KR102433903B1 - Method for displaying media contents in a personalized way - Google Patents

Abstract

The present disclosure relates to a method of displaying media content, executed by at least one processor. The method includes the steps of: selecting target media content from among a plurality of media contents in response to a user's input; determining a page movement speed of the target media content based on information about the target media content and a viewing history of previous media content; and displaying the target media content on the display based on the determined page movement speed of the target media content.

Description

How to display custom media content {METHOD FOR DISPLAYING MEDIA CONTENTS IN A PERSONALIZED WAY}

The present disclosure relates to a method for displaying user-customized media content, and more particularly, determining a page moving speed and/or page arrangement of a target media content based on a browsing history of previous media content, and displaying the target media content accordingly It relates to a method of displaying media content displayed on the screen.

As electronic devices such as PCs, smart phones, and tablet PCs become popular, users can view numerous media contents through the electronic devices. For example, users may view media content such as a webtoon or a web novel by using an application that provides media content such as a webtoon application or a web novel application. When the provided content cannot be viewed on one screen, the user needs to move the page, such as scrolling by touching or dragging the screen. In this case, it is inconvenient that the user has to directly manipulate the terminal while viewing the content. In particular, in a situation in which it is difficult for the user to directly manipulate the terminal (eg, when delicate manipulation is difficult due to a physical disability, wearing gloves in winter, riding on public transportation, etc.), there is a problem in that the content cannot be properly enjoyed. Accordingly, there has been a need for a technology capable of automatically moving a page so that a user can enjoy content without manipulating the terminal.

On the other hand, if the page movement speed is the same for all content or all users even if the page is automatically moved, or if the page movement speed is controlled only by the information of the target content (number of characters, image complexity, etc.), the content browsing speed according to the user Because the difference is not reflected, users with a slower or faster reading speed than the average may not be able to properly enjoy the content. Considering this point, there is a need to introduce an automatic page moving method that considers the difference in reading speed according to users.

The present disclosure provides a method for displaying a user-customized media content for solving the above problems, and a computer program stored in a recording medium.

The present disclosure may be implemented in various ways including a method, an apparatus (system), or a computer program stored in a computer-readable storage medium.

According to an embodiment of the present disclosure, a method for displaying user-customized media content, executed by at least one processor, includes: selecting a target media content from among a plurality of media content in response to a user input; and determining a page moving speed of the target media content based on the information on and a browsing history of the previous media content, and displaying the target media content on a display based on the determined page moving speed of the target media content. .

According to an embodiment of the present disclosure, the displaying includes playing background music playable together with the target media content within a total predicted viewing time associated with a page moving speed of the target media content.

According to an embodiment of the present disclosure, a method of displaying media content calculates a predicted reading time corresponding to each of a plurality of unit contents based on a page moving speed of the target media content and a plurality of unit contents included in the target media content. The method may further include determining, and the displaying may include playing background music associated with a plurality of unit contents together with the target media content at the determined predicted reading time.

According to an embodiment of the present disclosure, the determining may include inputting information on the target media content and the user's reading history of previous media content into the first machine learning model to determine the page moving speed of the target media content. include

According to an embodiment of the present disclosure, the method of displaying media content further includes collecting user's eye tracking information, and the determining includes: information about target media content, user's previous media and inputting the viewing history of the content and the collected user's eye tracking information into the second machine learning model to determine the page movement speed of the target media content and the page arrangement of the media content.

According to an embodiment of the present disclosure, the determining includes inputting information on the target media content and previous media content viewing histories of a plurality of users included in the user group to which the user belongs to the third machine learning model to input the target media content determining the page moving speed of

According to an embodiment of the present disclosure, the determining may include inputting information on the target media content and the reading histories of the target media content of a plurality of users who have read the target media content to the fourth machine learning model to determine the target media content. determining a page movement speed.

According to an embodiment of the present disclosure, in the determining step, information on the target media content and the reading histories of the target media content of a plurality of users who have read the target media content among users included in the user group to which the user belongs 5 input into the machine learning model to determine the page movement speed of the target media content.

According to an embodiment of the present disclosure, the displaying includes displaying by automatically scrolling or turning one or more pages included in the target media content based on the determined page moving speed of the target media content.

A computer program stored in a computer-readable recording medium is provided for executing the above-described method for displaying user-customized media content according to an embodiment of the present disclosure in a computer.

According to some embodiments of the present disclosure, the predicted viewing time and the corresponding page movement speed are determined based on the media content viewing history of the user(s), so that the user can view media contents at a speed suitable for the user without directly operating the terminal. Convenience is increased.

According to some embodiments of the present disclosure, the background music can be played according to the user's predicted reading time, so that the variety of content provision is increased by inserting the necessary background music or the background music of an appropriate length in the right place, and the interest of users is increased. and effective delivery of content is possible.

According to some embodiments of the present disclosure, more effective content delivery is possible by additionally collecting eye tracking information and additionally using it to provide a user-customized page arrangement.

According to some embodiments of the present disclosure, the page movement speed of the target media content (and based on the reading history of the target media content and/or the browsing history of the previous media content and information on the target media content using a machine learning model) / or page layout), a more accurate prediction result can be obtained, so that a user-customized service can be provided.

The effect of the present disclosure is not limited to the above-mentioned effects, and other effects not mentioned are clear from the description of the claims to those of ordinary skill in the art (referred to as "person of ordinary skill") will be able to be understood

DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS Embodiments of the present disclosure will be described with reference to the accompanying drawings described below, wherein like reference numerals denote like elements, but are not limited thereto.
1 is a diagram illustrating an example of displaying user-customized media content based on a page moving speed of target media content according to an embodiment of the present disclosure;
FIG. 2 is a schematic diagram illustrating a configuration in which an information processing system is connected to communicate with a plurality of user terminals in order to display user-customized media content according to an embodiment of the present disclosure.
3 is a block diagram illustrating an internal configuration of a user terminal and an information processing system according to an embodiment of the present disclosure.
4 is a flowchart illustrating a method of displaying user-customized media content according to an embodiment of the present disclosure.
5 is an exemplary diagram illustrating a process in which a specified background music is played when a specific unit content is displayed in a process of automatically moving a page, according to an embodiment of the present disclosure;
6 is a diagram illustrating an example of a first machine learning model according to an embodiment of the present disclosure.
7 is a diagram illustrating an example of a second machine learning model according to an embodiment of the present disclosure.
8 is a diagram illustrating an example of a third machine learning model according to an embodiment of the present disclosure.
9 is a diagram illustrating an example of a fourth machine learning model according to an embodiment of the present disclosure.
10 is a diagram illustrating an example of a fifth machine learning model according to an embodiment of the present disclosure.
11 is an exemplary diagram illustrating an artificial neural network model according to an embodiment of the present disclosure.

Hereinafter, specific contents for carrying out the present disclosure will be described in detail with reference to the accompanying drawings. However, in the following description, if there is a risk of unnecessarily obscuring the gist of the present disclosure, detailed descriptions of well-known functions or configurations will be omitted.

In the accompanying drawings, identical or corresponding components are assigned the same reference numerals. In addition, in the description of the embodiments below, overlapping description of the same or corresponding components may be omitted. However, even if description regarding components is omitted, it is not intended that such components are not included in any embodiment.

Advantages and features of the disclosed embodiments, and methods of achieving them, will become apparent with reference to the embodiments described below in conjunction with the accompanying drawings. However, the present disclosure is not limited to the embodiments disclosed below, but may be implemented in various different forms, and only the present embodiments allow the present disclosure to be complete, and the present disclosure provides those skilled in the art with the scope of the invention. It is provided for complete information only.

Terms used in this specification will be briefly described, and the disclosed embodiments will be described in detail. The terms used in the present specification have been selected as currently widely used general terms as possible while considering the functions in the present disclosure, but these may vary depending on the intention or precedent of a person skilled in the art, the emergence of new technology, and the like. In addition, in a specific case, there is a term arbitrarily selected by the applicant, and in this case, the meaning will be described in detail in the description of the corresponding invention. Therefore, the terms used in the present disclosure should be defined based on the meaning of the term and the contents of the present disclosure, rather than the simple name of the term.

Expressions in the singular herein include plural expressions unless the context clearly dictates the singular. Also, the plural expression includes the singular expression unless the context clearly dictates the plural.

In the entire specification, when a part includes a certain component, this means that other components may be further included, rather than excluding other components, unless otherwise stated.

In addition, the term 'module' or 'unit' used in the specification means a software or hardware component, and 'module' or 'unit' performs certain roles. However, 'module' or 'unit' is not meant to be limited to software or hardware. A 'module' or 'unit' may be configured to reside on an addressable storage medium or may be configured to reproduce one or more processors. Thus, as an example, a 'module' or 'unit' refers to components such as software components, object-oriented software components, class components, and task components, and processes, functions, and properties. , procedures, subroutines, segments of program code, drivers, firmware, microcode, circuitry, data, database, data structures, tables, arrays or at least one of variables. Components and 'modules' or 'units' are the functions provided within are combined into a smaller number of components and 'modules' or 'units' or additional components and 'modules' or 'units' can be further separated.

According to an embodiment of the present disclosure, a 'module' or a 'unit' may be implemented with a processor and a memory. 'Processor' should be construed broadly to include general purpose processors, central processing units (CPUs), microprocessors, digital signal processors (DSPs), controllers, microcontrollers, state machines, and the like. In some contexts, a 'processor' may refer to an application specific semiconductor (ASIC), a programmable logic device (PLD), a field programmable gate array (FPGA), or the like. 'Processor' refers to a combination of processing devices, such as, for example, a combination of a DSP and a microprocessor, a combination of a plurality of microprocessors, a combination of one or more microprocessors in combination with a DSP core, or any other such configurations. You may. Also, 'memory' should be construed broadly to include any electronic component capable of storing electronic information. 'Memory' means random access memory (RAM), read-only memory (ROM), non-volatile random access memory (NVRAM), programmable read-only memory (PROM), erase-programmable read-only memory (EPROM); may refer to various types of processor-readable media, such as electrically erasable PROM (EEPROM), flash memory, magnetic or optical data storage, registers, and the like. A memory is said to be in electronic communication with the processor if the processor is capable of reading information from and/or writing information to the memory. The memory integrated in the processor is in electronic communication with the processor.

In the present disclosure, a 'system' may refer to at least one of a server device and a cloud server device, but is not limited thereto.

In the present disclosure, a 'user terminal' is provided with a communication module to enable network connection, and any electronic device (eg, smartphone, PC, tablet PC, laptop) capable of outputting content by accessing a website, application, etc. PC, etc.). The user may be provided with any content accessible through the network by input through the user terminal's interface (eg, touch display, keyboard, mouse, touch pen or stylus, microphone, motion recognition sensor, etc.) through the user terminal. can

In this disclosure, a 'display' may refer to any display device associated with a computing device, for example, any display device that is controlled by or capable of displaying any information/data provided from the computing device. can refer to

In the present disclosure, 'media content' may include various works, information, or contents thereof that are digitally produced and may be provided by a content providing system. For example, media content includes image content, webtoon content, web novel content, text content, document content, cartoon content, advertisement content, article content, audio content, sound content, music content, video content, game content, or a combination thereof. It may include, but is not limited to, various types of content such as content. Here, in the case of content in which multiple content is serialized, such as webtoon content or web novel content, 'media content' may include an entire work or a part of the work (eg, a specific episode, etc.).

In the present disclosure, a 'media content providing application' may include an application that provides media content or an application (eg, a media content viewer application) that can view media content even if the media content is not provided by itself. For example, it may include, but is not limited to, a webtoon application, a webtoon viewer application, a web novel application, and the like. Even if the main purpose is not to provide media content, if the media content can be provided or viewed, it may be included in the 'media content providing application'. For example, even if it is a web browser that provides access to a search portal application or search portal, when webtoon content is provided through this application or web browser, the application or web browser may be included in the media content providing application.

In the present disclosure, a 'media content providing service' may include a service that provides media content or a service that allows viewing of media content (eg, a media content viewer service). For example, the media content providing service may include, but is not limited to, a webtoon providing service, a webtoon viewer service, a web novel providing service, and the like. Even if the main purpose of the service is not to provide media content, it is included in the 'media content provision service' if it provides a service for providing or viewing media content. For example, even if the main purpose of the service is to provide a search service, when webtoon content is provided, it is included in the 'media content providing service'.

In the present disclosure, the 'media content viewing history' may include information on media content viewed by a user and/or information recorded while viewing media content. For example, such information includes the title, type, genre, number of editions, number of unit content, number of characters, image complexity calculated from color difference information between adjacent pixels, viewing time, eye tracking information, cookie data, and clicks of the viewed media content. It may include, but is not limited to, any information related to viewing media content, such as data, touch data, drag data, or a combination thereof.

In the present disclosure, a 'previous media content viewing history' may refer to a media content viewing history of the user or other user(s) before the user selects the selected media content (hereinafter, target media content).

In the present disclosure, 'page movement' may refer to moving a page to view a part different from the currently displayed part of the target media content on a screen on which the target media content is displayed. In one embodiment, if the media content is composed of one page, it may include moving to another part of the same page. According to another embodiment, when the media content consists of several pages, moving to another part of the currently displayed page and/or moving from the currently displayed page to another page may include. In addition, the page movement may include automatically scrolling one or more pages included in the target media content at a constant or non-constant speed, continuously or discontinuously, and scrolling one or more pages included in the target media content at a constant speed. Alternatively, it may include passing at a non-constant speed.

In the present disclosure, the 'page moving speed' may refer to any speed associated with the movement of a page, for example, a continuous or discontinuous scrolling speed within one page, continuous or non-continuous scrolling from one page to another. Continuously scrolling speed, turning speed from one page to another, continuous or discontinuous scrolling within one unit of content, continuous or discontinuous scrolling from one unit of content to another, one unit of content It may include the speed at which content is transferred from one unit to another.

In the present disclosure, 'background music' may refer to anything that can be perceived by hearing, such as music, voice, sound effects, or a combination thereof inserted together with the content to effectively deliver the content of the media content. The background music may be played for the entire time or a part of the time for viewing the media content, and may be played for a specific time, for a specific part, and/or for a specific unit of content.

In the present disclosure, 'unit content' may refer to a part or all of one or more divided contents when the target media contents can be divided according to a specific criterion. For example, the unit content for webtoon content may include one story, one cut, one scene, one image, one speech bubble, or dialogue of a specific work. As another example, the unit content for the web novel content may include one story, one scene, one paragraph, and one sentence of a specific work.

In the present disclosure, 'information about the target media content' may include any information that can indicate or characterize the target media content, for example, the length of the target media content, the number of unit content, type, genre, It may include all information that can be collected from the target media content itself, such as the total number of characters, the number of characters in each unit content, image complexity calculated from color difference information between adjacent pixels, or a combination thereof.

In the present disclosure, 'page arrangement' may include the position, shape, distribution, etc. of all or part of the target media content displayed on the display unit, and further includes the arrangement of unit content within a page, arrangement between pages, etc. can do. For example, the page arrangement may include a display position, arrangement form, shape, arrangement order, and the like of each unit content.

In the present disclosure, an 'image' may include one or more images, and may also refer to a moving picture including a plurality of images.

In the present disclosure, a 'machine learning model' may include any model used to infer an answer to a given input. According to an embodiment, the machine learning model may include an artificial neural network model including an input layer (layer), a plurality of hidden layers, and an output layer. Here, each layer may include a plurality of nodes. In the present disclosure, a plurality of machine learning models such as a first machine learning model, a second machine learning model, a third machine learning model, a fourth machine learning model, and a fifth machine learning model are described as separate machine learning models. It is not limited, and some or all of the plurality of machine learning models may be implemented as one machine learning model. In this case, label information generated through annotation work or label work may be used to train the machine learning model. In addition, the machine learning model may include weights associated with a plurality of nodes included in the machine learning model. Here, the weight may include any parameter associated with the machine learning model. In the present disclosure, a machine learning model may refer to an artificial neural network model, and the artificial neural network model may refer to a machine learning model.

In the present disclosure, 'each of a plurality of A' or 'each of a plurality of A' may refer to each of all components included in the plurality of As or may refer to each of some components included in the plurality of As. .

1 is a diagram illustrating an example of displaying user-customized media content based on a page moving speed of target media content according to an embodiment of the present disclosure. As illustrated, the user may browse the media content through the media content application displayed on the user terminal. First, a user may select a target media content from among one or more media content displayed on a touchable display of a user terminal. For example, as shown, the upper left webtoon content may be selected from among the nine webtoon contents. In the present disclosure, although it is illustrated that target media content is selected through a touch input through a user's finger, the present disclosure is not limited thereto, and the user's input is any arbitrary selection of media content from one or more media content displayed on the user terminal. It can refer to an action. For example, the user's input may include any action of selecting the target media content through an input device, such as a keyboard input, a touch input, and a drag input. In another embodiment, when the user clicks or touches one media content in the media content list, the processor may select the corresponding media content as the target media content.

In an embodiment, when there is one media content included in the application or program, it may be considered that there is a user input only by executing the application or program, and the target media content may be considered selected.

The processor may determine a predicted viewing time of the target media content of the corresponding user and a page moving speed accordingly based on the information on the selected target media content and the collected previous media content viewing history. A time taken to view all/part of the target media content or each unit content selected by the user may be determined, and accordingly, the page moving speed of the entire corresponding media content or the page moving speed for each section may be determined. In an embodiment, the previous media content viewing history may be a previous media content viewing history of a user who selected the target media content. For example, the title, type, genre, total number of characters, number of characters included in each unit of content, viewing time, eye tracking information, cookie data, etc. of other media content that the user viewed before selecting the target media content can If there is a record of the user viewing the target media content, the previous media content viewing history is information recorded while the user reads the target media content, that is, the viewing time of the target media content, eye tracking information, cookie data, and touch data, click data, and the like.

In one embodiment, the previous media content viewing history may be the previous media content viewing history of other user(s). For example, it may be target media content viewing time, cookie data, click data, touch data, etc. of other user(s) who have read the target media content. If the user does not have a history of viewing both the target media content and other media content, the previous media content viewing history includes the title, type, genre, total number of characters, and content included in each unit of other media content viewed by other user(s). It may be the number of characters, reading time, eye tracking information, cookie data, and the like.

The processor may display the target media content on the display based on the determined page moving speed of the target media content. In an embodiment, the determined page moving speed of the target media content may be the same for all media content. For example, in webtoon content including 5 cuts, the same page moving speed may be continuously maintained while reading the entire target webtoon content.

In an embodiment, the page moving speed of the target media content may be determined to be different for each section of the target media content. For example, in webtoon content including 5 cuts, the page movement speed may vary while reading each cut.

In an embodiment, the page moving speed may be 0 for a predetermined time. That is, page movement may be stopped for a certain period of time. For example, in webtoon content including 5 cuts, the page movement speed becomes 0 during the predicted reading time of each cut, and the page may be moved to another cut after the predicted reading time has elapsed.

2 is a schematic diagram illustrating a configuration in which the information processing system 230 is connected to communicate with a plurality of user terminals 110_1 , 110_2 , and 110_3 in order to display customized media content according to an embodiment of the present disclosure. The information processing system 230 may include system(s) capable of providing media content providing services. In one embodiment, information processing system 230 includes one or more server devices and/or databases capable of storing, providing and executing computer executable programs (eg, downloadable applications) and data related to media content providing services; Alternatively, it may include one or more distributed computing devices and/or distributed databases based on cloud computing services. For example, the information processing system 230 may include separate systems (eg, servers) for providing a media content providing service.

The media content providing service provided by the information processing system 230 may be provided to the user through a media content providing application installed in each of the plurality of user terminals 110_1 , 110_2 , 110_3 . For example, the information processing system 230 may provide information corresponding to a media content viewing request received from the user terminals 110_1 , 110_2 , and 110_3 through a media content providing application or perform a corresponding process.

The plurality of user terminals 110_1 , 110_2 , and 110_3 may communicate with the information processing system 230 through the network 220 . The network 220 may be configured to enable communication between the plurality of user terminals 110_1 , 110_2 , and 110_3 and the information processing system 230 . Network 220 according to the installation environment, for example, Ethernet (Ethernet), wired home network (Power Line Communication), telephone line communication device and wired networks such as RS-serial communication, mobile communication network, WLAN (Wireless LAN), It may consist of a wireless network such as Wi-Fi, Bluetooth and ZigBee, or a combination thereof. The communication method is not limited, and the user terminals 110_1, 110_2, 110_3 as well as a communication method using a communication network (eg, a mobile communication network, a wired Internet, a wireless Internet, a broadcasting network, a satellite network, etc.) that the network 220 may include. ) may also include short-range wireless communication between

Although the mobile phone terminal 110_1, the tablet terminal 110_2, and the PC terminal 110_3 are illustrated as examples of the user terminal in FIG. 2, the present invention is not limited thereto, and the user terminals 110_1, 110_2, and 110_3 are wired and/or wireless communication. It may be any computing device capable of this and on which a media content providing application or the like may be installed and executed. For example, a user terminal is a smartphone, a mobile phone, a navigation device, a computer, a notebook computer, a digital broadcasting terminal, a PDA (Personal Digital Assistants), a PMP (Portable Multimedia Player), a tablet PC, a game console (game console), a wearable device ( wearable devices), Internet of things (IoT) devices, virtual reality (VR) devices, augmented reality (AR) devices, and the like. In addition, in FIG. 2, three user terminals 110_1, 110_2, and 110_3 are illustrated as communicating with the information processing system 230 through the network 220, but the present invention is not limited thereto. It may be configured to communicate with information processing system 230 via 220 .

In an embodiment, the information processing system 230 may receive a viewing request for the target media content from the user terminals 110_1 , 110_2 , and 110_3 . In response to the target media content viewing request, the information processing system 230 determines a predicted viewing time and corresponding page movement speed (and/or page placement) based on information on the target media content and previous media content viewing history, and , the determined page moving speed (and page arrangement) may be provided to the user terminals 110_1 , 110_2 , and 110_3 .

3 is a block diagram illustrating the internal configuration of the user terminal 110 and the information processing system 230 according to an embodiment of the present disclosure. The user terminal 110 may refer to any computing device capable of executing a media content providing application and the like and capable of wired/wireless communication, for example, the mobile phone terminal 110_1 of FIG. 2 , the tablet terminal 110_2, It may include a PC terminal 110_3 and the like. As shown, the user terminal 110 may include a memory 312 , a processor 314 , a communication module 316 , and an input/output interface 318 . Similarly, the information processing system 230 may include a memory 332 , a processor 334 , a communication module 336 , and an input/output interface 338 . As shown in FIG. 3 , the user terminal 110 and the information processing system 230 are configured to communicate information and/or data via the network 220 using the respective communication modules 316 and 336 . can be Also, the input/output device 320 may be configured to input information and/or data to the user terminal 110 or output information and/or data generated from the user terminal 110 through the input/output interface 318 .

The memories 312 and 332 may include any non-transitory computer-readable recording medium. According to one embodiment, the memories 312 and 332 are non-volatile mass storage devices such as random access memory (RAM), read only memory (ROM), disk drives, solid state drives (SSDs), flash memory, and the like. (permanent mass storage device) may be included. As another example, a non-volatile mass storage device such as a ROM, an SSD, a flash memory, a disk drive, etc. may be included in the user terminal 110 or the information processing system 230 as a separate permanent storage device distinct from the memory. Also, an operating system and at least one program code (eg, a code for a media content providing application, etc.) may be stored in the memories 312 and 332 .

These software components may be loaded from a computer-readable recording medium separate from the memories 312 and 332 . The separate computer-readable recording medium may include a recording medium directly connectable to the user terminal 110 and the information processing system 230, for example, a floppy drive, disk, tape, DVD/CD- It may include a computer-readable recording medium such as a ROM drive and a memory card. As another example, the software components may be loaded into the memories 312 and 332 through the communication modules 316 and 336 rather than the computer-readable recording medium. For example, the at least one program is a computer program (eg, a media content providing application, etc.) installed by files provided through the network 220 by developers or a file distribution system that distributes installation files of applications. may be loaded into the memories 312 and 332 based on the

The processors 314 and 334 may be configured to process instructions of a computer program by performing basic arithmetic, logic, and input/output operations. Instructions may be provided to the processor 314 , 334 by the memory 312 , 332 or the communication module 316 , 336 . For example, the processors 314 and 334 may be configured to execute received instructions according to program code stored in a recording device, such as the memories 312 and 332 .

The communication modules 316 and 336 may provide a configuration or function for the user terminal 110 and the information processing system 230 to communicate with each other via the network 220 , and the user terminal 110 and/or information processing The system 230 may provide a configuration or function for communicating with another user terminal or another system (eg, a separate cloud system). For example, a request or data (eg, a request to view target media content, etc.) generated by the processor 314 of the user terminal 110 according to a program code stored in a recording device such as the memory 312 is transmitted to the communication module It may be transmitted to the information processing system 230 through the network 220 under the control of 316 . Conversely, a control signal or command provided under the control of the processor 334 of the information processing system 230 is transmitted through the communication module 336 and the network 220 through the communication module 316 of the user terminal 110 . It may be received by the user terminal 110 . For example, the user terminal 110 may receive a page movement speed (and page arrangement) according to the predicted reading time from the information processing system 230 through the communication module 316 .

The input/output interface 318 may be a means for interfacing with the input/output device 320 . As an example, an input device may include a device such as a camera, keyboard, microphone, mouse, etc., including an audio sensor and/or an image sensor, and an output device may include a device such as a display, speaker, haptic feedback device, etc. can As another example, the input/output interface 318 may be a means for an interface with a device in which a configuration or function for performing input and output, such as a touch screen, is integrated into one. In FIG. 3 , the input/output device 320 is illustrated not to be included in the user terminal 110 , but the present invention is not limited thereto, and may be configured as a single device with the user terminal 110 . In addition, the input/output interface 338 of the information processing system 230 is connected to the information processing system 230 or means for interfacing with a device (not shown) for input or output that the information processing system 230 may include. can be In FIG. 3, the input/output interfaces 318 and 338 are illustrated as elements configured separately from the processors 314 and 334, but the present invention is not limited thereto, and the input/output interfaces 318 and 338 may be configured to be included in the processors 314 and 334. have.

The user terminal 110 and the information processing system 230 may include more components than those of FIG. 3 . However, there is no need to clearly show most of the prior art components. In an embodiment, the user terminal 110 may be implemented to include at least a portion of the above-described input/output device 320 . In addition, the user terminal 110 may further include other components such as a transceiver, a global positioning system (GPS) module, a camera, various sensors, and a database. For example, when the user terminal 110 is a smartphone, it may include components included in a smartphone in general, for example, an acceleration sensor, a gyro sensor, a microphone module, a camera module, and various physical components. Various components such as a button, a button using a touch panel, an input/output port, and a vibrator for vibration may be implemented to be further included in the user terminal 110 .

According to an embodiment, the processor 314 of the user terminal 110 may be configured to operate a media content providing application. In this case, the program code associated with the corresponding application may be loaded into the memory 312 of the user terminal 110 . While the application is being operated, the processor 314 of the user terminal 110 receives information and/or data provided from the input/output device 320 through the input/output interface 318 or through the communication module 316 to the information processing system ( Information and/or data may be received from 230 , and the received information and/or data may be processed and stored in the memory 312 . In addition, such information and/or data may be provided to the information processing system 230 via the communication module 316 .

While the media content providing application or the like is being operated, the processor 314 may generate audio data input or selected through an input device such as a touch screen, a keyboard, a camera including an audio sensor and/or an image sensor connected to the input/output interface 318, a microphone, etc. , text, image, video, etc. can be received, and the received voice data, text, image and/or video, etc. are stored in the memory 312 or through the communication module 316 and the network 220 through the information processing system ( 230) can be provided. In an embodiment, the processor 314 may receive a request for viewing of the target media content through the input device 320 such as a touch screen or a mouse, and transmit the request for viewing of the target media content to the communication module 316 . ) and the network 220 may be provided to the information processing system 230 .

The processor 314 of the user terminal 110 may transmit and output information and/or data to the input/output device 320 through the input/output interface 318 . For example, the processor 314 of the user terminal 110 may display information processed through the output device 320 such as a display output capable device (eg, a touch screen, a display, etc.) and an audio output capable device (eg, a speaker). and/or output data. In an embodiment, the processor 314 may display the target media content on the display of the user terminal 110 according to the determined page moving speed (and page layout). Additionally, the processor 314 may output background music through the speaker of the user terminal 110 .

The processor 334 of the information processing system 230 may be configured to manage, process, and/or store information and/or data received from the plurality of user terminals 110 and/or the plurality of external systems. The information and/or data processed by the processor 334 may be provided to the user terminal 110 via the communication module 336 and the network 220 . In one embodiment, the processor 334 of the information processing system 230 receives the target media content viewing request, and in response to the target media content viewing request, records the media content viewing history (and eye tracking information) and the target media content. Determines the page moving speed (and page arrangement) according to the predicted reading time based on the information about, and transmits the determined page moving speed (and page arrangement) to the user terminal 110 through the communication module 336 and the network 220 can provide

For example, the processor 334 may use the user's previous media content reading history and information on the target media content to predict the reading time and determine the page moving speed accordingly. Alternatively or additionally, the processor 334 may use the user's previous media content viewing history and eye tracking information to determine a page movement speed and page placement according to the predicted viewing time. Alternatively or additionally, the processor 334 may select background music suitable for the determined predicted reading time and page moving speed. Alternatively or additionally, the processor 334 may use other users' media content viewing histories to predict a user's viewing time, and determine a page moving speed accordingly.

4 is a flowchart illustrating a method of displaying user-customized media content according to an embodiment of the present disclosure. The media content presentation method 400 may be executed by a processor of any computing device. For example, any computing device may include a user terminal (eg, the user terminal 110 ), an information processing system (eg, the information processing system 230 ), and the like. The method 400 may begin with the processor selecting the target media content ( S410 ). The processor may select the target media content in response to the user's input.

The processor may determine the predicted viewing time of the target media content of the corresponding user and the corresponding page movement speed based on the information on the selected target media content and the collected previous media content browsing history ( S420 ). The processor may display the target media content on the display based on the page moving speed of the target media content determined in step S420 (S430). In an embodiment, the processor may determine the page movement speed as the page movement speed according to the average viewing time of users who have viewed the target media content. Then, based on the determined page moving speed, the target media content may be displayed on the billboard display.

5 is an exemplary diagram illustrating a process in which a specified background music is played when a specific unit content 510 is displayed in a process of automatically moving a page, according to an embodiment of the present disclosure. The processor may display the target media content on the display based on the determined predicted reading time of the target media content and the corresponding page moving speed. In this step, background music or sound effect may be played together with the target media content, and the background music or sound effect may be played from the start of reading of the target media content, or played while reading. In the present disclosure, background music may include not only music reproduced together with media content, but also sound effects reproduced in association with unit content.

In an embodiment, when the background music for each section of the total reading time is designated in advance, the processor may reproduce the background music corresponding to the user's total predicted reading time from the start of reading the media content. For example, if the total reading time is less than 3 minutes, background music 'a', if more than 3 minutes but less than 4 minutes, background music 'b', if the total reading time is more than 4 minutes, background music 'c', etc. In this case, in response to the user's request for viewing the target media content, the processor may play background music that matches the predicted viewing time together with the start of viewing the media content.

In an embodiment, if the background music is not specified in advance, the processor may select the background music that can be played within the total predicted reading time, and play the selected background music from the start of reading the target media content. In this case, the selected background music may be background music suitable for the theme or atmosphere of the target media content. For example, in the case of media content whose genre is horror, the processor may select background music from a list of background music to which a 'horror' tag is attached.

In an embodiment, background music (eg, sound effects, etc.) may be played while a predetermined unit content(s) is displayed. For example, at the predicted viewing start time of each unit content(s), that is, when the display of each unit content(s) starts, the background music may be played one or more times. In addition, background music may be played during the predicted viewing time of each unit content(s), that is, while each unit content(s) is displayed. In this case, when there is no preset background music in at least a portion of each of the plurality of unit contents, the processor may select background music suitable for the theme or atmosphere of each unit content(s).

6 is a diagram illustrating an example of a first machine learning model 610 according to an embodiment of the present disclosure. As shown, the first machine learning model 610 receives the user's previous media content reading history 620 and target media content information 630 as inputs, and the page movement speed 640 according to the predicted reading time. ) can be printed. It is possible to determine the time taken to read at least a portion of the target media content or each unit content, and accordingly, the page moving speed 640 for the entire corresponding media content or for each section may be determined.

In an embodiment, the user's previous media content viewing history 620 may be another media content viewing history that the user viewed before selecting the target media content.

In an embodiment, when there is a record of the user viewing the target media content, the user's previous media content viewing history 620 may be information recorded while the user views the target media content. For example, it may include at least one of a reading time, eye tracking information, cookie data, touch data, and click data recorded while the user reads the target media content.

7 is a diagram illustrating an example of a second machine learning model 710 according to an embodiment of the present disclosure. As shown, the second machine learning model receives the user's previous media content reading history 620, eye tracking information 720, and target media content information 630 as inputs, and the page according to the predicted reading time A movement speed 730 and a page layout 740 may be output. Based on the user's previous viewing history 620 of the media content and the information 630 on the target media content, a time taken to view at least a portion of the target media content or each unit content may be determined, and accordingly, the corresponding media content The page moving speed 730 for the entire content or for each section may be determined. Also, based on the eye tracking information, the page arrangement 740 including at least one of an arrangement order, arrangement form, or arrangement shape of each unit content may be determined.

8 is a diagram illustrating an example of a third machine learning model 810 according to an embodiment of the present disclosure. As shown, the third machine learning model 810 receives the previous media content viewing history 820 and information 630 on the target media content of a plurality of users included in the user group to which the user belongs, and predicts The page moving speed 830 according to the reading time may be output. It is possible to determine the time it takes to view all/part of the target media content or each unit content, and accordingly, the page moving speed 830 of the entire corresponding media content or each section may be determined.

In an embodiment, the user group may be a group clustered using a clustering method such as a K-means clustering method or a hierarchical clustering method. In another embodiment, the user group may be a grouped group based on user account information. For example, it may be a group clustered by a criterion including at least one of age, gender, occupation, or educational background of users. In another embodiment, the user group may be a grouped group based on the media content viewing history. For example, it may be a group grouped by a criterion including at least one of a title, type, genre, number, or media content viewing frequency of media contents viewed by users. In another embodiment, the user group may be a grouped group based on user account information and media content viewing history.

9 is a diagram illustrating an example of a fourth machine learning model 910 according to an embodiment of the present disclosure. As shown, the fourth machine learning model 910 receives the target media content viewing history 920 and information 630 on the target media content of a plurality of users who have viewed the target media content as inputs, and predicts the reading time. It is possible to output the page moving speed 930 according to It is possible to determine the time it takes to view all/part of the target media content or each unit content, and accordingly, the page moving speed 930 for all or each section of the corresponding media content may be determined.

10 is a diagram illustrating an example of a fifth machine learning model 1010 according to an embodiment of the present disclosure. As shown, the fifth machine learning model 1010 receives the target media content reading history 1020 and information 630 on the target media content of users included in the same user group as inputs, and according to the predicted reading time, A page moving speed 1030 may be output. Users included in the same user group may refer to a plurality of users included in a user group to which the user who selected the target media content belongs. It is possible to determine the time it takes to view all/part of the target media content or each unit content, and accordingly, the page moving speed 1030 for all or each section of the corresponding media content can be determined.

In an embodiment, the user group may be a group clustered using a clustering method such as a K-means clustering method or a hierarchical clustering method. In another embodiment, the user group may be a grouped group based on user account information. For example, it may be a group clustered by a criterion including at least one of age, gender, occupation, or educational background of users. In another embodiment, the user group may be a grouped group based on the media content viewing history. For example, it may be a group grouped by a criterion including at least one of a title, type, genre, number, or media content viewing frequency of media contents viewed by users. In another embodiment, the user group may be a grouped group based on user account information and media content viewing history.

According to an embodiment, the users 1020 included in the same user group may refer to a plurality of users who have a record of viewing the target media content among a plurality of users included in the user group to which the user belongs.

11 is an exemplary diagram illustrating an artificial neural network model 1100 according to an embodiment of the present disclosure. The artificial neural network model 1100 is an example of a machine learning model, and in machine learning technology and cognitive science, a statistical learning algorithm implemented based on the structure of a biological neural network or a structure for executing the algorithm.

According to an embodiment, in the artificial neural network model 1100, as in a biological neural network, nodes, which are artificial neurons that form a network by combining synapses, repeatedly adjust the weights of synapses, By learning to reduce the error between the output and the inferred output, it is possible to represent a machine learning model with problem-solving ability. For example, the artificial neural network model 1100 may include arbitrary probabilistic models, neural network models, etc. used in artificial intelligence learning methods such as machine learning and deep learning.

According to an embodiment, the artificial neural network model 1100 outputs a page movement speed (and page layout) according to a predicted reading time as information on media content viewing history (and eye tracking information) and target media content is input. It may include an artificial neural network model configured to do so. Here, the media content browsing history includes the title, type, genre, number of editions, number of unit content, number of characters, image complexity calculated from information on color difference between adjacent pixels, viewing time, eye tracking information, cookie data, and clicks of the viewed media content. It may include all information related to viewing media content, such as data, touch data, drag data, or a combination thereof.

The artificial neural network model 1100 is implemented as a multilayer perceptron (MLP) composed of multilayer nodes and connections between them. The artificial neural network model 1100 according to the present embodiment may be implemented using one of various artificial neural network model structures including MLP. 11 , the artificial neural network model 1100 includes an input layer 1120 that receives an input signal or data 1110 from the outside, and an output layer that outputs an output signal or data 1150 corresponding to the input data. (1140), which is located between the input layer 1120 and the output layer 1140, receives a signal from the input layer 1120, extracts characteristics, and transmits the characteristics to the output layer 1140 of n pieces (where n is a positive integer) It is composed of hidden layers 1130_1 to 1130_n. Here, the output layer 1140 receives signals from the hidden layers 1130_1 to 1130_n and outputs them to the outside.

The learning method of the artificial neural network model 1100 includes a supervised learning method that learns to be optimized to solve a problem by input of a teacher signal (correct answer), and an unsupervised learning method that does not require a teacher signal. ) is a way. In one embodiment, the information processing system supervised learning and/or unsupervised learning of the artificial neural network model 1100 to output a page movement speed according to the predicted reading time corresponding to the previous media content reading history and information on the target media content. can do it

The artificial neural network model 1100 learned in this way may be stored in the memory (not shown) of the information processing system, and information about the target media content received from the communication module and/or memory and the media content viewing history (and eye tracking information) ), it is possible to output the page movement speed (and page layout) according to the predicted reading time in response to the related information.

According to an embodiment, the input variable of the machine learning model, that is, the artificial neural network model 1100, may be a media content viewing history. For example, an input variable input to the input layer 1120 of the artificial neural network model 1100 may be a vector 1110 in which a media content browsing history is composed of one vector data element. In response to the media content browsing history, the output variable output from the output layer 1140 of the artificial neural network model 1100 may be a vector 1150 indicating or characterizing the page movement speed according to the predicted reading time. In the present disclosure, the output variable of the artificial neural network model 1100 is not limited to the type described above, and may include any information/data indicating the page movement speed according to the expected reading time.

In addition, the output layer 1140 of the artificial neural network model 1100 may be configured to output a vector indicating the reliability and/or accuracy of the page moving speed according to the output predicted reading time.

In this way, a plurality of output variables corresponding to a plurality of input variables are respectively matched to the input layer 1120 and the output layer 1140 of the artificial neural network model 1100, and the input layer 1120, the hidden layers 1130_1 to 1130_n, and By adjusting the synaptic value between nodes included in the output layer 1140 , it may be learned to extract a correct output corresponding to a specific input. Through this learning process, characteristics hidden in the input variable of the artificial neural network model 1100 can be identified, and the error between the output variable calculated based on the input variable and the target output is reduced. You can adjust the synapse value (or weight) between them. The information processing system and/or the user terminal input the information on the target media content and the media content viewing history to the learned artificial neural network model 1100, and use the page movement speed according to the output predicted viewing time to view the media content. can be displayed

The above-described method may be provided as a computer program stored in a computer-readable recording medium for execution by a computer. The medium may continuously store a computer executable program, or may be a temporary storage for execution or download. In addition, the medium may be various recording means or storage means in the form of a single or several hardware combined, it is not limited to a medium directly connected to any computer system, and may exist distributed on a network. Examples of the medium include a hard disk, a magnetic medium such as a floppy disk and a magnetic tape, an optical recording medium such as CD-ROM and DVD, a magneto-optical medium such as a floppy disk, and those configured to store program instructions, including ROM, RAM, flash memory, and the like. In addition, examples of other media may include recording media or storage media managed by an app store that distributes applications, sites that supply or distribute various other software, or servers.

The method, operation, or techniques of this disclosure may be implemented by various means. For example, these techniques may be implemented in hardware, firmware, software, or a combination thereof. Those of ordinary skill in the art will appreciate that the various illustrative logical blocks, modules, circuits, and algorithm steps described in connection with the disclosure herein may be implemented as electronic hardware, computer software, or combinations of both. To clearly illustrate this interchangeability of hardware and software, various illustrative components, blocks, modules, circuits, and steps have been described above generally in terms of their functionality. Whether such functionality is implemented as hardware or software depends upon the particular application and design requirements imposed on the overall system. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementations should not be interpreted as causing a departure from the scope of the present disclosure.

In a hardware implementation, the processing units used to perform the techniques include one or more ASICs, DSPs, digital signal processing devices (DSPDs), programmable logic devices (PLDs). ), field programmable gate arrays (FPGAs), processors, controllers, microcontrollers, microprocessors, electronic devices, other electronic units designed to perform the functions described in this disclosure. , a computer, or a combination thereof.

Accordingly, the various illustrative logic blocks, modules, and circuits described in connection with this disclosure are suitable for use in general purpose processors, DSPs, ASICs, FPGAs or other programmable logic devices, discrete gate or transistor logic, discrete hardware components, or the present disclosure. It may be implemented or performed in any combination of those designed to perform the functions described in A general purpose processor may be a microprocessor, but in the alternative, the processor may be any conventional processor, controller, microcontroller, or state machine. A processor may also be implemented as a combination of computing devices, eg, a combination of a DSP and a microprocessor, a plurality of microprocessors, one or more microprocessors in conjunction with a DSP core, or any other configuration.

In firmware and/or software implementations, the techniques include random access memory (RAM), read-only memory (ROM), non-volatile random access memory (NVRAM), PROM ( on computer-readable media such as programmable read-only memory), erasable programmable read-only memory (EPROM), electrically erasable PROM (EEPROM), flash memory, compact disc (CD), magnetic or optical data storage devices, etc. It may be implemented as stored instructions. The instructions may be executable by one or more processors, and may cause the processor(s) to perform certain aspects of the functionality described in this disclosure.

Although the embodiments described above have been described utilizing aspects of the presently disclosed subject matter in one or more standalone computer systems, the present disclosure is not limited thereto and may be implemented in connection with any computing environment, such as a network or distributed computing environment. . Still further, aspects of the subject matter in this disclosure may be implemented in a plurality of processing chips or devices, and storage may be similarly affected across the plurality of devices. Such devices may include PCs, network servers, and portable devices.

Although the present disclosure has been described in connection with some embodiments herein, various modifications and changes can be made without departing from the scope of the present disclosure that can be understood by those skilled in the art to which the present disclosure pertains. Further, such modifications and variations are intended to fall within the scope of the claims appended hereto.

110: user terminal

Claims (10)

A method for displaying user-customized media content, executed by at least one processor, the method comprising:
in response to a user input, selecting a target media content from among a plurality of media content;
determining a page movement speed of the target media content based on the information on the target media content and a browsing history of previous media content using a machine learning model; and
displaying the target media content on a display based on the determined page moving speed of the target media content
including,
The information on the target media content and the reading history of the previous media content include image complexity information associated with at least one image included in the media content,
The image complexity information is calculated from color difference information between adjacent pixels of an image.
According to claim 1,
The displaying step is
and playing background music playable within a total predicted reading time associated with the page moving speed of the target media content together with the target media content.
According to claim 1,
Further comprising the step of determining a predicted reading time corresponding to each of a plurality of unit content based on the page moving speed of the target media content and a plurality of unit content included in the target media content,
The displaying step is
Comprising the step of playing the background music associated with the plurality of unit content together with the target media content at the determined predicted reading time,
How to display media content.
According to claim 1,
The determining step is
and determining a page movement speed of the target media content by inputting the information on the target media content and the user's reading history of the previous media content into a first machine learning model.
5. The method of claim 4,
Further comprising the step of collecting the user's eye tracking information,
The determining step is
The information on the target media content, the reading history of the user's previous media content, and the collected user's eye tracking information are input into a second machine learning model, and the page movement speed of the target media content and the page arrangement of the media content A method for displaying media content, comprising determining
According to claim 1,
The determining step is
determining the page movement speed of the target media content by inputting the information on the target media content and the previous media content viewing histories of a plurality of users included in the user group to which the user belongs to a third machine learning model , how to display media content.
According to claim 1,
The determining step is
determining the page movement speed of the target media content by inputting the information on the target media content and the reading histories of the target media content of a plurality of users who have read the target media content into a fourth machine learning model , how to display media content.
According to claim 1,
The determining step is
By inputting the information on the target media content and the reading histories of the target media content of a plurality of users who have read the target media content among users included in the user group to which the user belongs, into a fifth machine learning model, the target media A method of displaying media content, comprising determining a page movement speed of the content.
According to claim 1,
The displaying step is
and displaying by automatically scrolling or turning over one or more pages included in the target media content based on the determined page moving speed of the target media content.
A computer program stored in a computer-readable recording medium for executing the method according to any one of claims 1 to 9 in a computer.

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