633 results about "Content delivery" patented technology

The invention provides a right content delivery strategy generation method and device based on AI big data, and relates to the technical field of strategy generation, and the right content delivery strategy generation method comprises the steps: obtaining user behavior data corresponding to a content platform type, so as to construct a corresponding target user portrait; dividing the to-be-put right and interest content into corresponding right and interest types according to content platform types; based on the target user portrait, analyzing preference degrees and response rates of user groups of the content platforms to different right and interest types; generating an adaptation relation matrix corresponding to the preference degree and the response rate; and distributing a corresponding right content type to each content platform according to the adaptation relation matrix, and determining a delivery frequency weight corresponding to each content platform. The cross-platform user portrait integration degree is improved, a right and platform dynamic matching mechanism is optimized, and the real-time adjustment capability of the delivery strategy is enhanced by constructing the cross-platform user portrait, dynamically generating the adaptation relation matrix and intelligently distributing the delivery strategy.

A system for the real-time delivery of personalized motivational content based on biometric information; the system includes: a biometric acquisition module configured to capture a variety of physiological signals from a user, wherein the physiological signals include at least heart rate variability, electrodermal activity, facial expressions and electroencephalographic (EEG) signals; a preprocessing module that is operationally coupled with the biometric acquisition module, wherein the preprocessing module is configured to remove noise, normalize and extract signal features from the physiological signals in real time; a multimodal biometric fusion engine configured to temporally align and synchronize the extracted features across signal modalities using dynamic time distortion and confidence-weighted interpolation; a motivational state inference model with a hybrid neural architecture comprising a Convolutional Neural Network (CNN) for spatial pattern recognition and a Recurrent Neural Network (RNN) for temporal sequence modeling, wherein the inference model is configured to output a motivational input score and an affective state classification; an engine for recommending motivational content, configured to select and prioritize content from a content repository based on motivational uptake score, user profile metadata, contextual signals including time of day and geolocation, and historical content effectiveness profiles; and a content delivery subsystem comprising one or more output modalities selected from an acoustic actuator, a visual display, a haptic actuator or an environmental controller, wherein the content delivery subsystem is capable of presenting the selected motivational content in a modality that is dynamically adapted to the user's current psychophysiological state.

The invention provides a disaster early warning information issuing method and system based on cooperation of a multi-modal product and an intelligent agent. The method and system are used for improving comprehensiveness and accuracy of disaster early warning and timeliness and effectiveness of emergency response. The method comprises the following steps: acquiring a multi-source monitoring data set comprising a satellite cloud picture data unit and a ground observation data unit, wherein the multi-source monitoring data set is acquired and generated by monitoring nodes distributed at different spatial positions in real time; joint feature analysis processing is performed on the multi-source monitoring data set through a multi-agent cooperative processing network, disaster risk trend features are generated, and the disaster risk trend features reflect a time evolution rule and a spatial diffusion mode of a disaster occurrence probability; calling a reinforcement learning algorithm to carry out early warning strategy optimization processing on the disaster risk trend characteristics, and generating an optimized early warning strategy containing early warning content types and delivery opportunities; and executing early warning content delivery operation according to the optimized early warning strategy, and linking an emergency response system to complete emergency response cooperative operation.

The invention discloses an online video content intelligent pushing method combined with a learning interest model. The method comprises the following steps: constructing a dynamic interest vector based on multi-source user behavior data, generating a user interest portrait vector set, and performing interest dimension clustering and weight distribution; generating a video content feature vector set according to a clustering result of the user interest portrait vector set; establishing a multi-dimensional association relationship between the user interest portrait vector set and the video content feature vector set, and outputting a user-video matching confidence matrix; converting the user-video matching confidence coefficient matrix into a push sequence based on a multi-objective optimization strategy and issuing the push sequence; and feedback behaviors of the user on the pushed video are collected in real time to realize closed-loop optimization. The method has the following advantages and effects: accurate perception and deep semantic matching of the dynamic learning interest of the user can be realized, and the accuracy, timeliness and user satisfaction of content distribution are remarkably improved, so that the learning efficiency and experience are optimized.

A modular system designed for privacy-preserving content recognition and supplemental content delivery across web and mobile environments. The system employs lightweight character sampling and vision-based recognition to generate unique content fingerprints without storing or replicating original data. It features a hybrid processing architecture, using local computing resources for intensive tasks while optimizing performance on resource-constrained devices. Core functionalities include multi-method content fingerprinting, real-time monitoring with adaptive sampling, and secure supplemental content association. Operating entirely on the client-side, it complies with website terms of service and privacy regulations. Advanced features include AI-driven content recognition, blockchain-based verification, and granular content targeting through resizable selection interfaces. This technology enables seamless delivery of supplemental content while preserving privacy, reducing resource usage, and ensuring scalability across browsers, mobile applications, and edge devices. It is particularly applicable in industries such as education, retail, and secure data sharing.

The invention discloses a personalized push and intelligent distribution system and method. The system comprises a data acquisition module, a content module, an intelligent decision module, a distribution execution module and a monitoring optimization module. Wherein the data acquisition module sends a feature vector to the intelligent decision-making module; the content module sends content metadata and an A / B experiment label to the intelligent decision-making module; the intelligent decision-making module sends the recommendation content, the distribution strategy and the A / B experiment label to a distribution execution module; the distribution execution module displays the received data to the user, collects feedback information of the user, and sends the feedback information of the user to the monitoring optimization module; and the monitoring optimization module analyzes feedback information of the user and sends an analysis result to the intelligent decision-making module, so that circulation is formed until the user completes the task of the round. According to the method, accurate distribution and personalized pushing are realized through multi-dimensional attribute and fine granularity decomposition.

The invention discloses a method for applying linear programming to CDN (Content Delivery Network) scheduling, which relates to the technical field of content delivery networks and comprises the steps of data preparation, strategy layer version smooth configuration, macroscopic layer and microscopic layer linear solution and online execution. Basic data are collected, cleaned and repaired, and a version change rule is set; the macroscopic layer constructs a linear programming model, and the cross-provincial bearing quota is solved with the aim of minimizing the cross-provincial cost; the micro layer takes the quota as a boundary and generates domain name class-node weight vectors in parallel; and adapting a routing request online through weighted rendezvous hashing and request features. According to the method, a dynamic cost matrix and a weight granularity control technology are integrated, the engineering problem of linear programming is solved, second-level response, approximate global optimal scheduling and accurate execution of floating-point-level weight are realized, memory overhead is reduced, smooth updating of a strategy and system stability are guaranteed, and CDN service quality and operation efficiency are improved.

Low- to ultralow-latency content delivery via real-time transport protocol (RTP) is provided. In an example, transport packets, which carry a packetized elementary stream (PES), are selectively marked based on frame size. More particularly, if a number or size of packets needed to transport a picture or PES packet exceeds a threshold, they are marked for preferential processing, such as low latency, low loss, and scalable throughput (L4S) processing. If the number or size of packets is below the threshold, they are marked for default or non-preferential processing. The marking may be applied to entire pictures, tiles, and / or slices. Related apparatuses, devices, techniques, and articles are also described.

The invention relates to the technical field of audio and video processing, in particular to an unmanned live broadcast management system based on a video streaming technology. The system comprises a data acquisition module used for synchronously acquiring multi-modal real-time data streams including videos and audios from multiple dimensions; the real-time analysis module is used for analyzing the competition and outputting structured data including key events and competition narrative states; the automatic narrative director module is used for generating a director instruction based on the narrative logic model, and controlling shot selection, image-text generation and automatic explanation; the manufacturing and distributing module is used for synthesizing and distributing the final program stream at low delay; and the management module is used for configuring, monitoring, scheduling and managing the live broadcast process. Through multi-modal fusion analysis and narrative logic driving, automation and intelligentization of the whole process from data acquisition to content distribution are realized, the ornamental value, accuracy and management efficiency of unmanned live broadcast are remarkably improved, and meanwhile, the manufacturing cost is reduced.

Techniques for implementing a fuzzy logic expert system / service to control sending of a requested media file and / or control triggering of a remedial action (e.g., not sending the media file) are described. According to some examples, a computer-implemented method includes receiving a request at a content delivery service from a user for a media file, generating a piracy risk score for the media file by a fuzzy logic expert service of the content delivery service, sending the media file to the user based on the piracy risk score not exceeding a threshold, and blocking the sending of the media file to the user based on the piracy risk score exceeding the threshold.

The invention relates to the field of communication, in particular to a method for delivering cache content of a mobile edge computing network for energy consumption optimization. For a cache-based MEC cellular network, the method reasonably utilizes user movement information, and calculates corresponding path fading, channel gain and shadow fading by utilizing future position information of a user. Determining the minimum transmission power by using the calculated channel gain and path fading, then setting a time slot, carrying out content delivery decision on data packets requested by different users, and allocating wireless communication resources; and with minimization of the total emission energy consumption of the base station as a target, modeling a cache content delivery problem into a mixed integer programming problem to carry out optimization solution so as to obtain an energy consumption minimization decision for the base station to deliver all data packets.

The invention provides an internet-based information intelligent directional accurate delivery system, which is characterized by comprising an analysis platform, the analysis platform comprises an intelligent matching module and an analysis engine, the intelligent matching module captures user key data and makes a mixed feature set, and the analysis engine is used for analyzing the mixed feature set; the analysis engine reads data of the mixed feature set and constructs a superficial portrait of the user; the fusion platform generates a user deep portrait according to an algorithm of the fusion platform and in combination with the user interest vector; the method relates to the technical field of Internet information accurate release. Through cooperative work of the analysis platform, the fusion platform and the delivery platform, deep mining, portrait construction and accurate content delivery of user data are realized; and the analysis platform, the fusion platform and the delivery platform realize a dynamic optimized pushing strategy of data by adopting a distributed crawling technology, time sequence prediction, transfer learning, reinforcement learning and intelligent routing decision, so that the information delivery accuracy and the user experience are effectively improved.

The present disclosure relates to methods, apparatus, systems, and non-transitory computer-readable storage media for training and using a multi-scale machine learning model for the enhancement of compressed video. According to some examples, a computer-implemented method includes receiving a video at a content delivery service; performing an encode on a frame of the video by the content delivery service that converts the frame from a pixel domain to a transform domain and back to the pixel domain to generate first pixel values and a first residual for a block of the frame at a first resolution; generating a first set of features, by a machine learning model of the content delivery service, for an input, at a first resolution, of the first pixel values and the first residual of the block; generating a second set of features, by the machine learning model of the content delivery service, for an input, at a second lower resolution, of second pixel values and a second residual of the block; upsampling the second set of features to the first resolution to generate an upsampled second set of features; generating a modified version of the frame based on the first set of features and the upsampled second set of features; and transmitting the modified version of the frame to a frame buffer or from the content delivery service to a viewer device.

Apparatus and associated methods relate to provide transient access rights to entities for creating, accessing, and / or sharing digital health content (DHC). In an illustrative example, a health content distribution system (HCDS) may generate a time-limited access token (TLAT) for authenticated users to access DHC. The TLAT, for example, may be generated based on a predetermined association of a corresponding DHC with a patient, a predetermined association of a requestor with the patient, a predetermined role of the requestor with relation to the patient, and a predetermined association between the requestor and a creator of the content. The TLAT may be further generated based on a predetermined association between the requestor and an organization associated with the patient. The HCDS may, for example, upon receiving the TLAT, transmit the corresponding DHC to be displayed at the requestor's device. Various embodiments may advantageously provide a secure on-demand health content access system.

Taxonomy driven multi-system networking and content delivery systems and methods are provided herein. An example systems includes a website authoring platform generating a taxonomy structure for a webpage, the taxonomy structure indicating assets to be included in the webpage a layout for the assets, a technical documentation authoring system for creating technical documents related to the assets, and a metadata binder providing the technical documentation authoring system with access to the taxonomy structure of the website authoring platform so that technical document authors are provided with the taxonomy structure to guide creation and structuring of the technical documents so that the technical documents comprise data that corresponds to the assets.

The invention relates to a cross-channel intelligent marketing decision-making system and method based on dynamic user behavior mining, and relates to the technical field of marketing data processing, and the method comprises the steps: obtaining a multi-channel user behavior data sequence, constructing a multi-dimensional channel priority scoring system, and achieving the precise classification of a core channel and a long-tail channel. And based on user conversion path data, quantifying a correlation coefficient of the core channel and the main channel, and generating a dynamic budget allocation strategy and a content delivery strategy in combination with a priority score. And finally, based on the budget allocation strategy and the content delivery strategy of each channel, automatically adjusting the operation parameters of the corresponding channel. The problems of one-sided channel value evaluation, unquantized synergistic effect, strategy stiffness and the like in traditional marketing are effectively solved, and the marketing resource utilization rate and the user conversion efficiency are improved.

A system and a method for managing an avatar across multiple three-dimensional (3D) rendering platforms are disclosed. The system includes a content database, a content delivery module, a Software Development Kit (SDK) module, and an Application Programming Interface (API) module. The content database stores a 3D avatar model and associated first assets. The SDK module integrates with an in-engine avatar customization module of each 3D rendering platform and allows users to use these assets to customize their avatar during runtime. The API module receives requests for avatar and asset data. The content delivery module fetches requested avatar data and assets in response to requests received at the API module and delivers the requests to the SDK module. Additionally, the content delivery module fetches, stores, and manages second assets used for avatar customization from the SDK module.

Low latency, over-the-top (OTT), and / or adaptive bitrate (ABR) content streaming is provided. Content delivery is enhanced by determining if a fragment of a content segment at a content delivery network (CDN) edge node meets a threshold for preferential encapsulation and transport. If met, preferential encapsulation and transport to the client device is provided; otherwise, it defaults to non-preferential encapsulation. The size of the fragment is quantified at a parser of the CDN edge node or an ABR segment encryption system. The ABR system may be connected between a content source and a CDN origin and may include an encryptor that sends CMAF video and audio segment's fragment byte offsets metadata. Also, the CDN edge node may include the ABR system and an encryptor that sends an encrypted CMAF segment's fragment size to a threshold calculator of an HTTP server. Related apparatuses, devices, techniques, and articles are also described.

Methods, systems, and apparatus for recognizing objects and providing content related to the recognized objects are described. In one aspect, a method includes detecting presence of one or more objects depicted in a viewfinder of a camera of the mobile device. In response to detecting the presence of the one or more objects, image data representing the one or more objects is sent to a content distribution system that selects content related to objects depicted in images. A location of each of the one or more objects in the viewfinder of the camera is tracked while waiting to receive content from the content distribution system. Content related to the one or more objects is received from the content distribution system. A current location of each object in the viewfinder is determined and the content related to the object is presented within the viewfinder at the current location of the object.

Apparatus and associated methods relate to management of therapeutic digital assets in a non-medical environment. In an illustrative example, non-medical digital asset(s) (NMDAs) may be evaluated relative to one or more therapeutic modalities. A corresponding therapeutic digital asset (TDA) may, for example, be generated for each therapeutically associated NMDA. The TDA may, for example, be traceably associated with corresponding risks and / or risk mitigations. Each TDA may be associated, for example, with a test profile corresponding to a risk and / or risk mitigation. A TDA may be released into a non-medical digital content delivery environment (DCDE), for example. The TDA may, for example, be monitored in the DCDE to maintain verification and / or validation (VV). A change in the DCDE affecting VV of the TDA may, for example, result in deactivation of the TDA in the DCDE. Various embodiments may advantageously permit VV of selected digital assets in a non-medical environment.

The invention discloses an Internet user behavior accurate analysis method based on artificial intelligence. The method comprises the following steps: obtaining user behavior data of an Internet platform; inputting the user behavior sequence into a Mamba network to generate a behavior state vector; inputting the commodity image set into an improved DeiT model, and setting a hierarchical feature routing controller; executing cross-modal feature interaction to generate a fusion feature tensor; inputting the fusion feature tensor into an IQL network; updating a fusion weight, high-level attention mapping and a routing control vector based on a value gradient resonance feedback mechanism; and outputting a user behavior analysis result and a personalized recommendation result. According to the method, the accuracy of Internet user behavior prediction and personalized recommendation can be remarkably improved, and the method is suitable for scenes needing high-precision user portraits and intelligent recommendation, such as a large-scale e-commerce platform and a content distribution platform.

The invention provides a multi-modal large model-based content generation method and system, and belongs to the technical field of digital content creation and dissemination, and the method comprises the steps: firstly building a demand association network comprising a user demand node, a content element node and a scene form node, and marking the association strength; constructing a multi-modal element coupling pool comprising a text description unit, a visual display unit and a semantic association unit on the basis, enabling the multi-modal element coupling pool to form mapping with demand association network nodes, calling a multi-modal large model to carry out situational mapping processing on the multi-modal element coupling pool, and generating an initial content framework set; and performing element collaborative optimization on the initial content framework set to obtain an optimized content framework conforming to content element node constraints, generating a target content set according to the optimized content framework, and pushing the target content set to a content distribution node to complete propagation, thereby efficiently generating and accurately propagating the multi-modal content.

Artificial intelligence (AI) techniques for connection networking are described. A method comprises receiving an input vector by a multi-tower machine learning (ML) model for a content delivery system of a connection network system, generating a set of user embeddings from a first vector of the input vector by a first tower of the multi-tower ML model based on the activity data associated with users of the connection network system, generating a set of campaign embeddings from a second vector of the input vector by a second tower of the multi-tower ML model based on the textual description of the content delivery campaign, generate a metric based on a subset of the user embeddings and a subset of the campaign embeddings, selecting audience user identifiers based on the metric, and sending content items for the content delivery campaign to electronic devices of the audience user identifiers.

To give a distributor, who is a player receiving backup from a viewer, a reward based on the backup in content distribution.SOLUTION: A distribution system for performing content distribution is configured to receive backup action to a distributor from a viewer viewing live distribution during the live distribution and give a reward to a distributor depending on the backup action.SELECTED DRAWING: Figure 33

Example implementations include methods and systems that relate to search results and recommendations in a media content delivery system. An example method includes providing a search query to a multi-task language model associated with a media content delivery system. The method also includes providing user engagement information to the multi-task language model. The user engagement information indicates user engagement activity with the media content delivery system. The method also includes retrieving, using the multi-task language model and based on the search query, one or more candidate media items from a media item database of the media content delivery system. The method also includes identifying, using the multi-task language model and based on the user engagement information, one or more recommended media items from the media item database.

The application provides a content generation method and system based on a multi-modal large model, belonging to the technical field of digital content creation and dissemination. First, a demand association network containing user demand nodes, content element nodes and scene form nodes is established, and the association strength is labeled. Based on this, a multi-modal element coupling pool containing text description units, visual display units and semantic association units is constructed, which is mapped with the demand association network nodes. A multi-modal large model is called to perform situational mapping processing on the multi-modal element coupling pool, generating an initial content framework set. The initial content framework set is optimized in terms of element coordination, obtaining an optimized content framework that meets the constraints of the content element nodes. According to the optimized content framework, a target content set is generated and pushed to the content distribution node for dissemination, thereby efficiently generating and accurately disseminating multi-modal content.

In various embodiments, machine learning-based customization for video stream content delivery systems and applications are provided. In some embodiments, a machine learning model-based content customization engine may modify in real-time how user-selected elements of content are presented at the user's equipment (UE). A request for video content from a UE may include user content selection data and user customization data. The user content selection data is used to initiate streaming of a selected title of video content from a content server, and the user customization data is used as the basis to modify selected elements of streaming video content prior to display by the UE. Video content data from the content server and user customization data may be input to a generative artificial intelligence (GAI) model that outputs customized video content data where one or more elements of content are modified based on the user customization data.

A merchant processing system accesses a user interface of a content delivery service, to identify media content items. In response to merchant inputs, the merchant processing system causes generation of a playlist of media content items in a playlist format of the content delivery service. In response to determining that the user is accessing an application of the merchant, the merchant processing system causes display of the playlist to the user on a device associated with the user, within one or more screen displays of the application. The merchant processing system then receives a playback request representing a selection by the user of a media content item on the playlist, in response to the display of the playlist to the user, and causes transmission of the selected media content item to the device associated with the user, to cause playback of the selected media content item, based on the playback request.

Systems and methods for customer-specific content delivery via an automated teller machine (ATM) may include one or more server(s) which receive, from an ATM, data indicative of a user profile corresponding to a user of the ATM, the user performing a transaction via the ATM; identify a transaction history associated with the user profile; determine, via one or more first machine learning models hosted on the one or more servers, a context corresponding to at least one transaction of the transaction history; generate, via one or more second machine learning models, a content item according to the context determined by the one or more first machine learning models; and transmit the content item for display by the ATM to the user, the ATM displaying the content item for at least a portion of a duration in which the transaction is performed via the ATM.