Electronic device for reducing power consumption due to wireless communication and operation method thereof

Abstract

An embodiment of the present invention relates to an apparatus and method for detecting a traffic pattern in an electronic device. According to an embodiment, the electronic device comprises: a communication circuit; at least one processor; and a memory storing instructions, wherein the instructions, when executed by the processor, may cause the electronic device to: detect traffic generated due to communication with an external electronic device that is in an RRC connected state; if a pattern in which traffic is periodically generated in the electronic device is identified by using a traffic classification model, predict whether traffic is generated during a specified first time, by using a traffic prediction model; and if it is determined that traffic is not generated during the specified first time, transmit, to the external electronic device, information related to switching to an RRC standby state or an RRC inactive state. Other embodiments may also be possible.

Description

Electronic device for reducing power consumption by wireless communication and method of operation thereof

[0001] An embodiment of the present disclosure relates to an electronic device for reducing power consumption by wireless communication and a method of operating the same.

[0002] With the advancement of wireless communication technology, electronic devices (e.g., communication electronic devices) are being widely used in daily life, and consequently, the demand for wireless data traffic is on the rise. Since the commercialization of 4G (4th generation) communication systems (e.g., LTE (long term evolution)), communication systems that transmit and / or receive signals using high-frequency bands (e.g., mmWave, approximately 3 GHz to 300 GHz band) (e.g., 5G (5th generation), pre-5G communication systems, or new radio (NR)) are being researched to meet the increasing demand for wireless data traffic.

[0003] 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.

[0004] RRC (radio resource control) may represent a radio resource control protocol between an electronic device and a base station. The RRC state of an electronic device may include an RRC connected state, in which the electronic device can immediately transmit and / or receive data with the base station, and an RRC inactive state and / or RRC idle state, in which the electronic device can receive paging signals from the base station.

[0005] The electronic device consumes more power in the RRC connection state, where a wireless connection with the base station is established, than in the RRC standby state (or RRC inactive state), where a wireless connection with the base station is not established. The electronic device may switch to the RRC standby state (or RRC inactive state) if there is no data being transmitted and / or received from the base station continuously for a specified time (e.g., an inactivity timer) while in the RRC connection state.

[0006] If an electronic device can predict in advance whether there is data to be continuously transmitted and / or received from a base station for a specified period while in an RRC connection state, it can reduce the power consumption of the electronic device by switching the RRC state of the electronic device to an RRC standby state (or RRC inactive state) before the specified time elapses.

[0007] If the electronic device incorrectly predicts whether there is data to be continuously transmitted and / or received from the base station for a specified period, communication may be delayed or unnecessary power consumption due to RRC state transitions may increase.

[0008] Embodiments of the present invention disclose an apparatus and method for increasing the accuracy of predicting whether data (or traffic) occurs in an electronic device.

[0009] The technical problems to be solved in this document are not limited to those mentioned above, and other unmentioned technical problems 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, when the instructions are executed individually or collectively by at least one processor, the electronic device may establish communication with an external electronic device through the communication circuit. According to one embodiment, when the instructions are executed individually or collectively by at least one processor, the electronic device may detect traffic generated through communication with an external electronic device while connected to a radio resource control (RRC). According to one embodiment, when the instructions are executed individually or collectively by at least one processor, the electronic device may identify a pattern of traffic generated by the electronic device using a traffic classification model. According to one embodiment, when the instructions are executed individually or collectively by at least one processor, the electronic device may predict whether traffic will occur during a specified first time period using a traffic prediction model if a pattern of periodically occurring traffic is identified. According to one embodiment, when instructions are executed individually or collectively by at least one processor, if the electronic device determines that no traffic has occurred during a specified first time, it may transmit information related to transitioning to an RRC standby state or an RRC inactive state to an external electronic device.

[0011] According to one embodiment, the method of operation of an electronic device may include an operation of establishing communication with an external electronic device. According to one embodiment, the method of operation of an electronic device may include an operation of detecting traffic generated through communication with an external electronic device while in a radio resource control (RRC) connected state. According to one embodiment, the method of operation of an electronic device may include an operation of identifying a pattern of traffic generated by the electronic device using a traffic classification model. According to one embodiment, if a pattern of periodically occurring traffic is identified, the method of operation of an electronic device may include an operation of predicting whether traffic will occur during a specified first time period using a traffic prediction model. According to one embodiment, if it is determined that no traffic will occur during the specified first time period, the method of operation of an electronic device may include an operation of transmitting information related to transitioning to an RRC standby state or an RRC inactive state to an external electronic device.

[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, the one or more programs may include instructions that, when executed by a processor of an electronic device, perform the following operations: establishing communication with an external electronic device; detecting traffic generated through communication with the external electronic device while in a radio resource control (RRC) connected state; identifying a pattern of traffic generated by the electronic device using a traffic classification model; predicting whether traffic will occur during a specified first time period using a traffic prediction model when a pattern of periodically occurring traffic is identified; and transmitting information related to transitioning to an RRC standby state or an RRC inactive state to the external electronic device when it is determined that no traffic will occur during the specified first time period.

[0013] According to an embodiment of the present invention, when an electronic device determines that traffic is occurring periodically based on a traffic pattern or an autocorrelation function, power consumption due to wireless communication can be reduced by predicting whether traffic occurs during a specified period and switching the RRC state between the electronic device and the base station to an RRC standby state (or RRC inactive state).

[0014] In addition, various effects that can be identified directly or indirectly through this document may be provided.

[0015] The effects obtainable from the present disclosure are not limited to those mentioned above, and other unmentioned effects will be clearly understood by those skilled in the art to which the present disclosure belongs from the description below.

[0016] In relation to the description of the drawings, the same or similar reference numerals may be used for identical or similar components.

[0017] FIG. 1 is a block diagram of an electronic device in a network environment according to one embodiment.

[0018] FIG. 2 is a block diagram showing an integrated intelligent system according to one embodiment.

[0019] FIG. 3 is a block diagram of an electronic device for switching the RRC state according to one embodiment.

[0020] FIG. 4 is a block diagram of a processor of an electronic device for traffic prediction according to one embodiment.

[0021] FIG. 5 is a flowchart for switching the RRC state based on traffic prediction in an electronic device according to one embodiment.

[0022] FIG. 6 is a flowchart for updating traffic data in an electronic device according to one embodiment.

[0023] FIG. 7 is an example of detecting a change in traffic data in an electronic device according to one embodiment.

[0024] FIG. 8 is an example of updating a traffic image based on a change in traffic data in an electronic device according to one embodiment.

[0025] FIG. 9 is a flowchart for detecting a traffic pattern having periodicity in an electronic device according to one embodiment.

[0026] FIG. 10 is an example of a traffic image in an electronic device according to one embodiment.

[0027] FIG. 11 is a flowchart for detecting a periodic traffic pattern using autocorrelation values ​​in an electronic device according to one embodiment.

[0028] FIG. 12 is an example of detecting a traffic pattern having periodicity using autocorrelation values ​​in an electronic device according to one embodiment.

[0029] FIG. 13 is a flowchart for detecting a traffic pattern having periodicity in an electronic device according to one embodiment.

[0030] FIG. 14 is an example of predicting whether traffic occurs in an electronic device according to one embodiment.

[0031] FIG. 15 is an example of a result of predicting the occurrence of traffic using a periodic traffic pattern in an electronic device according to one embodiment.

[0032] FIG. 16 is an example of a result of predicting the occurrence of traffic using a periodic traffic pattern 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 lower 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 described 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. 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 the specified information into a text form. The information changed into a text form may be in the form of a natural language utterance. According to one embodiment, the text-to-speech conversion module (232e) can change the information in the text form into information in the 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 switching RRC states according to one embodiment. FIG. 4 is a block diagram of a processor of an electronic device for traffic prediction 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 (e.g., an application processor and / or a communication processor) 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) can control the communication circuit (310) to establish communication with an external electronic device. For example, the communication connection with the external electronic device may include a series of operations in which the electronic device (101) and the external electronic device establish a communication link through RRC signaling. When communication with the external electronic device is established, the processor (300) can control the communication circuit (310) to be set to an RRC connected state to transmit and / or receive data with the external electronic device. For example, the external electronic device may include a base station, an eNB (evolved nodeB), or a gNB (next generation nodeB), as network elements to which the electronic device (101) connects for cellular communication.

[0102] According to one embodiment, the processor (300) can track and collect traffic (or traffic data) generated from the electronic device (101) in an RRC connection state. For example, the traffic (or traffic data) generated from the electronic device (101) may include data (or packets) transmitted and / or received in real time with an external electronic device based on the execution of an application program on the electronic device.

[0103] For example, the traffic tracking and feature extraction module (400) of the processor (300) can periodically track traffic (or traffic data) generated from the electronic device (101) based on a specified period. For example, traffic (or traffic data) generated in an electronic device (101) may include statistical information utilizing a class that provides traffic statistics (e.g., traffic stats API), such as a date (e.g., date), time (e.g., time), identification information of an application program (e.g., name), a user identifier (UID), the number of transmissions of data (or packets) per UID (e.g., Tx count), the number of receptions of data (or packets) per UID (e.g., Rx count), the amount of transmissions of data (or packets) per UID (e.g., Tx bytes), the amount of receptions of data (or packets) per UID (e.g., Rx bytes), the number of transmissions of data (or packets) in the electronic device (101), the number of receptions of data (or packets) in the electronic device (101), the amount of transmissions of data (or packets) in the electronic device (101), or the amount of receptions of data (or packets) in the electronic device (101). For example, traffic (or traffic data) generated from an electronic device (101) may be separated by application program based on UID.

[0104] For example, the traffic tracking and feature extraction module (400) of the processor (300) can track traffic (or traffic data) generated in the electronic device (101) by utilizing a Berkeley packet filter (BPF). For example, the traffic (or traffic data) generated in the electronic device (101) may include information about data (or packets) transmitted and / or received by the electronic device (101), and may include at least one of a date (e.g., date), time (e.g., time), transmitted data, received data, size of data (or size of packet), transmission delay time of data (e.g., inter-arrival time), or system clock. For example, the traffic (or traffic data) generated in the electronic device (101) may include at least one of identification information (e.g., name) of an application program running in the electronic device (101), radio access technology (RAT) information, wireless channel information, carrier information, usage of the processor (300), or usage of memory (320). For example, wireless channel information may include at least one of RSSI (received signal strength indicator), RSRQ (reference signal received quality), RSRP (reference signal received power), or SINR (signal to interference noise ratio).

[0105] According to one embodiment, the processor (300) can detect (or extract) characteristics of traffic (or traffic data) generated from an electronic device (101) collected in an RRC connection state. For example, the characteristics of traffic (or traffic data) generated from the electronic device (101) may include at least one of the number of data (or packets) transmitted during the previous cycle through an application program, the size of the data (or packets) transmitted during the previous cycle through an application program, the number of data (or packets) received during the previous cycle through an application program, or the size of the data (or packets) received during the previous cycle through an application program, based on information utilizing a class that provides traffic statistics (e.g., traffic stats API). For example, the characteristics of traffic (or traffic data) generated in an electronic device (101) include a change value (or amount of change) in the number of data (or packets) transmitted during the previous cycle through an application program detected based on information utilizing a class (e.g., traffic stats API) that provides traffic statistics, a change value (or amount of change) in the size of data (or packets) transmitted during the previous cycle through an application program, a change value (or amount of change) in the number of data (or packets) received during the previous cycle through an application program, a change value (or amount of change) in the size of data (or packets) received during the previous cycle through an application program, the average size of one data (or packet) transmitted during the previous cycle through an application program, the average size of one data (or packet) received during the previous cycle through an application program, the ratio of transmitted data (or packets) to the total size of data transmitted and received during the previous cycle through an application program, and the ratio of received data (or packets) to the total size of data transmitted and received during the previous cycle through an application program.It may include at least one of the following: the ratio of received data to data (or packets) transmitted during a previous cycle through an application program; the ratio of the number of transmissions of data (or packets) to the number of transmissions and receptions of data during a previous cycle through an application program; the ratio of the number of receptions of data (or packets) to the number of transmissions and receptions of data during a previous cycle through an application program; the change value (or amount of change) of the average number of transmissions of data extracted through current statistical information compared to the average value of transmissions of data extracted through previously collected statistical information; the change value (or amount of change) of the average number of receptions of data extracted through current statistical information compared to the average value of receptions of data extracted through previously collected statistical information; the change value (or amount of change) of the average transmission size of data extracted through current statistical information compared to the average transmission size of data extracted through previously collected statistical information; or the change value (or amount of change) of the average reception size of data extracted through current statistical information compared to the average reception size of data extracted through previously collected statistical information.

[0106] For example, the characteristics of traffic (or traffic data) generated in the electronic device (101) may include at least one of the following: a maximum value of the transmission delay time of uplink data, a maximum value of the transmission delay time of downlink data, an average value of the transmission delay time of uplink data, an average value of the transmission delay time of downlink data, the number of transmissions of uplink data, the number of receptions of downlink data, a minimum value of the transmission size of uplink data, a maximum value of the transmission size of uplink data, an average value of the transmission size of uplink data, the total sum of the transmission sizes of uplink data, a minimum value of the reception size of downlink data, a maximum value of the reception size of downlink data, an average value of the reception size of downlink data, or the total sum of the reception sizes of downlink data.

[0107] According to one embodiment, the processor (300) can generate a traffic image based on the features of traffic (or traffic data) generated in the electronic device (101). For example, the traffic tracking and feature extraction module (400) of the processor (300) can generate a traffic image based on the features of traffic collected over a specified period of time. For example, the traffic image may include two-dimensional image data containing traffic data and features of the traffic (e.g., 18 features).

[0108] According to one embodiment, the processor (300) can determine whether a pattern change in traffic data is detected. For example, the traffic data change detection and conversion module (402) of the processor (300) can determine whether a pattern change in traffic data is detected based on the number of times the value of the change in size of the traffic data exceeds a specified size during a specified time. For example, the traffic data change detection and conversion module (402) can determine that a pattern change in traffic data has occurred if the case where the value of the change in size of the data exceeds a specified size occurs a specified number of times (e.g., once) during a specified time. For example, the traffic data change detection and conversion module (402) can determine that a pattern change in traffic data has not occurred if the case where the value of the change in size of the data exceeds a specified size does not occur during a specified time. For example, the traffic data change detection and conversion module (402) can determine that a pattern change in traffic data has not occurred and that traffic with a relatively large change in size of data is detected if the case where the value of the change in size of the data exceeds a specified size occurs continuously during a specified time (e.g., two or more times). For example, the value of the change in data size is the value of the difference in data size over time, and may include the value of the difference in the size of traffic data at the i-1th time point compared to the size of traffic data at the i-th time point.

[0109] For example, the traffic data change detection and conversion module (402) of the processor (300) may determine that a change in the pattern of the traffic data has occurred if the application program generating the traffic is changed during a specified period. The traffic data change detection and conversion module (402) may determine that no change in the pattern of the traffic data has occurred if the application program generating the traffic is not changed during a specified period.

[0110] According to one embodiment, when a change in the pattern of traffic data is detected, the processor (300) can update (or convert) the traffic data (or traffic image) based on the change in the pattern of the traffic data. For example, the traffic data change detection and conversion module (402) of the processor (300) can delete traffic data collected at a previous time based on the time when the change in the pattern of the traffic data is detected.

[0111] According to one embodiment, the processor (300) can determine whether traffic data occurs periodically using traffic data collected over a specified period. For example, the periodicity inspection module (404) of the processor (300) can classify the category of traffic by applying a traffic image generated by the traffic tracking and feature extraction module (400) or a traffic image updated by the traffic data change recognition and transformation module (402) to a traffic classification model. The periodicity inspection module (404) can determine that traffic data occurs periodically if the category of traffic is included in a category of periodic traffic patterns (e.g., streaming). The periodicity inspection module (404) can determine that traffic data does not occur periodically if the category of traffic is not included in a category of periodic traffic patterns (e.g., streaming). For example, the traffic classification model may include an artificial intelligence model for classifying the category of traffic. For example, the traffic classification model may include an artificial intelligence model that learns using a deep clustering method.

[0112] For example, the periodicity inspection module (404) of the processor (300) can check (or calculate) the autocorrelation values ​​of traffic data (e.g., time series data) collected by the traffic tracking and feature extraction module (400). The periodicity inspection module (404) can determine that the traffic data occurs periodically if the number of autocorrelation values ​​among the traffic data that exceed a specified threshold value (e.g., about 0.2) exceeds a specified number (e.g., 1). The periodicity inspection module (404) can determine that the traffic data does not occur periodically if the number of autocorrelation values ​​among the traffic data that exceed a specified threshold value (e.g., about 0.2) is less than or equal to a specified number (e.g., 1).

[0113] For example, the periodicity inspection module (404) of the processor (300) can determine whether traffic data occurs periodically by using a traffic classification model to classify the traffic categories and the autocorrelation values ​​of the traffic data (e.g., time series data). The periodicity inspection module (404) can determine that traffic data occurs periodically if the category of traffic is included in the category of periodic traffic patterns (e.g., streaming) and the number of autocorrelation values ​​among the traffic data that exceed a specified threshold value (e.g., about 0.2) exceeds a specified number (e.g., 1). The periodicity inspection module (404) can determine that traffic data does not occur periodically if the category of traffic is not included in the category of periodic traffic patterns (e.g., streaming) or if the number of autocorrelation values ​​among the traffic data that exceed a specified threshold value (e.g., about 0.2) is less than or equal to a specified number (e.g., 1).

[0114] According to one embodiment, if the processor (300) determines that traffic data occurs periodically, it can predict whether traffic data will occur during a specified time. For example, the prediction module (406) of the processor (300) can predict whether traffic data will occur continuously during a specified time using a first traffic prediction model (e.g., classification). For example, the first traffic prediction model may include an artificial intelligence model for predicting whether traffic data will occur continuously during a specified time. For example, the specified time may be set by the operating time of an inactivity timer set to determine whether to switch from an RRC connection state to an RRC standby state (or RRC inactive state), or by a time delay value confirmed by an autocorrelation value.

[0115] For example, the prediction module (406) of the processor (300) can use a second traffic prediction model (e.g., a regressor) to determine the time when no traffic data is predicted to occur. The prediction module (406) can determine whether the time when no traffic data is predicted to occur exceeds a specified time. For example, the second traffic prediction model may include an artificial intelligence model for predicting whether traffic data occurs in units of time (e.g., about 1 second).

[0116] For example, the prediction module (406) of the processor (300) can predict whether traffic data occurs during a specified period using a first traffic prediction model or a second traffic prediction model based on the purpose of predicting whether traffic occurs or the environment of the electronic device.

[0117] According to one embodiment, the processor (300) may control the communication circuit (310) to switch the RRC state with the external electronic device to an RRC standby state (or RRC inactive state) when it is predicted that no traffic will occur for a specified period of time. For example, the resource management module (408) of the processor (300) may control the communication circuit (310) to transmit information related to the RRC state transition to the external electronic device. For example, information related to the RRC state transition may be transmitted to the external electronic device via a UAI (UE assistance information) message or a RAI (release assistance indication) message.

[0118] According to one embodiment, if the processor (300) determines that traffic data is not occurring periodically, it may restrict the operation of predicting whether traffic is occurring. If the processor (300) restricts the operation of predicting whether traffic is occurring, it cannot predict whether traffic is occurring for a specified period of time, so it may control the communication circuit (310) to maintain the RRC state of the electronic device (101) in an RRC connection state.

[0119] According to one embodiment, the electronic device (101) may be equipped with a processor (300) (e.g., an application processor and / or a communication processor) for predicting whether traffic data is occurring. The electronic device (101) may download training data from an external server to train artificial intelligence models related to predicting whether traffic data is occurring.

[0120] According to one embodiment, at least one of the traffic tracking and feature extraction module (400), traffic data change recognition and conversion module (402), periodicity inspection module (404), prediction module (406), or resource management module (408) related to predicting whether traffic data is generated in the electronic device (101) may be configured (or implemented) as a separate hardware device (e.g., circuit, chip, or chipset) different from the processor (300).

[0121] According to one embodiment, at least one of a traffic tracking and feature extraction module (400), a traffic data change recognition and conversion module (402), a periodicity inspection module (404), a prediction module (406), or a resource management module (408) related to predicting whether traffic data is generated in an electronic device (101) may be configured as software.

[0122] According to one embodiment, at least one of the traffic tracking and feature extraction module (400), traffic data change recognition and conversion module (402), periodicity inspection module (404), prediction module (406), or resource management module (408) related to predicting whether traffic data is generated in the electronic device (101) may be integrated into one module or a plurality of modules.

[0123] According to one embodiment, at least one of the traffic tracking and feature extraction module (400), traffic data change recognition and conversion module (402), periodicity inspection module (404), prediction module (406), or resource management module (408) related to predicting whether traffic data is generated in the electronic device (101) may be performed through an external server different from the electronic device (101) (e.g., intelligent server (230) of FIG. 2).

[0124] 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 be configured to establish communication with an external electronic device through the communication circuit. According to one embodiment, when the instructions are executed individually or collectively by at least one processor, the electronic device may be configured to detect traffic generated through communication with an external electronic device while connected to a radio resource control (RRC). According to one embodiment, when the instructions are executed individually or collectively by at least one processor, the electronic device may use a traffic classification model to identify a pattern of traffic generated by the electronic device. According to one embodiment, when the instructions are executed individually or collectively by at least one processor, the electronic device may use a traffic prediction model to predict whether traffic will occur during a specified first time period if a pattern of periodically occurring traffic is identified. According to one embodiment, when the instructions are executed individually or collectively by at least one processor, if the electronic device determines that no traffic will occur during a specified first time period, it may transmit information related to transitioning to an RRC standby state or an RRC inactive state to an external electronic device.

[0125] According to one embodiment, the memory may store instructions that, when executed individually or collectively by at least one processor, cause the electronic device to check the value of the autocorrelation function of traffic generated by the electronic device when a pattern of periodically occurring traffic is identified. According to one embodiment, the memory may store instructions that, when executed individually or collectively by at least one processor, cause the electronic device to predict whether traffic occurs during a specified first time period using a traffic prediction model when the value of the autocorrelation function satisfies a specified condition.

[0126] According to one embodiment, when instructions are executed individually or collectively by at least one processor, the electronic device may predict whether traffic will continuously occur during a specified first time using a traffic prediction model.

[0127] According to one embodiment, when instructions are executed individually or collectively by at least one processor, the electronic device may predict a time during which no traffic occurs continuously using a traffic prediction model. According to one embodiment, when instructions are executed individually or collectively by at least one processor, the electronic device may check whether the predicted time exceeds a specified first time.

[0128] According to one embodiment, the memory may store instructions that cause an electronic device to detect traffic generated through communication with an external electronic device when executed individually or collectively by at least one processor. According to one embodiment, the memory may store instructions that cause an electronic device to generate a traffic image for input to a traffic classification model based on the characteristics of the traffic detected during a specified second time period when executed individually or collectively by at least one processor, provided that the pattern of the traffic has not changed.

[0129] According to one embodiment, the memory may store instructions that, when executed individually or collectively by at least one processor, cause the electronic device to update a traffic image for input to a traffic classification model generated based on the characteristics of the traffic detected during a specified second time period when a change in the traffic pattern is detected.

[0130] According to one embodiment, the memory may store instructions that maintain an RRC connection state when, when executed individually or collectively by at least one processor, the electronic device determines that a pattern of periodically occurring traffic is not identified or that traffic occurs within a specified first time.

[0131] FIG. 5 is a flowchart (500) for switching the RRC state based on traffic prediction 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.

[0132] 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 establish communication with an external electronic device in operation 501. For example, when the processor (300) establishes communication with the external electronic device through RRC signaling with the external electronic device, the RRC state may be set to an RRC connected state. The processor (300) may control the communication circuit (310) to transmit and / or receive data with the external electronic device in the RRC connected state. For example, the external electronic device may include a base station, an eNB (evolved nodeB), or a gNB (next generation nodeB), as network elements to which the electronic device (101) connects for cellular communication.

[0133] According to one embodiment, an electronic device (e.g., electronic device (101)) or a processor (e.g., processor (120 or 300)) may detect traffic (or traffic data) generated from the electronic device (101) in an RRC connection state with an external electronic device in operation 503. For example, the traffic (or traffic data) generated from the electronic device (101) may include data transmitted and / or received in real time with the external electronic device based on the execution of an application program on the electronic device.

[0134] For example, the traffic tracking and feature extraction module (400) of the processor (300) can periodically track traffic (or traffic data) generated from the electronic device (101) based on a specified period. For example, the traffic (or traffic data) generated from the electronic device (101) may include statistical information utilizing a class that provides traffic statistics (e.g., traffic stats API).

[0135] For example, the traffic tracking and feature extraction module (400) of the processor (300) can track (or collect) traffic (or traffic data) generated from the electronic device (101) by utilizing a Berkeley packet filter (BPF). For example, the traffic tracking and feature extraction module (400) of the processor (300) can detect (or extract) features of the traffic (or traffic data) generated from the electronic device (101) collected in an RRC connection state.

[0136] For example, the traffic tracking and feature extraction module (400) of the processor (300) can generate a traffic image based on the features of the traffic collected over a specified period of time. For example, the traffic image may include two-dimensional image data containing traffic data and features of the traffic (e.g., 18 features).

[0137] For example, the traffic tracking and feature extraction module (400) of the processor (300) may update (or transform) the traffic data (or traffic image) based on the change in the pattern of the traffic data when a change in the pattern of the traffic data is detected. For example, the change in the pattern of the traffic data may be detected based on the number of times the value of the change in the size of the traffic data over a specified period exceeds a specified size, or based on a change in the application program that generates traffic over a specified period. For example, the update of the traffic data (or traffic image) may include a series of operations to delete traffic data collected at a previous time point based on the time point when the change in the pattern of the traffic data was detected.

[0138] According to one embodiment, an electronic device (e.g., electronic device (101)) or a processor (e.g., processor (120 or 300)) may, in operation 505, use traffic data collected over a specified period to determine whether traffic data occurs periodically. For example, a periodicity inspection module (404) of the processor (300) may classify the category of traffic by applying a traffic image generated by a traffic tracking and feature extraction module (400) or a traffic image updated by a traffic data change recognition and transformation module (402) to a traffic classification model. The periodicity inspection module (404) may determine that traffic data occurs periodically if the category of traffic falls within a category of periodic traffic patterns (e.g., streaming). The periodicity inspection module (404) may determine that traffic data does not occur periodically if the category of traffic does not fall within a category of periodic traffic patterns (e.g., streaming). For example, the traffic classification model may include an artificial intelligence model for classifying the category of traffic. For example, a traffic classification model may include an artificial intelligence model that learns using a deep clustering method.

[0139] For example, the periodicity inspection module (404) of the processor (300) can check (or calculate) the autocorrelation values ​​of traffic data (e.g., time series data) collected by the traffic tracking and feature extraction module (400). The periodicity inspection module (404) can determine that the traffic data occurs periodically if the number of autocorrelation values ​​among the traffic data that exceed a specified threshold value (e.g., about 0.2) exceeds a specified number (e.g., 1). The periodicity inspection module (404) can determine that the traffic data does not occur periodically if the number of autocorrelation values ​​among the traffic data that exceed a specified threshold value (e.g., about 0.2) is less than or equal to a specified number (e.g., 1).

[0140] For example, the periodicity inspection module (404) of the processor (300) can determine whether traffic data occurs periodically by using a traffic classification model to classify the traffic categories and the autocorrelation values ​​of the traffic data (e.g., time series data). The periodicity inspection module (404) can determine that traffic data occurs periodically if the category of traffic is included in the category of periodic traffic patterns (e.g., streaming) and the number of autocorrelation values ​​among the traffic data that exceed a specified threshold value (e.g., about 0.2) exceeds a specified number (e.g., 1). The periodicity inspection module (404) can determine that traffic data does not occur periodically if the category of traffic is not included in the category of periodic traffic patterns (e.g., streaming) or if the number of autocorrelation values ​​among the traffic data that exceed a specified threshold value (e.g., about 0.2) is less than or equal to a specified number (e.g., 1).

[0141] According to one embodiment, if an electronic device (e.g., electronic device (101)) or a processor (e.g., processor (120 or 300)) determines that traffic data is occurring periodically (e.g., 'Yes' in operation 505), in operation 507, it can predict whether traffic data will occur for a specified period of time. For example, the prediction module (406) of the processor (300) can predict whether traffic data will occur continuously for a specified period of time using a first traffic prediction model (e.g., classification). For example, the first traffic prediction model may include an artificial intelligence model for predicting whether traffic data will occur continuously for a specified period of time. For example, the specified period of time may be set by the running time of an inactivity timer set to determine whether to transition from an RRC connection state to an RRC standby state (or RRC inactive state), or by a time delay value determined by an autocorrelation value.

[0142] For example, the prediction module (406) of the processor (300) can use a second traffic prediction model (e.g., a regressor) to determine the time during which no traffic data is predicted to occur. The prediction module (406) can determine whether the time during which no traffic data is predicted to occur exceeds a specified time. If the time during which no traffic data is predicted to occur is longer than the specified time, the prediction module (406) can determine that no traffic data will occur during the specified time. If the time during which no traffic data is predicted to occur is less than the specified time, the prediction module (406) can determine that traffic data will occur within the specified time. For example, the second traffic prediction model may include an artificial intelligence model for predicting whether traffic data will occur in units of time (e.g., about 1 second).

[0143] According to one embodiment, if an electronic device (e.g., electronic device (101)) or a processor (e.g., processor (120 or 300)) determines that no traffic data has occurred for a specified period of time (e.g., 'Yes' in operation 507), then in operation 509, information related to an RRC state transition may be transmitted to an external electronic device. For example, the information related to an RRC state transition may include information requesting (or instructing) the electronic device (101) to transition its RRC state to an RRC standby state (or RRC inactive state). For example, the information related to an RRC state transition may be transmitted to an external electronic device via a UAI (UE assistance information) message or a RAI (release assistance indication) message.

[0144] 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 switching the RRC state if it determines that traffic data is not occurring periodically (e.g., 'No' of operation 505) or if it determines that traffic data is occurring within a specified time (e.g., 'No' of operation 507). For example, if the processor (300) determines that traffic data is not occurring periodically or if it determines that traffic data is occurring within a specified time, it may maintain the RRC state of the electronic device (101) in an RRC connected state.

[0145] FIG. 6 is a flowchart (600) for updating traffic data in an electronic device according to one embodiment. For example, at least part of FIG. 6 may include detailed operations of operation 503 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 and FIG. 8. FIG. 7 is an example of detecting a change in traffic data in an electronic device according to one embodiment. FIG. 8 is an example of updating a traffic image based on a change in traffic data in an electronic device according to one embodiment.

[0146] According to one embodiment with reference to FIGS. 6, 7 and 8, 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 detect traffic (or traffic data) generated from the electronic device (101) in the RRC connection state with the external electronic device in operation 601 when communication with the external electronic device is established (e.g., operation 501 of FIG. 5). For example, the traffic tracking and feature extraction module (400) of the processor (300) can track traffic (or traffic data) generated from the electronic device (101) based on statistical information utilizing a class (e.g., traffic stats API) that provides traffic statistics periodically acquired based on a specified period. For example, the traffic tracking and feature extraction module (400) of the processor (300) can track (or collect) traffic (or traffic data) generated from the electronic device (101) by utilizing a Berkeley packet filter (BPF).

[0147] For example, the traffic tracking and feature extraction module (400) of the processor (300) can detect (or extract) features of traffic (or traffic data) generated from the electronic device (101) collected in an RRC connection state.

[0148] For example, the traffic tracking and feature extraction module (400) of the processor (300) can generate a traffic image based on the features of the traffic collected over a specified period of time. For example, the traffic image may include two-dimensional image data containing traffic data and features of the traffic (e.g., 18 features), such as the first traffic image (700), the second traffic image (720), or the third traffic image (740) of FIG. 7.

[0149] According to one embodiment, an electronic device (e.g., electronic device (101)) or a processor (e.g., processor (120 or 300)) can determine whether a change in the pattern of traffic data is detected in operation 603. For example, a traffic data change detection and conversion module (402) of the processor (300) can determine whether a change in the pattern of traffic data is detected based on the number of times the value of change in the size of the traffic data over a specified time exceeds a specified size. For example, the value of change in the size of the data may include the value of difference in the size of the data over time, and may include the value of difference in the size of the traffic data at the i-1th time point relative to the size of the traffic data at the i-th time point.

[0150] For example, the traffic data change recognition and conversion module (402) can determine that a traffic data pattern change has occurred when the data size change value (712), such as the size change value (710) of the first traffic image (700), exceeds a specified size a specified number of times (e.g., once).

[0151] For example, the traffic data change detection and conversion module (402) may determine that no change in the pattern of traffic data occurs and that traffic with a relatively large change in the size of data is detected when the change in the size of the data (732) exceeds a specified size, such as the change in the size (730) of the second traffic image (720), occurs more than a specified number of times (e.g., once).

[0152] For example, the traffic data change recognition and conversion module (402) can determine that no change in the pattern of the traffic data has occurred if the change in the size of the data does not exceed a specified size, such as the change in the size value (750) of the third traffic image (740).

[0153] For example, the traffic data change detection and conversion module (402) of the processor (300) may determine that a change in the pattern of the traffic data has occurred if the application program generating traffic for a specified period of time is changed. The traffic data change detection and conversion module (402) may determine that no change in the pattern of the traffic data has occurred if the application program generating traffic for a specified period of time is maintained.

[0154] According to one embodiment, an electronic device (e.g., electronic device (101)) or a processor (e.g., processor (120 or 300)) may convert (or update) traffic data (or, traffic image) based on the traffic data pattern change in operation 605 when a traffic data pattern change is detected (e.g., 'Yes' of operation 603). For example, the traffic data change detection and conversion module (402) of the processor (300) may identify the time point (810) at which a traffic data pattern change is detected in the traffic image (800), as shown in FIG. 8. The traffic data change detection and conversion module (402) may update (or convert) the traffic image (820) by deleting traffic data collected at a previous time point based on the time point (810) at which a traffic data pattern change is detected. The traffic data change detection and conversion module (402) may provide the updated traffic image (820) to the periodicity inspection module (404).

[0155] 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 updating traffic data when no change in the pattern of the traffic data is detected (e.g., 'No' of operation 603). For example, the traffic data change detection and conversion module (402) of the processor (300) may provide the traffic image generated by the traffic tracking and feature extraction module (400) to the periodicity inspection module (404) when no change in the pattern of the traffic data is detected.

[0156] FIG. 9 is a flowchart (900) for detecting a periodic traffic pattern in an electronic device according to one embodiment. For example, at least part of FIG. 9 may include detailed operations of operations 505 to 507 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. 9 may be the electronic device (101) of FIG. 1, FIG. 2, or FIG. 3. For example, at least part of FIG. 9 may be described with reference to FIG. 10. FIG. 10 is an example of a traffic image in an electronic device according to one embodiment.

[0157] According to one embodiment with reference to FIGS. 9 and 10, 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) detects traffic (or traffic data) generated from the electronic device (101) in the RRC connection state of the electronic device (101) (e.g., operation 503 of FIG. 5), in operation 901, the traffic pattern can be detected using a traffic classification model. For example, the periodicity inspection module (404) of the processor (300) can classify the category of traffic by applying a traffic image generated by the traffic tracking and feature extraction module (400) or a traffic image updated (or converted) by the traffic data change recognition and conversion module (402) to the traffic classification model. For example, the traffic classification model may include an artificial intelligence model for classifying the category of traffic. For example, the traffic classification model may include an artificial intelligence model of a deep clustering method that classifies the category of traffic by clustering relatively similar traffic images into clusters.

[0158] According to one embodiment, an electronic device (e.g., electronic device (101)) or a processor (e.g., processor (120 or 300)) may, in operation 903, use a traffic classification model to determine whether a periodic traffic pattern has been detected. For example, a periodic traffic pattern may include a category of traffic in which traffic occurs periodically, such as streaming.

[0159] According to one embodiment, an electronic device (e.g., electronic device (101)) or a processor (e.g., processor (120 or 300)) can predict whether traffic data occurs during a specified time period in operation 905 when a periodic traffic pattern is detected using a traffic classification model (e.g., 'Yes' of operation 903). For example, the periodicity inspection module (404) of the processor (300) can determine that a periodic traffic pattern has been detected when traffic (or traffic data) occurs at specified intervals, such as the first traffic image (1000) of FIG. 10 (e.g., streaming pattern). For example, the prediction module (406) of the processor (300) can predict whether traffic data occurs continuously during a specified time period using a first traffic prediction model (e.g., classification). For example, the first traffic prediction model may include an artificial intelligence model for predicting whether traffic data occurs continuously during a specified time period. For example, the specified time may be set by the driving time of an inactivity timer configured to determine whether to transition from an RRC connection state to an RRC standby state (or RRC inactive state), or by a time delay value determined by an autocorrelation value.

[0160] For example, the prediction module (406) of the processor (300) can use a second traffic prediction model (e.g., a regressor) to determine the time during which no traffic data is predicted to occur. The prediction module (406) can determine whether the time during which no traffic data is predicted to occur exceeds a specified time. If the time during which no traffic data is predicted to occur is longer than the specified time, the prediction module (406) can determine that no traffic data will occur during the specified time. If the time during which no traffic data is predicted to occur is less than the specified time, the prediction module (406) can determine that traffic data will occur within the specified time. For example, the second traffic prediction model may include an artificial intelligence model for predicting whether traffic data will occur in units of time (e.g., about 1 second).

[0161] According to one embodiment, an electronic device (e.g., electronic device (101)) or a processor (e.g., processor (120 or 300)) may maintain the RRC state of the electronic device (101) in the RRC connection state in operation 907 when a periodic traffic pattern is not detected using a traffic classification model (e.g., 'No' in operation 903). For example, the periodicity inspection module (404) of the processor (300) may determine that a periodic traffic pattern is not detected when traffic (or traffic data) is continuously generated, such as the second traffic image (1010) (e.g., real-time pattern), the third traffic image (1020) (e.g., data transmission pattern), and the fourth traffic image (1030) (social network pattern) of FIG. 10. The resource management module (408) of the processor (300) may limit the operation of predicting whether traffic is generated when it is determined that a periodic traffic pattern is not detected. Since the resource management module (408) cannot predict whether traffic will occur during a specified time, it can maintain the RRC state of the electronic device (101) in an RRC connection state.

[0162] FIG. 11 is a flowchart (1100) for detecting a periodic traffic pattern using autocorrelation values ​​in an electronic device according to one embodiment. For example, at least part of FIG. 11 may include detailed operations of operations 505 and 507 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. 11 may be the electronic device (101) of FIG. 1, FIG. 2, or FIG. 3. For example, at least part of FIG. 11 may be described with reference to FIG. 12. FIG. 12 is an example of detecting a periodic traffic pattern using autocorrelation values ​​in an electronic device according to one embodiment.

[0163] According to one embodiment with reference to FIGS. 11 and 12, 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) detects traffic (or traffic data) generated from the electronic device (101) in the RRC connection state of the electronic device (101) (e.g., operation 503 of FIG. 5), in operation 1101, the autocorrelation value of the traffic data can be checked (or calculated). For example, the periodicity check module (404) of the processor (300) can calculate the autocorrelation value by applying the periodically generated traffic data (e.g., time series data) to an autocorrelation function (ACF).

[0164] According to one embodiment, an electronic device (e.g., electronic device (101)) or a processor (e.g., processor (120 or 300)) may, in operation 1103, determine whether the autocorrelation values ​​of the traffic data satisfy a specified autocorrelation condition. For example, the periodicity inspection module (404) of the processor (300) may determine that the specified autocorrelation condition is satisfied if, in the case of the first traffic image (1200) of FIG. 12, a plurality of autocorrelation values ​​(1212, 1214, and 1216) exceeding a specified reference value among the autocorrelation values ​​(1210) of the first traffic image (1200) are detected. For example, the state of satisfying the specified autocorrelation condition may include a state in which the traffic data is determined to have periodicity.

[0165] For example, the periodicity inspection module (404) of the processor (300) may determine that a specified autocorrelation condition is not satisfied if, in the case of the second traffic image (1220) of FIG. 12, one autocorrelation value (1232) exceeding a specified reference value among the autocorrelation values ​​(1230) of the second traffic image (1220) is detected. For example, a state in which a specified autocorrelation condition is not satisfied may include a state in which the traffic data is determined not to have periodicity.

[0166] For example, the periodicity inspection module (404) of the processor (300) may determine that the specified autocorrelation condition is not satisfied if, in the case of the third traffic image (1240) of FIG. 12, an autocorrelation value exceeding a specified reference value among the autocorrelation values ​​(1250) of the third traffic image (1240) is not detected.

[0167] According to one embodiment, if an electronic device (e.g., electronic device (101)) or a processor (e.g., processor (120 or 300)) determines that the autocorrelation value of the traffic data satisfies a specified autocorrelation condition (e.g., 'Yes' of operation 1103), then in operation 1105, it can predict whether traffic data will occur for a specified time. For example, the prediction module (406) of the processor (300) can predict whether traffic data will occur continuously for a specified time using a first traffic prediction model (e.g., classification). For example, the first traffic prediction model may include an artificial intelligence model for predicting whether traffic data will occur continuously for a specified time. For example, the specified time may be set by the running time of an inactivity timer set to determine whether to transition from an RRC connection state to an RRC standby state (or RRC inactive state), or by a time delay value determined by the autocorrelation value. For example, the time delay value identified by the autocorrelation value may include the delay value up to the point in time when the autocorrelation value exceeding a specified threshold value is first detected (e.g., 1212 in FIG. 12).

[0168] For example, the prediction module (406) of the processor (300) can use a second traffic prediction model (e.g., a regressor) to determine the time during which no traffic data is predicted to occur. The prediction module (406) can determine whether the time during which no traffic data is predicted to occur exceeds a specified time. If the time during which no traffic data is predicted to occur is longer than the specified time, the prediction module (406) can determine that no traffic data will occur during the specified time. If the time during which no traffic data is predicted to occur is less than the specified time, the prediction module (406) can determine that traffic data will occur within the specified time. For example, the second traffic prediction model may include an artificial intelligence model for predicting whether traffic data will occur in units of time (e.g., about 1 second).

[0169] According to one embodiment, if an electronic device (e.g., electronic device (101)) or a processor (e.g., processor (120 or 300)) determines that the autocorrelation value of the traffic data does not satisfy a specified autocorrelation condition (e.g., 'No' in operation 1103), then in operation 1107, the RRC state of the electronic device (101) may be maintained in an RRC connected state. For example, if the periodicity check module (404) of the processor (300) determines that the specified autocorrelation condition is not satisfied, it may determine that the traffic data does not have periodicity. If the resource management module (408) of the processor (300) determines that a traffic pattern having periodicity is not detected by the periodicity check module (404), it may limit the operation of predicting whether traffic occurs. Since the resource management module (408) cannot predict whether traffic occurs during a specified time, the RRC state of the electronic device (101) may be maintained in an RRC connected state.

[0170] FIG. 13 is a flowchart (1300) for detecting a periodic traffic pattern in an electronic device according to one embodiment. For example, at least part of FIG. 13 may include a detailed operation of operation 503 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. 13 may be the electronic device (101) of FIG. 1, FIG. 2, or FIG. 3.

[0171] According to one embodiment with reference to FIG. 13, 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) detects traffic (or traffic data) generated from the electronic device (101) in the RRC connection state of the electronic device (101) (e.g., operation 503 of FIG. 5), in operation 1301, a traffic pattern can be detected using a traffic classification model. For example, the periodicity inspection module (404) of the processor (300) can classify the category of traffic by applying a traffic image generated by the traffic tracking and feature extraction module (400) or a traffic image updated (or converted) by the traffic data change recognition and conversion module (402) to the traffic classification model. For example, the traffic classification model may include an artificial intelligence model for classifying the category of traffic.

[0172] According to one embodiment, an electronic device (e.g., electronic device (101)) or a processor (e.g., processor (120 or 300)) may, in operation 1303, use a traffic classification model to determine whether a periodic traffic pattern has been detected. For example, a periodic traffic pattern may include a category of traffic in which traffic occurs periodically, such as streaming.

[0173] According to one embodiment, an electronic device (e.g., electronic device (101)) or a processor (e.g., processor (120 or 300)) can check (or calculate) the autocorrelation value of the traffic data in operation 1305 when a periodic traffic pattern is detected using a traffic classification model (e.g., 'Yes' of operation 1303). For example, a periodicity check module (404) of the processor (300) can calculate the autocorrelation value by applying the periodically occurring traffic data to an autocorrelation function (ACF).

[0174] According to one embodiment, an electronic device (e.g., electronic device (101)) or a processor (e.g., processor (120 or 300)) may, in operation 1307, determine whether the autocorrelation values ​​of the traffic data satisfy a specified autocorrelation condition. For example, the periodicity check module (404) of the processor (300) may determine that the specified autocorrelation condition is satisfied if a plurality of autocorrelation values ​​exceeding a specified reference value are detected. For example, a state satisfying the specified autocorrelation condition may include a state in which the traffic data is determined to have periodicity.

[0175] For example, the periodicity check module (404) of the processor (300) may determine that a specified autocorrelation condition is not satisfied if a single autocorrelation value exceeding a specified threshold value is detected, or if an autocorrelation value exceeding a specified threshold value is not detected. For example, a state in which a specified autocorrelation condition is not satisfied may include a state in which traffic data is determined not to have periodicity.

[0176] According to one embodiment, if an electronic device (e.g., electronic device (101)) or a processor (e.g., processor (120 or 300)) determines that the autocorrelation value of the traffic data satisfies a specified autocorrelation condition (e.g., 'Yes' in operation 1307), then in operation 1309, it can predict whether traffic data will occur for a specified time. For example, the prediction module (406) of the processor (300) can predict whether traffic data will occur continuously for a specified time using a first traffic prediction model (e.g., classification). For example, the first traffic prediction model may include an artificial intelligence model for predicting whether traffic data will occur continuously for a specified time. For example, the specified time may be set by the running time of an inactivity timer set to determine whether to transition from an RRC connection state to an RRC standby state (or RRC inactive state), or by a time delay value determined by the autocorrelation value.

[0177] For example, the prediction module (406) of the processor (300) can use a second traffic prediction model (e.g., a regressor) to determine the time during which no traffic data is predicted to occur. The prediction module (406) can determine whether the time during which no traffic data is predicted to occur exceeds a specified time. If the time during which no traffic data is predicted to occur is longer than the specified time, the prediction module (406) can determine that no traffic data will occur during the specified time. If the time during which no traffic data is predicted to occur is less than the specified time, the prediction module (406) can determine that traffic data will occur within the specified time. For example, the second traffic prediction model may include an artificial intelligence model for predicting whether traffic data will occur in units of time (e.g., about 1 second).

[0178] According to one embodiment, if an electronic device (e.g., electronic device (101)) or a processor (e.g., processor (120 or 300)) determines using a traffic classification model that a periodic traffic pattern is not detected (e.g., 'No' in operation 1303) or that the autocorrelation value of the traffic data does not satisfy a specified autocorrelation condition (e.g., 'No' in operation 1307), then in operation 1311, the RRC state of the electronic device (101) can be maintained in an RRC connected state. For example, the periodicity inspection module (404) of the processor (300) may determine that a periodic traffic pattern is not detected when traffic (or traffic data) is continuously generated, such as in the second traffic image (1010), the third traffic image (1020), and the fourth traffic image (1030) of FIG. 10. The resource management module (408) of the processor (300) may limit the operation of predicting whether traffic occurs when it determines that a periodic traffic pattern has not been detected. Since the resource management module (408) cannot predict whether traffic occurs during a specified period, it may maintain the RRC state of the electronic device (101) in an RRC connection state.

[0179] FIG. 14 is an example of predicting whether traffic occurs in an electronic device according to one embodiment.

[0180] According to one embodiment with reference to FIG. 14, an electronic device (101) can predict whether traffic (or traffic data) occurs during a specified time using a traffic prediction model (e.g., a first traffic prediction model or a second traffic prediction model). For example, a prediction module (406) of a processor (300) can filter a traffic image (1400) for specific features (e.g., convolution) and detect specific features (e.g., ReLU (rectifier function)) in the filtered image (operation 1410). The prediction module (406) can compress the filtered image to enhance specific features (e.g., pooling) (operation 1420). The prediction module (406) can apply fully connected layers (e.g., fully connected layers) (operation 1430). The prediction module (406) can predict whether traffic (or traffic data) occurs during a specified time by applying a traffic image with a fully connected layer to the first traffic prediction model (1440) or the second traffic prediction model (1442).

[0181] FIG. 15 is an example of a result of predicting the occurrence of traffic using a periodic traffic pattern in an electronic device according to one embodiment.

[0182] According to one embodiment with reference to FIG. 15, an electronic device (101) can predict whether traffic will occur during a specified time period by applying traffic data of a periodic traffic pattern to a first traffic prediction model (1500). For example, a state in which traffic is predicted to occur can be represented as 0, and a state in which traffic is predicted not to occur can be represented as 1.

[0183] According to one embodiment, the electronic device (101) can predict whether traffic will occur with relatively high accuracy because the prediction result (1500) of whether traffic will occur is relatively similar to the state of traffic occurrence (1510) in the actual environment.

[0184] According to one embodiment, in the case of a first situation where the electronic device (101) incorrectly predicts that no traffic occurs (e.g., pred = 1) when traffic occurs within a specified time (e.g., true = 0), it may be switched to an RRC waiting state (or RRC inactive state) and may have to perform an unnecessary RACH (random access channel) process due to the transition to an RRC connected state. In the case of a second situation where the electronic device (101) incorrectly predicts that traffic occurs (e.g., pred = 0) when traffic does not occur within a specified time (e.g., true = 1), it maintains the RRC connected state, so that resource or power consumption due to the RRC state transition may not occur. Since the prediction result (1500) of whether traffic occurs contains only an error in the second situation and a traffic occurrence state (1510) in the actual environment, and the error in the first situation is predicted to be relatively low, the electronic device (101) can reduce resource or power consumption due to the RRC state transition caused by the incorrect prediction of traffic occurrence.

[0185] FIG. 16 is an example of a result of predicting the occurrence of traffic using a periodic traffic pattern in an electronic device according to one embodiment.

[0186] According to one embodiment with reference to FIG. 16, an electronic device (101) can predict the time when no traffic occurs by applying traffic data of a periodic traffic pattern to a second traffic prediction model.

[0187] According to one embodiment, when the electronic device (101) predicts whether traffic occurs without considering the characteristics of the traffic pattern (e.g., periodicity), it can accurately predict a situation where traffic occurs (e.g., 0) with a first probability (e.g., about 0.98) (1600) and accurately predict a situation where traffic does not occur (e.g., 1) with a second probability (e.g., about 0.86) (1602). When the electronic device (101) predicts whether traffic occurs without considering the characteristics of the traffic pattern (e.g., periodicity), it can incorrectly predict a situation where traffic occurs (e.g., 0) as a situation where traffic occurs (e.g., 1) with a third probability (e.g., about 0.02) (1610) and incorrectly predict a situation where traffic does not occur (e.g., 1) as a situation where traffic occurs (e.g., 0) with a fourth probability (e.g., about 0.14) (1612).

[0188] According to one embodiment, when the electronic device (101) predicts whether traffic occurs using traffic data of a periodic traffic pattern, it can accurately predict a situation where traffic occurs (e.g., 0) with a fifth probability (e.g., about 0.98) (1620) and accurately predict a situation where traffic does not occur (e.g., 1) with a sixth probability (e.g., about 0.94) (1622). When the electronic device (101) predicts whether traffic occurs using traffic data of a periodic traffic pattern, it can incorrectly predict a situation where traffic occurs (e.g., 0) as a situation where traffic does not occur (e.g., 1) with a seventh probability (e.g., about 0.02) (1630) and incorrectly predict a situation where traffic does not occur (e.g., 1) as a situation where traffic occurs (e.g., 0) with an eighth probability (e.g., about 0.06) (1632).

[0189] According to one embodiment, when an electronic device (101) predicts whether traffic occurs using traffic data of a traffic pattern having periodicity, the probability of incorrectly predicting a situation where traffic does not occur (e.g., 1) as a situation where traffic occurs (e.g., 0) may be lower than when predicting whether traffic occurs without considering the characteristics of the traffic pattern (e.g., periodicity).

[0190] According to one embodiment, when an electronic device predicts whether traffic is occurring using traffic data of a periodic traffic pattern, it can reduce power consumption by wireless communication by deactivating the antenna of the electronic device (e.g., a receiving antenna).

[0191] According to one embodiment, when an electronic device predicts whether traffic occurs using traffic data of a periodic traffic pattern, it can reduce power consumption due to short-range communication by switching short-range communication with an external electronic device (e.g., a wearable device) to a non-communication state.

[0192] 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 establishing communication with an external electronic device. According to one embodiment, a method of operation of the electronic device may include an operation of detecting traffic generated through communication with an external electronic device while in a radio resource control (RRC) connected state. According to one embodiment, a method of operation of the electronic device may include an operation of identifying a pattern of traffic generated by the electronic device using a traffic classification model. According to one embodiment, if a pattern of periodically occurring traffic is identified, a method of operation of the electronic device may include an operation of predicting whether traffic will occur during a specified first time period using a traffic prediction model. According to one embodiment, if it is determined that no traffic will occur during the specified first time period, a method of operation of the electronic device may include an operation of transmitting information related to transitioning to an RRC standby state or an RRC inactive state to an external electronic device.

[0193] According to one embodiment, the operation of predicting whether traffic occurs may include checking the value of the autocorrelation function of traffic generated by an electronic device when a pattern of periodically occurring traffic is identified. According to one embodiment, the operation of predicting whether traffic occurs may include predicting whether traffic occurs during a specified first time period using a traffic prediction model when the value of the autocorrelation function satisfies a specified condition.

[0194] According to one embodiment, the operation of predicting whether traffic occurs may include the operation of predicting whether traffic occurs continuously during a specified first time period using a traffic prediction model.

[0195] According to one embodiment, the operation of predicting whether traffic occurs may include the operation of predicting a time during which traffic does not continuously occur using a traffic prediction model. According to one embodiment, the operation of predicting whether traffic occurs may include the operation of checking whether the predicted time exceeds a specified first time.

[0196] According to one embodiment, the method of operating an electronic device may include an operation of checking whether the pattern of traffic generated through communication with an external electronic device changes. According to one embodiment, if the pattern of traffic does not change, the method of operating an electronic device may include an operation of generating a traffic image for input to a traffic classification model based on the characteristics of the traffic detected during a specified second time period.

[0197] According to one embodiment, the method of operating an electronic device may include, when a change in the traffic pattern is detected, an operation of updating a traffic image for input into a traffic classification model generated based on the characteristics of the traffic detected during a specified second time period, based on the time when the change in the traffic pattern is detected.

[0198] According to one embodiment, the method of operation of an electronic device may include an operation to maintain an RRC connection state when a pattern of periodically occurring traffic is not identified or when it is determined that traffic occurs within a specified first time.

[0199] 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), perform operations to establish communication with an external electronic device, detect traffic generated through communication with the external electronic device in a radio resource control (RRC) connected state, identify a pattern of traffic generated by the electronic device using a traffic classification model, predict whether traffic will occur during a specified first time period using a traffic prediction model when a pattern of periodically occurring traffic is identified, and transmit information related to transitioning to an RRC standby state or an RRC inactive state to the external electronic device when it is determined that no traffic will occur during the specified first time period.

[0200] 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, Communication is established with an external electronic device through the above communication circuit, and Detects traffic generated through communication with the external electronic device while in an RRC (radio resource control) connected state, and Identify the pattern of traffic generated by the electronic device using a traffic classification model, and If a pattern of periodically occurring traffic is identified, a traffic prediction model is used to predict whether traffic will occur during a specified first period, and An electronic device that transmits information related to transitioning to an RRC standby state or an RRC inactive state to the external electronic device when it is determined that no traffic occurs during the first specified time.

2. In Paragraph 1, When the above instructions are executed individually or collectively by at least one processor, the electronic device, If a pattern of periodically occurring traffic is identified, the value of the autocorrelation function of the traffic generated by the electronic device is checked, and An electronic device that predicts whether traffic occurs during a specified first time period using a traffic prediction model when the value of the above autocorrelation function satisfies a specified condition.

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 predicts whether traffic occurs continuously during the specified first time period using the above traffic prediction model.

4. In Paragraph 1, When the above instructions are executed individually or collectively by the at least one processor, the electronic device, Using the above traffic prediction model, continuously predict the time when no traffic occurs, and An electronic device that checks whether the above-mentioned predicted time exceeds the above-mentioned designated first time.

5. In Paragraph 1, When the above instructions are executed individually or collectively by at least one processor, the electronic device, Detecting traffic generated through communication with the above external electronic device, and An electronic device that generates a traffic image for input to a traffic classification model based on the characteristics of the traffic detected during a specified second time period, when the pattern of the above traffic has not changed.

6. In Paragraph 5, When the above instructions are executed individually or collectively by at least one processor, the electronic device, An electronic device that, when a change in the pattern of the traffic is detected, updates a traffic image for input into a traffic classification model generated based on the characteristics of the traffic detected during the specified second time period, based on the time when the change in the pattern of the traffic is detected.

7. In Paragraph 1, When the above instructions are executed individually or collectively by at least one processor, the electronic device, An electronic device that maintains the RRC connection state when the pattern of periodically occurring traffic is not identified or when it is determined that traffic occurs within the specified first time.

8. In the method of operating the electronic device (101), The operation of connecting communication with an external electronic device, Operation of detecting traffic generated through communication with the external electronic device in an RRC (radio resource control) connected state, The operation of identifying the pattern of traffic generated by the electronic device using a traffic classification model, If a pattern of periodically occurring traffic is identified, an action of predicting whether traffic occurs during a specified first time period using a traffic prediction model, and A method comprising the operation of transmitting information related to the transition to an RRC standby state or an RRC inactive state to the external electronic device when it is determined that no traffic occurs during the first specified time.

9. In Paragraph 8, The operation of predicting whether the above traffic occurs is When a pattern of periodically occurring traffic is identified, an operation to check the value of the autocorrelation function of the traffic generated by the electronic device, and A method comprising the operation of predicting whether traffic occurs during a specified first time period using a traffic prediction model when the value of the above autocorrelation function satisfies a specified condition.

10. In Paragraph 8, The operation of predicting whether the above traffic occurs is, A method comprising the operation of predicting whether traffic occurs continuously during the specified first time period using the above traffic prediction model.

11. In Paragraph 8, The operation of predicting whether the above traffic occurs is, The operation of predicting times when no traffic occurs continuously using the above traffic prediction model, and A method including an action to check whether the above-mentioned predicted time exceeds the above-mentioned designated first time.

12. In Paragraph 8, An operation to check whether the pattern of traffic generated through communication with the above external electronic device changes, and A method further comprising the operation of generating a traffic image for input to the traffic classification model generated based on the characteristics of the traffic detected during a specified second time period, if the pattern of the above traffic has not changed.

13. In Paragraph 12, A method further comprising, when a change in the pattern of the traffic is detected, an operation of updating a traffic image for input to a traffic classification model based on the characteristics of the traffic detected during the specified second time period, based on the time when the change in the pattern of the traffic is detected.

14. In Paragraph 8, A method further comprising an operation to maintain the RRC connection state when the pattern of periodically occurring traffic is not identified or when it is determined that traffic occurs within the specified first time.

15. In a non-transient computer-readable storage medium storing one or more programs, When the above one or more programs are executed by the processor (300) of the electronic device (101), the electronic device (101), The operation of connecting communication with an external electronic device, Operation of detecting traffic generated through communication with the external electronic device in an RRC (radio resource control) connected state, The operation of identifying the pattern of traffic generated by the electronic device using a traffic classification model, If a pattern of periodically occurring traffic is identified, an action of predicting whether traffic occurs during a specified first time period using a traffic prediction model, and A non-transient computer-readable storage medium comprising a command to perform an operation of transmitting information related to a transition to an RRC standby state or an RRC inactive state to the external electronic device when it is determined that no traffic occurs during the first specified time.

Images