KR102286638B1 - Computer program for automatic editing of highlights - Google Patents

Abstract

In one embodiment of the present disclosure for solving the above-described problems, a computer program stored in a computer-readable storage medium executable by one or more processors is disclosed. The computer program causes the one or more processors to perform the following operations for automatic highlight image editing, wherein the operations include: receiving one or more user reaction data for the real-time content from one or more user terminals viewing the real-time content The method may include receiving, identifying one or more highlight sections in the real-time content based on a result of analyzing the user reaction data, and generating highlight content based on the one or more highlight sections.

Description

COMPUTER PROGRAM FOR AUTOMATIC EDITING OF HIGHLIGHTS

The present disclosure relates to automatic highlight image editing, and more particularly, to a computer program for automatically editing highlight images using an artificial neural network.

Today, as the Internet becomes popular with the development of information and communication technology, users can share various information with other users through the Internet environment. In such an environment, with the advent of smartphones, an environment in which various contents can be easily consumed is being prepared, and one-person media-based platforms are spreading.

With the growth of media platforms, the number of single creators (or streamers) who transmit personal broadcasts through the Internet is also increasing. With the growth of these media platforms and the increase of single creators, the amount of media content has exploded. As the amount of video content increases explosively and various media provide video content, viewers are increasingly inclined to select and watch preferred content. In addition, in order to select and view a desired image in a short time, a tendency to prefer a short video clip highlighting a desired scene among all contents is also increasing.

Accordingly, a single creator creates and provides a highlight video by collecting, editing, and summarizing only the interesting parts (highlight parts) from the original video in order to attract the attention of the viewers. However, in order to produce a highlight video from original content, it takes a lot of time to search for all the original content, and it may be difficult for creators who do not have professional editing skills for content to create their own highlight video. In addition, if a separate professional editor is employed to produce a highlight video, manpower costs may occur. In addition, the criteria for selecting the highlight section from the original content may lack objectivity because the editor's subjective criteria are applied, and the quality of the generated highlight video may vary according to the editor's competence.

In line with a time when demand for various types of media is expanding, there may be a demand for a technology for automatically generating a highlight edited video by automatically identifying a highlight section from a creator's original content in response to a changing media environment.

Korean Patent Registration 2010-0085720

The present disclosure has been made in response to the above-described background art, and is intended to provide a computer program for automatically editing a highlight image.

A computer program stored in a computer-readable storage medium is disclosed in an embodiment of the present disclosure for solving the above-described problems. When the computer program is executed on one or more processors, the one or more processors cause the one or more processors to perform the following operations for automatically editing a highlight image, wherein the operations include: Receiving one or more user reaction data for , identifying one or more highlight sections in the real-time content based on a result of analyzing the user reaction data, and generating highlight content based on the one or more highlight sections may include.

In an alternative embodiment, the user reaction data may be based on at least one of a natural language input or an action input of a user viewing the real-time content, each user reaction data may be associated with at least a portion of the real-time content. .

In an alternative embodiment, an operation of receiving external reaction data for at least one of the real-time content or the relevant information of the real-time content, and the user reaction data for the real-time content and the external reaction data to the relevant information of the real-time content The method may further include generating a knowledge base for the related parties of the real-time content by storing in association with the .

In an alternative embodiment, the external reaction data may be reaction data for at least one of the real-time content or a person related to the real-time content, and may be data received through a different channel from the user reaction data for the real-time content. .

In an alternative embodiment, the operation of generating a knowledge base for the related party of the real-time content by storing the user reaction data and the external reaction data for the real-time content in association with the related party information of the real-time content includes the external reaction The knowledge base is created by extracting external narrative information related to the real-time content or related party information of the real-time content from data, and extracting internal narrative information related to the real-time content or related party information of the real-time content from the user response data. It may include an action to create.

In an alternative embodiment, the knowledge base is generated for each of the parties of the real-time content, and is used to identify a user reaction unique to each of the parties, and a semantic analysis of the externally narrated information or the internally narrated information. or based on at least one of quantitative analysis.

In an alternative embodiment, the identifying one or more highlight sections in the real-time content based on the analysis result of the user reaction data includes the real-time based on at least one of semantic analysis or quantitative analysis of the user reaction data. It may include the operation of identifying one or more highlight sections in the content.

In an alternative embodiment, the quantitative analysis may be performed based on a threshold determined based at least in part on the number of one or more user terminals viewing the real-time content.

In an alternative embodiment, the method may further include the operation of pre-processing the user reaction data based on a knowledge base of related information of the real-time content.

In an alternative embodiment, the user reaction data further includes user reaction user identification information related to the user terminal that has transmitted the user reaction data, and based on an analysis result of the user reaction data, one or more The operation of identifying the highlight section includes an operation of assigning a weight to each user reaction data based on the user reaction user identification information and an analysis result of the user reaction data based on a weight assigned to each user reaction data. and identifying one or more highlight sections in the real-time content based on the analysis result.

In an alternative embodiment, the operation of assigning a weight to each user response data based on the user response user identification information includes: a highlight section prediction contribution rate for real-time content of the user who generated the user response data; and assigning the weight to each of the user reaction data based on at least one of a viewing contribution rate for content matched with the related information of the real-time content and a matching rate for the knowledge base of the user reaction data. there is.

In an alternative embodiment, the highlight section prediction contribution rate for the real-time content is information on whether user reaction data is included in the pre-produced highlight content in relation to the person concerned with the real-time content, It may be calculated based on at least one of quantitative analysis and semantic analysis of the included user response data.

In an alternative embodiment, the viewing contribution rate for the content matched with the relevant information of the real-time content is information about the participation history of each of the one or more user terminals for the content matched with the relevant information of the real-time content, the matching It may be calculated based on the number of contents and the participation history.

In an alternative embodiment, the matching rate of the user reaction data to the knowledge base may be calculated based on the determination of a similarity between the user reaction data and at least one of external narration information and internal narration information included in the knowledge base. .

In another embodiment of the present disclosure, a method of automatically editing a highlight image performed by one or more processors of a computing device is disclosed. The method may include receiving one or more user reaction data for the real-time content from one or more user terminals viewing the real-time content, and identifying one or more highlight sections in the real-time content based on an analysis result of the user reaction data. and generating highlight content based on the one or more highlight sections.

In another embodiment of the present disclosure, a server for automatically editing a highlight image is disclosed. The server includes a processor including one or more cores, a memory including program codes executable in the processor, and a network unit for transmitting and receiving data to and from a user terminal and an external server, and the processor is one for viewing real-time content Receive one or more user reaction data for the real-time content from the user terminal, identify one or more highlight sections in the real-time content based on an analysis result of the user reaction data, and based on the one or more highlight sections You can create highlight content.

The present disclosure may provide a computer program for automatically editing a highlight image.

Various aspects are now described with reference to the drawings, in which like reference numbers are used to refer to like elements collectively. In the following examples, for purposes of explanation, numerous specific details are set forth in order to provide a thorough understanding of one or more aspects. It will be evident, however, that such aspect(s) may be practiced without these specific details.
1 is a block diagram of a computing device for automatically editing a highlight image according to an embodiment of the present disclosure.
2 is an exemplary diagram for explaining a method of identifying a highlight section based on user reaction data related to real-time content related to an embodiment of the present disclosure.
3 is an exemplary diagram for explaining a method of identifying a highlight section based on user reaction data related to real-time content related to an embodiment of the present disclosure related to another embodiment of the present disclosure.
4 is a diagram exemplarily illustrating a method of allocating different weights to each of user response data related to an embodiment of the present disclosure.
5 is a schematic diagram illustrating a network function according to an embodiment of the present disclosure.
6 is a flowchart for generating highlight content related to an embodiment of the present disclosure.
7 is a diagram illustrating logic for implementing a method for generating highlight content related to an embodiment of the present disclosure.
8 depicts a simplified, general schematic diagram of an exemplary computing environment in which embodiments of the present disclosure may be implemented.

Various embodiments are now described with reference to the drawings. In this specification, various descriptions are presented to provide an understanding of the present disclosure. However, it is apparent that these embodiments may be practiced without these specific descriptions.

The terms “component,” “module,” “system,” and the like, as used herein, refer to a computer-related entity, hardware, firmware, software, a combination of software and hardware, or execution of software. For example, a component can be, but is not limited to being, a process running on a processor, a processor, an object, a thread of execution, a program, and/or a computer. For example, both an application running on a computing device and the computing device may be a component. One or more components may reside within a processor and/or thread of execution. A component may be localized within one computer. A component may be distributed between two or more computers. In addition, these components can execute from various computer readable media having various data structures stored therein. Components may communicate via a network such as the Internet with another system, for example via a signal having one or more data packets (eg, data and/or signals from one component interacting with another component in a local system, distributed system, etc.) may communicate via local and/or remote processes depending on the data being transmitted).

In addition, the term “or” is intended to mean an inclusive “or” rather than an exclusive “or.” That is, unless otherwise specified or clear from context, "X employs A or B" is intended to mean one of the natural implicit substitutions. That is, X employs A; X employs B; or when X employs both A and B, "X employs A or B" may apply to either of these cases. It should also be understood that the term “and/or” as used herein refers to and includes all possible combinations of one or more of the listed related items.

Also, the terms "comprises" and/or "comprising" should be understood to mean that the feature and/or element in question is present. However, it should be understood that the terms "comprises" and/or "comprising" do not exclude the presence or addition of one or more other features, elements and/or groups thereof. Also, unless otherwise specified or otherwise clear from context to refer to a singular form, the singular in the specification and claims should generally be construed to mean "one or more."

Those skilled in the art will further appreciate that the various illustrative logical blocks, configurations, modules, circuits, means, logics, and algorithm steps described in connection with the embodiments disclosed herein may be implemented in electronic hardware, computer software, or combinations of both. It should be recognized that they can be implemented with To clearly illustrate this interchangeability of hardware and software, various illustrative components, blocks, configurations, means, logics, 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 constraints imposed on the overall system. Skilled artisans may implement the described functionality in varying ways for each particular application. However, such implementation decisions should not be interpreted as causing a departure from the scope of the present disclosure.

The network function of the present disclosure and an artificial neural network and a neural network may be used interchangeably.

According to an embodiment of the present disclosure, the streaming server, one or more user terminals and one or more streamer terminals and computing device 100 may transmit and receive data through an interconnection through a wireless and/or wired connection. there is.

The user terminal may be a terminal related to a plurality of users watching real-time content transmitted from the streamer terminal. The user terminal may access the streaming server to view real-time content transmitted from the streamer terminal, and may transmit user response data related to the real-time content to the streaming server.

The streamer terminal may be a terminal related to a streamer for transmitting real-time content to a plurality of user terminals. The streamer terminal may transmit various real-time content such as video content and audio content through a streaming server.

For example, the user terminal and the streamer terminal may include a personal computer (PC), a notebook (note book), a mobile terminal, a smart phone (smart phone), a tablet PC (tablet pc), etc., It may include all types of terminals capable of accessing a wired/wireless network. The detailed description of the user terminal and the streamer terminal described above is only an example, and the present disclosure is not limited thereto.

The streaming server may be a server that provides a service for transmitting and viewing real-time content through the Internet that interconnects real-time content related to each of one or more streamer terminals to each of one or more user terminals related to content viewing. In addition, the streaming server may receive user response data related to the corresponding real-time content from each user terminal in relation to the real-time content viewed by each of one or more user terminals and transmit it to the computing device 100 . The streaming server and the computing device 100 may include a streaming server in the computing device 100 according to an embodiment of the present disclosure to perform content transmission and highlight editing image providing functions in one integrated device. In this example, when the streaming server and the computing device 100 are implemented as one integrated device, the computing device 100 may receive user response data for real-time content from one or more user terminals.

According to an embodiment of the present disclosure, the computing device 100 may generate a highlight edited image of the streamer's real-time content. The computing device 100 may identify highlight sections of interest to viewers in the streamer's real-time content, and generate a highlight edited image based on the identified highlight sections. The computing device 100 of the present disclosure may include all kinds of computing devices capable of calculating electronic data, for example, general computing devices such as personal computers, server computers, and mobile terminals (smartphones (smartphones) It may include a computing device having a limited computing power, such as a smartphone), a tablet (tablet), and the like. In the present disclosure, a detailed description of a method in which the computing device 100 generates a highlight edited image from real-time content will be described later with reference to the following drawings.

1 is a block diagram of a computing device for providing an automatic highlight image editing service according to an embodiment of the present disclosure. 1 , the computing device 100 may include a network unit 150 , a processor 110 , and a memory 130 . The above-described components are exemplary, and the scope of the present disclosure is not limited to the above-described components. That is, additional components may be included or some of the above-described components may be omitted depending on implementation aspects for the embodiments of the present disclosure.

According to an embodiment of the present disclosure, the computing device 100 may include a network unit 150 for transmitting and receiving data. Specifically, the network unit 150 may transmit and receive information on real-time content and highlight content according to an embodiment of the present disclosure with at least one of one or more user terminals, a plurality of streamer terminals, and a streaming server. For example, the computing device 100 may receive real-time content and user reaction data related to reactions of a plurality of user terminals viewing real-time content from a streaming server or a streamer terminal through the network unit 150 . As another example, the computing device 100 may transmit the highlight content generated in response to the real-time content to the streamer terminal through the network unit 150 . The detailed description of the information transmitted and received by the above-described network unit is only an example, and the present disclosure is not limited thereto.

The memory 130 of the computing device 100 may store any type of information generated or determined by the processor 110 and any type of information received by the network unit 150 . Also, the memory 130 may store data supporting various functions of the computing device 100 . The memory 130 may store a plurality of application programs and commands driven by the computing device 100 . At least one of these applications may exist in the computing device 100 from the time of release. For example, the memory 130 may store real-time content and application programs for identifying a highlight section based on user reaction data related to the real-time content. As another example, the memory 130 may store a pre-trained neural network for generating a personalized knowledge base for each streamer of the computing device 100 . The description of the information stored in the above-described memory is only an example, and the present disclosure is not limited thereto.

According to an embodiment of the present disclosure, it may consist of one or more cores, and a central processing unit (CPU) of a computing device, a general purpose graphics processing unit (GPGPU), a tensor processing unit ( It may include a processor for data analysis and deep learning, such as a tensor processing unit (TPU). The processor 120 may read a computer program stored in the memory 130 to perform data processing for machine learning according to an embodiment of the present disclosure. According to an embodiment of the present disclosure, the processor 120 may perform an operation for learning the neural network. The processor 120 for learning of the neural network, such as processing input data for learning in deep learning (DL), extracting features from the input data, calculating an error, updating the weight of the neural network using backpropagation calculations can be performed. At least one of a CPU, a GPGPU, and a TPU of the processor 120 may process learning of a network function. For example, the CPU and the GPGPU can process learning of a network function and data classification using the network function together. In addition, in an embodiment of the present disclosure, learning of a network function and data classification using the network function may be processed by using the processors of a plurality of computing devices together. In addition, the computer program executed in the computing device according to an embodiment of the present disclosure may be a CPU, GPGPU, or TPU executable program.

In an embodiment of the present disclosure, the computing device 100 may distribute and process a network function using at least one of a CPU, a GPGPU, and a TPU. Also, in an embodiment of the present disclosure, the computing device 100 may distribute and process a network function together with other computing devices. Descriptions of specific details regarding network function distributed processing of the computing device 100 are in US patent applications US15/161080 (filed on May 20, 2016) and US15/217475 (filed on July 22, 2016), which are incorporated herein by reference in their entireties. specifically discussed.

According to an embodiment of the present disclosure, the processor 120 may typically process the overall operation of the computing device 100 . The processor 120 processes signals, data, information, etc. input or output through the above-described components, or by driving an application program stored in the memory 130, to provide or process appropriate information or functions to the user terminal. there is.

According to an embodiment of the present disclosure, the processor 110 may receive one or more user response data for the real-time content from one or more user terminals viewing the real-time content. The user response data may be based on at least one of a natural language input of a user of a user terminal viewing real-time content or an action input. In addition, the user reaction data may include user reaction user identification information related to the user terminal that has transmitted the user reaction data. The user response user identification information may be identification information for distinguishing each of one user terminal viewing real-time content.

Specifically, the user response data may be based on at least one of a natural language input input from a user of the user terminal in association with a part of real-time content, or an action input. For example, the natural language input of the user response data may include a chatting input input from a user of one or more user terminals through a chat window related to real-time content. Also, the action input of the user response data may mean an emoticon input, a donation input (eg, super chat, star balloon, etc.) input from a user of one or more user terminals, and a voice input.

As a specific example, the user response data may be a chat input received from the first user terminal through a chat window in association with a first time point in the game content transmitted in real time by the first streamer terminal. As another example, the user response data may be a donation input received from the second user terminal in association with the second time point in the communication content transmitted in real time by the second streamer terminal. The detailed description of the content transmitted by the above-described streamer terminal and the user reaction data corresponding thereto is only an example, and the content transmitted by the streamer of the present disclosure is not limited thereto, and the user reaction data is a user terminal viewing the content. It may include a variety of information related to the user's reaction that can be received from

That is, the processor 110 may receive user reaction data associated with a portion of the real-time content from each of one or more user terminals viewing the real-time content. In other words, the processor 110 identifies a point in time at which the user reaction data is received in the real-time content through the user reaction data having time correlation with the real-time content, and whether the corresponding user reaction data is data received from any user terminal. can be identified.

According to an embodiment of the present disclosure, the processor 110 may receive external reaction data. The processor 110 may receive external reaction data related to a post posted in relation to real-time content by one or more user terminals from an external server. The external server is a server that posts information posted through one or more user terminals, and may be, for example, an SNS, a blog, an Internet broadcasting bulletin board, or the like. The detailed description of the above-described external server is only an example, and the present disclosure is not limited thereto.

In detail, the processor 110 may receive external response data for at least one of real-time content and related information of real-time content from an external server. That is, the external reaction data is reaction data for at least one of real-time content or related information of real-time content, and a user's reaction input through a different channel (eg, a chat window of real-time content) from user reaction data for real-time content. data received over a different channel than the data).

The related party information of real-time content is information for identifying each of a plurality of real-time content exposed to one or more user terminals, and may include, for example, at least one of broadcast content information and streamer identification information. The broadcast content information is information about a category of a broadcast transmitted by a corresponding streamer, and may include, for example, game broadcast content, eating broadcast content, outdoor broadcast content, communication broadcast content, and the like. The streamer identification information is information for identifying the streamer who transmits the content and the performer appearing in the content, information about the name, age, and gender of the streamer and the performer, ID, nickname, nickname of the streamer and the performer may include information about The detailed description of the broadcast content information and the streamer identification information included in the related information of the real-time content described above is only an example, and the present disclosure is not limited thereto.

For a specific example, as for the related information of real-time content, the streamer related to the real-time content is 'A streamer', the category information related to the real-time content is 'invited singer live broadcast', and the performer information related to the real-time content is ' Singer Hong Gil-dong' may include information. The detailed description of the above-described relationship information of real-time content is merely an example, and the present disclosure is not limited thereto.

The processor 110 may use real-time content or relevant information of the real-time content as input to the natural language processing model to grasp the meaning and form of a sentence corresponding to the real-time content and the relevant information of the real-time content. The natural language processing model may be composed of one or more network functions learned through a training data set, and may be a model that performs semantic analysis and morphology analysis on the input sentence by taking the input sentence as an input. In addition, the processor 110 may crawl the real-time content and related information of the real-time content as external response data from an external server based on the meaning and form of a sentence output in response to the real-time content and related information of the real-time content.

For a specific example, when the related information of real-time content includes information that the streamer related to the real-time content is 'A streamer' and the performer information related to the real-time content includes information that the real-time content-related information is 'Singer Hong Gil-dong,' the processor 110 may These are input to the natural language processing model, and the meaning and shape of the sentence corresponding to the corresponding information can be grasped. In this case, the processor 110 may receive a post written in relation to 'A streamer' or 'Hong Gil-dong' corresponding to the meaning and form of a sentence corresponding to the related information of the real-time content from the external server. The detailed description of the related information of the real-time content described above is only an example, and the present disclosure is not limited thereto.

That is, the processor 110 may obtain external response data corresponding to specific real-time content by selecting and crawling only real-time content from an external server through a natural language processing model or external user reaction data corresponding to relevant information of real-time content. there is.

In addition, the processor 110 may determine the user's reaction of the user terminal to the real-time content based on the external reaction data. Specifically, the processor 110 acquires external response data for at least one of real-time content or related information of real-time content, and grasps the meaning of the external response data through a natural language processing model, so as to view one or more real-time content It may be determined whether the user terminals have a positive or negative response to the real-time content.

For example, when receiving external response data 'Hong Gil-dong can't sing' in relation to the first real-time content on the Internet broadcasting bulletin board, the processor 110 processes the sentence as an input of the natural language processing model, so that the sentence is It can be determined that the evaluation of Hong Gil-dong contains a negative meaning. Specific description of the above-described external reaction data is only an example, and the present disclosure is not limited thereto.

In addition, the processor 110 may provide feedback information to the terminal concerned of the real-time content corresponding to the external reaction data based on the external reaction data. Specifically, the processor 110 determines whether the user's response to the real-time content of the user terminal is positive or negative through the external reaction data, and provides feedback information to the terminal concerned with the real-time content based on the determined reaction. You can decide to send it.

As a specific example, when receiving external response data 'These days, BJ true content is not interesting..' in relation to the second real-time content on SNS, the processor 110 processes the corresponding sentence as an input of the natural language processing model, It can be determined that the viewer's reaction to the recent content of BJ True (ie, the streamer) is negative. The processor 110 generates feedback information indicating that there is room for improvement because the viewer's reaction to the latest content is negative based on the determined reaction, and thus the terminal concerned with the real-time content (eg, a terminal of BJ True that transmits real-time content) ) can be determined to be transmitted. Specific description of the above-described external reaction data and feedback information is only an example, and the present disclosure is not limited thereto.

In other words, the processor 110 receives, from an external server, external reaction data related to a post posted by a user of one or more user terminals who viewed the real-time content in relation to the real-time content, thereby reacting the user terminals who viewed the real-time content. can detect In addition, the processor 110 transmits feedback information to the relevant terminal of the corresponding real-time content based on the external reaction data of the user terminals who have viewed the real-time content, thereby allowing the person concerned with the real-time content to see the viewers' reaction to their content. By making it easy to recognize, it is possible to suggest the direction of improvement for the content. That is, the processor 110 may provide a streamer management effect for managing the streamer through feedback information based on external reaction data.

According to an embodiment of the present disclosure, the processor 110 may generate a knowledge base for a person concerned with real-time content. Specifically, the processor 110 may generate a knowledge base on the related parties of real-time contents by storing user reaction data and external reaction data for real-time contents in association with related information of the real-time contents.

A knowledge base is created corresponding to each of the parties in the real-time content, and the relation can be used to identify user responses unique to each. In addition, the knowledge base may be generated based on at least one of semantic analysis or quantitative analysis of external narration information or internal narration information. That is, the knowledge base may be a combination of keywords reflecting the individual characteristics of each streamer terminal that transmits a real-time video.

In more detail, the processor 110 extracts internal narration information related to real-time content or related information of real-time content from the user reaction data based on at least one of semantic analysis or quantitative analysis of the user reaction data, and the extracted internal A knowledge base can be created by matching the narration information with the relevant information of the real-time content.

As a specific example, when the related information of the first real-time content includes information such as 'Streamer Wild,' and the user response data includes information such as 'What's so bad, ㅇㅇㅇㅇ', the processor 110 is a natural language By performing semantic analysis on user response data through the processing model, internal narrative information called 'ㅇㅇㅇ' can be extracted. In this case, 'ㅇㅇㅇ' may be a neologism with a negative connotation for the streamer wild (for example, a list of consonants in the sentence 'wield away'). That is, the processor 110 extracts internal narration information from the user response data through semantic analysis of the user response data, and matches the extracted internal narration information with the related information of the first real-time content, thereby providing information to the related party of the first real-time content. You can create a knowledge base for In other words, in the knowledge base of the person concerned with the first real-time content, internal narration information including a negative meaning of “ㅇㅇㅇ” may be matched and stored. The detailed description of the above-described user reaction data and internal narrative information extracted from the user reaction data is an example, and the present disclosure is not limited thereto.

For another example, user response data including information on 'Godwiel' among a plurality of user response data received from each of one or more user terminals, in which the related information of the first real-time content includes information called “streamer wilde” When is more than a preset threshold number, the processor 110 determines that a plurality of user reaction data related to real-time content is more exposed through the chat window, and 'God Weil' corresponding to the user reaction data is internally descriptive information can be extracted as In addition, the processor 110 may generate a knowledge base for the related party of the first real-time content by matching the extracted internal narration information with the related party information of the first real-time content and storing the same. In other words, in the knowledge base of the person concerned with the first real-time content, internal narration information of “God Weil” may be matched and stored. The detailed description of the above-described user reaction data and internal narrative information extracted from the user reaction data is an example, and the present disclosure is not limited thereto.

That is, the processor 110 extracts a text semantically suitable for a person concerned with the real-time content as internal narrative information through semantic analysis of a plurality of user response data received from one or more user terminals through a real-time chatting window related to the real-time content. to create a knowledge base. In addition, the processor 110 extracts text frequently exposed in the real-time content as internal narrative information through quantitative analysis on a plurality of user response data received from one or more user terminals through a real-time chat window related to real-time content, and extracts knowledge You can create a base.

In addition, the processor 110 extracts external narration information related to real-time content or related information of real-time content from the external reaction data based on at least one of semantic analysis or quantitative analysis of the external response data, and extracts the extracted external narration information. A knowledge base can be created by matching with the relevant information of real-time content.

As a specific example, when the related information of the second real-time content includes information that the streamer is 'BJ True', and the external reaction data includes information that 'Today's true broadcast was a jackpot keke t ㅇㅇ!!!', the processor By performing semantic analysis on the external response data through the natural language processing model, 110 may extract external description information of 'Drㅇ'. In this case, 'tㄹㅇ' may be a buzzword (eg, 'true up') having a positive meaning for BJ true. That is, the processor 110 extracts external narration information from the external response data through semantic analysis of the external response data, and matches the extracted external narration information with the related information of the second real-time content, thereby providing information to the related party of the second real-time content. You can create a knowledge base for In other words, external narration information including the positive meaning of 'tlㅇ' may be matched and stored in the knowledge base of the person concerned with the second real-time content. The detailed description of the above-described external response data and external narrative information extracted from the external response data is merely an example, and the present disclosure is not limited thereto.

That is, the processor 110 extracts a text semantically suitable to a person concerned with the real-time content as external description information through semantic analysis of a plurality of external response data posted through one or more user terminals in relation to the real-time content, and extracts the knowledge base can create In addition, the processor 110 displays text frequently exposed in the external server in relation to the real-time content through quantitative analysis of a plurality of external reaction data posted through one or more user terminals to the external server related to the real-time content as external narrative information. can be extracted to create a knowledge base.

Accordingly, the processor 110 receives user reaction data and external reaction data from each of the postings of the real-time chat window and the external server in relation to specific real-time content, and performs semantic analysis and quantitative analysis for each of the user reaction data and the external reaction data. By extracting internal narration information and external narration information from user response data and external response data, respectively, it is possible to generate a knowledge base corresponding to a person concerned with a specific real-time content.

According to an embodiment of the present disclosure, the processor 110 may identify one or more highlight sections in real-time content based on an analysis result of the user reaction data. Specifically, the processor 110 may identify one or more highlight sections in the real-time content based on at least one of semantic analysis or quantitative analysis of the user reaction data.

In more detail, the processor 110 may generate one or more real-time sub-contents by dividing the real-time content into predetermined time intervals. Also, the processor 110 may identify one or more highlight sections in the real-time content based on the semantic analysis of the user response data received in response to each real-time sub content. The processor 110 performs semantic analysis corresponding to one or more user response data included in each of the one or more real-time sub-contents, and as a result of the semantic analysis, the number of user response data including positive meanings in each sub-content is the largest. The point of view can be identified as a highlight section.

As a specific example, when the first real-time content of the processor 110 is content corresponding to 5 minutes and the predetermined time period is 1 minute, the processor 110 divides the first real-time content into five periods and divides the first real-time content into five real-time content. Sub-contents (ie, first to fifth real-time sub-contents) may be generated. In addition, as a result of semantic analysis of a plurality of user response data received from one or more user terminals received for 1 minute in response to the first real-time sub-content to the fifth real-time sub content, a user including a positive meaning in each real-time sub content When the number of response data is 68, 157, 23, 14, or 66, the processor 110 may identify a time point (ie, 1 to 2 minutes) corresponding to the first real-time sub-content as a highlight section. there is. The detailed description of the above-described real-time content, the predetermined time interval, and the number of user reaction data is merely an example, and the present disclosure is not limited thereto.

According to an embodiment of the present disclosure, the processor 110 may identify one or more highlight sections in real-time content based on quantitative analysis of user reaction data. Specifically, the processor 110 may perform quantitative analysis on the number of user response data received in association with the real-time content, and may identify one or more highlight sections in the real-time content based on the quantitative analysis result. The quantitative analysis may be performed based on a threshold determined based at least in part on the number of one or more user terminals viewing the real-time content.

In more detail, the processor 110 may generate one or more real-time sub-contents by dividing the real-time content into predetermined time intervals. Also, the processor 110 may identify one or more real-time sub-contents in which the number of user response data received corresponding to each real-time sub-content exceeds a threshold value among the one or more real-time sub-contents as a highlight section. In this case, a threshold value serving as a criterion for identifying a highlight section through quantitative analysis based on the number of user reaction data may be determined based at least in part on the number of one or more user terminals viewing real-time content. The threshold value may have a positive correlation with the number of user terminals viewing the real-time content at a time point corresponding to each real-time sub-content. In other words, the threshold value, which is a criterion for identifying a highlight section through quantitative analysis based on the number of user response data, can be variably adjusted based on the number of user terminals viewing real-time content at a time point corresponding to each sub-content. there is. For example, the threshold value may be preset to 50% of the number of user terminals viewing the real-time content at a time point corresponding to each real-time sub-content. When the number of user terminals viewing the real-time content at a time point corresponding to the first real-time sub-content that is a time period of 1-2 minutes is 100, the threshold value may be 50, and the second real-time that is a time period of 2-3 minutes When the number of user terminals viewing the real-time content at the time corresponding to the sub-content is 50, the threshold value may be 25. That is, the threshold value was 50 as there were 100 viewers in the 1 to 2 minute section of the real-time content, but the threshold value can be adjusted to 25 as the number of viewers decreases to 50 in the 2-3 minute section, which is a later point in time. . The above-described real-time content, sub-content, and specific values of the threshold value and the number of user terminals viewed at a time corresponding to the sub-content are merely examples, and the present disclosure is not limited thereto.

For a specific example, referring to FIG. 2 , when the first real-time content corresponds to 7 minutes and the predetermined time period is 30 seconds, the processor 110 converts the first real-time content to the real-time sub content of 14 sections. (that is, the first real-time sub-contents to the fourteenth real-time sub-contents). Also, the number of one or more user terminals viewing the real-time content at a time point corresponding to each of the first to fourth real-time sub-contents may be flexibly changed. A threshold value for each real-time sub-content may be variably adjusted according to a fluid change in the number of user terminals viewing real-time content at a time point corresponding to each real-time sub-content. The number of a plurality of user response data received from one or more user terminals during the time period corresponding to the A section 210 (ie, the fourth real-time sub content) (ie, 1 minute 30 seconds to 2 minutes) is 90, and the corresponding section When the threshold value is 82 as the number of one or more user terminals that watched the real-time content at a time corresponding to 165 is 165, the processor 110 determines that the number of user response data received in the corresponding section exceeds the threshold value, Section A 210 may be identified as a highlight section. In addition, the number of a plurality of user response data received from one or more user terminals during the time period corresponding to the B section 220 (ie, the tenth real-time sub content) (ie, 4 minutes 30 seconds to 5 minutes) is 142, When the threshold value is 175 (eg, 50% of the number of user terminals) as the number of one or more user terminals that watched real-time content at a time point corresponding to the corresponding section is 350, the processor 110 controls the user received in the corresponding section. It is determined that the number of response data is less than or equal to a threshold value, so that section B 220 may not be identified as a highlight section. In addition, the number of a plurality of user response data received from one or more user terminals during the time period corresponding to the C section 230 (ie, the 12th real-time sub content) (ie, 5 minutes 30 seconds to 6 minutes) is 32, As the number of one or more user terminals that viewed real-time content at a time point corresponding to the section is 42, when the threshold value is 21, the processor 110 determines that the number of user response data received in the section exceeds the threshold value. It is determined that the section C 230 can be identified as a highlight section.

That is, the B section 220 may not be included in the highlight section despite receiving relatively more user response data compared to the A section 210 and the C section 230 . In other words, the processor 110 does not simply identify a section with a large number of user response data received from one or more user terminals as a highlight section, but identifies a section with a high number of user response data compared to the viewer as a highlight section in each section. can That is, even though relatively little user reaction data is received in section C 230 , the processor 110 may determine that a large amount of user reaction data has been received in relation to the number of viewers at that time, and may identify it as a highlight section.

Therefore, in response to a change in the number of viewers who watch content in a one-person media environment, different threshold values are set for each section, and quantitative analysis based on the number of user response data based on different threshold values corresponding to each section By calculating the result, the identification accuracy of the highlight section may be improved. In other words, it is possible to prevent the fear of misidentifying a section in which user reaction data received through a chat window increases as the number of viewers increases as a highlight section, which is not a major part inducing the reaction of viewers in real-time content. The detailed description of the method for identifying a highlight section with reference to FIG. 2 described above is merely an exemplary description for helping understanding of the present disclosure, and the present disclosure is not limited thereto.

According to another embodiment of the present disclosure, the processor 110 identifies a change amount of user reaction data for each section of real-time content, and identifies a highlight section based on a point in time when the identified change amount of user reaction data exceeds a threshold change amount. can In this case, the threshold change amount may be determined based at least in part on the change amount of the number of one or more user terminals viewing real-time content.

In more detail, the processor 110 may generate one or more real-time sub-contents by dividing the real-time content into predetermined time intervals. Also, the processor 110 may identify one or more real-time sub-contents in which a change amount of user response data received corresponding to each real-time sub-content among one or more real-time sub-contents exceeds a threshold change amount as one or more highlight sections.

For a specific example, referring to FIG. 3 , when the second real-time content corresponds to 7 minutes and the predetermined time interval is 30 seconds, the processor 110 converts the second real-time content into real-time sub-contents of 14 sections. (that is, the first real-time sub-contents to the fourteenth real-time sub-contents). In this case, the processor 110 may calculate a change amount of the user reaction data based on the user reaction data received from each of the real-time sub-contents.

For example, when user response data received from one or more user terminals in response to section A 310 is 20, and user response data received from one or more user terminals in response to section B 320 is 40, A The amount of change in user response data corresponding to the section 310 and section B 320 is 0.67 (that is, the slope ((40-20)/ 30)). Also, the number of one or more user terminals viewing the real-time content at a time point corresponding to each of the first to fourth real-time sub-contents may be flexibly changed. In this case, as the number of one or more user terminals viewing real-time content flexibly changes according to time points, the threshold change amount in each section may be variably adjusted.

Specifically, the amount of change in the plurality of user response data received from one or more user terminals during the time period corresponding to the A section 310 and the B section 320 (ie, the second real-time sub-content and the third real-time sub-content) 0.67 ((40-20)/30), and when the threshold change amount is 0.33 (eg, 50% of the change amount of the number of user terminals) at the time point corresponding to the section, the processor 110 determines the user corresponding to the section It is determined that the amount of change in the response data exceeds the threshold amount of change, so that the A section 310 and the B section 320 can be identified as the highlight section. In an additional embodiment, the processor 110 may identify only the section B 320 as the highlight section. In addition, the amount of change of the plurality of user response data received from one or more user terminals during the time period corresponding to the C section 330 and the D section 340 (ie, the ninth real-time sub-content and the tenth real-time sub-content) is 2 , and when the threshold change amount is 5 at the time point corresponding to the section, the processor 110 determines that the change amount of the user response data at the time point corresponding to the section is less than the threshold change amount C section 330 and D section 340 ) may not be identified as a highlight section.

That is, despite the fact that the view section corresponding to the C section 330 and the D section 340 has a large increase in user response data compared to the view section corresponding to the A section 310 and B section 320, the highlight It may not be included in the section. In other words, the processor 110 does not identify a highlight section based on the number of user response data in each section, but identifies a section in which the amount of change in the user response data is large in each section as a highlight section in relation to the amount of change of the viewer in each section. can The detailed description of the method for identifying a highlight section with reference to FIG. 3 described above is merely an exemplary description for helping understanding of the present disclosure, and the present disclosure is not limited thereto.

In a one-person media content broadcasting environment, a different number of viewers may be retained depending on the awareness of the streamer. For example, when user A, a popular streamer, transmits real-time content, an average of 3,000 user terminals can view the content, while user B, an unpopular streamer, receives an average of 50 users when sending real-time content. A corresponding user terminal may view the corresponding content. Accordingly, when identifying a highlight section based on the number of user response data received from a user terminal that simply watches real-time content, the highlight section is identified from the real-time content of each streamer as each streamer has a different number of viewers. There is a possibility that the standards for doing so may be different.

That is, the processor 110 identifies the highlight section through the amount of change in user response data at each point of time of the real-time content, so that the same criterion for identifying the highlight section from each of the real-time contents of a plurality of streamers having different numbers of viewers is used. can present Accordingly, the processor 110 may identify the highlight section with the same reference for each of the real-time contents of various streamers, such as popular streamers and unpopular streamers.

According to an embodiment of the present disclosure, the processor 110 may perform pre-processing on user response data received in response to real-time content. The preprocessing of the user reaction data may mean assigning different weights to the user reaction data received from each of one or more user terminals through a chat window related to real-time content.

Specifically, the processor 110 may assign a weight to each user reaction data based on the user reaction user identification information. The processor 110 determines at least one of a highlight section prediction contribution rate for the real-time content of the user who generated the user reaction data, a viewing contribution rate for the content matched with the related information of the user's real-time content, and a matching rate for the knowledge base of the viewer reaction data A weight may be assigned to each user response data based on one.

The highlight section prediction contribution rate for real-time content is information on whether user reaction data is included in the pre-produced highlight content in relation to the person involved in the real-time content, and quantitative analysis of the user reaction data included in the pre-produced highlight content and semantic analysis.

For example, when the user reaction data of the first user terminal is included in the highlight edited image pre-produced at a previous point in time in relation to the streamer A, which is a person concerned with the first real-time content, the processor 110 controls the first user terminal. A higher weight may be given to the first user response data of the first user terminal to the first real-time content based on the identification information. In the process of generating the highlight image by identifying the highlight section from the real-time content that was pre-produced at the previous time of the streamer A, the inclusion of the user reaction data of the first user terminal means that the user reaction data of the first user terminal is the previous real-time It may have contributed to identifying a highlight section in the content. That is, the processor 110 determines that the user of the first user terminal is a user who contributes a lot to detecting an interesting part in the real-time content, so that the user reaction data related to the first user terminal is more than the user reaction data related to other user terminals. A relatively high weight can be assigned. In this case, the degree of weight given to the user response data related to the first user terminal may be based on at least one of quantitative analysis and semantic analysis.

For a specific example, referring to FIG. 4 , a highlight edited video pre-produced in response to real-time content at a previous point in time received from each of the first user terminals to the fifth user terminals through a chatting input to the chatting window 400 . When including five user reaction data (first reaction data 410, second reaction data 420, third reaction data 430, fourth reaction data 440, and fifth reaction data 450) , the processor 110 may assign different weights to each of the user reaction data through semantic analysis of each user reaction data. Specifically, the processor 110 may perform semantic analysis on the first reaction data of the first user terminal, and as a result of the semantic analysis, the content included in the first reaction data (ie, 'aaaaaaaa') is real-time content. A weight of '0' may be assigned to the first response data 410 by identifying it as a meaningless sentence not related to . That is, the processor 110 determines that the meaning of the first reaction data 410 is not related to the real-time content, and assigns a weight of '0' to the first reaction data 410 to remove it as noise. By doing so, it is possible to prevent the first reaction data from being counted in the quantitative analysis process. In addition, the processor 110 may perform semantic analysis on the second reaction data 420 of the second user terminal, and as a result of the semantic analysis, the content included in the second reaction data 420 (that is, 'this is a highlight') 'Wow') means that the highlight section is more appropriately predicted, and a relatively high weight of '3' may be assigned to the second response data 420 . In addition, the processor 110 may perform semantic analysis on the third reaction data 430 of the third user terminal, and as a result of the semantic analysis, the content included in the third reaction data 430 (ie, 'Great!! !!!!!!') can be identified as contributing to the highlight section identification, and a weight of '2.5' can be assigned to the third response data 430 . In addition, the processor 110 may perform semantic analysis on the fourth reaction data 440 of the fourth user terminal, and as a result of the semantic analysis, the content included in the fourth reaction data 440 (ie, 'What is it? ') may be identified as having a meaning opposite to the highlight section prediction, and a weight of '-1' may be assigned to the fourth response data 440 . In addition, the processor 110 may perform semantic analysis on the fifth response data 450 of the fifth user terminal, and as a result of the semantic analysis, the contents included in the fifth response data 450 (that is, 'that ') as having a meaning opposite to the highlight section prediction, so that a weight of '-1' may be assigned to the fifth response data 450 .

That is, the processor 110 may remove meaningless user reaction data as noise based on the semantic analysis result for each of a plurality of user reaction data corresponding to each user terminal included in the pre-produced highlight image, and the highlight section A relatively high weight may be given to a user terminal that has predicted more clearly, and a negative weight may be given to user response data having a meaning opposite to that of the highlight section. The detailed description of the above-described user response data of each user terminal and a weight given corresponding thereto is only an example, and the present disclosure is not limited thereto.

For another example, when the user reaction data received from the user terminal A and the user terminal B are included twice and six times, respectively, in the highlight content pre-produced in response to the real-time content of the previous point in time, the processor 110 controls each user Through quantitative analysis of the reaction data, a higher weight may be given to the user reaction data of the fourth user terminal, to which the user reaction data of the third user terminal is inputted more times in the highlight section than the user reaction data of the third user terminal. That is, user reaction data of a user terminal that has inputted user reaction data a greater number of times at a time point corresponding to the highlight section through quantitative analysis of a plurality of user reaction data corresponding to each user terminal included in the pre-produced highlight image. can be given a relatively high weight. The detailed description of the above-described user response data of each user terminal and a weight given corresponding thereto is only an example, and the present disclosure is not limited thereto.

The viewing contribution rate for the content matched with the relevant information of the real-time content is information about the participation history of each of one or more user terminals for the content matched with the relationship between the real-time content information, and is based on the number of matched content and the participation history can be calculated by

For example, a user terminal that frequently views content related to a specific streamer may be more likely to recognize an interesting part in the real-time content image of the streamer. Accordingly, the processor 110 determines that each of the plurality of user terminals views the real-time content through the viewing contribution rate for the content matched with the relevant information of the particular real-time content (ie, information for identifying a particular streamer). different weights can be given to the user response data of

As a specific example, there are 30 contents matched with streamer A and transmitted at the previous time, and participation history (or viewing history) of 27 contents among 30 real-time contents matched with streamer A by the first user terminal. ), the processor 110 may give a relatively high weight to the first user terminal. That is, a different weight may be assigned to the user response data of each user terminal based on the degree of the participation history of each user terminal for the content of the previous point in time related to the streamer A. The participation history of the user terminal for the contents matched with the related information of the specific real-time content may be based on whether the user reaction data received from each user terminal is included in the content that is matched with the A streamer and transmitted at the previous point in time. there is. That is, the user terminal that has performed chatting input or action input corresponding to user reaction data in the previous content transmission process related to streamer A has a history of participating in content matched with streamer A by the processor 110 . can be judged to be The detailed description of the content matched to the above-mentioned specific streamer and the participation history of the specific user terminal is only an example, and the present disclosure is not limited thereto.

The matching rate of the viewer reaction data to the knowledge base may be calculated based on the determination of a similarity between the user reaction data and at least one of external narration information and internal narration information included in the knowledge base.

Specifically, the processor 110 may generate a personalized knowledge base for the person concerned with the real-time content, and view the real-time content and at least one of the external narration information and the internal narration information included in the knowledge base for the person concerned with the real-time content. A different weight may be assigned to each of the plurality of user response data received from each of the one or more user terminals viewing real-time content based on the similarity determination of each of the plurality of user response data received from the one or more user terminals.

For example, when the knowledge base of the person concerned with the first real-time content includes the internal narration information of “God Weil” and the external narration information of “T ㅇㅇ”, the plurality of pieces received from one or more user terminals viewing the first real-time content A high weight may be given to user terminals that have inputted user response data including information such as “God Weil” or “Duh” among the user response data of . Since the knowledge base personalized for each streamer may be composed of buzzwords or new words corresponding to the streamer, The user may be a user with a high degree of understanding of the content of the corresponding streamer. That is, the processor 110 may give a high weight to the user reaction data of the user terminals that have input the user reaction data included in the knowledge base of the person concerned with the real-time content in the process of watching the real-time content. In other words, it is possible to assign a high weight to valid user reaction data to a person involved in a specific real-time content by utilizing a personalized knowledge base for each of the parties involved in the plurality of real-time contents. For example, when a personalized knowledge base is used for each of the parties involved in a plurality of real-time contents, specific user reaction data is not valid as user reaction data of the first real-time content, but as user reaction data of the second real-time content can be determined to be valid. That is, each streamer may use a personalized knowledge base to give different weights to each of a plurality of user reaction data. The detailed description of the knowledge base of each person concerned is merely an example, and the present disclosure is not limited thereto.

Accordingly, the processor 110 performs pre-processing of adjusting the weights for the user reaction data received in relation to the real-time content and assigns different weights to each of the plurality of user reaction data for identifying the highlight section, thereby making it more sophisticated than the highlight section. can be clearly identified.

According to an embodiment of the present disclosure, the processor 110 generates an analysis result for user reaction data based on a weight assigned to each user reaction data, and selects one or more highlight sections in real-time content based on the analysis result. can be identified. Specifically, the processor 110 assigns different weights to each of the user reaction data through pre-processing of the user reaction data, and generates an analysis result for each of the user reaction data to which the different weights have been given, so as to highlight one or more highlights in real-time content. section can be identified.

For example, the first user reaction data and the second user reaction data are received from each of the first user terminal and the second user terminal in relation to the first real-time content, and a weight assigned to each received user reaction data is ' In the case of 1' and '2', the processor 110 may perform quantitative analysis based on user reaction data based on a weight assigned to each user reaction data. That is, a plurality of user reaction data received in the real-time content viewing environment may be recognized as different weights according to weights assigned to each user reaction data in the quantitative analysis process. For example, in the quantitative analysis process, as the weight given to the second user response data is '2', the second user response data may be recognized as having a weight twice that of the first user response data with a weight of '1'. there is. In other words, even when one user reaction data is received from each of the first user terminal and the second user terminal in the real-time content viewing environment, each user reaction data is different in the quantitative analysis process according to the weight assigned to each user reaction data. can be recognized as a proportion.

According to an embodiment of the present disclosure, the processor 110 may generate highlight content based on one or more highlight sections. Specifically, the processor 110 may generate highlight content including one or more highlight sections identified in the real-time content.

For example, as the first real-time content corresponds to 5 minutes and the predetermined time interval is 1 minute, the first real-time content includes five real-time sub-contents (ie, the first real-time sub-content to the fifth real-time sub content). ), and when a time section corresponding to the first real-time sub-content and the fourth real-time sub-content among the real-time content is identified as a highlight section, the processor 110 corresponds to the first real-time sub content and the fourth real-time sub content. It is possible to create highlight content including a time period. That is, the highlight content generated by the processor 110 may be composed of content at points in time corresponding to 0 to 1 minute and 3 to 4 minutes in the real-time content. The description of specific times of the above-described real-time content and real-time sub-content is merely an example, and the present disclosure is not limited thereto.

Also, the processor 110 may generate highlight content including at least partially the identified one or more highlight sections. Specifically, the processor 110 may generate highlight content by removing at least a portion of one or more highlight sections identified based on the reproduction time setting information or adding one or more supplementary highlight sections. The reproduction time setting information is information for limiting the reproduction time of the highlight content generated from the real-time content, and may be predetermined based on a user's input requesting the production of the highlight content for the real-time content. For example, when the reproduction time setting information is predetermined by the user as 5 minutes, the processor 110 may generate highlight content having a reproduction time of 5 minutes from the real-time content. That is, the reproduction time setting information may be predetermined in response to a user's request to produce highlight content having a specific reproduction time in relation to the real-time content.

Accordingly, the processor 110 removes at least some sections from the identified one or more highlight sections based on the comparison of the play time information of the identified one or more highlight sections and the predetermined play time setting information, or adds a supplementary highlight section You can create highlight content.

For example, three highlight sections identified by the processor 110 in the first real-time content (eg, section A corresponding to the first real-time sub-content, section B corresponding to the fourth real-time sub-content, and the eighth real-time sub When the playback time information of section C corresponding to the content is 3 minutes (that is, the sum of the playback times of the three highlight sections is 3 minutes), and the predetermined playback time setting information for the first real-time content is 2 minutes, the processor Step 110 may generate highlight content by removing at least a portion of the three identified highlight sections. In this case, the highlighted section partially removed from among the identified three highlight sections may be based on at least one of semantic analysis and quantitative analysis of user response data corresponding to each section. That is, the processor 110 may generate highlight content in which at least some of the three highlight sections are removed based on the results of semantic analysis and quantitative analysis of a plurality of user response data corresponding to each of the identified three highlight sections. . For example, as a result of quantitative analysis, when the number of user response data received in response to each of section A, section B, and section C is 67, 35, and 21, the processor 110 controls the user from among the identified three highlight sections. The highlight content may be generated by removing the section C (ie, the section corresponding to the eighth real-time sub-content), which is a section in which the reception of response data is least. The description of specific numerical values for the above-described one or more highlight sections, predetermined reproduction time setting information, and the number of user response data is merely an example, and the present disclosure is not limited thereto.

As another example, four highlight sections identified by the processor 110 in the second real-time content (eg, section D corresponding to the second real-time sub-content, section E corresponding to the fourth real-time sub-content, and the fifth real-time content) The reproduction time information of section F corresponding to the sub content and section H corresponding to the ninth real-time sub content) is 4 minutes (that is, the sum of the playback times of the four highlight sections is 4 minutes), When the predetermined reproduction time setting information is 5 minutes, the processor 110 may identify a supplementary highlight section in sections other than the four identified highlight sections among the second real-time contents. In this case, a section identified as a supplementary highlight section in a section other than the identified highlight section may be based on at least one of semantic analysis and quantitative analysis of user response data corresponding to each section. That is, the processor 110 may identify a supplementary highlight section based on the results of semantic analysis and quantitative analysis on a plurality of user response data corresponding to each of the identified four highlight sections. For example, as a result of quantitative analysis of user response data corresponding to each of the identified sections other than the four highlighted sections, the first real-time sub content is the section in which the number of user response data is the largest in sections other than the identified four highlight sections. When it is determined as the section A corresponding to , the processor 110 identifies section A as a supplementary highlight section, and adds the supplemental highlight section to the four highlight sections to generate highlight content. In this case, the supplementary highlight section may be added to the four highlight sections based on the viewpoint in the real-time content. That is, the processor 110 generates a section A (ie, a section corresponding to the first real-time sub-content), a section D (a section corresponding to the second real-time sub-content), and section E (a section corresponding to the fourth real-time sub-content). , section F (section corresponding to the fifth real-time sub content) and section H (section corresponding to the ninth real-time sub content) may be arranged in the order to generate the highlight content. The description of specific numerical values for the above-described one or more highlight sections, predetermined reproduction time setting information, and the number of user response data is merely an example, and the present disclosure is not limited thereto.

Accordingly, since the processor 110 may generate highlight content by removing or adding a specific section from the identified highlight section, it may be possible to produce highlight content having a specific reproduction time that meets the user's needs. That is, since it is possible to reproduce the highlight content corresponding to the playback time length desired by the user in the real-time content, the convenience of the user who wants to produce a highlight image may be increased.

5 is a schematic diagram illustrating a network function according to an embodiment of the present disclosure.

Throughout this specification, computational model, neural network, network function, and neural network may be used interchangeably. A neural network may be composed of a set of interconnected computational units, which may generally be referred to as nodes. These nodes may also be referred to as neurons. A neural network is configured by including at least one or more nodes. Nodes (or neurons) constituting the neural networks may be interconnected by one or more links.

In the neural network, one or more nodes connected through a link may relatively form a relationship between an input node and an output node. The concepts of an input node and an output node are relative, and any node in an output node relationship with respect to one node may be in an input node relationship in a relationship with another node, and vice versa. As described above, an input node-to-output node relationship may be created around a link. One or more output nodes may be connected to one input node through a link, and vice versa.

In the relationship between the input node and the output node connected through one link, the value of the output node may be determined based on data input to the input node. Here, a node interconnecting the input node and the output node may have a weight. The weight may be variable, and may be changed by the user or algorithm in order for the neural network to perform a desired function. For example, when one or more input nodes are interconnected to one output node by respective links, the output node sets values input to input nodes connected to the output node and links corresponding to the respective input nodes. An output node value may be determined based on the weight.

As described above, in a neural network, one or more nodes are interconnected through one or more links to form an input node and an output node relationship within the neural network. The characteristics of the neural network may be determined according to the number of nodes and links in the neural network, the correlation between the nodes and the links, and the value of a weight assigned to each of the links. For example, when the same number of nodes and links exist and there are two neural networks having different weight values between the links, the two neural networks may be recognized as different from each other.

A neural network may include one or more nodes. Some of the nodes constituting the neural network may configure one layer based on distances from the initial input node. For example, a set of nodes having a distance n from the initial input node may constitute n layers. The distance from the initial input node may be defined by the minimum number of links that must be passed to reach the corresponding node from the initial input node. However, the definition of such a layer is arbitrary for description, and the order of the layer in the neural network may be defined in a different way from the above. For example, a layer of nodes may be defined by a distance from the final output node.

The initial input node may refer to one or more nodes to which data is directly input without going through a link in a relationship with other nodes among nodes in the neural network. Alternatively, in a relationship between nodes based on a link in a neural network, it may mean nodes that other input nodes connected by a link do not have. Similarly, the final output node may refer to one or more nodes that do not have an output node in relation to other nodes among nodes in the neural network. Also, the hidden node may refer to nodes constituting the neural network other than the first input node and the last output node. The neural network according to an embodiment of the present disclosure may be a neural network in which the number of nodes in the input layer may be the same as the number of nodes in the output layer, and the number of nodes decreases and then increases again as progresses from the input layer to the hidden layer. can In addition, the neural network according to another embodiment of the present disclosure may be a neural network in which the number of nodes in the input layer may be less than the number of nodes in the output layer, and the number of nodes decreases as the number of nodes progresses from the input layer to the hidden layer. there is. In addition, the neural network according to another embodiment of the present disclosure may be a neural network in which the number of nodes in the input layer may be greater than the number of nodes in the output layer, and the number of nodes increases as the input layer progresses to the hidden layer. can The neural network according to another embodiment of the present disclosure may be a neural network in a combined form of the aforementioned neural networks.

A deep neural network (DNN) may refer to a neural network including a plurality of hidden layers in addition to an input layer and an output layer. Deep neural networks can be used to identify the latent structures of data. In other words, it can identify the potential structure of photos, texts, videos, voices, and music (e.g., what objects are in the photos, what the text and emotions are, what the texts and emotions are, etc.) . Deep neural networks include convolutional neural networks (CNNs), recurrent neural networks (RNNs), auto encoders, generative adversarial networks (GANs), and restricted Boltzmann machines (RBMs). boltzmann machine), a deep trust network (DBN), a Q network, a U network, a Siamese network, and the like. The description of the deep neural network described above is only an example, and the present disclosure is not limited thereto.

In an embodiment of the present disclosure, the network function may include an auto-encoder. The auto-encoder may be a kind of artificial neural network for outputting output data similar to input data. The auto encoder may include at least one hidden layer, and an odd number of hidden layers may be disposed between the input/output layers. The number of nodes in each layer may be reduced from the number of nodes of the input layer to an intermediate layer called the bottleneck layer (encoding), and then expanded symmetrically with reduction from the bottleneck layer to the output layer (symmetrically with the input layer). In this case, the nodes of the dimension reduction layer and the dimension reconstruction layer may or may not be symmetrical. The auto-encoder can perform non-linear dimensionality reduction. The number of input layers and output layers may correspond to the number of sensors remaining after pre-processing of input data. In the auto-encoder structure, the number of nodes of the hidden layer included in the encoder may have a structure that decreases as the distance from the input layer increases. If the number of nodes in the bottleneck layer (the layer with the fewest nodes between the encoder and the decoder) is too small, a sufficient amount of information may not be conveyed, so a certain number or more (e.g., more than half of the input layer, etc.) ) may be maintained.

The neural network may be trained by at least one of supervised learning, unsupervised learning, and semi-supervised learning. The training of the neural network is to minimize the error in the output. In the training of a neural network, iteratively inputs the training data to the neural network, calculates the output of the neural network and the target error for the training data, and calculates the error of the neural network from the output layer of the neural network to the input layer in the direction to reduce the error. It is a process of updating the weights of each node of the neural network by backpropagation in the direction. In the case of teacher learning, learning data in which the correct answer is labeled in each learning data is used (ie, labeled learning data), and in the case of comparative learning, the correct answer may not be labeled in each learning data. That is, for example, learning data in the case of teacher learning related to data classification may be data in which categories are labeled in each of the learning data. Labeled training data is input to the neural network, and an error can be calculated by comparing the output (category) of the neural network with the label of the training data. As another example, in the case of comparison learning related to data classification, an error may be calculated by comparing the input training data with the neural network output. The calculated error is back propagated in the reverse direction (ie, from the output layer to the input layer) in the neural network, and the connection weight of each node of each layer of the neural network may be updated according to the back propagation. A change amount of the connection weight of each node to be updated may be determined according to a learning rate. The computation of the neural network on the input data and the backpropagation of errors can constitute a learning cycle (epoch). The learning rate may be applied differently according to the number of repetitions of the learning cycle of the neural network. For example, a high learning rate can be used at the early stage of training of a neural network to increase efficiency by allowing the neural network to quickly acquire a certain level of performance, and a low learning rate can be used to increase accuracy at a later stage of learning.

In the training of neural networks, in general, the training data may be a subset of the actual data (that is, the data to be processed using the trained neural network), so the error on the training data is reduced but the error on the real data is reduced. There may be increasing learning cycles. Overfitting is a phenomenon in which errors on actual data increase by over-learning on training data as described above. For example, a phenomenon in which a neural network that has learned a cat by showing a yellow cat does not recognize that it is a cat when it sees a cat other than yellow may be a type of overfitting. Overfitting can act as a cause of increasing errors in machine learning algorithms. In order to prevent such overfitting, various optimization methods can be used. In order to prevent overfitting, methods such as increasing training data, regularization, and dropout in which a part of nodes in the network are omitted in the process of learning may be applied.

A computer-readable medium storing a data structure is disclosed according to an embodiment of the present disclosure.

The data structure may refer to the organization, management, and storage of data that enables efficient access and modification of data. A data structure may refer to an organization of data to solve a specific problem (eg, data retrieval, data storage, and data modification in the shortest time). A data structure may be defined as a physical or logical relationship between data elements designed to support a particular data processing function. The logical relationship between data elements may include a connection relationship between data elements that a user thinks. Physical relationships between data elements may include actual relationships between data elements physically stored on a computer-readable storage medium (eg, a hard disk). A data structure may specifically include a set of data, relationships between data, and functions or instructions applicable to data. Through an effectively designed data structure, a computing device can perform an operation while using the resources of the computing device to a minimum. Specifically, the computing device may increase the efficiency of operations, reads, insertions, deletions, comparisons, exchanges, and retrievals through effectively designed data structures.

A data structure may be classified into a linear data structure and a non-linear data structure according to the type of the data structure. The linear data structure may be a structure in which only one piece of data is connected after one piece of data. The linear data structure may include a list, a stack, a queue, and a deck. A list may mean a set of data in which an order exists internally. The list may include a linked list. The linked list may be a data structure in which data is linked in such a way that each data is linked in a line with a pointer. In a linked list, a pointer may contain information about a link with the next or previous data. A linked list may be expressed as a single linked list, a doubly linked list, or a circularly linked list according to a shape. A stack can be a data enumeration structure with limited access to data. A stack can be a linear data structure in which data can be processed (eg, inserted or deleted) at only one end of the data structure. The data stored in the stack may be a data structure LIFO-Last in First Out. A queue is a data listing structure that allows limited access to data. Unlike a stack, the queue may be a data structure that comes out later (FIFO-First in First Out) as data stored later. A deck can be a data structure that can process data at either end of the data structure.

The nonlinear data structure may be a structure in which a plurality of data is connected after one data. The nonlinear data structure may include a graph data structure. A graph data structure may be defined as a vertex and an edge, and the edge may include a line connecting two different vertices. A graph data structure may include a tree data structure. The tree data structure may be a data structure in which one path connects two different vertices among a plurality of vertices included in the tree. That is, it may be a data structure that does not form a loop in the graph data structure.

Throughout this specification, computational model, neural network, network function, and neural network may be used interchangeably. (Hereinafter, the neural network is unified and described.) The data structure may include a neural network. And the data structure including the neural network may be stored in a computer-readable medium. Data structures, including neural networks, may also include data input to the neural network, weights of the neural network, hyperparameters of the neural network, data obtained from the neural network, activation functions associated with each node or layer of the neural network, and loss functions for learning the neural network. there is. A data structure comprising a neural network may include any of the components disclosed above. That is, the data structure including the neural network includes all or all of the data input to the neural network, the weights of the neural network, the hyperparameters of the neural network, the data acquired from the neural network, the activation function associated with each node or layer of the neural network, and the loss function for training the neural network. may be configured including any combination of In addition to the above-described configurations, a data structure including a neural network may include any other information that determines a characteristic of a neural network. In addition, the data structure may include all types of data used or generated in the operation process of the neural network, and is not limited to the above. Computer-readable media may include computer-readable recording media and/or computer-readable transmission media. A neural network may be composed of a set of interconnected computational units, which may generally be referred to as nodes. These nodes may also be referred to as neurons. A neural network is configured by including at least one or more nodes.

The data structure may include data input to the neural network. A data structure including data input to the neural network may be stored in a computer-readable medium. The data input to the neural network may include learning data input in a neural network learning process and/or input data input to the neural network in which learning is completed. Data input to the neural network may include pre-processing data and/or pre-processing target data. The preprocessing may include a data processing process for inputting data into the neural network. Accordingly, the data structure may include data to be pre-processed and data generated by pre-processing. The above-described data structure is merely an example, and the present disclosure is not limited thereto.

The data structure may include the weights of the neural network. (In this specification, a weight and a parameter may be used interchangeably.) And a data structure including a weight of a neural network may be stored in a computer-readable medium. The neural network may include a plurality of weights. The weight may be variable, and may be changed by the user or algorithm in order for the neural network to perform a desired function. For example, when one or more input nodes are interconnected to one output node by respective links, the output node sets values input to input nodes connected to the output node and links corresponding to the respective input nodes. An output node value may be determined based on the parameter. The above-described data structure is merely an example, and the present disclosure is not limited thereto.

By way of example and not limitation, the weight may include a weight variable in a neural network learning process and/or a weight in which neural network learning is completed. The variable weight in the neural network learning process may include a weight at the start of the learning cycle and/or a variable weight during the learning cycle. The weight for which neural network learning is completed may include a weight for which a learning cycle is completed. Accordingly, the data structure including the weight of the neural network may include a data structure including the weight variable in the neural network learning process and/or the weight in which the neural network learning is completed. Therefore, it is assumed that the above-described weights and/or combinations of weights are included in the data structure including the weights of the neural network. The above-described data structure is merely an example, and the present disclosure is not limited thereto.

The data structure including the weights of the neural network may be stored in a computer-readable storage medium (eg, memory, hard disk) after being serialized. Serialization can be the process of converting a data structure into a form that can be reconstructed and used later by storing it on the same or a different computing device. The computing device may serialize the data structure to send and receive data over the network. A data structure including weights of the serialized neural network may be reconstructed in the same computing device or in another computing device through deserialization. The data structure including the weights of the neural network is not limited to serialization. Furthermore, the data structure including the weights of the neural network is a data structure to increase the efficiency of computation while using the resources of the computing device to a minimum (e.g., B-Tree, Trie, m-way search tree, AVL tree, Red-Black Tree). The foregoing is merely an example, and the present disclosure is not limited thereto.

The data structure may include hyper-parameters of the neural network. In addition, the data structure including the hyperparameters of the neural network may be stored in a computer-readable medium. The hyperparameter may be a variable variable by a user. Hyperparameters are, for example, learning rate, cost function, number of iterations of the learning cycle, weight initialization (e.g., setting the range of weight values subject to weight initialization), Hidden Unit The number (eg, the number of hidden layers, the number of nodes of the hidden layer) may be included. The above-described data structure is merely an example, and the present disclosure is not limited thereto.

6 is a flowchart for generating highlight content related to an embodiment of the present disclosure.

According to an embodiment of the present disclosure, the computing device 100 may receive one or more user response data to the real-time content from one or more user terminals viewing the real-time content ( 510 ).

According to an embodiment of the present disclosure, the computing device 100 may identify one or more highlight sections in real-time content based on the analysis result of the user reaction data ( 520 ).

According to an embodiment of the present disclosure, the computing device 100 may generate highlight content based on one or more highlight sections ( 530 ).

The order of the steps illustrated in the above-described FIG. ~ may be changed if necessary, and at least one or more steps may be omitted or added. That is, the above-described steps are merely embodiments of the present disclosure, and the scope of the present disclosure is not limited thereto.

7 is a diagram illustrating logic for implementing a method for generating highlight content related to an embodiment of the present disclosure.

According to an embodiment of the present disclosure, the computing device 100 may be implemented by the following logic.

According to an embodiment of the present disclosure, the computing device 100 includes a logic 610 for receiving one or more user reaction data for real-time content from one or more user terminals viewing real-time content, and an analysis result of the user reaction data It may include logic 620 for identifying one or more highlight sections in real-time content based on , and logic 630 for generating highlight content based on one or more highlight sections.

In an alternative embodiment, the user reaction data may be based on at least one of a natural language input or an action input of a user viewing the real-time content, each user reaction data may be associated with at least a portion of the real-time content. .

In an alternative embodiment, logic for receiving external reaction data for at least one of the real-time content or related party information of the real-time content, and user reaction data and the external reaction data for the real-time content are related to the real-time content person By storing in association with the information, it may further include logic for generating a knowledge base for the related parties of the real-time content.

In an alternative embodiment, the external reaction data may be reaction data for at least one of the real-time content or a person related to the real-time content, and may be data received through a different channel from the user reaction data for the real-time content. .

In an alternative embodiment, by storing the user reaction data and the external reaction data for the real-time content in association with the relevant information of the real-time content, the logic for generating the knowledge base for the relevant person of the real-time content may include: The knowledge base by extracting external narrative information related to the real-time content or related party information of the real-time content from reaction data, and extracting internal narrative information related to the real-time content or related party information of the real-time content from the user reaction data It may include logic for creating

In an alternative embodiment, the knowledge base is generated for each of the parties of the real-time content, and is used to identify a user reaction unique to each of the parties, and a semantic analysis of the externally narrated information or the internally narrated information. or based on at least one of quantitative analysis.

In an alternative embodiment, the logic for identifying one or more highlight sections in the real-time content based on an analysis result of the user reaction data may include: based on at least one of a semantic analysis or a quantitative analysis of the user reaction data. It may include logic for identifying one or more highlight sections in real-time content.

In an alternative embodiment, the quantitative analysis may be performed based on a threshold determined based at least in part on the number of one or more user terminals viewing the real-time content.

In an alternative embodiment, a logic for pre-processing the user reaction data based on a knowledge base of related information of the real-time content may be further included.

In an alternative embodiment, the user reaction data further includes user reaction user identification information related to the user terminal that has transmitted the user reaction data, and based on a result of analysis of the user reaction data, one or more items in the real-time content The logic for identifying the highlight section includes logic for assigning a weight to each user reaction data based on the user reaction user identification information and an analysis of the user reaction data based on a weight assigned to each of the user reaction data and logic for generating a result and identifying one or more highlight sections in the real-time content based on the analysis result.

In an alternative embodiment, the logic for assigning a weight to each user reaction data based on the user reaction user identification information may include a highlight section prediction contribution rate for real-time content of the user who generated the user reaction data, the user and logic for assigning the weight to each of the user reaction data based on at least one of a viewing contribution rate for content matched with the related information of the real-time content and a matching rate for the knowledge base of the user reaction data. can do.

In an alternative embodiment, the highlight section prediction contribution rate for the real-time content is information on whether user reaction data is included in the pre-produced highlight content in relation to the person concerned with the real-time content, It may be calculated based on at least one of quantitative analysis and semantic analysis of the included user response data.

In an alternative embodiment, the viewing contribution rate for the content matched with the relevant information of the real-time content is information about the participation history of each of the one or more user terminals for the content matched with the relevant information of the real-time content, the matching It may be calculated based on the number of contents and the participation history.

In an alternative embodiment, the matching rate of the user reaction data to the knowledge base may be calculated based on the determination of a similarity between the user reaction data and at least one of external narration information and internal narration information included in the knowledge base. .

According to an embodiment of the present disclosure, logic for implementing the computing device may be implemented by a module, circuit, or means for implementing the computing device.

Those skilled in the art will further appreciate that the various illustrative logical blocks, configurations, modules, circuits, means, logics, and algorithm steps described in connection with the embodiments disclosed herein may be combined with electronic hardware, computer software, or combinations of both. It should be recognized that it can be implemented as To clearly illustrate this interchangeability of hardware and software, various illustrative components, blocks, configurations, means, logics, 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 constraints imposed on the overall system. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present disclosure.

8 shows a simplified, general schematic diagram of an exemplary computing environment in which one embodiment of the present disclosure may be implemented, in conjunction with one embodiment of the present disclosure.

Although the present disclosure has been described above generally in the context of computer-executable instructions that may be executed on one or more computers, those skilled in the art will appreciate that the present disclosure may be implemented in combination with other program modules and/or in a combination of hardware and software. will be.

Generally, program modules include routines, procedures, programs, components, data structures, etc. that perform particular tasks or implement particular abstract data types. In addition, those skilled in the art will appreciate that the methods of the present disclosure are suitable for single-processor or multiprocessor computer systems, minicomputers, mainframe computers as well as personal computers, handheld computing devices, microprocessor-based or programmable consumer electronics, etc. (each of which is It will be appreciated that other computer system configurations may be implemented, including those that may operate in connection with one or more associated devices.

The described embodiments of the present disclosure may also be practiced in distributed computing environments where certain tasks are performed by remote processing devices that are linked through a communications network. In a distributed computing environment, program modules may be located in both local and remote memory storage devices.

Computers typically include a variety of computer-readable media. Any medium accessible by a computer may be a computer-readable medium, and the computer-readable medium may include a computer-readable storage medium and a computer-readable transmission medium. Such computer-readable storage media includes volatile and nonvolatile media, removable and non-removable media. Computer readable storage media includes volatile and nonvolatile media, removable and non-removable media implemented in any method or technology for storage of information such as computer readable instructions, data structures, program modules or other data. A computer-readable storage medium may be RAM, ROM, EEPROM, flash memory or other memory technology, CD-ROM, digital video disk (DVD) or other optical disk storage device, magnetic cassette, magnetic tape, magnetic disk storage device, or other magnetic storage device. device, or any other medium that can be accessed by a computer and used to store the desired information.

Computer-readable transmission media typically embody computer-readable instructions, data structures, program modules, or other data in a modulated data signal, such as a carrier wave or other transport mechanism. information delivery media. The term modulated data signal means a signal in which one or more of the characteristics of the signal is set or changed so as to encode information in the signal. By way of example, and not limitation, computer-readable transmission media includes wired media such as a wired network or direct-wired connection, and wireless media such as acoustic, RF, infrared, and other wireless media. Combinations of any of the above are also intended to be included within the scope of computer-readable transmission media.

An example environment 1100 implementing various aspects of the disclosure is shown including a computer 1102 , the computer 1102 including a processing unit 1104 , a system memory 1106 , and a system bus 1108 . do. The system bus 1108 couples system components, including but not limited to system memory 1106 , to the processing device 1104 . The processing device 1104 may be any of a variety of commercially available processors. Dual processor and other multiprocessor architectures may also be used as processing unit 1104 .

The system bus 1108 may be any of several types of bus structures that may further interconnect a memory bus, a peripheral bus, and a local bus using any of a variety of commercial bus architectures. System memory 1106 includes read only memory (ROM) 1110 and random access memory (RAM) 1112 . A basic input/output system (BIOS) is stored in non-volatile memory 1110, such as ROM, EPROM, EEPROM, etc., which is the basic input/output system (BIOS) that helps transfer information between components within computer 1102, such as during startup. contains routines. RAM 1112 may also include high-speed RAM, such as static RAM, for caching data.

The computer 1102 may also be configured with an internal hard disk drive (HDD) 1114 (eg, EIDE, SATA) - this internal hard disk drive 1114 may also be configured for external use within a suitable chassis (not shown). Yes—a magnetic floppy disk drive (FDD) 1116 (eg, for reading from or writing to removable diskette 1118), and an optical disk drive 1120 (eg, a CD-ROM) for reading from, or writing to, disk 1122, or other high capacity optical media such as DVD. The hard disk drive 1114 , the magnetic disk drive 1116 , and the optical disk drive 1120 are connected to the system bus 1108 by the hard disk drive interface 1124 , the magnetic disk drive interface 1126 , and the optical drive interface 1128 , respectively. ) can be connected to The interface 1124 for implementing an external drive includes at least one or both of Universal Serial Bus (USB) and IEEE 1394 interface technologies.

These drives and their associated computer-readable media provide non-volatile storage of data, data structures, computer-executable instructions, and the like. In the case of computer 1102, drives and media correspond to storing any data in a suitable digital format. Although the description of computer readable media above refers to HDDs, removable magnetic disks, and removable optical media such as CDs or DVDs, those skilled in the art will use zip drives, magnetic cassettes, flash memory cards, cartridges, etc. It will be appreciated that other tangible computer-readable media such as etc. may also be used in the exemplary operating environment and any such media may include computer-executable instructions for performing the methods of the present disclosure.

A number of program modules may be stored in the drive and RAM 1112 , including an operating system 1130 , one or more application programs 1132 , other program modules 1134 , and program data 1136 . All or portions of the operating system, applications, modules, and/or data may also be cached in RAM 1112 . It will be appreciated that the present disclosure may be implemented in various commercially available operating systems or combinations of operating systems.

A user may enter commands and information into the computer 1102 via one or more wired/wireless input devices, for example, a pointing device such as a keyboard 1138 and a mouse 1140 . Other input devices (not shown) may include a microphone, IR remote control, joystick, game pad, stylus pen, touch screen, and the like. Although these and other input devices are often connected to the processing unit 1104 through an input device interface 1142 that is connected to the system bus 1108, parallel ports, IEEE 1394 serial ports, game ports, USB ports, IR interfaces, It may be connected by other interfaces, etc.

A monitor 1144 or other type of display device is also coupled to the system bus 1108 via an interface, such as a video adapter 1146 . In addition to the monitor 1144, the computer typically includes other peripheral output devices (not shown), such as speakers, printers, and the like.

Computer 1102 may operate in a networked environment using logical connections to one or more remote computers, such as remote computer(s) 1148 via wired and/or wireless communications. Remote computer(s) 1148 may be workstations, server computers, routers, personal computers, portable computers, microprocessor-based entertainment devices, peer devices, or other common network nodes, and are generally Although including many or all of the components described, only memory storage device 1150 is shown for simplicity. The logical connections shown include wired/wireless connections to a local area network (LAN) 1152 and/or a larger network, eg, a wide area network (WAN) 1154 . Such LAN and WAN networking environments are common in offices and companies, and facilitate enterprise-wide computer networks, such as intranets, all of which can be connected to a worldwide computer network, for example, the Internet.

When used in a LAN networking environment, the computer 1102 is connected to the local network 1152 through a wired and/or wireless communication network interface or adapter 1156 . Adapter 1156 may facilitate wired or wireless communication to LAN 1152 , which also includes a wireless access point installed therein for communicating with wireless adapter 1156 . When used in a WAN networking environment, the computer 1102 may include a modem 1158 , connected to a communication server on the WAN 1154 , or otherwise establishing communications over the WAN 1154 , such as over the Internet. have the means A modem 1158 , which may be internal or external and a wired or wireless device, is coupled to the system bus 1108 via a serial port interface 1142 . In a networked environment, program modules described for computer 1102 , or portions thereof, may be stored in remote memory/storage device 1150 . It will be appreciated that the network connections shown are exemplary and other means of establishing a communication link between the computers may be used.

Computer 1102 may be associated with any wireless device or object that is deployed and operates in wireless communication, for example, printers, scanners, desktop and/or portable computers, portable data assistants (PDAs), communications satellites, wireless detectable tags. It operates to communicate with any device or place, and phone. This includes at least Wi-Fi and Bluetooth wireless technologies. Accordingly, the communication may be a predefined structure as in a conventional network or may simply be an ad hoc communication between at least two devices.

Wi-Fi (Wireless Fidelity) makes it possible to connect to the Internet, etc. without a wire. Wi-Fi is a wireless technology such as cell phones that allows these devices, eg, computers, to transmit and receive data indoors and outdoors, ie anywhere within range of a base station. Wi-Fi networks use a radio technology called IEEE 802.11 (a, b, g, etc.) to provide secure, reliable, and high-speed wireless connections. Wi-Fi can be used to connect computers to each other, to the Internet, and to wired networks (using IEEE 802.3 or Ethernet). Wi-Fi networks may operate in unlicensed 2.4 and 5 GHz radio bands, for example, at 11 Mbps (802.11a) or 54 Mbps (802.11b) data rates, or in products that include both bands (dual band). there is.

Those of ordinary skill in the art of the present disclosure will recognize that the various illustrative logical blocks, modules, processors, means, circuits, and algorithm steps described in connection with the embodiments disclosed herein include electronic hardware, (convenience For this purpose, it will be understood that it may be implemented by various forms of program or design code (referred to herein as "software") or a combination 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 constraints imposed on the overall system. A person of ordinary skill in the art of the present disclosure may implement the described functionality in various ways for each specific application, but such implementation decisions should not be interpreted as a departure from the scope of the present disclosure.

The various embodiments presented herein may be implemented as methods, apparatus, or articles of manufacture using standard programming and/or engineering techniques. The term “article of manufacture” includes a computer program, carrier, or media accessible from any computer-readable device. For example, computer-readable media include magnetic storage devices (eg, hard disks, floppy disks, magnetic strips, etc.), optical disks (eg, CDs, DVDs, etc.), smart cards, and flash memory. devices (eg, EEPROMs, cards, sticks, key drives, etc.). Also, various storage media presented herein include one or more devices and/or other machine-readable media for storing information. The term “machine-readable medium” includes, but is not limited to, wireless channels and various other media capable of storing, holding, and/or carrying instruction(s) and/or data.

It is understood that the specific order or layer structure of the steps in the presented processes is an example of exemplary approaches. Based on design priorities, it is understood that the specific order or layer structure of the steps in the processes may be rearranged within the scope of the present disclosure. The appended method claims present elements of the various steps in a sample order, but are not meant to be limited to the specific order or layer structure presented.

The description of the presented embodiments is provided to enable any person skilled in the art to make or use the present disclosure. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the generic principles defined herein may be applied to other embodiments without departing from the scope of the present disclosure. Thus, the present disclosure is not intended to be limited to the embodiments presented herein, but is to be construed in the widest scope consistent with the principles and novel features presented herein.

Claims (16)

A computer program stored in a computer-readable storage medium, wherein the computer program, when executed on one or more processors, causes the one or more processors to perform the following operations for automatic highlight image editing, the operations comprising:
receiving one or more user response data for the real-time content from one or more user terminals viewing the real-time content;
identifying one or more highlight sections in the real-time content based on a result of analyzing the user reaction data; and
generating highlight content based on the one or more highlight sections;
including,
The operation of identifying one or more highlight sections in the real-time content based on the analysis result of the user reaction data includes:
At least one of a highlight section prediction contribution rate for the real-time content of the user who generated the user reaction data, a viewing contribution rate for the content matched with the related information of the real-time content of the user, and a matching rate for the knowledge base of the user reaction data assigning a weight to each of the user response data based on and
generating an analysis result for the user reaction data based on a weight assigned to each user reaction data, and identifying one or more highlight sections in the real-time content based on the analysis result;
comprising,
A computer program stored on a computer-readable storage medium.
The method of claim 1,
The user reaction data is
based on at least one of a natural language input or an action input of a user viewing the real-time content, each user reaction data being associated with at least a portion of the real-time content,
A computer program stored on a computer-readable storage medium.

The method of claim 1,
receiving external response data for at least one of the real-time content and information on related parties of the real-time content; and
generating a knowledge base on the related party of the real-time content by storing the user reaction data and the external response data for the real-time content in association with the related party information of the real-time content;
further comprising,
A computer program stored on a computer-readable storage medium.
4. The method of claim 3,
The external reaction data is
response data for at least one of the real-time content or a person concerned with the real-time content, which is data received through a different channel from the user response data for the real-time content,
A computer program stored on a computer-readable storage medium.
4. The method of claim 3,
The operation of generating a knowledge base for the related party of the real-time content by storing the user reaction data and the external reaction data for the real-time content in association with the related party information of the real-time content includes:
By extracting, from the external reaction data, external narration information related to the real-time content or related party information of the real-time content, and extracting internal narration information related to the related party information of the real-time content or the real-time content from the user reaction data, the creating a knowledge base;
comprising,
A computer program stored on a computer-readable storage medium.
6. The method of claim 5,
The knowledge base is
Generated for each of the parties of the real-time content, used to identify a user reaction unique to each of the parties, and generated based on at least one of semantic analysis or quantitative analysis of the external narration information or the internal narration information ,
A computer program stored on a computer-readable storage medium.
The method of claim 1,
The operation of identifying one or more highlight sections in the real-time content based on the analysis result of the user reaction data includes:
identifying one or more highlight sections in the real-time content based on at least one of semantic analysis or quantitative analysis of the user reaction data;
comprising,
A computer program stored on a computer-readable storage medium.
8. The method of claim 7,
The quantitative analysis is
performed based on a threshold determined based at least in part on the number of one or more user terminals viewing the real-time content,
A computer program stored on a computer-readable storage medium.
The method of claim 1,
pre-processing the user response data based on a knowledge base on related information of the real-time content;
further comprising,
A computer program stored on a computer-readable storage medium.
The method of claim 1,
The user reaction data is
User reaction user identification information related to the user terminal that has transmitted the user reaction data;
further comprising,
A computer program stored on a computer-readable storage medium.
delete The method of claim 1,
The highlight section prediction contribution rate for the real-time content is,
Information on whether user reaction data is included in the pre-produced highlight content in relation to the person involved in the real-time content, based on at least one of quantitative analysis and semantic analysis of user reaction data included in the pre-produced highlight content calculated by
A computer program stored on a computer-readable storage medium.
The method of claim 1,
The viewing contribution rate for the content matched with the relevant information of the real-time content is,
Information on the participation history of each of the one or more user terminals for the content matched with the related information of the real-time content, calculated based on the number of the matched content and the participation history,
A computer program stored on a computer-readable storage medium.
The method of claim 1,
The matching rate for the knowledge base of the user response data is,
It is calculated based on the determination of the degree of similarity between the user response data and at least one of the external narration information and the internal narration information included in the knowledge base,
A computer program stored on a computer-readable storage medium.
A method of automatically editing a highlight image performed by one or more processors of a computing device, the method comprising:
Receiving one or more user reaction data for the real-time content from one or more user terminals viewing the real-time content;
identifying one or more highlight sections in the real-time content based on an analysis result of the user reaction data; and
generating highlight content based on the one or more highlight sections;
including,
The step of identifying one or more highlight sections in the real-time content based on the analysis result of the user reaction data,
At least one of a highlight section prediction contribution rate for the real-time content of the user who generated the user reaction data, a viewing contribution rate for the content matched with the related information of the real-time content of the user, and a matching rate for the knowledge base of the user reaction data assigning a weight to each of the user response data based on ; and
generating an analysis result for the user reaction data based on a weight assigned to each user reaction data, and identifying one or more highlight sections in the real-time content based on the analysis result;
containing,
A method of automatically editing a highlight video performed on one or more processors of a computing device.
As a server that automatically edits highlight video,
a processor including one or more cores;
a memory containing program codes executable by the processor; and
a network unit for transmitting and receiving data to and from a user terminal and an external server;
including, and
The processor is
Receive one or more user reaction data for the real-time content from one or more user terminals viewing real-time content,
Identifies one or more highlight sections in the real-time content based on the analysis result of the user reaction data, and
The highlight content is generated based on the one or more highlight sections, and the highlight section prediction contribution rate for the real-time content of the user who generated the user response data, the user's viewing contribution rate for the content matched with the related information of the real-time content, and assigning a weight to each of the user reaction data based on at least one of matching rates for the knowledge base of the user reaction data; and
generating an analysis result for the user reaction data based on a weight assigned to each user reaction data, and identifying one or more highlight sections in the real-time content based on the analysis result,
A server that automatically edits the highlight video.

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