57671results about "Selective content distribution" patented technology

The invention provides a live broadcast room content identification and intelligent distribution method and system based on multi-modal fusion, and relates to the technical field of machine learning, and the method comprises the steps: collecting video, audio and bullet screen data of a live broadcast room, and carrying out the feature extraction; performing cross-modal interaction, feature alignment and feature reconstruction on the multi-modal features by using the multi-modal feature fusion model to generate cross-modal fusion features; and carrying out content identification based on a pre-trained content classification model to obtain a category label and a time sequence feature. Furthermore, a live broadcast room content portrait and an association network are constructed, and a live broadcast room real-time score is generated and a live broadcast room display weight and a push strategy are dynamically adjusted in combination with user historical watching behavior data and a deep reinforcement learning model. Through multi-modal data fusion and deep reinforcement learning, accurate identification and personalized recommendation of the live broadcast room content are realized, and the user experience and the platform operation efficiency are effectively improved.

Some examples of the disclosure are directed to systems and methods for facilitating display of content and avatars in a multi-communication session including a first electronic device and a second electronic device. In some examples, the first electronic device presents a computer-generated environment including an avatar corresponding to a user of the second electronic device and a first object, wherein the computer-generated environment is presented based on a first set of display parameters satisfying a first set of criteria, including a spatial parameter for the user of the second electronic device, a spatial parameter for the first object, and a display mode parameter for the first object. In response to detecting a change in one or more of the first set of display parameters, the first electronic device updates presentation of the computer-generated environment in accordance with the one or more changes of the first set of display parameters.

A method is provided by which a subscriber accesses an SDV channel using a set top terminal. The method begins when the set top terminal receives a user request to tune to a first SDV channel. An active services list is also received over an access network. The active services list includes an entry for each currently available SDV program and a time-to-live (TTL) associated therewith. Tuning information is identified for the first SDV channel from its entry in the active services list. The set top terminal tunes to the first SDV channel using the identified tuning information. The channel change information associated with the user request is locally stored in set top terminal for transmission over the access network at a later time.

In some embodiments, a computer system displays media playback controls at a position based on an arrangement between a user and media content. In some embodiments, a computer system displays captions concurrently with media content at a position based on a spatial arrangement between the computer system and a reference point corresponding to the media content. In some embodiments, a computer system changes the spatial arrangement between captions and the viewpoint of the user based on user input.

In some embodiments, an electronic device generates virtual lighting effects while presenting a content item. In some embodiments, an electronic device enhances navigation to a respective playback position of a content item. In some embodiments, an electronic device displays media content in a three-dimensional environment. In some embodiments, an electronic device presents media content in different modes of presentation.

The invention provides a private domain live broadcast peak hot spot prediction and content scheduling method based on deep learning, and relates to the technical field of private domain live broadcast, and the method comprises the steps: extracting multi-scale time sequence features in private domain live broadcast historical data through employing a hybrid memory enhanced neural network, a live broadcast peak prediction result is generated through a spatial pyramid attention module and a progressive feature distillation network, a dynamic heterogeneous graph neural network is constructed, multi-modal data features and hierarchical comparative learning are combined, a live broadcast content scheduling strategy is output through a multi-agent reinforcement learning framework, a bidirectional interactive evaluation network is constructed, and a live broadcast peak prediction result is obtained. And performing online optimization on the scheduling strategy according to the real-time live broadcast data by using a fast adaptation algorithm of meta reinforcement learning to obtain an optimal content scheduling strategy.

Embodiments of the present disclosure provide a server, a display device, and a digital human processing method. The method comprises: receiving voice data inputted by a user and sent by a display device; on the basis of the voice data, determining a broadcast voice; extracting voice features of the broadcast voice; on the basis of the voice features, determining mouth shape parameters; determining emotion parameters and acquiring user image data; on the basis of the user image data, the emotion parameters, and the mouth shape parameters, generating digital human image data; and sending the broadcast voice and the digital human image data to the display device, so that the display device plays the broadcast voice and, on the basis of the digital human image data, displays a digital human image. According to the embodiments of the present disclosure, expression parameters and mouth shape parameters are determined on the basis of voice data inputted by a user, and are combined to generate a digital human image that has better facial expressions, thus achieving emotion customization and control.

The invention discloses an intelligent multi-modal virtual digital human interaction system based on an AI language large model. The system comprises a high-authenticity face generation module; the high-authenticity face generation module uses an AdaAN network, based on adaptive feature fusion and voice driving and time sequence modeling of voice features, feature information related to voice is extracted, the extracted voice features are processed through a deep neural network, it is ensured that the voice and facial expressions are highly aligned in time and space, and the face recognition accuracy is improved. Collecting a bio-electricity signal, mapping the signal to facial muscle movement, generating a final facial expression, and interacting with a user; the system further comprises an intelligent interaction module, a training optimization and efficient generation module, an efficient integration module, a multi-modal data acquisition module, an AI large model core processing module, a digital human image generation and driving module, an interaction scene adaptation module and a feedback optimization module. The invention further discloses a multi-mode digital human interaction method which has wide application value.

The invention relates to the technical field of resource allocation, in particular to a multi-channel video stream cooperative transmission method based on dynamic priority, which comprises the following steps of: acquiring task context characteristic parameters, system resource states, user behavior responses and computing node load data in real time, and constructing a priority allocation model to carry out pattern recognition to generate real-time task priority. And establishing a task scheduling strategy generation model, and performing resource allocation by adopting a priority weighting efficiency evaluation function, a memory-computing unit occupancy rate prediction matrix and a gradient optimization target under an adjustment resource constraint condition to form an initial scheduling strategy. And performing multi-dimensional parameter fusion analysis on the data through an adaptive optimization decision model, generating a resource redistribution correction vector, dynamically adjusting an initial scheduling strategy by adopting an online iterative optimization mechanism, and outputting a final real-time optimization task scheduling strategy. The task priority dynamic evaluation and the closed-loop optimization of the resource allocation are realized, and the efficient cooperative execution of the multi-channel video processing task is ensured.

The invention relates to an intelligent MV generation method, system and device based on AIGC and a medium, and belongs to the technical field of artificial intelligence content generation. The method comprises the following steps: receiving an original music file input by a user and a user instruction; performing audio preprocessing on the original music file to generate a standardized audio signal, and mapping a user instruction into a semantic vector; rhythm features and emotion features are extracted, music feature vectors are generated and subjected to feature alignment with semantic vectors, and an initial key frame sequence is generated based on a diffusion model; performing particle effect density dynamic adjustment and shot switching point generation on the initial key frame sequence, outputting an enhanced key frame sequence, performing optical flow frame insertion and long time sequence prediction, and generating an optimized video frame sequence; and performing block parallel rendering on the optimized video frame sequence according to the target platform parameters to generate an MV file and metadata. According to the invention, deep fusion of audio and visual contents can be realized, and high-quality video contents can be generated according to individual demands of users.

An aspect relates to determining if a request received from a user device is sufficiently similar to a cached request, wherein the request requesting an identification of streamable content meeting one or more criteria. The determination comprises comparing a vector corresponding to the received request with vectors of previously received, cached requests. If the vector corresponding to the received request is sufficiently similar to a first vector of a first previously received, cached request, then a previously generated response corresponding to the first previously received, cached request may be accessed and transmitted to the user device. If a sufficiently similar vector is not identified, then at least a portion of the received request may be transmitted, in association with an identification of content items in a library, to an artificial intelligence learning engine. The response from the artificial intelligence learning engine may be transmitted to the user device.

Systems, methods and non-transitory computer readable media for generating and operating artificial entities are provided. Some disclosed embodiments may involve receiving information related to a source individual; generating an artificial entity associated with the source individual based on the received information; receiving data reflecting an interaction with the artificial entity; and determining a manner for the artificial entity to respond to the interaction based on the collected information.

The invention discloses a digital human real-time interaction system and method. The interaction system comprises terminal equipment and a cloud server. The terminal device receives voice input by a user and transmits the voice to the cloud server. The cloud server comprises a video acquisition and processing module, an intelligent question and answer and voice generation module, an audio processing module and an image rendering module. A video capture and processing module captures original video data indicative of an object of a digital figure and generates successive image frames. The intelligent question and answer and voice generation module converts the voice into a text; performing semantic analysis on the text, understanding the intention of the user, and generating a corresponding reply text; the reply text is also converted into a reply voice. The audio processing module processes the reply voice and the continuous image frames to generate first video data. And the image rendering module fuses the first video data with the virtual background, and performs real-time processing on the fused video to generate a response video. And the terminal equipment outputs the response video.

The invention relates to the technical field of traffic management, in particular to a dynamic traffic scheduling method and system based on network configuration, and the method comprises the following steps: based on network configuration data, extracting network index data, network indexes including network delay, packet loss rate and bandwidth size, calculating a network quality score, evaluating a current network state, and obtaining network state information. According to the method, the key network indexes are extracted, the network quality score is calculated, and the current network state is comprehensively evaluated, so that network configuration can respond to network requirements and condition changes, and according to the real-time network quality, the bandwidth resources are dynamically adjusted, and a priority rating mechanism is introduced, the network quality is improved. The time sensitivity of different data contents is evaluated, it is ensured that important data can obtain enough transmission resources, the influence caused by network fluctuation is compensated by monitoring the network fluctuation in the data transmission process and adjusting bandwidth allocation in real time, and the continuity and efficiency of data transmission are ensured.

The invention relates to a high-quality video content automatic generation method and related equipment, and the method comprises the following steps: carrying out the multi-modal feature extraction of an input original video material, and obtaining a video feature vector set; performing content semantic analysis on the video feature vector set to obtain a video semantic map; performing narrative structure optimization on the video semantic map through a deep reinforcement learning technology to obtain an optimized video story line; performing multi-modal content generation based on the optimized video story line to obtain a candidate video clip set; and performing intelligent editing and synthesis on the candidate video clip set to obtain target high-quality video content, thereby solving the technical problem that the generated content is lack of depth and attraction due to the fact that many automatic attempts are limited to simple image or clip splicing and potential story clues in video materials cannot be deeply mined.

The invention discloses a multi-agent-based automatic generation method, system and terminal for a micro-drama, and the method comprises the steps: obtaining a user creation theme, and carrying out the story line generation processing and the split processing to obtain a plurality of split scripts which comprise commentaries, visual information, shot information and atmosphere information; performing dubbing conversion processing and subtitle generation processing according to the commentary to obtain a target dubbing audio and a target video subtitle; performing key frame generation processing according to the visual information to obtain a target key frame image, and performing video generation processing with the shot information to obtain a target video clip; performing background music generation processing according to the atmosphere information to obtain target background music; and performing time axis alignment processing and splicing processing on the target dubbing audio, the target video subtitles, the target video clips and the target background music to obtain a target synthesized episode. According to the invention, the production efficiency and the output quality of the micro-movie can be effectively improved.

A video streaming system including a video cache system having a cache database and a video storage device, and including a video server configured to communicate with a gateway system configured to store multiple video streams, communicate with a cloud backup system configured to store video in a file system, and communicate with a cloud server to receive a stream request, the stream request indicating requested video that comprises multiple video segments. The video server is further configured to receive the stream request and determine a playlist that indicates where on the video cache system, the cloud backup system, and the gateway system the video segments that are needed to fulfill the stream request are stored, and to communicate needed video segments to the video cache system in preparation for providing streaming video to fulfill the stream request.

The invention provides a private domain live broadcast room hotspot prediction and adaptive routing method based on swarm intelligence, and relates to the technical field of data processing, and the method comprises the steps: constructing a dynamic interest community through a dynamic graph neural network, extracting group interaction mode features through a multi-layer graph attention network, and carrying out the group interaction mode features; the method comprises the following steps: generating time sequence correlation features in combination with an improved bidirectional gating loop unit, extracting high-order semantic features, performing hotspot prediction through a multi-task learning framework, constructing a multi-level network topology model according to a prediction result, and performing path exploration in combination with an improved heuristic search algorithm to obtain an optimal transmission path. Flow distribution is carried out through a weighted polling algorithm, a deep learning distributed load balancing controller and a congestion control algorithm are combined, the data transmission rate is dynamically adjusted, and self-adaptive routing pushing of live broadcast content is achieved.

The invention discloses an automatic labeling and acquisition standardization method and system based on video content, and the method comprises the steps: decoding a video file into a processable video frame sequence, carrying out the standardization processing, unifying the format and the coding standard, and adjusting the resolution and the frame rate; performing content analysis on the standardized video frame sequence, extracting key frames, identifying objects, scenes and actions, and generating a video analysis result and a preliminary label; according to a video content analysis result, separating an audio stream from the video to carry out noise reduction processing and voice-to-text analysis, and combining the processed text with the video content analysis result to generate multi-modal comprehensive annotation information; based on the comprehensive labeling information, taking feedback of the user in video retrieval, label clicking and watching behaviors as an adjustment basis of label weights, and updating weight distribution of labels by analyzing user behavior data; labeling information and weight distribution are integrated into an index database, and indexes are updated in real time according to user behaviors and content changes.

The invention provides an AI-based marketing video auditing method, and relates to the technical field of AI marketing video auditing, and the method comprises the steps: obtaining a multi-modal data original structure set, and extracting image semantic features, voice expression features, text semantic features and scene label information, and obtaining an image semantic feature set, a visual rhythm feature set, a voice expression feature set, a voice and picture synchronous association vector structure, a text semantic feature set and a subtitle semantic and image main body linkage relation graph. By constructing an image semantic feature set, a voice expression feature set, a text semantic feature set and a visual rhythm feature set and fusing the image semantic feature set, the voice expression feature set, the text semantic feature set and the visual rhythm feature set into a multi-modal content fusion feature tensor, unified modeling of an AI marketing video at visual, auditory and semantic levels can be realized; and subsequent microscopic consistency detection, compliance knowledge graph and emotion semantic conflict identification are effectively performed, so that full-link risk perception and accurate auditing of video contents are realized.

A system and method for structuring and analyzing multimodal, unstructured data, as well as an article of manufacture and processor-readable storage medium implementing the same, may be used to provide a content creator with a tool to understand what actionable steps they could take to make a video perform well or go viral. Embodiments include a user interface simultaneously including a region for accepting a user query and a region that displays insight cards. The insight cards are generated based on retrieving a plurality of streaming contents, sorting the plurality of streaming contents into a viral set using one or more virality metrics, generating at least one token from the viral set, and performing a search of the viral set as a function of the at least one token to produce one or more results. The results are presented through the one or more insight cards in the user interface.

The invention discloses a video feature extraction and multi-dimensional matching-based movie advertisement real-time pushing system, and belongs to the technical field of digital advertisements. According to the system, multi-modal analysis is carried out on visual, audio, text and semantic features of a movie through a video feature extraction module, a dynamic interest tag constructed by a user portrait analysis module is combined, and the weighted matching degree of a video scene, user preference and an advertisement tag is calculated by utilizing a multi-dimensional matching engine; and millisecond-level advertisement putting is completed through the real-time pushing decision module. According to the method, context awareness and streaming computing technologies are fused, the relevance between advertisements and content scenes is remarkably improved, cross-platform deployment is supported, the method is suitable for short videos, live broadcast and other real-time scenes, the advertisement click rate is increased by 55%-72% through tests, the response delay is lower than 200 ms, and the method has the technical advantages of being efficient, accurate and low in delay.

The invention provides a private domain live broadcast bandwidth resource allocation and routing method and system based on reinforcement learning, and relates to the technical field of computer networks, and the method comprises the steps: collecting real-time data such as the number of users, geographical distribution, terminal types, content types, network delay, packet loss rate, bandwidth utilization rate and the like in a live broadcast room, and inputting the real-time data into a pre-trained reinforcement learning model; the model is based on a state space, features are extracted by using a deep neural network, bandwidth resource constraints are combined, and a bandwidth allocation and routing strategy is generated by adopting a strategy gradient algorithm; and according to the strategy, the software defined network controller adjusts bandwidth allocation parameters and routing table configuration in real time to realize differentiated bandwidth allocation and path optimization, establishes nearby access edge nodes based on user geographical distribution, and dynamically adjusts the path during congestion.

The invention provides a trajectory control video generation method and device based on depth information and time-frequency optimization, and relates to the technical field of image processing, and the method comprises the steps: optimizing a 3D trajectory through multi-entity segmentation, depth estimation and time-frequency decomposition in combination with a user instruction, and generating a control signal through a multi-scale fusion network; finally, the signals and original images are input into an improved Stable Video Diffusion model to generate a video potential representation sequence, the problems that an existing video generation method is insufficient in dynamic entity motion control precision and poor in cross-frame consistency are solved, and through 3D trajectory modeling guided by depth information and a time-frequency joint optimization mechanism, the video potential representation sequence is generated. And the motion smoothness, the space authenticity and the time-frequency stability of the generated video are obviously improved.

The invention relates to an AI intelligent short video generation method and system based on multi-agent collaboration. The method comprises the following steps: S1, input analysis and sub-shot script generation; s2, obtaining materials; s3, sub-shot video generation; step S4: editing and synthesizing; wherein the plurality of sub-shot video clips are edited and synthesized into a complete video according to a script sequence, and style fusion processing is performed on the whole video by using a picture style unification algorithm; step S5, background music matching; and S6, outputting the slices. Through the structured sub-shot script generation, intelligent material completion, multi-shot style fusion and music synchronization technology, a professional short video of multi-scene coherent narrative can be generated, the structured sub-shot script is automatically generated, and intelligent material completion, picture style unification and music rhythm and emotion expression synchronization are realized.

Methods, computer systems, computer-storage media, and graphical user interfaces are provided for efficiently generating video insights based on text representations of videos. In embodiments, text data associated with a video is obtained. Thereafter, a model prompt to be input into a large language model is generated. The model prompt includes the text data associated with the video. As output from the large language model, a text representation that represents the video in natural language based on the text data is obtained. The text representation is provided as input into a machine learning model to generate a video insight that indicates context of the video.

Systems, devices, and methods are provided for determining factually consistent generative narrations. A narrative may be generated by performing steps to determine one or more metadata messages for a first portion of a video stream, determine transcribed commentary for a second portion of the video stream, wherein the second portion includes the first portion, and determine a prompt based at least in part on the one or more metadata messages and the transcribed commentary. The prompt may be provided to a generative model that produces an output text. Techniques for performing a factual consistency evaluation may be used to determine a consistency score for the output text that indicates whether the output text is factually consistent with the one or more metadata messages and the transcribed commentary. A narrated highlight video may be generated using the consistent narrative.

Methods, systems, and media for timing presentation of content are provided. In some embodiments, the method comprises: causing a first video content item to be presented on a user device of a user; wherein the video content item is provided by a video sharing service; identifying a promotion for the video sharing service that is to be presented to the user during a time window; determining a probability that the user will select the promotion if the promotion is presented at a first time point within the time window in connection with the first video content item; determining whether to present the promotion at the first time point based on at least on the probability; in response to determining that the promotion is to be presented at the first time point, causing the promotion to be presented on the user device at the first time point in connection with the first video content item; and, in response to determining that the promotion is not to be presented at the first time point, causing the promotion to be presented on the user device at a second time point in connection with a second video content item, wherein the second time point is within the time window and wherein the second time point is after the first time point.

Systems, methods and non-transitory computer readable media for image analysis for personal interaction are provided. Audio data may be received. The audio data may include an input from an entity in a natural language. The input may include at least a first part and a second part. Further, image data may be received. The image data may depict a particular movement. The particular movement may be a movement of a particular portion of a particular body. The particular movement and the first part may be concurrent. The particular body may be associated with the entity. Further, a conversational artificial intelligence model may be used to analyze the audio data and the image data to generate a response in the natural language to the input. The response may be based on the input and the particular movement. Further, the generated response may be provided to the entity.

The invention discloses a media content production and distribution integrated system based on multi-agent collaboration, and relates to the technical field of media content production and distribution. The system comprises a content creation multi-task intelligent system, an audience analysis multi-task intelligent system and a media content distribution module, and the content creation multi-task intelligent system comprises a topic selection planning agent, a text creation agent, an article illustration agent and a content evaluation agent. The audience analysis multi-task intelligent system comprises a data processing agent, a user portrait agent and an interest prediction agent, and through functions and cooperation of the agents, an AI large language model and a multi-agent cooperation technology can be introduced, so that a full-service process from media content production to media content distribution can be automatically opened, and the efficiency is improved. And automatic production and personalized distribution of the media content are realized, the production efficiency and the propagation effect of the media content are greatly improved, and practical application and popularization are facilitated.