Electronic device and operation method thereof

Abstract

Provided may be an electronic device comprising at least one processor and a memory for storing one or more instructions, wherein the one or more instructions, when executed by the at least one processor, cause the electronic device to: obtain at least one failure prediction model trained to predict a failure of at least one block on the basis of state change data obtained from a plurality of external electronic devices and corresponding service history data; when a state change of the electronic device is detected, generate state change data; input the state change data into the at least one failure prediction model to obtain failure prediction information of each block of the electronic device; and on the basis of the failure prediction information, store failure occurrence time point information of a block predicted to have a failure and predetermined data before and after the time point.

Description

Electronic device and method of operation thereof

[0001] Various embodiments relate to an electronic device and a method of operating the same. More specifically, the invention relates to an electronic device and a method of operating the same that predicts a failure using a failure prediction model and stores a log.

[0002] As the use of digital content increases, so does dependence on display devices, such as smart TVs, that display digital content. Consequently, a malfunction of a display device causes significant inconvenience to users.

[0003] Conventionally, when a failure occurred in a display device, the faulty part was identified and resolved by utilizing customer statements or partial device data, or by using a diagnostic system that operates the device according to a predefined scenario.

[0004] However, when relying on customer statements, it may be difficult to accurately predict the faulty part and the time of failure because it must be based on the customer's memory.

[0005] When using internal device data, it is difficult to identify which data can be used for fault diagnosis among a large amount of data, and since there are limitations on storing all logs due to memory issues, engineers may face difficulties in securing logs at the time of the problem.

[0006] When using scenarios, there is a limitation in that diagnosis is impossible for failures not defined in the scenario.

[0007] An electronic device according to one embodiment may include at least one processor including a processing circuit.

[0008] An electronic device according to one embodiment may include a memory that stores one or more instructions.

[0009] One or more instructions may be executed individually or jointly by one or more processors to enable an electronic device to acquire at least one failure prediction model trained to predict failure of at least one block based on state change data and corresponding service history data acquired from multiple external electronic devices.

[0010] One or more instructions may be executed individually or jointly by one or more processors to enable the electronic device to generate state change data when a state change of the electronic device is detected.

[0011] One or more instructions can be executed individually or jointly by one or more processors to input state change data generated by the electronic device into at least one acquired fault prediction model to obtain fault prediction information for each block of the electronic device.

[0012] One or more instructions may be executed individually or jointly by one or more processors to enable the electronic device to store fault occurrence time information of a block predicted to have failed and predetermined data before and after that time based on the acquired fault prediction information.

[0013] A method of operating an electronic device according to one embodiment may include the step of acquiring at least one failure prediction model trained to predict failure of at least one block based on state change data and corresponding service history data acquired from a plurality of external electronic devices.

[0014] A method of operating an electronic device according to one embodiment may include the step of generating state change data when a state change of the electronic device is detected.

[0015] A method of operating an electronic device according to one embodiment may include the step of inputting generated state change data into at least one acquired fault prediction model to obtain fault prediction information for each block of the electronic device.

[0016] A method of operating an electronic device according to one embodiment may include the step of storing information on the time of failure of a block predicted to have failed and predetermined data before and after the time, based on acquired failure prediction information.

[0017] A computer-readable recording medium according to one embodiment may be a computer-readable recording medium having a program recorded thereon for implementing a method of operation of an electronic device comprising the step of acquiring at least one failure prediction model learned to predict failure of at least one block based on state change data acquired from a plurality of external electronic devices and corresponding service history data.

[0018] A computer-readable recording medium according to one embodiment may be a computer-readable recording medium having a program recorded thereon for implementing a method of operating an electronic device, which includes the step of generating state change data when a state change of the electronic device is detected.

[0019] A computer-readable recording medium according to one embodiment may be a computer-readable recording medium having a program recorded thereon for implementing a method of operation of an electronic device comprising the step of inputting generated state change data into at least one acquired fault prediction model to obtain fault prediction information for each block of the electronic device.

[0020] A computer-readable recording medium according to one embodiment may be a computer-readable recording medium having a program recorded thereon for implementing a method of operation of an electronic device, which includes the step of storing information on the time of failure of a block predicted to have failed and predetermined data before and after the time, based on acquired failure prediction information.

[0021] FIG. 1 is a drawing showing an example of an electronic device in operation according to one embodiment of the present disclosure.

[0022] FIG. 2 is a diagram showing an example of a state change of a block of an electronic device according to one embodiment of the present disclosure.

[0023] FIG. 3 is a diagram showing an example of a block-by-block state change of an electronic device according to one embodiment of the present disclosure.

[0024] FIG. 4 is a diagram showing the structure of state change data of an electronic device according to one embodiment of the present disclosure.

[0025] FIG. 5 is a diagram showing an example of state change data by block of an electronic device according to one embodiment of the present disclosure.

[0026] FIG. 6 is a diagram illustrating an example of a method for an electronic device according to one embodiment of the present disclosure to predict a failure of at least one block using artificial intelligence.

[0027] FIG. 7 is a flowchart of a method of operation of an electronic device according to one embodiment of the present disclosure.

[0028] FIG. 8 is a flowchart of a method of operation of an electronic device according to one embodiment of the present disclosure.

[0029] FIG. 9 is a detailed flowchart of a method of operation of an electronic device according to one embodiment of the present disclosure.

[0030] FIG. 10 is a block diagram of an electronic device according to one embodiment of the present disclosure.

[0031] FIG. 11 is a detailed block diagram of an electronic device according to one embodiment of the present disclosure.

[0032] FIG. 12 is a diagram illustrating an example of a structure in which an electronic device according to one embodiment of the present disclosure operates using a server.

[0033] FIG. 13 is a diagram illustrating an example of a structure in which an electronic device according to one embodiment of the present disclosure operates using on-device AI.

[0034] FIG. 14 is a structural diagram of a fault prediction system according to one embodiment of the present disclosure.

[0035] Embodiments of the present disclosure are described below in detail with reference to the attached drawings so that those skilled in the art can easily implement them. However, the present disclosure may be embodied in various different forms and is not limited to the embodiments described herein.

[0036] The terms used in this disclosure are described in their current, general form considering the functions mentioned herein; however, they may refer to various other terms depending on the intent of those skilled in the art, case law, the emergence of new technologies, etc. Accordingly, the terms used in this disclosure should not be interpreted solely by their names, but should be interpreted based on the meaning of the terms and the overall content of this disclosure.

[0037] Furthermore, the terms used in this disclosure are used merely to describe specific embodiments and are not intended to limit this disclosure.

[0038] In addition, when a component is described in the present disclosure as being “connected” or “connected” to another component, it should be understood that the component may be directly connected to or directly connected to the other component, but unless otherwise specifically stated, it may also be connected or connected through another component in between.

[0039] The terms “above” and similar designations used in this specification, particularly in the claims, may indicate both singular and plural forms. Furthermore, unless there is a description explicitly specifying the order of the steps describing the method according to this disclosure, the described steps may be performed in a suitable order. This disclosure is not limited by the order in which the described steps are described.

[0040] Phrases such as "in some embodiments" or "in one embodiment" appearing in various places in this specification do not necessarily refer to the same embodiment.

[0041] In this disclosure, "block" may refer to a minimum unit constituting the function of an electronic device. Some or all of the blocks may be implemented by various numbers of hardware and / or software configurations that execute specific functions. For example, the blocks of this disclosure may be implemented by one or more microprocessors or by circuit configurations for a specific function. Additionally, for example, the blocks of this disclosure may be implemented in various programming or scripting languages. The blocks may be implemented as algorithms executed on one or more processors. Furthermore, this disclosure may employ prior art for electronic configuration, signal processing, and / or data processing, etc. Examples of blocks may include memory blocks, network blocks, IOC blocks, audio blocks, display panel blocks, power blocks, HDMI blocks, sensor blocks, DDR blocks, IR blocks, etc.

[0042] Terms such as “mechanism,” “element,” “means,” and “configuration” may be used broadly and are not limited to mechanical and physical configurations.

[0043] Some embodiments of the present disclosure may be represented by functional block configurations and various processing steps.

[0044] Furthermore, the connecting lines or connecting members between the components depicted in the drawings are merely illustrative of functional connections and / or physical or circuit connections. In the actual device, connections between components may be represented by various alternative or added functional connections, physical connections, or circuit connections.

[0045] Additionally, terms such as "...part," "module," etc., as described in the specification refer to a unit that processes at least one function or operation, and this may be implemented in hardware or software, or as a combination of hardware and software.

[0046] The expression “configured to” as used in this disclosure may be replaced, depending on the context, with, for example, “suitable for,” “having the capacity to,” “designed to,” “adapted to,” “made to,” or “capable of.” The term “configured to” may not necessarily mean only “specifically designed to” in hardware. Instead, in some situations, the expression “system configured to” may mean that the system is “capable of” together with other devices or components. For example, the phrase “a processor configured (or set) to perform A, B, and C” may mean a dedicated processor for performing said operations (e.g., an embedded processor), or a generic-purpose processor (e.g., a CPU or an application processor) capable of performing said operations by executing one or more software programs stored in memory.

[0047] Additionally, in the specification, the term “user” may refer to a person who controls the function or operation of an electronic device using the electronic device.

[0048] In the specification, "at least one A or B" or "at least one A, or B" means that it may include any one of A, B, and A and B. Similarly, "at least one A, B, or C" or "at least one A, B, or C" means that it may include any one of A, B, C, A and B, A and C, B and C, or A, B, and C.

[0049] Below, embodiments of the present invention are described in detail with reference to the attached drawings so that those skilled in the art can easily implement the invention. Additionally, parts of the drawings that are irrelevant to the description are omitted to clearly explain the invention.

[0050] FIG. 1 is a drawing showing an example of an electronic device in operation according to one embodiment of the present disclosure.

[0051] In one embodiment, the electronic device (100) can predict failures of each block of the electronic device (100) in real time using a failure prediction model learned on a server (200) using data obtained from at least one external device (300).

[0052] In one embodiment, at least one external device (300) may include any electronic device operating in the market capable of providing meaningful data to the electronic device (100). The external device (300) may be a device of the same type as the electronic device (100) or a device of a different type.

[0053] Data obtained from at least one external device (300) to the server (200) may be state change data of the external device (300). The state change data may be data storing state changes that occurred block by block. Detailed information regarding state changes will be described later in FIGS. 2 and 3, etc., and detailed information regarding state change data will be described later in FIGS. 4 and 5, etc.

[0054] In the present disclosure, "state change data" may include data recording information about state changes for each block and event data generated when the environment of each block changes by more than a threshold value.

[0055] If a failure occurs in the external device (300), the external device (300) can transmit the previously stored state change data to the service center or server (200).

[0056] In one embodiment, the server (200) may periodically receive state change data from at least one external device (300). The server (200) may extract a learned failure prediction model based on the state change data and service history data of each external device (300) received from at least one external device (300). The service history data refers to service history data received by each external device (300) for troubleshooting when a failure occurs, and may be obtained from the external device (300) or a separate service center server to the server (200).

[0057] In one embodiment, service history data may be stored in a server (200). In this case, the server (200) may be a server that serves as a service center.

[0058] In one embodiment, the failure prediction model may be a model that receives state change data of an electronic device as input and outputs a failure probability of at least one block.

[0059] The failure prediction model can be continuously learned and updated based on state change data and service history data occurring in the market.

[0060] In one embodiment, the electronic device (100) may obtain a fault prediction model from a server (200) and use it for fault prediction for each block inside the electronic device (100).

[0061] In one embodiment, the electronic device (100) can generate state change data of the electronic device (100) itself in the same way that state change data is generated and stored in an external device (300) and provide it as an input value for a fault prediction model.

[0062] In one embodiment, the electronic device (100) may store state change data of a block predicted to fail and time information of the predicted failure. The stored state change data and time information may be used to diagnose the cause of the failure and solve the problem when a failure actually occurs.

[0063] In the present disclosure, the electronic device (100) and the external device (300) may be a smart TV, but this is merely one embodiment and may be implemented in various forms.

[0064] In particular, the electronic device (100) and the external device (300) can be easily implemented as an electronic device including a large video output unit, such as a TV, but are not limited thereto. Additionally, the electronic device (100) and the external device (300) may be fixed or mobile, and may be a digital broadcast receiver capable of receiving digital broadcasts.

[0065] In one embodiment, the electronic device (100) may be implemented in the form of a device that performs video output as part of its functions while performing other functions.

[0066] An electronic device (100) and an external device (300) according to one embodiment of the present disclosure may be implemented in various forms such as a tablet PC, a smartphone, a digital camera, a camcorder, a laptop computer, a smart TV, a netbook computer, a desktop, an e-book terminal, a video phone, a digital broadcasting terminal, a PDA (Personal Digital Assistants), a PMP (Portable Multimedia Player), a navigation device, a wearable device, a smart refrigerator, and other home appliances.

[0067] The electronic device (100) may have a built-in display, but is not limited thereto and may be implemented in a form that operates by connecting to an external display even without having a built-in display.

[0068] For example, the electronic device (100) may be implemented in a form that outputs video to a separate external display through a video or audio output port, such as an STB, Apple TV, etc., without a display or having a simple display for notifications, etc.

[0069] In this case, the electronic device (100) may have an output port for outputting a video or audio signal to a display. The output port may be in a form that can transmit video and audio signals simultaneously, such as HDMI, DP, Thunderbolt, etc., or may be in a form where there are separate ports for transmitting video and audio signals separately.

[0070] In one embodiment, the electronic device (100) can transmit video or audio signals via wired communication or wireless communication, etc.

[0071] The electronic device (100) may be implemented not only as an electronic device with a flat display, but also as an electronic device with a curved display having curvature or as a flexible electronic device with adjustable curvature. The output resolution of the electronic device (100) may include, for example, HD (High Definition), Full HD, Ultra HD, or a resolution sharper than Ultra HD.

[0072] FIG. 2 is a diagram showing an example of a state change of a block of an electronic device according to one embodiment of the present disclosure.

[0073] In one embodiment, examples of blocks of the electronic device (100) may include a memory block, a network block, a boot block, an IOC block, an audio block, a display panel block, a power block, an HDMI block, a sensor block, a DDR block, an IR block, etc.

[0074] Whenever the state of each block changes, the electronic device (100) can generate and store state change data.

[0075] The embodiment of FIG. 2 relates to a change in the state of a boot block.

[0076] In the embodiment of FIG. 2, the state of the boot block may be a power off state, a standby state, and a normal state.

[0077] Power off state is when the power cable is disconnected, standby state is when the power cable is connected but the power is not turned on, and normal state is when the power cable is connected and the power is turned on.

[0078] When the power cable is connected while the device is powered off, it may change to standby or normal state. When powered on from standby, it changes to normal state, and when the power cable is disconnected from standby, it may change to power off state. When powered off from normal state, it changes to standby state, and when the power cable is disconnected from normal state, it may change to power off state.

[0079] The electronic device (100) can generate and store state change data whenever there is a change in the three states determined in relation to the boot block.

[0080] FIG. 3 is a diagram showing an example of a block-by-block state change of an electronic device according to one embodiment of the present disclosure.

[0081] A block of an electronic device (100) can be defined as a minimum unit constituting a function, and such definition may vary depending on the embodiment. Each block may have predetermined state information.

[0082] One electronic device (100) can have a varying number of blocks.

[0083] The embodiment of FIG. 3 can show an example of state information determined by block.

[0084] For example, an HDMI block can have types 1, 2, and 3 states, where type 1 indicates a failed connection to an external device, type 2 indicates a successful connection to an external device, and type 3 indicates a disconnection occurred during the connection to an external device.

[0085] Additionally, for example, a network block can have states of type 4, type 5, and type 6, where type 4 indicates a state where the network connection has failed, type 5 indicates a state where the network connection has succeeded, and type 6 indicates a state where the network connection was interrupted.

[0086] Additionally, for example, the power block may have states of type 7, type 8, type 9, and type 10, where type 7 indicates an abnormal state of the power to A13VMain1, type 8 indicates an abnormal state of the standby power to B13V, type 9 indicates an abnormal state of the power to Main2 to A5V, and type 10 indicates an abnormal state of the IO power to B3.3V. In this case, an abnormal state may mean a state outside the defined management range. For example, an abnormal state may mean a case where a difference of more than 5% occurs compared to a value within the normal range.

[0087] Additionally, for example, a panel block may have states of type 11, type 12, and type 14, where type 11 means the panel's backlight is off, type 12 means some blocks of the panel's backlight are off, and type 14 means the panel connection cable is abnormally connected.

[0088] Additionally, for example, an audio block may have a type 15 and type 16 state, where type 15 indicates a state where amplifier initialization failed during device booting, and type 16 indicates a state where the amplifier failed I2C communication.

[0089] Additionally, for example, the main IC block may have states of type 17, type 18, type 19, and type 20, where type 17 indicates an abnormal temperature of the main IC, type 18 indicates a state where the temperature throttle of the main IC has entered a stepwise state, type 19 indicates a state where the connection block initialization failed, and type 20 indicates a state where a disconnection occurred in the connection block. In this case, an abnormal state may mean a state outside a defined management range. For example, an abnormal temperature of the main IC may mean a state where the temperature of the main IC is 125°C or higher.

[0090] Additionally, for example, the sensor block may have states of type 21, type 22, type 23, type 24, type 25, and type 26, where type 21 indicates an abnormal temperature of the accelerometer, type 22 indicates a failure to initialize the accelerometer during booting, type 23 indicates a disconnection of the accelerometer, type 24 indicates an abnormal temperature of the light sensor, type 25 indicates a failure to initialize the light sensor during booting, and type 26 indicates a disconnection of the light sensor. In this case, an abnormal state may mean a state outside a defined management range. For example, an abnormal temperature of the accelerometer or the light sensor may mean a state where the temperature of the accelerometer is 90°C or higher.

[0091] This definition is merely an example and is not limited to it.

[0092] FIG. 4 is a diagram showing the structure of state change data of an electronic device according to one embodiment of the present disclosure.

[0093] The electronic device (100) can generate state change data when a state change occurs in at least one block.

[0094] Figure 4 is a diagram showing an example of the structure of the data generated at this time.

[0095] In one embodiment, state change data may include Set ID, Boot ID, TimeStamp, Set state data, external environment data, and internal environment data.

[0096] In one embodiment, Set ID refers to a defined identifier of the electronic device (100), and Boot ID may refer to a segment identifier given once per boot segment from the time the electronic device (100) is booted until it is powered off. TimeStamp may refer to information about the time when the corresponding state change occurred. Information about the time may include information about the date and time. In the event that a failure occurs, this information may be used to check the time of failure and the data status at the time of failure.

[0097] In one embodiment, the Set state data may include information about the block where the state change occurred, and information about the type and degree of the state change.

[0098] In one embodiment, external environment data may refer to environmental information related to the connection between the electronic device (100) and an external device. For example, external environment data may include information about what type of device is connected to the HDMI port of the electronic device (100), what type of network is connected, and the quality or strength of the connected network.

[0099] In one embodiment, internal environment data may refer to information about the environment inside the electronic device (100). For example, internal environment data may include information about the temperature of each component of the electronic device (100), information about the version of the firmware, volume size information, and other information about various setting values ​​of the electronic device (100).

[0100] In one embodiment, state change data may include a Set ID, Boot ID, TimeStamp, event type, external environment data, and internal environment data. The event type field may refer to an event type defined for cases where the internal or external environment associated with the block changes by more than a threshold value, even if it is not defined as a state change of the block as in FIG. 3.

[0101] The electronic device (100) can store information about the state change that occurred during the first booting period as in (1).

[0102] After that, the electronic device (100) can be rebooted after being powered off.

[0103] The electronic device (100) can store information about the state change that occurred during the second boot period from when it was rebooted until it was powered off again, as in (2).

[0104] In the embodiment of Fig. 4, a failure of block A can be predicted from the 4th data of the data of (1).

[0105] The state change data of the electronic device generated in this way can be automatically deleted after a set period of time if it is predicted that no failure will occur. This can enable efficient use of the storage space of the storage device.

[0106] In one embodiment, the state change data of the electronic device generated in this way may be stored in a designated other storage space when a failure is predicted to occur. The stored data may be used for failure analysis when a failure occurs. In one embodiment, the stored data may be used for training a failure prediction model.

[0107] In the embodiment of FIG. 4, regarding the fourth data of (1) that is predicted to fail, in one embodiment, all data of (1) having the same Boot ID as the data can be stored in another storage space for future failure analysis. In one embodiment, data before and after within a predetermined time from the time of occurrence of the data can be stored in another storage space for future failure analysis.

[0108] FIG. 5 is a diagram showing an example of state change data by block of an electronic device according to one embodiment of the present disclosure.

[0109] In the embodiment of FIG. 5, the electronic device (100) had its state changed as follows: on July 8, 2024 at 13:53, the power cable was connected and the device was turned on; on July 8, 2024 at 13:57, the soundbar was connected to the HDMI; on July 8, 2024 at 14:03, the temperature of the main IC changed to an abnormal state of 128 degrees; on July 8, 2024 at 14:05, the power cable was disconnected and the device was turned off; and on July 8, 2024 at 7:15, the power cable was connected and the device was turned on again.

[0110] In this case, the electronic device (100) can generate and store state change data for each block as shown in the table (500).

[0111] The electronic device (100) can store the ID of the electronic device (100) as a fixed A000000000001, store data from 13:53 on 2024.07.08 to 14:05 on 2024.07.08 with the same Boot ID 000000-000000-000000-000001, and store data from 7:15 on 2024.07.08 with the next Boot ID 000000-000000-000000-000002.

[0112] The electronic device (100) can store the block (event field) where the state change occurred and the type of state change (event_type field) as a designated symbol or content.

[0113] In addition, the electronic device (100) may store various external environment information and internal environment information at the time when the data is stored. For example, the electronic device (100) may store the status of each HDIMI port and network status, etc. as external environment information, and may store the temperature of each component and voltage information of each part, etc. as internal environment information.

[0114] The external environment information and internal environment information stored in the table (500) are examples only and are not limited thereto.

[0115] FIG. 6 is a diagram illustrating an example of a method for an electronic device according to one embodiment of the present disclosure to predict a failure of at least one block using artificial intelligence.

[0116] An electronic device (100) according to one embodiment of the present disclosure can obtain fault prediction information for at least one block through a neural network learned based on state change data of the related block.

[0117] Artificial intelligence is a computer system that implements human-level intelligence, where machines learn and make judgments autonomously, and recognition accuracy improves with use. AI technology consists of machine learning (deep learning) technology, which utilizes algorithms to autonomously classify and learn the characteristics of input data, and component technologies that employ machine learning algorithms to mimic functions such as cognition and judgment of the human brain.

[0118] For example, the elemental technologies may include at least one of a linguistic understanding technology that recognizes human language / characters, a visual understanding technology that recognizes objects like human vision, an inference / prediction technology that judges information to logically infer and predict, a knowledge representation technology that processes human experience information into knowledge data, and a motion control technology that controls autonomous driving of a vehicle and the movement of a robot.

[0119] The artificial intelligence-related functions according to the present disclosure may be operated through the processor (110) and memory (120) of FIG. 10. The processor (110) may be composed of one or more processors. In this case, the one or more processors may be general-purpose processors such as CPUs, APs, DSPs (Digital Signal Processors), graphics-dedicated processors such as GPUs, VPUs (Vision Processing Units), or artificial intelligence-dedicated processors such as NPUs. The one or more processors (110) control the processing of input data according to predefined operation rules or artificial intelligence models stored in memory (120). Alternatively, if the one or more processors (110) are artificial intelligence-dedicated processors, the artificial intelligence-dedicated processors may be designed with a hardware structure specialized for processing a specific artificial intelligence model.

[0120] The predefined operation rules or artificial intelligence models are characterized by being created through learning. Here, being created through learning means that a predefined operation rules or artificial intelligence models are created by a basic artificial intelligence model being trained using multiple learning data by a learning algorithm to perform a desired characteristic (or purpose). Such learning may be performed within the electronic device (100) itself where the artificial intelligence according to the present disclosure is performed, or it may be performed through a separate server and / or system. Examples of learning algorithms include supervised learning, unsupervised learning, semi-supervised learning, or reinforcement learning, but are not limited to the examples described above.

[0121] An artificial intelligence model can be composed of multiple neural network layers. Each of the multiple neural network layers has multiple weight values ​​and performs neural network operations through calculations between the results of previous layers and the multiple weights. The multiple weights possessed by the multiple neural network layers can be optimized based on the learning results of the artificial intelligence model. For example, the multiple weights can be updated so that the loss or cost values ​​obtained by the artificial intelligence model during the learning process are reduced or minimized.

[0122] In an embodiment using a deep learning algorithm, the processor (110) can obtain fault prediction information for at least one block by using a pre-trained deep neural network model (610).

[0123] The previously trained deep neural network model (610) may be an artificial intelligence model trained through learning that takes state change data of a block as input and outputs failure prediction information for at least one block.

[0124] A deep neural network model may be, for example, a Convolutional Neural Network (CNN). However, it is not limited thereto, and the deep neural network model may be a known artificial intelligence model comprising at least one of a Recurrent Neural Network (RNN), a Restricted Boltzmann Machine (RBM), a Deep Belief Network (DBN), a Bidirectional Recurrent Deep Neural Network (BRDNN), and Deep Q-Networks.

[0125] The electronic device (100) can output fault prediction information for at least one block using various machine learning algorithms in addition.

[0126] FIG. 7 is a flowchart of a method of operation of an electronic device according to one embodiment of the present disclosure.

[0127] Referring to FIG. 7, an electronic device (100) according to one embodiment of the present disclosure may acquire at least one failure prediction model trained to predict failure of at least one block based on state change data and corresponding service history data obtained from a plurality of external electronic devices (S710).

[0128] In one embodiment, the electronic device (100) may obtain at least one failure prediction model trained to predict failure of at least one block based on state change data and corresponding service history data obtained from a plurality of external electronic devices (300) from a server (200).

[0129] In one embodiment, the electronic device (100) may obtain at least one failure prediction model trained to predict failure of at least one block based on state change data obtained from a plurality of external electronic devices (300) and corresponding service history data from a storage device connected to the electronic device (100) or a storage device inside the electronic device (100).

[0130] At least one fault prediction model may be a deep neural network model (610) described in FIG. 6. At least one fault prediction model may be a model learned from one of a server (200), an electronic device (100), or an external device.

[0131] The state change data obtained from a plurality of external electronic devices (300) may be data generated from each of the plurality of external electronic devices (300) in the same manner as the electronic device (100) in FIGS. 2 to 5 generates state change data.

[0132] State change data may include data recording information about the state change of each block and event data generated when the environment of each block changes by more than a threshold value.

[0133] In one embodiment, the failure prediction model may be a model that receives state change data of related blocks as input values ​​and outputs failure prediction information including whether the failure probability of at least one block is greater than or equal to a threshold value.

[0134] In one embodiment, the failure prediction model may be a model that receives state change data of all blocks as input values ​​and outputs failure prediction information including whether the failure probability of at least one block is greater than or equal to a threshold value.

[0135] In one embodiment, the service history data is data obtained from a device separate from a plurality of external electronic devices (300), and may include a service ID, set ID, service time, failure block, and failure symptom. For example, the service history data may be obtained from an AS center or a server of a service center.

[0136] In one embodiment, the failure prediction model obtained can be updated periodically or manually by user input.

[0137] An electronic device (100) according to one embodiment of the present disclosure can generate state change data when a state change of the electronic device (100) is detected (S720).

[0138] The details regarding the state change and the generation of state change data may be as described in FIGS. 2 through 5. The electronic device (100) may generate state change data. The electronic device (100) may generate state change data for each block in the manner described in FIGS. 2 through 5.

[0139] In one embodiment, the state change data generated can be shared with an external device (300).

[0140] An electronic device (100) according to one embodiment of the present disclosure can obtain fault prediction information for each block of the electronic device by inputting generated state change data into at least one acquired fault prediction model (S730).

[0141] In one embodiment, a single failure prediction model can output failure prediction information for a single block.

[0142] In one embodiment, a single fault prediction model can output fault prediction information for a plurality of blocks.

[0143] An electronic device (100) according to one embodiment of the present disclosure may store information on the time of failure of a block predicted to have failed based on acquired failure prediction information and predetermined data before and after the time (S740).

[0144] In one embodiment, the defined data before and after the time of failure may refer to all data having the same Boot ID as the time of failure.

[0145] In one embodiment, the predetermined data before and after the time of failure may refer to data generated or stored between the time before the time of failure and the time after the time of failure.

[0146] FIG. 8 is a flowchart of a method of operation of an electronic device according to one embodiment of the present disclosure.

[0147] Since steps S810 through S840 in FIG. 8 correspond to steps S710 through S740 in FIG. 7, redundant descriptions will be omitted.

[0148] Referring to FIG. 8, an electronic device (100) according to one embodiment of the present disclosure may acquire at least one failure prediction model trained to predict failure of at least one block based on state change data and corresponding service history data obtained from a plurality of external electronic devices (S810).

[0149] An electronic device (100) according to one embodiment of the present disclosure can generate state change data when a state change of the electronic device (100) is detected (S820).

[0150] An electronic device (100) according to one embodiment of the present disclosure can obtain fault prediction information for each block of the electronic device by inputting generated state change data into at least one acquired fault prediction model (S830).

[0151] An electronic device (100) according to one embodiment of the present disclosure may store fault occurrence time information of a block predicted to have a fault and predetermined data before and after the time based on acquired fault prediction information (S840).

[0152] In the event that a failure occurs, an electronic device (100) according to one embodiment of the present disclosure can identify the cause of the failure and a solution method using stored failure occurrence time information and predetermined data before and after the time (S850).

[0153] In one embodiment, when an actual failure occurs, the electronic device (100) can identify the cause of the failure and the solution method using stored failure occurrence time information and predetermined data before and after the time. This can solve the problem of difficulty in accurately predicting the failure location and time of failure when relying on customer statements because it must be based on the customer's memory, and the problem of difficulty in identifying data that can be used for diagnosing the failure among a large amount of data when using internal device data. The electronic device (100) can accurately obtain environmental information and state change information at the time of failure by using state change data and time information before and after the time when an abnormal state occurs.

[0154] FIG. 9 is a detailed flowchart of a method of operation of an electronic device according to one embodiment of the present disclosure.

[0155] In one embodiment, the electronic device (100) can generate state change data when the state changes (S910) (S930). The case where the state changes may mean the case where a state change occurs in a defined block-by-block as described in FIGS. 2 and FIGS. 3.

[0156] In one embodiment, the electronic device (100) may generate state change data (S930) when environmental information changes by more than a threshold value (S920). In one embodiment, the environmental information may be environmental information related to a block. Environmental information related to a block may refer to external environmental information and internal environmental information described in FIGS. 4 and 5.

[0157] In one embodiment, the electronic device (100) can obtain the failure probability of each block of the electronic device by inputting the generated state change data into at least one previously acquired failure prediction model (S940) in response to the generation of state change data (S930) (S950).

[0158] The failure probability of each block can be included in the failure prediction information for each block. The failure probability may refer to the probability that a failure has occurred in the corresponding block.

[0159] In one embodiment, the electronic device (100) can obtain fault prediction information for each block of the electronic device (100) based on the latest state data for each block and environment in response to the generation of state change data. That is, the electronic device (100) can obtain fault prediction information for one block based on the state change data for one block and the latest state change data for other blocks in response to the generation of state change data for one block. For example, when state change data for the HDMI block is generated, the electronic device (100) can obtain fault prediction information for the HDMI block by providing the latest state change data for the remaining blocks, excluding the HDMI block, along with the HDMI state change data, as input values ​​to the fault prediction model. Since not all blocks generate state change data simultaneously, when state change data for a specific block is generated, the electronic device (100) can provide input values ​​to the fault prediction model using the most recently generated state change data for other blocks.

[0160] In one embodiment, a fault prediction model that outputs fault prediction information for an HDMI block may require the input of state change data for all other blocks as well as state change information for the HDMI block as input values. This may be because consideration of the interaction between the HDMI block and other blocks is also necessary to predict a fault for the HDMI block.

[0161] In one embodiment, a fault prediction model that outputs fault prediction information for an HDMI block may require the input of state change information for an HDMI block as well as state change data for a defined related block as input values.

[0162] In one embodiment, failure prediction information for each block can be obtained through different failure prediction models.

[0163] In one embodiment, some blocks may share a fault prediction model for outputting fault prediction information.

[0164] In one embodiment, the electronic device (100) can identify whether the failure probability of each block is greater than or equal to a threshold value (S960).

[0165] In one embodiment, the electronic device (100) can store a log of the time of data occurrence in which the failure probability is identified as being greater than or equal to a threshold value in internal memory (S970).

[0166] In one embodiment, the log may include data generation time information and state change information for a block identified as having a failure probability above a threshold value.

[0167] In one embodiment, the log may refer to all state change data related to the state change data of a block identified as having a failure probability above a threshold value.

[0168] State change data associated with state change data identified as having a failure probability above a threshold value may refer, for example, to one of the following: state change data belonging to the same Boot ID range as the state change data, state change data for the same block as the state change data, or state change data generated within a predetermined time before or after the time of generation of the state change data.

[0169] In one embodiment, the electronic device (100) may transmit the information stored in step S970 to an engineer for analysis (S980). In one embodiment, if a failure actually occurs, the electronic device (100) may transmit the information stored in step S970 to an engineer so that it can be used to identify the cause of the failure and solve the problem.

[0170] FIG. 10 is a block diagram of an electronic device according to one embodiment of the present disclosure.

[0171] Referring to FIG. 10, the electronic device (100) may include a processor (110) and a memory (120).

[0172] The memory (120) can store a program for processing and controlling the processor (110). Additionally, the memory (120) can store data that is input to or output from the electronic device (100).

[0173] The memory (120) may include at least one of internal memory (not shown) and external memory (not shown).

[0174] The memory (120) may include at least one type of storage medium among flash memory type, hard disk type, multimedia card micro type, card type memory (e.g., SD or XD memory, etc.), RAM (Random Access Memory), SRAM (Static Random Access Memory), ROM (Read-Only Memory), EEPROM (Electrically Erasable Programmable Read-Only Memory), PROM (Programmable Read-Only Memory), magnetic memory, magnetic disk, and optical disk.

[0175] The built-in memory may include, for example, at least one of volatile memory (e.g., DRAM (Dynamic RAM), SRAM (Static RAM), SDRAM (Synchronous Dynamic RAM), etc.), non-volatile memory (e.g., OTPROM (One Time Programmable ROM), PROM (Programmable ROM), EPROM (Erasable and Programmable ROM), EEPROM (Electrically Erasable and Programmable ROM), Mask ROM, Flash ROM, etc.), a hard disk drive (HDD), or a solid-state drive (SSD).

[0176] According to one embodiment, the processor (110) can load instructions or data received from at least one of the non-volatile memory or other components into the volatile memory for processing. Additionally, the processor (110) can store data received from or generated from other components in the non-volatile memory.

[0177] The external memory may include, for example, at least one of CF (Compact Flash), SD (Secure Digital), Micro-SD (Micro Secure Digital), Mini-SD (Mini Secure Digital), xD (extreme Digital), and Memory Stick.

[0178] The memory (120) may store one or more instructions that can be executed by the processor (110).

[0179] In one embodiment, the memory (120) may store one or more instructions executable by the processor (110) separately in multiple memories (120).

[0180] In one embodiment, the memory (120) may store one or more instructions that can be executed individually or jointly by at least one processor (110).

[0181] In one embodiment, the memory (120) can store various information input through an input / output unit (not shown).

[0182] In one embodiment of the present disclosure, at least one of instructions, an algorithm, a data structure, program code, and an application program that can be read by a processor (110) may be stored in the memory (120). The instructions, algorithm, data structure, and program code stored in the memory (120) may be implemented in a programming or scripting language such as, for example, C, C++, Java, assembler, etc.

[0183] In one embodiment, the memory (120) may acquire at least one fault prediction model trained to predict a failure of at least one block based on state change data acquired from a plurality of external electronic devices and corresponding service history data, generate state change data when a state change of the electronic device (100) is detected, input the generated state change data into the acquired at least one fault prediction model to acquire fault prediction information for each block of the electronic device (100), and store instructions for controlling the processor (110) to store fault occurrence time information of the block predicted to have failed and predetermined data before and after the time based on the acquired fault prediction information.

[0184] The processor (110) can execute an OS (Operation System) and various applications stored in memory (120) when there is user input or when conditions stored in a preset state are satisfied.

[0185] The processor (110) may include RAM (RAM) used as a storage area corresponding to various tasks performed in the electronic device (100) or for storing signals or data input from outside the electronic device (100), and ROM (ROM) stored as a control program for controlling the electronic device (100).

[0186] The processor (110) may include at least one processing circuit.

[0187] The processor (110) may include a single core, dual core, triple core, quad core, and multiples thereof. Additionally, the processor (110) may include multiple processors. For example, the processor (110) may be implemented as a main processor (not shown) and a sub processor (not shown) operating in sleep mode.

[0188] Additionally, the processor (110) may include at least one of a CPU (Central Processing Unit), a GPU (Graphic Processing Unit), and a VPU (Video Processing Unit). Alternatively, depending on the embodiment, it may be implemented in the form of a System On Chip (SOC) integrating at least one of a CPU, a GPU, and a VPU.

[0189] The processor (120) may include various processing circuits and / or multiple processors. For example, the term “processor” as used herein, including in the claims, may include at least one processor and various processing circuits. In at least one processor, one or more processors may be configured to perform the various functions described herein in a distributed manner, individually and / or collectively. As used herein, “processor,” “at least one processor,” and “one or more processors” may be configured to perform various functions. However, these terms cover, for example but without limitation, situations where one processor performs some of the functions and other processor(s) perform other parts of the functions, and situations where a single processor can perform all functions. Additionally, at least one processor may include a combination of processors performing various functions of the disclosed functions in a distributed manner. At least one processor may execute program instructions to achieve or perform various functions.

[0190] The processor (110) can control components of various electronic devices (100) by executing one or more instructions stored in memory (120).

[0191] In one embodiment, the processor (110) may acquire at least one failure prediction model trained to predict failure of at least one block based on state change data and corresponding service history data obtained from a plurality of external electronic devices.

[0192] In one embodiment, the processor (110) can generate state change data when a state change of the electronic device (100) is detected.

[0193] In one embodiment, the processor (110) can input generated state change data into at least one acquired fault prediction model to obtain fault prediction information for each block of the electronic device (100).

[0194] In one embodiment, the processor (110) can store fault occurrence time information of a block predicted to have a fault and predetermined data before and after the time based on the acquired fault prediction information.

[0195] In one embodiment, the processor (110) may enable stored fault occurrence time information and predetermined data before and after the said time to be used to identify the cause of the fault and the solution method when an actual fault occurs.

[0196] In one embodiment, the processor (110) can control the generation of state change data in response to the generation of state change data by inputting the generated state change data into at least one fault prediction model to obtain fault prediction information for each block of the electronic device (100).

[0197] In one embodiment, the processor (110) can be controlled to obtain fault prediction information for each block of the electronic device (100) based on the latest state data for each block and environment in response to the generation of state change data by executing one or more instructions.

[0198] In one embodiment, the processor (110) can control at least one fault prediction model to be periodically updated by executing one or more instructions.

[0199] FIG. 11 is a detailed block diagram of an electronic device according to one embodiment of the present disclosure.

[0200] Referring to FIG. 11, the electronic device (100) may include a tuner unit (340), a processor (110), a display (320), a communication unit (350), a sensor unit (360), an input / output unit (370), a video processing unit (380), an audio processing unit (385), an audio output unit (390), a memory (120), and a power supply unit (395).

[0201] Hereinafter, the same reference numerals are assigned to configurations and steps identical to those described in FIG. 10, and redundant descriptions are omitted.

[0202] The processor (110) of FIG. 11 is configured to correspond to the processor (110) of FIG. 10, and the memory (120) of FIG. 11 is configured to correspond to the memory (120) of FIG. 10. Therefore, any content that overlaps with what was previously explained will be omitted.

[0203] A communication unit (350) according to one embodiment may include a Wi-Fi module, a Bluetooth module, an infrared communication module and a wireless communication module, a LAN module, an Ethernet module, a wired communication module, etc. At this time, each communication module may be implemented in the form of at least one hardware chip.

[0204] The Wi-Fi module and the Bluetooth module perform communication using the Wi-Fi method and the Bluetooth method, respectively. When using the Wi-Fi module or the Bluetooth module, various connection information such as SSID and session key is first transmitted and received, and then various information can be transmitted and received after establishing a communication connection using this information. The wireless communication module may include at least one communication chip that performs communication according to various wireless communication standards such as Zigbee, 3G (3rd Generation), 3GPP (3rd Generation Partnership Project), LTE (Long Term Evolution), LTE-A (LTE Advanced), 4G (4th Generation), and 5G (5th Generation).

[0205] A communication unit (350) according to one embodiment can receive user input from an external device.

[0206] A communication unit (350) according to one embodiment can communicate with an external device such as a server.

[0207] A communication unit (350) according to one embodiment may include a communication unit that performs wireless communication such as BT with a server, etc., and a communication unit that is connected to an external device such as an HDMI port, etc. At this time, the communication unit that performs wireless communication such as BT with a server, etc., can perform connection with other devices and transmission of video / audio data. The communication unit that is connected to an external device such as an HDMI port, etc., may include not only an input port for receiving input, but also an output port such as DP, HDMI, RGB, DVI, Thunderbolt, etc., for transmitting video or audio signals to an external display unit or speaker, etc.

[0208] A tuner unit (340) according to one embodiment can select only the frequency of the channel to be received by the electronic device (100) from among many radio wave components by tuning through amplification, mixing, resonance, etc. of a broadcast signal received via wired or wireless means. The broadcast signal includes audio, video, and additional information (e.g., EPG (Electronic Program Guide)).

[0209] The tuner unit (340) can receive broadcast signals from various sources such as terrestrial broadcasting, cable broadcasting, satellite broadcasting, internet broadcasting, etc. The tuner unit (340) can also receive broadcast signals from sources such as analog broadcasting or digital broadcasting.

[0210] The sensor unit (360) detects voice around the electronic device (100), image around the electronic device (100), or interaction with the surroundings of the electronic device (100), and may include at least one of a microphone (331), a camera (332), and an optical receiver (333). The sensor unit (360) detects the state of the electronic device (100) or the state around the electronic device (100) and can transmit the detected information to the processor (110).

[0211] The microphone (331) receives the user's uttered voice and voice generated around the electronic device (100). The microphone (331) can convert the received voice into an electrical signal and output it to the processor (110). The microphone (331) can use various noise removal algorithms to remove noise generated during the process of receiving external acoustic signals.

[0212] The camera (332) can obtain image frames such as still images or video. Images captured through the image sensor can be processed through a processor (110) or a separate image processing unit (not shown).

[0213] Image frames processed by the camera (332) can be stored in memory (120) or transmitted externally through the communication unit (350). Two or more cameras (332) may be provided depending on the configuration of the electronic device (100).

[0214] The optical receiver (333) receives an optical signal (including a control signal) received from an external remote control device (not shown). The optical receiver (333) can receive an optical signal corresponding to user input (e.g., touch, press, touch gesture, voice, or motion) from the remote control device (not shown). A control signal can be extracted from the received optical signal under the control of the processor (110). For example, the optical receiver (333) can receive a control signal corresponding to a channel up / down button for channel switching from the remote control device (not shown).

[0215] The sensor unit (360) is illustrated as including a microphone (331), a camera (332), and an optical receiver (333), but is not limited thereto. It may include at least one of a magnetic sensor, an acceleration sensor, a temperature / humidity sensor, an infrared sensor, a gyroscope sensor, a position sensor (e.g., GPS), a barometric pressure sensor, a proximity sensor, an RGB sensor, an illumination sensor, and a Wi-Fi signal receiver, but is not limited thereto. Since the function of each sensor can be intuitively inferred by a person skilled in the art from its name, a detailed description is omitted.

[0216] The sensor unit (360) is shown as being provided in the electronic device (100) itself, but is not limited thereto and may be provided in a control unit, such as a remote control, which is located independently of the electronic device (100) and communicates with the electronic device (100). When a sensing unit (130) is provided in the control unit of the electronic device (100), the control unit may digitize information detected by the sensing unit (130) and transmit it to the electronic device (100). The control unit may communicate with the electronic device (100) using short-range communication including infrared, Wi-Fi, or Bluetooth.

[0217] For example, the microphone may be provided in the electronic device (100) itself, but may also be provided in a control device, such as a remote control, which is located independently of the electronic device (100) and communicates with the electronic device (100).

[0218] In one embodiment, when a microphone is provided in the remote control, an analog voice signal is received through the microphone, and the remote control can digitize it and transmit it to an electronic device (100) such as a TV. At this time, the remote control can communicate with the electronic device (100) using short-range communication including infrared, Wi-Fi, Bluetooth, and BT.

[0219] In one embodiment, the electronic device (100) may be equipped with a plurality of communication units (350) capable of various short-range communication including infrared, Wi-Fi, or Bluetooth.

[0220] In one embodiment, the electronic device (100) may have a plurality of communication units (350) that are different from each other, such as a communication unit that communicates with a server (200) and a communication unit that communicates with a remote control. For example, the communication unit that communicates with the server may be a communication unit that uses an Ethernet modem, a Wi-Fi module, etc., while the communication unit that communicates with the remote control may be a communication unit that uses a BT module.

[0221] In one embodiment, the electronic device (100) may have a communication unit (350) in which a communication unit communicating with a server and a communication unit communicating with a remote control are identical. For example, both the communication unit communicating with the server and the communication unit communicating with the remote control may be communication units that use a Wi-Fi module.

[0222] In one embodiment, a device such as a smartphone with a remote control application installed can perform the same role as the remote control described above. That is, a device with a remote control application installed can control an electronic device (100) and perform voice recognition functions.

[0223] Devices on which the remote control application can be installed may include all devices capable of operating by installing an application, such as AI speakers, in addition to smartphones.

[0224] In one embodiment, a device with a remote control application installed may be able to receive user voice.

[0225] In one embodiment, the electronic device (100) may include a plurality of communication units capable of implementing the communication method to transmit and receive data using Wi-Fi, BT, infrared, etc., with a device on which a remote control or a remote control application can be installed, and to control the device on which a remote control or a remote control application can be installed.

[0226] The input / output unit (370) receives video (e.g., video, etc.), audio (e.g., voice, music, etc.), and additional information (e.g., EPG, etc.) from outside the electronic device (100) under the control of the processor (110). The input / output unit (370) may include any one of HDMI (High-Definition Multimedia Interface), MHL (Mobile High-Definition Link), USB (Universal Serial Bus), DP (Display Port), Thunderbolt, VGA (Video Graphics Array) port, RGB port, D-SUB (D-subminiature), DVI (Digital Visual Interface), component jack, and PC port.

[0227] The video processing unit (380) performs processing on video data received by the electronic device (100). The video processing unit (380) can perform various image processing on the video data, such as decoding, scaling, noise filtering, frame rate conversion, and resolution conversion.

[0228] The display (320) converts video signals, data signals, OSD signals, control signals, etc., processed by the processor (110) to generate driving signals. The display (320) can be implemented as a PDP, LCD, OLED, flexible display, etc., and can also be implemented as a 3D display. Additionally, the display (320) can be configured as a touch screen and used as an input device in addition to an output device.

[0229] The display (320) can output various content input through a communication unit (not shown) or an input / output unit (370), or output an image stored in memory (120). Additionally, the display (320) can output information input by a user through the input / output unit (370) to the screen.

[0230] The display (320) may include a display panel. The display panel may be a Liquid Crystal Display (LCD) panel or a panel containing various light-emitting elements such as a Light Emitting Diode (LED), Organic Light Emitting Diode (OLED), or Cold Cathode Fluorescent Lamp (CCFL). Additionally, the display panel may include not only a flat display device but also a curved display device having a curvature or a flexible display device with adjustable curvature. The display panel may also be a 3D display or an electrophoretic display.

[0231] The output resolution of the display panel may include, for example, HD (High Definition), Full HD, Ultra HD, or a resolution sharper than Ultra HD.

[0232] In the embodiment of FIG. 11, the electronic device (100) is shown to include a display, but is not limited thereto. The electronic device (100) may be configured to be connected via wired or wireless communication to a separate display device including a display, and to transmit video / audio signals to the display device.

[0233] In one embodiment, the electronic device (100) may be implemented in a form that operates by connecting to an external display even without having a built-in display.

[0234] For example, the electronic device (100) may be implemented in a form that outputs video to a separate external display through a video or audio output port, such as an STB, Apple TV, etc., without a display or having a simple display for notifications, etc.

[0235] In this case, the electronic device (100) may have an output port for outputting a video or audio signal to a display. The output port may be in a form that can transmit video and audio signals simultaneously, such as HDMI, DP, Thunderbolt, etc., or may be in a form where there are separate ports for transmitting video and audio signals separately.

[0236] In one embodiment, the electronic device (100) can transmit video or audio signals via wired communication or wireless communication, etc.

[0237] The audio processing unit (385) performs processing on audio data. Various processing such as decoding, amplification, and noise filtering on audio data can be performed in the audio processing unit (385). Meanwhile, the audio processing unit (385) may be equipped with multiple audio processing modules to process audio corresponding to multiple contents.

[0238] The audio output unit (390) outputs audio included in a broadcast signal received through the tuner unit (340) under the control of the processor (110). The audio output unit (390) can output audio (e.g., voice, sound) input through the communication unit (350) or the input / output unit (370). Additionally, the audio output unit (390) can output audio stored in the memory (120) under the control of the processor (110). The audio output unit (390) may include at least one of a speaker, a headphone output terminal, or an S / PDIF (Sony / Philips Digital Interface) output terminal.

[0239] The power supply unit (395) supplies power input from an external power source to the components inside the electronic device (100) under the control of the processor (110). Additionally, the power supply unit (395) can supply power output from one or more batteries (not shown) located inside the electronic device (100) to the internal components under the control of the processor (110).

[0240] The memory (120) may store various data, programs, or applications for driving and controlling the electronic device (100) under the control of the processor (110). The memory (120) may include a broadcast receiving module, a channel control module, a volume control module, a communication control module, a voice recognition module, a motion recognition module, an optical receiving module, a display control module, an audio control module, an external input control module, a power control module, a power control module for an external device connected wirelessly (e.g., Bluetooth), a voice database (DB), or a motion database (DB), which are not illustrated. The modules not illustrated and the database of the memory (120) may be implemented in software form to perform broadcast reception control functions, channel control functions, volume control functions, communication control functions, voice recognition functions, motion recognition functions, optical reception control functions, display control functions, audio control functions, external input control functions, power control functions, or power control functions for an external device connected wirelessly (e.g., Bluetooth) in the electronic device (100). The processor (110) can perform each of these functions using the software stored in memory (120).

[0241] In FIG. 11, the processor (110) is depicted as a single element, but is not limited thereto. In one embodiment, the processor (110) may be composed of one or more elements.

[0242] In one embodiment of the present disclosure, the processor (110) may be composed of a dedicated hardware chip that performs artificial intelligence (AI) learning.

[0243] A 'module' included in memory (120) refers to a unit that processes a function or operation performed by a processor (110), and can be implemented as software such as instructions, algorithms, data structures, or program code.

[0244] Meanwhile, the block diagram of the electronic device (100) illustrated in FIGS. 10 and FIGS. 11 is a block diagram for one embodiment. Each component of the block diagram may be integrated, added, or omitted according to the specifications of the actual electronic device (100) being implemented. That is, as needed, two or more components may be combined into one component, or one component may be subdivided into two or more components. Furthermore, the functions performed in each block are intended to explain the embodiments, and the specific operations or devices thereof do not limit the scope of the present invention.

[0245] FIG. 12 is a diagram illustrating an example of a structure in which an electronic device according to one embodiment of the present disclosure operates using a server, and FIG. 13 is a diagram illustrating an example of a structure in which an electronic device according to one embodiment of the present disclosure operates using on-device AI.

[0246] The electronic device (100) according to the embodiment of FIG. 12 can obtain a fault prediction model from the server (200) using state change data obtained from one or more external devices (300) and service history data obtained from the service center server (400).

[0247] In the event of a failure, the external device (300) can receive service by transmitting state change data generated by itself to the service center server (400). This service history information can be stored in the service center server.

[0248] In the event of a failure, an electronic device (100) according to one embodiment of the present disclosure can receive service by transmitting state change data generated within itself to a service center server (400), just like an external device (300).

[0249] In the embodiment of FIG. 12, the fault prediction model can be learned and extracted from the server (200). The fault prediction model extracted by the server (200) can be transmitted to the electronic device (100) and the external device (300) for sharing and use.

[0250] The electronic device (100) according to the embodiment of FIG. 13 can extract a learned fault prediction model using state change data obtained from one or more external devices (300) and service history data obtained from a service center server (400).

[0251] The electronic device (100) according to the embodiment of FIG. 13 can update the fault prediction model by continuously receiving state change data from one or more external devices (300) even after the fault prediction model has been extracted and training the fault prediction model.

[0252] In one embodiment, the electronic device (100) can also directly perform the role of a service center server.

[0253] In one embodiment, the electronic device (100) can share a fault prediction model extracted by itself with one or more external devices (300).

[0254] FIG. 14 is a structural diagram of a fault prediction system according to one embodiment of the present disclosure.

[0255] The server (200) can acquire state change data from at least one external device (300) and acquire service history data from the service center server (400). The state change data of the external device (300) may have the same data structure as described in FIG. 4. The service history data may include a service ID, a set ID, information on the time of occurrence, a failure block, failure symptom information, etc.

[0256] The server (200) can preprocess the acquired state change data and service history data. In one embodiment, the preprocessing may include a process of sorting the data by Set ID and Boot ID. In one embodiment, the preprocessing may include a process of matching the state change data and service history data by matching the Set ID of the state change data and the service history data, and matching the Boot ID of the state change data with information regarding the time of occurrence of the service history data.

[0257] The server (200) can extract a learned failure prediction model based on state change data and service history data.

[0258] The electronic device (100) can obtain a fault prediction model from the server (200) and periodically update it.

[0259] In one embodiment, the electronic device (100) can obtain the failure probability of each block using a failure prediction model for each block.

[0260] In one embodiment, the electronic device (100) may use two or more failure prediction models for a single block. In this case, if a block is predicted to have failed by even one model, it may be determined that a failure has occurred.

[0261] In one embodiment, the electronic device (100) can obtain the failure probability of each block in real time by running a failure prediction model whenever state change data is generated. If the failure probability exceeds a threshold value, the electronic device (100) can extract logs from before and after the time of failure occurrence and store them in internal memory. The generated logs can be uploaded to a server (200) or stored via a storage medium such as a USB, and then used to resolve the failure when an actual failure occurs. The logs from before and after the time of failure occurrence may be identical to the failure time information of the block described in FIGS. 7 to 9 and the predetermined data information or logs from before and after said time.

[0262] The method of operation of an electronic device (100) according to one embodiment may also be implemented in the form of a computer-readable medium containing instructions executable by a computer, such as a program module executed by a computer. The computer-readable medium may be any available medium accessible by a computer and includes both volatile and non-volatile media, and both removable and non-removable media. The computer-readable medium may include program instructions, data files, data structures, etc., either alone or in combination. The program instructions recorded on the medium may be those specifically designed and configured for the present invention, or may be those known and available to those skilled in the art of computer software. Examples of computer-readable recording media include magnetic media such as hard disks, floppy disks, and magnetic tapes; optical recording media such as CD-ROMs and DVDs; magneto-optical media such as floptical disks; and hardware devices specifically configured to store and execute program instructions, such as ROM, RAM, and flash memory. Examples of program instructions may include machine code, such as that generated by a compiler, as well as high-level language code that can be executed by a computer using an interpreter, etc.

[0263] According to one embodiment, the method according to the various embodiments disclosed herein may be provided by being included in a computer program product. The computer program product may be traded between a seller and a buyer as a product. The computer program product may be distributed in the form of a device-readable storage medium (e.g., compact disc read-only memory (CD-ROM)), or distributed online (e.g., download or upload) through an application store or directly between two user devices (e.g., smartphones). In the case of online distribution, at least a portion of the computer program product (e.g., downloadable app) may be temporarily stored or temporarily created on a device-readable storage medium, such as the memory of a manufacturer's server, an application store's server, or a relay server.

[0264] The foregoing description is for illustrative purposes only, and those skilled in the art will understand that other specific forms can be easily modified without altering the technical concept or essential features of the invention. Therefore, the embodiments described above should be understood as illustrative in all respects and not restrictive. For example, each component described as a single unit may be implemented in a distributed manner, and components described as distributed may likewise be implemented in a combined form.

[0265] An electronic device according to one embodiment may include at least one processor including a processing circuit and a memory for storing one or more instructions, wherein the one or more instructions are executed individually or jointly by the one or more processors to obtain at least one fault prediction model trained to predict a failure of at least one block based on state change data obtained from a plurality of external electronic devices and corresponding service history data, and when a state change of the electronic device is detected, state change data is generated, and the generated state change data is input into the at least one fault prediction model obtained to obtain fault prediction information for each block of the electronic device, and based on the obtained fault prediction information, may store information on the time of failure of a block predicted to have failed and predetermined data before and after the time.

[0266] The above state change data may include data recording information about the state change of each block and event data generated when the environment of each block changes by more than a threshold value.

[0267] The above fault prediction model may be a model that receives state change data of related blocks as input values ​​and outputs fault prediction information including whether the failure probability of at least one block is greater than or equal to a threshold value.

[0268] The above service history data is data obtained from a device separate from the plurality of external electronic devices, and may include a service ID, device ID, service time, failure block, and failure symptom.

[0269] The above at least one failure prediction model may be learned on a server and acquired from the server to the electronic device.

[0270] The electronic device can enable the stored fault occurrence time information and predetermined data before and after the time to be used to identify the cause of the fault and the solution method when an actual fault occurs, by having the one or more instructions executed individually or jointly by the one or more processors.

[0271] The fixed data before and after the above point in time may refer to all data having the same boot ID as the above point in time.

[0272] The electronic device may input the generated state change data into the acquired at least one fault prediction model in response to the generation of state change data by the execution of the one or more instructions individually or jointly by the one or more processors, thereby obtaining fault prediction information for each block of the electronic device.

[0273] The electronic device may be configured to obtain fault prediction information for each block of the electronic device based on the latest state data for each block and environment in response to the generation of state change data by the execution of one or more instructions individually or jointly by one or more processors.

[0274] The electronic device can enable the at least one fault prediction model to be periodically updated by having the one or more instructions executed individually or jointly by the one or more processors.

[0275] A method of operating an electronic device according to one embodiment may include the steps of: acquiring at least one fault prediction model trained to predict a failure of at least one block based on state change data acquired from a plurality of external electronic devices and corresponding service history data; generating state change data when a state change of the electronic device is detected; inputting the generated state change data into the at least one fault prediction model acquired to acquire fault prediction information for each block of the electronic device; and storing, based on the acquired fault prediction information, information on the time of failure of a block predicted to have failed and predetermined data before and after the time.

[0276] The above fault prediction model may be a model that receives state change data of related blocks as input values ​​and outputs fault prediction information including whether the failure probability of at least one block is greater than or equal to a threshold value.

[0277] The above service history data is data obtained from a device separate from the plurality of external electronic devices, and may include a service ID, device ID, service time, failure block, and failure symptom.

[0278] The above at least one failure prediction model may be learned on a server and acquired from the server to the electronic device.

[0279] The stored failure occurrence time and the predetermined data before and after said time can be used to identify the cause of the failure and the solution method in the event that an actual failure occurs.

[0280] The fixed data before and after the above point in time may refer to all data having the same boot ID as the above point in time.

[0281] The step of obtaining fault prediction information for each block of the electronic device may include, in response to the generation of state change data, the step of inputting the generated state change data into at least one acquired fault prediction model to obtain fault prediction information for each block of the electronic device.

[0282] The step of obtaining fault prediction information for each block of the electronic device may include obtaining fault prediction information for each block of the electronic device based on the latest state data for each block and environment in response to the generation of state change data.

[0283] A computer-readable recording medium may be provided on which a program for performing the operation method of the above electronic device on a computer is recorded.

Claims

1. At least one processor (110) including a processing circuit; and The electronic device (100) includes a memory (120) for storing one or more instructions, wherein the one or more instructions are executed individually or collectively by the at least one processor (110): At least one failure prediction model is obtained that is trained to predict failure of at least one block based on state change data and corresponding service history data obtained from multiple external electronic devices (300), and When a change in the state of the above electronic device (100) is detected, state change data is generated, and The above-mentioned generated state change data is input into at least one acquired fault prediction model to obtain fault prediction information for each block of the electronic device (100), and An electronic device that stores fault occurrence time information of a block predicted to have failed and predetermined data before and after the time based on the above-mentioned fault prediction information.

2. In Paragraph 1, The above state change data is an electronic device comprising data recording information about the state change of each block and event data generated when the environment of each block changes by more than a threshold value.

3. In any one of paragraphs 1 to 2, An electronic device that is a model in which the above-mentioned fault prediction model receives state change data of related blocks as input values ​​and outputs fault prediction information including whether the failure probability of at least one block is greater than or equal to a threshold value.

4. In any one of paragraphs 1 through 3, The above service history data is data obtained from a device separate from the plurality of external electronic devices, and is an electronic device including a service ID, set ID, service time point, failure block, and failure symptom.

5. In any one of paragraphs 1 through 4, The electronic device, wherein at least one fault prediction model is learned on a server (200) and acquired from the server (200) to the electronic device.

6. In any one of paragraphs 1 to 5, the one or more instructions are executed individually or jointly by the one or more processors (110). An electronic device that allows the stored fault occurrence time information and predetermined data before and after the said time to be used to identify the cause of the fault and the solution method when an actual fault occurs.

7. In any one of paragraphs 1 through 6, An electronic device in which the fixed data before and after the above point in time refers to all data having the same Boot ID as the above point in time.

8. In any one of paragraphs 1 through 7, The above one or more instructions are executed individually or jointly by the above one or more processors (110), thereby, An electronic device that, in response to the generation of the above state change data, inputs the generated state change data into at least one acquired fault prediction model to obtain fault prediction information for each block of the electronic device (100).

9. In any one of paragraphs 1 through 8, The above one or more instructions are executed individually or jointly by the above one or more processors (110), thereby, An electronic device that obtains fault prediction information for each block of the electronic device (100) based on the latest state data for each block and environment in response to the generation of the above state change data.

10. In any one of paragraphs 1 through 9, The above one or more instructions are executed individually or jointly by the above one or more processors (110), thereby, An electronic device that enables at least one failure prediction model to be periodically updated.

11. A method for operating an electronic device (100), A step of obtaining at least one failure prediction model trained to predict failure of at least one block based on state change data and corresponding service history data obtained from a plurality of external electronic devices (300); When a state change of the electronic device (100) is detected, a step of generating state change data; A step of inputting the generated state change data into at least one acquired fault prediction model to obtain fault prediction information for each block of the electronic device (100); and A method of operating an electronic device comprising the step of storing fault occurrence time information of a block predicted to have failed and predetermined data before and after the time based on the above-mentioned fault prediction information.

12. In Paragraph 11, A method of operation of an electronic device comprising state change data of the above-mentioned blocks, data recording information about the state change of each block, and event data generated when the environment of each block changes by more than a threshold value.

13. In any one of paragraphs 11 to 12, A method of operation of an electronic device, wherein the above-mentioned fault prediction model receives state change data of related blocks as input values ​​and outputs fault prediction information including whether the failure probability of at least one block is greater than or equal to a threshold value.

14. In any one of paragraphs 11 through 13, The above service history data is data obtained from a device separate from the plurality of external electronic devices (300), and a method of operation of an electronic device including a service ID, set ID, service time, failure block, and failure symptom.

15. A computer-readable recording medium having a program recorded thereon for performing the method of any one of paragraphs 11 through 14 on a computer.

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