Electronic device, and method by which electronic device classifying category of object by using position tracking device

Abstract

An electronic device according to one embodiment comprises a memory, a communication module and a processor, wherein the memory can store instructions that, when executed by the processor, cause the electronic device to: be connected through the communication module; collect, at each specified interval, position data capable of tracking the trajectory of an object from a position tracking device attached to the object; change the center point of trajectory data, which includes position data collected at each specified interval; normalize the trajectory data by predicting missing data not collected at the specified intervals; generate, on the basis of the normalized trajectory data, supplementary data corresponding to the missing data; generate, as a trajectory image, the trajectory data including the supplementary data; and classify a category of the object on the basis of a feature extracted from the trajectory image.

Description

A method for classifying objects by using electronic devices and location tracking devices in electronic devices.

[0001] The present disclosure relates to an electronic device and a method for classifying categories of objects using a location tracking device in the electronic device.

[0002] Various types of smart tags are being developed to manage or prevent loss of personal items, and electronic devices can track personal items based on location information received from smart tags while communicating with the smart tags attached to the personal items.

[0003] Electronic devices can track the location of personal items with attached smart tags by connecting to them using communication methods such as Bluetooth Low Energy (BLE) or Ultra Wide Band (UWB), thereby reducing battery consumption of the electronic devices and enabling stable location tracking.

[0004] Additionally, by building a large-scale crowd-sourced location tracking network, electronic devices can track the location of personal items with smart tags attached both indoors and outdoors, and with offline-finding capabilities, electronic devices can track the location of personal items with smart tags attached even in offline situations.

[0005] An electronic device can classify or predict the category of an object based on the trajectory of the object to which the location tracking device is attached, and can provide various functions for each category.

[0006] An electronic device according to an embodiment may include a memory, a communication module, and a processor. The memory according to an embodiment may store a command that, when executed by the processor, allows the electronic device to collect location data capable of tracking a trajectory of an object from a location tracking device attached to the object and connected via the communication module at designated intervals. The memory according to an embodiment may store a command that, when executed by the processor, allows the electronic device to change a center point of trajectory data including location data collected at designated intervals and to normalize the trajectory data by predicting missing data not collected during the designated intervals. The memory according to an embodiment may store a command that, when executed by the processor, allows the electronic device to generate supplementary data corresponding to the missing data based on the normalized trajectory data. The memory according to an embodiment may store a command that, when executed by the processor, allows the electronic device to generate a trajectory image from the trajectory data including the supplementary data. In one embodiment, the memory may store instructions that, when executed by the processor, cause the electronic device to classify a category for the object based on features extracted from the trajectory image.

[0007] In one embodiment, a method for classifying a category of an object using a location tracking device in an electronic device may include an operation of collecting location data capable of tracking a trajectory of the object from a location tracking device attached to the object at designated intervals, the location data being connected via a communication module of the electronic device. In one embodiment, the method may include an operation of changing a center point of trajectory data including location data collected at designated intervals, and normalizing the trajectory data by predicting missing data that was not collected during the designated intervals. In one embodiment, the method may include an operation of generating supplementary data corresponding to the missing data based on the normalized trajectory data. In one embodiment, the method may include an operation of generating the trajectory data including the supplementary data into a trajectory image. In one embodiment, the method may include an operation of classifying a category for the object based on a feature extracted from the trajectory image.

[0008] In one embodiment, a non-volatile storage medium storing commands is provided, wherein the commands, when executed by an electronic device, are configured to cause the electronic device to perform at least one operation, wherein the at least one operation may include an operation of collecting location data capable of tracking a trajectory of an object from a location tracking device attached to the object at designated intervals and connected via a communication module of the electronic device. The operation according to one embodiment may include an operation of changing a center point of trajectory data including location data collected at designated intervals and normalizing the trajectory data by predicting missing data that has not been collected during the designated intervals. The operation according to one embodiment may include an operation of generating supplementary data corresponding to the missing data based on the normalized trajectory data. The operation according to one embodiment may include an operation of generating the trajectory data including the supplementary data into a trajectory image. The operation according to one embodiment may include an operation of classifying a category for the object based on a feature extracted from the trajectory image.

[0009] FIG. 1 is a block diagram of an electronic device within a network environment according to one embodiment.

[0010] FIG. 2 is a drawing for explaining an operation of receiving location data from a location tracking device attached to an object in an electronic device according to an embodiment.

[0011] Figure 3 is a block diagram of an electronic device according to one embodiment.

[0012] FIG. 4 is a diagram illustrating a time vector for predicting missing data in an electronic device according to an embodiment.

[0013] Figure 5 is a graph showing the result of supplementing missing data in an electronic device according to an example.

[0014] FIGS. 6A and 6B are diagrams for explaining the generation of trajectory images corresponding to trajectory data for each of multiple channels in an electronic device according to one embodiment.

[0015] FIG. 7 is a diagram illustrating a result of nonlinearly changing a trajectory image in an electronic device according to one embodiment.

[0016] FIG. 8 is a diagram for explaining an operation of classifying an object category using an artificial intelligence model in an electronic device according to one embodiment.

[0017] FIG. 9 is a diagram illustrating a UI that provides a recommendation function according to an object category in an electronic device according to one embodiment.

[0018] FIG. 10 is a flowchart illustrating an operation of classifying a category of an object to which a location tracking device is attached in an electronic device according to one embodiment.

[0019] FIG. 1 is a block diagram of an electronic device (101) within a network environment (100) according to an embodiment. Referring to FIG. 1, in the network environment (100), the electronic device (101) may communicate with the electronic device (102) via a first network (198) (e.g., a short-range wireless communication network), or may communicate with at least one of the electronic device (104) or the server (108) via a second network (199) (e.g., a long-range wireless communication network). According to an embodiment, the electronic device (101) may communicate with the electronic device (104) via the server (108). According to one embodiment, the electronic device (101) may include a processor (120), a memory (130), an input module (150), an audio output module (155), a display module (160), an audio module (170), a sensor module (176), an interface (177), a connection terminal (178), a haptic module (179), a camera module (180), a power management module (188), a battery (189), a communication module (190), a subscriber identification module (196), or an antenna module (197). In some embodiments, the electronic device (101) may omit at least one of these components (e.g., the connection terminal (178)), or may have one or more other components added. In some embodiments, some of these components (e.g., the sensor module (176), the camera module (180), or the antenna module (197)) may be integrated into one component (e.g., the display module (160)).

[0020] The processor (120) may 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, for example, software (e.g., a program (140)), and may perform various data processing or calculations. According to one embodiment, as at least a part of the data processing or calculation, the processor (120) may store a command or data received from another component (e.g., a sensor module (176) or a communication module (190)) in a volatile memory (132), process the command or data stored in the volatile memory (132), and store the resulting data in a 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 a secondary processor (123) (e.g., a graphics processing unit, a neural processing unit (NPU), an image signal processor, a sensor hub processor, or a communication processor) that can operate independently or together therewith. For example, if the electronic device (101) includes a main processor (121) and a secondary processor (123), the secondary processor (123) may be configured to use less power than the main processor (121) or to be specialized for a specified function. The secondary processor (123) may be implemented separately from the main processor (121) or as a part thereof.

[0021] The auxiliary processor (123) may control at least a part of functions or states associated with at least one component (e.g., a display module (160), a sensor module (176), or a communication module (190)) of the electronic device (101), for example, 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. In one embodiment, the auxiliary processor (123) (e.g., an image signal processor or a communication processor) may be implemented as a part of another functionally related component (e.g., a camera module (180) or a communication module (190)). In one embodiment, the auxiliary processor (123) (e.g., a neural network processing unit) may include a hardware structure specialized for processing artificial intelligence models. The artificial intelligence models may be generated through machine learning. This learning can be performed, for example, on the electronic device (101) itself where the artificial intelligence model is executed, or can be performed through a separate server (e.g., server (108)). The learning algorithm can 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 can include multiple artificial neural network layers.The artificial neural network may be one of 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 the above, but is not limited to the examples described above. In addition to, or alternatively to, a hardware structure, an artificial intelligence model may include a software structure.

[0022] The memory (130) can store various data used by at least one component (e.g., processor (120) or sensor module (176)) of the electronic device (101). The data can include, for example, software (e.g., program (140)) and input data or output data for commands related thereto. The memory (130) can include volatile memory (132) or non-volatile memory (134).

[0023] The program (140) may be stored as software in the memory (130) and may include, for example, an operating system (142), middleware (144), or an application (146).

[0024] The input module (150) can receive commands or data to be used in a component of the electronic device (101) (e.g., a processor (120)) from an external source (e.g., a user) of the electronic device (101). The input module (150) can include, for example, a microphone, a mouse, a keyboard, a key (e.g., a button), or a digital pen (e.g., a stylus pen).

[0025] The audio output module (155) can output audio signals to the outside of the electronic device (101). The audio output module (155) can include, for example, a speaker or a receiver. The speaker can be used for general purposes, such as multimedia playback or recording playback. The receiver can be used to receive incoming calls. According to one embodiment, the receiver can be implemented separately from the speaker or as part of the speaker.

[0026] The display module (160) can visually provide information to an external party (e.g., a 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 the 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 a force generated by the touch.

[0027] The audio module (170) can convert sound into an electrical signal, or vice versa, convert an electrical signal into sound. According to one embodiment, the audio module (170) can acquire sound through the input module (150), output sound through the sound output module (155), or an external electronic device (e.g., electronic device (102)) (e.g., speaker or headphone) directly or wirelessly connected to the electronic device (101).

[0028] The sensor module (176) can detect the operating status (e.g., power or temperature) of the electronic device (101) or the external environmental status (e.g., user status) and generate an electrical signal or data value corresponding to the detected status. According to one embodiment, the sensor module (176) can include, for example, a gesture sensor, a gyro sensor, a barometric pressure sensor, a magnetic sensor, an acceleration sensor, a grip sensor, a proximity sensor, a color sensor, an IR (infrared) sensor, a biometric sensor, a temperature sensor, a humidity sensor, or an illuminance sensor.

[0029] The interface (177) may support one or more designated protocols that may be used to directly or wirelessly connect the electronic device (101) with an external electronic device (e.g., the electronic device (102)). In 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.

[0030] The connection terminal (178) may include a connector through which the electronic device (101) may 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).

[0031] A haptic module (179) can convert electrical signals into mechanical stimuli (e.g., vibration or movement) or electrical stimuli that a user can perceive through tactile or kinesthetic sensations. According to one embodiment, the haptic module (179) can include, for example, a motor, a piezoelectric element, or an electrical stimulation device.

[0032] The camera module (180) can capture still images and videos. According to one embodiment, the camera module (180) may include one or more lenses, image sensors, image signal processors, or flashes.

[0033] 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 as, for example, at least a part of a power management integrated circuit (PMIC).

[0034] A battery (189) may power at least one component of the electronic device (101). In one embodiment, the battery (189) may include, for example, a non-rechargeable primary battery, a rechargeable secondary battery, or a fuel cell.

[0035] The communication module (190) may support the establishment of a direct (e.g., wired) communication channel or a wireless communication channel between the 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 operate independently from the processor (120) (e.g., application processor) and may include one or more communication processors that 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., a cellular communication module, a short-range wireless communication module, or a global navigation satellite system (GNSS) communication module) or a wired communication module (194) (e.g., a local area network (LAN) communication module, or a power line communication module). Among these communication modules, the corresponding communication module can communicate with an external electronic device (104) via a first network (198) (e.g., a short-range communication network such as Bluetooth, wireless fidelity (WiFi) direct, or infrared data association (IrDA)) or a second network (199) (e.g., a long-range communication network such as 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 can 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 verify or authenticate the electronic device (101) within a communication network such as the first network (198) or the second network (199) by using subscriber information (e.g., an international mobile subscriber identity (IMSI)) stored in the subscriber identification module (196).

[0036] The wireless communication module (192) can support 5G networks and next-generation communication technologies following the 4G network, such as NR access technology (new radio access technology). The NR access technology can support high-speed transmission of high-capacity data (eMBB (enhanced mobile broadband)), minimization of terminal power and connection of multiple terminals (mMTC (massive machine type communications)), or high reliability and low latency (URLLC (ultra-reliable and low-latency communications)). The wireless communication module (192) can support, for example, a high-frequency band (e.g., mmWave band) to achieve a high data transmission rate. The wireless communication module (192) can support various technologies for securing performance in a high-frequency band, such as beamforming, massive multiple-input and multiple-output (MIMO), 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), an external electronic device (e.g., the electronic device (104)), or a network system (e.g., the second network (199)). According to one embodiment, the wireless communication module (192) may support a peak data rate (e.g., 20 Gbps or more) for eMBB realization, a loss coverage (e.g., 164 dB or less) for mMTC realization, or a U-plane latency (e.g., 0.5 ms or less for downlink (DL) and uplink (UL), or 1 ms or less for round trip) for URLLC realization.

[0037] The antenna module (197) can transmit or receive signals or power to or from an external device (e.g., an external electronic device). According to one embodiment, the antenna module (197) may include an antenna including a radiator formed 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 the first network (198) or the second network (199), may be selected from the plurality of antennas, for example, by the communication module (190). A signal or power may be transmitted or received between the communication module (190) and an external electronic device via the selected at least one antenna. According to some embodiments, in addition to the radiator, another component (e.g., a radio frequency integrated circuit (RFIC)) may be additionally formed as a part of the antenna module (197).

[0038] In one embodiment, the antenna module (197) may form a mmWave antenna module. In one embodiment, the mmWave antenna module may include a printed circuit board, an RFIC disposed on or adjacent a first side (e.g., a bottom side) of the printed circuit board and capable of supporting a designated high frequency band (e.g., a mmWave band), and a plurality of antennas (e.g., an array antenna) disposed on or adjacent a second side (e.g., a top side or a side side) of the printed circuit board and capable of transmitting or receiving signals in the designated high frequency band.

[0039] At least some of the above components can be interconnected and exchange signals (e.g., commands or data) with each other via a communication method between peripheral devices (e.g., a bus, GPIO (general purpose input and output), SPI (serial peripheral interface), or MIPI (mobile industry processor interface)).

[0040] According to one embodiment, commands or data may be transmitted or received between the electronic device (101) and an external electronic device (104) via 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 executed in the electronic device (101) may be executed in one or more of the external electronic devices (102, 104, or 108). For example, when the electronic device (101) is to perform a certain function or service automatically or in response to a request from a user or another device, the electronic device (101) may, instead of or in addition to executing the function or service itself, request one or more external electronic devices to perform the function or at least a part of the service. One or more external electronic devices that receive the request may execute at least a portion of the requested function or service, or an 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 process the result as is or additionally and provide it as at least a portion of a response to the request. For this purpose, cloud computing, distributed computing, mobile edge computing (MEC), or client-server computing technology may be used, for example. The electronic device (101) may provide an ultra-low latency service by using distributed computing or mobile edge computing, for example. In another embodiment, the external electronic device (104) may include an Internet of Things (IoT) device. The server (108) may be an intelligent server utilizing machine learning and / or a neural network. According to one embodiment, the external electronic device (104) or the server (108) may be included in the 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.

[0041] FIG. 2 is a drawing for explaining an operation of receiving location data from a location tracking device attached to an object in an electronic device according to an embodiment.

[0042] Referring to FIG. 2, an electronic device (201) according to an embodiment may be connected to a location tracking device (301) attached to an object (not shown) for communication (211). The electronic device (201) according to an embodiment may be connected to the location tracking device (301) for communication using BLE (Bluetooth low engine) communication and / or UWB (ultra wide band) communication. The electronic device (201) according to an embodiment may collect (receive) location data capable of tracking the trajectory of the object from the location tracking device (301) to which the communication is connected at a specified period. When the communication connection with the location tracking device (301) is released and the “offline search” function is activated in the electronic device (201), the electronic device (201) according to an embodiment may collect (receive) location data of the encrypted location tracking device from a server (335) (217). According to one embodiment, the electronic device (201) can decrypt the location data of the encrypted location tracking device to confirm the location data of the location tracking device.

[0043] A location tracking device (301) according to an embodiment can be connected to an electronic device (201) for communication (211). The location tracking device (301) according to an embodiment can be connected to the electronic device (201) for communication using BLE (Bluetooth low-energy) communication and / or UWB (ultra-wide band) communication. The location tracking device (301) according to an embodiment can transmit location data capable of tracking the trajectory of the object to the electronic device (201) with which the communication is connected. The location tracking device (301) according to an embodiment can be attached to various objects such as a companion dog, a bag, a wallet, a car, a bicycle, a motorcycle, etc. The location tracking device (301) according to an embodiment can include a smart tag.

[0044] According to one embodiment, when an external electronic device (331) is connected to a location tracking device (301) using BLE (Bluetooth low engine) communication, the location data of the external electronic device (331) can be encrypted as location data of the location tracking device and transmitted to a server (335) (215).

[0045] According to one embodiment, when a server (335) receives location data of an encrypted location tracking device (301) from an external electronic device (301), confirms that the location tracking device (301) is registered as a location tracking device of the electronic device (201), and confirms that an “offline find” function is activated in the electronic device (201), the server (335) can transmit the location data of the location tracking device (301) to the electronic device (201).

[0046] Figure 3 is a block diagram of an electronic device according to one embodiment.

[0047] Referring to the above drawing 3, the electronic device (201) may include a processor (220), a memory (230), a display (260), and a communication module (290).

[0048] According to one embodiment, the processor (220) may be implemented substantially identically or similarly to the processor (120) of FIG. 1.

[0049] According to one embodiment, the processor (220) may collect (receive) location data capable of tracking the trajectory of the object from a location tracking device attached to the object (e.g., the location tracking device (301) of FIG. 2) at specified intervals.

[0050] According to one embodiment, when the processor (220) is connected to the location tracking device through a communication module (290) (e.g., a BLE communication module and / or an ultra wide band (UWB) communication module), the processor (220) can collect location data capable of tracking the trajectory of the object from the location tracking device at a specified period.

[0051] According to one embodiment, when the processor (220) confirms activation of the “offline find” function while the communication connection with the location tracking device is disconnected, the processor (220) may collect location data of the location tracking device from a server (e.g., server (335) of FIG. 2) at specified intervals.

[0052] According to one embodiment, the processor (220) may change the center point of the trajectory data including the position data collected from the position tracking device (e.g., the position tracking device (301) of FIG. 2) attached to the object at each specified period and normalize the trajectory data by predicting missing data that was not collected during the specified period, thereby making the trajectory data into standardized data.

[0053] According to one embodiment, if the location data collected from the location tracking device is defined as “P” and the trajectory data including the location data collected at each specified period is defined as a continuous sequence “S”, it can be expressed as in <Mathematical Formula 1> below.

[0054]

[0055] According to one embodiment, location data collected from the location tracking device ( ) may include a plurality of feature values ​​including a timestamp value (timestamp t), a latitude value (latitude lat), an altitude value (longitude lng), an accuracy value (accuracy acc) for location information of the electronic device (201) or an external electronic device (e.g., the external electronic device (331) of FIG. 2) connected to the location tracking device, a speed value (speed sp), a signal strength value (rssi) between the location tracking device and the electronic device (201) or the external electronic device (e.g., the external electronic device (331) of FIG. 2) connected to the communication, and a value (method mt) indicating a communication connection method between the location tracking device and the electronic device (201) or the external electronic device (e.g., the external electronic device (301) of FIG. 2) connected to the communication. The types of the plurality of feature values ​​included in the location data according to one embodiment are described as examples, and are not limited thereto, and may additionally include other feature values.

[0056] According to one embodiment, the processor (220) generates trajectory data ( ) has a large deviation in latitude (lat) and longitude (lng) values ​​and different center points, so the trajectory data ( ) of the center point ( , ) and calculate the trajectory data ( ) can be changed to a reference center point (e.g. (0.0)).

[0057] [Correction pursuant to Rule 91, October 21, 2024]

[0058] [Correction pursuant to Rule 91, October 21, 2024]

[0059] According to an embodiment, the processor (220) is configured such that, when the location tracking device is disconnected from the communication with an external electronic device (e.g., the external electronic device (331) of FIG. 2) and the location data of the location tracking device is not collected during a specified period, depending on the distribution of the external electronic device, the processor (220) is configured such that, through the following <Mathematical Expression 3> and <Mathematical Expression 4>, the processor (220) is configured such that, when the location tracking device is disconnected from the communication with an external electronic device (e.g., the external electronic device (331) of FIG. 2) and the location data of the location tracking device is not collected during a specified period, the processor (220) is configured such that, the location data of the location tracking device is not collected during a specified period, the location data of the location tracking device is not collected during a specified period, depending on the distribution of the external electronic device, the location data of the location tracking device is not collected during a specified period, based on the following <Mathematical Expression 3> and <Mathematical Expression 4>. ) can be calculated.

[0060] [Correction pursuant to Rule 91, October 21, 2024]

[0061] d is It can display latitude (lat) values, altitude (lng) values, accuracy (acc) values, speed (sp) values, and signal strength (rssi) values.

[0062] [Correction pursuant to Rule 91, October 21, 2024]

[0063] FIG. 4 is a diagram for explaining a time vector for predicting missing data in an electronic device according to an embodiment. Referring to FIG. 4, in (a) according to an embodiment, the time series is trajectory data ( ) represents each location information value included in the data, and missing data is indicated as “nan” (a1, a2, a3, a4).

[0064] In (b) according to one embodiment, the Masking Vectors m represent the result values ​​calculated through the above <Mathematical Formula 3>, and missing data is represented as “0” (b1, b2, b3, b4).

[0065] In (d) according to one embodiment, Time Gap represents the result value calculated through the above <Mathematical Formula 4>, and is a time vector ( ) can be used to predict the occurrence of missing data when Time Gap is “2” (c1, c2, c3) and “7” (c4).

[0066] According to one embodiment, the processor (220) can predict uncollected missing data (P2) by collecting location data (P3) after a specified period (e.g., 5 minutes) has passed since collecting location data (P1), as in (a).

[0067] According to one embodiment, the processor (220) can specify or predict a cycle for collecting location data by category of objects.

[0068] A processor (220) according to one embodiment can generate supplementary data corresponding to missing data based on normalized trajectory data.

[0069] According to one embodiment, the processor (220) may generate the supplementary data corresponding to the missing data based on the normalized trajectory data using the first artificial intelligence model.

[0070] According to one embodiment, when the processor (220) inputs normalized trajectory data as input data into a first artificial intelligence model, the first artificial intelligence model can generate the supplementary data corresponding to the missing data as result data.

[0071] According to one embodiment, the processor (220) may use at least one of a long short-term memory (LSTM) or a bidirectional recurrent deep neural network (BRDNN) of the RNN (recurring neural network) series as the first artificial intelligence model for generating the supplementary data corresponding to the missing data.

[0072] Fig. 5 is a graph illustrating the results of supplementing missing data in an electronic device according to an example. In Fig. 5, hollow circles represent location data, and filled circles represent supplementary data corresponding to missing data generated using the first artificial intelligence model.

[0073] A processor (220) according to one embodiment can generate trajectory data including supplementary data corresponding to missing data as a trajectory image.

[0074] According to one embodiment, the processor (220) comprises first position data (P) among the trajectory data. i ) and the second location data (P) which is the next location of the first location data i+1 ) can generate pixel values ​​of the trajectory connecting them.

[0075] According to one embodiment, the processor (220) can generate the pixel value of the trajectory through the following <Mathematical Formula 5>.

[0076] [Correction pursuant to Rule 91, October 21, 2024]

[0077] [Correction under Rule 91 21.10.2024] In the above <Mathematical Formula 5>, L is represents a line segment, and one pixel of the line segment can be defined as. The above pixel value is can be determined according to , d is It can display the accuracy (acc) value, speed (sp) value, signal strength (rssi) value, and value indicating the connection method (mt).

[0078] According to one embodiment, the processor (220) can generate a trajectory image corresponding to the trajectory data based on a plurality of feature values ​​included in the position data and the pixel value.

[0079] According to one embodiment, the processor (220) may generate at least one trajectory image corresponding to trajectory data for each channel corresponding to at least some values ​​(e.g., accuracy (acc) value, speed (sp) value, signal strength (rssi) value, and value (mt) indicating a connection method) among a plurality of feature values ​​included in the position data, and may generate a final trajectory image by merging the at least one generated trajectory image.

[0080] FIGS. 6A and 6B are diagrams for explaining the generation of trajectory images corresponding to trajectory data for each of multiple channels in an electronic device according to an embodiment. Referring to FIGS. 6A and 6B, the processor (220) according to an embodiment may generate trajectory data (610) into multiple trajectory images (630) corresponding to multiple channels. The processor (220) according to an embodiment may generate a first trajectory image (631) corresponding to a first channel using an accuracy (acc) value, a second trajectory image (633) corresponding to a second channel using a speed (sp) value, and a third trajectory image (635) corresponding to a third channel using a signal intensity (rssi) value. According to one embodiment, the processor (220) can generate a final trajectory image (637) by merging the first trajectory image (631), the second trajectory image (633), and the third trajectory image (635).

[0081] According to one embodiment, the processor (220) can non-linearly change the resolution of the trajectory image.

[0082] According to one embodiment, the processor (220) can change the trajectory image expressed in a Cartesian Coordinate System into a Polar Coordinate System through the following <Mathematical Formula 6>.

[0083]

[0084]

[0085] x,y: of the trajectory image Coordinate points

[0086] r: distance between the origin of the polar coordinate system and the coordinates of the trajectory image

[0087] : The angle between the origin of the polar coordinate system and the coordinates of the trajectory image

[0088] According to one embodiment, the processor (220) can calculate the value by distorting the position of the distance (r) between the coordinates of the trajectory image from the origin of the polar coordinate system (Mathematical Formula 7) below.

[0089]

[0090] : Distortion coefficient

[0091] According to one embodiment, the processor (220) can change the polar coordinate system of the trajectory image back to the rectangular coordinate system through the following <Mathematical Formula 8>.

[0092]

[0093] FIG. 7 is a diagram illustrating a result of nonlinearly changing a trajectory image in an electronic device according to one embodiment. <711> When the resolution of the trajectory image is expressed linearly as shown above, the resolution of the trajectory image is reduced, which can alleviate the problem of sparsity in position data, but a lot of information about the trajectory corresponding to the same pixel (e.g., information about trajectories with little movement) may be lost. <731> When the resolution of the trajectory image is expressed nonlinearly, trajectories with a small movement radius can be enlarged, and trajectories with a large movement radius can be reduced, thereby reducing the deviation in mobility while reducing the resolution of the trajectory image.

[0094] According to one embodiment, a processor (220) can classify a category for an object to which a position tracking device (e.g., the position tracking device (301) of FIG. 2) is attached based on features extracted from a trajectory image.

[0095] According to one embodiment, the processor (220) can classify a category for the object based on features extracted from the trajectory image using a second artificial intelligence model.

[0096] According to one embodiment, the second artificial intelligence model may include a Convelutional Neural Network (CNN).

[0097] FIG. 8 is a diagram for explaining an operation of classifying an object category using an artificial intelligence model in an electronic device according to an embodiment. Referring to FIG. 10, when the processor (220) according to an embodiment inputs a trajectory image (630) as input data to a second artificial intelligence model (e.g., CNN (Convelutional Neural Network)) (811), the second artificial intelligence model (811) extracts the object's features through a convolution layer (811) and classifies the object through a fully connected layer (813), thereby outputting the object's category information (1030) as result data.

[0098] According to one embodiment, a processor (220) may provide a recommendation function that can recommend from the identified category when identifying the category of an object based on the trajectory of a location tracking device attached to the object (e.g., location tracking device (301) of FIG. 2).

[0099] According to an embodiment, the processor (220) may identify the category of an object based on trajectory data of an object to which the location tracking device is attached using a second artificial intelligence model trained to classify the category of an object based on trajectory data, and may provide a function that can recommend within the identified category of the object. For example, if the object is a key, the processor (220) may provide a "notify when the key is moved away" function, and if the object is a pet, the processor (220) may provide a "notify when there is no movement or the pet moves away from home" function as a recommendation function.

[0100] FIG. 9 is a diagram illustrating a UI that provides a recommendation function according to an object category in an electronic device according to one embodiment.

[0101] Referring to the above FIG. 9, a processor (220) according to one embodiment may provide a UI that provides a "notify when key is moved away" function as a reminder function (913) when the category of an object is confirmed as a "key" (911) based on trajectory data collected from a location tracking device attached to the object (e.g., location tracking device (301) of FIG. 2).

[0102] According to one embodiment, the memory (230) may be implemented substantially identically or similarly to the memory (130) of FIG. 1.

[0103] In one embodiment, the memory (230) may store trajectory data including position data collected at specified intervals from a position tracking device attached to an object (e.g., position tracking device (301) of FIG. 2).

[0104] In one embodiment, the memory (230) may store at least one trajectory image corresponding to the trajectory data.

[0105] In the memory (230) according to one embodiment, trajectory data corresponding to each category of an object may be stored.

[0106] In the memory (230) according to one embodiment, a recommendation function may be stored for each category of objects.

[0107] According to one embodiment, the memory (230) can store data or commands (instructions) of the electronic device (201). For example, the commands (instructions) stored in the memory (230) can be set to perform specific operations by the processor (220).

[0108] According to one embodiment, the display (260) may be implemented substantially identically or similarly to the display (160) of FIG. 1.

[0109] According to one embodiment, the display (260) can display a UI that can recommend functions in the category of the object identified based on trajectory data of the object to which the location tracking device (e.g., the location tracking device (301) of FIG. 2) is attached.

[0110] According to one embodiment, the communication module (290) may be implemented substantially identically or similarly to the communication module (190) of FIG. 1, and may include a plurality of communication circuits using different communication technologies.

[0111] According to one embodiment, the communication module (290) may include at least one of a wireless LAN module (not shown) and a short-range communication module (not shown), and the short-range communication module (not shown) may include a UWB (ultra wide band) communication module, a Wi-Fi communication module, an NFC communication module, a Bluetooth legacy communication module, and / or a BLE communication module.

[0112] An electronic device (e.g., an electronic device (101) of FIG. 1 and / or an electronic device (201) of FIG. 2 and / or an electronic device (201) of FIG. 3) according to an embodiment may include a memory (e.g., a memory (130) of FIG. 1 and / or a memory (230) of FIG. 3), a communication module (e.g., a communication module (190) of FIG. 1 and / or a communication module (290) of FIG. 3), and a processor (e.g., a processor (120) of FIG. 1 and / or a processor (220) of FIG. 3). The memory according to an embodiment may store instructions that, when executed by the processor, cause the electronic device to collect location data from a location tracking device connected to the object through the communication module and attached to the object at designated intervals, thereby tracking a trajectory of the object. In one embodiment, the memory may store a command that, when executed by the processor, causes the electronic device to change a center point of trajectory data including location data collected at each specified period and to normalize the trajectory data by predicting missing data that was not collected during the specified period. In one embodiment, the memory may store a command that, when executed by the processor, causes the electronic device to generate supplementary data corresponding to the missing data based on the normalized trajectory data. In one embodiment, the memory may store a command that, when executed by the processor, causes the electronic device to generate the trajectory data including the supplementary data into a trajectory image. In one embodiment, the memory may store a command that, when executed by the processor, causes the electronic device to classify a category for the object based on a feature extracted from the trajectory image.

[0113] According to one embodiment, the location data may be set to include a plurality of feature values ​​including a timestamp value, a latitude value, an altitude value, an accuracy value, a speed value, a signal strength value, and a value indicating a connection method.

[0114] According to an embodiment, the memory (e.g., the memory (130) of FIG. 1 and / or the memory (230) of FIG. 3) may, when executed by the processor (e.g., the processor (120) of FIG. 1 and / or the processor (220) of FIG. 3), cause the electronic device (e.g., the electronic device (101) of FIG. 1 and / or the electronic device (201) of FIG. 2 and / or the electronic device (201) of FIG. 3) to change the center point of the trajectory data to a reference center point. According to an embodiment, the processor (e.g., the processor (120) of FIG. 1 and / or the processor (220) of FIG. 3) may store instructions that may be set to normalize the trajectory data by calculating a time vector for predicting missing data that has not been collected from the position tracking device during the specified period.

[0115] According to one embodiment, the memory (e.g., memory (130) of FIG. 1 and / or memory (230) of FIG. 3) may store a command that, when executed by the processor (e.g., processor (120) of FIG. 1 and / or processor (220) of FIG. 3), may set the electronic device (e.g., electronic device (101) of FIG. 1 and / or electronic device (201) of FIG. 2 and / or electronic device (201) of FIG. 3) to generate the supplementary data corresponding to the missing data based on the normalized trajectory data using a first artificial intelligence model.

[0116] According to an embodiment, the memory (e.g., the memory (130) of FIG. 1 and / or the memory (230) of FIG. 3), when executed by the processor (e.g., the processor (120) of FIG. 1 and / or the processor (220) of FIG. 3), may cause the electronic device (e.g., the electronic device (101) of FIG. 1 and / or the electronic device (201) of FIG. 2 and / or the electronic device (201) of FIG. 3) to generate pixel values ​​of a trajectory connecting first location data among the trajectory data and second location data that is a next location of the first location data. According to an embodiment, the processor (e.g., the processor (120) of FIG. 1 and / or the processor (220) of FIG. 3) may generate the trajectory image corresponding to the trajectory data based on a plurality of feature values ​​included in the location data and the pixel values. A processor according to one embodiment (e.g., processor (120) of FIG. 1 and / or processor (220) of FIG. 3) may store a command that can be set to non-linearly change the resolution of the trajectory image.

[0117] According to an embodiment, the memory (e.g., the memory (130) of FIG. 1 and / or the memory (230) of FIG. 3) may store a command that, when executed by the processor (e.g., the processor (120) of FIG. 1 and / or the processor (220) of FIG. 3), may set the electronic device (e.g., the electronic device (101) of FIG. 1 and / or the electronic device (201) of FIG. 2 and / or the electronic device (201) of FIG. 3) to generate the pixel value based on a value representing an accuracy value, a speed value, a signal strength value, and a connection method among a plurality of feature values ​​included in the second location data.

[0118] According to one embodiment, the memory (e.g., the memory (130) of FIG. 1 and / or the memory (230) of FIG. 3) may store a command that, when executed by the processor (e.g., the processor (120) of FIG. 1 and / or the processor (220) of FIG. 3), may set the electronic device (e.g., the electronic device (101) of FIG. 1 and / or the electronic device (201) of FIG. 2 and / or the electronic device (201) of FIG. 3) to generate a plurality of trajectory images based on a plurality of feature values ​​and the pixel values ​​included in each of the position data included in the trajectory data, and to generate the trajectory image by merging the plurality of trajectory images.

[0119] According to an embodiment, the memory (e.g., the memory (130) of FIG. 1 and / or the memory (230) of FIG. 3) may store a command that, when executed by the processor (e.g., the processor (120) of FIG. 1 and / or the processor (220) of FIG. 3), may cause the electronic device (e.g., the electronic device (101) of FIG. 1 and / or the electronic device (201) of FIG. 2 and / or the electronic device (201) of FIG. 3) to change the rectangular coordinate system of the trajectory image to a polar coordinate system, distort a distance value between an origin of the polar coordinate system and a coordinate of the trajectory image, and change the polar coordinate system having the distorted distance value to a rectangular coordinate system to change the resolution of the trajectory image non-linearly.

[0120] According to one embodiment, the memory (e.g., memory (130) of FIG. 1 and / or memory (230) of FIG. 3) may store a command that, when executed by the processor (e.g., processor (120) of FIG. 1 and / or processor (220) of FIG. 3), may set the electronic device (e.g., electronic device (101) of FIG. 1 and / or electronic device (201) of FIG. 2 and / or electronic device (201) of FIG. 3) to classify a category for the object based on features extracted from the trajectory image using a second artificial intelligence model.

[0121] According to one embodiment, the memory (e.g., memory (130) of FIG. 1 and / or memory (230) of FIG. 3) may store a command that, when executed by the processor (e.g., processor (120) of FIG. 1 and / or processor (220) of FIG. 3), may set the electronic device (e.g., electronic device (101) of FIG. 1 and / or electronic device (201) of FIG. 2 and / or electronic device (201) of FIG. 3) to provide a recommended function in the identified category when the electronic device identifies the category of the object based on the trajectory of a location tracking device attached to the object.

[0122] FIG. 10 is a flowchart illustrating an operation of classifying a category of an object to which a location tracking device is attached in an electronic device according to an embodiment. The operation of classifying an object to which a location tracking device is attached by category in the device may include operations 1001 to 1009. 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, at least two operations may be performed in parallel, or another operation may be added.

[0123] In operation 1001, an electronic device (e.g., the electronic device (101) of FIG. 1, the electronic device (201) of FIG. 2, and / or the electronic device (201) of FIG. 3) may collect position data capable of tracking the trajectory of an object from a position tracking device attached to the object.

[0124] According to one embodiment, the electronic device may collect (receive) location data capable of tracking the trajectory of the object from a location tracking device attached to the object (e.g., location tracking device (301) of FIG. 2) at specified intervals.

[0125] According to one embodiment, when the electronic device is connected to the location tracking device through a communication module of the electronic device (e.g., a communication module (290) of FIG. 3) (e.g., a BLE communication module and / or an ultra wide band (UWB) communication module), the electronic device can collect location data capable of tracking the trajectory of the object from the location tracking device at designated intervals.

[0126] According to one embodiment, the electronic device may collect location data of the location tracking device from a server (e.g., server (335) of FIG. 2) at specified intervals when the "offline find" function is activated while the communication connection with the location tracking device is disconnected.

[0127] In operation 1003, an electronic device (e.g., electronic device (101) of FIG. 1, electronic device (201) of FIG. 2, and / or electronic device (201) of FIG. 3) may normalize trajectory data including location data collected at specified intervals.

[0128] According to one embodiment, the electronic device may change the center point of trajectory data including position data collected from a position tracking device attached to an object (e.g., position tracking device (301) of FIG. 2) at specified intervals, and normalize the trajectory data by predicting missing data that was not collected during the specified intervals.

[0129] The electronic device according to one embodiment, tracks trajectory data ( ) has a large deviation in latitude (lat) and longitude (lng) values ​​and different center points, so the trajectory data ( ) and calculate the center point of the trajectory data ( ) can be changed to a reference center point (e.g. (0.0)).

[0130] According to an embodiment, the electronic device, when the location data of the location tracking device is not collected during a specified period, can predict the missing data not collected from the location tracking device during the specified period through the <Mathematical Formula 3> and the <Mathematical Formula 4>, a time vector ( ) can be calculated.

[0131] In operation 1005, an electronic device (e.g., the electronic device (101) of FIG. 1, the electronic device (201) of FIG. 2, and / or the electronic device (201) of FIG. 3) may generate supplementary data corresponding to the missing data based on normalized trajectory data.

[0132] According to one embodiment, the electronic device can generate the supplementary data corresponding to the missing data based on the normalized trajectory data using the first artificial intelligence model.

[0133] In one embodiment, the electronic device inputs normalized trajectory data as input data into the first artificial intelligence model, and the first artificial intelligence model can generate the supplementary data corresponding to the missing data as result data.

[0134] According to one embodiment, the first artificial intelligence model may include at least one of a long short-term memory (LSTM) of the recurring neural network (RNN) series or a bidirectional recurrent deep neural network (BRDNN).

[0135] In operation 1007, an electronic device (e.g., the electronic device (101) of FIG. 1, the electronic device (201) of FIG. 2, and / or the electronic device (201) of FIG. 3) can generate the trajectory data including the supplementary data into a trajectory image.

[0136] According to one embodiment, the electronic device comprises first position data (P) among the trajectory data. i ) and the second location data (P) which is the next location of the first location data i+1 ) can be generated using the above <Mathematical Formula 5>.

[0137] According to one embodiment, the electronic device can generate a trajectory image corresponding to the trajectory data based on a plurality of feature values ​​included in the location data and the pixel values.

[0138] According to one embodiment, the electronic device may generate at least one trajectory image corresponding to trajectory data for each channel corresponding to at least some values ​​(e.g., an accuracy (acc) value, a speed (sp) value, a signal strength (rssi) value, and a value indicating a connection method (mt)) among a plurality of feature values ​​included in the location data, and generate a final trajectory image by merging the at least one generated trajectory image.

[0139] According to one embodiment, the electronic device can non-linearly change the resolution of the trajectory image.

[0140] According to one embodiment, the electronic device may change the trajectory image expressed in a Cartesian Coordinate System into a Polar Coordinate System through the <Mathematical Formula 6>, calculate the ru value by distorting the position of the distance (r) between the coordinates of the trajectory image from the origin of the polar coordinate system through the <Mathematical Formula 7>, and change the polar coordinate system of the trajectory image back into a Cartesian coordinate system through the <Mathematical Formula 8>, thereby nonlinearly changing the resolution of the trajectory image.

[0141] In operation 1009, an electronic device (e.g., electronic device (101) of FIG. 1, electronic device (201) of FIG. 2, and / or electronic device (201) of FIG. 3) can classify (predict) a category for an object based on features extracted from a trajectory image.

[0142] According to one embodiment, the electronic device can classify a category for the object based on features extracted from the trajectory image using a second artificial intelligence model.

[0143] According to one embodiment, the second artificial intelligence model may include a Convelutional Neural Network (CNN).

[0144] According to an embodiment, a method for classifying a category of an object using a location tracking device in an electronic device (e.g., the electronic device (101) of FIG. 1 or the electronic device (201) of FIGS. 2 and 3) may include an operation of collecting location data capable of tracking a trajectory of the object from a location tracking device attached to the object at designated intervals, the location data being connected via a communication module of the electronic device. According to an embodiment, the method may include an operation of changing a center point of trajectory data including the location data collected at designated intervals, and normalizing the trajectory data by predicting missing data that was not collected during the designated intervals. According to an embodiment, the method may include an operation of generating supplementary data corresponding to the missing data based on the normalized trajectory data. According to an embodiment, the method may include an operation of generating the trajectory data including the supplementary data into a trajectory image. According to an embodiment, the method may include an operation of classifying a category for the object based on a feature extracted from the trajectory image.

[0145] The location data according to one embodiment may include a plurality of feature values ​​including a timestamp value, a latitude value, an altitude value, an accuracy value, a speed value, a signal strength value, and a value indicating a connection method.

[0146] The method according to one embodiment may further include an operation of normalizing the trajectory data by changing the center point of the trajectory data to a reference center point and calculating a time vector for predicting missing data not collected from the position tracking device during the specified period.

[0147] The method according to one embodiment may further include an operation of generating the supplementary data corresponding to the missing data based on the normalized trajectory data using the first artificial intelligence model.

[0148] According to one embodiment, the method may include an operation of generating a pixel value of a trajectory connecting first position data and second position data that is a next position of the first position data among the trajectory data, and generating the trajectory image corresponding to the trajectory data based on a plurality of feature values ​​included in the position data and the pixel value. According to one embodiment, the method may further include an operation of non-linearly changing the resolution of the trajectory image.

[0149] The method according to one embodiment may further include an operation of generating the pixel value based on a value indicating an accuracy value, a speed value, a signal strength value, and a connection method among a plurality of feature values ​​included in the second location data.

[0150] According to one embodiment, the method may include an operation of generating a plurality of trajectory images based on a plurality of feature values ​​and pixel values ​​included in each of the position data included in the trajectory data. According to one embodiment, the method may further include an operation of generating the trajectory image by merging the plurality of trajectory images.

[0151] According to one embodiment, the method may include an operation of changing the rectangular coordinate system of the trajectory image to a polar coordinate system. According to one embodiment, the method may further include an operation of distorting a distance value between an origin of the polar coordinate system and a coordinate of the trajectory image, and changing the polar coordinate system having the distorted distance value to a rectangular coordinate system to non-linearly change the resolution of the trajectory image.

[0152] The method according to one embodiment may further include an operation of classifying a category for the object based on features extracted from the trajectory image using a second artificial intelligence model.

[0153] The method according to one embodiment may include an operation of identifying the category of the object based on the trajectory of a location tracking device attached to the object. The method according to one embodiment may further include an operation of providing a recommended function based on a function that can be recommended within the identified category.

[0154] Electronic devices according to embodiments disclosed herein may take various forms. Electronic devices may include, for example, portable communication devices (e.g., smartphones), computer devices, portable multimedia devices, portable medical devices, cameras, wearable devices, or home appliances. Electronic devices according to embodiments disclosed herein are not limited to the aforementioned devices.

[0155] The embodiments of this document and the terminology used herein are not intended to limit the technical features described in this document to specific embodiments, but should be understood to include various modifications, equivalents, or substitutes of the 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 the items, unless the context clearly indicates otherwise. In this document, each of the phrases "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" can include any one of the items listed together in the corresponding phrase among those phrases, or all possible combinations thereof. Terms such as "first," "second," or "first" or "second" may be used merely to distinguish one component from another, and do not limit the components in any other respect (e.g., importance or order). When a component (e.g., a first component) is referred to as "coupled" or "connected" to another (e.g., a second component), with or without the terms "functionally" or "communicatively," it means that the component can be connected to the other component directly (e.g., wired), wirelessly, or through a third component.

[0156] The term "module" used in one embodiment of this document 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. A module may be an integral component, or a minimum unit or part of such a component 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).

[0157] An embodiment of the present document may be implemented as software (e.g., a program (140)) including one or more instructions stored in a storage medium (e.g., an internal memory (136) or an external memory (138)) readable by a machine (e.g., an electronic device (101) or an electronic device (301)). For example, a processor (e.g., a processor (520)) of the machine (e.g., an electronic device (301)) may call at least one instruction among the one or more instructions stored from the storage medium and execute it. This enables the machine to operate 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 executable by an interpreter. The machine-readable storage medium may be provided in the form of a non-transitory storage medium. Here, 'non-transitory' simply means that the storage medium is a tangible device and does not contain signals (e.g., electromagnetic waves), and the term does not distinguish between cases where data is stored semi-permanently or temporarily on the storage medium.

[0158] According to one embodiment, the method according to one embodiment disclosed in the present document may be provided as a computer program product. The computer program product may be traded between sellers and buyers 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 may be provided through an application store (e.g., Play Store). TM ) or directly between two user devices (e.g., smart phones), online distribution (e.g., downloading or uploading). In the case of online distribution, at least a portion of the computer program product may be at least temporarily stored or temporarily created in a machine-readable storage medium, such as the memory of a manufacturer's server, an application store's server, or an intermediary server.

[0159] According to one embodiment, each component (e.g., a module or a program) of the above-described components may include one or more entities, and some of the entities may be separated and placed in other components. According to one embodiment, one or more components or operations of the aforementioned components may be omitted, or one or more other components or operations may be added. Alternatively or additionally, a plurality of components (e.g., a module or a program) may be integrated into a single component. In such a case, the integrated component may perform one or more functions of each of the plurality of components identically or similarly to those performed by the corresponding component among the plurality of components prior to the integration. According to one embodiment, the operations performed by a module, program, or other component 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.

Claims

1. In an electronic device (101 of FIG. 1; 201 of FIGS. 2 and 3), Memory (130 in Fig. 1; 230 in Fig. 3); a communication module (190 in Fig. 1; 290 in Fig. 3); and Contains a processor (120 in FIG. 1; 220 in FIG. 3), The above memory, when executed by the processor, causes the electronic device to: Connected through the above communication module, collects location data capable of tracking the trajectory of the object from a location tracking device attached to the object at specified intervals, Changing the center point of the trajectory data including the location data collected at each of the above-mentioned specified periods, and normalizing the trajectory data by predicting the missing data that was not collected during the above-mentioned specified periods, Generate supplementary data corresponding to the missing data based on the above normalized trajectory data, Generating the trajectory data including the above supplementary data into a trajectory image, An electronic device storing a command set to classify a category for the object based on features extracted from the trajectory image.

2. In paragraph 1, An electronic device wherein the above location data is set to include a plurality of feature values ​​including a timestamp value, a latitude value, an altitude value, an accuracy value, a speed value, a signal strength value, and a value indicating a connection method.

3. In paragraph 1 or 2, The above memory, when executed by the processor, causes the electronic device to: Change the center point of the above trajectory data to the reference center point, An electronic device storing instructions set to normalize the trajectory data by calculating a time vector for predicting missing data not collected from the position tracking device during the specified period.

4. In any one of paragraphs 1 to 3, The above memory, when executed by the processor, causes the electronic device to: An electronic device storing a command set to generate the supplementary data corresponding to the missing data based on the normalized trajectory data using the first artificial intelligence model.

5. In any one of paragraphs 1 to 4, The above memory, when executed by the processor, causes the electronic device to: Generate pixel values ​​of a trajectory connecting the first location data and the second location data, which is the next location of the first location data, among the above trajectory data, and Based on the plurality of feature values ​​included in the above location data and the pixel value, the trajectory image corresponding to the trajectory data is generated, An electronic device storing a command set to non-linearly change the resolution of the above trajectory image.

6. In any one of paragraphs 1 to 5, The above memory, when executed by the processor, causes the electronic device to: An electronic device storing a command set to generate the pixel value based on a value representing an accuracy value, a speed value, a signal strength value, and a connection method among a plurality of feature values ​​included in the second location data.

7. In any one of paragraphs 1 to 6, The above memory, when executed by the processor, causes the electronic device to: An electronic device storing a command set to generate a plurality of trajectory images based on a plurality of feature values ​​and pixel values ​​included in each of the position data included in the trajectory data, and to generate the trajectory image by merging the plurality of trajectory images.

8. In any one of paragraphs 1 to 7, The above memory, when executed by the processor, causes the electronic device to: Change the orthogonal coordinate system of the above trajectory image to polar coordinate system, Distort the distance value between the origin of the above polar coordinate system and the coordinates of the above trajectory image, An electronic device storing a command set to change the resolution of the trajectory image non-linearly by changing the polar coordinate system having the distorted distance value into an orthogonal coordinate system.

9. In any one of paragraphs 1 to 8, The above memory, when executed by the processor, causes the electronic device to: An electronic device storing a command set to classify a category for the object based on features extracted from the trajectory image using a second artificial intelligence model.

10. In any one of paragraphs 1 to 9, The above memory, when executed by the processor, causes the electronic device to: An electronic device storing a command set to provide a recommended function by identifying a category of an object based on a trajectory of a location tracking device attached to the object, and then recommending a function that can be recommended from the identified category.

11. A method for classifying an object category using a location tracking device in an electronic device, An action of collecting location data capable of tracking the trajectory of an object from a location tracking device attached to the object at specified intervals, the location data being connected via a communication module of the electronic device; An operation of changing the center point of trajectory data including location data collected at each of the above-mentioned specified periods, and normalizing the trajectory data by predicting missing data not collected during the above-mentioned specified periods; An operation of generating supplementary data corresponding to the missing data based on the normalized trajectory data; An operation of generating the trajectory data including the supplementary data into a trajectory image; and A method comprising an action of classifying a category for the object based on features extracted from the trajectory image.

12. In paragraph 11, A method wherein the above location data includes a plurality of feature values ​​including a timestamp value, a latitude value, an altitude value, an accuracy value, a speed value, a signal strength value, and a value indicating a connection method.

13. In clause 11 or 12, A method further comprising the steps of changing the center point of the trajectory data to a reference center point and normalizing the trajectory data by calculating a time vector for predicting missing data not collected from the position tracking device during the specified period.

14. In any one of paragraphs 11 to 13, A method further comprising an action of generating the supplementary data corresponding to the missing data based on the normalized trajectory data using the first artificial intelligence model.

15. In a nonvolatile storage medium storing commands, the commands are set to cause the electronic device to perform at least one operation when executed by the electronic device, the at least one operation being: An action of collecting location data capable of tracking the trajectory of an object from a location tracking device attached to the object at specified intervals, the location data being connected via a communication module of the electronic device; An operation of changing the center point of trajectory data including location data collected at each of the above-mentioned specified periods, and normalizing the trajectory data by predicting missing data not collected during the above-mentioned specified periods; An operation of generating supplementary data corresponding to the missing data based on the normalized trajectory data; An operation of generating the trajectory data including the supplementary data into a trajectory image; and A storage medium comprising an operation of classifying a category for the object based on features extracted from the trajectory image.

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