Electronic device and control method thereof

Abstract

An electronic device and a control method thereof are provided. The method for controlling the electronic device can allow obtaining information associated with a user based on an image obtained by a camera of the electronic device, identifying at least one advertisement category among multiple advertisement categories, sending information about at least one advertisement category to an advertisement platform server when an event for displaying an advertisement occurs, and receiving advertisements in at least one advertisement category from the advertisement platform server.

Description

Technical Field

[0001] The present disclosure relates to an electronic device and a control method thereof. More particularly, the present disclosure relates to an electronic device and a control method thereof for providing an optimal advertisement to a user based on information related to the user. Background Art

[0002] As the use of electronic devices, such as smartphones, becomes more common and the time the devices are in use increases, there is a growing need to provide advertisements using electronic devices, such as smartphones, to companies that want to advertise products and services.

[0003] In particular, in the case of providing advertisements using electronic devices, it is very important to identify user preferences or interests. This is because if any advertisement is provided regardless of the user's preferences or interests, the effect of providing the advertisement may be very small.

[0004] In recent years, there have emerged advertising platform companies that collect users' personal information, such as website visit records, search records, etc., and provide services that provide users with optimal advertisements based on the collected information.

[0005] However, user personal information, such as website visit records or search records, must be restricted by user consent due to personal information protection issues, and there is a problem that services cannot be provided without user consent. Therefore, it is required to find a way to provide users with the best advertisements without sending user personal information by the electronic device itself. Summary of the invention

[0006] Technical issues

[0007] The present disclosure aims to solve the above problems, and an object of the present disclosure is to provide an electronic device and a method for controlling the same, which sends advertisement category information determined based on information related to a user to an advertisement platform server, and provides the best advertisement to the user without sending the user's personal information.

[0008] Technical Solution

[0009] According to an embodiment of the present disclosure, an electronic device includes: a camera, a communication interface, a memory including at least one command, and a processor, which is configured to be connected to the camera, the communication interface and the memory to control the electronic device, wherein the processor is configured to obtain information related to a user of the electronic device by executing at least one command, determine at least one category of multiple advertising categories based on the information related to the user, control the communication interface to send information about at least one advertising category to an advertising platform server based on an event occurring for displaying an advertisement, and receive advertisements in at least one advertising category from the advertising platform server through the communication interface.

[0010] According to an embodiment of the present disclosure, a method for controlling an electronic device includes: acquiring information related to a user of the electronic device based on an image obtained by a camera of the electronic device, determining at least one of a plurality of advertising categories based on the information related to the user, sending information about at least one advertising category to an advertising platform server based on an event occurring for displaying an advertisement, and receiving advertisements in at least one advertising category from the advertising platform server through a communication interface.

[0011] According to an embodiment of the present disclosure, in a non-transitory computer-readable recording medium storing a program for executing a method for controlling an electronic device, the method for controlling an electronic device includes: acquiring information related to a user of the electronic device based on an image obtained by a camera of the electronic device, determining at least one of a plurality of advertising categories based on the information related to the user, sending information about at least one advertising category to an advertising platform server based on an event occurring for displaying an advertisement, and receiving advertisements in at least one advertising category from the advertising platform server through a communication interface.

[0012] Beneficial Effects

[0013] Through the present disclosure as described above, optimal advertisements can be provided to users without the risk of leaking user personal information to the outside, and advertisers can provide optimal advertisements corresponding to advertisement categories determined based on user information, so that the effect of providing advertisements can be improved. BRIEF DESCRIPTION OF THE DRAWINGS

[0014] Figure 1 is a diagram showing the use of a system for providing advertisements based on information associated with a user according to an embodiment;

[0015] Figure 2 and Figure 3 is a block diagram showing a configuration of an electronic device according to an embodiment;

[0016] Figure 4 is a block diagram showing a configuration for providing an advertisement based on information associated with a user according to an embodiment;

[0017] Figure 5 is a view showing information associated with a user stored in a user information database according to an embodiment;

[0018] Figure 6 is a diagram showing a matching table stored in an advertisement category database according to an embodiment;

[0019] Figure 7 is a sequence diagram illustrating a method of providing an advertisement based on information associated with a user according to an embodiment;

[0020] Figure 8is a view showing a method of displaying advertisements according to priorities of advertisement categories according to an embodiment;

[0021] Fig. 9 is a flow chart showing a method of controlling an electronic device according to an embodiment of the present disclosure;

[0022] Fig.10 is a view illustrating an operation of an electronic device using an artificial intelligence model according to an embodiment; and

[0023] Fig.11A and Fig. 11B is a block diagram showing a specific configuration of a learning unit and a recognition unit according to an embodiment of the present disclosure. DETAILED DESCRIPTION

[0024] The above and other aspects, features and advantages of certain embodiments of the present disclosure will become more apparent through the following description in conjunction with the accompanying drawings, in which: However, it should be understood that the present disclosure is not limited to the specific embodiments described below, but includes various modifications, equivalents and / or alternatives of the embodiments of the present disclosure. With respect to the description of the drawings, similar reference numerals may be used for similar constituent elements.

[0025] The terms “having”, “may have”, “including” and “may include” used in the exemplary embodiments of the present disclosure indicate the existence of corresponding features (for example, elements such as values, functions, operations or parts), and do not exclude the existence of additional features.

[0026] In the description, the term "A or B", "at least one of A or / and B", or "one or more of A or / and B" may include all possible combinations of the items listed together. For example, the term "A or B" or "at least one of A or / and B" may specify (1) at least one A, (2) at least one B, or (3) at least one A and at least one B.

[0027] The expressions "1", "2", "first", or "second" used herein may modify various elements without regard to their order and / or importance, and only distinguish one element from another element. Therefore, without limiting the corresponding elements.

[0028] If it is described that a certain element (e.g., a first element) is “operably or communicatively coupled with another element (e.g., a second element)” / “coupled to another element (e.g., a second element)” or “connected to” another element (e.g., a second element), it should be understood that the certain element may be connected to the other element directly or through another element (e.g., a third element). At the same time, if it is described that a certain element (e.g., a first element) is “directly coupled with another element (e.g., a second element)” / “directly coupled to another element (e.g., a second element)” or “directly connected to” another element (e.g., a second element), it should be understood that there are no other elements (e.g., a third element) between the certain element and the other element.

[0029] In the specification, the term "configured to" may be changed to, for example, "suitable for", "capable of", "designed to", "appropriate for", "manufactured to", or "capable" in some cases. The term "configured to" does not necessarily mean "specially designed to" at the hardware level. In some cases, the term "device is configured to" may mean that "the device is able to" do something together with another device or component. For example, the phrase "a sub-processor configured to perform A, B, and C" may refer to a general-purpose processor (such as a CPU or an application processor) that is capable of performing the corresponding operations by executing a dedicated processor (such as an embedded processor) for performing the corresponding operations or executing one or more software programs stored in a memory device.

[0030] The electronic device according to various exemplary embodiments may include, for example, at least one of a smart phone, a tablet PC, a mobile phone, a video phone, an e-book reader, a desktop PC, a laptop PC, a netbook computer, a workstation, a server, a PDA, a portable multimedia player (PMP), an MP3 player, a medical device, a camera, or a wearable device. The wearable device may include at least one of an accessory (e.g., a watch, a ring, a bracelet, an anklet, a necklace, glasses, contact lenses, or a head-mounted device (HMD)), a fabric or clothing (e.g., an electronic clothing), a body attachment type (e.g., a skin pad or a tattoo), or a biological implantable circuit. The electronic device according to the various embodiments described above may include at least one of a TV, a digital video disk (DVD) player, an audio, a refrigerator, an air conditioner, a cleaner, an oven, a microwave oven, a washing machine, an air purifier, a set-top box, a home automation control panel, a security control panel, a media box (e.g., Samsung HomeSync TM, Apple TV TM, or Google TV TM), a game console (e.g., Xbox TM, PlayStation TMs), an electronic dictionary, an electronic key, a camera, or a digital photo frame.

[0031] In another embodiment, the electronic device may include at least one of various medical devices (e.g., various portable medical measuring devices (such as blood glucose meters, heart rate monitors, blood pressure monitors, thermometers, etc.), magnetic resonance angiography (MRA), magnetic resonance imaging (MRI), computed tomography (CT), cameras or ultrasound, etc.), navigation equipment, global navigation satellite system (GNSS), event data recorder (EDR), flight data recorder (FDR), automotive infotainment equipment, marine electronic equipment (e.g., ship navigation equipment, gyrocompass, etc.), avionics equipment, security equipment, vehicle host, industrial or household robot, drone, ATM of financial institution, store point of sale (POS) or Internet of Things device (e.g., light bulbs, various sensors, sprinkler equipment, fire alarm, thermostat, street lamp, toaster, sports equipment, hot water tank, heater, boiler, etc.).

[0032] In the present disclosure, the term "user" may refer to a person using an electronic device or a device using an electronic device (eg, an artificial intelligence electronic device).

[0033] One or more exemplary embodiments will be described in detail with reference to the accompanying drawings. Figure 1 is a diagram showing the use of a system for providing advertisements based on information associated with a user according to an embodiment of the present disclosure.

[0034] The electronic device 100 may obtain an image. In this case, the electronic device 100 may obtain an image through at least one camera and may receive the image from an external device (or an external server). The image obtained by the electronic device 100 may include at least one person.

[0035] The electronic device 100 may obtain information associated with the user of the electronic device 100 based on the obtained image.

[0036] Specifically, the electronic device 100 may identify a person who appears most frequently among a plurality of persons included in a plurality of images obtained by a camera of the electronic device 100 (particularly, a front camera provided on a side where a display is placed) during the self-portrait mode as a user of the electronic device 100. In addition, the electronic device 100 may obtain various information (e.g., age, gender, body shape information, etc.) about a person identified as a user obtained by using an artificial intelligence model for object recognition.

[0037] In another embodiment, the electronic device 100 may analyze images and texts uploaded by the user on the SNS, or analyze images and texts sent by the user to an external device to identify images including the user. For example, when the user uploads the text "the photo of me that I took today" together with an image on the SNS, the electronic device 100 may identify that the user is included in the image uploaded on the SNS through text analysis. In addition, the electronic device 100 may obtain information about the user included in the image uploaded to the SNS or the image sent to the external device by using an artificial intelligence model for object recognition.

[0038] In addition, the electronic device 100 can identify a person who appears more than a preset number of times among a plurality of persons included in a plurality of images stored in the memory as a person related to the user (e.g., family, friends, etc.). In addition, the electronic device 100 can obtain various information (e.g., age, gender, body shape information, etc.) about a person related to the user obtained by using an artificial intelligence model for object recognition.

[0039] In another embodiment, the electronic device 100 can identify an image including a person related to the user by analyzing an image and text uploaded by the user on the SNS or an image and text sent to an external device. For example, when a user uploads the text "Isn't my son pretty?" and an image including a baby on the SNS, the electronic device 100 can identify people related to the user (especially family members, etc.) included in the image uploaded to the SNS through text analysis. In addition, the electronic device 100 can obtain various information about people related to the user included in the image uploaded on the SNS or the image sent to the external device by using an artificial intelligence model for object recognition.

[0040] The electronic device 100 may determine at least one of a plurality of advertising categories based on the information obtained about the user. Specifically, the electronic device 100 may store a matching table in a memory, which matches and stores at least one advertising category predetermined according to the user type. For example, the matching table may store at least one advertising category predetermined according to the user type, such as teenage boys, women in their 20s, and the like. In this case, the matching table may match and store a plurality of advertising categories predetermined according to the user type in order of priority. For example, for women in their 20s, the matching table may store a work advertising category as a first priority, a travel advertising category as a second priority, and a cosmetics advertising category as a third priority. In this case, the matching table may be periodically updated by an external server and may be updated based on the user's contextual information.

[0041] The electronic device 100 can determine the advertisement category information corresponding to the obtained information related to the user by using the matching table. For example, if the information related to the user obtained through the image is "female in her 30s", the electronic device 100 can use the matching table to determine "parenting advertisement category", "travel advertisement category" and "clothing advertisement category", which are advertisement categories matching female in her 30s, as advertisement categories corresponding to the user.

[0042] Furthermore, in addition to the information related to the user as described above, the electronic device 100 may also determine advertisement category information based on user context information (eg, user preference information, user location information, etc.).

[0043] Specifically, information about the user's movement pattern can be obtained based on the location information of the electronic device 100 obtained by the sensor provided in the electronic device 100. In addition, the electronic device 100 can identify the user's area of ​​interest based on the user's search results and website visit history. In addition, the electronic device 100 can obtain information about the user's product purchase history.

[0044] In addition, the electronic device 100 may determine the advertising category information based on the user context information. For example, the electronic device 100 may identify the location where the user lives, the workplace or school where the user goes, etc., based on the information about the user's mobility pattern, and determine the advertising category information based on the location where the user lives or the workplace or school where the user goes. As another example, when the user's interest is traveling, the electronic device 100 may determine the "travel advertising category" that is the user's interest as the advertising category corresponding to the user. As another example, if the products mainly purchased by the user are infant products, the electronic device 100 may determine the "parenting advertising category" as the advertising category corresponding to the user based on the user's purchase information.

[0045] Meanwhile, in the above-mentioned embodiments, it has been described that the advertisement category is determined based on information related to the user or user context information, but this is only an embodiment, and the electronic device 100 may determine the user context information together with the information related to the user. The electronic device 100 may determine the advertisement category information or prioritize the advertisement category based on the user context information as well as the information related to the user. For example, if the information related to the user is "male in his 30s" and the user's interest is "game", the electronic device 100 may determine the "game advertisement category" together with the "parenting advertisement category" and the "home appliance advertisement category" as an advertisement category corresponding to the user, which is an advertisement category corresponding to males in their 30s. Optionally, the electronic device 100 may also determine the advertisement category by giving high priority to the advertisement category corresponding to the user's interest.

[0046] When an event for displaying an advertisement occurs, the electronic device 100 may send information about at least one advertisement category to the advertisement platform server 50. In this case, the event for displaying an advertisement may include, but is not limited to, an event for a user to visit a specific website or an event for watching a video on a website that provides the video. In addition, the electronic device 100 may send information about at least one advertisement category corresponding to the user to the advertisement platform server 50 before the event for displaying an advertisement occurs. The advertisement platform server 50 may be a server owned by an advertisement platform company that provides advertisements requested by multiple advertisers, but this is only an embodiment, and the technical idea of ​​the present disclosure may also be applied to servers provided by advertisers.

[0047] Furthermore, when transmitting information on a plurality of advertisement categories, the electronic device 100 may also transmit priority information on the plurality of advertisement categories.

[0048] The advertisement platform server 50 may determine an advertisement to be provided to the user based on at least one advertisement category or priority information, and may transmit the determined advertisement to the electronic device 100 .

[0049] The electronic device 100 may display the advertisement received from the advertising platform server 50. In this case, the electronic device 100 may determine at least one of the size and position of the advertisement received from the advertising platform server to be displayed based on the priority of the advertisement category. For example, when the priority of the advertisement category is higher, the electronic device 100 may increase the size of the advertisement to be displayed and determine the position of the advertisement to be displayed to be the top.

[0050] At the same time, the object recognition model in the above-mentioned embodiment is a judgment model based on artificial intelligence algorithm learning, which can be, for example, a model based on a neural network. In addition, the electronic device 100 can obtain information related to the user using the object recognition model described above, determine the advertising category corresponding to the obtained information related to the user, and use a personal assistant program that is only an artificial intelligence program (or an artificial intelligence agent) to receive advertisements corresponding to the advertising category determined from the advertising platform server 50. In this case, the personal assistant program is a dedicated program for providing services based on artificial intelligence (AI), and can be executed by an existing general-purpose processor (such as a CPU) or a separate pure AI processor (such as a GPU).

[0051] Specifically, when a user input for entering a predetermined mode (e.g., an active advertisement providing mode) is detected, the electronic device 100 may operate (or execute) an artificial intelligence agent. In addition, the artificial intelligence agent may analyze an image stored in the electronic device 100 based on the usage information of the electronic device 100 to obtain information related to the user or obtain user context information, determine advertisement category information based on the obtained information related to the user or user context information, and receive advertisements corresponding to the determined advertisement category from an external advertisement platform company.

[0052] According to the embodiments of the present disclosure as described above, the electronic device 100 can provide the best advertisement to the user without the risk of leaking the user's personal information.

[0053] Figure 2 is a block diagram showing the configuration of the electronic device 100. Figure 2 As shown, the electronic device 100 may include a camera 110, a communication interface (including circuits) 120, a memory 130, and a processor 140. In this case, Figure 2 The configuration shown in FIG. 1 is an exemplary view showing an embodiment of the present disclosure, and appropriate hardware / software configurations obvious to those skilled in the art may be additionally included in the electronic device 100, or may be omitted. Figure 2 The configuration shown in .

[0054] The camera 110 can obtain an image. In addition, a plurality of cameras 110 may be provided on the front and rear surfaces of the electronic device 100. Specifically, the electronic device 100 may obtain a self-portrait image through a camera located in front of the camera 110 (the surface on which the display exists). Meanwhile, the camera 110 may be provided on the body of the electronic device 100, but this is only an embodiment, and may be provided outside the electronic device 100 and electrically connected to the electronic device 100.

[0055] The communication interface 120 can communicate with an external device. The communication interface 120 is a configuration for performing communication with an external device. At the same time, the communication of the communication interface 120 with the external device may include communication through a third device (e.g., a repeater, a hub, an access point, a server, or a gateway). For example, wireless communication may include cellular communication using at least one of Long Term Evolution (LTE), LTE Advanced (LTE-A), Code Division Multiple Access (CDMA), Wideband CDMA (WCDMA), Universal Mobile Telecommunications System (UMTS), Wireless Broadband (WiBro), and Global System for Mobile Communications (GSM). According to an exemplary embodiment, wireless communication may include, for example, wireless fidelity (Wi-Fi), Bluetooth, Bluetooth Low Energy (BLE), Zigbee, near field communication (NFC), magnetic security transmission, radio frequency RF, or body area network BAN. Wired communication may include, for example, at least one of Universal Serial Bus (USB), High Definition Multimedia Interface (HDMI), Recommended Standard 232 (RS-232), Power Line Communication, or Plain Old Telephone Service (POTS). The network performing wireless communication or wired communication may include at least one of a telecommunication network (eg, a computer network (eg, LAN or WAN)), the Internet, or a telephone network.

[0056] For example, the communication interface 120 may transmit information about the advertisement category to the external advertisement platform server 50 , and may receive an advertisement determined based on the information about the advertisement category from the advertisement platform server 50 .

[0057] In addition, the communication interface 120 may receive an image for acquiring information related to a user or various information for acquiring context information from an external electronic device.

[0058] For example, the memory 130 may store commands or data about at least one other element of the electronic device 100. The memory 130 may be implemented as a non-volatile memory, a volatile memory, a flash memory, a hard disk drive (HDD), or a solid state drive (SDD). The memory 130 may be accessed by the processor 140, and the processor 140 may read, record, correct, delete, update, etc. the data. According to an embodiment of the present disclosure, the term storage may include the memory 130, a read-only memory (ROM) (not shown) and a random access memory (RAM) (not shown) within the processor 140, and a memory card (not shown) (e.g., a micro secure digital (SD) card or a memory stick) connected to the electronic device 100. In addition, the memory 130 may store programs, data, etc. to constitute various screens to be displayed on the display area of ​​the display.

[0059] In addition, the memory 130 may store an artificial intelligence agent for operating an object recognition model. Specifically, the electronic device 100 may use an artificial intelligence agent to drive an object recognition model, which is used to obtain information related to the user by analyzing an image. In this case, the artificial intelligence agent is a dedicated program for providing services based on artificial intelligence (AI) (e.g., speech recognition services, secretarial services, translation services, search services, advertising provision services, etc.). In particular, the artificial intelligence agent can be executed by an existing general-purpose processor (e.g., a CPU) or a separate AI-specific processor (e.g., a GPU, etc.).

[0060] like Figure 3 As shown, the memory 130 may include multiple components (or modules) for providing advertisements to users. Figure 4 This is described in detail.

[0061] Specifically, the memory 130 may store a matching table for matching and storing at least one advertisement category predetermined according to the user type. In this case, the matching table may match and store multiple advertisement categories predetermined according to the user type in order of priority. In addition, the matching table may be periodically updated by an external server.

[0062] The processor 140 may be electrically connected to the memory 130 to control the overall operation and functions of the electronic device 100. Specifically, the processor may obtain information related to the user of the electronic device 100 based on the image obtained by the camera 110 by executing at least one command stored in the memory 130, and determine at least one of a plurality of advertisement categories based on the information related to the user, and when an event for displaying an advertisement occurs, control the communication interface 120 to send information about at least one advertisement category to the advertisement platform server 50, and receive advertisements about at least one advertisement category from the advertisement platform server 50 through the communication interface 120.

[0063] Specifically, the processor 140 may identify the person who appears most frequently among the multiple persons included in the multiple images obtained by the camera 110 (particularly, the camera set in front of the electronic device 100) during the self-portrait mode as a user. In addition, the processor 140 may obtain the age, gender, and body shape information of the person identified as the user by using an artificial intelligence model for object recognition.

[0064] In addition, the processor 140 may identify a person who appears more than a predetermined number of times among the multiple persons included in the multiple images stored in the memory 130 as a person related to the user (e.g., family, friends, etc.). In addition, the processor 140 may obtain age, gender, and body shape information of the person related to the user by using an artificial intelligence model for object recognition.

[0065] As described above, the processor 140 may determine the advertisement category based on the obtained information about the user or the information about the person related to the user. Specifically, the processor 140 may determine the advertisement category information corresponding to the obtained information related to the user by using the matching table stored in the memory 130.

[0066] In addition, the processor 140 may obtain information about the advertisement category based on context information of the user and information related to the user.

[0067] Specifically, the processor 140 may obtain information about a user's movement pattern based on location information of the electronic device obtained by a sensor provided in the electronic device 100 , and determine at least one of a plurality of advertisement categories based on the information about the user and the user's movement pattern.

[0068] Furthermore, the processor 140 may identify the user's interests based on the user's search results and the visit history of the website, and determine at least one of a plurality of advertisement categories based on the information related to the user and the user's interests.

[0069] In addition, the processor 140 may obtain information about a product purchase history of the user, and may determine at least one advertisement category among a plurality of advertisement categories based on the information about the user and the product purchase history of the user.

[0070] In addition, the processor 140 may control the communication interface 120 to transmit information about the determined advertisement category to the advertisement platform server 50. In this case, the advertisement category information may include priority information of a plurality of advertisement categories.

[0071] In addition, the processor 140 may receive an advertisement corresponding to the advertisement category information from the advertisement platform server 50 through the communication interface 120 and display the received advertisement. Specifically, the processor 140 may determine at least one of the size and position of displaying the advertisement received from the advertisement platform server 50 based on the priority of the advertisement category.

[0072] Figure 3 1 is a block diagram showing a detailed configuration of an electronic device according to an embodiment of the present disclosure. The electronic device 100 may include a camera 110, a communication interface 120, a display 150, a speaker 160, a memory 130, an input interface 170, a sensor 180, and a processor 140. Figure 2 Described in Figure 3 The camera 110 , the communication interface 120 , the memory 130 , and the processor 140 are shown, and thus repeated descriptions will be omitted.

[0073] The display 150 may display various information under the control of the processor 140. Specifically, the display 150 may display advertisements corresponding to the advertisement categories. In this case, the display 150 may provide advertisements corresponding to the advertisement categories to the plurality of advertisement areas included in the screen. In this case, the display 150 may provide advertisements to the plurality of advertisement areas based on the display size and display area of ​​the advertisement determined by the processor 140 according to the priority of the advertisement category. Meanwhile, the display 150 may be implemented as a touch screen together with the touch panel.

[0074] The speaker 160 may include various audio output circuits and be configured to output various alarm sounds or voice messages in addition to various audio data for which various processing operations such as decoding, amplification, and noise filtering are performed by the audio processor. Specifically, the speaker 160 may output a voice message included in an advertisement. Meanwhile, the configuration for outputting audio may be implemented as a speaker, but this is only an example, and may be implemented as an output terminal capable of outputting audio data.

[0075] The input interface 170 may receive a user input for controlling the electronic device 100. Specifically, the input interface 170 may include a microphone for receiving a user voice, a touch panel for receiving a user touch using a user's hand or a stylus, a button for receiving a user manipulation, etc. However, Figure 3 The example of the input interface 170 shown is only an embodiment and may be implemented with other input devices (eg, a keyboard, a mouse, a motion input unit, etc.).

[0076] The sensor 180 may detect various status information of the electronic device 100. For example, the sensor 180 may include a motion sensor capable of detecting motion information of the electronic device 100 (e.g., a gyroscope sensor, an acceleration sensor, etc.), a sensor capable of detecting position information (e.g., a global positioning system (GPS) sensor), a sensor capable of detecting environmental information around the electronic device 100 (e.g., a temperature sensor, a humidity sensor, an air pressure sensor, etc.), a sensor capable of detecting user information of the electronic device 100 (e.g., a blood pressure sensor, a blood sugar sensor, a pulse rate sensor, etc.), and the like.

[0077] Figure 4 is a block diagram showing a configuration for providing advertisements based on information related to a user according to an embodiment of the present disclosure. Figure 4 As shown, the electronic device 100 may include a user information acquisition module 410 , an object recognition model 420 , a context information acquisition module 430 , a user information DB 440 , an advertisement category determination module 450 , an advertisement category DB 460 , and an advertisement display module 470 . Figure 4The illustrated configurations may be implemented as software and stored in the memory 130 , but this is merely an example, and at least one configuration may be implemented with separate dedicated hardware (eg, a dedicated processor chip).

[0078] The user information acquisition module 410 may obtain information related to the user by analyzing an image obtained by the camera 110 or an image received from an external device. Specifically, the user information acquisition module 410 may analyze an image obtained by a camera located in front of the electronic device during a self-portrait mode to determine a user detected with the highest frequency among the people included in the image. Specifically, the user information acquisition module 410 may classify the faces of the people included in the image based on a facial recognition algorithm, and determine the person detected with the highest frequency among the classified faces as the user.

[0079] Optionally, the user information acquisition module 410 may determine a person detected more than a predetermined value in the plurality of images stored in the memory 130 as a person related to the user. Specifically, the user information acquisition module 410 may classify the faces of the people included in the plurality of images stored in the memory 130 based on a facial recognition algorithm, and identify a person detected with a frequency greater than or equal to a predetermined value in the classified faces as a person related to the user.

[0080] The user information acquisition module 410 can obtain information about the user or the person related to the user (e.g., gender, age, body shape, etc.) by inputting an image including the user or the person related to the user into the object recognition model 420. In this case, the object recognition model can be an artificial intelligence model that is trained to input an image to obtain information about the object included in the image. In this case, the object recognition model 420 can be, for example, a model based on a neural network. The object recognition model can be designed to simulate the structure of the human brain on a computer, and can include a plurality of network nodes with weights that simulate neurons of a human neural network. A plurality of network nodes can form a connection relationship so as to respectively simulate the synaptic activity of neurons that send and receive signals through synapses. In addition, the object recognition model 420 can include, for example, a neural network model or a deep learning model developed from a neural network model. In a deep learning model, a plurality of network nodes can exchange data according to a convolutional connection relationship when located at different depths (or layers). For example, models such as a deep neural network (DNN), a recursive neural network (RNN), and a bidirectional recursive deep neural network (BRDNN) can be used as a data recognition model, but are not limited thereto.

[0081] The context information acquisition module 430 may obtain context information of the user based on the usage information of the electronic device 100. Specifically, the context information acquisition module 430 may obtain the user's mobility pattern information based on the location information of the electronic device 100 obtained by the sensor 180. For example, the context information acquisition module 430 may obtain information about the user's mobility pattern (e.g., home, work, school) based on the location information of the electronic device 100.

[0082] The context information acquisition module 430 can obtain information about the user's interests (or preferred fields) based on the user's search results and the website's visit history. For example, when the user frequently searches for football or frequently visits football sites, the context information acquisition module 430 can determine the user's interests as football.

[0083] The context information acquisition module 430 may obtain information about the user's interests based on the user's product purchase history. For example, when the user has a purchase history of parenting products, the context information acquisition module 430 may determine parenting as the user's interest.

[0084] The context information acquisition module 430 may obtain context information of the user based on data or applications stored in the electronic device 100. For example, if there is a folder named "My Son" in the electronic device 100 and there is an application used in a kindergarten / daycare center, the context information acquisition module 430 may obtain context information that the user has a son.

[0085] The context information acquisition module 430 may obtain the user's interest based on the user's emotional state detected when viewing a specific screen. For example, if it is recognized that the user's emotion is positive when viewing football content, the context information acquisition module 430 may obtain football as the user's interest. In this case, the user's emotion may be determined based on the user's voice obtained through the microphone and the text input through the input interface 170.

[0086] In addition, the context information acquisition module 430 may obtain context information of the user based on a text included in a message, an SNS, an email, etc. obtained by the electronic device 100 .

[0087] The user information DB 440 may store the user information obtained by the user information acquisition module 410 or the context information obtained by the context information acquisition module 430. In this case, the user information DB 440 may store the user information or the context information in the form of a knowledge graph such as RDF, OWL, etc. In other words, Figure 5As shown, the user information DB 440 can store information and the relationship between the information in a graphical form with the user as the center. In other words, the user information DB 440 can store entities, relationships between objects, and attribute types / names and attribute values ​​of objects in the form of tables or graphs, and include data in which relationships or attributes are stored in a specific object in various forms. For example, Figure 5 As shown, the user information DB 440 can store the relationship between the user and another object (e.g., family, friends, pets, etc.) and the user's attributes (e.g., age, gender, items of interest, where the user lives, etc.) in the form of a relationship graph.

[0088] In this case, whenever the user information acquisition module 410 acquires user information or the context information acquisition module 430 acquires context information, the information stored in the user information DB 440 may be updated. In addition, the relationship graph stored in the user information DB 440 may be updated by acquiring additional information from an external server. For example, as context information, when a user has a pet named Chihuahua, additional information about Chihuahua (e.g., life span, body shape, personality, etc.) may be received from an external server and stored in the user DB 440 to update the relationship graph.

[0089] The advertisement category determination module 450 may determine an advertisement category that is optimal for the user among a plurality of advertisement categories based on the user information or context information stored in the user information DB 440 .

[0090] Specifically, the advertisement category determination module 450 may determine the advertisement category that is best for the user among the multiple advertisement categories using the matching table stored in the advertisement category DB 460. Specifically, the matching table stored in the advertisement category DB 460 may match and store at least one advertisement category predetermined according to the user type. Figure 6 As shown, the matching table can store multiple advertising categories corresponding to the gender and age of the user. In other words, the matching category can store multiple advertising categories corresponding to males in their teens, males in their twenties, males in their thirties, males in their forties, males in their fifties, females in their teens, females in their twenties, females in their thirties, females in their forties, and females in their fifties. Figure 6 In the embodiment, it has been described that a plurality of advertisement categories are matched and stored according to the age and gender of the user. However, this is only an embodiment, and a plurality of advertisement categories may be matched and stored according to information related to various users (such as the region, occupation, etc. of the user).

[0091] In this case, the matching table may match and store multiple advertisement categories predetermined according to user types in order of priority. Figure 6As shown, the first priority ad category for teenage males may be the education category. The second priority ad category for teenage males may be the games category. The third priority ad category for teenage males may be the clothing category. The first priority ad category for females in their 20s may be the employment category. The second priority ad category for females in their 20s may be the travel category. The third priority ad category for females in their 20s may be the clothing category.

[0092] When the program is installed, the matching table may be matched with a predetermined advertisement category according to the user type, but this is only an embodiment and may be periodically updated by an external server.

[0093] In addition, the matching table can be updated based on user context information. For example, when the user is a male in his 30s and the user's preferred field is games, the matching table can match and store the game category of the user's preferred field with male in his 30s.

[0094] Meanwhile, the advertisement category determination module 450 may determine a plurality of advertisement categories corresponding to the user among the plurality of advertisement categories based on the user information stored in the user information DB 440. For example, if the user is a woman in her 30s, the advertisement category determination module 450 may determine the first priority advertisement category as the parenting category, the second priority advertisement category as the clothing category, and the third priority advertisement category as the travel category.

[0095] In addition, the advertisement category determination module 450 may determine at least one advertisement category corresponding to the user based on the user context information stored in the user information DB 440. For example, when the user's interest is travel, the advertisement category determination module 450 may determine the travel category as the advertisement category without considering the advertisement category stored in the matching table.

[0096] In this case, the advertisement category determination module 450 may give priority to advertisement categories corresponding to the user's interests in the user context information. For example, if the user is a woman in her 30s and the user's interest is travel, the advertisement category determination module 450 may change the priorities of the plurality of advertisement categories stored in the matching table and determine that the first priority advertisement category is the travel category, the second priority advertisement category is the parenting category, and the third priority advertisement category is the clothing category.

[0097] Optionally, the advertisement category determination module 450 may exclude at least one of the advertisement categories stored in the matching table based on the user context information. For example, if the user is a woman in her 30s or a single person, the advertisement category determination module 450 may exclude the parenting category from the advertisement categories stored in the matching table.

[0098] Alternatively, the advertisement category determination module 450 may determine the advertisement category according to the user's emotional state or health state detected based on the sensing values ​​obtained by various sensors. For example, if it is determined that the user's health state is poor, the advertisement category determination module 450 may determine the health category as the advertisement category. As another example, if it is determined that the user is in an angry state, the advertisement category determination module 450 may determine the category corresponding to the user's preferred interests as the advertisement category.

[0099] At the same time, the advertising category determination module 450 can determine at least one advertising category corresponding to the user by using only information related to the user or by using both information related to the user and context information, but this is only an embodiment, and at least one advertising category corresponding to the user can be determined using only context information.

[0100] The advertisement display module 470 may display at least one advertisement received from the advertisement platform company 50 on the display 150. In this case, the advertisement display module 470 may determine at least one of a size and a position at which the advertisement received from the advertisement platform server 50 will be displayed based on the priority of the advertisement category. For example, the advertisement display module 470 may place an advertisement with a higher priority in an advertisement area with a higher position or a larger size among the plurality of advertisement areas, and place an advertisement with a lower priority in an advertisement area with a lower position or a smaller size among the plurality of advertisement areas.

[0101] In addition, when a plurality of advertisements per category are received, the advertisement display module 470 may periodically change and display the plurality of advertisements.

[0102] Figure 7 is a flowchart illustrating a method for controlling the electronic device 100 according to an embodiment.

[0103] The electronic device 100 may obtain an image (S710). In this case, the electronic device 100 may obtain an image including a user through the camera 110, but this is only an exemplary embodiment, and the image may be received from an external electronic device.

[0104] The electronic device 100 may obtain information related to the user based on the obtained image (S720). Specifically, the electronic device 100 may identify the user or a person related to the user by analyzing the obtained image. For example, the electronic device 100 may identify the person who appears most frequently among the multiple persons included in the multiple images obtained by the camera 110 during the self-portrait mode as the user, and identify the person who appears more than a predetermined number of times among the multiple persons included in the multiple images stored in the memory 130 as a person related to the user. In addition, the electronic device 100 may obtain various information related to the user, such as the age, gender, body shape information, etc. of the person identified as the user, by using an artificial intelligence model for object recognition.

[0105] The electronic device 100 may obtain context information (S730). In this case, the electronic device 100 may obtain context information based on various usage information of the electronic device 100. For example, the electronic device 100 may obtain information about the user's movement pattern based on the location information of the electronic device obtained by the sensor 180, obtain the user's interests based on the user's search results and the website's visit history, and obtain the user's interests based on information about the user's product purchase history.

[0106] The electronic device 100 may determine an advertisement category based on the information related to the user and the context information (S740). In this case, the electronic device 100 may determine the advertisement category corresponding to the user by using a matching table stored by matching a plurality of advertisement categories for each user type. In other words, the electronic device 100 may determine at least one advertisement category information corresponding to the obtained information related to the user by using the matching table. Alternatively, the electronic device 100 may add, change, or delete at least one advertisement category determined based on the information related to the user based on the context information.

[0107] The electronic device 100 may detect the occurrence of an event for displaying an advertisement (S750). For example, the electronic device 100 may detect various events, such as an event for accessing a specific website or an event for logging into a specific SNS.

[0108] The electronic device 100 may transmit information about the determined advertisement category to the advertisement platform server 50 (S760). In this case, the electronic device 100 may transmit not only information about the determined advertisement category but also priority information about a plurality of advertisement categories.

[0109] The advertisement platform server 50 may determine the advertisement based on the advertisement category (S770). In other words, the advertisement platform server 50 may determine the advertisement corresponding to the advertisement category among a plurality of pre-stored advertisements.

[0110] The advertising platform server 50 may transmit information about the determined advertisement to the electronic device 100 (S780). In this case, the advertising platform server 50 may directly transmit information about the advertisement to the electronic device 100, but this is only an embodiment, and transmits information about the advertisement to the electronic device 100 through a server corresponding to the advertiser.

[0111] The electronic device 100 may provide the advertisement received from the advertisement platform server (S790). In this case, the electronic device 100 may determine the size and position of the advertisement display area based on the priority of the advertisement category corresponding to the received advertisement. For example, when the user is a woman in her 30s, such as Figure 8 As shown, the electronic device 100 may provide an advertisement corresponding to the parenting category, which is the first priority advertisement category for women in their 30s, to the largest advertisement area 810 located at the top of the screen, provide an advertisement corresponding to the clothing category, which is the second priority advertisement category for women in their 30s, to the advertisement area 820 located at the center area of ​​the screen, and provide an advertisement corresponding to the travel category, which is the third priority advertisement category for women in their 30s, to the advertisement area 830 located at the bottom of the screen. In other words, the electronic device 100 may provide advertisements with high advertisement priorities in areas where user recognition can be easily performed.

[0112] Fig. 9 is a flowchart illustrating a method for controlling an electronic device according to an embodiment.

[0113] The electronic apparatus 100 may obtain an image (S910). In this case, the electronic apparatus 100 may obtain the image through the camera 110, but this is only an embodiment, and the image may be obtained from an external device.

[0114] The electronic device 100 may obtain information related to the user based on the obtained image (S920). In this case, the electronic device 100 may obtain the user's gender, age, and body type as the information related to the user.

[0115] The electronic device 100 may determine the advertisement category based on the information related to the user (S930). Specifically, the electronic device 100 may determine at least one advertisement category corresponding to the user based on a matching table that matches and stores the advertisement category for each user type.

[0116] The electronic device 100 may detect that an event for displaying an advertisement occurs (S940).

[0117] The electronic device 100 may transmit information about the advertisement category to the advertisement platform server 50 (S950), and receive advertisements included in the advertisement category from the advertisement platform server 50 (S960).

[0118] In addition, the electronic device 100 may provide the advertisement received from the advertisement platform server 50 (S970).

[0119] By disclosing as described above, users can receive optimal advertisements without the risk of leakage of user personal information, and advertisers can provide users with optimal advertisements corresponding to advertisement categories determined based on user information, thereby increasing advertising effectiveness.

[0120] Fig.10 is a view illustrating a method for controlling a display device according to an embodiment.

[0121] Reference Fig.10 , the electronic device 1000 may include at least one of a learning unit 1010 and a recognition unit 1020 . Fig.10 The learning unit 1010 and the recognition unit 1020 may be implemented as a configuration of software stored in the memory 130 of the electronic device 100, but this is merely an embodiment and may be implemented as a separate dedicated hardware chip.

[0122] The learning unit 1010 may generate or train an artificial intelligence model for identifying information of a plurality of objects included in an image by using the learning data. The learning unit 1010 may generate a training model having a recognition criterion by using the collected learning data. For example, the learning unit 1010 may generate, learn, or update an object recognition model for acquiring information related to a plurality of users included in an image by using an image and information about an object included in the image as learning data.

[0123] The recognition unit 1020 can obtain various information by using predetermined data as input data of the training model. For example, the recognition unit 1020 can recognize (or estimate, infer) information related to multiple users included in the image by using the image as input data of the training object recognition model.

[0124] At least a portion of the learning unit 1010 and at least a portion of the recognition unit 1020 may be implemented as a software module, or manufactured in the form of at least one hardware chip and installed on an electronic device. For example, at least one of the learning unit 1010 and the recognition unit 1020 may be manufactured in the form of a dedicated hardware chip for artificial intelligence (AI), or may be manufactured as part of an existing general-purpose processor (e.g., a CPU or an application processor) or a graphics-specific processor (e.g., a GPU) and installed in the above-mentioned various electronic devices. In this case, the dedicated hardware chip for artificial intelligence is a dedicated processor specifically for probability calculation, which has higher parallel processing performance than existing general-purpose processors, and can therefore quickly process computing tasks in the field of artificial intelligence such as machine learning. When the learning unit 1010 and the recognition unit 1020 are implemented as software modules (or program modules including instructions), the software modules may be stored in a non-temporary computer-readable recording medium. In this case, the software module may be provided by an operating system (OS), or may be provided by a predetermined application. Alternatively, some software modules may be provided by an operating system (OS), and some software modules may be provided by a predetermined application.

[0125] In this case, the learning unit 1010 and the recognition unit 1020 may be installed on one electronic device, or may be installed on separate electronic devices, respectively. For example, one of the learning unit 1010 and the recognition unit 1020 may be included in the electronic device 100, and the other may be included in an external server. In addition, the learning unit 1010 and the recognition unit 1020 may provide the recognition unit 1020 with model information constructed by the learning unit 1010 via wired or wireless communication, or data input to the learning unit 1010 may be provided to the learning unit 1010 as additional learning data.

[0126] Fig.11A is a block diagram of a learning unit 1010 and a recognition unit 1020 according to various embodiments.

[0127] Reference Fig.11A (a), according to some embodiments, the learning unit 1010 may include a learning data acquisition unit 1010-1 and a model learning unit 1010-4. In addition, the learning unit 1010 may also selectively include at least one of a learning data preprocessing unit 1010-2, a learning data selection unit 1010-3, and a model evaluation unit 1010-5.

[0128] The learning data acquisition unit 1010-1 may obtain the learning data required for an artificial intelligence model (e.g., an object recognition model). According to an embodiment of the present disclosure, the learning data acquisition unit 1010-1 may obtain an image and information about an object included in the image as learning data. The learning data may be data collected or tested by the learning unit 1010 or a manufacturer of the learning unit 1010.

[0129] The model learning unit 1010-4 can learn a standard about how to identify an object (especially a person or an animal) included in an image by using learning data. For example, the model learning unit 1010-4 can learn an artificial intelligence model by using supervised learning of learning as a determination criterion, and the learning uses at least a portion of the learning data. As another example, the artificial intelligence model can learn a data recognition model by unsupervised learning, which finds criteria for determining the environment by learning the data type used to determine the situation without further guidance. As another example, the model learning unit 1010-4 can learn an artificial intelligence model by using reinforcement learning of feedback on whether the result determined according to the learning situation is correct. In addition, for example, the model learning unit 1010-4 can learn an artificial intelligence model using a learning algorithm including an error back propagation method or a gradient descent method.

[0130] If there are multiple pre-established artificial intelligence models, the model learning unit 1010-4 can determine an artificial intelligence model with a high correlation between the input learning data and the basic learning data as the artificial intelligence model to be learned. In this case, the basic learning data can be pre-classified according to the data type, and the data recognition model can be pre-established according to the data type. For example, the basic learning data can be pre-classified according to various criteria, such as the area where the learning data is generated, the time when the learning data is generated, the size of the learning data, the type of learning data, the creator of the learning data, the type of object in the learning data, etc.

[0131] In addition, when learning the artificial intelligence model, the model learning unit 1010-4 can store the learned artificial intelligence model. In this case, the model learning unit 1010-4 can store the learned artificial intelligence model in the memory 130 of the electronic device 100. Alternatively, the model learning unit 1010-4 can store the learned artificial intelligence model in the memory of a server connected to the electronic device 100 via a wired or wireless network.

[0132] The learning unit 1010 may also include a learning data preprocessing unit 1010-2 and a learning data selection unit 1010-3 to improve the recognition result of the artificial intelligence model or save resources or time required to generate the artificial intelligence model.

[0133] The learning data preprocessing unit 1010-2 may preprocess the acquired data so that the acquired data can be used for object recognition. The training data preprocessing unit 1010-2 may process the acquired data into a predetermined format so that the model learning unit 1010-4 may use the acquired data for learning object recognition.

[0134] The learning data selection unit 1010-3 can select data required for learning from the data obtained by the learning data acquisition unit 1010-1 or the data preprocessed by the learning data preprocessing unit 1010-2. The selected learning data can be provided to the model learning unit 1010-4. The learning data selection unit 1010-3 can select data required for learning from the preprocessed data. In addition, the learning data selection unit 1010-3 can select data according to a predetermined criterion by learning the model learning unit 1010-4 described below.

[0135] The learning unit 1010 may also include a model evaluation unit 1010-5 to improve the recognition results of the artificial intelligence model.

[0136] The model evaluation unit 1010-5 may input evaluation data to the artificial intelligence model, and if the recognition result output from the evaluation data does not meet the predetermined criteria, allow the model learning unit 1010-4 to learn again. In this case, the evaluation data may be predefined data for evaluating the artificial intelligence model.

[0137] For example, if the number or ratio of evaluation data whose recognition results are inaccurate in the recognition results of the learning data recognition model for the evaluation data exceeds a predetermined threshold, the model evaluation unit 1010 - 5 may evaluate that the predetermined criterion is not satisfied.

[0138] At the same time, when there are multiple learning data recognition models, the model evaluation unit 1010-5 can evaluate whether each learning mobile image recognition model meets the predetermined criteria, and determine the model that meets the predetermined criteria as the final data recognition model. In this case, when there are multiple models that meet the predetermined criteria, the model evaluation unit 1010-5 can determine any one or a predetermined number of models previously set in descending order of evaluation scores as the final data recognition model.

[0139] Reference Fig.11A In (b), according to some embodiments, the recognition unit 1020 may include an input data acquisition unit 1020-1 and a recognition result providing unit 1020-4.

[0140] In addition, the recognition unit 1020 may also selectively include at least one of an input data preprocessor 1020 - 2 , an input data selection unit 1020 - 3 , and a model updating unit 1020 - 5 .

[0141] The input data acquisition unit 1020-1 may obtain data required for recognizing an object included in an image. The recognition result providing unit 1020-4 may recognize an object included in an image by applying the input data obtained by the input data acquisition unit 1021-1 to an artificial intelligence model learned as an input value. The recognition result providing unit 1020-4 may apply data selected by the input data preprocessing unit 1020-2 or the input data selecting unit 1020-3 to be described below as an input value to the artificial intelligence model to obtain a recognition result. The recognition result may be determined by the artificial intelligence model.

[0142] As an embodiment, the recognition result providing unit 1020-4 may apply the image data obtained by the input data acquiring unit 1020-1 to the learned first model to recognize (or estimate) an object (eg, a person or an animal) included in the image.

[0143] The recognition unit 1020 may further include an input data preprocessor 1020-2 and an input data selection unit 1020-3 to improve the recognition result of the artificial intelligence model or save resources or time used to provide the recognition result.

[0144] The input data preprocessing unit 1020-2 can preprocess the obtained data so that the obtained data can be input to the artificial intelligence model. The input data preprocessing unit 1020-2 can process the obtained data into a predetermined format so that the recognition result providing unit 1020-4 described below can process the obtained data obtained in the predetermined format.

[0145] The input data selection unit 1020-3 can select data required for object recognition from the data obtained by the input data acquisition unit 1020-1 or the data preprocessed by the input data preprocessor 1020-2. The selected data can be provided to the recognition result providing unit 1020-4. The input data selection unit 1020-3 can select part or all of the obtained or preprocessed data according to a predetermined selection criterion for object recognition. In addition, the input data selection unit 1020-3 can select data according to a predetermined selection criterion through learning of the model learning unit 1010-4.

[0146] The model updating unit 1020-5 may control the artificial intelligence model to be updated based on the evaluation of the recognition result provided by the recognition result providing unit 1020-4. For example, the model updating unit 1020-5 may provide the recognition result provided by the recognition result providing unit 1020-4 to the model learning unit 1010-4, so that the model learning unit 1010-4 may request additional learning or updating of the artificial intelligence model.

[0147] Fig. 11Bis a diagram illustrating an example in which the electronic device 100 and the external server S cooperate with each other to learn and determine data according to an embodiment.

[0148] Reference Fig. 11B , the external server S may learn a criterion for recognizing an object included in an image, and the electronic device 100 may recognize the object included in the image by using a model generated based on a learning result of the server S.

[0149] In this case, the model learning unit 1010-4 of server S can execute Fig.10 Functions of the learning unit 1010 shown. The model learning unit 1010-4 of the server S can learn object recognition criteria (or recognition criteria) for the artificial intelligence model.

[0150] In addition, the recognition result providing unit 1020-4 of the electronic device 100 may apply the data selected by the input data selecting unit 1020-3 to the artificial intelligence model generated by the server S, and recognize the object included in the image. Alternatively, the recognition result providing unit 1020-4 of the electronic device 100 may receive the artificial intelligence model generated by the server 50 from the server 50, and recognize the object included in the image using the received artificial intelligence model.

[0151] The term "module" used herein includes a unit composed of hardware, software or firmware, and can be used interchangeably with terms such as logic, logic block, component or circuit. A "module" can be a component built as a whole or a minimum unit or part thereof that performs one or more functions.

[0152] According to an embodiment, the various embodiments described above may be implemented as software including instructions stored in a machine-readable storage medium readable by a machine (e.g., a computer). A device may include an electronic device according to the disclosed embodiment, as a device that calls stored instructions from a storage medium and is operable according to the called instructions. When the instructions are executed by a processor, the processor may use other components to perform functions corresponding to the instructions, or may perform these functions under the control of the processor. The instructions may include code generated or executed by a compiler or interpreter. The machine-readable storage medium may be provided in the form of a non-transitory storage medium. "Non-transitory" means that the storage medium does not include a signal and is tangible, but does not distinguish whether the data is stored in the storage medium semi-permanently or temporarily.

[0153] In addition, according to an embodiment, the method according to the various embodiments described above may be provided as part of a computer program product. The computer program product may be traded between a seller and a buyer. The computer program product may be distributed in the form of a machine-readable storage medium (e.g., a compact disc read-only memory (CD-ROM)) or distributed through an application store (e.g., PlayStore). TM ) Online distribution. In the case of online distribution, at least a part of the computer program product may be at least temporarily stored or temporarily generated on a storage medium, such as a memory in a manufacturer's server, an application store's server, or a relay server.

[0154] In addition, each of the components (e.g., modules or programs) according to the various embodiments described above may be composed of a single entity or multiple entities, and some of the subcomponents of the subcomponents described above may be omitted, or other subcomponents may be further included in various embodiments. Typically, or in addition, some components (e.g., modules or programs) may be integrated into a single entity to perform the same or similar functions performed by each corresponding component prior to integration. According to various exemplary embodiments, the operations performed by modules, program modules, or other components may be sequential, parallel, or both, performed iteratively or heuristically, or at least some operations may be performed in a different order, omitted, or other operations may be added.

Claims

1. An electronic device, comprising: camera; Communication interface; a memory comprising at least one command; as well as a processor configured to connect to the camera, the communication interface and the memory to control the electronic device, The processor is configured to execute the at least one command to: identifying a person who appears most frequently among a plurality of persons included in a plurality of images obtained by the camera during a self-portrait mode as a user of the electronic device, and obtaining age, gender, and body shape information about a person identified as a user by using an artificial intelligence model for object recognition that is trained using the age, gender, and body shape information as learning data; identifying at least one category among a plurality of advertisement categories as a preferred field for the user based on the obtained age, gender and body shape information about the person identified as the user, Based on the event for displaying the advertisement being occurred, controlling the communication interface to send information about the at least one advertisement category to the advertisement platform server, receiving an advertisement corresponding to the advertisement category from the advertisement platform server through a communication interface, identifying at least one of a size and a position of the received advertisement to be displayed on the display based on a priority order corresponding to the at least one advertisement category, and controlling display of the received advertisement on a display based on at least one of the identified size or position, wherein the processor is configured to identify the user's interests by determining the user's emotions, and At least one of the plurality of categories is identified based on the obtained age, gender, and body shape information about the person identified as the user and user interests.

2. The electronic device according to claim 1, wherein: The processor is configured to identify a person who appears more than a predetermined number of times among a plurality of persons included in a plurality of images stored in the memory as a person related to the user, and Age, gender and body type information about a person identified as being related to the user is obtained by using an artificial intelligence model for object recognition.

3. The electronic device according to claim 1, further comprising: Sensors for obtaining location information of electronic devices, The processor is configured to obtain information about the user's movement pattern based on the location information of the electronic device obtained by the sensor, and At least one of the plurality of advertisement categories is identified based on the obtained age, gender, and body shape information about the person identified as the user and the user's movement pattern.

4. The electronic device according to claim 1, wherein: The processor is configured to identify the user's interests based on the user's search results and website visit history, and At least one of the plurality of categories is identified based on the obtained age, gender, and body shape information about the person identified as the user and user interests.

5. The electronic device according to claim 1, wherein: The processor is configured to obtain information about the user's product purchase history, and At least one of the plurality of advertisement categories is identified based on the obtained age, gender, and body size information about the person identified as the user and the user's product purchase history.

6. The electronic device according to claim 1, wherein: The memory is configured to store a matching table for matching and storing at least one advertisement category predetermined according to a user type, and The processor is configured to identify advertisement category information corresponding to the obtained age, gender and body shape information about the person identified as the user by using a matching table.

7. The electronic device according to claim 6, wherein: The matching table is configured to match and store the plurality of advertisement categories predetermined according to user types in order of priority.

8. The electronic device according to claim 6, wherein: The matching table is configured to be updated periodically by an external server.

9. A method for controlling an electronic device, comprising: identifying a person who appears most frequently among a plurality of persons included in a plurality of images obtained by the camera during a self-portrait mode as a user of the electronic device, and obtaining age, gender, and body shape information about a person identified as a user by using an artificial intelligence model for object recognition that is trained using the age, gender, and body shape information as learning data; identifying at least one category among a plurality of advertisement categories as a preferred field for the user based on the obtained age, gender and body shape information about the person identified as the user, Based on an event for displaying an advertisement occurring, sending information about the at least one advertisement category to an advertisement platform server; receiving an advertisement corresponding to the advertisement category from the advertisement platform server through a communication interface, identifying at least one of a size and a position of the received advertisement to be displayed on the display based on a priority order corresponding to the at least one advertisement category, and controlling display of the received advertisement on a display based on at least one of the identified size or position, wherein identifying the user's interests by determining the user's emotions, and At least one of the plurality of categories is identified based on the obtained age, gender, and body shape information about the person identified as the user and user interests.

10. The method according to claim 9, wherein: The acquisition includes identifying a person who appears more than a predetermined number of times among a plurality of persons included in a plurality of images stored in the memory as a person related to the user, and By using an artificial intelligence model for object recognition, age, gender and body shape information is obtained about the person who is identified as being related to the user.

11. The method according to claim 9, further comprising: acquiring information about a user's movement pattern based on location information of the electronic device obtained by a sensor, and The identifying includes identifying at least one of the plurality of advertisement categories based on the obtained age, gender, and body shape information about the person identified as the user and the user's movement pattern.

12. The method according to claim 9, further comprising: Identify the user's interests based on their search results and website visits, and The identifying includes identifying at least one of the multiple categories based on the obtained age, gender and body shape information about the person identified as the user and the user's interests.

13. The method according to claim 9, further comprising: Get information about the user's product purchase history, and Wherein, identifying includes identifying at least one category of the plurality of advertisement categories based on the obtained age, gender and body shape information about the person identified as the user and the user's product purchase history.

Images