874 results about "Content delivery" patented technology

A system and method for AI-driven advertising and content generation that integrates connectionist and symbolic AI techniques to deliver personalized, contextually relevant user experiences. The invention leverages specialized agent networks, knowledge graphs, and retrieval-augmented generation to create dynamic, adaptive content with seamlessly integrated advertisements. It employs a sophisticated ad integration layer and experience broker to ensure relevance and engagement. The system prioritizes security, traceability, and user preferences while optimizing ad placement and content delivery. By bridging connectionist and symbolic AI, the platform enables immersive, tailored interactions across multiple scenarios and time horizons, enhancing user engagement and advertiser value in an AI-driven digital landscape.

This invention introduces an adaptive system for multi-modal media processing and delivery, addressing challenges in modern digital content distribution. The technology dynamically analyzes and processes media content in real-time, optimizing delivery across diverse devices, networks, and content types. Key features include adaptive processing that adjusts compression, encoding, and delivery protocols based on content characteristics and delivery constraints. The system incorporates artificial intelligence for continuous improvement, learning from historical data and user feedback. It addresses network variability and device diversity, adapting to changing conditions and optimizing content for different platforms. Security and personalization features enable protected content distribution and tailored user experiences. The invention's cross-media optimization approach allows efficient handling of various media formats within a unified framework. Its scalable, modular design suits applications from consumer streaming to enterprise-level distribution. This comprehensive solution aims to enhance content distribution efficiency and user experience in the complex, evolving digital media landscape.

The invention discloses a network content propagation method and system based on fusion of cognitive understanding and intelligent governance, and the method comprises the steps: constructing a sub-graph network of each mode, and generating cross-modal feature graph representation; performing topic modeling on texts in the text knowledge base, extracting potential topic words, and constructing an event graph; nodes related to the hot spots are retrieved from the event atlas, a hot spot mode is formed, content features similar to the hot spot mode are matched in cross-modal feature graph representation, and potential hot spot content is recognized; constructing a cross-modal content graph, learning an abnormal mode by using the labeled abnormal data, training an abnormal detection model according to the abnormal mode, and identifying abnormal content in the cross-modal content graph; predicting a content propagation link of the hot content and the abnormal content; by adjusting the weight of the key node in the content propagation link, the content delivery time, the content delivery position and the target audience are determined. According to the method and the device, the accurate personalized recommendation and the maximization of the propagation effect are realized.

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.

Systems and methods for a media content delivery service. A media content provider includes a data store for storing and serving media content to subscribers. The media content provider continually records and or otherwise stores video and audio content from around the world. Stored content will generally correspond to a large number of other media content providers, such as cable television operators, Internet based media content providers, television networks, and so on. Subscribers to the media provider interact with the media content provider via hardware and / or software mechanisms either provided by the media content provider or conforming to specifications of the media content provider. Based on these subscriber's subscriptions to the other content providers, the subscribers will have viewing, listening, or application execution privileges for content maintained in the data store. Content of a subscriber corresponding to multiple video content providers is maintained and served by a single provider.

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.

Systems and methods for verifying content delivery in a content delivery network (CDN) are described. Content retrieval and routing in a CDN are tested using multiple test sequences that are each assigned to a different value of a parameter P and are generated using the assigned value of P. A client device can verify whether a test sequence received at the client device matches a test sequence requested by the client device (e.g., the correct content was) and verify that the received test sequence is assigned to the same value of P as the requested test sequence. The value of P assigned to the received test sequence can be extracted, by the client device, from the received test sequence to determine which test sequence was actually received, thereby providing information that can be used to help debug content delivery errors.

A dynamic experience curation platform utilizing AI and ML techniques, including neural networks and generative models, to enhance user interactions with content on the Internet. The platform employs an Experience Broker (EB) service to disintermediate user devices from the internet (or other content / information sources), improving security and user experience. It processes content requests, extracts relevant information, filters out unwanted content like ads, transforms, combines, and / or mutates existing content, and generates curated content consistent with user preferences and other constraints. The platform's generative AI process renders content into a user interface consistent with user preferences. It allows for personalized content delivery across devices, including virtual and augmented reality environments. The platform supports a spectrum of personalization, allowing users to fine-tune their content consumption experience. Additionally, it manages user sessions across devices and integrates various databases for storing user profiles, preferences, and other relevant information.

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.

Techniques for performing a machine learning (ML) cinematic (e.g., movie) question answering are described. According to some examples, a computer-implemented method includes receiving a request from a viewer device at a content delivery service to play a video; sending the video from the content delivery service to the viewer device; receiving, by the content delivery service, a question from the viewer device during playing of the video; generating, by a script context retrieval machine learning model of the content delivery service, a proper subset of a script of the video based on an input of the question; generating, by a cinematic question answering machine learning model of the content delivery service, an answer based on an input of the proper subset of the script of the video; and sending, by the content delivery service, the answer to the viewer device.

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.

Apparatus and associated methods relate to a dynamic inducement package generation system (DIPGS). In an illustrative example, the DIPGS may include at least one validated digital therapeutics (DTx), user profiles, and media profiles associated with wild media content. The DIPGS, for example, may include an assessment engine configured to generate or update a user profile based on a journal of user input and observation of user behavior. The DIPGS may include a medical media package generation engine (MMPGE) configured to generate a dynamic inducement media package (DIMP). In an inducement mode, the DIMP may play the user-selected wild media content regularly. After transitioning from the inducement mode to a therapeutic mode, the MMPGE generates an interventive monitoring content (IMC) comprising a validated DTx to provide therapeutic treatment to the user. Various embodiments may advantageously provide continuous medical guidance and progress monitoring to a user.

A computer-implemented method generates, using training frames of training image data in combination with a first set of data derived from the training frames of training image data, a set of fine-tuning weights for a pre-trained diffusion model implemented by a first artificial neural network. A first digital signature is generated based upon values of the fine-tuning weights. The values of fine-tuning weights and the first digital signature are sent to a computing device configured to, after verifying the first digital signature, use the fine-tuning weights to establish a specialized diffusion model implemented by a second artificial neural network.

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.

This disclosure relates to systems and techniques that can be implemented by content platforms to optimize (a) demographic-based digital component distribution used to categorize each user into a particular demographic so as to appropriately target that user for purposes of maximizing the efficacy of digital components shown to that user, and (b) demographic reporting used to report to digital component providers the effectiveness of the digital component.

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.

Technologies for content delivery and presentation tracking are disclosed. An example method includes tracking tokens generated during playback of a content stream, associating each of the tracked viewer measurement tokens with metadata that indicates timing, and merging the tracked viewer measurement tokens with baseline format tokens associated with the playback of the content stream, to generate merged token data.

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.

The invention provides a self-media content streaming matching method and system based on dynamic semantic analysis, and the method comprises the steps: carrying out the dynamic semantic analysis of the text content of a voice stream of a creator, generating a semantic theme sequence, aligning the semantic theme sequence with an emotion fluctuation curve of the voice stream of the creator, and generating an emotion semantic incidence matrix; dividing a mapping relationship between an emotional intensity numerical range in the emotional fluctuation curve and a semantic topic type in the semantic topic sequence; performing behavior association fitting on the mapping relationship based on user historical feedback behavior data, and generating a dynamic association rule between the emotion intensity numerical range and the user feedback behavior; and adjusting a preset self-media content delivery strategy in real time, and generating an adjusted delivery strategy so as to carry out self-media content delivery stream matching. According to the method, the emotion semantic association matrix is constructed, accurate space-time matching of the content theme and the emotion expression is realized, and the target of adaptively optimizing the flow casting matching according to the real-time emotion state of the creator is achieved.

An example operation includes one or more of determining an expected charging duration of an electric vehicle (EV) at a charging point, determining content preferences of an occupant associated with the EV, wherein the content preferences are based on profile data of the occupant associated with the occupant, and delivering content to the EV, based on the expected charging duration and the content preferences.

To provide a content distribution system capable of distributing product information or store information related to customer purchase history information while increasing affinity with user preference.SOLUTION: A content distribution system 10, which distributes content tailored to user preference, includes: a user information acquisition unit 28 that acquires user attribute information including a group identifier; a history information storage unit 44 that records user purchase information in association with the group identifier; a user feature calculation unit 30 that calculates, on the basis of the user purchase information and the group identifier, the user feature of a user; and a content distribution unit 38 that stores content information including information on a product or a store recommended to the user, and distributes it to user terminals of each user. The content distribution unit 38 determines, on the basis of the user feature calculated by the user feature calculation unit 30, the content information that is expected to interest the user.SELECTED DRAWING: Figure 1

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.

A game tracking system is presented having gamification features and functionalities that enable system scaling and enhancement for various game structures and directed content distribution continuously and for the duration of gaming activity. Distinct game modules are seamlessly enabled through a user interface to integrate with a base game module used to determine the fundamental game outcomes from information received from the game tracking system. The game modules affect play and scoring through new game rules while maintaining use of the base game module and using the data derived therefrom in application to the new game rules. In accordance with the distinct game modules, content distribution is directionally applied to a series of display devices in accordance with tracked game outcomes, game results and other conditions. A number of distinct game modules can be selected having a distinct set of game rules with the accompanying content distribution varying with each.

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.