KR102593763B1 - Method and server for guiding action of internet broadcasting host based on reaction of listeners - Google Patents

Abstract

A server that guides the broadcasting direction of a broadcasting host based on listener responses is disclosed. This server includes a server communication unit that communicates with a host terminal and a listener terminal of a voice-based live Internet broadcast, a memory storing broadcast data; and a server processor including one or more cores. The server processor includes a collection module that collects the listener's reaction information, an analysis module that analyzes the listener's emotion and participation based on the listener's reaction information collected by the collection module, and a host according to the analyzed listener's emotion and participation. It may include a progress guidance module that guides the direction of the broadcast. By providing this server, the host's broadcast can be performed more smoothly and user convenience can be improved.

Description

Method for inducing the broadcasting direction of a broadcasting host based on listener response and a server for the same {METHOD AND SERVER FOR GUIDING ACTION OF INTERNET BROADCASTING HOST BASED ON REACTION OF LISTENERS}

This disclosure relates to a voice-based live Internet broadcasting service. More specifically, the present disclosure relates to a method for inducing a broadcasting host's broadcasting direction based on listener responses and a server for the same.

The content described below simply provides background information related to this embodiment and does not constitute prior art.

With the recent rapid spread of the Internet in the past terrestrial-centered broadcasting system, viewer demand for platforms that support broadcasting services through the Internet has increased explosively, and Internet-based streaming services are being actively provided, and video-based streaming services are being actively provided. In addition to streaming services, audio-based streaming services are also steadily growing.

In addition, voice-based live Internet broadcasting is being actively conducted in response to the needs of listeners who want to hear the host's voice directly and communicate in real time, as well as broadcasts recorded in audio-based streaming services. In voice-based live Internet broadcasting, the broadcast host broadcasts based on audio, and real-time communication takes place through messenger chatting and multi-party live calls.

Hosts who conduct voice-based live Internet broadcasts are single or small in number and broadcast while actually speaking, so they are sensitive to listeners' reactions, omit content that needs to be taken care of during the broadcast, or do not listen to what the listener wants. There is a problem where the broadcast flow can be missed.

Accordingly, there is a need for a method to assist the broadcast host to make broadcasting proceed more smoothly.

Registered Patent Publication No. 10-244905 (Registration date: 2022.09.14)

The purpose of the embodiment disclosed in the present disclosure is to provide a method of inducing a broadcasting host's direction of broadcasting based on listener responses in voice-based live Internet broadcasting.

The problems to be solved by the present disclosure are not limited to the problems mentioned above, and other problems not mentioned can be clearly understood by those skilled in the art from the description below.

The server that guides the broadcasting direction of the broadcasting host based on the listener response according to the present disclosure to achieve the above-described technical problem includes: a server communication unit that communicates with the host terminal and the listener terminal of the voice-based live Internet broadcast; A memory in which broadcast data is stored; and a server processor including one or more cores.

The server processor includes a collection module that collects listener response information; an analysis module that analyzes the listener's emotions and participation based on the listener's reaction information collected by the collection module; and a progress guidance module that guides the host's broadcast direction according to the analyzed listener's emotions and participation.

The server processor may further include a grouping module that groups the listeners and assigns labels to each group based on the analyzed emotions and participation levels of the listeners.

The progress guidance module may be configured to guide the host's broadcast progress direction in a customized manner for each group.

The server processor may further include a monitoring module that monitors the host's compliance with the guided broadcast direction.

The server processor may be configured to determine the appropriateness of the guided broadcast direction through listener feedback when the host acts compliantly.

During the live Internet broadcast, if there is an inquiry from a listener in an Internet call or chat window involving the host, the server processor lists the inquiry and provides it to the host terminal through the server communication unit, and selects the inquiry from the inquiry list. It may be configured to provide inquiries excluding inquiries that have been answered to the host terminal at a predetermined period.

The server processor may be configured to merge inquiries that are duplicated or have a predetermined similarity range among listener inquiries, sort inquiries with the highest frequency of inquiries, and provide the inquiries to the host terminal through the server communication unit.

The server processor provides data on the analyzed emotions and participation of the listener to the host terminal through the server communication unit so that the data on the analyzed emotions and participation of the listener are visually displayed on the host terminal. It can be configured to do so.

In addition, a method of inducing a broadcasting direction of a broadcasting host based on a listener response performed by a computing device according to the present disclosure for achieving the above-described technical problem includes collecting response information of the listener; Analyzing the listener's emotions and participation based on the collected response information of the listener; And it may include guiding the host's direction of broadcasting according to the analyzed listener's emotions and participation.

The derivation method may further include, after the analyzing step, grouping the listeners and assigning labels to each group based on the analyzed emotions and participation of the listeners.

The guiding step may include guiding the host's broadcast direction in a customized manner for each group.

The guidance method includes, after the guiding step, monitoring whether the host is compliant with the guided broadcast direction; And when the host behaves compliantly, the method may further include determining the appropriateness of the guided broadcasting direction through listener feedback.

The induction method includes the following steps: during a voice-based live Internet broadcast, if a listener has an inquiry in an Internet call or chat window involving the host, listing the inquiry and providing the inquiry to the host terminal; and providing inquiries excluding answers that have been answered from the inquiry list to the host terminal at a predetermined period.

In addition to this, a computer program stored in a computer-readable recording medium may be further provided to execute the method for implementing the present disclosure.

In addition, a computer-readable recording medium recording a computer program for executing a method for implementing the present disclosure may be further provided.

According to the means for solving the above-described problem of the present disclosure, the direction of the host's broadcast can be guided during voice-based live Internet broadcasting, so the burden of the host's broadcast can be somewhat alleviated, and listeners can also be encouraged to participate in the broadcast. .

The effects of the present disclosure are not limited to the effects mentioned above, and other effects not mentioned may be clearly understood by those skilled in the art from the description below.

1 is a diagram schematically illustrating a voice-based live Internet broadcasting system according to the present disclosure.
FIG. 2 is a relative block diagram showing the configurations of a server and a host terminal that guide the broadcasting host's broadcasting direction based on listener responses according to the present disclosure.
Figure 3 is a sequence diagram showing the processing process of the server that guides the broadcasting direction of the broadcasting host according to the present disclosure.
FIG. 4 is a diagram illustrating a processing process when learning and applying an emotion analysis model according to the present disclosure.
FIG. 5 is a diagram illustrating a method in which a server that guides the broadcasting direction of a broadcasting host according to the present disclosure guides the broadcasting progress based on listener responses.

Like reference numerals refer to like elements throughout this disclosure. The present disclosure does not describe all elements of the embodiments, and general content or overlapping content between the embodiments in the technical field to which the present disclosure pertains is omitted. The term 'unit, module, member, block' used in the specification may be implemented as software or hardware, and depending on the embodiment, a plurality of 'unit, module, member, block' may be implemented as a single component, or It is also possible for one 'part, module, member, or block' to include multiple components.

Throughout the specification, when a part is said to be “connected” to another part, this includes not only direct connection but also indirect connection, and indirect connection includes connection through a wireless communication network. do.

Additionally, when a part "includes" a certain component, this means that it may further include other components rather than excluding other components, unless specifically stated to the contrary.

Throughout the specification, when a member is said to be located “on” another member, this includes not only cases where a member is in contact with another member, but also cases where another member exists between the two members.

Terms such as first and second are used to distinguish one component from another component, and the components are not limited by the above-mentioned terms.

Singular expressions include plural expressions unless the context clearly makes an exception.

The identification code for each step is used for convenience of explanation. The identification code does not explain the order of each step, and each step may be performed differently from the specified order unless a specific order is clearly stated in the context. there is.

Hereinafter, the operating principle and embodiments of the present disclosure will be described with reference to the attached drawings.

In this specification, the 'server that guides the broadcasting direction of the broadcasting host according to the present disclosure' can be implemented not only by a cloud system but also by various devices that can perform computational processing and provide results to users. For example, the server that guides the broadcasting direction of the broadcasting host according to the present disclosure may include all of a computer, a server device, and a portable terminal, or may be in any one form.

Here, the computer may include, for example, a laptop, desktop, laptop, tablet PC, slate PC, etc. equipped with a web browser.

The server device is a server that processes information by communicating with external devices, and may include an application server, computing server, database server, file server, game server, mail server, proxy server, and web server.

The portable terminal is, for example, a wireless communication device that guarantees portability and mobility, such as PCS (Personal Communication System), GSM (Global System for Mobile communications), PDC (Personal Digital Cellular), PHS (Personal Handyphone System), and PDA. (Personal Digital Assistant), IMT (International Mobile Telecommunication)-2000, CDMA (Code Division Multiple Access)-2000, W-CDMA (W-Code Division Multiple Access), WiBro (Wireless Broadband Internet) terminal, smart phone ), all types of handheld wireless communication devices, and wearable devices such as watches, rings, bracelets, anklets, necklaces, glasses, contact lenses, or head-mounted-device (HMD). may include.

Functions related to artificial intelligence according to the present disclosure are operated through a processor and memory. The processor may consist of one or multiple processors. At this time, one or more processors may be a general-purpose processor such as a CPU, AP, or DSP (Digital Signal Processor), a graphics-specific processor such as a GPU or VPU (Vision Processing Unit), or an artificial intelligence-specific processor such as an NPU. One or more processors control input data to be processed according to predefined operation rules or artificial intelligence models stored in memory. Alternatively, when one or more processors are dedicated artificial intelligence processors, the artificial intelligence dedicated processors may be designed with a hardware structure specialized for processing a specific artificial intelligence model.

Predefined operation rules or artificial intelligence models are characterized by being created through learning. Here, created through learning means that a basic artificial intelligence model is learned using a large number of learning data by a learning algorithm, thereby creating a predefined operation rule or artificial intelligence model set to perform the desired characteristics (or purpose). It means burden. This learning may be accomplished in the device itself that performs artificial intelligence according to the present disclosure, or may be accomplished through a separate server and/or system. Examples of learning algorithms include supervised learning, unsupervised learning, semi-supervised learning, or reinforcement learning, but are not limited to the examples described above.

An artificial intelligence model may be composed of multiple neural network layers. Each of the plurality of neural network layers has a plurality of weight values, and neural network calculation is performed through calculation between the calculation result of the previous layer and the plurality of weights. Multiple weights of multiple neural network layers can be optimized by the learning results of the artificial intelligence model. For example, a plurality of weights may be updated so that loss or cost values obtained from the artificial intelligence model are reduced or minimized during the learning process. Artificial neural networks may include deep neural networks (DNN), for example, Convolutional Neural Network (CNN), Deep Neural Network (DNN), Recurrent Neural Network (RNN), Restricted Boltzmann Machine (RBM), Deep Belief Network (DBN), Bidirectional Recurrent Deep Neural Network (BRDNN), or Deep Q-Networks, etc., but are not limited to the examples described above.

The processor creates a neural network, trains or learns a neural network, performs calculations based on received input data, generates an information signal based on the results, or generates a neural network. The network can be retrained.

Neural networks include CNN (Convolutional Neural Network), RNN (Recurrent Neural Network), perceptron, multilayer perceptron, FF (Feed Forward), RBF (Radial Basis Network), DFF (Deep Feed Forward), and LSTM. (Long Short Term Memory), GRU (Gated Recurrent Unit), AE (Auto Encoder), VAE (Variational Auto Encoder), DAE (Denoising Auto Encoder), SAE (Sparse Auto Encoder), MC (Markov Chain), HN (Hopfield) Network), BM (Boltzmann Machine), RBM (Restricted Boltzmann Machine), DBN (Depp Belief Network), DCN (Deep Convolutional Network), DN (Deconvolutional Network), DCIGN (Deep Convolutional Inverse Graphics Network), GAN (Generative Adversarial Network) ), Liquid State Machine (LSM), Extreme Learning Machine (ELM), Echo State Network (ESN), Deep Residual Network (DRN), Differential Neural Computer (DNC), Neural Turning Machine (NTM), Capsule Network (CN), Those skilled in the art will understand that it may include any neural network, including, but not limited to, KN (Kohonen Network) and AN (Attention Network).

According to an exemplary embodiment of the present disclosure, the processor may support a Convolution Neural Network (CNN), a Region with Convolution Neural Network (R-CNN), a Region Proposal Network (RPN), a Recurrent Neural Network (RNN), such as GoogleNet, AlexNet, VGG Network, etc. ), S-DNN (Stacking-based deep Neural Network), S-SDNN (State-Space Dynamic Neural Network), Deconvolution Network, DBN (Deep Belief Network), RBM (Restrcted Boltzman Machine), Fully Convolutional Network, LSTM (Long Short-Term Memory) Network, Classification Network, Generative Modeling, eXplainable AI, Continual AI, Representation Learning, AI for Material Design, BERT for natural language processing, SP-BERT, MRC/QA, Text Analysis, Dialog System, GPT-3 , GPT-4, Visual Analytics for vision processing, Visual Understanding, Video Synthesis, and Anomaly Detection, Prediction, Time-Series Forecasting, Optimization, Recommendation, and Data Creation for ResNet data intelligence. , but is not limited to this. Hereinafter, embodiments of the present disclosure will be described in detail with reference to the attached drawings.

1 is a diagram schematically illustrating a voice-based live Internet broadcasting system 1000 according to the present disclosure.

The live Internet broadcasting system 1000 may include components necessary to provide voice-based live Internet broadcasting. The live Internet broadcasting system 1000 guides the broadcasting host's broadcasting direction based on listener responses. a server (100, hereinafter referred to as “broadcast direction guidance server”), a host terminal (200) corresponding to the host (H) of the live Internet broadcast, and a listener terminal (300) corresponding to the listener (L) of the live Internet broadcast. ) may include.

The broadcast direction guidance server 100 receives broadcast data for voice-based live Internet broadcasting from the host terminal 200 and provides it to the listener terminal 300, so that the live Internet broadcast is provided to the listener (L). (S11).

In an embodiment, the broadcast direction guidance server 100 may be implemented not only as a server/client server, but also as a cloud. In this case, the broadcast direction guidance server 100 may be equipped with storage, servers, virtualization modules/systems, operating systems, middleware, runtime modules, data, and applications. Accordingly, the host terminal 200 and the listener terminal 300 of the client side can use various functions provided by the cloud system as long as they are equipped with a service application, and can be charged according to the amount of usage. For example, the broadcast direction guidance server 100 may be implemented based on SaaS (Server as a software), but the embodiment is not limited to this.

The broadcast direction guidance server 100 can provide messenger and multi-party Internet call functions to the host terminal (H) and the listener terminal (L) so that the host (H) and the listener (L) can communicate (S13) , S15).

The broadcast direction guidance server 100 collects the listener's reaction information, analyzes the listener's emotion and participation level based on the listener's reaction information collected by the collection module, and analyzes the listener's emotion and participation level according to the analyzed listener's emotion and participation level. The direction of the host's broadcast can be guided (S17).

FIG. 2 is a relative block diagram showing the configurations of the broadcast direction guidance server 100 and the host terminal 200 according to the present disclosure.

First, the broadcast direction guidance server 100 according to the present disclosure may include a server communication unit 110, a server input unit 120, a server memory 150, and at least one server processor 190. The components of the broadcast direction guidance server 100 shown in FIG. 2 are not essential for implementing the broadcast direction guidance server 100 according to the present disclosure, so the broadcast direction guidance server 100 described in this specification ) may have more or fewer components than those listed above.

Among the above components, the server communication unit 110 may include one or more components that enable communication with external devices (e.g., the host terminal 200 and the listener terminal 300 of voice-based live Internet broadcasting). , For example, it may include at least one of a broadcast reception module, a wired communication module, a wireless communication module, a short-range communication module, and a location information module. The server communication unit 110 can communicate with the host terminal 200 and the listener terminal 300 of a voice-based live Internet broadcast.

The server input unit 120 is for inputting image information (or signal), audio information (or signal), data, or information input from a user, and includes at least one of at least one camera, at least one microphone, and a user input unit. It can be included. Voice data or image data collected from the server input unit 120 may be analyzed and processed as a user's control command.

The server memory 150 can store data supporting various functions of the broadcasting direction guidance server 100 and a program for the operation of the control unit, and can store input/output data (e.g., music files, still images, etc.). , video, etc.), a plurality of application programs (application programs or applications) running on the broadcasting direction guidance server 100, data for operation of the server, and commands can be stored. At least some of these applications may be downloaded from an external server via wireless communication.

The server memory 150 is of flash memory type, hard disk type, solid state disk type, SDD type (Silicon Disk Drive type), and multimedia card micro type. card micro type), card type memory (e.g. SD or XD memory, etc.), random access memory (RAM), static random access memory (SRAM), read-only memory (ROM), EEPROM (electrically It may include at least one type of storage medium among erasable programmable read-only memory (PROM), programmable read-only memory (PROM), magnetic memory, magnetic disk, and optical disk. In addition, the server memory 150 is separate from the broadcast direction guidance server 100, but may be a database connected by wire or wirelessly, and may be implemented as a database system.

The server memory 150 can store broadcast data and can store an emotion analysis model 151. The emotion analysis model 151 may be a model whose learning has been completed, and may be saved at any time even during training.

The server processor 190 includes at least one core, a memory for storing data about an algorithm for controlling the operation of components within the broadcast direction guidance server 100 or a program that reproduces the algorithm, and data stored in the memory. It can be implemented with at least one processor (not shown) that performs the above-described operations using. At this time, the memory and the processor can each be implemented as separate chips. Alternatively, the memory and the processor can be implemented as a single chip. It may be possible.

The server processor 190 includes a collection module 191 that collects the listener's reaction information, an analysis module 193 that analyzes the listener's emotions and participation based on the listener's reaction information collected by the collection module, and an analysis module 193 that analyzes the listener's reaction information. It may include a progress guidance module 195 that guides the direction of the host's broadcast according to the listener's emotions and participation level.

In addition, the server processor 190 includes a grouping module 197 that groups listeners and assigns labels to each group based on the analyzed emotions and participation of the listeners, and monitors the host's compliance with the guided broadcast direction. It may further include a monitoring module 199.

In addition, the server processor 190 includes one or more of the components described above in order to implement various embodiments according to the present disclosure described in FIGS. 3 to 5 below on the broadcast direction guidance server 100. They can be controlled by combining them.

The host terminal 200 shown in FIG. 2 may include a terminal communication unit 210, a terminal input unit 220, a terminal display 230, a terminal memory 250, and a terminal processor 290. The components of the host terminal 200 shown in FIG. 2 are not essential for implementing the host terminal 200 according to the present disclosure, so the host terminal 200 described in this specification has more components than the components listed above. It may have many or few components.

To explain the configurations and differences of the broadcasting direction guidance server 100, the terminal display 230 can output messenger chat for voice-based live Internet broadcasting and can output various interfaces for live Internet broadcasting. You can.

In an embodiment, the terminal processor 290 may include a filtering module that recognizes and filters the content of the speech of the host (H), and the filtering module can select the content of the speech of the host (H) and the listener (L) through speech to speech (STT). Using the text) technique, the voice can be converted into text and the filtering target phrase (slang or swear words, etc.) can be highlighted and displayed, and the filtering target phrase can be processed (e.g., in the voice signal of the host (H) received). There are ways to replace it with a specific sound or lower the output, but the embodiment is not limited to this) and output it or provide it to the broadcasting direction guidance server 100.

At least one component may be added or deleted in response to the performance of the components shown in FIG. 2. Additionally, it will be easily understood by those skilled in the art that the mutual positions of the components may be changed in response to the performance or structure of the system.

Meanwhile, each component shown in FIG. 2 refers to software and/or hardware components such as Field Programmable Gate Array (FPGA) and Application Specific Integrated Circuit (ASIC).

Figure 3 is a sequence diagram showing the processing process of the broadcast direction guidance server 100 according to the present disclosure. FIG. 4 is a diagram for explaining the processing process when learning/applying the emotion analysis model 153 according to the present disclosure, and FIG. 5 shows the broadcasting direction guidance server 100 according to the present disclosure performing broadcasting based on listener responses. This is a drawing to explain a method of guiding progress "??*. While explaining FIG. 3, necessary parts will be explained with reference to FIGS. 4 and 5 together.

The collection module 191 of the server processor 190 may collect listener reaction information (S310).

Here, the listener's reaction information is derived from the listener's (L) reaction to the host's (H) voice, the listener's reaction to the music provided by the host, and recorded conversation records (chat records, text conversation records, etc.). It may include information related to the extracted listener's response, and the server processor 190 can collect it directly from the listener terminal 300.

At this time, the server processor 190 may analyze the biometric data collected through the terminal sensing unit 330 of the listener terminal 300 and determine the response of the listener (L).

In addition, the server processor 190 can convert the listener's chat content and Internet call utterance into text by applying natural language processing (NLP) or a speech to text (STT) algorithm. In the case of the natural language processing algorithm, a sequence-to-sequence model It may include a sequence to sequence model, a deep learning model, etc., and a bag of words model, a stopword removal model, a stemming model, and a lemma extraction model may be applied, but the embodiment is not limited thereto.

After the listener's reaction information is collected in step S310, the analysis module 193 of the server processor 190 may analyze the listener's emotions and participation based on the listener's reaction information collected by the collection module. (S320).

Referring to FIG. 4, the analysis module 193 converts the listener's reaction information into text in a chat window or Internet call and inputs it into the emotion analysis model 151, and the emotion analysis model 151 outputs the listener's emotional state. You can. The emotion analysis model 151 may be a pre-trained model based on a neural network.

In an embodiment, the emotion analysis model 151 is based on textual keywords and the listener's biometric data collected from the listener terminal 300 (e.g., heart rate, electrocardiogram, blood pressure, pulse, sugar level, etc.) to identify the listener. Emotional states can be analyzed. The emotion analysis model 151 can analyze and output emotional states such as joy, sadness, boredom, fun, and anger. The server processor 190 may separately store the analyzed specific emotional state in the server memory 150.

In addition, the analysis module 193 can utilize various indicators to measure listener participation. The analysis module 193 analyzes listeners based on the amount of chat activity, number of sponsorships, broadcast viewing time, number of shares, number of likes, etc. The degree of participation can be determined, but the embodiment is not limited to this.

After step S320, the grouping module 195 may group listeners and assign labels to each group based on the analyzed emotions and participation of the listeners.

The grouping module 195 may group listeners using a K-means algorithm, DBSCAN (density based spatial clustering of application with noise) algorithm, hierarchical clustering algorithm, etc.

Here, the K-means algorithm is an algorithm that groups given data into K clusters, and operates by minimizing the variance of the distance difference between each cluster. The DBSCAN algorithm is a density-based clustering method among clustering algorithms, and an instrumental algorithm. The clustering algorithm can be said to be an algorithm that reduces the number of clusters by selecting the two clusters with the highest similarity or the closest distance among several clusters and combining them into one.

For example, the grouping module 195 creates excitement among groups with high participation, small groups with a sponsorship amount of more than a certain amount among groups with high participation, a group with low participation, and small groups with low participation but with a sponsorship amount of more than a certain amount. Listeners can be grouped into good groups, cynical groups, etc., but grouping of emotions/participation is not limited to the above-described embodiments.

At this time, when determining the degree of participation, degree of excitement, and degree of cynicism, the grouping module 195 may set standard levels for participation and emotion for each field. That is, the grouping module 195 can determine participation and emotions based on different criteria depending on the type of broadcast channel (eg, game, blind date, music, sports, etc.). To this end, the grouping module 195 may use an artificial intelligence-based reference level determination model that sets a reference level to determine emotion and participation according to the type of broadcast channel. The grouping module 195 can determine the standard level of a group with low participation, the standard level of a happy state, the standard level of a sad state, the standard level of excitement, and the standard level of cynicism using a standard level determination model. .

Here, the standard level determination model receives broadcast channel type information, chat record information, Internet call conversation record information, listener biometric information, etc., and can determine the standard level of emotion and participation for each broadcast channel.

Additionally, when grouping listeners based on emotion and participation, the server processor 190 may perform all of the above-described grouping algorithms and then select a grouping algorithm that produces clear results. For example, the server processor 190 may select a grouping algorithm by evaluating the degree of grouping based on the number of groups, a situation where the groups are far apart, etc., and use data grouped by the selected grouping algorithm.

After step S330, the progress guidance module 195 of the server processor 190 may guide the host's broadcast direction according to the analyzed listener's emotions and participation (S340).

At this time, the progress guidance module 195 can guide the host's broadcast progress direction in a customized manner for each group.

For example, the progress guidance module 195 may guide the host to say thank you to a group that feels happy and has high participation, and may guide the group that feels depressed and has high participation to play pleasant music. .

After step S340, the monitoring module 199 of the server processor 190 may monitor the host's compliance with the guided broadcast direction (S350).

If the host behaves compliantly (S360), the server processor 190 may determine the appropriateness of the guided broadcasting direction through listener feedback (S370).

If the host behaves compliantly, the server processor 190 analyzes the number of changes in the number of listeners of the live Internet broadcast, the trend of changes in the increase or decrease of sponsors, the trend of increase or decrease in sponsorship amount, the activity of the chat window, and the trend of changes in increase or decrease in listener participation. It can be provided to the host terminal 200. The server processor 190 may first perform the broadcasting guide and then show the resulting effect to a host who has a negative opinion about charging for the broadcasting guide, so that charging can be performed after the broadcasting guide. Before proceeding with the guide, a contract for deferred payment may be provided to the host terminal 200.

In addition, if the host behaves compliantly, the server processor 190 collects data on the listener terminal 300's satisfaction with inducing the broadcast to proceed and whether the action is appropriate for the broadcast, and provides the data to the host terminal 200. can do.

In an embodiment, the server processor 190 sends data about the analyzed listener's emotion and participation to the server communication unit 110 so that the data about the analyzed listener's emotion and participation is visually displayed on the host terminal 200. It can be provided to the host terminal 200 through .

Referring to FIG. 5, during a live Internet broadcast, if there is an inquiry from a listener in an Internet call or chat window involving the host, the server processor 190 lists the inquiry and sends it to the host terminal through the server communication unit 110. It is provided to (200) (S410), and inquiries excluding inquiries that have been answered in the inquiry list can be provided to the host terminal 200 at a predetermined period (S420). At this time, the server processor 190 may convert the inquiry generated from the Internet call into text and provide it to the host terminal 200.

In this case, the server processor 190 merges inquiries that are duplicated or have a predetermined similarity range among the listener's inquiries, sorts the inquiries with the highest frequency of inquiries, and sends them to the host terminal 200 through the server communication unit 110. can be provided.

The server processor 190 may use a similarity detection algorithm that outputs the similarity as a percentage when two inquiries are input in order to determine whether the inquiries overlap or have a certain degree of similarity. At this time, the server processor 190 may correspond to each inquiry with a vector in space, and if the cosine similarity value between the two vectors is 0.8 or more and 1 or less, it may be determined that the inquiries are similar. The server processor 190 may determine the degree of similarity between inquiries based on the noun of each inquiry. The cosine similarity value can be calculated by the scalar product and size between vectors.

In an embodiment, the host terminal 200 can respond or reject unanswered inquiries, and if rejected, the server processor 190 displays the rejected inquiries so that only the host terminal 200 can see them. This can be provided to the host terminal 200.

In an embodiment, the server processor 190 may provide a warning message to the listener terminal through the server communication unit 110 to listeners who ask difficult questions beyond a predetermined level.

In another embodiment, the server processor 190 may generate a highlight image according to various criteria, which will be described in detail below.

Highlight videos are not only the content actually used in the live broadcast, but also data not used in the live broadcast, audio data, video data, used by generation or situation to increase the value of the highlight video, make it more interesting and attractive, or increase the number of views. It can be implemented as content containing a variety of language data, hot keywords from live Internet broadcasts, letters, and symbols. Accordingly, the highlight video can be generated based on voice and can also be generated based on video.

The server processor 190 may set trigger conditions for generating one or more highlight videos for a live Internet broadcast, where the server processor 190 determines the chat progress speed, the number of sponsored listeners, and the sponsorship amount of the sponsored listeners in the live Internet broadcast. , a condition in which at least one of the frequency of use of a specific keyword, a specific emotional state implied by the keyword, the listener's participation, the intensity of the host's voice, the frequency of the host's speech, and the speech time is above a predetermined level can be set as the trigger condition.

The server processor 190 may generate a highlight video for each section of a live Internet broadcast that satisfies trigger conditions. The server processor 190 may combine reinforcement data for each broadcast section in which the trigger condition is satisfied.

Here, the reinforcement data is data that is not actually broadcast or used in the live Internet broadcast, and is at least one of audio data to increase the number of views of the highlight video, video data, language data used by generation or situation, and hot keywords in the live Internet broadcast. may include.

The server processor 190 can recreate a highlight video by appropriately combining data that is actually broadcast or not used in a live Internet broadcast in the broadcast section, and audio data (music, specific voice, hot content) to increase the number of views of the highlight video. (sounds corresponding to keywords, etc.) can be added, avatars corresponding to the host and avatar content corresponding to the listeners can be added, and conversations can be reconstructed in chat or Internet calls. At this time, the server processor 190 may reconstruct the conversation and add a comment evaluating the conversation by adding a virtual third party. The server processor 190 may generate a highlight video so that the avatar also shows emotions in situations where the host or listener shows emotions in a conversation between the host and listeners. At this time, the server processor 190 may use the above-described artificial intelligence-based analysis model that identifies the emotion implied in the keyword.

Additionally, the server processor 190 may generate a highlight image by combining language data used by generation or situation, hot keywords of live Internet broadcasting, etc., together in the determined broadcast section. The server processor 190 can insert keywords with high priority among hot keywords at appropriate points in the highlight video based on video or audio.

In addition, when there are multiple sections that satisfy the trigger conditions, the server processor 190 integrates some of the multiple sections into one highlight video and adjusts the playback order within the video based on the weight between the trigger conditions in the integrated image. It can be arranged very differently.

In an embodiment, the server processor 190 may separately generate a highlight image corresponding to a section with a large sponsorship amount by adding reinforcement data, and may additionally generate a separate highlight image. In the case of a separate highlight video, the server processor 190 may include a section where the chat progress speed is above a predetermined level, a section where the frequency of use of a specific keyword or a specific emotional state implied by the keyword is above a predetermined level, a section where the listener's participation rate is above a predetermined level, A highlight video can be created by dividing the host into sections where the voice intensity/utterance frequency/utterance time is above a certain level and adding reinforcement data for each section.

In an embodiment, when generating an integrated image, the server processor 190 places points that simultaneously satisfy a plurality of trigger conditions in the first half of the highlight image, and sequentially places points that satisfy only one trigger condition in the second half of the highlight image. It can be placed. Additionally, the server processor 190 may place a trigger condition with a high weight in the first half of the integrated highlight image. The weight may be determined according to the selection of the host, but the embodiment is not limited thereto. In an embodiment, if the playback time of the combined image is longer than a predetermined time, the server processor 190 may generate the combined image into a plurality of partial combined images.

Meanwhile, the disclosed embodiments may be implemented in the form of a recording medium that stores instructions executable by a computer. Instructions may be stored in the form of program code, and when executed by a processor, may create program modules to perform operations of the disclosed embodiments. The recording medium may be implemented as a computer-readable recording medium.

Computer-readable recording media include all types of recording media storing instructions that can be decoded by a computer. For example, there may be Read Only Memory (ROM), Random Access Memory (RAM), magnetic tape, magnetic disk, flash memory, optical data storage device, etc.

As described above, the disclosed embodiments have been described with reference to the attached drawings. A person skilled in the art to which this disclosure pertains will understand that the present disclosure may be practiced in forms different from the disclosed embodiments without changing the technical idea or essential features of the present disclosure. The disclosed embodiments are illustrative and should not be construed as limiting.

100: Broadcast direction guidance server, 200: Host terminal, 300: Listener terminal.

Claims (10)

As a server that guides the direction of the broadcast host's broadcast based on listener responses,
a server communication unit that communicates with a host terminal and a listener terminal of a voice-based live Internet broadcast; A memory in which broadcast data is stored; and a server processor including one or more cores,
The server processor is,
a collection module that collects listener reaction information;
an analysis module that analyzes the listener's emotions and participation based on the listener's reaction information collected by the collection module; and
It includes a progression guidance module that guides the direction of the host's broadcast according to the analyzed listener's emotions and participation,
The server processor is,
During the live Internet broadcast, in order to guide the host's broadcast progress, if there is an inquiry from a listener in an Internet call or chat window involving the host, the inquiry is listed and provided to the host terminal through the server communication unit. , Inquiries excluding inquiries that have been answered in the inquiry list are provided to the host terminal at a predetermined period,
The server processor is,
If an unanswered inquiry among the listed inquiries is rejected by the host terminal, information on the rejected inquiry is provided to the host terminal through the server communication unit,
The server processor monitors the host's compliance with the guided broadcast direction through a monitoring module,
The server processor is,
If the host complies and acts, the appropriateness of the guided broadcast direction is determined through listener feedback,
If the host behaves compliantly, the server processor analyzes the number of changes in the number of listeners of the live Internet broadcast, the trend of changes in the increase or decrease of sponsors, the trend of increase or decrease in sponsorship amount, the activity of the chat window, and the trend of changes in increase or decrease in listener participation, and the server Provided to the host terminal through the communication department,
The server processor is configured to, when the host behaves compliantly, provide a satisfaction level according to the listener's guidance in broadcasting to the host terminal through the server communication unit. According to paragraph 1,
The server processor is,
Based on the analyzed emotions and participation of the listeners, it further includes a grouping module that groups the listeners and assigns labels to each group,
The progress guidance module is,
A broadcasting direction guidance server configured to guide the broadcasting direction of the host in a customized manner for each group. delete delete According to paragraph 2,
The server processor is,
A broadcasting direction guidance server configured to merge inquiries that overlap or have a predetermined similarity range among listener inquiries, sort inquiries with the highest frequency of inquiries, and provide the information to the host terminal through the server communication unit. According to clause 5,
The server processor is,
Broadcasting configured to provide data on the analyzed emotion and participation of the listener to the host terminal through the server communication unit, so that the data on the analyzed emotion and participation of the listener are visually displayed in the host terminal. Direction guidance server. A method of deriving the broadcasting direction of a broadcasting host based on listener responses performed by a server processor of a broadcasting direction inducing server,
collecting listener reaction information;
Analyzing the listener's emotions and participation based on the collected response information of the listener; and
It includes guiding the direction of the host's broadcast according to the analyzed listener's emotions and participation,
The server processor is,
During the live Internet broadcast, in order to guide the host's broadcast progress, if there are inquiries from listeners in an Internet call or chat window involving the host, the inquiries are listed and provided to the host terminal through the server communication unit, Inquiries excluding answers that have been answered from the inquiry list are provided to the host terminal at a predetermined interval,
The server processor is,
If an unanswered inquiry among the listed inquiries is rejected by the host terminal, information about the rejected inquiry is provided to the host terminal.
The server processor monitors the host's compliance with the guided broadcast direction through a monitoring module,
The server processor is,
If the host complies and acts, the appropriateness of the guided broadcast direction is determined through listener feedback,
If the host behaves compliantly, the server processor analyzes the number of changes in the number of listeners of the live Internet broadcast, the trend of increase or decrease in sponsors, the trend of increase or decrease in sponsorship amount, the activity of the chat window, and the trend of increase or decrease in listener participation, and Provided to,
The server processor is configured to provide the host terminal with satisfaction based on the listener's guidance in broadcasting when the host behaves compliantly. In clause 7,
After the above analysis step,
Based on the analyzed emotions and participation of the listeners, further comprising grouping the listeners and assigning labels to each group,
The guiding steps are:
A broadcasting direction guidance method comprising guiding the host's broadcasting direction in a customized manner for each group. delete delete

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