13 results about "Content discovery" patented technology

Technology is disclosed for dynamically creating and / or validating new URLs based on a user's query and / or contextual data. This enables the discovery of relevant content that may not be pre-existing in the search engine's index. This process enhances the search engine's ability to deliver the most current and relevant results by generating potential content sources in real-time, validating them, and incorporating the discovered content into the index for immediate query execution. Various embodiments are additionally or alternatively directed to a process that involves iteratively discovering, crawling, and indexing new content by following links from seed URLs, which may be either pre-existing or generated. This process deepens the search engine's index by continuously exploring outlinks, fetching new content, and updating the index in real-time.

Disclosed are computerized systems and methods for a decision intelligence (DI)-based framework that automatically and / or dynamically provides an interactive content viewing and content discovery experience to users. The framework includes functionality for real-time video content analysis and interactive entity identification that, inter alia, provides novel capabilities to viewing users related to the extraction, analysis and subsequent interaction with content depicted within video frames during playback of such content. The framework implements artificial intelligence / machine learning (AI / ML) approaches for video processing, user interaction and information delivery through a series of interconnected processes and subsystems. In some implementations, rendered content can be parsed and mined for real-world and / or digital content depicted therein that relate to real-world entities and / or digital resources, whereby interaction with such entities is provided in the form of a provided interface, electronic message and / or recommendations for further information discovery, or some combination thereof.

Disclosed are computerized systems and methods for a decision intelligence (Dl)-based framework that automatically and / or dynamically provides an interactive content viewing and content discovery experience to users. The framework includes functionality for real-time video content analysis and interactive entity identification that, inter alia, provides novel capabilities to viewing users related to the extraction, analysis and subsequent interaction with content depicted within video frames during playback of such content. The framework implements artificial intelligence / machine learning (AI / ML) approaches for video processing, user interaction and information delivery through a series of interconnected processes and subsystems. In some implementations, rendered content can be parsed and mined for real -world and / or digital content depicted therein that relate to real-world entities and / or digital resources, whereby interaction with such entities is provided in the form of a provided interface, electronic message and / or recommendations for further information discovery, or some combination thereof.

Disclosed are computerized systems and methods for a decision intelligence (DI)-based framework that automatically and / or dynamically provides an interactive content viewing and content discovery experience to users. The framework includes functionality for real-time video content analysis and interactive entity identification that, inter alia, provides novel capabilities to viewing users related to the extraction, analysis and subsequent interaction with content depicted within video frames during playback of such content. The framework implements artificial intelligence / machine learning (AI / ML) approaches for video processing, user interaction and information delivery through a series of interconnected processes and subsystems. In some implementations, rendered content can be parsed and mined for real-world and / or digital content depicted therein that relate to real-world entities and / or digital resources, whereby interaction with such entities is provided in the form of a provided interface, electronic message and / or recommendations for further information discovery, or some combination thereof.

Disclosed are computerized systems and methods for a decision intelligence (DI)-based framework that automatically and / or dynamically provides an interactive content viewing and content discovery experience to users. The framework includes functionality for real-time video content analysis and interactive entity identification that, inter alia, provides novel capabilities to viewing users related to the extraction, analysis and subsequent interaction with content depicted within video frames during playback of such content. The framework implements artificial intelligence / machine learning (AI / ML) approaches for video processing, user interaction and information delivery through a series of interconnected processes and subsystems. In some implementations, rendered content can be parsed and mined for real-world and / or digital content depicted therein that relate to real-world entities and / or digital resources, whereby interaction with such entities is provided in the form of a provided interface, electronic message and / or recommendations for further information discovery, or some combination thereof.

According to one aspect of the present disclosure, a method of thumbnail personalization is provided. The method includes initializing a thumbnail-personalization engine that includes a multi-armed bandit (MAB) model. The method includes allocating first user traffic to the plurality of thumbnails. The method includes calculating a first thumbnail-conversion rate of each thumbnail of the plurality of thumbnails based on performance data associated with the first user traffic. The method includes allocating second user traffic to the plurality of thumbnails based on the first thumbnail-conversion rate. The method includes calculating a second thumbnail-conversion rate of the plurality of thumbnails based on performance data associated with the second user traffic. The method includes determining an updated set of parameters for each arm of the MAB model based on a data decay function or a sliding-window period applied to the first thumbnail-conversion rate and the second thumbnail-conversion rate.

Technology is disclosed for dynamically creating and / or validating new URLs based on a user's query and / or contextual data. This enables the discovery of relevant content that may not be pre-existing in the search engine's index. This process enhances the search engine's ability to deliver the most current and relevant results by generating potential content sources in real-time, validating them, and incorporating the discovered content into the index for immediate query execution. Various embodiments are additionally or alternatively directed to a process that involves iteratively discovering, crawling, and indexing new content by following links from seed URLs, which may be either pre-existing or generated. This process deepens the search engine's index by continuously exploring outlinks, fetching new content, and updating the index in real-time.

Disclosed are computerized systems and methods for a decision intelligence (DI)-based framework that automatically and / or dynamically provides an interactive content viewing and content discovery experience to users. The framework includes functionality for real-time video content analysis and interactive entity identification that, inter alia, provides novel capabilities to viewing users related to the extraction, analysis and subsequent interaction with content depicted within video frames during playback of such content. The framework implements artificial intelligence / machine learning (AI / ML) approaches for video processing, user interaction and information delivery through a series of interconnected processes and subsystems. In some implementations, rendered content can be parsed and mined for real-world and / or digital content depicted therein that relate to real-world entities and / or digital resources, whereby interaction with such entities is provided in the form of a provided interface, electronic message and / or recommendations for further information discovery, or some combination thereof.

Disclosed are computerized systems and methods for a decision intelligence (DI)-based framework that automatically and / or dynamically provides an interactive content viewing and content discovery experience to users. The framework includes functionality for real-time video content analysis and interactive entity identification that, inter alia, provides novel capabilities to viewing users related to the extraction, analysis and subsequent interaction with content depicted within video frames during playback of such content. The framework implements artificial intelligence / machine learning (AI / ML) approaches for video processing, user interaction and information delivery through a series of interconnected processes and subsystems. In some implementations, rendered content can be parsed and mined for real-world and / or digital content depicted therein that relate to real-world entities and / or digital resources, whereby interaction with such entities is provided in the form of a provided interface, electronic message and / or recommendations for further information discovery, or some combination thereof.

The disclosed techniques can overcome challenges that arise when large language models are used for summarizing documents that exceed the token limit of a LLM. The disclosed techniques communicate contextually relevant segments of a document with customized prompts to an LLM for generating a summary. A subset of segments can be selected from a document based one or more factors, including but not limited to, a token count of content of the select segments, a determination that the token count of content of the select segments does not exceed a token limit of the large language model. The subset of segments can also be selected based on a determination that the selected segments meet one or more criteria with respect to specific user activity. Thus, the system can also provide technical solutions that can use an LLM to discover content of interest stored in large files.

A system is disclosed that integrates a context-aware, domain-specific artificial intelligence architecture into a location-based or interest-based peer-to-peer communication platform. The system improves operation of such platforms by enabling streamlined content discovery, enhanced personalization, efficient user and group administration, and dynamic content and user moderation with minimal computational requirements. Technical improvements include leveraging a pre-trained language model in conjunction with a procedural function framework, semantic search, and content retrieval modules to generate context-aware responses without resource-intensive retraining. The architecture supports classification, parameter extraction, and sentiment analysis to provide accurate and scalable query handling while reducing latency and computational load.

A system is disclosed that integrates a context-aware, domain-specific artificial intelligence architecture into a location-based or interest-based peer-to-peer communication platform. The system improves operation of such platforms by enabling streamlined content discovery, enhanced personalization, efficient user and group administration, and dynamic content and user moderation with minimal computational requirements. Technical improvements include leveraging a pre-trained language model in conjunction with a procedural function framework, semantic search, and content retrieval modules to generate context-aware responses without resource-intensive retraining. The architecture supports classification, parameter extraction, and sentiment analysis to provide accurate and scalable query handling while reducing latency and computational load.