Electronic device for detecting traffic patterns, and operating method thereof

Abstract

One embodiment of the present invention relates to an apparatus and a method for detecting traffic patterns in an electronic device. According to one embodiment, the electronic device comprises a communication circuit, at least one processor, and a memory for storing instructions, wherein, when executed by the at least one processor, the instructions can instruct the electronic device to: identify, when it is time to detect a traffic pattern corresponding to traffic data in a CDRX mode, whether an active period in the CDRX mode arrives; and, on the basis of a traffic classification model, detect, when the active period arrives, a traffic pattern corresponding to traffic data generated by the electronic device. Other embodiments are also possible.

Description

Electronic device for detecting traffic patterns and method of operation thereof

[0001] An embodiment of the present disclosure relates to an electronic device for detecting traffic patterns and a method of operating the same.

[0002] With the advancement of information and communication technology and semiconductor technology, various electronic devices are evolving into multimedia devices that provide various multimedia functions. Multimedia functions may include at least one of voice call functions, video call functions, messaging functions, broadcasting functions, wireless internet functions, camera functions, electronic payment functions, or content playback functions.

[0003] An electronic device may execute at least one application program to provide various services required by a user. An application program executed on the electronic device may include unique data transmission patterns depending on the situation. If the electronic device detects a pattern of traffic generated by the application program, it may efficiently manage wireless resources based on the traffic pattern.

[0004] The information described above may be provided as related art for the purpose of aiding understanding of this document. None of the above is to be claimed as prior art related to this document, nor can it be used to determine prior art.

[0005] An electronic device can detect (or classify) traffic patterns of an application program based on the features of traffic collected during a specified time (e.g., time window) at specified intervals.

[0006] When there is no traffic to transmit and / or receive over a network, the electronic device may operate in discontinuous reception (DRX) (or connected mode DRX) to reduce unnecessary power consumption of the electronic device. When the electronic device operates in DRX, it may operate in a low-power mode (e.g., sleep mode) and periodically switch to an active state (e.g., active mode) based on a specified DRX period to check whether there is data to receive from an external electronic device.

[0007] If the period for detecting traffic patterns and the DRX period of the electronic device do not match, the detection of traffic patterns may be limited or the transition to low-power mode may be delayed, which may increase the power consumption of the electronic device.

[0008] Embodiments of the present invention disclose an apparatus and method for detecting (or classifying, identifying) traffic patterns corresponding to traffic data based on a DRX period (or CDRX period) in an electronic device.

[0009] The technical problems to be solved in this document are not limited to those mentioned above, and other technical problems not mentioned will be clearly understood by those skilled in the art to which this invention belongs from the description below.

[0010] According to one embodiment, the electronic device may include at least one processor comprising a communication circuit and a processing circuit, and a memory for storing instructions. According to one embodiment, the instructions may, when executed individually or collectively by at least one processor, cause the electronic device to check whether an active period (on duration) in CDRX mode has arrived when a time has arrived for detecting a traffic pattern corresponding to traffic data generated by the electronic device based on a traffic classification model in CDRX mode. According to one embodiment, the instructions may, when executed individually or collectively by at least one processor, cause the electronic device to detect a traffic pattern corresponding to traffic data generated by the electronic device based on a traffic classification model when an active period in CDRX mode has arrived.

[0011] According to one embodiment, the method of operating an electronic device may include an operation of checking whether an active period (on duration) within the CDRX mode has arrived when a time has arrived for detecting a traffic pattern corresponding to traffic data generated by the electronic device based on a traffic classification model within the CDRX mode. According to one embodiment, the method of operating an electronic device may include an operation of detecting a traffic pattern corresponding to traffic data generated by the electronic device based on a traffic classification model when an active period within the CDRX mode has arrived.

[0012] According to one embodiment, a non-transient computer-readable storage medium (or computer program product) storing one or more programs may be described. According to one embodiment, one or more programs may include instructions that, when executed by a processor of an electronic device, when a time has come to detect a traffic pattern corresponding to traffic data generated by the electronic device based on a traffic classification model within a CDRX mode, check whether an active period (on duration) within the CDRX mode has arrived, and when the active period within the CDRX mode has arrived, perform an operation to detect a traffic pattern corresponding to traffic data generated by the electronic device based on the traffic classification model.

[0013] According to an embodiment of the present invention, by detecting (or classifying, identifying) traffic patterns based on setting values ​​(e.g., period or start time) related to the identification of updated traffic patterns based on information related to the DRX (discontinuous reception) (or CDRX (connected mode DRX)) mode in an electronic device, unnecessary power consumption resulting from the detection of traffic patterns can be reduced.

[0014] The effects obtainable from the various embodiments of the present invention are not limited to those mentioned above, and other unmentioned effects will be clearly understood by those skilled in the art to which the various embodiments of the present invention belong from the description below.

[0015] FIG. 1 is a block diagram of an electronic device in a network environment according to one embodiment.

[0016] FIG. 2 is a block diagram showing an integrated intelligent system according to one embodiment.

[0017] FIG. 3 is a block diagram of an electronic device for detecting traffic patterns according to one embodiment.

[0018] FIG. 4 is a block diagram of a processor of an electronic device for detecting traffic patterns according to one embodiment.

[0019] FIG. 5 is a flowchart for detecting traffic patterns based on information related to CDRX in an electronic device according to one embodiment.

[0020] FIG. 6 is a flowchart for limiting the detection of traffic patterns during a CDRX interval in an electronic device according to one embodiment.

[0021] FIG. 7 is an example of limiting the detection of traffic patterns during a CDRX interval in an electronic device according to one embodiment.

[0022] FIG. 8a is a flowchart for detecting traffic patterns based on the CDRX period in an electronic device according to one embodiment.

[0023] FIG. 8b is a flowchart for detecting traffic patterns during a CDRX interval in an electronic device according to one embodiment.

[0024] FIG. 9 is an example showing a section for RRM measurement in the CDRX section of an electronic device according to one embodiment.

[0025] FIG. 10 is a graph showing the detection error rate of a traffic pattern according to a change in the detection period of a traffic pattern in an electronic device according to one embodiment.

[0026] FIG. 11 is a graph showing the length of a waiting period according to a data transmission category in an electronic device according to one embodiment.

[0027] FIG. 12 is a flowchart for detecting traffic patterns based on the active time in the CDRX section of an electronic device according to one embodiment.

[0028] FIG. 13 is an example of detecting a traffic pattern based on the active time in a CDRX section in an electronic device according to one embodiment.

[0029] FIG. 14 is a flowchart for detecting traffic patterns using a virtual traffic image in an electronic device according to one embodiment.

[0030] FIG. 15 is an example of detecting traffic patterns using a virtual traffic image in an electronic device according to one embodiment.

[0031] FIG. 16 is a flowchart for selectively detecting traffic patterns based on information related to CDRX in an electronic device according to one embodiment.

[0032] FIG. 17 is a flowchart for detecting traffic patterns during a CDRX interval in an electronic device according to one embodiment.

[0033] Various embodiments are described below in detail with reference to the attached drawings.

[0034] FIG. 1 is a block diagram of an electronic device (101) in a network environment (100) according to one embodiment. Referring to FIG. 1, in the network environment (100), the electronic device (101) may communicate with an electronic device (102) through a first network (198) (e.g., a short-range wireless communication network) or may communicate with at least one of an electronic device (104) or a server (108) through a second network (199) (e.g., a long-range wireless communication network). According to one embodiment, the electronic device (101) may communicate with the electronic device (104) through the server (108). According to one embodiment, the electronic device (101) may include a processor (120), memory (130), input module (150), sound output module (155), display module (160), audio module (170), sensor module (176), interface (177), connection terminal (178), haptic module (179), camera module (180), power management module (188), battery (189), communication module (190), subscriber identification module (196), or antenna module (197). In some embodiments, at least one of these components (e.g., connection terminal (178)) may be omitted from the electronic device (101), or one or more other components may be added. In some embodiments, some of these components (e.g., sensor module (176), camera module (180), or antenna module (197)) may be integrated into a single component (e.g., display module (160)).

[0035] The processor (120) can control at least one other component (e.g., a hardware or software component) of the electronic device (101) connected to the processor (120) by executing software (e.g., a program (140)), and can perform various data processing or operations. According to one embodiment, as at least part of the data processing or operations, the processor (120) can store commands or data received from other components (e.g., a sensor module (176) or a communication module (190)) in volatile memory (132), process the commands or data stored in volatile memory (132), and store the resulting data in non-volatile memory (134). According to one embodiment, the processor (120) may include a main processor (121) (e.g., a central processing unit or an application processor) or an auxiliary processor (123) that can operate independently or together with it (e.g., a graphics processing unit, a neural processing unit (NPU), an image signal processor, a sensor hub processor, or a communication processor). For example, if the electronic device (101) includes a main processor (121) and an auxiliary processor (123), the auxiliary processor (123) may be configured to use less power than the main processor (121) or to be specialized for a designated function. The auxiliary processor (123) may be implemented separately from the main processor (121) or as part thereof.

[0036] The auxiliary processor (123) may control at least some of the functions or states associated with at least one component of the electronic device (101) (e.g., display module (160), sensor module (176), or communication module (190)) on behalf of the main processor (121) while the main processor (121) is in an inactive (e.g., sleep) state, or together with the main processor (121) while the main processor (121) is in an active (e.g., application execution) state. According to one embodiment, the auxiliary processor (123) (e.g., image signal processor or communication processor) may be implemented as part of another functionally related component (e.g., camera module (180) or communication module (190)). According to one embodiment, the auxiliary processor (123) (e.g., neural network processing unit) may include a hardware structure specialized for processing an artificial intelligence model. The artificial intelligence model may be generated through machine learning. Such learning may be performed, for example, on the electronic device (101) itself where the artificial intelligence model is executed, or through a separate server (e.g., server (108)). The learning algorithm may include, for example, supervised learning, unsupervised learning, semi-supervised learning, or reinforcement learning, but is not limited to the examples described above. The artificial intelligence model may include a plurality of artificial neural network layers.An artificial neural network may be a deep neural network (DNN), a convolutional neural network (CNN), a recurrent neural network (RNN), a restricted Boltzmann machine (RBM), a deep belief network (DBN), a bidirectional recurrent deep neural network (BRDNN), a deep Q-network, or a combination of two or more of these, but is not limited to the examples mentioned above. In addition to the hardware structure, the artificial intelligence model may include a software structure, either additionally or substantially.

[0037] The memory (130) can store various data used by at least one component of the electronic device (101) (e.g., processor (120) or sensor module (176)). The data may include, for example, input data or output data for software (e.g., program (140)) and related commands. The memory (130) may include volatile memory (132) or non-volatile memory (134).

[0038] The program (140) may be stored as software in memory (130) and may include, for example, an operating system (142), middleware (144), or an application (146).

[0039] The input module (150) can receive commands or data to be used for a component of the electronic device (101) (e.g., processor (120)) from outside the electronic device (101) (e.g., user). The input module (150) may include, for example, a microphone, a mouse, a keyboard, a key (e.g., a button), or a digital pen (e.g., a stylus pen).

[0040] The sound output module (155) can output a sound signal to the outside of the electronic device (101). The sound output module (155) may include, for example, a speaker or a receiver. The speaker may be used for general purposes, such as multimedia playback or recording playback. The receiver may be used to receive incoming calls. According to one embodiment, the receiver may be implemented separately from the speaker or as part thereof.

[0041] The display module (160) can visually provide information to an external (e.g., user) of the electronic device (101). The display module (160) may include, for example, a display, a holographic device, or a projector and a control circuit for controlling said device. According to one embodiment, the display module (160) may include a touch sensor configured to detect a touch, or a pressure sensor configured to measure the intensity of the force generated by the touch.

[0042] The audio module (170) can convert sound into an electrical signal or, conversely, convert an electrical signal into sound. According to one embodiment, the audio module (170) can acquire sound through the input module (150) or output sound through the sound output module (155) or an external electronic device (e.g., electronic device (102)) (e.g., speaker or headphones) connected directly or wirelessly to the electronic device (101).

[0043] The sensor module (176) can detect the operating state of the electronic device (101) (e.g., power or temperature) or the external environmental state (e.g., user state) and generate an electrical signal or data value corresponding to the detected state. According to one embodiment, the sensor module (176) may include, for example, a gesture sensor, a gyroscope sensor, a barometric pressure sensor, a magnetic sensor, an accelerometer sensor, a grip sensor, a proximity sensor, a color sensor, an IR (infrared) sensor, a biosensor, a temperature sensor, a humidity sensor, or an illuminance sensor.

[0044] The interface (177) may support one or more specified protocols that can be used for the electronic device (101) to be connected directly or wirelessly to an external electronic device (e.g., electronic device (102)). According to one embodiment, the interface (177) may include, for example, a high definition multimedia interface (HDMI), a universal serial bus (USB) interface, an SD card interface, or an audio interface.

[0045] The connection terminal (178) may include a connector through which the electronic device (101) can be physically connected to an external electronic device (e.g., electronic device (102)). According to one embodiment, the connection terminal (178) may include, for example, an HDMI connector, a USB connector, an SD card connector, or an audio connector (e.g., a headphone connector).

[0046] The haptic module (179) can convert an electrical signal into a mechanical stimulus (e.g., vibration or movement) or an electrical stimulus that can be perceived by the user through tactile or kinesthetic senses. According to one embodiment, the haptic module (179) may include, for example, a motor, a piezoelectric element, or an electric stimulation device.

[0047] The camera module (180) can capture still images and video. According to one embodiment, the camera module (180) may include one or more lenses, image sensors, image signal processors, or flashes.

[0048] The power management module (188) can manage power supplied to the electronic device (101). According to one embodiment, the power management module (188) can be implemented, for example, as at least part of a power management integrated circuit (PMIC).

[0049] The battery (189) can supply power to at least one component of the electronic device (101). According to one embodiment, the battery (189) may include, for example, a non-rechargeable primary battery, a rechargeable secondary battery, or a fuel cell.

[0050] The communication module (190) can support the establishment of a direct (e.g., wired) communication channel or a wireless communication channel between an electronic device (101) and an external electronic device (e.g., electronic device (102), electronic device (104), or server (108)), and the performance of communication through the established communication channel. The communication module (190) may include one or more communication processors that operate independently of the processor (120) (e.g., application processor) and support direct (e.g., wired) communication or wireless communication. According to one embodiment, the communication module (190) may include a wireless communication module (192) (e.g., cellular communication module, short-range wireless communication module, or GNSS (global navigation satellite system) communication module) or a wired communication module (194) (e.g., LAN (local area network) communication module, or power line communication module). The corresponding communication module among these communication modules can communicate with an external electronic device (104) through a first network (198) (e.g., a short-range communication network such as Bluetooth, Wi-Fi (wireless fidelity) direct or IrDA (infrared data association)) or a second network (199) (e.g., a legacy cellular network, a 5G network, a next-generation communication network, the Internet, or a computer network (e.g., a LAN or WAN)). These various types of communication modules may be integrated into a single component (e.g., a single chip) or implemented as multiple separate components (e.g., multiple chips). The wireless communication module (192) can identify or authenticate the electronic device (101) within a communication network such as the first network (198) or the second network (199) using subscriber information (e.g., International Mobile Subscriber Identifier (IMSI)) stored in the subscriber identification module (196).

[0051] The wireless communication module (192) can support 5G networks and next-generation communication technologies following 4G networks, for example, new radio access technology. The NR access technology can support high-speed transmission of high-capacity data (enhanced mobile broadband (eMBB)), minimization of terminal power and connection of multiple terminals (massive machine type communications (mMTC)), or high reliability and low latency (ultra-reliable and low-latency communications (URLLC)). The wireless communication module (192) can support a high-frequency band (e.g., mmWave band) to achieve a high data throughput, for example. The wireless communication module (192) can support various technologies for securing performance in the high-frequency band, such as beamforming, massive MIMO (multiple-input and multiple-output), full-dimensional MIMO (FD-MIMO), array antenna, analog beam-forming, or large-scale antenna. The wireless communication module (192) can support various requirements specified in the electronic device (101), external electronic device (e.g., electronic device (104)), or network system (e.g., second network (199)). According to one embodiment, the wireless communication module (192) may support a peak data rate for eMBB realization (e.g., 20 Gbps or more), loss coverage for mMTC realization (e.g., 164 dB or less), or U-plane latency for URLLC realization (e.g., downlink (DL) and uplink (UL) each 0.5 ms or less, or round trip 1 ms or less).According to one embodiment, the subscriber identification module (196) may include a plurality of subscriber identification modules. For example, the plurality of subscriber identification modules may store different subscriber information.

[0052] An antenna module (197) can transmit a signal or power to or from an external source (e.g., an external electronic device). According to one embodiment, the antenna module (197) may include an antenna comprising a radiator made of a conductor or a conductive pattern formed on a substrate (e.g., a PCB). According to one embodiment, the antenna module (197) may include a plurality of antennas (e.g., an array antenna). In this case, at least one antenna suitable for a communication method used in a communication network, such as a first network (198) or a second network (199), may be selected from the plurality of antennas, for example, by a communication module (190). A signal or power may be transmitted or received between the communication module (190) and an external electronic device through the selected at least one antenna. According to some embodiments, in addition to the radiator, other components (e.g., a radio frequency integrated circuit (RFIC)) may be additionally formed as part of the antenna module (197).

[0053] According to one embodiment, the antenna module (197) may form a high frequency (e.g., mmWave) antenna module. According to one embodiment, the high frequency (e.g., mmWave) antenna module may include a printed circuit board, an RFIC disposed on or adjacent to a first surface (e.g., bottom surface) of the printed circuit board and capable of supporting a specified high frequency band (e.g., mmWave band), and a plurality of antennas (e.g., array antennas) disposed on or adjacent to a second surface (e.g., top surface or side surface) of the printed circuit board and capable of transmitting or receiving a signal of the specified high frequency band. For example, the plurality of antennas may include a patch array antenna and / or a dipole array antenna.

[0054] At least some of the components can be connected to each other via a communication method between peripheral devices (e.g., bus, GPIO (general purpose input and output), SPI (serial peripheral interface), or MIPI (mobile industry processor interface)) and exchange signals (e.g., commands or data) with each other.

[0055] According to one embodiment, commands or data may be transmitted or received between an electronic device (101) and an external electronic device (104) through a server (108) connected to a second network (199). Each of the external electronic devices (102, or 104) may be the same or a different type of device as the electronic device (101). According to one embodiment, all or part of the operations performed on the electronic device (101) may be performed on one or more of the external electronic devices (102, 104, or 108). For example, if the electronic device (101) needs to perform a function or service automatically or in response to a request from a user or another device, the electronic device (101) may request one or more external electronic devices to perform at least part of the function or service instead of performing the function or service itself or additionally. One or more external electronic devices that receive the above request may execute at least part of the requested function or service, or additional function or service related to the request, and transmit the result of the execution to the electronic device (101). The electronic device (101) may provide the result as is or additionally processed as at least part of the response to the request. For this purpose, for example, cloud computing, distributed computing, mobile edge computing (MEC), or client-server computing technology may be used. The electronic device (101) may provide ultra-low latency services using, for example, distributed computing or mobile edge computing. In another embodiment, the external electronic device (104) may include an Internet of Things (IoT) device. The server (108) may be an intelligent server using machine learning and / or neural networks. According to one embodiment, the external electronic device (104) or the server (108) may be included within a second network (199).The electronic device (101) can be applied to intelligent services (e.g., smart home, smart city, smart car, or healthcare) based on 5G communication technology and IoT-related technology.

[0056] An electronic device according to one embodiment disclosed in this document may be of various forms. The electronic device may include, for example, a portable communication device (e.g., a smartphone), a computer device, a portable multimedia device, a portable medical device, a camera, a wearable device, or a consumer electronics device. The electronic device according to the embodiment of this document is not limited to the aforementioned devices.

[0057] The embodiments of this document and the terms used therein are not intended to limit the technical features described in this document to specific embodiments, and should be understood to include various modifications, equivalents, or substitutions of said embodiments. In connection with the description of the drawings, similar reference numerals may be used for similar or related components. The singular form of a noun corresponding to an item may include one or more of said items unless the relevant context clearly indicates otherwise. In this document, phrases such as "A or B," "at least one of A and B," "at least one of A or B," "A, B or C," "at least one of A, B and C," and "at least one of A, B, or C" may each include any one of the items listed together in the corresponding phrase, or all possible combinations thereof. Terms such as "first," "second," or "first" or "second" may be used simply to distinguish said components from other said components and do not limit said components in any other aspect (e.g., importance or order). Where any (e.g., 1st) component is referred to as “coupled” or “connected” to another (e.g., 2nd) component, with or without the terms “functionally” or “communicationly,” it means that said any component may be connected to said other component directly (e.g., via a wire), wirelessly, or through a third component.

[0058] As used in one embodiment of this document, the term “module” may include a unit implemented in hardware, software, or firmware, and may be used interchangeably with terms such as logic, logic block, component, or circuit, for example. A module may be a component formed integrally, or a minimum unit of said component or a part thereof that performs one or more functions. For example, according to one embodiment, a module may be implemented in the form of an application-specific integrated circuit (ASIC).

[0059] One embodiment of the present document may be implemented as software (e.g., program (140)) comprising one or more instructions stored in a storage medium (e.g., internal memory (136) or external memory (138)) readable by a machine (e.g., electronic device (101)). For example, a processor (e.g., processor (120)) of the machine (e.g., electronic device (101)) may call at least one of the one or more instructions stored in the storage medium and execute it. This enables the machine to be operated to perform at least one function according to the at least one called instruction. The one or more instructions may include code generated by a compiler or code that can be executed by an interpreter. The storage medium readable by the machine may be provided in the form of a non-transitory storage medium. Here, 'non-temporary' simply means that the storage medium is a tangible device and does not contain a signal (e.g., electromagnetic waves), and the term does not distinguish between cases where data is stored semi-permanently and cases where it is stored temporarily.

[0060] According to one embodiment, the method according to one embodiment 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 an application store (e.g., Play Store). TM It can be distributed online (e.g., downloaded or uploaded) through ) or directly between two user devices (e.g., smartphones). In the case of online distribution, at least a portion of the computer program product 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.

[0061] According to one embodiment, each component (e.g., module or program) of the components described above may include a singular or multiple entities, and some of the multiple entities may be separated and placed in other components. According to one embodiment, one or more of the components or operations among the aforementioned components may be omitted, or one or more other components or operations may be added. Generally or additionally, multiple components (e.g., module or program) may be integrated into a single component. In this case, the integrated component may perform one or more functions of each of the multiple components in the same or similar manner as those performed by the corresponding component among the multiple components prior to integration. According to one embodiment, operations performed by the module, program, or other components may be executed sequentially, in parallel, iteratively, or heuristically, or one or more of the operations may be executed in a different order, omitted, or one or more other operations may be added.

[0062] FIG. 2 is a block diagram illustrating an integrated intelligence system according to one embodiment. For example, the electronic device (101) of FIG. 2 may include at least some of the configuration and / or functions of the electronic device (101) of FIG. 1.

[0063] According to one embodiment with reference to FIG. 2, the integrated intelligent system may include an electronic device (101), an intelligent server (230) (e.g., the server (108) of FIG. 1), and a service server (250) (e.g., the server (108) of FIG. 1).

[0064] According to one embodiment, the electronic device (101) may be a terminal device (UE: user equipment) capable of connecting to the Internet. For example, the electronic device (101) may be a mobile phone, a smartphone, a PDA (personal digital assistant), a laptop computer, a TV, a home appliance, a wearable device, an HMD, or a smart speaker.

[0065] According to one embodiment, the electronic device (101) may include a communication interface (213) (e.g., interface (177) of FIG. 1), a microphone (212) (e.g., input module (150) of FIG. 1), a speaker (216) (e.g., sound output module (155) of FIG. 1), a display module (211) (e.g., display module (160) of FIG. 1), a memory (215) (e.g., memory (130) of FIG. 1), or a processor (214) (e.g., processor (120) of FIG. 1). The components included in the electronic device (101) may be operatively or electrically connected to each other.

[0066] According to one embodiment, the communication interface (213) may be configured to be connected to an external device to transmit and receive data. According to one embodiment, the microphone (212) may receive sound (e.g., user speech) and convert it into an electrical signal (e.g., audio signal). According to one embodiment, the speaker (216) may output the electrical signal (e.g., audio signal) as sound (e.g., voice).

[0067] According to one embodiment, the display module (211) may be configured to display an image or video. For example, the display module (211) may display a graphic user interface (GUI) of an app (or application program) running on the electronic device (101). According to one embodiment, the display module (211) may receive touch input through a touch sensor. For example, the display module (211) may receive text input through a touch sensor in an image keyboard area displayed within the display module (211).

[0068] According to one embodiment, memory (215) may store a client module (218), a software development kit (SDK) (217), and a plurality of apps (219a, 219b). For example, the client module (218) and the SDK (217) may form a framework (or solution program) for performing general-purpose functions. Additionally, the client module (218) or the SDK (217) may form a framework for processing user input (e.g., voice input, text input, touch input).

[0069] According to one embodiment, a plurality of apps (219a, 219b) stored in memory (215) may be programs for performing a designated function. According to one embodiment, the plurality of apps may include a first app (219a) and a second app (219b). According to one embodiment, each of the plurality of apps (219a, 219b) may include a plurality of operations for performing a designated function. For example, the plurality of apps (219a, 219b) may include an alarm app, a message app, and / or a schedule app. According to one embodiment, the plurality of apps (219a, 219b) may be executed by a processor (214) to sequentially execute at least some of the plurality of operations.

[0070] According to one embodiment, the processor (214) can control the overall operation of the electronic device (101). For example, the processor (214) can perform a specified operation by being electrically connected to a communication interface (213), a microphone (212), a speaker (216), and a display module (211).

[0071] According to one embodiment, the processor (214) may execute a program stored in memory (215) individually or collectively to perform a specified function. For example, the processor (214) may execute at least one of a client module (218) or an SDK (217) to perform the following operations for processing user input. For example, the processor (214) may control the operation of a plurality of apps (219a, 219b) through the SDK (217). The following operations described as the operation of the client module (218) or the SDK (217) may be operations performed by the execution of the processor (214).

[0072] According to one embodiment, the client module (218) can receive user input. For example, the client module (218) can receive a voice signal corresponding to a user utterance detected through a microphone (212). Alternatively, the client module (218) can receive touch input detected through a display module (211). Alternatively, the client module (218) can receive text input detected through a keyboard or a virtual keyboard. In addition, various forms of user input can be received through an input module included in the electronic device (101) or an input module connected to the electronic device (101). The client module (218) can transmit the user input received through the input module to an intelligent server (230). Along with the user input received through the input module, the client module (218) can transmit status information of the electronic device (101) to the intelligent server (230). For example, the status information may be execution status information of an app.

[0073] According to one embodiment, the client module (218) can receive a result corresponding to a user input received through an input module from an intelligent server (230). For example, if the intelligent server (230) can produce a result corresponding to a user input received through an input module, the client module (218) can receive a result corresponding to a user input received through an input module from the intelligent server (230). The client module (218) can display the result corresponding to the user input received from the intelligent server (230) on a display module (211). Additionally, the client module (218) can output the result corresponding to the user input received from the intelligent server (230) as audio through a speaker (216).

[0074] According to one embodiment, the client module (218) may receive a plan corresponding to user input received through the input module. The client module (218) may display the results of executing a plurality of actions of the app according to the plan on the display module (211). For example, the client module (218) may sequentially display the results of executing a plurality of actions on the display module (211) and output audio through the speaker (216). For example, the electronic device (101) may display only some of the results of executing a plurality of actions (e.g., the result of the last action) on the display module (211) and output audio through the speaker (216).

[0075] According to one embodiment, the client module (218) may receive a request from the intelligent server (230) to obtain information necessary to produce a result corresponding to the voice input. According to one embodiment, the client module (218) may transmit information necessary to produce a result corresponding to the voice input from the intelligent server (230) to the intelligent server (230) in response to the request from the intelligent server (230).

[0076] According to one embodiment, the client module (218) can transmit result information of executing a plurality of operations according to a plan to the intelligent server (230). The intelligent server (230) can use the result information of executing a plurality of operations according to a plan to confirm that the user input received through the input module has been processed correctly.

[0077] According to one embodiment, the client module (218) may include a voice recognition module. According to one embodiment, the client module (218) may recognize voice input that performs a limited function through the voice recognition module. For example, the client module (218) may execute an intelligent app for processing voice input to perform an organic action through a specified input (e.g., Wake Up!).

[0078] According to one embodiment, the intelligent server (230) can receive information related to user voice input from the electronic device (101) via a communication network. According to one embodiment, the intelligent server (230) can convert the data related to the voice input received from the electronic device (101) into text data. According to one embodiment, the intelligent server (230) can generate a plan for performing a task corresponding to the user voice input based on the text data.

[0079] According to one embodiment, a plan may be generated by an artificial intelligence (AI) system. The AI ​​system may be at least one of a rule-based system, a neural network-based system (e.g., a feedforward neural network (FNN) or a recurrent neural network (RNN)). Alternatively, the AI ​​system may be a combination of the foregoing or a different AI system. According to one embodiment, the plan may be selected from a set of predefined plans or may be generated in real time in response to a user request. For example, the AI ​​system may select at least one plan from a plurality of predefined plans.

[0080] According to one embodiment, the intelligent server (230) may transmit the result according to the plan generated by the artificial intelligence system to the electronic device (101) or transmit the plan generated by the artificial intelligence system to the electronic device (101). According to one embodiment, the electronic device (101) may display the result according to the plan on the display module (211). According to one embodiment, the electronic device (101) may display the result of executing the operation according to the plan on the display module (211).

[0081] According to one embodiment, the intelligent server (230) may include a front end (231), a natural language platform (232), a capsule database (238), an execution engine (233), an end user interface (234), a management platform (235), a big data platform (236), or an analytic platform (237).

[0082] According to one embodiment, the front end (231) can transmit a response corresponding to user input received from the electronic device (101).

[0083] According to one embodiment, the natural language platform (232) may include an automatic speech recognition module (ASR module) (232a), a natural language understanding module (NLU module) (232b), a planner module (232c), a natural language generator module (NLG module) (232d), or a text to speech module (TTS module) (232e).

[0084] According to one embodiment, an automatic speech recognition module (232a) can convert voice input received from an electronic device (101) into text data. According to one embodiment, a natural language understanding module (232b) can identify the user's intent using the text data of the voice input. For example, the natural language understanding module (232b) can identify the user's intent by performing a syntactic analysis or a semantic analysis on the user input in the form of text data. According to one embodiment, the natural language understanding module (232b) can identify the meaning of a word extracted from the voice input using linguistic features (e.g., grammatical elements) of a morpheme or phrase, and determine the user's intent by matching the meaning of the word to the intent. The natural language understanding module (223b) can acquire intent information corresponding to the user's utterance. The intent information may be information indicating the user's intent determined by interpreting the text data. The intent information may include information indicating an action or function that the user intends to execute using the device.

[0085] According to one embodiment, the planner module (232c) can generate a plan using intentions and parameters determined by the natural language understanding module (232b). According to one embodiment, the planner module (232c) can determine multiple domains necessary to perform a task based on intentions determined by the natural language understanding module (232b). The planner module (232c) can determine multiple actions included in each of the multiple domains determined based on intentions determined by the natural language understanding module (232b). According to one embodiment, the planner module (232c) can determine parameters necessary to execute multiple actions included in each of the multiple domains, or result values ​​output by the execution of multiple actions. Parameters and result values ​​may be defined as concepts of a specified format (or class). Accordingly, the plan may include multiple actions and multiple concepts determined by the user's intention. The planner module (232c) can determine the relationships between the multiple actions and multiple concepts in a stepwise (or hierarchical) manner. For example, the planner module (232c) can determine the execution order of multiple actions determined based on the user's intentions based on multiple concepts. In other words, the planner module (232c) can determine the execution order of multiple actions based on parameters required for the execution of multiple actions and results output by the execution of multiple actions. Accordingly, the planner module (232c) can generate a plan that includes association information (e.g., ontology) between multiple actions and multiple concepts. The planner module (232c) can generate a plan using information stored in a capsule database in which a set of relationships between concepts and actions is stored.

[0086] According to one embodiment, the natural language generation module (232d) can change specified information into a text form. The information changed into a text form may be in the form of natural language utterance. According to one embodiment, the text-to-speech conversion module (232e) can change information in a text form into information in a speech form.

[0087] According to one embodiment, some or all functions of the natural language platform (232) may also be implemented in an electronic device (101).

[0088] According to one embodiment, a capsule database may store information regarding the relationships between multiple concepts and actions corresponding to multiple domains. A capsule according to one embodiment may include multiple action objects (or action information) and concept objects (or concept information) included in a plan. According to one embodiment, a capsule database may store multiple capsules in the form of a concept action network (CAN). According to one embodiment, multiple capsules may be stored in a function registry included in the capsule database.

[0089] According to one embodiment, the capsule database may include a strategy registry that stores strategy information necessary for determining a plan corresponding to user input. The strategy information may include reference information for determining one plan when there are multiple plans corresponding to user input. According to one embodiment, the capsule database may include a follow-up registry that stores information on a follow-up action for suggesting a follow-up action to the user in a specified situation. For example, the follow-up action may include a follow-up utterance. According to one embodiment, the capsule database may include a layout registry that stores layout information of information output through an electronic device (101). According to one embodiment, the capsule database may include a vocabulary registry that stores vocabulary information included in the capsule information. According to one embodiment, the capsule database may include a dialogue registry that stores information on a conversation (or interaction) with the user. The capsule database may update stored objects through a developer tool. For example, the developer tool may include a function editor for updating action objects or concept objects. The developer tool may include a vocabulary editor for updating vocabulary. The developer tool may include a strategy editor for creating and registering strategies for determining plans. The developer tool may include a dialog editor for creating conversations with the user.The developer tool may include a follow-up editor that can edit follow-up utterances that activate follow-up goals and provide hints. The follow-up goal may be determined based on the currently set goal, user preferences, or environmental conditions. In one embodiment, the capsule database may also be implemented within the electronic device (101).

[0090] According to one embodiment, the execution engine (233) can produce a result using a plan generated by an artificial intelligence system. The end user interface (234) can transmit the result produced using the plan to the electronic device (101). Accordingly, the electronic device (101) can receive the result produced by the execution engine (233) and provide it to the user. According to one embodiment, the management platform (235) can manage information used in the intelligent server (230). According to one embodiment, the big data platform (236) can collect user data. According to one embodiment, the analysis platform (237) can manage the quality of service (QoS) of the intelligent server (230). For example, the analysis platform (237) can manage the components and processing speed (or efficiency) of the intelligent server (230).

[0091] According to one embodiment, the service server (250) may provide a service designated to the electronic device (101) (e.g., food ordering or hotel reservation). According to one embodiment, the service server (250) may be a server operated by a third party. According to one embodiment, the service server (250) may provide information to the intelligent server (230) for generating a plan corresponding to a received voice input. The information provided to the intelligent server (230) may be stored in a capsule database. Additionally, the service server (250) may provide result information according to the plan to the intelligent server (230). The service server (250) may include a plurality of service providers (e.g., CP service A (251), CP service B (252), CP service C (253)), and each service provider (251, 252, 253) may provide a function for a domain associated with each capsule stored in the capsule database (238) of the intelligent server (230).

[0092] In the integrated intelligent system described above, the electronic device (101) can provide various intelligent services to the user in response to user input. For example, user input may include input via a physical button, touch input, or voice input.

[0093] According to one embodiment, the electronic device (101) may provide a voice recognition service through an intelligent app (or voice recognition app) stored internally. For example, the electronic device (101) may recognize a user utterance or voice input received through a microphone (212) and provide a service to the user corresponding to the user utterance or voice input.

[0094] According to one embodiment, an electronic device (101) may perform a specified action, either alone or in conjunction with an intelligent server (230) and / or a service server (250), based on voice input received through an input module (e.g., a microphone (212)). For example, the electronic device (101) may execute an app corresponding to the voice input received through the input module (e.g., a microphone (212)) and perform a specified action through the executed app.

[0095] According to one embodiment, when an electronic device (101) provides services together with an intelligent server (230) and / or a service server (250), the electronic device (101) can detect user speech using a microphone (212) and generate a signal (or voice data) corresponding to the detected user speech. The electronic device (101) can transmit the voice data to the intelligent server (230) via a network (240) using a communication interface (213).

[0096] An intelligent server (230) according to one embodiment may generate a plan for performing a task corresponding to a voice input, or a result of performing an operation according to the plan, as a response to a voice input received from an electronic device (101). For example, the plan may include a plurality of operations for performing a task corresponding to a user's voice input, and a plurality of concepts related to the plurality of operations. A concept may define a parameter input to the execution of the plurality of operations or a result value output by the execution of the plurality of operations. The plan may include association information between the plurality of operations and the plurality of concepts.

[0097] According to one embodiment, the electronic device (101) can receive a response using a communication interface (213). The electronic device (101) can output a voice signal generated inside the electronic device (101) to the outside using a speaker (216) or output an image generated inside the electronic device (101) to the outside using a display module (211).

[0098] FIG. 2 describes an example in which voice recognition of user input received from an electronic device (101), natural language understanding and generation, and output of results using a plan are performed on an intelligent server (230), but the embodiments of this document are not limited thereto. For example, at least some components of the intelligent server (230) (e.g., natural language platform (232), execution engine (233), capsule database (238)) may be embedded in the electronic device (101) so that the operation is performed by the electronic device (101).

[0099] FIG. 3 is a block diagram of an electronic device for detecting a traffic pattern according to one embodiment. FIG. 4 is a block diagram of a processor of an electronic device for detecting a traffic pattern according to one embodiment. For example, the electronic device (101) of FIG. 3 and FIG. 4 may be at least partially similar to the electronic device (101) of FIG. 1 or FIG. 2, or may include other embodiments of the electronic device.

[0100] According to one embodiment with reference to FIGS. 3 and 4, the electronic device (101) may include at least one of a processor (300), a communication circuit (310), or a memory (320). For example, the processor (300) may be substantially identical to the processor (120) of FIG. 1 or may include the processor (120). The communication circuit (310) may be substantially identical to the wireless communication module (192) of FIG. 1 or may include the wireless communication module (192). The memory (320) may be substantially identical to the memory (130) of FIG. 1 or may include the memory (130). For example, the processor (300) may be operatively, functionally, or electrically connected to at least one of the communication circuit (310) or the memory (320). For example, the processor (300) may include at least one processor including a processing circuit.

[0101] According to one embodiment, the processor (300) may include a first processor (400) (e.g., an application processor (AP)) and a second processor (410) (e.g., a communication processor (CP)). For example, the first processor (400) and the second processor (410) may be configured (or implemented) as different hardware devices (e.g., circuits, chips, or chipsets). For example, the first processor (400) and the second processor (410) may be implemented as different software on a single hardware device.

[0102] According to one embodiment, the processor (300) can detect (or classify, identify) a traffic pattern generated in the electronic device (101) based on at least one of the state information of the electronic device (101) or the characteristics of the traffic generated in the electronic device (101). For example, the device state check module (412) of the second processor (410) can check the state information of the electronic device (101). For example, the state information of the electronic device (101) confirmed by the device state check module (412) may be provided to the traffic pattern classification module (418). For example, the state information of the electronic device (101) may include at least one of the state of a communication connection between the electronic device (101) and an external electronic device or an application program running in the electronic device (101). For example, the communication connection state may include at least one of an RRC (radio resource control) connection state (e.g., RRC connected state), an RRC inactive state (e.g., RRC inactive state), or an RRC idle state (e.g., RRC idle state).

[0103] For example, the traffic status check module (414) of the second processor (410) can check the characteristics of traffic generated in the electronic device (101). For example, information related to the characteristics of traffic generated in the electronic device (101) identified by the traffic status check module (414) can be provided to the traffic pattern classification module (418). For example, the characteristics of the traffic may include, but are not limited to, at least one of the following: the number of uplink packets, a log value for the number of uplink packets, the size of the uplink packets (or, sum), a log value for the size of the uplink packets (or, sum), the number of downlink packets, a log value for the number of downlink packets, the size of the downlink packets (or, sum), a log value for the size of the downlink packets (or, sum), the degree of increase or decrease in the number of uplink packets, the degree of increase or decrease in the size of the uplink packets, the degree of increase or decrease in the number of downlink packets, the result of comparing the number of packets between the uplink and downlink, the result of comparing the size of packets between the uplink and downlink, the time interval between downlink packets, the time interval between uplink packets, or statistical information on the size of the uplink / downlink packets (e.g., minimum value, maximum value, sum, standard deviation, and / or average value), and may include other characteristics. For example, the characteristics of the traffic may further include at least one of the type of protocol, the time of packet transmission and reception, or information provided by the OS (operating system) (e.g., Android's TrafficStats). For example, the characteristics of the traffic can be identified by corresponding to at least one of a timestamp or an application program.

[0104] For example, when a designated first cycle arrives, the traffic pattern classification module (418) of the second processor (410) can detect (or classify, identify) the traffic pattern generated in the electronic device (101) by inputting at least one of the status information of the electronic device (101) provided by the device status check module (412) or the characteristics of the traffic generated in the electronic device (101) provided by the traffic status check module (414) into the traffic classification model.

[0105] According to one embodiment, when the processor (300) confirms that the electronic device (101) has entered the CDRX (connected mode discontinuous reception) mode (or DRX mode), it may update a setting value related to the identification (or detection) of a traffic pattern based on information related to the CDRX mode. For example, the device status check module (412) of the second processor (410) may confirm the entry of the electronic device (101) into the CDRX mode based on information related to the CDRX mode obtained from an RRC message (e.g., RRC connection reconfiguration) received from an external electronic device (e.g., base station, eNB (evolved nodeB) or gNB (next generation nodeB)). For example, the information related to the CDRX mode may include at least one of the CDRX period (or DRX period) or the duration of an active period within the CDRX interval.

[0106] For example, the classification control module (416) of the second processor (410) can update a setting value related to the classification of traffic patterns based on information related to the CDRX mode. For example, the setting value related to the classification of traffic patterns may include at least one of the detection period of the traffic pattern, the detection time of the traffic pattern, or the size of the input batch of the traffic classification model. For example, a batch may represent a unit of data (or data samples) processed in batches (or at once) by the traffic classification model for the detection (or learning) of traffic patterns.

[0107] For example, the classification control module (416) of the second processor (410) can update a setting value related to the classification of traffic patterns based on user input and information related to the CDRX mode. For example, the classification information providing module (402) of the first processor (400) can provide setting information regarding an update method related to traffic classification within the CDRX interval to the second processor (300) (e.g., classification control module (416)). For example, the setting information regarding an update method related to traffic classification may include information related to an update method set (or selected) by user input for traffic classification within the CDRX interval. For example, the setting information regarding an update method related to traffic classification may be provided from the first processor (400) (e.g., classification information providing module (402)) to the second processor (410) (e.g., classification control module (416)) via inter-process communication (IPC). For example, if the classification control module (416) of the second processor (410) determines, based on the setting information regarding the update method related to traffic classification provided by the classification information provision module (402), that there is no update method related to traffic classification set by user input, it may update the setting value related to the classification of traffic patterns so that the detection of traffic patterns is restricted during the CDRX period. If the classification control module (416) of the second processor (410) determines, based on the setting information regarding the update method related to traffic classification provided by the classification information provision module (402), that there is an update method related to traffic classification set by user input, it may update the setting value related to the classification of traffic patterns based on the update method related to traffic classification.For example, the update of a setting value related to the classification of traffic patterns can be performed whenever it is determined that an update method related to traffic classification has been set by user input, based on setting information regarding an update method related to traffic classification provided by the classification information providing module (402).

[0108] According to one embodiment, the processor (300) may limit the detection of traffic patterns during the CDRX period in which the electronic device (101) operates in CDRX mode. For example, if the classification control module (416) of the second processor (410) determines that the electronic device (101) operates in CDRX mode, the processor may control the traffic pattern classification module (418) to limit the detection of traffic patterns using a traffic classification model during the CDRX period in which the electronic device (101) operates in CDRX mode. If the electronic device (101) does not operate in CDRX mode, the processor may control the traffic pattern classification module (418) to detect (or classify, identify) traffic patterns based on a designated first period.

[0109] According to one embodiment, the processor (300) can update the detection period of a traffic pattern based on information related to the CDRX mode. For example, the classification control module (416) of the second processor (410) can update the detection period of a traffic pattern based on the first update method when it is determined that a first update method for updating the detection period of a traffic pattern is selected based on setting information regarding an update method related to traffic classification received from the classification information providing module (402). For example, the detection period of a traffic pattern can be updated to a multiple (or integer multiple) of a specified first period. For example, the detection period of a traffic pattern can be updated to a value not related to a multiple (or integer multiple) of a specified first period.

[0110] For example, the classification control module (416) of the second processor (410) may update the detection cycle of the traffic pattern to a specified second cycle that is different from the specified first cycle, based on the performance (e.g., error rate) required in the function corresponding to the traffic pattern of the electronic device (101) detected by the traffic pattern classification module (418). For example, the detection cycle of the traffic pattern may be updated to a relatively short cycle if a relatively low error rate is required in the function corresponding to the traffic pattern. For example, the detection cycle of the traffic pattern may be updated to a relatively long cycle if a relatively low error rate is not required in the function corresponding to the traffic pattern. For example, the function corresponding to the traffic pattern may be set based on the current traffic pattern detected by the traffic pattern classification module (418). For example, the current traffic pattern may include the most recently detected traffic pattern by the traffic pattern classification module (418).

[0111] For example, the classification control module (416) of the second processor (410) may select at least one traffic classification model to be used for traffic classification among a plurality of traffic classification models. If the classification control module (416) determines that a first update method for updating the detection cycle of a traffic pattern is selected based on the setting information for the update method related to traffic classification provided by the classification information providing module (402), the detection cycle of a traffic pattern using at least one traffic classification model based on the first update method may be updated. For example, at least one traffic classification model may be selected based on at least one of the version of the traffic classification model, the status information of the electronic device (101), or a previously detected traffic pattern.

[0112] For example, when the classification control module (416) of the second processor (410) receives (or obtains) information related to a change in the CDRX period (or CDRX interval) of the electronic device (101) from the device status check module (412), it can update the detection period of the traffic pattern to correspond to the changed CDRX period of the electronic device (101) based on the first update method.

[0113] For example, the traffic pattern classification module (418) can detect (or classify, identify) a traffic pattern occurring in the electronic device (101) based on a designated second period updated by the classification control module (416) during the CDRX period of the electronic device (101). For example, when the designated second period updated by the classification control module (416) during the CDRX period of the electronic device (101) arrives, the traffic pattern classification module (418) can determine whether an active period (e.g., on duration) within the CDRX period has arrived based on information related to the CDRX mode. If the active period within the CDRX period has not arrived, the traffic pattern classification module (418) can delay the detection of the traffic pattern until the time when the active period arrives. When the active period within the CDRX period arrives, the traffic pattern classification module (418) can detect (or classify) the traffic pattern using a traffic classification model. For example, a traffic classification model may include an artificial intelligence model for detecting (or classifying) traffic patterns.

[0114] According to one embodiment, the processor (300) may set a time for detecting (or classifying) a traffic pattern within a period in which the electronic device (101) operates in CDRX mode based on information related to the CDRX mode. For example, if the classification control module (416) of the second processor (410) determines that a second update method for updating the time of detection of a traffic pattern is selected based on setting information regarding an update method related to traffic classification received from the classification information providing module (402), the time of detection of a traffic pattern using at least one traffic classification model during the CDRX period may be set (or updated) based on the second update method.

[0115] For example, the classification control module (416) of the second processor (410) can identify the radio resource management (RRM) measurement interval included within the CDRX interval based on information related to the CDRX mode. The classification control module (416) can set (or update) the detection time of the traffic pattern to perform detection of the traffic pattern using a traffic classification model during the RRM measurement interval included within the CDRX interval. For example, the RRM measurement interval may include a time interval for the electronic device (101) to perform measurement through a reference signal received from an external electronic device and to report (or transmit) the measurement result to the external electronic device in order to efficiently use radio resources during wireless communication between the electronic device (101) and an external electronic device (e.g., base station, eNB or gNB).

[0116] For example, when the classification control module (416) of the second processor (410) receives (or obtains) information related to a change in the RRM measurement interval of the electronic device (101) from the device status check module (412), it can set (or update) the detection time of the traffic pattern within the CDRX interval to correspond to the changed RRM measurement interval of the electronic device (101) based on the second update method.

[0117] For example, the traffic pattern classification module (418) can detect (or classify) a traffic pattern using a traffic classification model when an RRM measurement period arrives during the CDRX period of the electronic device (101).

[0118] According to one embodiment, the processor (300) may update the batch input size of the traffic classification model when it is determined that the electronic device (101) is operating in CDRX mode based on information related to the CDRX mode. For example, the classification control module (416) of the second processor (410) may update the batch input size of the traffic classification model used by the traffic pattern classification module (418) when it is determined that the electronic device (101) is operating in CDRX mode based on information related to the CDRX mode. For example, the update of the batch input size of the traffic classification model may be performed when the classification control module (416) of the second processor (410) determines that a second update method for updating the batch input size of the traffic classification model is selected based on setting information regarding an update method related to traffic classification provided by the classification information providing module (402). For example, the batch input size may represent the input size of data (or data samples) that can detect (or infer) traffic patterns in batches (or at once) in the traffic classification model.

[0119] For example, if the classification control module (416) of the second processor (410) determines that a third update method for updating the batch input size of the traffic classification model is selected based on the setting information for the update method related to traffic classification received from the classification information providing module (402), the batch input size of the traffic classification model can be updated based on the third update method.

[0120] For example, when the traffic pattern classification module (418) determines that the electronic device (101) is operating in CDRX mode, it can generate virtual traffic images corresponding to the CDRX section based on the traffic characteristics identified by the traffic status verification module (414). For example, the number of virtual traffic images can be determined based on the batch input size updated by the classification control module (416). For example, virtual traffic images can be generated by adding a zero vector to the traffic characteristics identified by the traffic status verification module (414) when it is determined that no traffic of the electronic device (101) occurs within the CDRX section. For example, the traffic images can represent the form of data input to the traffic classification model for detecting traffic patterns.

[0121] For example, the traffic pattern classification module (418) can detect (or classify) traffic patterns by inputting a plurality of virtual traffic images into a traffic classification model based on the batch input size updated in the classification control module (416).

[0122] For example, when the traffic pattern classification module (418) arrives at the traffic pattern detection period during the CDRX interval, it may use virtual traffic images to recognize some of the previously detected traffic patterns as traffic patterns corresponding to the traffic pattern detection period. For example, the traffic pattern detection period may include a designated first period for detecting traffic patterns or a designated second period updated based on information related to the CDRX mode, as a period for detecting traffic patterns during the CDRX interval.

[0123] For example, the traffic pattern classification module (418) can detect traffic patterns based on a traffic pattern detection period (e.g., a designated first period or a designated second period) when there are no previously detected traffic patterns using virtual traffic images during the CDRX period. For example, when the traffic pattern classification module (418) arrives at the traffic pattern detection period (e.g., a designated first period or a designated second period) during the CDRX period of the electronic device (101), it can determine whether an active period (e.g., on duration) within the CDRX period has arrived based on information related to the CDRX mode. If the active period within the CDRX period has not arrived, the traffic pattern classification module (418) can delay the detection of traffic patterns until the active period arrives. When the active period within the CDRX period has arrived, the traffic pattern classification module (418) can detect (or classify) traffic patterns using a traffic classification model.

[0124] For example, the traffic pattern classification module (418) may limit the detection of traffic patterns during the CDRX period if there are no detected traffic patterns using virtual traffic images during the CDRX period.

[0125] According to one embodiment, when the processor (300) detects (or classifies) a pattern of traffic generated by the electronic device (101), it can perform a function corresponding to the pattern of traffic generated by the electronic device (101). For example, if the resource management module (420) of the second processor (410) determines that the traffic pattern has been switched based on the pattern of traffic generated by the electronic device (101) detected by the traffic pattern classification module (418), it can control the communication circuit (310) to transmit information related to the pattern of traffic generated by the electronic device (101) to an external electronic device (e.g., base station, eNB, or gNB). For example, the information related to the pattern of traffic may be a specified standard (e.g., 3GPP (3 thIt can be transmitted to an external electronic device by being included in an RRC message (e.g., UAI (user equipment assistance information)) defined in the generation partnership project). For example, the DRX (discontinuous reception) cycle (e.g., long DRX cycle) of the electronic device (101) can be updated by the external electronic device based on the transmission of information related to the traffic pattern. For example, the DRX cycle (e.g., long DRX cycle) of the electronic device (101) can be set relatively short by the external electronic device (e.g., base station) based on the traffic pattern generated by the electronic device (101) when the interval between packets transmitted and / or received by the electronic device (101) is relatively short (e.g., a category relatively sensitive to delay). For example, the DRX cycle of the electronic device (101) (e.g., long DRX cycle) may be set relatively long from an external electronic device (e.g., base station) based on the pattern of traffic generated in the electronic device (101), when the interval between packets transmitted and / or received by the electronic device (101) is relatively long (e.g., a category relatively insensitive to delay). For example, the DRX cycle of the electronic device (101) may be obtained from an external electronic device (e.g., base station) via an RRC message.

[0126] For example, the resource management module (420) of the second processor (410) may determine (or select) a data transmission and reception path for transmitting and / or receiving traffic with an external electronic device based on a pattern of traffic generated in the electronic device (101) detected by the traffic pattern classification module (418). For example, the determination of the data transmission and reception path may include a series of operations to select a data transmission and reception path associated with the pattern of traffic generated in the electronic device (101) (or application program) among a plurality of data transmission and reception paths associated with an application program running in the electronic device (101). For example, the data transmission and reception path may include at least one of a PDU (packet data unit) session, a PDN (packet data name) connection, or a network slice.

[0127] For example, the resource management module (420) of the second processor (410) can control the communication circuit (310) to adjust the number of antennas to be used for wireless communication with an external electronic device based on the pattern of traffic generated in the electronic device (101) detected by the traffic pattern classification module (420). For example, the antenna may include at least one of a transmitting antenna or a receiving antenna.

[0128] According to one embodiment, the communication circuit (310) can support the electronic device (101) in transmitting or receiving at least one of at least one of a signal or data to an external device (e.g., the electronic device (102 or 104) of FIG. 1 or a server (108) or an intelligent server (230) of FIG. 2) via a wireless resource.

[0129] According to one embodiment, the memory (320) may store various data used by at least one component of the electronic device (101) (e.g., processor (300) or communication circuit (310)). For example, the memory (320) may store various instructions that can be executed individually or collectively by the processor (300) (e.g., at least one processor). For example, the various data may include information related to a plurality of traffic classification models that can be used to detect traffic patterns in the processor (300).

[0130] According to one embodiment, a classification information providing module (402) for setting an update method related to traffic classification in an electronic device (101) may be configured (or implemented) as a separate hardware device (e.g., a second processor (410)) or software different from the first processor (400).

[0131] According to one embodiment, at least one of the device status checking module (412), traffic status checking module (414), classification control module (416), traffic pattern classification module (418), or resource management module (420) related to the detection of traffic patterns occurring in the electronic device (101) may be configured (or implemented) as a separate hardware device (e.g., circuit, chip, or chipset) different from the second processor (410).

[0132] According to one embodiment, at least one of a device status check module (412), a traffic status check module (414), a classification control module (416), a traffic pattern classification module (418), or a resource management module (420) related to the detection of a traffic pattern generated in an electronic device (101) may be configured as software.

[0133] According to one embodiment, at least one of the device status checking module (412), traffic status checking module (414), classification control module (416), traffic pattern classification module (418), or resource management module (420) related to the detection of traffic patterns generated in the electronic device (101) may be integrated into one module or a plurality of modules.

[0134] According to one embodiment, at least one of the device status check module (412), traffic status check module (414), classification control module (416), traffic pattern classification module (418), or resource management module (420) related to the detection of traffic patterns occurring in the electronic device (101) may be performed through an external server different from the electronic device (101) (e.g., the intelligent server (230) of FIG. 2).

[0135] According to one embodiment, an electronic device (e.g., the electronic device (101) of FIG. 1, FIG. 2, or FIG. 3) may include a communication circuit (e.g., the wireless communication module (192) of FIG. 1 or the communication circuit (310) of FIG. 3), at least one processor including a processing circuit (e.g., the processor (120) of FIG. 1 or the processor (300) of FIG. 3), and a memory for storing instructions (e.g., the memory (130) of FIG. 1 or the memory (320) of FIG. 3). According to one embodiment, when the instructions are executed individually or collectively by at least one processor, the electronic device may check whether an active period (on duration) in the CDRX mode has arrived when a time (or a period for classifying traffic patterns) has arrived for detecting a traffic pattern corresponding to traffic data generated by the electronic device based on a traffic classification model in the CDRX mode. According to one embodiment, when instructions are executed individually or collectively by at least one processor, the electronic device may detect a traffic pattern corresponding to traffic data generated by the electronic device based on a traffic classification model when an active period in CDRX mode arrives.

[0136] According to one embodiment, when instructions are executed individually or collectively by at least one processor, the electronic device may limit the detection of a traffic pattern corresponding to traffic data generated by the electronic device based on a traffic classification model when an active period in CDRX mode has not arrived.

[0137] According to one embodiment, when instructions are executed individually or collectively by at least one processor, the electronic device may update a period for classifying traffic patterns (or a time point for detecting traffic patterns) based on information related to the CDRX mode.

[0138] According to one embodiment, when instructions are executed individually or collectively by at least one processor, the electronic device may check whether an active period in CDRX mode has arrived when an updated period for classifying traffic patterns has arrived.

[0139] According to one embodiment, when instructions are executed individually or collectively by at least one processor, the electronic device may update the period for classifying traffic patterns by a multiple of a designated first period for classifying traffic patterns based on information related to the CDRX mode.

[0140] According to one embodiment, when the instructions are executed individually or collectively by at least one processor, the electronic device may identify a radio resource management (RRM) measurement interval within the CDRX mode based on information related to the CDRX mode. According to one embodiment, when the instructions are executed individually or collectively by at least one processor, the electronic device may set a time point for classifying traffic patterns to correspond to the RRM measurement interval.

[0141] According to one embodiment, when instructions are executed individually or collectively by at least one processor, the electronic device may update a cycle for classifying traffic patterns based on a pattern of traffic of the electronic device detected at a previous time.

[0142] FIG. 5 is a flowchart (500) for detecting a traffic pattern based on information related to CDRX in an electronic device according to one embodiment. In the following embodiments, each operation may be performed sequentially, but is not necessarily performed sequentially. For example, the order of each operation may be changed, and at least two operations may be performed in parallel. For example, the electronic device of FIG. 5 may be the electronic device (101) of FIG. 1, FIG. 2, or FIG. 3.

[0143] According to one embodiment with reference to FIG. 5, an electronic device (e.g., electronic device (101)) or a processor (e.g., processor (120) of FIG. 1 or processor (300) of FIG. 3) may obtain information related to the CDRX mode in operation 501. For example, a second processor (410) may obtain information related to the CDRX mode from a DRX-config information element of an RRC message (e.g., RRC connection reconfiguration) such as Table 1 (e.g., 3GPP 38.331 V17.3.0) received from an external electronic device (e.g., base station, eNB or gNB).

[0144] DRX-Config ::= SEQUENCE {drx-onDurationTimer CHOICE {subMilliSeconds INTEGER (1..31),milliSeconds ENUMERATED {ms1, ms2, ms3, ms4, ms5, ms6, ms8, ms10, ms20, ms30, ms40, ms50, ms60, ms80, ms100, ms200, ms300, ms400, ms500, ms600, ms800, ms1000, ms1200, ms1600, spare8, spare7, spare6, spare5, spare4, spare3, spare2, spare1}},drx-InactivityTimer ENUMERATED {ms0, ms1, ms2, ms3, ms4, ms5, ms6, ms8, ms10, ms20, ms30, ms40, ms50, ms60, ms80, ms100, ms200, ms300, ms500, ms750, ms1280, ms1920, ms2560, spare9, spare8, spare7, spare6, spare5, spare4, spare3, spare2, spare1},drx-HARQ-RTT-TimerDL INTEGER (0..56),drx-HARQ-RTT-TimerUL INTEGER (0..56),drx-RetransmissionTimerDL ENUMERATED {sl0, sl1, sl2, sl4, sl6, sl8, sl16, sl24, sl33, sl40, sl64, sl80, sl96, sl112, sl128, sl160, sl320, spare15, spare14, spare13, spare12, spare11, spare10, spare9, spare8, spare7, spare6, spare5, spare4, spare3, spare2, spare1},drx-RetransmissionTimerUL ENUMERATED {sl0, sl1, sl2, sl4, sl6, sl8, sl16, sl24, sl33, sl40, sl64, sl80, sl96, sl112, sl128, sl160, sl320, spare15, spare14, spare13, spare12, spare11, spare10, spare9, spare8, spare7, spare6, spare5, spare4, spare3, spare2, spare1},drx-LongCycleStartOffset CHOICE {ms10 INTEGER(0..9),ms20 INTEGER(0..19),ms32 INTEGER(0..31),ms40 INTEGER(0..39),ms60 INTEGER(0..59),ms64 INTEGER(0..63),ms70 INTEGER(0..69),ms80 INTEGER(0..79),ms128 INTEGER(0..127),ms160 INTEGER(0..159),ms256 INTEGER(0..255),ms320 INTEGER(0..319),ms512 INTEGER(0..511),ms640 INTEGER(0..639),ms1024 INTEGER(0..1023),ms1280 INTEGER(0..1279),ms2048 INTEGER(0..2047),ms2560 INTEGER(0..2559),ms5120 INTEGER(0..5119),ms10240 INTEGER(0..10239)},shortDRX SEQUENCE {drx-ShortCycle ENUMERATED {ms2, ms3, ms4, ms5, ms6, ms7, ms8, ms10, ms14, ms16, ms20, ms30, ms32, ms35, ms40, ms64, ms80, ms128, ms160, ms256, ms320, ms512, ms640, spare9, spare8, spare7, spare6, spare5, spare4, spare3, spare2, spare1},drx-ShortCycleTimer INTEGER (1..16)} OPTIONAL, -- Need Rdrx-SlotOffset INTEGER (0..31)}DRX-ConfigExt-v1700 ::= SEQUENCE {drx-HARQ-RTT-TimerDL-r17 INTEGER (0..448),drx-HARQ-RTT-TimerUL-r17 INTEGER (0..448)}.

[0145] For example, information related to the CDRX mode may include at least one of the CDRX period (e.g., drx-onDurationTimer) or the duration of the active period within the CDRX interval (e.g., drx-InactivityTimer).

[0146] According to one embodiment, an electronic device (e.g., electronic device (101)) or a processor (e.g., processor (120 or 300)) may, in operation 503, update a setting value (or variable) related to the identification (or detection, classification, inference) of a traffic pattern based on information related to a CDRX mode. For example, a setting value related to the classification of a traffic pattern may include at least one of a detection period of a traffic pattern, a detection time of a traffic pattern, or the size of an input batch of a traffic classification model.

[0147] For example, the classification control module (416) of the second processor (410) can check the update method related to traffic classification set by user input based on the setting information regarding the update method related to traffic classification received from the classification information providing module (402) of the first processor (400). The classification control module (416) of the second processor (410) can update the setting value (or variable) related to the identification (or detection, classification, inference) of the traffic pattern based on the update method related to traffic classification set by user input.

[0148] For example, if the classification control module (416) of the second processor (410) determines that the electronic device (101) is operating in CDRX mode, it can update the setting value (or variable) related to the identification (or detection) of traffic patterns so that the detection (or classification, inference) of traffic patterns using a traffic classification model is restricted during the CDRX period in which the electronic device (101) is operating in CDRX mode.

[0149] For example, the classification control module (416) can set (or update) the detection time of the traffic pattern to perform detection of the traffic pattern using a traffic classification model during the RRM measurement period within the CDRX section.

[0150] For example, the classification control module (416) of the second processor (410) may update the detection cycle of the traffic pattern to a specified second cycle that is different from the specified first cycle, based on the performance (e.g., error rate) required in the function corresponding to the traffic pattern of the electronic device (101) detected by the traffic pattern classification module (418). For example, the detection cycle of the traffic pattern may be updated to a relatively short cycle if a relatively low error rate is required in the function corresponding to the traffic pattern. For example, the detection cycle of the traffic pattern may be updated to a relatively long cycle if a relatively low error rate is not required in the function corresponding to the traffic pattern. For example, the function corresponding to the traffic pattern may be set based on the current traffic pattern detected by the traffic pattern classification module (418). For example, the current traffic pattern may include the most recently detected traffic pattern by the traffic pattern classification module (418). For example, the detection cycle of the traffic pattern may be updated to a multiple (or integer multiple) of the specified first cycle. For example, the detection period of a traffic pattern can be updated to a value not related to a multiple (or integer multiple) of a specified first period.

[0151] For example, the classification control module (416) of the second processor (410) can update the batch input size of the traffic classification model used by the traffic pattern classification module (418) when it is determined that the electronic device (101) is operating in CDRX mode based on information related to the CDRX mode. For example, the batch input size may represent the input size of data (or data samples) that can detect (or infer) traffic patterns in batches (or at once) in the traffic classification model.

[0152] According to one embodiment, an electronic device (e.g., electronic device (101)) or a processor (e.g., processor (120 or 300)) may selectively detect (or classify, identify, infer) a traffic pattern occurring in the electronic device (101) based on an updated setting value (or variable) related to the identification (or detection) of a traffic pattern in operation 505.

[0153] For example, the traffic pattern classification module (418) of the second processor (410) can limit the detection of traffic patterns using a traffic classification model during the CDRX period in which the electronic device (101) operates in CDRX mode based on an updated setting value (or variable) related to the identification (or detection) of traffic patterns.

[0154] For example, the traffic pattern classification module (418) of the second processor (410) can detect (or classify, identify, infer) a traffic pattern generated in the electronic device (101) based on the detection time of the traffic pattern set (or updated) by the classification control module (416) during the CDRX period of the electronic device (101). For example, the detection time of the traffic pattern can be set (or updated) based on the RRM measurement period included in the CDRX period.

[0155] For example, the traffic pattern classification module (418) can detect (or classify, identify) a traffic pattern occurring in the electronic device (101) based on a designated second period updated by the classification control module (416) during the CDRX period of the electronic device (101). For example, when the designated second period updated by the classification control module (416) during the CDRX period of the electronic device (101) arrives, the traffic pattern classification module (418) can determine whether an active period (e.g., on duration) within the CDRX period has arrived based on information related to the CDRX mode. If the active period within the CDRX period has not arrived, the traffic pattern classification module (418) can delay the detection of the traffic pattern until the time when the active period arrives. When the active period within the CDRX period arrives, the traffic pattern classification module (418) can detect the traffic pattern using a traffic classification model.

[0156] For example, when it is determined that the electronic device (101) is operating in CDRX mode, the traffic pattern classification module (418) can generate virtual traffic images corresponding to the CDRX section based on the characteristics of the traffic confirmed by the traffic status check module (414). The traffic pattern classification module (418) can detect traffic patterns by inputting a plurality of virtual traffic images into a traffic classification model based on the batch input size updated by the classification control module (416). When the detection period of the traffic pattern arrives during the CDRX section, the traffic pattern classification module (418) can use the virtual traffic images to recognize a portion of the previously detected traffic patterns as traffic patterns corresponding to the detection period of the traffic pattern. For example, the detection period of the traffic pattern may include a designated first period for detecting traffic patterns or a designated second period updated based on information related to the CDRX mode, as a period for detecting traffic patterns during the CDRX section. For example, the number of virtual traffic images can be determined based on the batch input size updated in the classification control module (416). For example, virtual traffic images can be generated by adding a zero vector to the traffic characteristics identified by the traffic status check module (414) by determining that no traffic of the electronic device (101) occurs within the CDRX section. For example, the traffic images can represent the form of data input to the traffic classification model for detecting traffic patterns.

[0157] For example, the traffic pattern classification module (418) can detect traffic patterns based on a traffic pattern detection cycle (e.g., a designated first cycle or a designated second cycle) when there are no previously detected traffic patterns using virtual traffic images during the CDRX period. For example, when the traffic pattern detection cycle (e.g., a designated first cycle or a designated second cycle) arrives during the CDRX period of the electronic device (101), the traffic pattern classification module (418) can determine whether an active period within the CDRX period has arrived based on information related to the CDRX mode. If the active period within the CDRX period has not arrived, the traffic pattern classification module (418) can delay the detection of traffic patterns until the time when the active period arrives. When the active period within the CDRX period arrives, the traffic pattern classification module (418) can detect traffic patterns.

[0158] FIG. 6 is a flowchart (600) for limiting the detection of traffic patterns during a CDRX interval in an electronic device according to one embodiment. For example, at least part of FIG. 6 may include detailed operations of operation 505 of FIG. 5. In the following embodiments, each operation may be performed sequentially, but is not necessarily performed sequentially. For example, the order of each operation may be changed, and at least two operations may be performed in parallel. For example, the electronic device of FIG. 6 may be the electronic device (101) of FIG. 1, FIG. 2, or FIG. 3. For example, at least part of FIG. 6 may be described with reference to FIG. 7. FIG. 7 is an example for limiting the detection of traffic patterns during a CDRX interval in an electronic device according to one embodiment.

[0159] According to one embodiment with reference to FIGS. 6 and 7, when an electronic device (e.g., electronic device (101)) or a processor (e.g., processor (120) of FIG. 1 or processor (300) of FIG. 3) updates a setting value (or variable) related to the identification (or detection) of a traffic pattern based on information related to the CDRX mode (e.g., operation 503 of FIG. 5), in operation 601, the electronic device (101) can be checked whether it has entered the CDRX section based on information related to the CDRX mode. For example, the processor (300) (e.g., device status check module (412) of the second processor (410)) can check the entry of the electronic device (101) into the CDRX mode based on information related to the CDRX mode obtained from an RRC message received from an external electronic device (e.g., base station, eNB or gNB). For example, the CDRX period may include a time period during which the electronic device (101) operates in CDRX mode.

[0160] According to one embodiment, an electronic device (e.g., electronic device (101)) or a processor (e.g., processor (120 or 300)) may perform classification of traffic patterns occurring in the electronic device (101) based on a designated first period in operation 603 when the electronic device (101) does not enter a CDRX section (e.g., 'No' of operation 601). For example, the processor (300) (e.g., traffic pattern classification module (418) of the second processor (410)) may perform detection of traffic patterns in a designated first period (714) when the electronic device (101) does not enter a CDRX section (704) (702). For example, the time (712) for detecting traffic patterns may vary based on the operating state (e.g., load) of the electronic device (101) (e.g., processor (300)) at the time of detecting traffic patterns. For example, the designated first period (714) may represent a time interval during which the electronic device (101) detects a traffic pattern during the time interval (702) in which it transmits and / or receives data through the network.

[0161] According to one embodiment, an electronic device (e.g., electronic device (101)) or a processor (e.g., processor (120 or 300)) may restrict the classification of traffic patterns using a traffic classification model during the CDRX section in operation 605 when the electronic device (101) enters the CDRX section (e.g., 'Yes' of operation 601). For example, when the electronic device (101) enters the CDRX section (704), the processor (300) (e.g., traffic pattern classification module (418)) may restrict the classification of traffic patterns in the low-power section (706) and active section (708) within the CDRX section (704). For example, the processor (300) (e.g., traffic pattern classification module (418)) can recognize that during the CDRX section (704) of the electronic device (101), the same traffic pattern (e.g., game or streaming) as the traffic pattern detected immediately before entering the CDRX section is present (e.g., game or streaming).

[0162] According to one embodiment, an electronic device (e.g., electronic device (101)) (or a processor (e.g., processor (120 or 300))) may perform (or resume) classification of traffic patterns occurring in the electronic device (101) based on a designated first cycle (714) when the CDRX period of the electronic device (101) expires and it enters an active period (703) (716). For example, a processor (300) (e.g., a traffic pattern classification module (418) of a second processor (410)) may perform detection of traffic patterns using a traffic classification model when the electronic device (101) enters an active period (703) (716) (717). A processor (300) (e.g., a traffic pattern classification module (418) of a second processor (410)) can perform detection of traffic patterns using a traffic classification model based on a specified first period (714) during an active period (703) (718).

[0163] FIG. 8a is a flowchart (800) for detecting a traffic pattern based on a CDRX period in an electronic device according to one embodiment. For example, at least part of FIG. 8a may include detailed operations of operation 505 of FIG. 5. In the following embodiments, each operation may be performed sequentially, but is not necessarily performed sequentially. For example, the order of each operation may be changed, and at least two operations may be performed in parallel. For example, the electronic device of FIG. 8a may be the electronic device (101) of FIG. 1, FIG. 2, or FIG. 3. For example, at least part of FIG. 8a may be described with reference to FIG. 10 and FIG. 11. FIG. 10 is a graph showing the traffic pattern detection error rate according to the change in the traffic pattern detection period in an electronic device according to one embodiment. FIG. 11 is a graph showing the length of the waiting period according to the data transmission category in an electronic device according to one embodiment.

[0164] According to one embodiment with reference to FIGS. 8a, FIGS. 10 and FIGS. 11, when an electronic device (e.g., electronic device (101)) or a processor (e.g., processor (120) of FIG. 1 or processor (300) of FIG. 3) updates a setting value (or variable) related to the identification (or detection) of a traffic pattern based on information related to the CDRX mode (e.g., operation 503 of FIG. 5), in operation 801, it can determine whether the electronic device (101) has entered a CDRX interval based on information related to the CDRX mode. For example, the CDRX interval may include a time interval during which the electronic device (101) operates in CDRX mode.

[0165] According to one embodiment, an electronic device (e.g., electronic device (101)) or a processor (e.g., processor (120 or 300)) may classify traffic patterns occurring in the electronic device (101) based on a specified first period in operation 803 when the electronic device (101) has not entered a CDRX interval (e.g., 'No' in operation 801). For example, the specified first period (714) may represent a time interval for detecting traffic patterns during a time interval (702) in which the electronic device (101) transmits and / or receives data through a network.

[0166] According to one embodiment, an electronic device (e.g., electronic device (101)) or a processor (e.g., processor (120 or 300)) may classify traffic patterns occurring in the electronic device (101) based on a designated second period in operation 805 when the electronic device (101) enters a CDRX section (e.g., 'Yes' of operation 801). For example, the designated second period may be a period updated based on information related to the CDRX mode and may be set to be relatively longer than the designated first period.

[0167] For example, the error rate (or error occurrence rate) of a traffic pattern may increase as the detection period of the traffic pattern becomes relatively longer, as shown in FIG. 10. For example, the error rate of a traffic pattern may increase as the detection period of the traffic pattern becomes longer. For example, the error rate of a traffic pattern may be higher when using a detection period of a traffic pattern of a second interval (1001) than when using a first interval (1000) that is shorter than the second interval (1001). For example, the error rate of a traffic pattern may be lower when using a detection period of a traffic pattern of a second interval (1001) than when using a third interval (1002) that is longer than the second interval (1001). For example, the detection period of a traffic pattern may be set to be relatively longer as the reference number of the detection period of the traffic pattern in FIG. 10 increases. The classification control module (416) of the second processor (410) can update the detection cycle of the traffic pattern to a specified second cycle based on the performance (e.g., error rate) required in the function corresponding to the traffic pattern of the electronic device (101) detected by the traffic pattern classification module (418). For example, the detection cycle of the traffic pattern may be updated to a relatively short cycle if a relatively low error rate is required in the function corresponding to the traffic pattern. For example, the detection cycle of the traffic pattern may be updated to a relatively long cycle if a relatively low error rate is not required in the function corresponding to the traffic pattern. For example, the function corresponding to the traffic pattern may be set based on the current traffic pattern detected by the traffic pattern classification module (418). For example, the current traffic pattern is the most recently detected traffic pattern by the traffic pattern classification module (418) and may include at least one of streaming, real-time transmission, or data transfer.

[0168] For example, the waiting time of a streaming pattern can be evenly distributed from a relatively short time point to a relatively long time point, as shown in (a) of FIG. 11. When a streaming pattern is detected by the traffic pattern classification module (418), the classification control module (416) determines that a relatively low error rate is not required in the function corresponding to the traffic pattern, and can update the detection period of the traffic pattern within the CDRX section to be relatively long.

[0169] For example, the waiting time of a first type of real-time transmission (e.g., light RT) pattern may be distributed at relatively short intervals, as shown in (b) of FIG. 11. When a first type of real-time transmission pattern is detected by the traffic pattern classification module (418), the classification control module (416) determines that a relatively low error rate is required in the function corresponding to the traffic pattern, and can update the detection cycle of the traffic pattern within the CDRX interval to be shorter than that of the streaming type. For example, real-time transmission may include a data transmission method that can respond in real-time to user input of the electronic device (101).

[0170] For example, the waiting time of a second type of real-time transmission (e.g., heavy RT) pattern can be distributed at a relatively short time, as shown in (c) of FIG. 11. When a second type of real-time transmission pattern is detected by the traffic pattern classification module (418), the classification control module (416) determines that a relatively low error rate is required in the function corresponding to the traffic pattern, and can update the detection period of the traffic pattern within the CDRX section to be shorter than that of the first type of real-time transmission and streaming type.

[0171] For example, the waiting time of a data transfer pattern can be evenly distributed from a relatively short time point to a relatively long time point, as shown in (d) of FIG. 11. When the classification control module (416) detects a data transfer pattern in the traffic pattern classification module (418), it determines that a relatively low error rate is not required in the function corresponding to the traffic pattern, and can update the detection period of the traffic pattern within the CDRX section to be shorter than the streaming type.

[0172] For example, the waiting time of other patterns can be evenly distributed from a relatively short time point to a relatively long time point, as shown in (e) of FIG. 11. When a different pattern is detected by the traffic pattern classification module (418), the classification control module (416) determines that a relatively low error rate is not required in the function corresponding to the traffic pattern, and can update the detection period of the traffic pattern within the CDRX section to be longer than the data transmission pattern but shorter than the streaming type.

[0173] For example, a processor (300) (e.g., a traffic pattern classification module (418)) can detect (or classify, identify) a traffic pattern occurring in the electronic device (101) based on an active period within a designated second cycle and a CDRX period updated by a classification control module (416) during the CDRX period of the electronic device (101).

[0174] FIG. 8b is a flowchart (810) for detecting a traffic pattern in a CDRX section in an electronic device according to one embodiment. For example, at least part of FIG. 8b may include a detailed operation of operation 505 of FIG. 5. In the following embodiments, each operation may be performed sequentially, but is not necessarily performed sequentially. For example, the order of each operation may be changed, and at least two operations may be performed in parallel. For example, the electronic device of FIG. 8b may be the electronic device (101) of FIG. 1, FIG. 2, or FIG. 3. For example, at least part of FIG. 8b may be described with reference to FIG. 9. FIG. 9 is an example showing a section for RRM measurement in a CDRX section in an electronic device according to one embodiment.

[0175] According to one embodiment with reference to FIGS. 8b and FIGS. 9, when an electronic device (e.g., electronic device (101)) or a processor (e.g., processor (120) of FIG. 1 or processor (300) of FIG. 3) updates a setting value (or variable) related to the identification (or detection) of a traffic pattern based on information related to the CDRX mode (e.g., operation 503 of FIG. 5), in operation 811, it can determine whether the electronic device (101) has entered a CDRX interval based on information related to the CDRX mode. For example, the CDRX interval may include a time interval during which the electronic device (101) operates in CDRX mode.

[0176] According to one embodiment, an electronic device (e.g., electronic device (101)) or a processor (e.g., processor (120 or 300)) may classify traffic patterns occurring in the electronic device (101) based on a specified first period in operation 813 when the electronic device (101) has not entered a CDRX interval (e.g., 'No' in operation 811). For example, the specified first period (714) may represent a time interval for detecting traffic patterns during a time interval (702) in which the electronic device (101) transmits and / or receives data through a network.

[0177] According to one embodiment, an electronic device (e.g., electronic device (101)) or a processor (e.g., processor (120 or 300)) can check whether an RRM measurement interval has arrived in operation 815 when the electronic device (101) has entered the CDRX interval (e.g., 'Yes' of operation 811).

[0178] According to one embodiment, if an electronic device (e.g., electronic device (101)) or a processor (e.g., processor (120 or 300)) does not encounter an RRM measurement period within a CDRX period (e.g., 'No' in operation 815), then in operation 815, the RRM measurement period may be encountered again. For example, a traffic pattern classification module (418) may continuously or periodically encounter an RRM measurement period during the CDRX period of the electronic device (101).

[0179] According to one embodiment, an electronic device (e.g., electronic device (101)) or a processor (e.g., processor (120 or 300)) may perform classification of traffic patterns occurring in the electronic device (101) during the RRM measurement period in operation 817 when an RRM measurement period arrives within the CDRX period (e.g., 'Yes' of operation 815). For example, in the RRM measurement period (900) within the CDRX period, as shown in FIG. 9, the electronic device (101) may maintain an active state for a longer period than the active period (910 or 920) within the CDRX period. The classification control module (416) of the second processor (410) may set the detection time of the traffic pattern to perform detection of the traffic pattern using the traffic classification module during the RRM measurement period (900) within the CDRX period in order to secure time for classification of the traffic pattern in the traffic pattern classification module (418). The traffic pattern classification module (418) can detect (or classify, identify) a traffic pattern generated in the electronic device (101) using a traffic classification model when the time for detection of a traffic pattern set (or updated) by the classification control module (416) during the CDRX period of the electronic device (101) arrives.

[0180] According to one embodiment, when the electronic device (101) detects a traffic pattern during an RRM measurement period within a CDRX section, the timing of the traffic pattern detection may be limited to the RRM measurement period rather than the timing required by the electronic device (101). In this case, the classification control module (416) may set (or update) the timing of the traffic pattern detection by considering the RRM measurement period when the electronic device (101) does not require a relatively low error rate. For example, the timing required by the electronic device (101) may include the timing of the traffic pattern detection corresponding to the performance (e.g., error rate) required by the function corresponding to the traffic pattern of the electronic device (101).

[0181] FIG. 12 is a flowchart (1200) for detecting a traffic pattern based on the active time in a CDRX section in an electronic device according to one embodiment. For example, at least part of FIG. 12 may include a detailed operation of operation 803 of FIG. 8. In the following embodiments, each operation may be performed sequentially, but is not necessarily performed sequentially. For example, the order of each operation may be changed, and at least two operations may be performed in parallel. For example, the electronic device of FIG. 12 may be the electronic device (101) of FIG. 1, FIG. 2, or FIG. 3. For example, at least part of FIG. 12 may be described with reference to FIG. 13. FIG. 13 is an example for detecting a traffic pattern based on the active time in a CDRX section in an electronic device according to one embodiment.

[0182] According to one embodiment with reference to FIGS. 12 and 13, an electronic device (e.g., electronic device (101)) or a processor (e.g., processor (120) of FIG. 1 or processor (300) of FIG. 3) can check whether a designated second cycle arrives within the CDRX interval in operation 1201 when the electronic device (101) enters the CDRX interval (e.g., 'Yes' of operation 801 of FIG. 8). For example, the traffic pattern classification module (418) of the second processor (410) can check whether a designated second cycle, updated by the classification control module (416), arrives during the CDRX interval of the electronic device (101).

[0183] According to one embodiment, if an electronic device (e.g., electronic device (101)) or a processor (e.g., processor (120 or 300)) has not arrived at a specified second cycle (e.g., 'No' in operation 1201), then in operation 1201, the electronic device (e.g., electronic device (101)) may check again whether a specified second cycle arrives within a CDRX interval. For example, a traffic pattern classification module (418) may check whether a specified second cycle arrives continuously or periodically during a CDRX interval of the electronic device (101).

[0184] According to one embodiment, an electronic device (e.g., electronic device (101)) or a processor (e.g., processor (120 or 300)) can determine whether an active period within a CDRX interval has arrived in operation 1203 when a designated second period arrives (e.g., 'Yes' of operation 1201). For example, a traffic pattern classification module (418) can determine whether an active period within a CDRX interval has arrived based on information related to the CDRX mode when a designated second period (1320) updated by the classification control module (416) arrives during the CDRX interval (1304) of the electronic device (101).

[0185] According to one embodiment, an electronic device (e.g., electronic device (101)) or a processor (e.g., processor (120 or 300)) may check again whether an active period within a CDRX interval has arrived in operation 1203 when an active period within a CDRX interval has not arrived (e.g., 'No' in operation 1203). For example, a traffic pattern classification module (418) may delay the detection of a traffic pattern if an active period within a CDRX interval (1304) has not arrived.

[0186] According to one embodiment, an electronic device (e.g., electronic device (101)) or a processor (e.g., processor (120 or 300)) may perform classification of traffic patterns generated by the electronic device (101) in operation 1205 when an active period within the CDRX interval arrives (e.g., 'Yes' of operation 1203). For example, when an active period within the CDRX interval (1304) has not arrived, the traffic pattern classification module (418) may update information related to the start of traffic pattern detection so that the active period (1330) within the CDRX interval and the traffic pattern detection period are at least partially synchronized (1322). When an active period (1330) within the CDRX interval arrives, the traffic pattern classification module (418) may detect traffic patterns using a traffic classification model. For example, the update of information related to the start of traffic pattern detection may include a series of operations that delay the start of the operation of the traffic classification model to correspond to the active section (1330) within the CDRX section.

[0187] FIG. 14 is a flowchart (1400) for detecting a traffic pattern using a virtual traffic image in an electronic device according to one embodiment. For example, at least part of FIG. 14 may include a detailed operation of operation 505 of FIG. 5. In the following embodiments, each operation may be performed sequentially, but is not necessarily performed sequentially. For example, the order of each operation may be changed, and at least two operations may be performed in parallel. For example, the electronic device of FIG. 14 may be the electronic device (101) of FIG. 1, FIG. 2, or FIG. 3. For example, at least part of FIG. 14 may be described with reference to FIG. 15. FIG. 15 is an example for detecting a traffic pattern using a virtual traffic image in an electronic device according to one embodiment.

[0188] According to one embodiment with reference to FIGS. 14 and 15, when an electronic device (e.g., electronic device (101)) or a processor (e.g., processor (120) of FIG. 1 or processor (300) of FIG. 3) updates a setting value (or variable) related to the identification (or detection) of a traffic pattern based on information related to the CDRX mode (e.g., operation 503 of FIG. 5), in operation 1401, virtual traffic images may be generated based on traffic information generated by the electronic device (101) during a specified time. For example, the classification control module (416) of the second processor (410) may update the batch input size of the traffic classification model used by the traffic pattern classification module (418) when it is determined that the electronic device (101) is operating in CDRX mode based on information related to the CDRX mode. For example, the batch input size may represent an input size that can detect (or infer) a traffic pattern at once in the traffic classification model. For example, the updated size of the batch input can be determined based on the performance of the component using the traffic classification model (e.g., the second processor (410) or the traffic pattern classification module (418)).

[0189] For example, if the traffic pattern classification module (418) determines that the electronic device (101) is operating in CDRX mode based on information related to the CDRX mode, it may generate virtual traffic images (1510) corresponding to the CDRX section based on the traffic characteristics (1500) confirmed by the traffic status check module (414). For example, the number of virtual traffic images may be determined based on the batch input size updated by the classification control module (416). For example, the virtual traffic images (1510) may include a plurality of traffic images (1512, 1514, 1516 and / or 1518) in which zero vectors of different lengths corresponding to the flow of time are added to the traffic characteristics confirmed by the traffic status check module (414) when it is determined that no traffic of the electronic device (101) occurs within the CDRX section. For example, traffic images can represent the form of data input to a traffic classification model for detecting traffic patterns.

[0190] According to one embodiment, an electronic device (e.g., electronic device (101)) or a processor (e.g., processor (120 or 300)) can detect traffic patterns corresponding to virtual traffic images in operation 1403. For example, a traffic pattern classification module (418) can generate a multi-vector (1520) to provide to a traffic classification model using virtual traffic images (1510). The traffic pattern classification module (418) can input the multi-vector (1520) into a traffic classification model to detect (or classify, identify) traffic patterns corresponding to the virtual traffic images (1530 and 1540). The traffic pattern classification module (418) can store the detection results of the traffic patterns in memory (320) (e.g., a classification result queue).

[0191] According to one embodiment, an electronic device (e.g., electronic device (101)) or a processor (e.g., processor (120 or 300)) can determine in operation 1405 whether a detection period of a traffic pattern (e.g., a first period or a second period) arrives within the CDRX interval.

[0192] According to one embodiment, if the detection cycle of a traffic pattern has not arrived within the CDRX interval (e.g., electronic device (101)) or the processor (e.g., processor (120 or 300)), the electronic device (e.g., electronic device (101)) or the processor (e.g., processor (120 or 300)) can, in operation 1405, check again whether the detection cycle of a traffic pattern (e.g., first cycle or second cycle) arrives within the CDRX interval. For example, the traffic pattern classification module (418) can check whether the detection cycle of a traffic pattern arrives continuously or periodically during the CDRX interval of the electronic device (101).

[0193] According to one embodiment, an electronic device (e.g., electronic device (101)) or a processor (e.g., processor (120 or 300)) can determine whether there is at least one previously detected traffic pattern using virtual traffic images in operation 1407 when the detection cycle of a traffic pattern within a CDRX interval arrives (e.g., 'yes' of operation 1405).

[0194] According to one embodiment, an electronic device (e.g., electronic device (101)) or a processor (e.g., processor (120 or 300)) can recognize a traffic pattern within a CDRX interval using at least one traffic pattern that has been previously detected using virtual traffic images (e.g., 'yes' of operation 1407) in operation 1409. For example, when the first detection cycle of a traffic pattern arrives during a CDRX interval, the traffic pattern classification module (418) can recognize the first detected traffic pattern (e.g., streaming) among the traffic patterns detected using virtual traffic images as a traffic pattern corresponding to the detection cycle of the first traffic pattern within the CDRX interval. For example, when the second detection cycle of a traffic pattern arrives during the CDRX period, the traffic pattern classification module (418) can recognize the second detected traffic pattern (e.g., streaming) among the detected traffic patterns using virtual traffic images as the traffic pattern corresponding to the detection cycle of the second traffic pattern within the CDRX period. For example, when the third detection cycle of a traffic pattern arrives during the CDRX period, the traffic pattern classification module (418) can recognize the third detected traffic pattern (e.g., others) among the detected traffic patterns using virtual traffic images as the traffic pattern corresponding to the detection cycle of the third traffic pattern within the CDRX period. For example, the detection cycle of a traffic pattern may include a designated first cycle for detecting traffic patterns or a designated second cycle updated based on information related to the CDRX mode as a cycle for detecting traffic patterns during the CDRX period.

[0195] According to one embodiment, an electronic device (e.g., electronic device (101)) or a processor (e.g., processor (120 or 300)) may detect a traffic pattern using a traffic classification model in operation 1411 when there is no pre-detected traffic pattern using virtual traffic images (e.g., 'No' in operation 1407). For example, the traffic pattern classification module (418) may determine whether an active period within the CDRX period has arrived based on information related to the CDRX mode when there is no pre-detected traffic pattern using virtual traffic images when the traffic pattern detection period (e.g., a designated first period or a designated second period) has arrived during the CDRX period of the electronic device (101). If the active period within the CDRX period has not arrived, the traffic pattern classification module (418) may delay the detection of the traffic pattern until the time when the active period arrives. The traffic pattern classification module (418) can detect a traffic pattern using a traffic classification model when an active period within the CDRX section arrives.

[0196] According to one embodiment, an electronic device (e.g., electronic device (101)) or a processor (e.g., processor (120 or 300)) may perform post-processing to increase the reliability of detected traffic patterns using virtual traffic images corresponding to the detection cycle of traffic patterns within a CDRX section. For example, when a traffic pattern classification module (418) detects traffic patterns using virtual traffic images, it may perform post-processing of the detected traffic patterns using a traffic classification model. For example, when the detection cycle of a traffic pattern arrives within a CDRX section, the traffic pattern classification module (418) may perform post-processing of the traffic pattern corresponding to the detection cycle of the traffic pattern among the traffic patterns using virtual traffic images.

[0197] According to one embodiment, an electronic device (e.g., electronic device (101)) or a processor (e.g., processor (120 or 300)) may limit the detection of traffic patterns during a CDRX period when there are no detected traffic patterns by using virtual traffic images corresponding to the detection period of traffic patterns within a CDRX period.

[0198] FIG. 16 is a flowchart (1600) for selectively detecting traffic patterns based on information related to CDRX in an electronic device according to one embodiment. In the following embodiments, each operation may be performed sequentially, but is not necessarily performed sequentially. For example, the order of each operation may be changed, and at least two operations may be performed in parallel. For example, the electronic device of FIG. 16 may be the electronic device (101) of FIG. 1, FIG. 2, or FIG. 3.

[0199] According to one embodiment with reference to FIG. 16, an electronic device (e.g., electronic device (101)) or a processor (e.g., processor (120) of FIG. 1 or processor (300) of FIG. 3) can determine in operation 1601 whether the electronic device (101) is connected (or connected, registered) to a wireless network. For example, the processor (300) (e.g., second processor (410)) can determine whether it is connected to an external electronic device (e.g., base station, eNB or gNB) through a communication circuit (310) and transmits and / or receives data with the external electronic device.

[0200] According to one embodiment, an electronic device (e.g., electronic device (101)) or a processor (e.g., processor (120 or 300)) may terminate one embodiment for optionally detecting a traffic pattern when the electronic device (101) is not connected (or connected) to a wireless network (e.g., 'No' of operation 1601).

[0201] According to one embodiment, an electronic device (e.g., electronic device (101)) or a processor (e.g., processor (120 or 300)) may obtain information related to the CDRX mode in operation 1603 when the electronic device (101) is connected (or connected, registered) to a wireless network (e.g., 'Yes' in operation 1601). For example, the second processor (410) may obtain information related to the CDRX mode from a DRX configuration information element of an RRC message (e.g., RRC connection reconfiguration) such as Table 1 (e.g., 3GPP 38.331 V17.3.0) received from an external electronic device (e.g., base station, eNB, or gNB). For example, the information related to the CDRX mode may include at least one of the CDRX period (e.g., drx-onDurationTimer) or the duration of the active period within the CDRX interval (e.g., drx-InactivityTimer).

[0202] According to one embodiment, an electronic device (e.g., electronic device (101)) or a processor (e.g., processor (120 or 300)) can determine in operation 1605 whether the electronic device (101) has entered a CDRX period based on information related to the CDRX mode. For example, the CDRX period may include a time period during which the electronic device (101) operates in CDRX mode.

[0203] According to one embodiment, an electronic device (e.g., electronic device (101)) or a processor (e.g., processor (120 or 300)) can check in operation 1607 whether an update method related to traffic classification has been set when the electronic device (101) enters a CDRX section (e.g., 'Yes' of operation 1605). For example, the classification control module (416) of the second processor (410) can check whether setting information regarding an update method related to traffic classification within the CDRX section has been received from the classification information providing module (402) of the first processor (400). For example, the setting information regarding an update method related to traffic classification may include information related to an update method set (or selected) by user input for traffic classification within the CDRX section. For example, configuration information regarding an update method related to traffic classification can be provided from a first processor (400) (e.g., classification information providing module (402)) to a second processor (410) (e.g., classification control module (416)) via inter-process communication (IPC).

[0204] According to one embodiment, an electronic device (e.g., electronic device (101)) or a processor (e.g., processor (120 or 300)) may classify (or detect) a pattern of traffic generated by the electronic device (101) based on the update method related to traffic classification in operation 1609, when an update method related to traffic classification is set (e.g., 'Yes' of operation 1607). For example, the update method related to the classification of traffic patterns may include at least one of a first update method that updates the detection cycle of the traffic pattern, a second update method that sets (or updates) the detection time of the traffic pattern, or a third update method that updates the size of the input batch of the traffic classification model.

[0205] For example, when a first update method is set, the classification control module (416) of the second processor (410) may update the detection cycle of the traffic pattern to a specified second cycle different from the specified first cycle, based on the performance (e.g., error rate) required in the function corresponding to the traffic pattern of the electronic device (101) detected by the traffic pattern classification module (418). For example, the detection cycle of the traffic pattern may be updated to a relatively short cycle if a relatively low error rate is required in the function corresponding to the traffic pattern. For example, the detection cycle of the traffic pattern may be updated to a relatively long cycle if a relatively low error rate is not required in the function corresponding to the traffic pattern. For example, the function corresponding to the traffic pattern may be set based on the current traffic pattern detected by the traffic pattern classification module (418). For example, the current traffic pattern may include the most recently detected traffic pattern by the traffic pattern classification module (418).

[0206] For example, the traffic pattern classification module (418) can detect (or classify, identify, infer) a traffic pattern occurring in the electronic device (101) based on a designated second cycle updated by the classification control module (416) during the CDRX period of the electronic device (101). For example, when the designated second cycle updated by the classification control module (416) during the CDRX period of the electronic device (101) arrives, the traffic pattern classification module (418) can determine whether an active period within the CDRX period has arrived based on information related to the CDRX mode. If the active period within the CDRX period has not arrived, the traffic pattern classification module (418) can delay the detection of the traffic pattern until the time when the active period arrives. When the active period within the CDRX period arrives, the traffic pattern classification module (418) can detect the traffic pattern using a traffic classification model. For example, the detection period of a traffic pattern may be updated as a multiple (or integer multiple) of a specified first period. For example, the detection period of a traffic pattern may be updated as a value not related to a multiple (or integer multiple) of a specified first period.

[0207] For example, if the second update method is set, the classification control module (416) can set (or update) the detection time of the traffic pattern to perform detection of the traffic pattern using the traffic classification model during the RRM measurement period within the CDRX section.

[0208] For example, the traffic pattern classification module (418) can detect (or classify, identify, infer) the traffic pattern generated in the electronic device (101) using a traffic classification model when the time for detection of the traffic pattern set (or updated) by the classification control module (416) during the CDRX period of the electronic device (101) arrives.

[0209] For example, the classification control module (416) of the second processor (410) can update the batch input size of the traffic classification model used by the traffic pattern classification module (418) when the third update method is set. For example, the batch input size may represent the input size that the traffic classification model can detect (or infer) a traffic pattern at once.

[0210] For example, when it is determined that the electronic device (101) is operating in CDRX mode, the traffic pattern classification module (418) can generate virtual traffic images corresponding to the CDRX section based on the characteristics of the traffic confirmed by the traffic status check module (414). The traffic pattern classification module (418) can detect traffic patterns by inputting a plurality of virtual traffic images into a traffic classification model based on the batch input size updated by the classification control module (416). When the detection period of the traffic pattern arrives during the CDRX section, the traffic pattern classification module (418) can recognize some of the detected traffic patterns using the virtual traffic images as traffic patterns corresponding to the detection period of the traffic pattern. For example, the detection period of the traffic pattern may include a designated first period for detecting traffic patterns or a designated second period updated based on information related to the CDRX mode, as a period for detecting traffic patterns during the CDRX section. For example, the number of virtual traffic images can be determined based on the batch input size updated in the classification control module (416). For example, virtual traffic images can be generated by adding a zero vector to the traffic characteristics identified by the traffic status check module (414) by determining that no traffic of the electronic device (101) occurs within the CDRX section. For example, the traffic images can represent the form of data input to the traffic classification model for detecting traffic patterns.

[0211] For example, the traffic pattern classification module (418) can detect a traffic pattern based on a traffic pattern detection period (e.g., a designated first period or a designated second period) when there are no detected traffic patterns using virtual traffic images during the CDRX period. For example, when the traffic pattern classification module (418) arrives at the traffic pattern detection period (e.g., a designated first period or a designated second period) during the CDRX period of the electronic device (101), it can determine whether an active period within the CDRX period has arrived based on information related to the CDRX mode. If the active period within the CDRX period has not arrived, the traffic pattern classification module (418) can delay the detection of the traffic pattern until the time when the active period arrives. When the active period within the CDRX period has arrived, the traffic pattern classification module (418) can detect the traffic pattern.

[0212] According to one embodiment, an electronic device (e.g., electronic device (101)) or a processor (e.g., processor (120 or 300)) may restrict the classification of traffic patterns during the CDRX period in operation 1611 if no update method related to traffic classification is set (e.g., 'No' in operation 1607). For example, a traffic pattern classification module (418) of the second processor (410) may restrict the detection of traffic patterns during the CDRX period in which the electronic device (101) operates in CDRX mode based on an updated setting value (or variable) related to the identification (or detection) of traffic patterns. For example, a classification control module (416) of the second processor (410) may update a setting value related to the classification of traffic patterns so that the detection of traffic patterns during the CDRX period is restricted if no update method related to traffic classification set based on user input exists. The traffic pattern classification module (418) can restrict the classification of traffic patterns within the CDRX section when the electronic device (101) enters the CDRX section. For example, the traffic pattern within the CDRX section may be recognized as having the same traffic pattern as the traffic pattern detected immediately before the electronic device (101) enters the CDRX section.

[0213] According to one embodiment, an electronic device (e.g., electronic device (101)) or a processor (e.g., processor (120 or 300)) may perform classification of traffic patterns occurring in the electronic device (101) based on a designated first cycle in operation 1613 when the electronic device (101) does not enter a CDRX section (e.g., 'No' in operation 1605). For example, a traffic pattern classification module (418) of a second processor (410) may perform detection of traffic patterns in a designated first cycle when the electronic device (101) does not enter a CDRX section.

[0214] FIG. 17 is a flowchart (1700) for detecting a traffic pattern during a CDRX interval in an electronic device according to one embodiment. For example, at least part of FIG. 17 may include a detailed operation of operation 1609 of FIG. 16. In the following embodiments, each operation may be performed sequentially, but is not necessarily performed sequentially. For example, the order of each operation may be changed, and at least two operations may be performed in parallel. For example, the electronic device of FIG. 17 may be the electronic device (101) of FIG. 1, FIG. 2, or FIG. 3. For example, FIG. 17 is described assuming there are three update methods related to traffic classification, but it can be applied in the same way if there are multiple update methods related to traffic classification.

[0215] According to one embodiment with reference to FIG. 17, an electronic device (e.g., electronic device (101)) or a processor (e.g., processor (120) of FIG. 1 or processor (300) of FIG. 3) can check in operation 1701 whether a first priority update method, which has the highest priority among a plurality of update methods related to traffic classification, has been set (or selected) when an update method related to traffic classification is set (e.g., 'Yes' of operation 1607 of FIG. 16). For example, the classification control module (416) of the second processor (410) can check whether the first priority update method has been selected as a method for updating an updated setting value related to traffic pattern identification by user input, based on setting information regarding an update method related to traffic classification within a CDRX section received from the classification information providing module (402) of the first processor (400). For example, the priority of the update method may be set based on at least one of the importance of the method of updating the setting value related to the identification of traffic patterns within the CDRX section, or the time when the updated setting value related to the identification of traffic patterns is applied.

[0216] According to one embodiment, an electronic device (e.g., electronic device (101)) or a processor (e.g., processor (120 or 300)) may classify (or detect) a pattern of traffic generated by the electronic device (101) based on the first priority update method related to traffic classification in operation 1703 when a first priority update method related to traffic classification is set (e.g., 'Yes' of operation 1701). For example, the classification control module (416) of the second processor (410) may update the batch input size of the traffic classification model used by the traffic pattern classification module (418) when a third update method of the first priority is set. For example, the batch input size may represent an input size that allows the traffic classification model to detect (or infer) a traffic pattern at once.

[0217] For example, when the traffic pattern classification module (418) determines that the electronic device (101) is operating in CDRX mode, it can detect traffic patterns based on the updated batch input size of the traffic classification model, as in operations 1401 to 1411 of FIG. 14. For example, when the traffic pattern classification module (418) determines that the electronic device (101) is operating in CDRX mode, it can generate virtual traffic images corresponding to the CDRX section based on the traffic characteristics confirmed by the traffic status check module (414). The traffic pattern classification module (418) can detect traffic patterns by inputting a plurality of virtual traffic images into the traffic classification model based on the updated batch input size of the classification control module (416). When the detection cycle of the traffic pattern arrives during the CDRX section, the traffic pattern classification module (418) can recognize a portion of the detected traffic patterns using the virtual traffic images as traffic patterns corresponding to the detection cycle of the traffic pattern. For example, the traffic pattern detection period may include a designated first period for detecting traffic patterns or a designated second period updated based on information related to the CDRX mode, as a period for detecting traffic patterns during the CDRX interval. For example, the number of virtual traffic images may be determined based on the batch input size updated in the classification control module (416). For example, virtual traffic images may be generated by adding a zero vector to the traffic characteristics identified by the traffic status check module (414) by determining that no traffic of the electronic device (101) occurs within the CDRX interval. For example, the traffic image may represent the form of data input to the traffic classification model for detecting traffic patterns.

[0218] For example, the traffic pattern classification module (418) can detect a traffic pattern based on a traffic pattern detection period (e.g., a designated first period or a designated second period) when there are no detected traffic patterns using virtual traffic images during the CDRX period. For example, when the traffic pattern classification module (418) arrives at the traffic pattern detection period (e.g., a designated first period or a designated second period) during the CDRX period of the electronic device (101), the traffic pattern classification module (418) can determine whether an active period within the CDRX period has arrived based on information related to the CDRX mode. If the active period within the CDRX period has not arrived, the traffic pattern classification module (418) can delay the detection of the traffic pattern until the time when the active period arrives. When the active period within the CDRX period arrives, the traffic pattern classification module (418) can detect the traffic pattern.

[0219] According to one embodiment, if an electronic device (e.g., electronic device (101)) or a processor (e.g., processor (120 or 300)) is not set for a first priority update method related to traffic classification (e.g., 'No' in operation 1701), in operation 1705, it can check whether a second priority update method is set (or selected) among a plurality of update methods related to traffic classification. For example, the classification control module (416) of the second processor (410) can check whether the second priority update method is selected as a method for updating an updated setting value related to traffic pattern identification by user input, based on setting information regarding an update method related to traffic classification within a CDRX interval received from the classification information providing module (402) of the first processor (400). For example, the second priority update method may include an update method with a higher priority than the first priority among a plurality of update methods related to traffic classification.

[0220] According to one embodiment, an electronic device (e.g., electronic device (101)) or a processor (e.g., processor (120 or 300)) may classify (or detect) a pattern of traffic generated by the electronic device (101) based on the second priority update method related to traffic classification in operation 1707, when a second priority update method related to traffic classification is set (e.g., 'Yes' in operation 1705). For example, when a second priority first update method is set, the classification control module (416) of the second processor (410) may update the detection cycle of the traffic pattern to a specified second cycle different from the specified first cycle, based on the performance (e.g., error rate) required in the function corresponding to the traffic pattern of the electronic device (101) detected by the traffic pattern classification module (418). For example, the detection cycle of the traffic pattern may be updated to a relatively short cycle if a relatively low error rate is required in the function corresponding to the traffic pattern. For example, the detection period of the traffic pattern may be updated at a relatively long period if the function corresponding to the traffic pattern does not require a relatively low error rate. For example, the function corresponding to the traffic pattern may be set based on the current traffic pattern detected by the traffic pattern classification module (418). For example, the current traffic pattern may include the most recently detected traffic pattern by the traffic pattern classification module (418).

[0221] For example, the traffic pattern classification module (418) can detect (or classify, identify, infer) a traffic pattern occurring in the electronic device (101) using a traffic classification model, such as operations 801 to 803 of FIG. 8a, based on a designated second cycle updated by the classification control module (416) during the CDRX period of the electronic device (101). For example, when the designated second cycle updated by the classification control module (416) during the CDRX period of the electronic device (101) arrives, the traffic pattern classification module (418) can determine whether an active period within the CDRX period has arrived based on information related to the CDRX mode. If the active period within the CDRX period has not arrived, the traffic pattern classification module (418) can delay the detection of the traffic pattern until the time when the active period arrives. When the active period within the CDRX period arrives, the traffic pattern classification module (418) can detect the traffic pattern using a traffic classification model. For example, the detection period of a traffic pattern may be updated to a multiple (or integer multiple) of a specified first period. For example, the detection period of a traffic pattern may be updated to a value not related to a multiple (or integer multiple) of a specified first period.

[0222] According to one embodiment, if an electronic device (e.g., electronic device (101)) or a processor (e.g., processor (120 or 300)) is not set to a second priority update method related to traffic classification (e.g., 'No' in operation 1705), in operation 1709, the electronic device (101) may classify (or detect) a pattern of traffic generated by the electronic device (101) based on a third priority update method related to traffic classification. For example, if the second update method of the third priority is set, the classification control module (416) may set (or update) the detection time of the traffic pattern to perform detection of the traffic pattern using a traffic classification model during the RRM measurement interval within the CDRX interval.

[0223] For example, the traffic pattern classification module (418) can detect (or classify, identify, infer) a traffic pattern generated in the electronic device (101) using a traffic classification model, as in operations 811 to 817 of FIG. 8b, based on the detection time of a traffic pattern set by the classification control module (416) during the CDRX period of the electronic device (101). For example, when the detection time of a traffic pattern set (or updated) by the classification control module (416) during the CDRX period of the electronic device (101) arrives, the traffic pattern classification module (418) can detect (or classify, identify, infer) a traffic pattern generated in the electronic device (101) using a traffic classification model.

[0224] According to one embodiment, a method of operation of an electronic device (e.g., the electronic device (101) of FIG. 1, FIG. 2 or FIG. 3) may include an operation of checking whether an active period (on duration) in the CDRX mode has arrived when a time has arrived to detect a traffic pattern corresponding to traffic data generated by the electronic device based on a traffic classification model in the CDRX mode. According to one embodiment, a method of operation of the electronic device may include an operation of detecting a traffic pattern corresponding to traffic data generated by the electronic device based on a traffic classification model when an active period in the CDRX mode has arrived.

[0225] According to one embodiment, the method of operating an electronic device may include an operation of limiting the detection of a traffic pattern corresponding to traffic data generated by the electronic device based on a traffic classification model when an active period in the CDRX mode has not arrived.

[0226] According to one embodiment, the method of operating an electronic device may include an operation of updating a period for classifying traffic patterns based on information related to a CDRX mode.

[0227] According to one embodiment, the operation of checking whether an active period arrives may include the operation of checking whether an active period arrives within the CDRX mode when an updated cycle for classifying traffic patterns arrives.

[0228] According to one embodiment, the operation of updating the period for classifying traffic patterns may include the operation of updating the period for classifying traffic patterns by a multiple of a designated first period for classifying traffic patterns based on information related to the CDRX mode.

[0229] According to one embodiment, the method of operating an electronic device may include an operation of identifying a radio resource management (RRM) measurement interval within a CDRX mode based on information related to the CDRX mode. According to one embodiment, the method of operating an electronic device may include an operation of setting a time point for classifying traffic patterns to correspond to the RRM measurement interval.

[0230] According to one embodiment, the operation of updating the period for classifying traffic patterns may include updating the period for classifying traffic patterns based on the pattern of traffic of an electronic device detected at a previous time.

[0231] According to one embodiment, a non-transient computer-readable storage medium (or computer program product) storing one or more programs may be described. According to one embodiment, one or more programs may include instructions that, when executed by a processor (e.g., processor (120) of FIG. 1 or processor (300) of FIG. 3) of an electronic device (e.g., electronic device (101) of FIG. 1, FIG. 2 or FIG. 3), when a time has come to detect a traffic pattern corresponding to traffic data generated by the electronic device based on a traffic classification model within the CDRX mode, check whether an active period (on duration) within the CDRX mode has arrived, and when an active period within the CDRX mode has arrived, perform an operation to detect a traffic pattern corresponding to traffic data generated by the electronic device based on the traffic classification model.

[0232] The embodiments of the present invention disclosed in this specification and drawings are merely specific examples provided to facilitate the explanation of the technical content according to the embodiments of the present invention and to aid in understanding the embodiments of the present invention, and are not intended to limit the scope of the embodiments of the present invention. Accordingly, the scope of an embodiment of the present invention should be interpreted as including all modifications or variations derived based on the technical concept of an embodiment of the present invention, in addition to the embodiments disclosed herein.

Claims

1. In an electronic device (101), communication circuit (310), At least one processor (300) including a processing circuit, and It includes a memory (320) for storing instructions, When the above instructions are executed individually or collectively by at least one processor, the electronic device, When the time arrives to detect traffic patterns within CDRX (connected discontinuous reception) mode, check whether the active period (on duration) within said CDRX mode has arrived, and An electronic device that detects a traffic pattern corresponding to traffic data generated by the electronic device based on a traffic classification model when an active period within the above CDRX mode arrives.

2. In Paragraph 1, When the above instructions are executed individually or collectively by the at least one processor, the electronic device, An electronic device that limits the detection of a traffic pattern corresponding to traffic data generated by the electronic device based on the traffic classification model when the active period within the above CDRX mode has not arrived.

3. In Paragraph 1, When the above instructions are executed individually or collectively by the at least one processor, the electronic device, An electronic device that updates the cycle for classifying the traffic pattern based on information related to the above CDRX mode.

4. In Paragraph 3, When the above instructions are executed individually or collectively by the at least one processor, the electronic device, An electronic device that checks whether the active period within the CDRX mode arrives when an updated period for classifying the above traffic pattern arrives.

5. In Paragraph 3, When the above instructions are executed individually or collectively by the at least one processor, the electronic device, An electronic device that updates the period for classifying the traffic pattern by a multiple of a designated first period for classifying the traffic pattern based on information related to the above CDRX mode.

6. In Paragraph 3, When the above instructions are executed individually or collectively by the at least one processor, the electronic device, An electronic device that updates the period for classifying the traffic pattern based on the traffic pattern of the electronic device detected at a previous point in time.

7. In Paragraph 1, When the above instructions are executed individually or collectively by the at least one processor, the electronic device, Based on the information related to the above CDRX mode, identify the RRM (radio resource management) measurement interval within the above CDRX mode, and An electronic device for setting a point in time for classifying the traffic pattern to correspond to the above RRM measurement interval.

8. In the method of operating the electronic device (101), When the time for detecting a traffic pattern within a CDRX (connected discontinuous reception) mode arrives, an operation to check whether an active period (on duration) within the CDRX mode arrives, and A method comprising detecting a traffic pattern corresponding to traffic data generated by the electronic device based on a traffic classification model when an active period within the above CDRX mode arrives.

9. In Paragraph 8, A method further comprising an operation to limit the detection of a traffic pattern corresponding to traffic data generated by the electronic device based on the traffic classification model when the active period within the above CDRX mode has not arrived.

10. In Paragraph 8, A method further comprising an operation to update a period for classifying the traffic pattern based on information related to the above CDRX mode.

11. In Paragraph 10, The operation of checking whether the above active period arrives is, A method including an operation to check whether the active period within the CDRX mode arrives when an updated period for classifying the above traffic pattern arrives.

12. In Paragraph 10, The operation of updating the period for classifying the above traffic patterns is, A method comprising the operation of updating the period for classifying the traffic pattern as a multiple of a designated first period for classifying the traffic pattern based on information related to the above CDRX mode.

13. In Paragraph 10, The operation of updating the period for classifying the above traffic patterns is, A method comprising an operation to update a period for classifying the traffic pattern based on the traffic pattern of the electronic device detected at a previous point in time.

14. In Paragraph 8, An operation to identify the RRM (radio resource management) measurement section within the CDRX mode based on information related to the above CDRX mode, and A method further comprising the operation of setting a timing point for classifying the traffic pattern to correspond to the above RRM measurement interval.

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