] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0202] Step 1:

[0203] The terminal collects operational information such as temperature, power consumption, and operating status in real time through sensors installed on each electronic device in the home. At this point, the input is real-time data from the sensors, and the output is an information packet summarizing this data. This information packet is sent to the server via a security protocol.

[0204] Step 2:

[0205] The server receives operational information sent from the terminal. The input data is analyzed using machine learning algorithms such as Python or TensorFlow. The output is a determination result indicating whether or not an anomaly is present. If an anomaly is detected based on this result, detailed anomaly information is generated. This information is sent back to the terminal.

[0206] Step 3:

[0207] The terminal notifies the user based on anomaly information received from the server. At this stage, the input is anomaly information from the server, and the output is visual and audible notification to the user. Specifically, this may involve displaying a warning message on the terminal's display and having a voice assistant verbally inform the user of the anomaly.

[0208] Step 4:

[0209] The device uses built-in voice and camera sensors to analyze the user's facial expressions and voice tone. The input is real-time audio and video data, which is processed by an emotion analysis engine. The output is data indicating the user's emotional state, and this data is also sent to the server.

[0210] Step 5:

[0211] The server receives the user's emotional state and adjusts the content of notifications and suggestions accordingly. The input to this process is the user's emotional state data, and the output is the text of notifications and suggestions adapted to that emotion. The server sends this text to the terminal and displays it to the user in an emotionally sensitive manner.

[0212] Step 6:

[0213] The server uses an AI model to search for the most suitable repair provider based on the user's schedule information and the availability of repair providers. The input is the user's time information and provider availability data, and the output is a list of potential optimal repair providers. This list is sent to the terminal and presented to the user.

[0214] Step 7:

[0215] The terminal presents the user with a list of potential repair companies received from the server and prompts them to make a final selection. The input is the list of potential repair companies, and the output is the user's selection data. Once the user agrees, the terminal completes the reservation and displays a confirmation notification.

[0216] Through these processing steps, the terminal and server work together to quickly detect abnormalities in home electronic devices and provide notifications and countermeasures that are sensitive to the user's feelings.

[0217] (Application Example 2)

[0218] 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".

[0219] In recent years, the number of devices in homes has increased, and consequently, the frequency of malfunctions has also risen. While conventional systems can detect and notify users of device malfunctions, they do not provide countermeasures that take into account the emotional burden on the user. Furthermore, even when arranging repairs or proposing alternative solutions after a malfunction is detected, it is difficult to provide support that is tailored to the emotional state of each individual user. Therefore, there is a need for a system that enables flexible and efficient responses that take into account the user's feelings.

[0220] 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.

[0221] In this invention, the server includes means for collecting operational information from devices within the home, means for analyzing the collected operational information and detecting anomalies, and means for analyzing the user's emotions and providing an optimal notification method based on those emotions. This makes it possible to provide notifications tailored to the user's emotions after detecting an anomaly, thereby reducing the user's mental burden and enabling prompt repair arrangements and the suggestion of alternative solutions.

[0222] "Household appliances" refers to electronic devices and electrical appliances used within the home, and specifically includes refrigerators, washing machines, air conditioners, etc.

[0223] "Operational information" refers to information such as operating status, power consumption, and temperature collected from household appliances.

[0224] "Means for detecting abnormalities" refers to technologies or devices for analyzing collected operational information and identifying states that differ from normal operation.

[0225] "Means of sending notifications" refer to methods for informing users of information when an anomaly is detected, and can take the form of voice, messages, alerts, etc.

[0226] "A means of automatically arranging and scheduling repair work" refers to a method for selecting the appropriate repair technician based on detected abnormalities and automatically scheduling repairs.

[0227] "Electronic devices" refers to various electrical and electronic equipment that operates within the home.

[0228] "Means of proposing alternative solutions" refer to methods of using machine learning and algorithms to present other possible options or methods when an anomaly occurs.

[0229] "Methods for analyzing user emotions" refers to technologies that analyze a user's emotional state using data collected from sensors such as audio and video.

[0230] "Means of providing the optimal notification method" refers to means of delivering notifications in the most appropriate format and content based on the user's emotional state.

[0231] A system for carrying out this invention includes a server that collects operational information from household devices and analyzes that information, and a terminal that receives input from a user and proposes the optimal action based on that input.

[0232] The server collects operational information in real time using various sensors installed in the home. Small computers such as Raspberry Pi or NVIDIA Jetson Nano can be used as hardware. This allows diverse data, such as temperature, power consumption, and device operating status, to be transmitted to the server in a secure environment.

[0233] If an anomaly is detected, the server analyzes the collected data using a pre-trained machine learning algorithm (for example, a model built using TensorFlow or PyTorch). This analysis identifies anomaly patterns. Based on this information, the terminal sends an anomaly notification to the user.

[0234] The user's emotional state is evaluated using emotion analysis software (e.g., OpenCV or Google® Cloud Vision API) based on data from the device's camera and microphone. The results of this emotion analysis are sent to a server, which generates personalized notifications and suggestions. The content of the notifications is generated using a natural language generation API (e.g., OpenAI®'s GPT-3®).

[0235] For example, when the temperature sensor inside the refrigerator indicates an abnormality, the server recognizes the abnormality based on that information, and the terminal uses sentiment analysis to determine whether the user is feeling anxious. After obtaining the result, the terminal provides a reassuring message such as, "There is a problem with the refrigerator temperature, but we will guide you on how to deal with it shortly."

[0236] An example of a prompt message for the generating AI model would be: "Generate a gentle message to alleviate user anxiety when a malfunction is detected in a home device." This allows the user to quickly understand the necessary countermeasures while minimizing their emotional burden.

[0237] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0238] Step 1:

[0239] The server collects operational information from sensors placed throughout the home. It receives data from the sensors (temperature, power consumption, operating status, etc.) as input, which is then transmitted to the server using the appropriate protocol. The server receives a real-time stream of operational data as output.

[0240] Step 2:

[0241] The server analyzes received operational data using machine learning algorithms to detect anomalies. The input is an operational data stream, which is passed through a machine learning model for analysis. The output includes the presence or absence of anomaly patterns and, if anomalies are present, specific information.

[0242] Step 3:

[0243] If an anomaly is detected, the server sends that information to the terminal. The input is anomaly information, which is transmitted to the terminal using a communication module. The output is the terminal receiving the anomaly.

[0244] Step 4:

[0245] The device notifies the user based on the received anomaly information. Furthermore, the device uses its built-in camera and microphone to analyze the user's emotions. Inputs include anomaly information and real-time audio and video data, and emotion analysis software is used to determine the emotional state. Outputs include the user's emotion analysis results and appropriate notification content.

[0246] Step 5:

[0247] The server uses a natural language generation API to generate an appropriate message based on the sentiment analysis results. It takes user sentiment data as input, sends prompt sentences to a generation AI model, and obtains a message. The output is a customized message to notify the user.

[0248] Step 6:

[0249] The terminal displays the generated message to the user and automatically arranges repair work as needed. The system receives the generated message and the repair technician's availability as input, and then proposes the optimal repair schedule based on this information. The output completes the display of information to the user and the arrangement of the work.

[0250] 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.

[0251] Data generation model 58 is a 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.

[0252] 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.

[0253] [Second Embodiment]

[0254] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0255] 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.

[0256] 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).

[0257] 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.

[0258] 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.

[0259] 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).

[0260] 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.

[0261] 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.

[0262] 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.

[0263] 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.

[0264] 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.

[0265] 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".

[0266] This invention provides a system for detecting anomalies and responding quickly by collecting and analyzing operational data from household devices in real time. The system operates with the cooperation of a server, terminals, and users as follows:

[0267] Data collection and anomaly detection

[0268] The device collects operational data such as temperature, power consumption, and operating time from various devices in the home in real time. This data is securely transmitted to a server via the internet.

[0269] The server stores the received data and analyzes it using an anomaly detection algorithm. When an anomaly is detected, it performs an evaluation according to the type and severity of the anomaly.

[0270] Anomaly notification and repair arrangement

[0271] If the server detects an anomaly as a result of its analysis, it generates a notification based on a pre-configured urgency level. This notification is sent to the terminal for verification.

[0272] The device displays a notification from the server to the user, informing them that immediate repair is required.

[0273] The server initiates a process to select the most suitable repair service provider and date / time based on the user's calendar information and a database of repair service providers.

[0274] Collaboration with repair companies

[0275] The device proposes a repair date to the user, and once approved, that information is sent to the server, and a reservation is automatically made with the repair company.

[0276] Proposal of alternative measures

[0277] When an anomaly occurs, the server calculates possible alternative solutions based on information from other household devices. For example, if the refrigerator breaks down, it will present the user with specific alternative solutions via the terminal, such as using the air conditioner to adjust the room temperature to help preserve food while the appliance is unusable.

[0278] Utilization of community data

[0279] The server comprehensively analyzes data collected from multiple households to identify frequently occurring failure patterns. Based on this analysis, the terminal advises the user, "Many problems in this range have been reported with the same model. We recommend regular inspections."

[0280] As a specific example, when abnormal data is reported from the temperature sensor of a refrigerator, the server analyzes it and determines that the temperature is outside the allowable range. This information is immediately notified to the user through the terminal. A repairman is automatically arranged in a form that matches the user's schedule, and at the same time, auxiliary alternative means are proposed and implemented in accordance with official recommendations. As a result, the user can enjoy a quick and efficient fault response.

[0281] The following describes the processing flow.

[0282] Step 1:

[0283] The user activates the devices in the home. The sensors connected to the terminal start operating and continuously measure operation data such as temperature, power consumption, and operating time in real time.

[0284] Step 2:

[0285] The terminal transmits the operation data collected to the server via the Internet. The data is transferred through a secure channel.

[0286] Step 3:

[0287] The server accumulates the raw data received and starts analyzing the data using an anomaly detection algorithm. A comparative analysis with past data is performed to check for deviations from the normal operation pattern.

[0288] Step 4:

[0289] When the server detects an anomaly, it evaluates the type and severity of the anomaly. Once the anomaly is identified, notification content for the user is generated based on that information.

[0290] Step 5:

[0291] A notification of the anomaly is sent from the server to the terminal. The notification includes the specific content of the anomaly and its urgency.

[0292] Step 6:

[0293] The device displays notifications received from the server, immediately informing the user of any anomalies. Based on this information, the user can then consider prompt countermeasures.

[0294] Step 7:

[0295] When a home appliance malfunctions, the server searches its database for a reliable repair company. It then analyzes the user's schedule and the company's availability to prepare to suggest the optimal repair date.

[0296] Step 8:

[0297] The terminal notifies the user of the repair company and proposed schedule based on the server's calculation results. The user can then approve or request changes to the proposal.

[0298] Step 9:

[0299] After the user approves the repair arrangement proposal, that information is sent back to the server. The server automatically makes a reservation with the repair company and completes the arrangement.

[0300] Step 10:

[0301] The server calculates alternative solutions based on data obtained from other electronic devices in the home, depending on the abnormal situation. For example, it considers ways to mitigate the problem by using other devices if a specific device fails.

[0302] Step 11:

[0303] The device presents the user with calculated alternative solutions. The user reviews the proposed alternatives and, if necessary, approves their implementation, after which the alternatives are automatically applied.

[0304] (Example 1)

[0305] Next, Example 1 will be described. In the following description, the data processing device 12 is referred to as a "server", and the smart glasses 214 are referred to as a "terminal".

[0306] In a conventional system, even when collecting operation data of devices in a household and detecting an abnormality, the mechanism for quickly responding was not sufficient. In particular, there was a concern that delays in notifications when an abnormality occurred and arrangements for repair technicians would cause the problem to persist and further damage. Also, there was a lack of a system that comprehensively utilized data from multiple devices as well as individual devices and provided reasonable alternative measures.

[0307] The specific processing by the specific processing unit 290 of the data processing device 12 in Example 1 is realized by the following respective means.

[0308] In this invention, the server includes means for acquiring operation information from devices within the living environment, means for specifying an abnormality and distributing a notification, and means for automatically arranging and reserving a repair person. Thereby, the resident can quickly grasp the abnormality of the device and realize prompt countermeasures and optimal repair arrangements.

[0309] "Devices within the living environment" refers to various electronic and electrical devices installed in a household, and those are the targets for collecting their operation data.

[0310] "Operation information" refers to various data such as temperature, power consumption, and operating time generated when a device is operating, and is used for monitoring and analyzing the system.

[0311] "Abnormality" refers to activities that deviate from the normal operation range based on operation information, and is specified using a machine learning algorithm or the like.

[0312] "Notification" refers to messages or alerts sent to inform the user of the fact when the system detects an abnormality.

[0313] A "repair technician" refers to a specialist or contractor dispatched to address equipment malfunctions, and is arranged with the user's approval.

[0314] An "alternative solution" refers to a proposal for resolving a problem by coordinating with other devices when one device malfunctions, and is provided to maintain continuity of daily life.

[0315] "Information analysis" refers to the process of using acquired operational information to detect anomalies, identify patterns, and derive appropriate countermeasures based on the situation.

[0316] This invention is a comprehensive system for collecting operational information from multiple devices within a living environment and for detecting and addressing abnormalities. The system is realized through the cooperation of a server, terminals, and users.

[0317] The server first receives operational information sent from the terminal. The hardware used here includes storage devices for storing data and processors for processing the data. The software includes a database system (e.g., MongoDB) and machine learning algorithms for anomaly detection. Machine learning algorithms are typically implemented using the Python language or TensorFlow libraries. This allows the server to analyze the operational information and quickly identify anomalies.

[0318] The terminal collects operational information in real time from various devices within the living environment. This includes temperature sensors, power measurement sensors, and Wi-Fi modules. The collected data is transmitted to a server via secure communication such as the HTTPS protocol. The terminal also receives notifications from the server and informs the user. Notifications are sent to the user visually through smartphone applications, etc.

[0319] When a user receives a notification of an anomaly, they check the situation through the application interface and take appropriate action. The server proposes the optimal repair date, taking into account the user's schedule information and the availability of repair personnel. This proposal is also displayed to the user via their terminal.

[0320] For example, if the refrigerator's temperature sensor reports unusual data, the server analyzes the data and determines that there is a temperature anomaly. The server immediately generates an anomaly notification and sends it to the user's smartphone via a terminal. Subsequently, the server automatically schedules a repair appointment and suggests an alternative to the user, such as using the air conditioner to adjust the room temperature. This allows the user to respond quickly and efficiently.

[0321] An example of a prompt for a generated AI model might be, "Please explain in detail the process of data collection and anomaly detection when a home appliance malfunctions." This would deepen the understanding of the entire process and allow the user to obtain more detailed information about how the system works.

[0322] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0323] Step 1:

[0324] The terminal collects operational data in real time from various devices placed within the home. The input here is raw data from various sensors (temperature sensors, power measurement sensors, etc.) installed in the devices. This data is temporarily stored inside the terminal and sent to the server using the HTTPS protocol. In this process, the data is formatted and compressed and sent to the server in the appropriate format.

[0325] Step 2:

[0326] The server stores the operational data received from the terminal in a database. The input for this step is formatted sensor data. The server stores the data in an unstructured database (e.g., MongoDB) and creates an index to make the data easily accessible when needed. The data is stored in a time-series format and used for subsequent analysis.

[0327] Step 3:

[0328] The server performs anomaly detection using stored data. The input is operational data stored in a database. The server applies machine learning algorithms to identify abnormal patterns. Here, an anomaly detection model using Python and TensorFlow is in operation. If an anomaly is detected as a result of the analysis, that information is generated as an anomaly notification.

[0329] Step 4:

[0330] The server sends the generated anomaly notification to the terminal. The input is the result of the anomaly detection, and the output is the content that the terminal notifies the user of. This notification includes the type of anomaly, its urgency, and recommended actions, which are then communicated to the user by the terminal.

[0331] Step 5:

[0332] The user receives a notification from their device and checks the nature of the anomaly. The input is the notification information from the device. The user views the notification using a smartphone application and considers countermeasures as needed. This includes interface operations on the device.

[0333] Step 6:

[0334] The server selects a repair date based on the user's schedule information and the availability of repair technicians. Inputs include the user's calendar and database information on repair technicians. An algorithm is used to calculate the optimal date, and the result is returned to the terminal. This enables rapid repair arrangements.

[0335] Step 7:

[0336] The server calculates possible alternative solutions based on information from other devices when an anomaly occurs and proposes them to the user via the terminal. Inputs are operational data from other devices and the results of previous anomaly detection. The server calculates the alternative solutions and sends the results as output to the terminal.

[0337] (Application Example 1)

[0338] 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."

[0339] The objectives are to enable early detection and rapid response to malfunctions in household electronic devices, while also suppressing wasteful energy consumption and managing it efficiently. Furthermore, the objectives are to minimize the impact of household malfunctions on other devices and to quickly provide necessary alternative solutions, thereby creating a more comfortable and sustainable living environment.

[0340] 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.

[0341] In this invention, the server includes means for collecting operational data from devices in the home, means for analyzing the collected data to detect anomalies, means for sending notifications regarding anomalies, means for automatically arranging and booking repair services according to the anomaly, means for coordinating with other electronic devices in the home to propose alternative solutions, means for analyzing data from multiple homes to derive common problems and solutions, means for collecting and optimizing energy usage data, and means for detecting anomalies in energy consumption and sending alerts. This enables rapid and effective fault response and energy management in the living environment.

[0342] "Household appliances" refers to electrical, electronic, and mechanical devices installed in a residence, and includes, but is not limited to, refrigerators, air conditioners, washing machines, and lighting.

[0343] "Operational data" refers to information indicating the operating status of a device, such as its temperature, power consumption, and operating time.

[0344] "Analysis" refers to the process of using collected data to identify patterns and anomalies in the data, employing machine learning algorithms and other techniques.

[0345] An "abnormal" state refers to a condition that deviates from the normal operation of a household appliance, which can lead to malfunction or excessive energy consumption.

[0346] "Notification" refers to the means of informing users of anomaly detections, and is carried out via smartphones or other communication devices.

[0347] "Automatic arrangement of repair service providers" refers to the process by which the server automatically schedules the appropriate repair service when an anomaly is detected.

[0348] An "alternative solution" is a method of proposing a temporary solution for a malfunctioning device, aiming to minimize the impact by utilizing other household devices.

[0349] "Energy usage data" refers to information about the energy consumed within a household, and analyzing this data can lead to energy conservation and increased efficiency.

[0350] An "alert" refers to a notification that warns or alerts the user when energy waste or abnormalities are detected.

[0351] This invention is a system that collects operational data in real time from various devices in the home and enables rapid response when an anomaly is detected. The server centrally manages the operational data collected through various sensors and IoT devices, and analyzes the collected data to detect anomalies. Python is used for the analysis, utilizing data analysis libraries such as Pandas and NumPy, and machine learning algorithms using TensorFlow are applied for anomaly detection.

[0352] The device sends a notification to the user when an anomaly is detected, prompting immediate verification and action. The notification is delivered via a smartphone application, which allows users to arrange for a repair service within the application. This includes a feature that suggests the optimal repair date and time in conjunction with the user's schedule.

[0353] Furthermore, the server proposes alternative measures to compensate for the malfunctioning equipment, based on data from other devices. For example, if a refrigerator malfunctions, it will provide the user with specific suggestions via the terminal, such as adjusting the air conditioner temperature to stabilize the indoor environment.

[0354] Energy usage data is also collected simultaneously, enabling efficient energy management. When energy waste is detected based on the data, an alert is sent, and the user receives suggestions on how to optimize their usage.

[0355] This technology can also be enhanced using generative AI models. For example, if the refrigerator is running longer than usual, it can generate a prompt message to advise the user, such as, "Please check that the refrigerator door is properly closed."

[0356] Examples of prompts for generative AI models:

[0357] An abnormal operating pattern has been detected in the refrigerator data. Please create a notification message for the appropriate user.

[0358] Through this system, users can maintain comfort within their homes while being supported in a sustainable and efficient lifestyle.

[0359] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0360] Step 1:

[0361] The server collects operational data from devices within each home via IoT devices. This data includes temperature, power consumption, and operating time. The input is real-time operational data transmitted from each device, and the output is the storage of this data in a centrally managed database. Specifically, it receives data packets from each device, converts the format as needed, and stores them securely.

[0362] Step 2:

[0363] The server analyzes the collected data using data analysis libraries such as Pandas and NumPy, and applies anomaly detection algorithms. The input is behavioral data obtained from the database, and the output is a flag indicating whether an anomaly was detected. Specifically, it compares the data with historical data and searches for patterns that exceed a set threshold.

[0364] Step 3:

[0365] If an anomaly is detected, the server sends a notification to the user's device. The input is the anomaly detection result and the user's contact information, and the output is an alert message sent via the notification system. The anomaly information is displayed as a text message or push notification via the user's smartphone app.

[0366] Step 4:

[0367] The device provides a real-time interface to prompt users who receive notifications to take action. Input is notification data from the server, and output is notification confirmation and option selection via the user interface. Specifically, it supports operations such as displaying a repair provider selection screen by tapping the notification.

[0368] Step 5:

[0369] The server automatically arranges repair technicians in conjunction with the user's schedule. Inputs include the user's preferred date and time and a database of available repair technicians; output is confirmed reservation information. A scheduling algorithm is used to select the optimal date, time, and technician, and automatically confirm the reservation.

[0370] Step 6:

[0371] The terminal presents the user with alternative solutions to compensate for a malfunctioning device. Inputs include operating data from other household appliances and action plan templates, while output is a suggested alternative solution. Specifically, if the refrigerator malfunctions, it creates and sends a message suggesting temperature adjustment using the air conditioner.

[0372] Step 7:

[0373] The server uses a generative AI model to enhance the analysis of anomalies and energy consumption. The input consists of collected data and past anomaly cases, while the output is a prompt message suggesting improvements or actions to take. For example, it might generate a prompt such as, "An anomaly has been detected in the refrigerator data. Please create a notification message for the appropriate user."

[0374] In this way, the entire system achieves efficient and sustainable maintenance of a home environment.

[0375] 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.

[0376] This invention provides optimal responses that take into account the user's emotions by integrating an emotion engine into a system that analyzes operational data acquired from household devices and detects abnormalities. This system is configured as follows.

[0377] Data collection and analysis

[0378] The device collects various data in real time through sensors installed in household appliances. This data includes temperature, power consumption, and appliance operating status. The collected data is transmitted to a server in a secure environment.

[0379] After receiving the data, the server uses machine learning algorithms to analyze the data and detect anomaly patterns. If an anomaly is detected, that information is sent to the terminal.

[0380] User notifications and sentiment analysis

[0381] When the device receives information about an anomaly detection, it displays an appropriate notification to the user. At this time, the emotion engine is activated and analyzes the user's emotions in real time through voice and camera sensors.

[0382] Based on sentiment analysis results, the server adapts notifications and suggestions to the user's emotions. For example, if the user is feeling stressed, a simple and gentle notification message will be used.

[0383] Repair arrangements and alternative solutions proposed

[0384] The server searches for and suggests the most suitable repair service provider based on the user's schedule and emotional state. In this process, an emotional engine considers arrangements to minimize the user's stress.

[0385] The device presents the user with repair arrangement proposals provided by the server, and completes the reservation after receiving approval. It also coordinates with other electronic devices in the home as needed to calculate alternative solutions.

[0386] Utilization of community data

[0387] The server analyzes operational data collected from multiple households to identify frequently occurring anomaly patterns and derive adaptive failure prevention measures.

[0388] The device provides this information to the user and offers advice tailored to their emotional state. For example, it might use gentle language to warn them and alleviate anxiety.

[0389] For example, if an abnormality is detected in the refrigerator, when the user returns home and sentiment analysis begins, the sentiment engine recognizes that the user is experiencing stress. Based on this, the device displays a reassuring notification such as, "A minor abnormality has been found in the refrigerator, but we will address it immediately, so there is no need to worry." Subsequently, it arranges for a repair service and provides a suggestion that best suits the user's schedule and emotions, thereby achieving comprehensive home management.

[0390] The following describes the processing flow.

[0391] Step 1:

[0392] The device collects motion data in real time from sensors connected to devices within the home and transmits that data to a server via the internet.

[0393] Step 2:

[0394] The server stores the operational data it receives in a database and uses machine learning algorithms to analyze patterns that should be detected as anomalies. When an anomaly is detected, detailed information is generated.

[0395] Step 3:

[0396] The server sends the results of the anomaly detection to the terminal. At this time, it creates a notification message according to the type and urgency of the anomaly.

[0397] Step 4:

[0398] The device receives a notification message and displays it to the user. Simultaneously, the device's built-in emotion engine uses the camera and microphone to collect data in order to analyze the user's emotions.

[0399] Step 5:

[0400] The device uses an emotion engine to analyze the user's voice and facial expressions to determine their emotional state. The results are sent to a server and used to optimize notification content and response strategies.

[0401] Step 6:

[0402] The server takes the user's emotional state into account and modifies notification messages and response processes accordingly. For example, if the user is feeling anxious, it generates a more reassuring message.

[0403] Step 7:

[0404] The server searches a database of repair companies and suggests the most suitable repair company and date based on the user's schedule and emotional state. This information is then sent to the terminal.

[0405] Step 8:

[0406] The device displays a list of repair service providers optimized for the user's device and asks for their approval. The user can either approve the proposal or submit a request for modifications.

[0407] Step 9:

[0408] Once the user approves the proposal, the device sends that information to the server. The server then automatically makes a reservation with a selected repair company.

[0409] Step 10:

[0410] The server calculates alternative solutions for devices experiencing malfunctions. It considers ways to coordinate with other home devices to ensure the user experiences no inconvenience.

[0411] Step 11:

[0412] The device presents the user with a calculated alternative solution and requests their approval to implement it. If the user approves, the alternative solution is automatically applied.

[0413] (Example 2)

[0414] 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".

[0415] Modern homes contain many electronic devices, and malfunctions can occur in these devices. However, if these malfunctions are not detected early and dealt with appropriately, they can significantly disrupt daily life. Furthermore, when dealing with malfunctions, it is necessary to not only perform mechanical actions but also to consider the user's feelings and provide appropriate notifications and suggestions. However, current systems face the challenge of making it difficult to provide optimal responses based on emotions.

[0416] 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.

[0417] In this invention, the server includes means for collecting operational information from devices within the home and analyzing the collected information, means for sending notifications when an abnormality is detected, and means for adjusting the content of notifications based on the user's emotions. This enables early detection of abnormalities and rapid response while taking the user's emotions into consideration.

[0418] "Household devices" refers to electronic devices installed in a home that provide various operational information.

[0419] "Operational information" refers to information including various data such as temperature, power consumption, and operating status obtained from the device.

[0420] An "abnormality" refers to a state or pattern that deviates from the normal operating range of a device.

[0421] "Notification" refers to a means of informing users of the occurrence of an anomaly or other important information.

[0422] "User emotions" refers to the psychological state of a user and includes data analyzed from voice, facial expressions, and other factors.

[0423] "Emotion-based notification content adjustment" refers to procedures and methods for conveying information in a more appropriate format and content depending on the user's emotional state.

[0424] "Information from multiple households" refers to operational information collected from devices within multiple households and stored in a database.

[0425] "Deriving general problems and solutions" refers to the process of identifying common problems from a large amount of operational information and finding countermeasures to address them.

[0426] This invention requires a terminal installed in the home and a server that interacts with it. The terminal collects operational information such as temperature, power consumption, and operating status in real time from sensors built into electronic devices in each home. This information is transmitted to the server via a security protocol.

[0427] The server uses programming frameworks such as Python and TensorFlow to analyze the received operational information. Machine learning algorithms identify anomalous patterns and, if necessary, send anomaly notifications to the terminal. A database management system is used to improve the speed and accuracy of the analysis, and the collected data is continuously used to update the learning model.

[0428] The device provides notifications to the user based on the received anomaly information. These notifications are displayed on the screen or announced by voice. Furthermore, the device is equipped with voice and camera sensors, which allow for real-time analysis of the user's emotions, and the analysis results are sent back to the server.

[0429] The server uses the results of sentiment analysis to generate optimal notifications and suggestions tailored to the user's psychological state. This ensures that even when an anomaly occurs, information is provided in a way that minimizes anxiety and stress for the user. For example, if the analysis reveals that the user is stressed when the refrigerator temperature rises, the device will send a notification such as, "There is a slight problem with the refrigerator, but we will address it immediately, so please don't worry."

[0430] Furthermore, the server optimizes repair responses by considering the schedule information of both the user and the repair company. It also enhances user convenience by coordinating with other electronic devices in the home and suggesting alternative solutions to the user.

[0431] As a concrete example, here is an example of a prompt message: "Please describe the design of a system that detects abnormalities in home devices and provides notifications and suggestions tailored to the user's emotional state."

[0432] In this way, it becomes possible to detect abnormalities in various devices within the home early on and to provide prompt and accurate responses that take into consideration the user's feelings.

[0433] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0434] Step 1:

[0435] The terminal collects operational information such as temperature, power consumption, and operating status in real time through sensors installed on each electronic device in the home. At this point, the input is real-time data from the sensors, and the output is an information packet summarizing this data. This information packet is sent to the server via a security protocol.

[0436] Step 2:

[0437] The server receives operational information sent from the terminal. The input data is analyzed using machine learning algorithms such as Python or TensorFlow. The output is a determination result indicating whether or not an anomaly is present. If an anomaly is detected based on this result, detailed anomaly information is generated. This information is sent back to the terminal.

[0438] Step 3:

[0439] The terminal notifies the user based on anomaly information received from the server. At this stage, the input is anomaly information from the server, and the output is visual and audible notification to the user. Specifically, this may involve displaying a warning message on the terminal's display and having a voice assistant verbally inform the user of the anomaly.

[0440] Step 4:

[0441] The device uses built-in voice and camera sensors to analyze the user's facial expressions and voice tone. The input is real-time audio and video data, which is processed by an emotion analysis engine. The output is data indicating the user's emotional state, and this data is also sent to the server.

[0442] Step 5:

[0443] The server receives the user's emotional state and adjusts the content of notifications and suggestions accordingly. The input to this process is the user's emotional state data, and the output is the text of notifications and suggestions adapted to that emotion. The server sends this text to the terminal and displays it to the user in an emotionally sensitive manner.

[0444] Step 6:

[0445] The server uses an AI model to search for the most suitable repair provider based on the user's schedule information and the availability of repair providers. The input is the user's time information and provider availability data, and the output is a list of potential optimal repair providers. This list is sent to the terminal and presented to the user.

[0446] Step 7:

[0447] The terminal presents the user with a list of potential repair companies received from the server and prompts them to make a final selection. The input is the list of potential repair companies, and the output is the user's selection data. Once the user agrees, the terminal completes the reservation and displays a confirmation notification.

[0448] Through these processing steps, the terminal and server work together to quickly detect abnormalities in home electronic devices and provide notifications and countermeasures that are sensitive to the user's feelings.

[0449] (Application Example 2)

[0450] 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."

[0451] In recent years, the number of devices in homes has increased, and consequently, the frequency of malfunctions has also risen. While conventional systems can detect and notify users of device malfunctions, they do not provide countermeasures that take into account the emotional burden on the user. Furthermore, even when arranging repairs or proposing alternative solutions after a malfunction is detected, it is difficult to provide support that is tailored to the emotional state of each individual user. Therefore, there is a need for a system that enables flexible and efficient responses that take into account the user's feelings.

[0452] 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.

[0453] In this invention, the server includes means for collecting operational information from devices within the home, means for analyzing the collected operational information and detecting anomalies, and means for analyzing the user's emotions and providing an optimal notification method based on those emotions. This makes it possible to provide notifications tailored to the user's emotions after detecting an anomaly, thereby reducing the user's mental burden and enabling prompt repair arrangements and the suggestion of alternative solutions.

[0454] "Household appliances" refers to electronic devices and electrical appliances used within the home, and specifically includes refrigerators, washing machines, air conditioners, etc.

[0455] "Operational information" refers to information such as operating status, power consumption, and temperature collected from household appliances.

[0456] "Means for detecting abnormalities" refers to technologies or devices for analyzing collected operational information and identifying states that differ from normal operation.

[0457] "Means of sending notifications" refer to methods for informing users of information when an anomaly is detected, and can take the form of voice, messages, alerts, etc.

[0458] "A means of automatically arranging and scheduling repair work" refers to a method for selecting the appropriate repair technician based on detected abnormalities and automatically scheduling repairs.

[0459] "Electronic devices" refers to various electrical and electronic equipment that operates within the home.

[0460] "Means of proposing alternative solutions" refer to methods of using machine learning and algorithms to present other possible options or methods when an anomaly occurs.

[0461] "Methods for analyzing user emotions" refers to technologies that analyze a user's emotional state using data collected from sensors such as audio and video.

[0462] "Means of providing the optimal notification method" refers to means of delivering notifications in the most appropriate format and content based on the user's emotional state.

[0463] A system for carrying out this invention includes a server that collects operational information from household devices and analyzes that information, and a terminal that receives input from a user and proposes the optimal action based on that input.

[0464] The server collects operational information in real time using various sensors installed in the home. Small computers such as Raspberry Pi or NVIDIA Jetson Nano can be used as hardware. This allows diverse data, such as temperature, power consumption, and device operating status, to be transmitted to the server in a secure environment.

[0465] If an anomaly is detected, the server analyzes the collected data using a pre-trained machine learning algorithm (for example, a model built using TensorFlow or PyTorch). This analysis identifies anomaly patterns. Based on this information, the terminal sends an anomaly notification to the user.

[0466] The user's emotional state is evaluated using emotion analysis software (e.g., OpenCV or Google Cloud Vision API) based on data from the device's camera and microphone. The results of this emotion analysis are sent to a server, which generates personalized notifications and suggestions. The content of the notifications is generated using a natural language generation API (e.g., OpenAI's GPT-3).

[0467] For example, when the temperature sensor inside the refrigerator indicates an abnormality, the server recognizes the abnormality based on that information, and the terminal uses sentiment analysis to determine whether the user is feeling anxious. After obtaining the result, the terminal provides a reassuring message such as, "There is a problem with the refrigerator temperature, but we will guide you on how to deal with it shortly."

[0468] An example of a prompt message for the generating AI model would be: "Generate a gentle message to alleviate user anxiety when a malfunction is detected in a home device." This allows the user to quickly understand the necessary countermeasures while minimizing their emotional burden.

[0469] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0470] Step 1:

[0471] The server collects operational information from sensors placed throughout the home. It receives data from the sensors (temperature, power consumption, operating status, etc.) as input, which is then transmitted to the server using the appropriate protocol. The server receives a real-time stream of operational data as output.

[0472] Step 2:

[0473] The server analyzes received operational data using machine learning algorithms to detect anomalies. The input is an operational data stream, which is passed through a machine learning model for analysis. The output includes the presence or absence of anomaly patterns and, if anomalies are present, specific information.

[0474] Step 3:

[0475] If an anomaly is detected, the server sends that information to the terminal. The input is anomaly information, which is transmitted to the terminal using a communication module. The output is the terminal receiving the anomaly.

[0476] Step 4:

[0477] The device notifies the user based on the received anomaly information. Furthermore, the device uses its built-in camera and microphone to analyze the user's emotions. Inputs include anomaly information and real-time audio and video data, and emotion analysis software is used to determine the emotional state. Outputs include the user's emotion analysis results and appropriate notification content.

[0478] Step 5:

[0479] The server uses a natural language generation API to generate an appropriate message based on the sentiment analysis results. It takes user sentiment data as input, sends prompt sentences to a generation AI model, and obtains a message. The output is a customized message to notify the user.

[0480] Step 6:

[0481] The terminal displays the generated message to the user and automatically arranges repair work as needed. The system receives the generated message and the repair technician's availability as input, and then proposes the optimal repair schedule based on this information. The output completes the display of information to the user and the arrangement of the work.

[0482] 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.

[0483] 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.

[0484] 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.

[0485] [Third Embodiment]

[0486] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0487] 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.

[0488] 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).

[0489] 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.

[0490] 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.

[0491] 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).

[0492] 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.

[0493] 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.

[0494] 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.

[0495] 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.

[0496] 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.

[0497] 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".

[0498] This invention provides a system for detecting anomalies and responding quickly by collecting and analyzing operational data from household devices in real time. The system operates with the cooperation of a server, terminals, and users as follows:

[0499] Data collection and anomaly detection

[0500] The device collects operational data such as temperature, power consumption, and operating time from various devices in the home in real time. This data is securely transmitted to a server via the internet.

[0501] The server stores the received data and analyzes it using an anomaly detection algorithm. When an anomaly is detected, it performs an evaluation according to the type and severity of the anomaly.

[0502] Anomaly notification and repair arrangement

[0503] If the server detects an anomaly as a result of its analysis, it generates a notification based on a pre-configured urgency level. This notification is sent to the terminal for verification.

[0504] The device displays a notification from the server to the user, informing them that immediate repair is required.

[0505] The server initiates a process to select the most suitable repair service provider and date / time based on the user's calendar information and a database of repair service providers.

[0506] Collaboration with repair companies

[0507] The device proposes a repair date to the user, and once approved, that information is sent to the server, and a reservation is automatically made with the repair company.

[0508] Proposal of alternative measures

[0509] When an anomaly occurs, the server calculates possible alternative solutions based on information from other household devices. For example, if the refrigerator breaks down, it will present the user with specific alternative solutions via the terminal, such as using the air conditioner to adjust the room temperature to help preserve food while the appliance is unusable.

[0510] Utilization of community data

[0511] The server comprehensively analyzes data collected from multiple households to identify frequently occurring failure patterns. Based on this analysis, the terminal advises the user, "Many problems in this range have been reported with the same model. We recommend regular inspections."

[0512] For example, if abnormal data is reported from the refrigerator's temperature sensor, the server analyzes it and determines that the temperature is outside the acceptable range. This information is immediately notified to the user via their device. A repair technician is automatically arranged to fit the user's schedule, and at the same time, supplementary alternative measures are suggested and implemented according to official recommendations. This allows the user to enjoy a quick and efficient breakdown response.

[0513] The following describes the processing flow.

[0514] Step 1:

[0515] The user activates a device in their home. Sensors connected to the terminal begin operating and continuously measure operational data such as temperature, power consumption, and operating time in real time.

[0516] Step 2:

[0517] The device collects operational data and sends it to the server via the internet. The data is transferred through a secure channel.

[0518] Step 3:

[0519] The server stores the raw data it receives and begins analyzing the data using an anomaly detection algorithm. It performs comparative analysis with past data to check for deviations from normal operating patterns.

[0520] Step 4:

[0521] When the server detects an anomaly, it evaluates the type and severity of the anomaly. Once the anomaly is identified, it generates a notification for the user based on that information.

[0522] Step 5:

[0523] An anomaly notification is sent from the server to the terminal. The notification includes the specific nature of the anomaly and its urgency.

[0524] Step 6:

[0525] The device displays notifications received from the server, immediately informing the user of any anomalies. Based on this information, the user can then consider prompt countermeasures.

[0526] Step 7:

[0527] When a home appliance malfunctions, the server searches its database for a reliable repair company. It then analyzes the user's schedule and the company's availability to prepare to suggest the optimal repair date.

[0528] Step 8:

[0529] The terminal notifies the user of the repair company and proposed schedule based on the server's calculation results. The user can then approve or request changes to the proposal.

[0530] Step 9:

[0531] After the user approves the repair arrangement proposal, that information is sent back to the server. The server automatically makes a reservation with the repair company and completes the arrangement.

[0532] Step 10:

[0533] The server calculates alternative solutions based on data obtained from other electronic devices in the home, depending on the abnormal situation. For example, it considers ways to mitigate the problem by using other devices if a specific device fails.

[0534] Step 11:

[0535] The device presents the user with calculated alternative solutions. The user reviews the proposed alternatives and, if necessary, approves their implementation, after which the alternatives are automatically applied.

[0536] (Example 1)

[0537] 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."

[0538] Conventional systems lacked sufficient mechanisms to quickly respond to malfunctions, even when they collected operational data from household devices. In particular, delays in notification and dispatching repair technicians raised concerns about prolonged problems and further damage. Furthermore, there was a lack of systems that comprehensively utilized data from multiple devices, not just individual ones, to provide rational alternative solutions.

[0539] 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.

[0540] In this invention, the server includes means for acquiring operational information from devices within the living environment, means for identifying abnormalities and distributing notifications, and means for automatically arranging and scheduling repair personnel. This enables residents to quickly identify equipment abnormalities, take prompt action, and arrange for optimal repairs.

[0541] "Devices within the living environment" refers to various electronic and electrical devices installed in the home, and these are the devices from which operational data is collected.

[0542] "Operational information" refers to various data such as temperature, power consumption, and operating time generated when a device is in operation, and is used for system monitoring and analysis.

[0543] An "anomaly" refers to activity that deviates from the normal operating range based on operational information, and is identified using machine learning algorithms or similar methods.

[0544] A "notification" refers to a message or alert sent to a user to inform them of an anomaly detected by the system.

[0545] A "repair technician" refers to a specialist or contractor dispatched to address equipment malfunctions, and is arranged with the user's approval.

[0546] An "alternative solution" refers to a proposal for resolving a problem by coordinating with other devices when one device malfunctions, and is provided to maintain continuity of daily life.

[0547] "Information analysis" refers to the process of using acquired operational information to detect anomalies, identify patterns, and derive appropriate countermeasures based on the situation.

[0548] This invention is a comprehensive system for collecting operational information from multiple devices within a living environment and for detecting and addressing abnormalities. The system is realized through the cooperation of a server, terminals, and users.

[0549] The server first receives operational information sent from the terminal. The hardware used here includes storage devices for storing data and processors for processing the data. The software includes a database system (e.g., MongoDB) and machine learning algorithms for anomaly detection. Machine learning algorithms are typically implemented using the Python language or TensorFlow libraries. This allows the server to analyze the operational information and quickly identify anomalies.

[0550] The terminal collects operational information in real time from various devices within the living environment. This includes temperature sensors, power measurement sensors, and Wi-Fi modules. The collected data is transmitted to a server via secure communication such as the HTTPS protocol. The terminal also receives notifications from the server and informs the user. Notifications are sent to the user visually through smartphone applications, etc.

[0551] When a user receives a notification of an anomaly, they check the situation through the application interface and take appropriate action. The server proposes the optimal repair date, taking into account the user's schedule information and the availability of repair personnel. This proposal is also displayed to the user via their terminal.

[0552] For example, if the refrigerator's temperature sensor reports unusual data, the server analyzes the data and determines that there is a temperature anomaly. The server immediately generates an anomaly notification and sends it to the user's smartphone via a terminal. Subsequently, the server automatically schedules a repair appointment and suggests an alternative to the user, such as using the air conditioner to adjust the room temperature. This allows the user to respond quickly and efficiently.

[0553] An example of a prompt for a generated AI model might be, "Please explain in detail the process of data collection and anomaly detection when a home appliance malfunctions." This would deepen the understanding of the entire process and allow the user to obtain more detailed information about how the system works.

[0554] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0555] Step 1:

[0556] The terminal collects operational data in real time from various devices placed within the home. The input here is raw data from various sensors (temperature sensors, power measurement sensors, etc.) installed in the devices. This data is temporarily stored inside the terminal and sent to the server using the HTTPS protocol. In this process, the data is formatted and compressed and sent to the server in the appropriate format.

[0557] Step 2:

[0558] The server stores the operational data received from the terminal in a database. The input for this step is formatted sensor data. The server stores the data in an unstructured database (e.g., MongoDB) and creates an index to make the data easily accessible when needed. The data is stored in a time-series format and used for subsequent analysis.

[0559] Step 3:

[0560] The server performs anomaly detection using stored data. The input is operational data stored in a database. The server applies machine learning algorithms to identify abnormal patterns. Here, an anomaly detection model using Python and TensorFlow is in operation. If an anomaly is detected as a result of the analysis, that information is generated as an anomaly notification.

[0561] Step 4:

[0562] The server sends the generated anomaly notification to the terminal. The input is the result of the anomaly detection, and the output is the content that the terminal notifies the user of. This notification includes the type of anomaly, its urgency, and recommended actions, which are then communicated to the user by the terminal.

[0563] Step 5:

[0564] The user receives a notification from their device and checks the nature of the anomaly. The input is the notification information from the device. The user views the notification using a smartphone application and considers countermeasures as needed. This includes interface operations on the device.

[0565] Step 6:

[0566] The server selects a repair date based on the user's schedule information and the availability of repair technicians. Inputs include the user's calendar and database information on repair technicians. An algorithm is used to calculate the optimal date, and the result is returned to the terminal. This enables rapid repair arrangements.

[0567] Step 7:

[0568] The server calculates possible alternative solutions based on information from other devices when an anomaly occurs and proposes them to the user via the terminal. Inputs are operational data from other devices and the results of previous anomaly detection. The server calculates the alternative solutions and sends the results as output to the terminal.

[0569] (Application Example 1)

[0570] 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."

[0571] The objectives are to enable early detection and rapid response to malfunctions in household electronic devices, while also suppressing wasteful energy consumption and managing it efficiently. Furthermore, the objectives are to minimize the impact of household malfunctions on other devices and to quickly provide necessary alternative solutions, thereby creating a more comfortable and sustainable living environment.

[0572] 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.

[0573] In this invention, the server includes means for collecting operational data from devices in the home, means for analyzing the collected data to detect anomalies, means for sending notifications regarding anomalies, means for automatically arranging and booking repair services according to the anomaly, means for coordinating with other electronic devices in the home to propose alternative solutions, means for analyzing data from multiple homes to derive common problems and solutions, means for collecting and optimizing energy usage data, and means for detecting anomalies in energy consumption and sending alerts. This enables rapid and effective fault response and energy management in the living environment.

[0574] "Household appliances" refers to electrical, electronic, and mechanical devices installed in a residence, and includes, but is not limited to, refrigerators, air conditioners, washing machines, and lighting.

[0575] "Operational data" refers to information indicating the operating status of a device, such as its temperature, power consumption, and operating time.

[0576] "Analysis" refers to the process of using collected data to identify patterns and anomalies in the data, employing machine learning algorithms and other techniques.

[0577] An "abnormal" state refers to a condition that deviates from the normal operation of a household appliance, which can lead to malfunction or excessive energy consumption.

[0578] "Notification" refers to the means of informing users of anomaly detections, and is carried out via smartphones or other communication devices.

[0579] "Automatic arrangement of repair service providers" refers to the process by which the server automatically schedules the appropriate repair service when an anomaly is detected.

[0580] An "alternative solution" is a method of proposing a temporary solution for a malfunctioning device, aiming to minimize the impact by utilizing other household devices.

[0581] "Energy usage data" refers to information about the energy consumed within a household, and analyzing this data can lead to energy conservation and increased efficiency.

[0582] An "alert" refers to a notification that warns or alerts the user when energy waste or abnormalities are detected.

[0583] This invention is a system that collects operational data in real time from various devices in the home and enables rapid response when an anomaly is detected. The server centrally manages the operational data collected through various sensors and IoT devices, and analyzes the collected data to detect anomalies. Python is used for the analysis, utilizing data analysis libraries such as Pandas and NumPy, and machine learning algorithms using TensorFlow are applied for anomaly detection.

[0584] The device sends a notification to the user when an anomaly is detected, prompting immediate verification and action. The notification is delivered via a smartphone application, which allows users to arrange for a repair service within the application. This includes a feature that suggests the optimal repair date and time in conjunction with the user's schedule.

[0585] Furthermore, the server proposes alternative measures to compensate for the malfunctioning equipment, based on data from other devices. For example, if a refrigerator malfunctions, it will provide the user with specific suggestions via the terminal, such as adjusting the air conditioner temperature to stabilize the indoor environment.

[0586] Energy usage data is also collected simultaneously, enabling efficient energy management. When energy waste is detected based on the data, an alert is sent, and the user receives suggestions on how to optimize their usage.

[0587] This technology can also be enhanced using generative AI models. For example, if the refrigerator is running longer than usual, it can generate a prompt message to advise the user, such as, "Please check that the refrigerator door is properly closed."

[0588] Examples of prompts for generative AI models:

[0589] An abnormal operating pattern has been detected in the refrigerator data. Please create a notification message for the appropriate user.

[0590] Through this system, users can maintain comfort within their homes while being supported in a sustainable and efficient lifestyle.

[0591] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0592] Step 1:

[0593] The server collects operational data from devices within each home via IoT devices. This data includes temperature, power consumption, and operating time. The input is real-time operational data transmitted from each device, and the output is the storage of this data in a centrally managed database. Specifically, it receives data packets from each device, converts the format as needed, and stores them securely.

[0594] Step 2:

[0595] The server analyzes the collected data using data analysis libraries such as Pandas and NumPy, and applies anomaly detection algorithms. The input is behavioral data obtained from the database, and the output is a flag indicating whether an anomaly was detected. Specifically, it compares the data with historical data and searches for patterns that exceed a set threshold.

[0596] Step 3:

[0597] If an anomaly is detected, the server sends a notification to the user's device. The input is the anomaly detection result and the user's contact information, and the output is an alert message sent via the notification system. The anomaly information is displayed as a text message or push notification via the user's smartphone app.

[0598] Step 4:

[0599] The device provides a real-time interface to prompt users who receive notifications to take action. Input is notification data from the server, and output is notification confirmation and option selection via the user interface. Specifically, it supports operations such as displaying a repair provider selection screen by tapping the notification.

[0600] Step 5:

[0601] The server automatically arranges repair technicians in conjunction with the user's schedule. Inputs include the user's preferred date and time and a database of available repair technicians; output is confirmed reservation information. A scheduling algorithm is used to select the optimal date, time, and technician, and automatically confirm the reservation.

[0602] Step 6:

[0603] The terminal presents the user with alternative solutions to compensate for a malfunctioning device. Inputs include operating data from other household appliances and action plan templates, while output is a suggested alternative solution. Specifically, if the refrigerator malfunctions, it creates and sends a message suggesting temperature adjustment using the air conditioner.

[0604] Step 7:

[0605] The server uses a generative AI model to enhance the analysis of anomalies and energy consumption. The input consists of collected data and past anomaly cases, while the output is a prompt message suggesting improvements or actions to take. For example, it might generate a prompt such as, "An anomaly has been detected in the refrigerator data. Please create a notification message for the appropriate user."

[0606] In this way, the entire system achieves efficient and sustainable maintenance of a home environment.

[0607] 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.

[0608] This invention provides optimal responses that take into account the user's emotions by integrating an emotion engine into a system that analyzes operational data acquired from household devices and detects abnormalities. This system is configured as follows.

[0609] Data collection and analysis

[0610] The device collects various data in real time through sensors installed in household appliances. This data includes temperature, power consumption, and appliance operating status. The collected data is transmitted to a server in a secure environment.

[0611] After receiving the data, the server uses machine learning algorithms to analyze the data and detect anomaly patterns. If an anomaly is detected, that information is sent to the terminal.

[0612] User notifications and sentiment analysis

[0613] When the device receives information about an anomaly detection, it displays an appropriate notification to the user. At this time, the emotion engine is activated and analyzes the user's emotions in real time through voice and camera sensors.

[0614] Based on sentiment analysis results, the server adapts notifications and suggestions to the user's emotions. For example, if the user is feeling stressed, a simple and gentle notification message will be used.

[0615] Repair arrangements and alternative solutions proposed

[0616] The server searches for and suggests the most suitable repair service provider based on the user's schedule and emotional state. In this process, an emotional engine considers arrangements to minimize the user's stress.

[0617] The device presents the user with repair arrangement proposals provided by the server, and completes the reservation after receiving approval. It also coordinates with other electronic devices in the home as needed to calculate alternative solutions.

[0618] Utilization of community data

[0619] The server analyzes operational data collected from multiple households to identify frequently occurring anomaly patterns and derive adaptive failure prevention measures.

[0620] The device provides this information to the user and offers advice tailored to their emotional state. For example, it might use gentle language to warn them and alleviate anxiety.

[0621] For example, if an abnormality is detected in the refrigerator, when the user returns home and sentiment analysis begins, the sentiment engine recognizes that the user is experiencing stress. Based on this, the device displays a reassuring notification such as, "A minor abnormality has been found in the refrigerator, but we will address it immediately, so there is no need to worry." Subsequently, it arranges for a repair service and provides a suggestion that best suits the user's schedule and emotions, thereby achieving comprehensive home management.

[0622] The following describes the processing flow.

[0623] Step 1:

[0624] The device collects motion data in real time from sensors connected to devices within the home and transmits that data to a server via the internet.

[0625] Step 2:

[0626] The server stores the operational data it receives in a database and uses machine learning algorithms to analyze patterns that should be detected as anomalies. When an anomaly is detected, detailed information is generated.

[0627] Step 3:

[0628] The server sends the results of the anomaly detection to the terminal. At this time, it creates a notification message according to the type and urgency of the anomaly.

[0629] Step 4:

[0630] The device receives a notification message and displays it to the user. Simultaneously, the device's built-in emotion engine uses the camera and microphone to collect data in order to analyze the user's emotions.

[0631] Step 5:

[0632] The device uses an emotion engine to analyze the user's voice and facial expressions to determine their emotional state. The results are sent to a server and used to optimize notification content and response strategies.

[0633] Step 6:

[0634] The server takes the user's emotional state into account and modifies notification messages and response processes accordingly. For example, if the user is feeling anxious, it generates a more reassuring message.

[0635] Step 7:

[0636] The server searches a database of repair companies and suggests the most suitable repair company and date based on the user's schedule and emotional state. This information is then sent to the terminal.

[0637] Step 8:

[0638] The device displays a list of repair service providers optimized for the user's device and asks for their approval. The user can either approve the proposal or submit a request for modifications.

[0639] Step 9:

[0640] Once the user approves the proposal, the device sends that information to the server. The server then automatically makes a reservation with a selected repair company.

[0641] Step 10:

[0642] The server calculates alternative solutions for devices experiencing malfunctions. It considers ways to coordinate with other home devices to ensure the user experiences no inconvenience.

[0643] Step 11:

[0644] The device presents the user with a calculated alternative solution and requests their approval to implement it. If the user approves, the alternative solution is automatically applied.

[0645] (Example 2)

[0646] 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."

[0647] Modern homes contain many electronic devices, and malfunctions can occur in these devices. However, if these malfunctions are not detected early and dealt with appropriately, they can significantly disrupt daily life. Furthermore, when dealing with malfunctions, it is necessary to not only perform mechanical actions but also to consider the user's feelings and provide appropriate notifications and suggestions. However, current systems face the challenge of making it difficult to provide optimal responses based on emotions.

[0648] 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.

[0649] In this invention, the server includes means for collecting operational information from devices within the home and analyzing the collected information, means for sending notifications when an abnormality is detected, and means for adjusting the content of notifications based on the user's emotions. This enables early detection of abnormalities and rapid response while taking the user's emotions into consideration.

[0650] "Household devices" refers to electronic devices installed in a home that provide various operational information.

[0651] "Operational information" refers to information including various data such as temperature, power consumption, and operating status obtained from the device.

[0652] An "abnormality" refers to a state or pattern that deviates from the normal operating range of a device.

[0653] "Notification" refers to a means of informing users of the occurrence of an anomaly or other important information.

[0654] "User emotions" refers to the psychological state of a user and includes data analyzed from voice, facial expressions, and other factors.

[0655] "Emotion-based notification content adjustment" refers to procedures and methods for conveying information in a more appropriate format and content depending on the user's emotional state.

[0656] "Information from multiple households" refers to operational information collected from devices within multiple households and stored in a database.

[0657] "Deriving general problems and solutions" refers to the process of identifying common problems from a large amount of operational information and finding countermeasures to address them.

[0658] This invention requires a terminal installed in the home and a server that interacts with it. The terminal collects operational information such as temperature, power consumption, and operating status in real time from sensors built into electronic devices in each home. This information is transmitted to the server via a security protocol.

[0659] The server uses programming frameworks such as Python and TensorFlow to analyze the received operational information. Machine learning algorithms identify anomalous patterns and, if necessary, send anomaly notifications to the terminal. A database management system is used to improve the speed and accuracy of the analysis, and the collected data is continuously used to update the learning model.

[0660] The device provides notifications to the user based on the received anomaly information. These notifications are displayed on the screen or announced by voice. Furthermore, the device is equipped with voice and camera sensors, which allow for real-time analysis of the user's emotions, and the analysis results are sent back to the server.

[0661] The server uses the results of sentiment analysis to generate optimal notifications and suggestions tailored to the user's psychological state. This ensures that even when an anomaly occurs, information is provided in a way that minimizes anxiety and stress for the user. For example, if the analysis reveals that the user is stressed when the refrigerator temperature rises, the device will send a notification such as, "There is a slight problem with the refrigerator, but we will address it immediately, so please don't worry."

[0662] Furthermore, the server optimizes repair responses by considering the schedule information of both the user and the repair company. It also enhances user convenience by coordinating with other electronic devices in the home and suggesting alternative solutions to the user.

[0663] As a concrete example, here is an example of a prompt message: "Please describe the design of a system that detects abnormalities in home devices and provides notifications and suggestions tailored to the user's emotional state."

[0664] In this way, it becomes possible to detect abnormalities in various devices within the home early on and to provide prompt and accurate responses that take into consideration the user's feelings.

[0665] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0666] Step 1:

[0667] The terminal collects operational information such as temperature, power consumption, and operating status in real time through sensors installed on each electronic device in the home. At this point, the input is real-time data from the sensors, and the output is an information packet summarizing this data. This information packet is sent to the server via a security protocol.

[0668] Step 2:

[0669] The server receives operational information sent from the terminal. The input data is analyzed using machine learning algorithms such as Python or TensorFlow. The output is a determination result indicating whether or not an anomaly is present. If an anomaly is detected based on this result, detailed anomaly information is generated. This information is sent back to the terminal.

[0670] Step 3:

[0671] The terminal notifies the user based on anomaly information received from the server. At this stage, the input is anomaly information from the server, and the output is visual and audible notification to the user. Specifically, this may involve displaying a warning message on the terminal's display and having a voice assistant verbally inform the user of the anomaly.

[0672] Step 4:

[0673] The device uses built-in voice and camera sensors to analyze the user's facial expressions and voice tone. The input is real-time audio and video data, which is processed by an emotion analysis engine. The output is data indicating the user's emotional state, and this data is also sent to the server.

[0674] Step 5:

[0675] The server receives the user's emotional state and adjusts the content of notifications and suggestions accordingly. The input to this process is the user's emotional state data, and the output is the text of notifications and suggestions adapted to that emotion. The server sends this text to the terminal and displays it to the user in an emotionally sensitive manner.

[0676] Step 6:

[0677] The server uses an AI model to search for the most suitable repair provider based on the user's schedule information and the availability of repair providers. The input is the user's time information and provider availability data, and the output is a list of potential optimal repair providers. This list is sent to the terminal and presented to the user.

[0678] Step 7:

[0679] The terminal presents the user with a list of potential repair companies received from the server and prompts them to make a final selection. The input is the list of potential repair companies, and the output is the user's selection data. Once the user agrees, the terminal completes the reservation and displays a confirmation notification.

[0680] Through these processing steps, the terminal and server work together to quickly detect abnormalities in home electronic devices and provide notifications and countermeasures that are sensitive to the user's feelings.

[0681] (Application Example 2)

[0682] 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."

[0683] In recent years, the number of devices in homes has increased, and consequently, the frequency of malfunctions has also risen. While conventional systems can detect and notify users of device malfunctions, they do not provide countermeasures that take into account the emotional burden on the user. Furthermore, even when arranging repairs or proposing alternative solutions after a malfunction is detected, it is difficult to provide support that is tailored to the emotional state of each individual user. Therefore, there is a need for a system that enables flexible and efficient responses that take into account the user's feelings.

[0684] 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.

[0685] In this invention, the server includes means for collecting operational information from devices within the home, means for analyzing the collected operational information and detecting anomalies, and means for analyzing the user's emotions and providing an optimal notification method based on those emotions. This makes it possible to provide notifications tailored to the user's emotions after detecting an anomaly, thereby reducing the user's mental burden and enabling prompt repair arrangements and the suggestion of alternative solutions.

[0686] "Household appliances" refers to electronic devices and electrical appliances used within the home, and specifically includes refrigerators, washing machines, air conditioners, etc.

[0687] "Operational information" refers to information such as operating status, power consumption, and temperature collected from household appliances.

[0688] "Means for detecting abnormalities" refers to technologies or devices for analyzing collected operational information and identifying states that differ from normal operation.

[0689] "Means of sending notifications" refer to methods for informing users of information when an anomaly is detected, and can take the form of voice, messages, alerts, etc.

[0690] "A means of automatically arranging and scheduling repair work" refers to a method for selecting the appropriate repair technician based on detected abnormalities and automatically scheduling repairs.

[0691] "Electronic devices" refers to various electrical and electronic equipment that operates within the home.

[0692] "Means of proposing alternative solutions" refer to methods of using machine learning and algorithms to present other possible options or methods when an anomaly occurs.

[0693] "Methods for analyzing user emotions" refers to technologies that analyze a user's emotional state using data collected from sensors such as audio and video.

[0694] "Means of providing the optimal notification method" refers to means of delivering notifications in the most appropriate format and content based on the user's emotional state.

[0695] A system for carrying out this invention includes a server that collects operational information from household devices and analyzes that information, and a terminal that receives input from a user and proposes the optimal action based on that input.

[0696] The server collects operational information in real time using various sensors installed in the home. Small computers such as Raspberry Pi or NVIDIA Jetson Nano can be used as hardware. This allows diverse data, such as temperature, power consumption, and device operating status, to be transmitted to the server in a secure environment.

[0697] If an anomaly is detected, the server analyzes the collected data using a pre-trained machine learning algorithm (for example, a model built using TensorFlow or PyTorch). This analysis identifies anomaly patterns. Based on this information, the terminal sends an anomaly notification to the user.

[0698] The user's emotional state is evaluated using emotion analysis software (e.g., OpenCV or Google Cloud Vision API) based on data from the device's camera and microphone. The results of this emotion analysis are sent to a server, which generates personalized notifications and suggestions. The content of the notifications is generated using a natural language generation API (e.g., OpenAI's GPT-3).

[0699] For example, when the temperature sensor inside the refrigerator indicates an abnormality, the server recognizes the abnormality based on that information, and the terminal uses sentiment analysis to determine whether the user is feeling anxious. After obtaining the result, the terminal provides a reassuring message such as, "There is a problem with the refrigerator temperature, but we will guide you on how to deal with it shortly."

[0700] An example of a prompt message for the generating AI model would be: "Generate a gentle message to alleviate user anxiety when a malfunction is detected in a home device." This allows the user to quickly understand the necessary countermeasures while minimizing their emotional burden.

[0701] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0702] Step 1:

[0703] The server collects operational information from sensors placed throughout the home. It receives data from the sensors (temperature, power consumption, operating status, etc.) as input, which is then transmitted to the server using the appropriate protocol. The server receives a real-time stream of operational data as output.

[0704] Step 2:

[0705] The server analyzes received operational data using machine learning algorithms to detect anomalies. The input is an operational data stream, which is passed through a machine learning model for analysis. The output includes the presence or absence of anomaly patterns and, if anomalies are present, specific information.

[0706] Step 3:

[0707] If an anomaly is detected, the server sends that information to the terminal. The input is anomaly information, which is transmitted to the terminal using a communication module. The output is the terminal receiving the anomaly.

[0708] Step 4:

[0709] The device notifies the user based on the received anomaly information. Furthermore, the device uses its built-in camera and microphone to analyze the user's emotions. Inputs include anomaly information and real-time audio and video data, and emotion analysis software is used to determine the emotional state. Outputs include the user's emotion analysis results and appropriate notification content.

[0710] Step 5:

[0711] The server uses a natural language generation API to generate an appropriate message based on the sentiment analysis results. It takes user sentiment data as input, sends prompt sentences to a generation AI model, and obtains a message. The output is a customized message to notify the user.

[0712] Step 6:

[0713] The terminal displays the generated message to the user and automatically arranges repair work as needed. The system receives the generated message and the repair technician's availability as input, and then proposes the optimal repair schedule based on this information. The output completes the display of information to the user and the arrangement of the work.

[0714] 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.

[0715] 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.

[0716] 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.

[0717] [Fourth Embodiment]

[0718] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0719] 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.

[0720] 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).

[0721] 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.

[0722] 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.

[0723] 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).

[0724] 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.

[0725] 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.

[0726] 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.

[0727] 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.

[0728] 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.

[0729] 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.

[0730] 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".

[0731] This invention provides a system for detecting anomalies and responding quickly by collecting and analyzing operational data from household devices in real time. The system operates with the cooperation of a server, terminals, and users as follows:

[0732] Data collection and anomaly detection

[0733] The device collects operational data such as temperature, power consumption, and operating time from various devices in the home in real time. This data is securely transmitted to a server via the internet.

[0734] The server stores the received data and analyzes it using an anomaly detection algorithm. When an anomaly is detected, it performs an evaluation according to the type and severity of the anomaly.

[0735] Anomaly notification and repair arrangement

[0736] If the server detects an anomaly as a result of its analysis, it generates a notification based on a pre-configured urgency level. This notification is sent to the terminal for verification.

[0737] The device displays a notification from the server to the user, informing them that immediate repair is required.

[0738] The server initiates a process to select the most suitable repair service provider and date / time based on the user's calendar information and a database of repair service providers.

[0739] Collaboration with repair companies

[0740] The device proposes a repair date to the user, and once approved, that information is sent to the server, and a reservation is automatically made with the repair company.

[0741] Proposal of alternative measures

[0742] When an anomaly occurs, the server calculates possible alternative solutions based on information from other household devices. For example, if the refrigerator breaks down, it will present the user with specific alternative solutions via the terminal, such as using the air conditioner to adjust the room temperature to help preserve food while the appliance is unusable.

[0743] Utilization of community data

[0744] The server comprehensively analyzes data collected from multiple households to identify frequently occurring failure patterns. Based on this analysis, the terminal advises the user, "Many problems in this range have been reported with the same model. We recommend regular inspections."

[0745] For example, if abnormal data is reported from the refrigerator's temperature sensor, the server analyzes it and determines that the temperature is outside the acceptable range. This information is immediately notified to the user via their device. A repair technician is automatically arranged to fit the user's schedule, and at the same time, supplementary alternative measures are suggested and implemented according to official recommendations. This allows the user to enjoy a quick and efficient breakdown response.

[0746] The following describes the processing flow.

[0747] Step 1:

[0748] The user activates a device in their home. Sensors connected to the terminal begin operating and continuously measure operational data such as temperature, power consumption, and operating time in real time.

[0749] Step 2:

[0750] The device collects operational data and sends it to the server via the internet. The data is transferred through a secure channel.

[0751] Step 3:

[0752] The server stores the raw data it receives and begins analyzing the data using an anomaly detection algorithm. It performs comparative analysis with past data to check for deviations from normal operating patterns.

[0753] Step 4:

[0754] When the server detects an anomaly, it evaluates the type and severity of the anomaly. Once the anomaly is identified, it generates a notification for the user based on that information.

[0755] Step 5:

[0756] An anomaly notification is sent from the server to the terminal. The notification includes the specific nature of the anomaly and its urgency.

[0757] Step 6:

[0758] The device displays notifications received from the server, immediately informing the user of any anomalies. Based on this information, the user can then consider prompt countermeasures.

[0759] Step 7:

[0760] When a home appliance malfunctions, the server searches its database for a reliable repair company. It then analyzes the user's schedule and the company's availability to prepare to suggest the optimal repair date.

[0761] Step 8:

[0762] The terminal notifies the user of the repair company and proposed schedule based on the server's calculation results. The user can then approve or request changes to the proposal.

[0763] Step 9:

[0764] After the user approves the repair arrangement proposal, that information is sent back to the server. The server automatically makes a reservation with the repair company and completes the arrangement.

[0765] Step 10:

[0766] The server calculates alternative solutions based on data obtained from other electronic devices in the home, depending on the abnormal situation. For example, it considers ways to mitigate the problem by using other devices if a specific device fails.

[0767] Step 11:

[0768] The device presents the user with calculated alternative solutions. The user reviews the proposed alternatives and, if necessary, approves their implementation, after which the alternatives are automatically applied.

[0769] (Example 1)

[0770] 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".

[0771] Conventional systems lacked sufficient mechanisms to quickly respond to malfunctions, even when they collected operational data from household devices. In particular, delays in notification and dispatching repair technicians raised concerns about prolonged problems and further damage. Furthermore, there was a lack of systems that comprehensively utilized data from multiple devices, not just individual ones, to provide rational alternative solutions.

[0772] 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.

[0773] In this invention, the server includes means for acquiring operational information from devices within the living environment, means for identifying abnormalities and distributing notifications, and means for automatically arranging and scheduling repair personnel. This enables residents to quickly identify equipment abnormalities, take prompt action, and arrange for optimal repairs.

[0774] "Devices within the living environment" refers to various electronic and electrical devices installed in the home, and these are the devices from which operational data is collected.

[0775] "Operational information" refers to various data such as temperature, power consumption, and operating time generated when a device is in operation, and is used for system monitoring and analysis.

[0776] An "anomaly" refers to activity that deviates from the normal operating range based on operational information, and is identified using machine learning algorithms or similar methods.

[0777] A "notification" refers to a message or alert sent to a user to inform them of an anomaly detected by the system.

[0778] A "repair technician" refers to a specialist or contractor dispatched to address equipment malfunctions, and is arranged with the user's approval.

[0779] An "alternative solution" refers to a proposal for resolving a problem by coordinating with other devices when one device malfunctions, and is provided to maintain continuity of daily life.

[0780] "Information analysis" refers to the process of using acquired operational information to detect anomalies, identify patterns, and derive appropriate countermeasures based on the situation.

[0781] This invention is a comprehensive system for collecting operational information from multiple devices within a living environment and for detecting and addressing abnormalities. The system is realized through the cooperation of a server, terminals, and users.

[0782] The server first receives operational information sent from the terminal. The hardware used here includes storage devices for storing data and processors for processing the data. The software includes a database system (e.g., MongoDB) and machine learning algorithms for anomaly detection. Machine learning algorithms are typically implemented using the Python language or TensorFlow libraries. This allows the server to analyze the operational information and quickly identify anomalies.

[0783] The terminal collects operational information in real time from various devices within the living environment. This includes temperature sensors, power measurement sensors, and Wi-Fi modules. The collected data is transmitted to a server via secure communication such as the HTTPS protocol. The terminal also receives notifications from the server and informs the user. Notifications are sent to the user visually through smartphone applications, etc.

[0784] When a user receives a notification of an anomaly, they check the situation through the application interface and take appropriate action. The server proposes the optimal repair date, taking into account the user's schedule information and the availability of repair personnel. This proposal is also displayed to the user via their terminal.

[0785] For example, if the refrigerator's temperature sensor reports unusual data, the server analyzes the data and determines that there is a temperature anomaly. The server immediately generates an anomaly notification and sends it to the user's smartphone via a terminal. Subsequently, the server automatically schedules a repair appointment and suggests an alternative to the user, such as using the air conditioner to adjust the room temperature. This allows the user to respond quickly and efficiently.

[0786] An example of a prompt for a generated AI model might be, "Please explain in detail the process of data collection and anomaly detection when a home appliance malfunctions." This would deepen the understanding of the entire process and allow the user to obtain more detailed information about how the system works.

[0787] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0788] Step 1:

[0789] The terminal collects operational data in real time from various devices placed within the home. The input here is raw data from various sensors (temperature sensors, power measurement sensors, etc.) installed in the devices. This data is temporarily stored inside the terminal and sent to the server using the HTTPS protocol. In this process, the data is formatted and compressed and sent to the server in the appropriate format.

[0790] Step 2:

[0791] The server stores the operational data received from the terminal in a database. The input for this step is formatted sensor data. The server stores the data in an unstructured database (e.g., MongoDB) and creates an index to make the data easily accessible when needed. The data is stored in a time-series format and used for subsequent analysis.

[0792] Step 3:

[0793] The server performs anomaly detection using stored data. The input is operational data stored in a database. The server applies machine learning algorithms to identify abnormal patterns. Here, an anomaly detection model using Python and TensorFlow is in operation. If an anomaly is detected as a result of the analysis, that information is generated as an anomaly notification.

[0794] Step 4:

[0795] The server sends the generated anomaly notification to the terminal. The input is the result of the anomaly detection, and the output is the content that the terminal notifies the user of. This notification includes the type of anomaly, its urgency, and recommended actions, which are then communicated to the user by the terminal.

[0796] Step 5:

[0797] The user receives a notification from their device and checks the nature of the anomaly. The input is the notification information from the device. The user views the notification using a smartphone application and considers countermeasures as needed. This includes interface operations on the device.

[0798] Step 6:

[0799] The server selects a repair date based on the user's schedule information and the availability of repair technicians. Inputs include the user's calendar and database information on repair technicians. An algorithm is used to calculate the optimal date, and the result is returned to the terminal. This enables rapid repair arrangements.

[0800] Step 7:

[0801] The server calculates possible alternative solutions based on information from other devices when an anomaly occurs and proposes them to the user via the terminal. Inputs are operational data from other devices and the results of previous anomaly detection. The server calculates the alternative solutions and sends the results as output to the terminal.

[0802] (Application Example 1)

[0803] 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".

[0804] The objectives are to enable early detection and rapid response to malfunctions in household electronic devices, while also suppressing wasteful energy consumption and managing it efficiently. Furthermore, the objectives are to minimize the impact of household malfunctions on other devices and to quickly provide necessary alternative solutions, thereby creating a more comfortable and sustainable living environment.

[0805] 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.

[0806] In this invention, the server includes means for collecting operational data from devices in the home, means for analyzing the collected data to detect anomalies, means for sending notifications regarding anomalies, means for automatically arranging and booking repair services according to the anomaly, means for coordinating with other electronic devices in the home to propose alternative solutions, means for analyzing data from multiple homes to derive common problems and solutions, means for collecting and optimizing energy usage data, and means for detecting anomalies in energy consumption and sending alerts. This enables rapid and effective fault response and energy management in the living environment.

[0807] "Household appliances" refers to electrical, electronic, and mechanical devices installed in a residence, and includes, but is not limited to, refrigerators, air conditioners, washing machines, and lighting.

[0808] "Operational data" refers to information indicating the operating status of a device, such as its temperature, power consumption, and operating time.

[0809] "Analysis" refers to the process of using collected data to identify patterns and anomalies in the data, employing machine learning algorithms and other techniques.

[0810] An "abnormal" state refers to a condition that deviates from the normal operation of a household appliance, which can lead to malfunction or excessive energy consumption.

[0811] "Notification" refers to the means of informing users of anomaly detections, and is carried out via smartphones or other communication devices.

[0812] "Automatic arrangement of repair service providers" refers to the process by which the server automatically schedules the appropriate repair service when an anomaly is detected.

[0813] An "alternative solution" is a method of proposing a temporary solution for a malfunctioning device, aiming to minimize the impact by utilizing other household devices.

[0814] "Energy usage data" refers to information about the energy consumed within a household, and analyzing this data can lead to energy conservation and increased efficiency.

[0815] An "alert" refers to a notification that warns or alerts the user when energy waste or abnormalities are detected.

[0816] This invention is a system that collects operational data in real time from various devices in the home and enables rapid response when an anomaly is detected. The server centrally manages the operational data collected through various sensors and IoT devices, and analyzes the collected data to detect anomalies. Python is used for the analysis, utilizing data analysis libraries such as Pandas and NumPy, and machine learning algorithms using TensorFlow are applied for anomaly detection.

[0817] The device sends a notification to the user when an anomaly is detected, prompting immediate verification and action. The notification is delivered via a smartphone application, which allows users to arrange for a repair service within the application. This includes a feature that suggests the optimal repair date and time in conjunction with the user's schedule.

[0818] Furthermore, the server proposes alternative measures to compensate for the malfunctioning equipment, based on data from other devices. For example, if a refrigerator malfunctions, it will provide the user with specific suggestions via the terminal, such as adjusting the air conditioner temperature to stabilize the indoor environment.

[0819] Energy usage data is also collected simultaneously, enabling efficient energy management. When energy waste is detected based on the data, an alert is sent, and the user receives suggestions on how to optimize their usage.

[0820] This technology can also be enhanced using generative AI models. For example, if the refrigerator is running longer than usual, it can generate a prompt message to advise the user, such as, "Please check that the refrigerator door is properly closed."

[0821] Examples of prompts for generative AI models:

[0822] An abnormal operating pattern has been detected in the refrigerator data. Please create a notification message for the appropriate user.

[0823] Through this system, users can maintain comfort within their homes while being supported in a sustainable and efficient lifestyle.

[0824] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0825] Step 1:

[0826] The server collects operational data from devices within each home via IoT devices. This data includes temperature, power consumption, and operating time. The input is real-time operational data transmitted from each device, and the output is the storage of this data in a centrally managed database. Specifically, it receives data packets from each device, converts the format as needed, and stores them securely.

[0827] Step 2:

[0828] The server analyzes the collected data using data analysis libraries such as Pandas and NumPy, and applies anomaly detection algorithms. The input is behavioral data obtained from the database, and the output is a flag indicating whether an anomaly was detected. Specifically, it compares the data with historical data and searches for patterns that exceed a set threshold.

[0829] Step 3:

[0830] If an anomaly is detected, the server sends a notification to the user's device. The input is the anomaly detection result and the user's contact information, and the output is an alert message sent via the notification system. The anomaly information is displayed as a text message or push notification via the user's smartphone app.

[0831] Step 4:

[0832] The device provides a real-time interface to prompt users who receive notifications to take action. Input is notification data from the server, and output is notification confirmation and option selection via the user interface. Specifically, it supports operations such as displaying a repair provider selection screen by tapping the notification.

[0833] Step 5:

[0834] The server automatically arranges repair technicians in conjunction with the user's schedule. Inputs include the user's preferred date and time and a database of available repair technicians; output is confirmed reservation information. A scheduling algorithm is used to select the optimal date, time, and technician, and automatically confirm the reservation.

[0835] Step 6:

[0836] The terminal presents the user with alternative solutions to compensate for a malfunctioning device. Inputs include operating data from other household appliances and action plan templates, while output is a suggested alternative solution. Specifically, if the refrigerator malfunctions, it creates and sends a message suggesting temperature adjustment using the air conditioner.

[0837] Step 7:

[0838] The server uses a generative AI model to enhance the analysis of anomalies and energy consumption. The input consists of collected data and past anomaly cases, while the output is a prompt message suggesting improvements or actions to take. For example, it might generate a prompt such as, "An anomaly has been detected in the refrigerator data. Please create a notification message for the appropriate user."

[0839] In this way, the entire system achieves efficient and sustainable maintenance of a home environment.

[0840] 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.

[0841] This invention provides optimal responses that take into account the user's emotions by integrating an emotion engine into a system that analyzes operational data acquired from household devices and detects abnormalities. This system is configured as follows.

[0842] Data collection and analysis

[0843] The device collects various data in real time through sensors installed in household appliances. This data includes temperature, power consumption, and appliance operating status. The collected data is transmitted to a server in a secure environment.

[0844] After receiving the data, the server uses machine learning algorithms to analyze the data and detect anomaly patterns. If an anomaly is detected, that information is sent to the terminal.

[0845] User notifications and sentiment analysis

[0846] When the device receives information about an anomaly detection, it displays an appropriate notification to the user. At this time, the emotion engine is activated and analyzes the user's emotions in real time through voice and camera sensors.

[0847] Based on sentiment analysis results, the server adapts notifications and suggestions to the user's emotions. For example, if the user is feeling stressed, a simple and gentle notification message will be used.

[0848] Repair arrangements and alternative solutions proposed

[0849] The server searches for and suggests the most suitable repair service provider based on the user's schedule and emotional state. In this process, an emotional engine considers arrangements to minimize the user's stress.

[0850] The device presents the user with repair arrangement proposals provided by the server, and completes the reservation after receiving approval. It also coordinates with other electronic devices in the home as needed to calculate alternative solutions.

[0851] Utilization of community data

[0852] The server analyzes operational data collected from multiple households to identify frequently occurring anomaly patterns and derive adaptive failure prevention measures.

[0853] The device provides this information to the user and offers advice tailored to their emotional state. For example, it might use gentle language to warn them and alleviate anxiety.

[0854] For example, if an abnormality is detected in the refrigerator, when the user returns home and sentiment analysis begins, the sentiment engine recognizes that the user is experiencing stress. Based on this, the device displays a reassuring notification such as, "A minor abnormality has been found in the refrigerator, but we will address it immediately, so there is no need to worry." Subsequently, it arranges for a repair service and provides a suggestion that best suits the user's schedule and emotions, thereby achieving comprehensive home management.

[0855] The following describes the processing flow.

[0856] Step 1:

[0857] The device collects motion data in real time from sensors connected to devices within the home and transmits that data to a server via the internet.

[0858] Step 2:

[0859] The server stores the operational data it receives in a database and uses machine learning algorithms to analyze patterns that should be detected as anomalies. When an anomaly is detected, detailed information is generated.

[0860] Step 3:

[0861] The server sends the results of the anomaly detection to the terminal. At this time, it creates a notification message according to the type and urgency of the anomaly.

[0862] Step 4:

[0863] The device receives a notification message and displays it to the user. Simultaneously, the device's built-in emotion engine uses the camera and microphone to collect data in order to analyze the user's emotions.

[0864] Step 5:

[0865] The device uses an emotion engine to analyze the user's voice and facial expressions to determine their emotional state. The results are sent to a server and used to optimize notification content and response strategies.

[0866] Step 6:

[0867] The server takes the user's emotional state into account and modifies notification messages and response processes accordingly. For example, if the user is feeling anxious, it generates a more reassuring message.

[0868] Step 7:

[0869] The server searches a database of repair companies and suggests the most suitable repair company and date based on the user's schedule and emotional state. This information is then sent to the terminal.

[0870] Step 8:

[0871] The device displays a list of repair service providers optimized for the user's device and asks for their approval. The user can either approve the proposal or submit a request for modifications.

[0872] Step 9:

[0873] Once the user approves the proposal, the device sends that information to the server. The server then automatically makes a reservation with a selected repair company.

[0874] Step 10:

[0875] The server calculates alternative solutions for devices experiencing malfunctions. It considers ways to coordinate with other home devices to ensure the user experiences no inconvenience.

[0876] Step 11:

[0877] The device presents the user with a calculated alternative solution and requests their approval to implement it. If the user approves, the alternative solution is automatically applied.

[0878] (Example 2)

[0879] 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".

[0880] Modern homes contain many electronic devices, and malfunctions can occur in these devices. However, if these malfunctions are not detected early and dealt with appropriately, they can significantly disrupt daily life. Furthermore, when dealing with malfunctions, it is necessary to not only perform mechanical actions but also to consider the user's feelings and provide appropriate notifications and suggestions. However, current systems face the challenge of making it difficult to provide optimal responses based on emotions.

[0881] 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.

[0882] In this invention, the server includes means for collecting operational information from devices within the home and analyzing the collected information, means for sending notifications when an abnormality is detected, and means for adjusting the content of notifications based on the user's emotions. This enables early detection of abnormalities and rapid response while taking the user's emotions into consideration.

[0883] "Household devices" refers to electronic devices installed in a home that provide various operational information.

[0884] "Operational information" refers to information including various data such as temperature, power consumption, and operating status obtained from the device.

[0885] An "abnormality" refers to a state or pattern that deviates from the normal operating range of a device.

[0886] "Notification" refers to a means of informing users of the occurrence of an anomaly or other important information.

[0887] "User emotions" refers to the psychological state of a user and includes data analyzed from voice, facial expressions, and other factors.

[0888] "Emotion-based notification content adjustment" refers to procedures and methods for conveying information in a more appropriate format and content depending on the user's emotional state.

[0889] "Information from multiple households" refers to operational information collected from devices within multiple households and stored in a database.

[0890] "Deriving general problems and solutions" refers to the process of identifying common problems from a large amount of operational information and finding countermeasures to address them.

[0891] This invention requires a terminal installed in the home and a server that interacts with it. The terminal collects operational information such as temperature, power consumption, and operating status in real time from sensors built into electronic devices in each home. This information is transmitted to the server via a security protocol.

[0892] The server uses programming frameworks such as Python and TensorFlow to analyze the received operational information. Machine learning algorithms identify anomalous patterns and, if necessary, send anomaly notifications to the terminal. A database management system is used to improve the speed and accuracy of the analysis, and the collected data is continuously used to update the learning model.

[0893] The device provides notifications to the user based on the received anomaly information. These notifications are displayed on the screen or announced by voice. Furthermore, the device is equipped with voice and camera sensors, which allow for real-time analysis of the user's emotions, and the analysis results are sent back to the server.

[0894] The server uses the results of sentiment analysis to generate optimal notifications and suggestions tailored to the user's psychological state. This ensures that even when an anomaly occurs, information is provided in a way that minimizes anxiety and stress for the user. For example, if the analysis reveals that the user is stressed when the refrigerator temperature rises, the device will send a notification such as, "There is a slight problem with the refrigerator, but we will address it immediately, so please don't worry."

[0895] Furthermore, the server optimizes repair responses by considering the schedule information of both the user and the repair company. It also enhances user convenience by coordinating with other electronic devices in the home and suggesting alternative solutions to the user.

[0896] As a concrete example, here is an example of a prompt message: "Please describe the design of a system that detects abnormalities in home devices and provides notifications and suggestions tailored to the user's emotional state."

[0897] In this way, it becomes possible to detect abnormalities in various devices within the home early on and to provide prompt and accurate responses that take into consideration the user's feelings.

[0898] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0899] Step 1:

[0900] The terminal collects operational information such as temperature, power consumption, and operating status in real time through sensors installed on each electronic device in the home. At this point, the input is real-time data from the sensors, and the output is an information packet summarizing this data. This information packet is sent to the server via a security protocol.

[0901] Step 2:

[0902] The server receives operational information sent from the terminal. The input data is analyzed using machine learning algorithms such as Python or TensorFlow. The output is a determination result indicating whether or not an anomaly is present. If an anomaly is detected based on this result, detailed anomaly information is generated. This information is sent back to the terminal.

[0903] Step 3:

[0904] The terminal notifies the user based on anomaly information received from the server. At this stage, the input is anomaly information from the server, and the output is visual and audible notification to the user. Specifically, this may involve displaying a warning message on the terminal's display and having a voice assistant verbally inform the user of the anomaly.

[0905] Step 4:

[0906] The device uses built-in voice and camera sensors to analyze the user's facial expressions and voice tone. The input is real-time audio and video data, which is processed by an emotion analysis engine. The output is data indicating the user's emotional state, and this data is also sent to the server.

[0907] Step 5:

[0908] The server receives the user's emotional state and adjusts the content of notifications and suggestions accordingly. The input to this process is the user's emotional state data, and the output is the text of notifications and suggestions adapted to that emotion. The server sends this text to the terminal and displays it to the user in an emotionally sensitive manner.

[0909] Step 6:

[0910] The server uses an AI model to search for the most suitable repair provider based on the user's schedule information and the availability of repair providers. The input is the user's time information and provider availability data, and the output is a list of potential optimal repair providers. This list is sent to the terminal and presented to the user.

[0911] Step 7:

[0912] The terminal presents the user with a list of potential repair companies received from the server and prompts them to make a final selection. The input is the list of potential repair companies, and the output is the user's selection data. Once the user agrees, the terminal completes the reservation and displays a confirmation notification.

[0913] Through these processing steps, the terminal and server work together to quickly detect abnormalities in home electronic devices and provide notifications and countermeasures that are sensitive to the user's feelings.

[0914] (Application Example 2)

[0915] 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".

[0916] In recent years, the number of devices in homes has increased, and consequently, the frequency of malfunctions has also risen. While conventional systems can detect and notify users of device malfunctions, they do not provide countermeasures that take into account the emotional burden on the user. Furthermore, even when arranging repairs or proposing alternative solutions after a malfunction is detected, it is difficult to provide support that is tailored to the emotional state of each individual user. Therefore, there is a need for a system that enables flexible and efficient responses that take into account the user's feelings.

[0917] 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.

[0918] In this invention, the server includes means for collecting operational information from devices within the home, means for analyzing the collected operational information and detecting anomalies, and means for analyzing the user's emotions and providing an optimal notification method based on those emotions. This makes it possible to provide notifications tailored to the user's emotions after detecting an anomaly, thereby reducing the user's mental burden and enabling prompt repair arrangements and the suggestion of alternative solutions.

[0919] "Household appliances" refers to electronic devices and electrical appliances used within the home, and specifically includes refrigerators, washing machines, air conditioners, etc.

[0920] "Operational information" refers to information such as operating status, power consumption, and temperature collected from household appliances.

[0921] "Means for detecting abnormalities" refers to technologies or devices for analyzing collected operational information and identifying states that differ from normal operation.

[0922] "Means of sending notifications" refer to methods for informing users of information when an anomaly is detected, and can take the form of voice, messages, alerts, etc.

[0923] "A means of automatically arranging and scheduling repair work" refers to a method for selecting the appropriate repair technician based on detected abnormalities and automatically scheduling repairs.

[0924] "Electronic devices" refers to various electrical and electronic equipment that operates within the home.

[0925] "Means of proposing alternative solutions" refer to methods of using machine learning and algorithms to present other possible options or methods when an anomaly occurs.

[0926] "Methods for analyzing user emotions" refers to technologies that analyze a user's emotional state using data collected from sensors such as audio and video.

[0927] "Means of providing the optimal notification method" refers to means of delivering notifications in the most appropriate format and content based on the user's emotional state.

[0928] A system for carrying out this invention includes a server that collects operational information from household devices and analyzes that information, and a terminal that receives input from a user and proposes the optimal action based on that input.

[0929] The server collects operational information in real time using various sensors installed in the home. Small computers such as Raspberry Pi or NVIDIA Jetson Nano can be used as hardware. This allows diverse data, such as temperature, power consumption, and device operating status, to be transmitted to the server in a secure environment.

[0930] If an anomaly is detected, the server analyzes the collected data using a pre-trained machine learning algorithm (for example, a model built using TensorFlow or PyTorch). This analysis identifies anomaly patterns. Based on this information, the terminal sends an anomaly notification to the user.

[0931] The user's emotional state is evaluated using emotion analysis software (e.g., OpenCV or Google Cloud Vision API) based on data from the device's camera and microphone. The results of this emotion analysis are sent to a server, which generates personalized notifications and suggestions. The content of the notifications is generated using a natural language generation API (e.g., OpenAI's GPT-3).

[0932] For example, when the temperature sensor inside the refrigerator indicates an abnormality, the server recognizes the abnormality based on that information, and the terminal uses sentiment analysis to determine whether the user is feeling anxious. After obtaining the result, the terminal provides a reassuring message such as, "There is a problem with the refrigerator temperature, but we will guide you on how to deal with it shortly."

[0933] An example of a prompt message for the generating AI model would be: "Generate a gentle message to alleviate user anxiety when a malfunction is detected in a home device." This allows the user to quickly understand the necessary countermeasures while minimizing their emotional burden.

[0934] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0935] Step 1:

[0936] The server collects operational information from sensors placed throughout the home. It receives data from the sensors (temperature, power consumption, operating status, etc.) as input, which is then transmitted to the server using the appropriate protocol. The server receives a real-time stream of operational data as output.

[0937] Step 2:

[0938] The server analyzes received operational data using machine learning algorithms to detect anomalies. The input is an operational data stream, which is passed through a machine learning model for analysis. The output includes the presence or absence of anomaly patterns and, if anomalies are present, specific information.

[0939] Step 3:

[0940] If an anomaly is detected, the server sends that information to the terminal. The input is anomaly information, which is transmitted to the terminal using a communication module. The output is the terminal receiving the anomaly.

[0941] Step 4:

[0942] The device notifies the user based on the received anomaly information. Furthermore, the device uses its built-in camera and microphone to analyze the user's emotions. Inputs include anomaly information and real-time audio and video data, and emotion analysis software is used to determine the emotional state. Outputs include the user's emotion analysis results and appropriate notification content.

[0943] Step 5:

[0944] The server uses a natural language generation API to generate an appropriate message based on the sentiment analysis results. It takes user sentiment data as input, sends prompt sentences to a generation AI model, and obtains a message. The output is a customized message to notify the user.

[0945] Step 6:

[0946] The terminal displays the generated message to the user and automatically arranges repair work as needed. The system receives the generated message and the repair technician's availability as input, and then proposes the optimal repair schedule based on this information. The output completes the display of information to the user and the arrangement of the work.

[0947] 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.

[0948] 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.

[0949] In the above embodiment, an example was given in which the 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.

[0950] 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.

[0951] 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.

[0952] 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.

[0953] 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.

[0954] 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.

[0955] 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."

[0956] 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.

[0957] 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.

[0958] 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.

[0959] 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.

[0960] 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.

[0961] 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.

[0962] 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.

[0963] 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.

[0964] 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.

[0965] 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.

[0966] 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.

[0967] 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.

[0968] The following is further disclosed regarding the embodiments described above.

[0969] (Claim 1)

[0970] A means of collecting operational data from devices within the home,

[0971] A means for analyzing collected operational data and detecting anomalies,

[0972] A means of sending a notification when an anomaly is detected,

[0973] A method for automatically arranging and booking repair services to address abnormalities,

[0974] A means of suggesting alternative solutions in conjunction with other electronic devices in the home,

[0975] A method for analyzing data from multiple households to derive common problems and solutions,

[0976] A system that includes this.

[0977] (Claim 2)

[0978] The system according to claim 1, which uses a machine learning algorithm to identify abnormality patterns when detecting abnormalities in equipment.

[0979] (Claim 3)

[0980] The system according to claim 1, which optimizes repair reservations based on the user's schedule information and the availability of repair technicians.

[0981] "Example 1"

[0982] (Claim 1)

[0983] A means for acquiring operational information from devices within the living environment,

[0984] A means for analyzing acquired operational information and identifying anomalies,

[0985] A means of distributing notifications when an anomaly is detected,

[0986] A method for automatically arranging and scheduling repair technicians to address abnormalities,

[0987] A means of proposing alternative solutions in cooperation with other electronic devices in the living environment,

[0988] A means of analyzing information from multiple living environments to derive common problems and solutions,

[0989] A system that includes this.

[0990] (Claim 2)

[0991] The system according to claim 1, which uses machine learning techniques to identify abnormal patterns when identifying abnormalities in a device.

[0992] (Claim 3)

[0993] The system according to claim 1, which optimizes repair reservations based on user schedule information and the availability of repair technicians.

[0994] "Application Example 1"

[0995] (Claim 1)

[0996] A means of collecting operational data from devices within the home,

[0997] A means for analyzing collected operational data and detecting anomalies,

[0998] A means of sending a notification when an anomaly is detected,

[0999] A method for automatically arranging and booking repair services to address abnormalities,

[1000] A means of suggesting alternative solutions in conjunction with other electronic devices in the home,

[1001] A method for analyzing data from multiple households to derive common problems and solutions,

[1002] Means for collecting and optimizing energy usage data,

[1003] A means for detecting abnormal energy consumption and sending an alert,

[1004] A system that includes this.

[1005] (Claim 2)

[1006] The system according to claim 1, which uses a machine learning algorithm to identify abnormality patterns when detecting abnormalities in equipment.

[1007] (Claim 3)

[1008] The system according to claim 1, which optimizes repair reservations based on the user's schedule information and the availability of repair technicians.

[1009] "Example 2 of combining an emotion engine"

[1010] (Claim 1)

[1011] A means of collecting operational information from devices within the home,

[1012] A means for analyzing collected operational information and detecting anomalies,

[1013] A means of sending a notification when an anomaly is detected,

[1014] A method for automatically arranging and booking repair services to address abnormalities,

[1015] A means of analyzing user emotions and adjusting notification and suggestion content,

[1016] Based on the user's emotional state and scheduling information, the most effective means of making repair provider suggestions,

[1017] A means of coordinating with other electronic devices in the home to suggest alternative solutions,

[1018] A method for analyzing information from multiple households to derive common problems and solutions,

[1019] A system that includes this.

[1020] (Claim 2)

[1021] The system according to claim 1, which uses a machine learning algorithm to identify abnormality patterns when detecting abnormalities in equipment.

[1022] (Claim 3)

[1023] The system according to claim 1, which optimizes repair reservations by taking into account the user's time information and the availability of repair technicians.

[1024] "Application example 2 of combining emotional engines"

[1025] (Claim 1)

[1026] A means of collecting operational information from household devices,

[1027] A means for analyzing collected operational information and detecting anomalies,

[1028] A means of sending a notification when an anomaly is detected,

[1029] A means of automatically arranging and scheduling repair work to respond to abnormalities,

[1030] A means of suggesting alternative solutions in conjunction with other electronic devices in the home,

[1031] A method for analyzing data from multiple households to derive common problems and solutions,

[1032] A means of analyzing user emotions and providing the optimal notification method based on those emotions,

[1033] A system that includes this.

[1034] (Claim 2)

[1035] The system according to claim 1, which uses a machine learning algorithm to identify abnormality patterns when detecting abnormalities in equipment.

[1036] (Claim 3)

[1037] The system according to claim 1, which optimizes repair reservations based on the user's schedule information and the availability of repair technicians. [Explanation of Symbols]

[1038] 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 collecting operational data from devices within the home, A means for analyzing collected operational data and detecting anomalies, A means of sending a notification when an anomaly is detected, A method for automatically arranging and booking repair services to address abnormalities, A means of suggesting alternative solutions in conjunction with other electronic devices in the home, A method for analyzing data from multiple households to derive common problems and solutions, A system that includes this.

2. The system according to claim 1, which uses a machine learning algorithm to identify abnormality patterns when detecting abnormalities in equipment.

3. The system according to claim 1, which optimizes repair reservations based on the user's schedule information and the availability of repair technicians.

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