14514 results about "MediaFLO" patented technology

Systems and methods for determining attributes in media content. A computing device may be configured to obtain the media content based on a received media content identifier and segment the media content into scenes. The computing device may analyze viewer engagement metrics to identify a scene associated with a viewer engagement score that exceeds a threshold, select video frames from the identified scene, and identify primary objects in the series of images in the scene. The computing device may add a bounding box around the identified primary objects in one or more selected frames and perform text extraction within the bounding box. The computing device may determine object attributes of the identified primary objects, querying a database to identify topics of interest (ToIs) based on the extracted text and the determined object attributes, and performing a responsive action in response to identifying the one or more ToIs.

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.

A system and method for adaptive image and video processing using a Persistent Cognitive Machine (PCM) architecture with visual strategy caching. The system receives degraded input media and extracts degradation fingerprints to query a PCM-based visual strategy cache containing previously successful processing strategies. When matching cached strategies are found above a relevance threshold, they are retrieved and applied directly. When no match exists, the input is processed through transform-domain networks to generate new strategies. A pattern synthesizer combines multiple strategies for complex degradation types. The system evaluates processing effectiveness using a feedback controller and stores successful strategies in the hierarchical cache. This cognitive approach enables real-time processing with continuously improving performance as the cache learns from successful patterns. The adaptive architecture eliminates redundant processing while maintaining high-quality output, making it suitable for diverse imaging and video applications requiring efficient enhancement capabilities with superior performance over traditional methods.

A system and method for generating multimedia artifacts with managed scene continuity in visual and multimedia using an AI-based and scene continuity aware media generation platform. The system receives a user or AI agent specification or simulation result(s), selects or trains generative models based on the specification, preprocesses relevant data, and generates scene narrative or frame-specific, sequence specific or broader continuity aware content using the selected or trained model(s). The generated content may be further enhanced using frame interpolation and view synthesis techniques to create smooth transitions or novel viewpoints or to aid in more efficient transmission or viewing or persistence of resultant content. The system enables efficient and customizable generation of high-quality scene continuity aware content for various applications in visual and multimedia production using neuro-symbolic and simulation enhanced compression, representation and generation processes.

Media user interfaces are described, including user interfaces for capturing media (e.g., capturing a photo, recording a video), displaying media (e.g., displaying a photo, playing a video), editing media (e.g., modifying a photo, modifying a video), accessing media controls or settings (e.g., accessing controls or settings to capture photos or videos to capture videos), automatically adjusting media (e.g., automatically modifying a photo, automatically modifying a video), and automatically managing a media capture mode (e.g., a photo media capturing mode, a portrait media capturing mode, a video media capturing mode) based on a set of conditions.

The present disclosure relates to systems, non-transitory computer-readable media, and methods for utilizing a context transformer engine, a smart topic agent, and a large language model to generate a smart topic output. In particular, in one or more embodiments, the disclosed systems generate a smart topic output from a transcript of a video call. In some embodiments, the disclosed systems provide a smart topic interface that provides the smart topic output on a client device and receives selections of smart topic elements. In one or more embodiments, the disclosed systems generate a combined smart topic from transcripts of video calls in which client devices that participated are associated with a collaborating user account group.

Techniques are disclosed for enabling creators to create multimodal chatbots that are based on or simulate / model characters. The characters may be from audiovisual (AV) media such as films and TV shows or real people. The application leverages a combination of Visual Interpretation AI, Retrieval-Augmented Generation (RAG), Low-Rank Adaptation (LoRA), and function calling to provide rich, interactive experiences. The inferencing performed by an instant multimodal chatbot utilizes base weights, character weights, relationship weights, experience weights as we as environmental inputs. An instant chatbot uses a number of AI models including a video to text model, an image to text model, a sensory to text model, a large language model, a text to video model, a text to image model and a text to voice model. A user can interact with the chatbot in a variety of ways including text, audio and video.

Disclosed systems and methods convert user-supplied textual content into animated videos. Textual input is received and processed to identify narrative elements such as characters, settings, and events. These elements are then transformed into visual scene components. Still images generated based on these components are subsequently animated in line with the narrative context. The system can automatically implement storytelling techniques adapted to incorporate neuroscience principles. The system also synthesizes speech for dialogues or narrations using voice synthesis technology that considers emotional markers, tone, and pace. Generated media and metadata are stored in a data storage system that maintains data integrity and enables efficient retrieval. Users can interact with an export interface to choose video resolution, format, and sharing options. A feedback system employing machine learning algorithms collects and analyzes user feedback for real-time adjustments to the generated animated video.

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.

Example systems, methods, and non-transitory computer readable media are directed to an AI-assisted interview system that provides real-time analysis and suggestions during video and audio interviews. The system can analyze questions, response text, audio, and video in real-time and suggest follow-up topics and questions. Suggested topics and questions can optimize for breadth by sampling under-sampled semantic regions and / or depth by identifying related questions. The system can provide clarification suggestions for ambiguous responses and measure quality metrics for questions and answers.

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.

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.

The present disclosure generally relates to methods and interfaces for capturing and viewing immersive media. In some examples, in response to a hardware input, immersive media is captured and generated using multiple sensors of a head-mounted display device. In some examples, while displaying a media item, additional information related to the media item is displayed in response to detecting a viewer's attention departing from the media item.

A mobile application with an improved user interface facilitates generating stylized media content items including images and videos. An end-user selects a desired visual effect from a set of options. The mobile application captures or accesses an image. The image is processed on a server using a generative neural network pre-trained to apply stylizations based on the selected effect. The server sends back the stylized image to the mobile application for display. The end-user can then save the stylized image or generate a video (e.g., an animation) showing the original image transition to the stylized image. The user interface provides an efficient creative workflow to apply aesthetic enhancements in a visual style chosen by the end-user. Generative machine learning techniques automate stylization to enable accessible media customization and sharing.

The invention relates, inter alia, to a metering device (10) for metering and supplying media via fluid lines (14a, 14b, 14c, 14e, 14d, 14e, 14f) to several target devices (12a, 12b, 12c), in particular to several commercial textile washing machines, wherein the metering device (10) comprises an inlet disc (16) with several inlet-side connections (18a, 18b, 18c, 18d, 18e, 18f, 18g, 18h), each of which can be connected to a container (11a, 11b, 11c), and an actuator (17) that is displaceable relative to the inlet disc (16) and which controls a plurality of through-channels (19a, 19b, 19c, 19e, 19f, 19g, 19h), wherein each through-channel (19a, 19b, 19c, 19e, 19f, 19g, 19h) is connectable to a target device (12a, 12b, 12c), wherein as a result of a displacement of the actuator (17) relative to the input disk (16) at least one of the through-channels (19a, 19b, 19c, 19e, 19f, 19g, 19h) is connected to one of the terminals (18a, 18b, 18c, 18d, 18e, 18f, 18g,18h) is communicable, wherein the dosing device (10) comprises several pumps (21a, 21b, 21c, 21d, 21e) that can be addressed by a control unit (20) of the dosing device (10), which are designed to extract medium (13a, 13b, 13c) from the container (11a, 11b, 11c) and to convey it to the target device (12a, 12b, 12c), and wherein each through-channel (19a, 19b, 19c, 19e, 19f, 19g, 19h) is assigned a pump (21a, 21b, 21c, 21d, 21e), each of which is located downstream of the through-channel (19a, 19b, 19c, 19e, 19f, 19g, 19h) is arranged.

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 present disclosure generally relates to techniques and interfaces for managing media playback devices. In some embodiments, the techniques include varying a feedback based on movement of a computer system toward or away from an external device. In some embodiments, the techniques include displaying an interface that includes controls for controlling media playback on an external device when the computer system and the external device are playing media. In some embodiments, the techniques include performing operations at a computer system in response to an input having a size that is less than or greater than a size threshold. In some embodiments, the techniques include performing different operations at a computer system when status lights have states that indicate different states of the computer system.

An AI-driven system for personalized advertising in real time, where: ◯ an analytics unit to monitor and analyze user behavior in real time across multiple digital platforms such as websites, mobile applications, social media, and smart devices; the unit collects data on user engagement, browsing patterns, time spent, and content preferences to enable targeted, personalized advertising; ◯ a prediction module that predicts preferences and interests of a user, operatively connected to the user interaction data collection unit, wherein the prediction module uses machine learning models such as deep learning, recurrent neural networks (RNNs) and transformer-based architectures to predict interests of users based on historical interactions and inferred preferences; ◯ an emotion and sentiment analysis unit that assesses the user's mood in real time through computer vision, natural language processing (NLP) and voice analysis, whereby the analysis of facial expressions, voice pitch and linguistic mood is used to determine emotional states and receptivity to advertising content; ◯ an embodiment of a context awareness component in operational communication with the emotion analysis and mood unit, in some cases further augmented by various environmental and situational data such as the device type and its physical location, date and time, and the content processed in the device, processing methods, etc., in establishing adaptability and automatic ad placement to be as relevant as possible to the user and their status as prescribed; ◯ Use reinforcement learning algorithms and generative AI models to drive advertising with personalization engines. Creative elements, messaging, and presentations are dynamically adjusted in real time based on user responses to ensure advertising is personalized and always optimized for best performance; o a privacy-focused AI system with federated learning, differential privacy methods, and on-device AI processing to reduce targeted advertising while complying with international data protection laws such as the General Data Protection Regulation (GDPR) and California consumer privacy laws; o an operationally adapted ad delivery mechanism to engage with real-time bidding (RTB) networks, programmatic advertising exchanges and demand-side platforms (DSPs) and place advertisements through digital advertising networks, which guarantees the delivery of tailored advertising to the most relevant audience in real time; and o a contextual feedback loop in which the machine learning models used in the preference and interest prediction module and in advertising personalization The engine is continuously updated to reflect the latest user engagement data, improving personalization over time and optimizing advertising performance.

A large language model (LLM) is used to broaden a search for supporting material for a media project. The LLM is provided with contextual material from the media project and optional grounding material and generates a list of types of supporting material. The list is provided to a machine-learning-based encoder, which encodes the list items into embedding space vectors. A body of source material, which may multimodal, is also encoded into the embedding space. A search engine is used to locate items of source material having embedded space vectors closest to vectors corresponding to the list of types of supporting material. Located items are provided to a media editing application as suggested supporting material. The body of source material may include live external sources. A small language model fine-tuned with a training data set generated by a LLM may be used instead of the LLM.

Systems and methods are presented herein for requesting a version of media content from a server that includes haptic feedback rending criteria compatible with the haptics capabilities of a client device. At a server, a request is received for a media asset for interaction on a haptic enabled client device, wherein the media asset comprises haptic feedback rendering criteria. Based on the request, haptic feedback capabilities of the haptic enabled client device associated with the request is determined. The haptic feedback capabilities of the haptic enabled client device are compared to the haptic feedback rendering criteria of one or more versions of the media asset available via the server. In response to the comparing, a version of the media asset comprising haptic feedback rendering criteria compatible with the haptic feedback capabilities of the haptic enabled client device is transmitted from the server to the haptic enabled client device.

The present disclosure generally relates to connecting to one or more external devices. In some examples, one or more techniques are described, such as automatically connecting an external device, connecting to an external device, managing a set of external devices, controlling media playback, and managing media controls.

Methods and apparatus are described for evaluating dubbing of media content. Emotions are identified based on combinations of attributes determined for segments of a source language audio and a dubbed audio. The emotions may be compared to determine emotional prosody transfer between the source audio and dubbed audio. Based on the comparison, a notification is generated indicating whether an emotion classification associated with the source audio matches an emotion classification associated with the dubbed audio.

The invention discloses a convergent media content intelligent label generation and target classification method, and belongs to the technical field of artificial intelligence and convergent media. In order to solve the problems of low labor efficiency, poor rule label flexibility and the like of traditional fusion media content label generation, the deep learning and multi-modal fusion technology is innovatively adopted. In a data processing layer, through deep integration of NLP and CV technologies, feature extraction of multi-source data such as texts, pictures, videos, audios and the like is realized. In a label generation layer, an intelligent label engine is constructed based on a CNN and RNN hybrid model, and a dynamic label library is established in cooperation with a rule matching strategy. And in the classification optimization level, the K-means clustering and hierarchical classification methods are combined to realize adaptive classification and optimization of the labels. Through continuous data training and a user feedback mechanism, the label generation accuracy, the classification efficiency and the system adaptability are remarkably improved, and an innovative solution is provided for intelligent management of the convergence media content.

The invention discloses a new media-oriented interactive marketing method, which comprises the following steps of: 1, multi-source data acquisition: acquiring behavior data of a user in social media, a short video platform and an e-commerce scene in real time through an API (Application Program Interface), a burying point technology and cross-platform data crawling, comprising click tracks, staying duration, comment content, sharing behaviors and shopping cart operation; through the technical innovation of a dynamic content engine, a real-time feedback closed loop, edge calculation, reinforcement learning, a multi-modal material library, a GAN automatic synthesis technology, federated learning, a block chain and the like, the core benefits of improving the interactive conversion efficiency, data-driven real-time decision, reducing the content generation cost, balancing the safety and the effect, performing scene-based accurate touch and the like are realized; the user conversion rate, the click rate, the marketing response speed, the negative comment processing efficiency, the portrait accuracy and the target user coverage rate are remarkably improved.

This disclosure provides methods, devices, and systems for streaming media from a Bluetooth-enabled peripheral device to a WLAN device over a wireless link (such as a P2P link). In some instances, a first peripheral device of a pair of peripheral devices selects values of a time-to-play (TTP) and a sequence number of a first audio stream, and synchronizes the selected TTP and sequence number with a TTP and a sequence number of a second audio stream. The first peripheral device rate-converts first audio data using a sample rate conversion ratio based on the selected TTP and sequence number, encapsulates the rate-converted first audio data into first RTP packets, and transmits the first RTP packets to the WLAN device concurrently with a transmission of second RTP packets from the second peripheral device to the WLAN device.

A method relating to determining similarities between media, and systems and computer-readable media used to implement said method, includes determining an input image for comparison with a plurality of stored images in a media database; vectorising the input image to determine an input image vector representation; and comparing the input image with each of the plurality of stored images in a media database. The method further comprises outputting a set of direct matches and a set of exact matches as a detected comparison output for the input image. Further embodiments also relate to systems and methods for associating digital media with provenance information and creating verified copyright assets for attributing copyright ownership using the systems and methods disclosed herein.