Electronic device and control method of electronic device

Abstract

An electronic device and a method for controlling the electronic device are disclosed. Specifically, the electronic device according to the present disclosure comprises: a communication unit; a memory including information related to a neural network model for predicting a target device for each time slot of the advertisement content; and a processor. In addition, the processor may: acquire usage history data of a user from the plurality of external devices; distributing and acquiring a plurality of feature values corresponding to the plurality of external devices for each time slot in the plurality of time slots based on the use history data; inputting the plurality of feature values into a neural network model so as to divide score information of a plurality of probabilities, related to the tag, of the plurality of external devices for each time slot in the plurality of time slots, and obtaining the score information; identifying a plurality of target devices for providing advertisement content for each of the plurality of time slots based on the score information; and control the communication unit to transmit information about the advertisement content to the plurality of target devices. Various other embodiments are also possible.

Description

Technical Field

[0001] The present disclosure relates to an electronic device and a control method of the electronic device, and more particularly, to an electronic device for effectively providing advertising content to a user and a control method thereof. Background Art

[0002] As the online advertising market grows substantially, technologies for providing appropriate advertising content to users are developing. For example, the development of advertising targeting technologies for providing advertising content to users who have an interest in and purchase intent for the advertised product is accelerating.

[0003] As a related technology related to targeted advertising, there is a technology for improving user conversion rate based on the user's content viewing pattern, the user's preference information for products (e.g., preference information), etc. In addition, in particular, with the recent development of technology in the field of artificial intelligence (AI), attempts have been made to more effectively identify target devices that become objects of advertising exposure by using neural network models and deep learning.

[0004] However, regarding these related technologies, the usage pattern of the user device in each time zone is not fully considered, thereby limiting the effectiveness of these technologies. For example, according to the conventional technology, advertising content is provided without considering different users who use one user device for each time zone, and even in the case where one user uses one user device, the user's advertising conversion rate may vary depending on the time zone in which the advertising content is provided, etc. Therefore, the advertising conversion rate may deteriorate, and thus the advertising cost may be wasted. Summary of the invention

[0005] Technical Solution

[0006] Provided are an electronic device for effectively providing advertisement contents by considering a usage pattern of a user device per time zone and a control method of the electronic device.

[0007] According to one aspect of the present disclosure, an electronic device includes: a communicator; at least one memory storing information associated with a neural network model, the neural network model predicting a target device for advertising content in each of a plurality of time zones; and at least one processor configured to: obtain data corresponding to a user's usage history from a plurality of external devices; based on the data corresponding to the usage history, obtain the plurality of feature values ​​by assigning each of a plurality of feature values ​​corresponding to a corresponding external device in the plurality of external devices to a corresponding time zone in the plurality of time zones, input the plurality of feature values ​​into the neural network model, and obtain score information of the plurality of probabilities associated with the plurality of external devices and tags by assigning each of a plurality of probabilities to a corresponding time zone in the plurality of time zones, identify a plurality of target devices for providing the advertising content in each of the plurality of time zones based on the score information, and control the communicator to send information about the advertising content to the plurality of target devices.

[0008] The at least one processor may also be configured to obtain information about the number of external devices corresponding to the label among the multiple external devices by assigning each of the multiple external devices to a corresponding time zone among the multiple time zones, and identify the multiple target devices in each of the multiple time zones based on the score information and the information about the number of external devices corresponding to the label.

[0009] The at least one processor may also be configured to assign a number of target devices to each of the multiple time zones based on information about the number of external devices corresponding to the tag, and identify the multiple target devices in each of the multiple time zones based on identifying external devices with the highest score information up to the number of target devices assigned to each of the multiple time zones.

[0010] The at least one processor may be further configured to identify a time zone having a number of assigned target devices less than a predetermined threshold from among the plurality of time zones, and determine not to provide the advertisement content in the identified time zone.

[0011] The data corresponding to the use history of the user may include information about the use time of the plurality of external devices, information about applications of the plurality of external devices used by the user, and information about content viewed by the user.

[0012] The at least one processor may also be configured to: receive, through the communicator, data corresponding to the user's usage history, information about the user's profile, and information about the user's product preferences; and obtain the multiple feature values ​​by assigning each of the multiple feature values ​​to a corresponding time zone among the multiple time zones based on the data corresponding to the user's usage history, information about the user's profile, and information about the user's product preferences.

[0013] The plurality of feature values ​​may be acquired in the form of feature vectors after being allocated to the plurality of time zones.

[0014] According to one aspect of the present disclosure, a control method of an electronic device includes: acquiring data corresponding to a user's usage history from multiple external devices; based on the data corresponding to the usage history, acquiring multiple feature values ​​by assigning each of multiple feature values ​​corresponding to corresponding external devices among the multiple external devices to corresponding time zones among multiple time zones; inputting the multiple feature values ​​into a neural network model that predicts a target device for advertising content in each time zone, and acquiring score information of the multiple probabilities by assigning each of multiple probabilities related to the multiple external devices and tags to corresponding time zones among the multiple time zones; identifying multiple target devices for providing the advertising content in each of the multiple time zones based on the score information; and sending information about the advertising content to the multiple target devices.

[0015] Identifying multiple target devices may include: obtaining information about the number of external devices corresponding to the label among the multiple external devices by assigning each of the multiple external devices to a corresponding time zone among the multiple time zones; and identifying multiple target devices in each of the multiple time zones based on the score information and the information about the number of external devices corresponding to the label.

[0016] Identifying the multiple target devices may also include: assigning the number of target devices to each of the multiple time zones based on information about the number of external devices corresponding to the tag; and identifying multiple target devices in each of the multiple time zones based on identifying external devices with the highest score information up to the number of target devices assigned to each of the multiple time zones.

[0017] The control method may further include identifying a time zone having a number of assigned target devices less than a predetermined threshold from among the plurality of time zones, and determining not to provide the advertisement content in the identified time zone.

[0018] The data corresponding to the use history of the user includes information about the use time of the plurality of external devices, information about applications of the plurality of external devices used by the user, and information about content viewed by the user.

[0019] Obtaining the multiple feature values ​​may include: receiving data corresponding to the user's usage history, information about the user's profile, and information about the user's product preferences from the multiple external devices; and obtaining the multiple feature values ​​by dividing the multiple feature values ​​according to each of the multiple time zones based on the data corresponding to the user's usage history, information about the user's profile, and information about the user's product preferences.

[0020] The plurality of feature values ​​are acquired in the form of feature vectors after being divided in units of predetermined time periods. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] 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:

[0022] Figure 1 is a flowchart illustrating a control method of an electronic device according to one or more embodiments of the present disclosure;

[0023] Figure 2 is a flowchart illustrating a control method of an electronic device according to one or more embodiments of the present disclosure;

[0024] Figure 3 is a diagram for illustrating in detail a standard for dividing a plurality of time zones and a process for identifying the number of used devices corresponding to a tag according to one or more embodiments of the present disclosure;

[0025] Figure 4 is a diagram for illustrating in detail a process of acquiring score information and a process of identifying a target device according to one or more embodiments of the present disclosure;

[0026] Figure 5 is a block diagram schematically illustrating a configuration of an electronic device according to one or more embodiments of the present disclosure; and

[0027] Figure 6 is a block diagram illustrating in detail the configuration of an electronic device according to one or more embodiments of the present disclosure. DETAILED DESCRIPTION

[0028] Various modifications may be made to the embodiments of the present disclosure, and various types of embodiments may exist. Therefore, specific embodiments will be shown in the drawings, and the embodiments will be described in detail in the specific embodiments. However, it should be noted that the various embodiments are not intended to limit the scope of the present disclosure to specific embodiments, but they should be interpreted as including various modifications, equivalents and / or alternatives of the embodiments of the present disclosure. In addition, with respect to the detailed description of the drawings, similar components may be referred to by similar reference numerals.

[0029] Furthermore, in describing the present disclosure, in the case where it is determined that a detailed explanation of a related known function or feature may unnecessarily obscure the gist of the present disclosure, the detailed explanation will be omitted.

[0030] In addition, the embodiments described below can be modified in various forms, and the scope of the technical ideas of the present disclosure is not limited to the following embodiments. On the contrary, these embodiments are provided to make the present disclosure more complete and complete, and to fully convey the technical ideas of the present disclosure to those skilled in the art.

[0031] In addition, the terms used in the present disclosure are only used to explain specific embodiments and are not intended to limit the scope of the present disclosure.In addition, unless defined differently in the context, a singular expression includes a plural expression.

[0032] Furthermore, in the present disclosure, expressions such as “having”, “may have”, “including”, and “may include” indicate the existence of such characteristics (for example, elements such as numbers, functions, operations, and components), and do not exclude the existence of additional characteristics.

[0033] Furthermore, in the present disclosure, the expression "A or B", "at least one of A and / or B", or "one or more of A and / or B", etc. may include all possible combinations of the listed items. For example, "A or B", "at least one of A and B", or "at least one of A or B" may refer to all of the following: (1) only A, (2) only B, or (3) both A and B.

[0034] In addition, the expressions "first", "second", etc. used in the present disclosure may describe various elements regardless of any order and / or importance. In addition, such expressions are only used to distinguish one element from another element and are not intended to limit the elements.

[0035] The description in the present disclosure that one element (e.g., a first element) is “(operably or communicatively) coupled to / coupled to another element (e.g., a second element)” or “connected to another element (e.g., a second element)” should be interpreted to include both the case where one element is directly coupled to another element and the case where one element is coupled to another element via yet another element (e.g., a third element).

[0036] In contrast, description that one element (eg, a first element) is “directly coupled” or “directly connected” to another element (eg, a second element) may be interpreted to mean that another element (eg, a third element) does not exist between the one element and the other element.

[0037] In addition, the expression "configured to" used in the present disclosure may be used interchangeably with other expressions such as "suitable for", "capable of", "designed to", "suitable for", "manufactured to", and "capable of", depending on the circumstances. Meanwhile, the term "configured to" may not necessarily mean that a device is "specially designed to" in terms of hardware.

[0038] Conversely, in some cases, the expression "a device is configured to" may mean that the device is "capable" of performing an operation together with another device or component. For example, the phrase "the processor is configured to perform A, B, and C" may represent a dedicated processor (e.g., an embedded processor) for performing the corresponding operations, or a general-purpose processor (e.g., a CPU or an application processor) that can perform the corresponding operations by executing one or more software programs stored in a memory device.

[0039] In addition, in the embodiments of the present disclosure, a "module" or "component" may perform at least one function or operation, and may be implemented as hardware or software, or as a combination of hardware and software. In addition, multiple 'modules' or 'components' may be integrated into at least one module and implemented as at least one processor, excluding 'modules' or 'components' that need to be implemented as specific hardware.

[0040] Various elements and regions in the drawings are schematically shown. Therefore, the technical concept of the present disclosure is not limited by the relative sizes or intervals shown in the drawings.

[0041] Hereinafter, embodiments according to the present disclosure will be described in detail with reference to the accompanying drawings so that those skilled in the art to which the present disclosure pertains can easily implement the present disclosure.

[0042] Figure 1 is a flowchart illustrating a control method of the electronic device 100 according to one or more embodiments of the present disclosure.

[0043] The electronic device 100 according to the present disclosure refers to a device for predicting a target device for advertisement content in each time zone. Figure 5An example electronic device is described in more detail. In one or more examples, the electronic device 100 may predict a target device for providing specific advertising content among multiple external devices connected to the electronic device 100, and send information about the advertising content to the predicted target device. For example, the electronic device 100 according to the present disclosure may be implemented as a server for managing advertisement provision. However, as understood by those of ordinary skill in the art, the electronic device 100 is not limited to a server, and may be any suitable device known to those of ordinary skill in the art, such as a computer, a laptop computer, a tablet computer, a smart phone, etc.

[0044] like Figure 1 As shown, the electronic device 100 according to one or more embodiments of the present disclosure may acquire data corresponding to a user's usage history from a plurality of external devices in operation S110.

[0045] In one or more examples, according to the present disclosure, multiple external devices may refer to external devices that are objects for providing content. For example, multiple external devices may be implemented as one of a TV, a smart phone, or a tablet PC. However, these devices are merely examples, and the types of multiple external devices may include any suitable device known to those of ordinary skill in the art.

[0046] In one or more examples, the term "data corresponding to the user's usage history" may generally be used to refer to data of a history of a user's use of multiple external devices. Specifically, the data corresponding to the user's usage history may include at least one of information about usage time of the multiple external devices, information about applications used by the user of the multiple external devices, or information about content viewed by the user.

[0047] For example, in the case where the electronic device 100 is a TV, the data corresponding to the user's usage history may include the user's viewing history of TV programs, the user's usage history of applications for the TV, and the user's usage history of devices connected to the TV, etc. According to the present disclosure, any additional information indicating the user's usage pattern or the characteristics of the external device may be included in the data corresponding to the user's usage history.

[0048] The present disclosure describes an embodiment using data corresponding to a user's usage history. However, as understood by those of ordinary skill in the art, various information may be used, such as information about a user profile (e.g., the user's gender, age, etc.), or information about a user's product preferences (e.g., information about products purchased by the user, etc.), or any other information known to those of ordinary skill in the art.

[0049] In operation S120, based on the data corresponding to the usage history, the electronic device 100 may determine a plurality of feature values ​​by allocating each of a plurality of feature values ​​corresponding to a corresponding external device among a plurality of external devices to a corresponding time zone among a plurality of time zones.

[0050] According to one or more embodiments, the data corresponding to the usage history may be formatted so that the data can be used for learning of a neural network model. For example, the electronic device 100 may obtain a plurality of feature values ​​corresponding to each of the plurality of external devices by converting the data corresponding to the usage history into a feature vector (or embedding into a feature vector). In the process of obtaining the plurality of feature values, preprocessing processes such as processing, deleting, and extracting the data may be performed.

[0051] In one or more examples, the electronic device 100 according to the present disclosure can obtain multiple feature values ​​by dividing the multiple feature values ​​according to each of the multiple predefined time zones. For example, the multiple time zones may include at least one of the information about the day of the week or the information about the time of the day. For example, the multiple time zones may be the result of dividing the 24 hours constituting multiple days into morning, afternoon, evening, late night and early morning according to a predetermined standard, or may be the result of dividing a week according to the day of the week and then dividing each day according to a predetermined standard. In addition, multiple time zones may be defined by various methods according to system developers or user settings.

[0052] According to one or more embodiments, the electronic device 100 may determine multiple feature values ​​by assigning each of the feature values ​​to a corresponding time zone among multiple time zones based on data corresponding to the user's usage history, information about the user's profile, and information about the user's product preferences.

[0053] According to one or more embodiments, a plurality of feature values ​​may be obtained in the form of feature vectors after being divided in units of predetermined time periods. For example, in order to minimize the inclusion of sparse data in data used for learning a neural network model, the electronic device 100 may aggregate data corresponding to the usage history while dividing the data in units of predetermined time periods (e.g., one month), and then obtain a plurality of feature values.

[0054] In operation S130, the electronic device 100 may input the plurality of feature values ​​into the neural network model, and acquire score information of probability that the plurality of external devices are associated with a predetermined tag by assigning each score to a corresponding time zone among the plurality of time zones.

[0055] In one or more examples, the term "tag" may refer to a characteristic (or feature, pattern, etc.) that may be a criterion for advertisement targeting in a user's usage history. For example, a tag may indicate at least one of information that a user has used a specific application or information that a user has viewed a specific category of content. For example, a tag may be set to "user used a gaming application" or may be set to "user viewed a war movie."

[0056] According to one or more embodiments, the tag may include not only at least one of information that the user has used a specific application or information that the user has viewed a specific category of content, but may also further include information limiting a specific time zone among multiple time zones. For example, the tag may be set to "the user used a game application in the early morning."

[0057] According to one or more embodiments, the neural network model may refer to a neural network model trained to obtain score information for identifying a target device that is an object of advertising content provision. For example, when a plurality of feature values ​​corresponding to a plurality of external devices are obtained and a predetermined label is set as described above, the neural network model may calculate the probability that the plurality of external devices are associated with the set label, normalize the calculated probability, and output the normalized calculated probability as score information.

[0058] In one or more examples, with respect to the neural network model according to the present disclosure, a separate learning process may be performed for each of the multiple time zones. For example, the separate learning process may include: obtaining score information of the probability that multiple external devices are associated with a predetermined tag in the first time zone based on a feature value according to the usage history in the first time zone, and obtaining score information of the probability that multiple external devices are associated with a predetermined tag in the second time zone based on a feature value according to the usage history in the second time zone.

[0059] According to one or more embodiments of the present disclosure, a neural network model may refer to an artificial intelligence model including a neural network, wherein the artificial intelligence model may be trained by deep learning. The neural network model according to one or more embodiments of the present disclosure may include various types of neural networks, such as a deep neural network (DNN), a convolutional neural network (CNN), a recurrent neural network (RNN), and a generative adversarial network (GAN), etc. However, as understood by those of ordinary skill in the art, the type of neural network may include any type of neural network known to those of ordinary skill in the art.

[0060] In operation S140, the electronic device 100 may identify a plurality of target devices for providing advertisement content for each of a plurality of time zones based on the score information in operation S140.

[0061] According to one or more embodiments, the electronic device 100 may organize multiple external devices among the multiple target devices in an order starting with the highest score information obtained for each of the multiple time zones. For example, the number of target devices may be determined based on a request for an advertisement that exposes advertising content to a user. When identifying the number of target devices among the multiple external devices, the electronic device 100 may identify a device having higher score information obtained for each of the multiple time zones as a target device.

[0062] Reference Figure 4 Various embodiments related to a process of acquiring score information and a process of identifying a target device according to the present disclosure are described in more detail.

[0063] In operation S150, when multiple target devices are identified, the electronic device 100 can send information about the advertising content to the multiple target devices. For example, the electronic device 100 can send information about the time zone for providing the advertising content together with the information about the advertising content to the multiple target devices, so that the multiple target devices can output the output advertising content in the time zone.

[0064] According to the above reference Figure 1 According to one or more described embodiments, the electronic device 100 may provide advertising content to appropriate target devices in each time zone in consideration of the user's usage patterns of multiple external devices in each time zone, and thus, advantageously improve the user conversion rate of the advertising content.

[0065] Specifically, according to one or more embodiments of the present disclosure, when there are different users who use one external device in each time zone, appropriate advertising content is provided in consideration of the usage pattern of each time zone, and thus, advertising targeting suitable for each of multiple users using one external device can be performed. In addition, when one user uses one user device, precise advertising targeting that takes into account the user usage pattern of each time zone can be performed. A time zone may correspond to one or more time intervals within a set time period. For example, if the set time period is a 24-hour day, the associated time zone may be morning, afternoon, evening, early morning, etc. Therefore, the efficiency of providing advertising content can be improved, and this can lead to a reduction in advertising costs and an increase in advertising revenue.

[0066] Figure 2 is a flowchart illustrating a control method of the electronic device 100 according to one or more embodiments of the present disclosure. Figure 3 is a diagram for illustrating in detail a criterion for dividing a plurality of time zones and a process of identifying the number of used devices corresponding to a tag according to one or more embodiments of the present disclosure.

[0067] Figure 2 The process shown involves the process of identifying multiple target devices. It can be performed based on the following premises: Figure 2 The process shown: As mentioned above in Figure 1 As described in the description, score information about each of multiple external devices in each time zone is obtained through a neural network model.

[0068] Figure 2 The process shown can also be performed based on the premise that multiple time zones are divided and Figure 3 As in the embodiment of the present invention, the number of used devices corresponding to the tag is identified.

[0069] Reference Figure 3 According to the present disclosure, the plurality of time zones may include, for example, a late night time zone, an early morning time zone, a morning time zone, an afternoon time zone, and an evening time zone. Figure 3 As shown, the start time and end time of the late night time zone may be from 23:00 to 2:00 of the next day. For example, the start time of 23:00 corresponds to the time starting from 23:00:00, and the end time of 2:00 corresponds to the time before 2:00:00. In addition, the start time and end time of the early morning time zone may be from 2:00 to 6:00, the start time and end time of the morning time zone may be from 6:00 to 10:00, and the start time and end time of the afternoon time zone may be from 10:00 to 17:00, and the start time and end time of the evening time zone may be from 17:00 to 23:00.

[0070] In operation S210, the electronic device 100 may obtain information about the number of devices corresponding to the tag among the plurality of external devices by assigning each device to a corresponding time zone among the plurality of time zones in operation S210. For example, the electronic device 100 may identify a device corresponding to a predetermined tag among the plurality of external devices based on data corresponding to a user's usage history, thereby calculating the number of devices corresponding to the tag for each of the plurality of time zones. In one or more examples, the device corresponding to the tag may refer to a device among the plurality of external devices that meets a standard or characteristic specified at the tag.

[0071] For example, when the tag is set to "user used a game application", the electronic device 100 can identify a device having a history of "user used a game application" among multiple external devices, and identify "the time when the user used the game application" based on the data corresponding to the user's usage history. Figure 3, the electronic device 100 can identify the number of devices used in the late night time zone, the early morning time zone, the morning time zone, the afternoon time zone, and the evening time zone. For example, the "number of devices that the user has used the game application" in the late night time zone, the early morning time zone, the morning time zone, the afternoon time zone, and the evening time zone can be 570,000, 200,000, 480,000, 450,000, and 1,300,000, respectively. In one or more examples, if the number of devices used in each time zone is expressed as a ratio, these numbers in the late night time zone, the early morning time zone, the morning time zone, the afternoon time zone, and the evening time zone become 19%, 7%, 16%, 15%, and 43%, respectively.

[0072] In operation S220, the electronic device 100 may assign the number of target devices for each of the multiple time zones based on information about the number of devices corresponding to the tag. For example, the electronic device 100 may assign a predetermined number of all target devices according to a ratio of the number of devices corresponding to the tag for each of the multiple time zones. The predetermined number of all target devices may refer to the number of all target devices to which the advertising content will be exposed (e.g., displayed). The predetermined number of all target devices may be determined according to a request by an advertiser, according to the number of times the advertising content is exposed or displayed.

[0073] For example, when the label is set to "User used game applications" and the number of devices with a history of user using game applications in the late night time zone, early morning time zone, morning time zone, afternoon time zone, and evening time zone is 570,000, 200,000, 480,000, 450,000, and 1,300,000, respectively, the electronic device 100 can allocate 600,000 corresponding to the number of all target devices into 114,000, 40,000, 96,000, 90,000, and 260,000 for the late night time zone, early morning time zone, morning time zone, afternoon time zone, and evening time zone, respectively.

[0074] In operation S230, the electronic device 100 may identify a time zone in which the number of assigned target devices is less than a predetermined threshold value among the plurality of time zones. Hereinafter, an example is described based on a case where the threshold value is 500,000. However, the threshold value according to the present disclosure may be changed according to the advertiser's request or the developer's setting.

[0075] If no time zone in which the number of assigned target devices is less than a predetermined threshold is identified in the multiple time zones in operation S230-No (for example, the "No" branch of operation S230), then in operation S240, the electronic device 100 can identify external devices with high score information up to the number of target devices assigned to each of the multiple time zones, thereby identifying the target device for each of the multiple time zones.

[0076] For example, when the number of all target devices is 600,000, the target devices may be allocated to 300,000, 240,000, and 60,000 in the first, second, and third time zones, respectively. The electronic device 100 may identify 300,000 target devices with high score information in the first time zone, 240,000 target devices with high score information in the second time zone, and 60,000 target devices with high score information in the third time zone.

[0077] If a time zone in which the number of assigned target devices is less than a predetermined threshold is identified in multiple time zones in operation S230-Yes (eg, the "Yes" branch of operation S230), the electronic device 100 may determine not to provide advertising content in the identified time zone in operation S250.

[0078] For example, the electronic device 100 may not identify target devices in a time zone in which the number of assigned target devices is less than a predetermined threshold among multiple time zones, and reallocate the number of target devices assigned in the time zone in which the number of assigned target devices is less than the predetermined threshold to the number of target devices assigned in the remaining time zones, thereby identifying the final number of target devices.

[0079] For example, when the total number of target devices is 600,000, and 300,000, 240,000, and 60,000 are allocated in the first, second, and third time zones, respectively, the electronic device 100 may identify 333,333 target devices with high score information in the first time zone, 266,667 target devices with high score information in the second time zone, and may not identify target devices in the third time zone (for example, 60,000 devices are below the threshold and are therefore allocated to other time zones).

[0080] According to the above reference Figure 2 and Figure 3In the described embodiment, the electronic device 100 can identify appropriate target devices for each time zone in consideration of the usage patterns of multiple users in each time zone. In addition, when the usage rate of a specific time zone is less than a threshold, the electronic device 100 can perform control so that advertisements are not exposed (e.g., displayed) in the time zone, thereby improving the efficiency of providing advertising content.

[0081] Figure 4 is a diagram for describing in detail a process of acquiring score information and a process of identifying a target device according to one or more embodiments of the present invention.

[0082] like Figure 4 As shown, the multiple time zones may include a first time zone, a second time zone, and a third time zone. In addition, the label may be set to "user used the game application". In addition, the ratio of devices having a history of user using the game application in the first time zone, the second time zone, and the third time zone may be 60%, 20%, and 20%, respectively.

[0083] In About Figure 4 In the explanation of , the number of the plurality of external devices according to the present disclosure may be n, where n is a positive integer. However, for ease of explanation, the description is based on the premise that there are only five external devices (e.g., a first device, a second device, a third device, a fourth device, and a fifth device). Figure 4 In addition, for the sake of convenience of explanation, the description is based on the premise that the number of all target devices according to the advertiser's request is also five. Figure 4 The embodiment shown.

[0084] according to Figure 4 In the illustrated embodiment, the electronic device 100 can obtain 0.9, 0.8, 0.6, 0.2 and 0.1 as the score information of the first device, the second device, the third device, the fourth device and the fifth device in the first time zone through the neural network model. In addition, the electronic device 100 can obtain 0.1, 0.8, 0.1, 0.2 and 0.1 as the score information of the first device, the second device, the third device, the fourth device and the fifth device in the second time zone through the neural network model. In addition, the electronic device 100 can obtain 0.3, 0.7, 0.3, 0.4 and 0.3 as the score information of the first device, the second device, the third device, the fourth device and the fifth device in the third time zone through the neural network model.

[0085] In one or more examples, the electronic device 100 may assign a number of target devices to each of multiple time zones based on information about the number of devices corresponding to the tag, or assign a number of target devices to each of multiple time zones by using only score information without considering information about the number of devices corresponding to the tag.

[0086] According to one or more embodiments, the electronic device 100 may assign the number of target devices to each of the multiple time zones based on the number of devices corresponding to the tag. Figure 4 In the illustrated embodiment, the number of all target devices may be 5, and the electronic device 100 may assign the target devices to be three, one, and one in the first time zone, the second time zone, and the third time zone, respectively, based on the fact that the ratio of devices used in each of the first time zone, the second time zone, and the third time zone (e.g., “the ratio of devices in which the user has used the game application”) is 60%, 20%, and 20%, respectively. Subsequently, the electronic device 100 may identify three devices, i.e., the first device, the second device, and the third device, whose score information in the first time zone is in the order of highest to lowest, as target devices (e.g., Figure 4 , the same below), and identify one device with the highest score information in the second time zone, namely the second device, and one device with the highest score information in the third time zone, namely the second device, as target devices.

[0087] According to one or more embodiments, the electronic device 100 may assign the number of target devices to each of the plurality of time zones by using only the score information without considering the information about the number of devices corresponding to the tag. Figure 4 In the illustrated embodiment, the electronic device 100 may identify a first device in a first time zone, a second device in a first time zone, a third device in a third time zone, a second device in a second time zone, and a second device in a third time zone as five devices as target devices in the order of the highest score information among all score information obtained for each of the multiple time zones.

[0088] Figure 5 1 is a block diagram schematically showing a configuration of an electronic device 100 according to one or more embodiments of the present disclosure. Figure 6 is a block diagram illustrating in detail the configuration of the electronic device 100 according to one or more embodiments of the present disclosure.

[0089] like Figure 5 As shown, the electronic device 100 according to one or more embodiments of the present disclosure includes a communicator 110, a memory 120 and a processor 130. Figure 6As shown, the electronic device 100 according to one or more embodiments of the present disclosure may further include an input device 140 and an output device 150. Figure 5 and Figure 6 The components shown are examples only, and as will be appreciated by one of ordinary skill in the art, other than Figure 5 and Figure 6 In addition to the components shown, new components may be added or some components may be omitted.

[0090] In one or more examples, at least one instruction about the electronic device 100 may be stored in the memory 120. In addition, an operating system (O / S) for operating the electronic device 100 may be stored in the memory 120. In addition, various software programs or applications for the electronic device 100 to operate according to various embodiments of the present disclosure may be stored in the memory 120. In addition, the memory 120 may include a semiconductor memory such as a flash memory, or a magnetic storage medium such as a hard disk, etc.

[0091] For example, in the memory 120, various software modules for the electronic device 100 to operate according to various embodiments of the present disclosure may be stored, and the processor 130 may control the operation of the electronic device 100 by executing the various software modules stored in the memory 120. For example, the memory 120 may be accessed by the processor 130, and reading / recording / correction / deletion / update of data by the processor 130 may be performed.

[0092] In one or more examples, the term memory 120 may be used to mean including the memory 120, a ROM (not shown) or a RAM (not shown) inside the processor 130, or a memory card (not shown) (e.g., a micro SD card, a memory stick) installed on the electronic device 100.

[0093] Specifically, in various embodiments according to the present disclosure, in the memory 120, information about advertising content, information about a neural network model, data corresponding to a user's usage history, multiple feature values, information about a predetermined tag, score information, information about an identified target device, etc. may be stored. In one or more examples, within the scope of achieving the purpose of the present disclosure, various types of information may be stored in the memory 120, and the information stored in the memory 120 may be updated when received from an external device or input by a user.

[0094] The processor 130 may control the overall operation of the electronic device 100. For example, the processor 130 may be connected with components of the electronic device 100 including the communicator 110 and the memory 120, and may control the overall operation of the electronic device 100 by executing at least one instruction stored in the memory 120 as described above.

[0095] The processor 130 may be implemented in various ways. For example, the processor 130 may be implemented as at least one of an application specific integrated circuit (ASIC), an embedded processor, a microprocessor, a hardware control logic, a hardware finite state machine (FSM), or a digital signal processor (DSP). Meanwhile, in the present disclosure, the term processor 130 may be used as a meaning including a central processing unit (CPU), a graphics processing unit (GPU), a main processing unit (MPU), and the like.

[0096] According to one or more embodiments, the processor 130 may acquire data corresponding to the user's usage history from multiple external devices. Then, the processor 130 may determine multiple feature values ​​by assigning each of the multiple feature values ​​corresponding to each of the multiple external devices to a corresponding time zone in a plurality of predefined time zones based on the data corresponding to the usage history. Specifically, the processor 130 may determine multiple feature values ​​by assigning each of the multiple feature values ​​to a corresponding time zone in a plurality of predetermined time zones.

[0097] The processor 130 may input the plurality of feature values ​​into the neural network model, and obtain score information of the probability that the plurality of external devices are associated with a predetermined tag by assigning each score to a corresponding time zone in the plurality of time zones. In one or more examples, the tag may indicate at least one of information that the user has used a specific application or information that the user has viewed a specific category of content.

[0098] Based on the score information, the processor 130 may identify a plurality of target devices for providing the advertisement content for each of the plurality of time zones. According to one or more embodiments, the processor 130 may include the plurality of external devices in the plurality of target devices in the order of having higher score information acquired for each of the plurality of time zones among the plurality of external devices.

[0099] The processor 130 may control the communicator 110 (e.g., a communication circuit) to send information about the advertising content to multiple target devices. For example, the processor 130 may control the communicator 110 to send information about a time zone for providing the advertising content together with the information about the advertising content to multiple target devices. In one or more examples, the processor 130 may control the multiple target devices to output the advertising content in the time zone.

[0100] According to one or more embodiments, the processor 130 can obtain information about the number of devices corresponding to a tag among multiple external devices by assigning each of the multiple devices to multiple time zones, and identify multiple target devices in each of the multiple time zones based on the score information and the information about the number of devices corresponding to the tag.

[0101] The processor 130 can assign the number of target devices for each of the multiple time zones based on information about the number of devices corresponding to the tags, and identify as many external devices with high score information as the number of target devices assigned to each of the multiple time zones, thereby identifying multiple target devices for each of the multiple time zones.

[0102] In one or more examples, if a time zone having a number of assigned target devices less than a predetermined threshold is identified among the plurality of time zones, the processor 130 may determine not to provide the advertising content in the identified time zone.

[0103] The control process of the processor 130 can be implemented by a plurality of modules according to the present disclosure. Figure 6 As shown, the multiple modules according to one or more embodiments of the present disclosure may include a usage history data acquisition module 121, a feature value acquisition module 122, a score information acquisition module 123, a target device identification module 124, and an advertising content providing module 125. Each of the multiple modules may be implemented as a hardware module or a software module. In addition, information about the multiple modules may be stored in the memory 120, and the processor 130 may implement the embodiments according to the present disclosure through the multiple modules by loading the information about the multiple modules stored in the memory 120.

[0104] The usage history data acquisition module 121 may refer to a module capable of acquiring data corresponding to the user's usage history. For example, the usage history data acquisition module 121 may receive data from a plurality of external devices through the communicator 110 to thereby acquire data corresponding to the user's usage history.

[0105] The feature value acquisition module 122 may refer to a module that may acquire a plurality of feature values ​​corresponding to each of a plurality of external devices. For example, when acquiring data corresponding to a user's usage history by using the history data acquisition module 121, the feature value acquisition module 122 may determine a plurality of feature values ​​by assigning each of the plurality of feature values ​​to a corresponding time zone in a plurality of predefined time zones. In one or more embodiments, the plurality of feature values ​​may be acquired by the feature value acquisition module 122 after a preprocessing process such as processing, deleting, and extracting is performed on the data corresponding to the user's usage history.

[0106] The score information acquisition module 123 may refer to a module that can acquire score information of each of the plurality of external devices. Specifically, when the plurality of feature values ​​are acquired by the feature value acquisition module 122, the score information acquisition module 123 may acquire score information of the probability of the plurality of external devices being associated with a predetermined tag by using a neural network model.

[0107] The target device identification module 124 may refer to a module that can identify a plurality of target devices for providing advertising content in each time zone based on the score information. For example, when the score information is acquired through the score information acquisition module 123, the target device identification module 124 may assign the number of target devices to each of the plurality of time zones based on the information about the number of devices corresponding to the tag, or assign the number of target devices to each of the plurality of time zones by using only the score information without considering the information about the number of devices corresponding to the tag.

[0108] The advertisement content providing module 125 may refer to a module that can send information about the advertisement content to a plurality of target devices. For example, when information about a plurality of target devices is acquired through the target device identification module 124, the advertisement content providing module 125 may control the communicator 110 to send the information about the advertisement content to the plurality of target devices. In addition, the advertisement content providing module 125 may send information about a time zone for providing the advertisement content together with the information about the advertisement content to the plurality of target devices, so that the advertisement content is output by the plurality of target devices in the time zone.

[0109] Due to the above reference Figures 1 to 4 Other embodiments according to the present disclosure based on the control of the processor 130 are described, and thus repeated explanations on the same contents are omitted.

[0110] The communicator 110 may include a circuit and may perform communication with an external device. For example, the processor 130 may receive various data or information from an external device connected through the communicator 110 or transmit various data or information to the external device.

[0111] The communicator 110 may include at least one of a WiFi module, a Bluetooth module, a wireless communication module, an NFC module, or an ultra-wideband (UWB) module. For example, each of the WiFi module and the Bluetooth module may perform communication by a WiFi method and a Bluetooth method, respectively. When using a WiFi module or a Bluetooth module, various types of connection information such as SSID are first sent and received, and a connection for communication is performed by using the information, and then various types of information may be sent and received.

[0112] In addition, the wireless communication module can perform communication according to various communication protocols such as IEEE, Zigbee, third generation (3G), third generation partnership project (3GPP), LTE (Long Term Evolution), fifth generation (5G), etc. In addition, the NFC module can perform communication by using a near field communication (NFC) method of the 13.56MHz band among various RF-ID bands such as 135kHz, 13.56MHz, 433MHz, 860-960MHz and 2.45GHz. In addition, the UWB module can accurately measure the time of arrival (ToA) and the angle of arrival (AoA) at the transmitter device through communication between UWB antennas. ToA is the time when the pulse arrives at the target, and AoA is the angle of arrival of the pulse. Therefore, the UWB module can perform accurate distance and position recognition within an error range of centimeters (cm) indoors.

[0113] Specifically, in various embodiments according to the present disclosure, the processor 130 may receive data corresponding to the user's usage history, information about the user's profile, and information about the user's product preferences, etc., from a plurality of external devices through the communicator 110. The processor 130 may control the communicator 110 to send information about the advertising content to a plurality of target devices. The processor 130 may control the communicator 110 to send information about a time zone for providing the advertising content together with the information about the advertising content to a plurality of target devices.

[0114] The input device 140 may include a circuit, and the processor 130 may receive a user instruction for controlling the operation of the electronic device 100 through the input device 140. For example, the input device 140 may be composed of components such as a microphone, a camera, and a remote control signal receiver (not shown). In addition, the input device 140 may be implemented in the form of being included in a display as a touch screen. In particular, the microphone may receive a voice signal and convert the received voice signal into an electronic signal.

[0115] Specifically, according to various embodiments of the present disclosure, the processor 130 may receive a user input for transmitting the advertisement content to a plurality of target devices through the input 140. In addition, according to the present disclosure, the processor 130 may receive information about a criterion for dividing each of a plurality of time zones, information about the number of target devices, information about a predetermined threshold, etc. through the input 140.

[0116] The outputter 150 may include a circuit, and the processor 130 may output various functions that the electronic device 100 may perform through the outputter 150. In addition, the outputter 150 may include at least one of a display, a speaker, or an indicator.

[0117] The display can output image data by the control of the processor 130. For example, the display can output an image pre-stored in the memory 120 by the control of the processor 130. Specifically, the display according to one or more embodiments of the present disclosure can display a user interface stored in the memory 120. The display can be implemented as a liquid crystal display panel (LCD), an organic light emitting diode (OLED), etc., and the display can also be implemented as a flexible display, a transparent display, etc. depending on the situation. However, the display according to the present disclosure is not limited to a specific type. The speaker can output audio data by the control of the processor 130, and the indicator can be turned on by the control of the processor 130.

[0118] Specifically, according to various embodiments of the present disclosure, the processor 130 may display various types of information, such as score information for each of a plurality of time zones and information about a plurality of target devices for each time zone, through the outputter 150. In addition, the processor 130 may display a guide message indicating that information about the advertisement content is transmitted to a plurality of target devices through the outputter 150.

[0119] Furthermore, the control method of the electronic device 100 according to the above-described embodiment may be implemented as a program and provided to the electronic device 100. Specifically, the program including the control method of the electronic device 100 may be provided while being stored in a non-transitory computer-readable medium.

[0120] Specifically, in a non-transitory computer-readable medium including a program for executing a control method of the electronic device 100, the control method of the electronic device 100 may include the following steps: acquiring data corresponding to a user's usage history from multiple external devices, and based on the data corresponding to the usage history, acquiring multiple feature values ​​by dividing a plurality of feature values ​​corresponding to each of the multiple external devices according to each of a plurality of predefined time zones, and inputting the plurality of feature values ​​into a neural network model of a target device for each time zone for predicting advertising content, and acquiring score information of the probability that multiple external devices will be associated with predetermined tags by dividing the scores for each of the multiple time zones, and identifying a plurality of target devices for providing advertising content for each of the multiple time zones based on the score information, and sending information about the advertising content to the plurality of target devices.

[0121] Various embodiments regarding the electronic device 100 may be applied to a control method of the electronic device 100 , and a computer-readable recording medium including a program for executing the control method of the electronic device 100 .

[0122] According to various embodiments of the present disclosure as described above, considering the usage patterns of multiple external devices by users in each time zone, the electronic device 100 can provide advertising content to appropriate target devices for each time zone, thereby significantly improving the user conversion rate of the advertising content.

[0123] In particular, according to the present disclosure, in the case where there are different users using one external device in each time zone, appropriate advertising content is provided in consideration of the usage pattern of each time zone, and thus, advertising targeting suitable for each of a plurality of users using one external device can be performed. In addition, in the case where one user uses one user device, accurate advertising targeting in consideration of the user usage pattern of each time zone can be performed. Therefore, the efficiency of providing advertising content can be improved, thereby leading to a reduction in advertising costs and an increase in advertising revenue.

[0124] The functions related to the neural network model as described above may be performed by the memory 120 and the processor 130 .

[0125] The processor 130 may be composed of one or more processors 130. Here, the one or more processors 130 may be a general-purpose processor 130 (such as a CPU, AP, etc.), a graphics-specific processor 130 (such as a GPU, VPU, etc.), or an artificial intelligence-specific processor 130 (such as an NPU).

[0126] The one or more processors 130 perform control to process input data according to predefined operation rules or artificial intelligence models stored in the nonvolatile memory 120 and the volatile memory 120. The predefined operation rules or artificial intelligence models are characterized in that they are generated by learning.

[0127] In one or more examples, generating by learning can refer to applying a learning algorithm to a plurality of learning data, and thereby generating a predefined operating rule or artificial intelligence model with desired characteristics. Such learning can be performed in the device itself that performs artificial intelligence according to the present disclosure, or by a separate server / system.

[0128] The artificial intelligence model may include multiple neural network layers. Each layer has multiple weight values, and the operation of the layer is performed by the operation result of the previous layer and the operation of multiple weight values. As examples of neural networks, there are convolutional neural networks (CNNs), deep neural networks (DNNs), recurrent neural networks (RNNs), restricted Boltzmann machines (RBMs), deep belief networks (DBNs), bidirectional recurrent deep neural networks (BRDNNs), generative adversarial networks (GANs), deep Q networks, or any other suitable neural networks known to those of ordinary skill in the art.

[0129] A learning algorithm may be a method of training a specific object device (e.g., a robot) by using a plurality of learning data so that the specific object device makes a decision or makes a prediction by itself. As examples of learning algorithms, there are supervised learning, unsupervised learning, semi-supervised learning, reinforcement learning, or any other learning algorithm known to those of ordinary skill in the art.

[0130] The machine-readable storage medium may be provided in the form of a non-transitory storage medium. In one or more examples, the term "non-transitory" only means that the storage medium is a tangible device and does not include signals (e.g., electromagnetic waves), and the term does not distinguish between a case where data is semi-permanently stored in the storage medium and a case where data is temporarily stored in the storage medium. For example, a "non-transitory storage medium" may include a buffer that temporarily stores data.

[0131] According to one or more embodiments, the methods according to various embodiments disclosed herein may be provided while being included in a computer program product. A computer program product refers to a product, and it can 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 disk read-only memory (CD-ROM)), or distributed directly between two user devices (e.g., smartphones), or distributed through an application store (e.g., Play Store TM ) Online distribution (e.g., downloading or uploading). In the case of online distribution, at least a portion of the computer program product (e.g., downloadable application) may be at least temporarily stored in a storage medium readable by a machine such as a manufacturer's server, an application store's server, and a memory 120 of a relay server, or may be temporarily generated.

[0132] In addition, each component (e.g., module or program) according to the various embodiments of the present disclosure described above may be composed of a single object or multiple objects. In addition, in the aforementioned corresponding subcomponents, some subcomponents may be omitted in various embodiments, or other subcomponents may be further included. Alternatively or additionally, some components (e.g., modules or programs) may be integrated as objects, and the functions performed by each component before integration are performed identically or in a similar manner.

[0133] The operations performed by the modules, programs or other components according to various embodiments may be performed sequentially, in parallel, repeatedly or heuristically. Alternatively, at least some of the operations may be performed in a different order or omitted, or other operations may be added.

[0134] In addition, the term "component" or "module" used in the present disclosure may include a unit composed of hardware, software or firmware, and may be used interchangeably with terms such as logic, logic block, component or circuit. In addition, a "component" or "module" may be a component that is constructed to perform one or more functions as an integrated body or minimum unit or a part thereof. For example, a module may be constructed as an application specific integrated circuit (ASIC).

[0135] In addition, various embodiments of the present disclosure may be implemented as software including instructions that can be read by a machine (e.g., a computer) and stored in a machine-readable storage medium. A machine refers to a device that calls instructions stored in a storage medium and can operate according to the called instructions, and the device may include an electronic device according to the aforementioned embodiment (e.g., electronic device 100).

[0136] When the instruction is executed by a processor, the processor may perform the function corresponding to the instruction by itself or by using other components under its control. The instruction may include a code generated or executed by a compiler or an interpreter.

[0137] In addition, the present disclosure is not limited to the above-described embodiments, and it is apparent that various modifications may be made by a person skilled in the art without departing from the gist of the present disclosure as claimed in the appended claims. In addition, it is intended that such modifications are not interpreted independently of the technical concept or prospect of the present disclosure.

Claims

1. An electronic device, comprising: communicator; at least one memory storing information associated with a neural network model for predicting a target device for an advertisement content for each of a plurality of time zones; as well as At least one processor configured to: acquiring data corresponding to a user's usage history from a plurality of external devices; acquiring a plurality of feature values ​​by assigning each of a plurality of feature values ​​corresponding to a corresponding external device among the plurality of external devices to a corresponding time zone among the plurality of time zones based on data corresponding to the usage history, inputting a plurality of feature values ​​into the neural network model, and obtaining score information of a plurality of probabilities by assigning each of a plurality of probabilities related to the plurality of external devices and the tags to a corresponding time zone among the plurality of time zones, identifying a plurality of target devices for providing the advertisement content in each of the plurality of time zones based on the score information, and The communicator is controlled to transmit information about the advertisement content to the plurality of target devices.

2. The electronic device according to claim 1, wherein: The at least one processor is further configured to: acquiring information about the number of external devices corresponding to the tag among the plurality of external devices by assigning each of the plurality of external devices to a corresponding time zone among the plurality of time zones, and A plurality of target devices for each of the plurality of time zones are identified based on the score information and information about the number of external devices corresponding to the tag.

3. The electronic device according to claim 2, wherein: The at least one processor is further configured to: allocating the number of target devices to each of the plurality of time zones based on information about the number of external devices corresponding to the tag, and A plurality of target devices for each of the plurality of time zones are identified based on identifying external devices having the highest score information up to the number of target devices assigned for each of the plurality of time zones.

4. The electronic device according to claim 3, wherein: The at least one processor is further configured to: identifying a time zone from the plurality of time zones having a number of assigned target devices less than a predetermined threshold, and A determination is made not to provide the advertising content during the identified time zone.

5. The electronic device according to claim 1, wherein: The data corresponding to the use history of the user includes information about the use time of the plurality of external devices, information about applications of the plurality of external devices used by the user, and information about content viewed by the user.

6. The electronic device according to claim 1, wherein: The at least one processor is further configured to: receiving, through the communicator, data corresponding to the user's usage history, information about the user's profile, and information about the user's product preference, and Based on data corresponding to the user's usage history, information about the user's profile, and information about the user's product preference, multiple feature values ​​are acquired by allocating each of the multiple feature values ​​to a corresponding time zone among the multiple time zones.

7. The electronic device according to claim 1, wherein: The plurality of feature values ​​are acquired in the form of feature vectors after being allocated to the plurality of time zones.

8. A method for controlling an electronic device, the method comprising: acquiring data corresponding to a user's usage history from a plurality of external devices; acquiring a plurality of feature values ​​by assigning each of a plurality of feature values ​​corresponding to a corresponding external device among the plurality of external devices to a corresponding time zone among a plurality of time zones based on data corresponding to the usage history; inputting the plurality of feature values ​​into a neural network model of a target device for predicting advertisement content in each time zone, and obtaining score information of a plurality of probabilities by assigning each of a plurality of probabilities related to the plurality of external devices and tags to a corresponding time zone in the plurality of time zones; identifying a plurality of target devices for providing the advertisement content in each of the plurality of time zones based on the score information; as well as Information about the advertisement content is transmitted to the plurality of target devices.

9. The control method of the electronic device according to claim 8, wherein: Identify multiple target devices including: acquiring information about the number of external devices corresponding to the tag among the plurality of external devices by assigning each of the plurality of external devices to a corresponding time zone among the plurality of time zones; and A plurality of target devices for each of the plurality of time zones are identified based on the score information and information about the number of external devices corresponding to the tag.

10. The control method of the electronic device according to claim 9, wherein: Identifying multiple target devices also includes: allocating the number of target devices to each of the plurality of time zones based on information about the number of external devices corresponding to the tag; and A plurality of target devices for each of the plurality of time zones are identified based on identifying external devices having the highest score information up to the number of target devices assigned for each of the plurality of time zones.

11. The control method of the electronic device according to claim 10, further comprising: identifying a time zone from the plurality of time zones having a number of assigned target devices less than a predetermined threshold, and A determination is made not to provide the advertising content during the identified time zone.

12. The control method of the electronic device according to claim 8, wherein: The data corresponding to the use history of the user includes information about the use time of the plurality of external devices, information about applications of the plurality of external devices used by the user, and information about content viewed by the user.

13. The control method of the electronic device according to claim 8, wherein: Getting multiple feature values ​​includes: receiving data corresponding to the user's usage history, information about the user's profile, and information about the user's product preference from the plurality of external devices, and A plurality of feature values ​​are acquired by dividing the plurality of feature values ​​by each of the plurality of time zones based on data corresponding to the user's usage history, information about the user's profile, and information about the user's product preference.

14. The control method of the electronic device according to claim 8, wherein: The plurality of feature values ​​are acquired in the form of feature vectors after being divided in units of predetermined time periods.

Images