KR102650337B1 - Electronic device and method of providing recommended contents - Google Patents

Abstract

An electronic device according to various embodiments of the present disclosure collects time-dependent location information of the first user during a predetermined period from the terminal device of the first user, and collects location information of the first user according to time during the predetermined period. Based on location information over time, analyze at least one movement pattern of the first user, collect current location information of the first user from the terminal device of the first user, and collect current location information of the first user. Based on location information and the current time, determine a current movement pattern among the at least one movement pattern, and determine recommended content for the first user among the plurality of contents based on the current movement pattern, It may be configured to transmit the determined recommended content to the terminal device of the first user.

Description

Electronic device and method for providing recommended content {ELECTRONIC DEVICE AND METHOD OF PROVIDING RECOMMENDED CONTENTS}

This disclosure relates to an electronic device and method for providing recommended content. Specifically, it relates to technology that determines and provides recommended content for a user based on the user's real-time location information.

Recently, as OTT (over the top) services have become popular, users can use OTT services through their own terminal devices. Users can receive a variety of content through terminal devices without restrictions of time and space. OTT service providers are continuing their efforts to provide recommended content suitable for users in order to encourage users to use OTT services for a long time.

Rather than providing personalized recommended content to users, OTT service providers have been providing recommended content in a general way. For example, recommended content has been provided based on the user's past viewing history. For example, the content with the highest number of views is currently provided as recommended content, or the content determined by the OTT service provider is provided as recommended content.

Korean Patent Publication No. 10-2019-0033719 Korean Patent Publication No. 10-2018-0015586 Korean Patent Publication No. 10-2014-0066269

According to various embodiments of the present disclosure, a technical problem is to analyze a user's movement pattern and determine and provide recommended content for the user based on the movement pattern.

According to various embodiments of the present disclosure, the technical task is to analyze the user's movement pattern based on an artificial intelligence model and provide recommended content for the user based on the analyzed movement pattern.

According to various embodiments of the present disclosure, a technical task is to provide recommended content for a user based not only on the user's movement pattern but also on the content viewing history of other users with similar movement patterns as the user.

An electronic device according to various embodiments of the present disclosure includes a communication circuit connected to communicate with a terminal device of each of a plurality of users; A memory that stores a plurality of contents; and a processor. The processor according to various embodiments collects location information according to the time of the first user during a predetermined period from the terminal device of the first user, and collects location information according to the time of the first user during the predetermined period. Based on the location information, analyze at least one movement pattern of the first user, collect current location information of the first user from the terminal device of the first user, current location information of the first user, and Based on the current time, determine a current movement pattern among the at least one movement pattern, determine recommended content for the first user among the plurality of contents based on the current movement pattern, and recommend the determined recommendation. It may be configured to transmit content to the first user's terminal device.

The memory according to various embodiments may store an artificial intelligence model. The processor according to various embodiments inputs time-dependent location information of the first user for the predetermined period into the artificial intelligence model, and determines at least one movement pattern of the first user from the artificial intelligence model. Configured to obtain, the artificial intelligence model uses a machine learning algorithm to include an input data set containing time-dependent location information for each of a plurality of users for a predetermined period of time, and at least one data set for each of the plurality of users. It may be a correlation model built by modeling the correlation between output data sets containing movement patterns of .

Each of the at least one movement pattern according to various embodiments may include at least one information of movement time, movement time, movement speed, departure and arrival location, movement route, and day of the week.

The processor according to various embodiments determines the movement time and movement speed corresponding to the current movement pattern, and recommends content for the first user among the plurality of contents based on the confirmed movement time and movement speed. It can be configured to determine .

The processor according to various embodiments determines at least one user among the plurality of users having a movement pattern similar to the current movement pattern of the first user, checks the content viewing history of each of the at least one user, and , Based on the content viewing history of each of the at least one user, it may be configured to determine recommended content for the first user among the plurality of content.

The plurality of contents according to various embodiments may be video contents.

Each of the plurality of contents according to various embodiments may include at least one tag. The processor according to various embodiments may be configured to determine recommended content for the first user among the plurality of contents based on the current movement pattern and at least one tag included in each of the plurality of contents. there is.

The processor according to various embodiments may be configured to transmit, along with the determined recommended content, a movement path corresponding to the current movement pattern and the current location information to the terminal device of the first user.

The communication circuit according to various embodiments is connected to a first external server that provides weather information, and the processor collects weather information corresponding to the current location information of the first user from the first external server, It may be configured to determine recommended content for the first user among the plurality of content based on weather information corresponding to the current location information of the first user and the current movement pattern.

The communication circuit according to various embodiments is connected to a second external server that provides traffic information, and the processor collects traffic information corresponding to the current location information of the first user from the second external server, It may be configured to determine recommended content for the first user among the plurality of content based on traffic information corresponding to the current location information of the first user and the current movement pattern.

The processor according to various embodiments may receive, from the terminal device of the first user, information that the vehicle assistance application has been activated, and in response to receiving the information that the vehicle assistance application has been activated, It may be configured to collect traffic information corresponding to location information from the second external server.

A method of providing recommended content performed by an electronic device according to various embodiments of the present disclosure includes: collecting time-dependent location information of the first user for a predetermined period of time from the first user's terminal device; Analyzing at least one movement pattern of the first user based on time-dependent location information of the first user during the predetermined period of time; collecting current location information of the first user from the first user's terminal device; determining a current movement pattern among the at least one movement pattern based on the current location information and current time of the first user; determining recommended content for the first user among the plurality of content based on the current movement pattern; and transmitting the determined recommended content to the terminal device of the first user.

According to various embodiments of the present disclosure, the user's movement pattern may be analyzed, and recommended content for the user may be determined and provided based on the movement pattern.

According to various embodiments of the present disclosure, the user's movement pattern can be analyzed based on an artificial intelligence model, and recommended content for the user can be provided based on the analyzed movement pattern.

According to various embodiments of the present disclosure, recommended content for a user may be provided based on not only the user's movement pattern but also the content viewing history of other users with similar movement patterns as the user.

1 is a diagram illustrating a system according to various embodiments of the present disclosure.
FIG. 2 is a block diagram illustrating an electronic device and a terminal device according to various embodiments of the present disclosure.
3 is a flowchart of an operation of an electronic device according to various embodiments of the present disclosure.
4A and 4B are diagrams illustrating a method of building an artificial intelligence model and a method of utilizing the artificial intelligence model according to various embodiments of the present disclosure.
5 is a flowchart of an operation of an electronic device according to various embodiments of the present disclosure.
FIG. 6 is a diagram illustrating a screen of a first user's terminal device according to various embodiments of the present disclosure.
FIG. 7 is a diagram illustrating a screen of a first user's terminal device according to various embodiments of the present disclosure.
FIG. 8 is a diagram illustrating a screen of a first user's terminal device according to various embodiments of the present disclosure.

Embodiments of the present disclosure are illustrated for the purpose of explaining the technical idea of the present disclosure. The scope of rights according to the present disclosure is not limited to the embodiments presented below or the specific description of these embodiments.

All technical terms and scientific terms used in this disclosure, unless otherwise defined, have meanings commonly understood by those skilled in the art to which this disclosure pertains. All terms used in this disclosure are selected for the purpose of more clearly explaining this disclosure and are not selected to limit the scope of rights according to this disclosure.

Expressions such as “comprising,” “comprising,” “having,” and the like used in the present disclosure are open terms that imply the possibility of including other embodiments, unless otherwise stated in the phrase or sentence containing the expression. It should be understood as (open-ended terms).

The singular forms described in this disclosure may include plural forms unless otherwise stated, and this also applies to the singular forms recited in the claims.

Expressions such as “first” and “second” used in the present disclosure are used to distinguish a plurality of components from each other and do not limit the order or importance of the components.

The term “unit” used in this disclosure refers to software or hardware components such as a field-programmable gate array (FPGA) or an application specific integrated circuit (ASIC). However, “wealth” is not limited to hardware and software. The “copy” may be configured to reside on an addressable storage medium and may be configured to reproduce on one or more processors. Thus, as an example, “part” refers to software components, such as object-oriented software components, class components, and task components, processors, functions, properties, procedures, subroutines, Includes segments of program code, drivers, firmware, microcode, circuits, data, databases, data structures, tables, arrays, and variables. The functionality provided within components and “parts” may be combined into smaller numbers of components and “parts” or may be further separated into additional components and “parts”.

As used in this disclosure, the expression "based on" is used to describe one or more factors that influence the act or action of decision, judgment, or action described in the phrase or sentence containing the expression, and this expression It does not exclude additional factors that may influence the decision, act or action of judgment.

In this disclosure, when a component is referred to as being “connected” or “connected” to another component, it means that the component can be directly connected or connected to the other component, or as a new component. It should be understood that it can be connected or connected through other components.

Hereinafter, embodiments of the present disclosure will be described with reference to the attached drawings. In the accompanying drawings, identical or corresponding components are given the same reference numerals. Additionally, in the description of the following embodiments, overlapping descriptions of identical or corresponding components may be omitted. However, even if descriptions of components are omitted, it is not intended that such components are not included in any embodiment.

FIG. 1 is a diagram illustrating a system 10 that operates an OTT (over the top) service according to various embodiments of the present disclosure. System 10 may include an electronic device 110 and a plurality of terminal devices 120. The electronic device 110 may be a server device for operating an OTT (over the top) service. The term plurality of terminal devices 120 used in this document may refer to terminal devices for each of a plurality of users. Multiple users may refer to users who have signed up to use the OTT service.

The terminal device 120 of each of a plurality of users may refer to a user's (or consumer's) device for using an OTT service. The terminal device 120 may be a device capable of connecting to the Internet, for example, a mobile phone, a smartphone, a laptop computer, a wearable device, or a head mounted display (HMD). A plurality of terminal devices 120 used by a plurality of users are connected to the electronic device 110 through a network and can transmit and receive various data. The electronic device 110 may provide a plurality of contents to the terminal device 120 through an application or website related to an OTT service. The plurality of contents may be, for example, video contents. In this drawing, for convenience of explanation, it is assumed that the plurality of terminal devices 120 includes a first user's terminal device 120a, a second user's terminal device 120b, and a third user's terminal device 120c. Although this is explained, it goes without saying that the number of user terminal devices may be four or more.

The electronic device 110 according to various embodiments may collect and store personal information of multiple users. The electronic device 110 may store personal information of the first user who has subscribed to the OTT service. The first user's personal information may include at least one of information about the first user's age, gender, marital status, or information about whether the first user has children. For example, the electronic device 110 may store personal information of the first user, such as that the first user is 30 years old, male, married, and has no children. The electronic device 110 may receive and store the first user's personal information from the first user's terminal device 120a.

According to various embodiments, multiple terminal devices 120 may be connected (logged in) to one account. A plurality of user profiles can be registered in one account, and each of the plurality of users can connect the account from each of the plurality of terminal devices owned by the user. For example, the first user, the second user, and the third user using the same account each have the first user's terminal device 120a, the second user's terminal device 120b, and the third user's terminal device ( You can connect the above account to 120c). The processor 111 may collect and store content viewing history for each user profile and/or terminal device for each of a plurality of users connected to the account.

The electronic device 110 according to various embodiments may collect and store time-dependent location information of a plurality of users. The electronic device 110 may collect time-dependent location information of the first user for a predetermined period of time from the terminal device 120a of the first user who has subscribed to the OTT service. For example, location information over time of the first user over the past year may be collected. Prior to collecting location information, consent to collect location information must first be obtained from the first user through the first user's terminal device 120a.

The electronic device 110 may analyze at least one movement pattern of the first user based on time-dependent location information of the first user for a predetermined period. For example, the electronic device 110 may collect time-dependent location information of the first user over the past six months and analyze at least one movement pattern of the first user based on the collected information. The specific analysis method will be described later.

The electronic device 110 may check the current location information of the first user in real time and determine the current movement pattern based on the current location information and current time of the first user. For example, when there are three analyzed movement patterns of the first user, it is possible to determine which movement pattern corresponds to the current location information and current time of the first user.

The electronic device 110 may classify the content viewing history according to the first user's movement pattern. For example, among the movement patterns, the content history watched by the first user during the commuting pattern, the content history watched by the first user during the leaving work pattern, and the content history watched by the first user during the weekend rest pattern can be classified. there is.

The electronic device 110 may determine recommended content for the first user from among the plurality of content based on the first user's current movement pattern and provide the recommended content to the first user's terminal device 120a. The specific method of determining recommended content will be described later.

FIG. 2 is a block diagram illustrating an electronic device 110 and a terminal device 120 according to various embodiments of the present disclosure.

Referring to FIG. 2 , an electronic device 110 according to various embodiments may include a processor 111, a memory 113, and a communication circuit 115. At least one of the components included in the electronic device 110 may be omitted, or another component may be added to the electronic device 110 . Additionally or alternatively, some components may be integrated and implemented, or may be implemented as a single or plural entity. At least some components in the electronic device 110 are connected to each other through a bus, general purpose input/output (GPIO), serial peripheral interface (SPI), or mobile industry processor interface (MIPI), and provide data and/or Or you can send and receive signals.

According to various embodiments, the processor 111 of the electronic device 110 performs operations or data processing related to control and/or communication of each component (e.g., memory 113) of the electronic device 110. It may be a possible configuration. The processor 111 may be operatively connected to components of the electronic device 110, for example. The processor 111 may load commands or data received from other components of the electronic device 110 into the memory 113, process the commands or data stored in the memory 113, and store the resulting data. there is. The processor 111 disclosed in this document may refer to a set of one or more processors 111.

According to various embodiments, the memory 113 of the electronic device 110 may store instructions for operating the processor 111.

According to various embodiments, the communication circuit 115 of the electronic device 110 may establish a wired or wireless communication channel with an external device (e.g., the terminal device 120) and transmit and receive various data with the external device. According to one embodiment, the communication circuit 115 may include at least one port for connecting to an external device via a wired cable in order to communicate with the external device via a wired cable. In the above case, the communication circuit 115 can communicate with a wired external device through at least one port. According to one embodiment, the communication circuit 115 may include a cellular communication module and be configured to be connected to a cellular network (eg, 3G, LTE, 5G, Wibro, or Wimax). According to various embodiments, the communication circuit 115 includes a short-range communication module and can transmit and receive data with an external device using short-range communication (e.g., Wi-Fi, Bluetooth, Bluetooth Low Energy (BLE), UWB). However, it is not limited to this. According to one embodiment, the communication circuit 115 may include a non-contact communication module for non-contact communication. Non-contact communication may include at least one non-contact proximity communication technology, such as, for example, near field communication (NFC) communication, radio frequency identification (RFID) communication, or magnetic secure transmission (MST) communication.

The terminal device 120 according to various embodiments may include a controller 121, a memory 123, a display 125, and a communication circuit 127. Even if some of the components shown in FIG. 2 are omitted or replaced, there will be no problem in implementing the various embodiments disclosed in this document. Additionally, the first user's terminal device 120a disclosed in this document is the same as the terminal device 120 described in this drawing.

The controller 121 of the terminal device 120 according to various embodiments is a component that can perform operations or data processing related to control and/or communication of each component of the terminal device 120. The controller 121 may be operatively connected to components of the terminal device 120, for example. The controller 121 loads commands or data received from other components of the terminal device 120 into the memory 123 of the terminal device 120, processes the commands or data stored in the memory 123, and , the resulting data can be saved.

The memory 123 according to various embodiments may store instructions for the operation of the controller 121.

The display 125 of the terminal device 120 according to various embodiments may display various screens based on control of the controller 121. The display 125 may be implemented in the form of a touch sensor panel (TSP) that can recognize the contact or proximity of various external objects (eg, fingers). The touch sensor panel may have various structures and types, and the present disclosure can be applied regardless of the structure and type of the touch sensor panel. The display 125 may include a capacitive sensor to recognize contact or proximity of various external objects. A capacitance sensor may be composed of a plurality of capacitors, and the capacitance sensor may apply an electric signal to the capacitors. A capacitor can charge and discharge charge in response to the application of an electrical signal. When an electrical signal is applied to a capacitor, the capacitor may be charged depending on the magnitude of the voltage of the electrical signal. The display 125 may receive a touch input based on signals collected from the capacitive sensor. For example, the display 125 can receive the user's tap touch, double tap touch, sliding touch, drag & drop touch, and long touch. there is.

FIG. 3 is a flowchart of the operation of the electronic device 110 according to various embodiments of the present disclosure.

Referring to the operation flowchart 300, in operation 310, the processor 111 of the electronic device 110 according to various embodiments receives the time of the first user for a predetermined period from the terminal device 120a of the first user. Location information can be collected. For example, the processor 111 may collect GPS information according to the time of the first user for the past two years from the first user's terminal device 120a. For example, the processor 111 may collect location information tagged with time information from the first user's terminal device 120a during a predetermined period.

In operation 320, the processor 111 according to various embodiments may analyze at least one movement pattern of the first user based on time-dependent location information of the first user for a predetermined period. Each of the at least one movement patterns may include at least one information of movement time, movement time, movement speed, departure and arrival locations, movement route, and day of the week. For example, a specific travel pattern includes information about starting from location M and traveling along a specific travel route to location N at the speed of the subway, for one hour between 11:00 AM and 1:00 PM on Saturday. can do.

For example, the processor 111 analyzed the time-dependent location information of the first user, and as a result, the first user generally moved from location You can see the pattern. In this case, the processor 111 may analyze that the first user has a movement pattern of moving from location X to location Y during one hour between 8:00 AM and 9:00 AM on weekdays. The above-described travel pattern can be classified as a “commuting pattern.”

For example, the processor 111 analyzed the time-dependent location information of the first user, and as a result, the first user was generally in the vicinity of the Z location for 14 hours from 6 PM on Sunday to 8 AM on Monday over the past two years ( You can see the pattern that it moves only within a 1km radius from the Z location. In this case, the processor 111 may analyze that the Z location is the first user's home, and that there is a movement pattern in which the first user only moves around the house for 14 hours from 6 PM on Sunday to 8 AM on Monday. The above-described movement pattern can be classified as a “weekend rest pattern.”

Like the method described above, the processor 111 may analyze at least one movement pattern of the first user by analyzing location information of the first user over time. The number of at least one movement pattern of the first user may be one or two or more.

According to one embodiment, the processor 111 may analyze at least one movement pattern of the first user through a learned artificial intelligence model. The artificial intelligence model may be a correlation model learned to analyze at least one movement pattern of the user when the user inputs location information according to time for a predetermined period. The specific learning method of the artificial intelligence model will be described later. The processor 111 may input time-dependent location information of the first user for a predetermined period into the learned artificial intelligence model. In this case, the artificial intelligence model may output at least one movement pattern of the first user.

The processor 111 according to various embodiments may collect the first user's current location information from the first user's terminal device 120a in operation 330. The processor 111 may collect real-time location information of the first user from the first user's terminal device 120a. For example, GPS information on the current location of the first user's terminal device 120a may be collected from the first user's terminal device 120a.

The processor 111 according to various embodiments may determine a current movement pattern among at least one movement pattern based on the current location information and current time of the first user in operation 340. The processor 111 may determine which of the at least one movement patterns of the first user analyzed in advance is the current movement pattern corresponding to the current location information and current time of the first user. For example, as described above, assume that the first user is analyzed to have two movement patterns: a “commuting pattern” and a “weekend rest pattern.” If the current time is 8:00 am on Tuesday and the current user's location is located on the movement path corresponding to the "commuting pattern", the current movement pattern of the first user is expected (judged) to be a "commuting pattern". You can. Accordingly, the processor 111 may predict (determine) that the first user will move for the next hour according to the commuting pattern.

The processor 111 according to various embodiments may determine recommended content for the first user among a plurality of contents based on the current movement pattern in operation 350. According to one embodiment, the processor 111 determines content whose playback time is similar to the estimated remaining movement time among a plurality of contents as recommended content, based on the movement time (or the remaining expected movement time) according to the current movement pattern. You can. For example, if the estimated remaining travel time is 10 minutes according to the current movement pattern, short content with a playback time of 10 minutes may be determined as recommended content.

According to one embodiment, the processor 111 may determine recommended content for the first user based on the movement time and movement speed corresponding to the current movement pattern. The processor 111 may check the movement time and movement speed corresponding to the current movement pattern. For example, the processor 111 can confirm that, according to the current travel pattern, the vehicle is scheduled to travel for approximately 1 hour and will travel at subway travel speed. The corresponding information may be matched to the current movement pattern and stored in the memory 113. Thereafter, the processor 111 may determine recommended content for the first user among the plurality of contents based on the confirmed movement time and movement speed.

For example, if the first user is expected to travel in the subway for about 1 hour in the future, the processor 111 may determine entertainment content with a playback time of about 1 hour as recommended content. Alternatively, for example, if the first user is expected to rest at home for the next 6 hours, the processor 111 may determine 6 hours of drama as recommended content.

According to one embodiment, the processor 111 may determine recommended content for the first user among the plurality of contents based on the current movement pattern and at least one tag included in each of the plurality of contents. Each of the plurality of contents may include at least one tag. At least one tag may include, for example, a genre tag such as comedy, thriller, or historical drama, or a mood tag such as humorous, light, or gloomy, liked by women in their 20s, men in their 30s. You can also include preference tags, such as likes. In addition, various tags can be assigned to contents. The processor 111 may determine content having a tag corresponding to the current movement pattern as recommended content for the first user.

The processor 111 according to various embodiments may transmit the determined recommended content to the terminal device 120a of the first user in operation 360. The first user may receive recommended content through the first user's terminal device 120a.

The processor 111 according to an embodiment may determine recommended content for the first user among a plurality of content based on the first user's current movement pattern and the first user's personal information. For example, recommended content for the first user among a plurality of contents may be determined based on the current movement pattern of the first user, information on the first user's age, gender, marital status, and information on whether the first user has children. there is.

The processor 111 according to an embodiment may determine recommended content for the first user among a plurality of content based on content viewing history corresponding to the first user's current movement pattern. For example, if the first user watches a lot of current affairs content during the commuting pattern and a lot of entertainment content during the leaving work pattern, and the current movement pattern of the first user is a commuting pattern, the current affairs content is used as recommended content. You can decide.

According to one embodiment, multiple terminal devices 120 may be connected (logged in) to one account. For example, the first user, the second user, and the third user using the same account each have the first user's terminal device 120a, the second user's terminal device 120b, and the third user's terminal device ( You can connect the above account to 120c). In the above case, the processor 111 may collect and store content viewing history for each user profile for each of a plurality of users connected to the account. The processor 111 can collect time-dependent location information for each of a plurality of users from a plurality of user terminal devices 120 connected to the same account, determine the movement pattern of each of the plurality of users, and determine the movement pattern of each of the plurality of users. Content can be recommended based on the movement patterns of multiple users. For example, if the processor 111 determines that a plurality of users using the same account correspond to a movement pattern called a weekend rest pattern in which they only move around the current home, the processor 111 may recommend content appropriate for the movement pattern.

According to one embodiment, the processor 111 selects the user who has watched the content the most or the user who has most recently viewed the content among the plurality of users connected to one account for the terminal device 120a. It is also possible to determine the main user, collect location information of the main user over time, and analyze the main user's movement patterns. The processor 111 may determine recommended content for the main user based on the main user's content viewing history.

4A and 4B are diagrams illustrating a method of building an artificial intelligence model 400 and a method of utilizing the artificial intelligence model 400 according to various embodiments of the present disclosure.

Referring to FIG. 4A, the processor 111 of the electronic device 110 according to various embodiments uses a machine learning algorithm to generate input data including time-dependent location information for each of a plurality of users for a predetermined period of time. An artificial intelligence model 400 can be constructed by modeling the correlation between the set 410 and the output data set 420 including at least one movement pattern of each of the plurality of users. In other words, the artificial intelligence model uses the above-described input data set 410 as input data for learning (training) and uses the above-described output data set 420 as output data for learning (training), so that the artificial intelligence model ( 400) can be created.

For example, a specific user may depart from location A for one hour from 7 PM to 8 PM on weekdays over the past year, travel along a specific travel route, and then return to location A. That is, the specific user can move according to the evening exercise pattern for one hour starting at 7 PM on weekdays. In other words, data that is location information of a specific user over time over the past year, and a movement pattern that starts from location A for 1 hour from 7:00 PM to 8:00 PM on weekdays, moves along a specific travel route, and then returns to location A. By learning the correlation between the two, an artificial intelligence model 400 can be built.

In the same way as above, data called location information over time for a plurality of users can be used as learning data to build the artificial intelligence model 400. The above-described learning data can be obtained through the terminal devices of each of a plurality of users.

In the present disclosure, an artificial intelligence model, machine learning model, or neural network model may be designed to implement the human brain structure on a computer, and may include a plurality of network nodes that simulate neurons of a human neural network and have weights. You can. A plurality of network nodes may have a connection relationship with each other by simulating the synaptic activity of neurons in which neurons send and receive signals through synapses. In an artificial intelligence learning model, multiple network nodes are located in layers of different depths and can exchange data according to convolutional connection relationships. The artificial intelligence learning model may be, for example, an artificial neural network model, a convolution neural network model, etc.

In this disclosure, a machine learning algorithm may mean one of a deep neural network, a recurrent neural network, a convolutional neural network, a machine learning model for classification-regression analysis, or a reinforcement learning model.

Referring to FIG. 4B, as described above, the artificial intelligence model 400 may be built according to a machine learning algorithm. That is, the artificial intelligence model 400 is a correlation model learned to output at least one movement pattern 440 of the first user when time-dependent location information 430 of a specific user for a predetermined period is input. You can.

The processor 111 of the electronic device 110 according to various embodiments uses the constructed artificial intelligence model 400 to obtain information about the first user from the time-dependent location information 430 of the first user for a predetermined period of time. At least one movement pattern 440 can be obtained. The processor 111 may input time-dependent location information 430 of the first user for a predetermined period as input data to the learned (trained) artificial intelligence model 400. In this case, the artificial intelligence model 400 may output at least one movement pattern 440 of the first user as output data. The processor 111 can obtain one movement pattern 440 even if the first user moves as output by the artificial intelligence model 400. That is, at least one movement pattern 440 of the first user can be accurately and quickly analyzed using the constructed artificial intelligence model 400.

FIG. 5 is a flowchart of the operation of the electronic device 110 according to various embodiments of the present disclosure. Specifically, FIG. 5 is a flowchart of operation 350 of FIG. 3.

Referring to the operation flowchart 500, in operation 510, the processor 111 according to various embodiments may determine at least one user among a plurality of users having a movement pattern similar to the current movement pattern of the first user. For example, assume that the current movement pattern determined based on the current location information and current time of the first user is a “commuting pattern.” A “commuting pattern” may be a travel pattern that involves traveling by subway for about an hour on a weekday morning. The processor 111 may determine at least one user among a plurality of users having a movement pattern similar to the commuting pattern of the first user. That is, the processor 111 may determine at least one user who has a commuting pattern of traveling by subway for about one hour on weekday mornings.

The processor 111 according to various embodiments may check the content viewing history of each at least one user in operation 520. The processor 111 may check the viewing history of each of at least one user having a movement pattern similar to that of the first user.

In operation 530, the processor 111 according to various embodiments may determine recommended content for the first user among a plurality of content based on the content viewing history of each of at least one user. For example, the processor 111 determines the entertainment content as recommended content for the first user when each of at least one user having a similar movement pattern as the first user views the 30-minute entertainment content the most. You can.

According to one embodiment, the processor 111 may classify the content viewing history of each at least one user by movement pattern. For example, among the travel patterns, there is content most viewed by at least one user during the commute pattern, content most viewed by at least one user during the commute pattern, and content most viewed by at least one user during the weekend rest pattern. Content can be classified. The processor 111 may determine recommended content for the first user based on the content viewing history of each of at least one user classified by movement pattern. For example, when the current movement pattern of the first user is a commute pattern, the content that at least one user viewed most during the commute pattern may be determined as recommended content for the first user.

FIG. 6 is a diagram illustrating a screen 600 of the first user's terminal device 120a according to various embodiments of the present disclosure. In this drawing, the description is made assuming the screen of the first user's terminal device 120a, but of course, the same technical features can be provided to other users' terminal devices.

The first user's terminal device 120a according to various embodiments may execute an application that provides an OTT service. The first user's terminal device 120a may display various contents through the home screen 600 of the application.

The first user's terminal device 120a according to various embodiments may display the movement path and current location information corresponding to the first user's current movement pattern on the first area 610 of the home screen 600 of the application. You can. The first area 610 may be a map area for displaying the current movement pattern. The first user's terminal device 120a may receive movement path and current location information corresponding to the first user's current movement pattern from the electronic device 110. The first user's terminal device 120a may display the movement path and current location information corresponding to the current movement pattern on the first area 610. The map may be a map with actual scale, or it may be a map to briefly display only the movement route according to the movement pattern.

The first user's terminal device 120a according to various embodiments may display recommended content determined based on the first user's current movement pattern on the second area 620 of the home screen 600. In this figure, the second area 620 may be an area that provides recommended content determined based on the current movement pattern of the first user. The number of recommended contents may be one or more.

The first user's terminal device 120a according to various embodiments may display recommended content determined based on the first user's viewing history in the third area 630 of the home screen 600. The third area 630 may be an area that provides recommended content determined based on the first user's past content viewing history.

FIG. 7 is a diagram illustrating a screen of the first user's terminal device 120a according to various embodiments of the present disclosure. Specifically, FIG. 7 is a diagram illustrating the home screen 700 of an application that provides recommended content determined based on current weather information.

The electronic device 110 according to various embodiments may collect weather information corresponding to the current location information of the first user from a first external server. The communication circuit of the electronic device 110 may be connected to a first external server that provides weather information. The first external server may be, for example, a Korea Meteorological Administration server. After collecting the current location information of the first user from the first user's terminal device 120a, the processor 111 of the electronic device 110 sends weather information corresponding to the current location information of the first user to the first external server. It can be collected from

The electronic device 110 according to various embodiments may determine recommended content among a plurality of content based on weather information corresponding to the current location information of the first user and the current movement pattern. For example, when the first user is currently located at home and the weather information corresponding to the current location information indicates rainy weather, the processor 111 of the electronic device 110 may determine a sad drama as recommended content.

The processor 111 of the electronic device 110 may transmit weather information corresponding to the first user's current location information and recommended content determined based on the current movement pattern to the first user's terminal device 120a. The processor 111 may also transmit weather information corresponding to the current location information to the first user's terminal device 120a along with the recommended content.

The first user's terminal device 120a according to various embodiments may display weather information corresponding to the first user's current location information in the first area 710 of the application home screen 700.

The first user's terminal device 120a according to various embodiments may display recommended content determined based on weather information and the current movement pattern corresponding to the first user's current location information in the second area 720 of the home screen 700. ) can be displayed. In this figure, the second area 720 may be an area that provides weather information corresponding to current location information and recommended content determined based on the current movement pattern. The number of recommended contents may be one or more.

The first user's terminal device 120a according to various embodiments may display recommended content determined based on the first user's viewing history in the third area 730 of the home screen 700. The third area 730 may be an area that provides recommended content determined based on the first user's past content viewing history.

FIG. 8 is a diagram illustrating a screen 800 of the first user's terminal device 120a according to various embodiments of the present disclosure. Specifically, FIG. 8 is a diagram illustrating a home screen 800 of an application that provides recommended content determined based on traffic information corresponding to the current location information of the first user and the current movement pattern.

The processor 111 of the electronic device 110 according to various embodiments may collect traffic information corresponding to the current location information of the first user from the second external server. The communication circuit of the electronic device 110 may be a server that provides traffic information. The second external server may be, for example, a server of a road traffic corporation.

The processor 111 may receive information that the vehicle assistance application is activated from the first user's terminal device 120a. A vehicle support application is, for example, an application that can be used in a terminal device and may refer to software that connects the terminal device and the vehicle for communication and allows some functions of the terminal device to be performed in the vehicle. The processor 111 may collect traffic information corresponding to the current location information of the first user from the second external server in response to receiving information that the vehicle assistance application has been activated from the terminal device 120a of the first user. .

According to another embodiment, the processor 111 of the electronic device 110 may collect traffic information corresponding to the first user's current location information from the first user's vehicle. The communication circuit of the electronic device 110 may be connected to the first user's vehicle.

Traffic information according to various embodiments may be information indicating traffic conditions on roads around the current location of the first user. For example, traffic information corresponding to the current location information of the first user may be information indicating traffic conditions on roads within a 5 km radius from the current location of the first user. Traffic conditions may be, for example, smooth, normal or congested.

The processor 111 according to various embodiments may determine recommended content for the first user among the plurality of contents based on traffic information corresponding to the current location information of the first user and the current movement pattern.

For example, if traffic information corresponding to the current location information of the first user indicates a congestion situation and the first user is currently located at home, the processor 111 provides drama content suitable for viewing at home to the first user. You can decide on the recommended content for you.

For example, if traffic information corresponding to the current location information of the first user indicates a congestion situation and the first user is moving, the processor 111 recommends audio content suitable for viewing in the traffic jam situation for the first user. You can decide based on content.

The processor 111 according to various embodiments may transmit traffic information corresponding to the first user's current location information and recommended content determined based on the current movement pattern to the first user's terminal device 120a.

The first user's terminal device 120a according to various embodiments may display the movement path and current location information corresponding to the first user's current movement pattern on the first area 810 of the home screen 800 of the application. You can. The first user's terminal device 120a may display traffic information corresponding to the first user's current location information in the first area 810. The first area may be a map area for displaying current movement patterns and surrounding traffic information. The map may be a map with actual scale, or it may be a map to briefly display only the movement route and surrounding traffic information according to the movement pattern.

The first user's terminal device 120a according to various embodiments provides traffic information corresponding to the first user's current location information and recommended content determined based on the current movement pattern in the second area of the home screen 800 ( 820). In this figure, the second area 820 may be an area that provides traffic information corresponding to the current location information of the first user and recommended content determined based on the current movement pattern. The number of recommended contents may be one or more.

The first user's terminal device 120a according to various embodiments may display recommended content determined based on the first user's viewing history in the third area 830 of the home screen 800. The third area 830 may be an area that provides recommended content determined based on the first user's past content viewing history.

Although process steps, method steps, algorithms, etc. are described in a sequential order in the flow diagrams shown in the figures, such processes, methods, and algorithms may be configured to operate in any suitable order. In other words, the steps of the processes, methods and algorithms described in various embodiments of this disclosure do not need to be performed in the order described in this disclosure. Additionally, although some steps are described as being performed asynchronously, in other embodiments, some such steps may be performed concurrently. Additionally, illustration of a process by depiction in the drawings does not mean that the illustrated process excludes other variations and modifications thereto, and that any of the illustrated process or steps thereof may be used in various embodiments of the present disclosure. It does not imply that more than one is required, nor does it imply that the illustrated process is preferred.

Although the method has been described through specific embodiments, the method can also be implemented as computer-readable code on a computer-readable recording medium. Computer-readable recording media include all types of recording devices that store data that can be read by a computer system. Examples of computer-readable recording media may include ROM, RAM, CD-ROM, magnetic tape, floppy disk, and optical data storage devices. Additionally, computer-readable recording media can be distributed across networked computer systems, so that computer-readable code can be stored and executed in a distributed manner. And, functional programs, codes, and code segments for implementing the above embodiments can be easily deduced by programmers in the technical field to which this disclosure pertains.

Claims (12)

In electronic devices,
A communication circuit connected to communicate with each terminal device of a plurality of users;
Memory for storing multiple contents and artificial intelligence models; and
Includes a processor,
The processor,
From the terminal device of the first user, collect time-dependent location information of the first user for a predetermined period and input it into the artificial intelligence model,
From the artificial intelligence model, obtain at least one movement pattern of the first user based on time-dependent location information of the first user during the predetermined period,
Collecting current location information of the first user from the terminal device of the first user,
Based on the current location information and current time of the first user, determine a current movement pattern among the at least one movement pattern,
Based on the current movement pattern, determine recommended content for the first user among the plurality of content,
configured to transmit the determined recommended content to the terminal device of the first user,
The artificial intelligence model uses a machine learning algorithm to include an input data set containing time-dependent location information for each of a plurality of users for a predetermined period of time, and at least one movement pattern of each of the plurality of users. Electronic devices, a correlation model built by modeling the correlation between output data sets. delete According to paragraph 1,
Each of the at least one movement pattern includes at least one information of movement time, movement time, movement speed, departure and arrival location, movement route, and day of the week. According to paragraph 3,
The processor,
Check the movement time and movement speed corresponding to the current movement pattern,
An electronic device configured to determine recommended content for the first user among the plurality of contents, based on the confirmed movement time and movement speed. According to paragraph 3,
The processor,
Determining at least one user among the plurality of users having a movement pattern similar to the current movement pattern of the first user,
Check the content viewing history of each of the at least one user,
An electronic device configured to determine recommended content for the first user among the plurality of content based on the content viewing history of each of the at least one user. According to paragraph 1,
An electronic device wherein the plurality of contents are video contents. According to clause 6,
Each of the plurality of contents includes at least one tag,
The processor,
An electronic device configured to determine recommended content for the first user among the plurality of contents, based on the current movement pattern and at least one tag included in each of the plurality of contents. According to paragraph 1,
The processor,
An electronic device configured to transmit, along with the determined recommended content, a movement path corresponding to the current movement pattern and the current location information to the terminal device of the first user. According to paragraph 1,
The communication circuit is connected to a first external server that provides weather information,
The processor,
Collect weather information corresponding to the current location information of the first user from the first external server,
An electronic device configured to determine recommended content for the first user among the plurality of content based on weather information corresponding to the current location information of the first user and the current movement pattern. According to paragraph 1,
The communication circuit is connected to a second external server that provides traffic information,
The processor,
Collect traffic information corresponding to the current location information of the first user from the second external server,
An electronic device configured to determine recommended content for the first user among the plurality of content based on traffic information corresponding to the current location information of the first user and the current movement pattern. According to clause 10,
The processor,
Receiving information that a vehicle assistance application is activated from the terminal device of the first user,
The electronic device, configured to collect traffic information corresponding to current location information of the first user from the second external server in response to receiving information that the vehicle assistance application has been activated. In a method of providing recommended content performed by an electronic device,
An operation of collecting time-dependent location information of the first user for a predetermined period from the first user's terminal device and inputting it into an artificial intelligence model;
Obtaining at least one movement pattern of the first user based on time-dependent location information of the first user during the predetermined period from the artificial intelligence model;
collecting current location information of the first user from the first user's terminal device;
determining a current movement pattern among the at least one movement pattern based on the current location information and current time of the first user;
determining recommended content for the first user among a plurality of content based on the current movement pattern; and
An operation of transmitting the determined recommended content to the terminal device of the first user,
The artificial intelligence model uses a machine learning algorithm to include an input data set containing time-dependent location information for each of a plurality of users for a predetermined period of time, and at least one movement pattern of each of the plurality of users. A method of providing recommended content, a correlation model built by modeling the correlation between output data sets.

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