Media Object Queries for Selective Personalized Supplemental Content Delivery
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Users often struggle to understand complex media content due to confusion about depicted objects, leading to a subpar viewing experience, and existing supplemental content systems are not personalized or efficient in resource usage.
Innovation Solution
A system that generates personalized supplemental content by identifying relevant presentation points within a media asset based on user queries, using machine learning and knowledge graphs to determine the identity of objects and provide tailored information.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If supplemental content is provided for every scene throughout the media asset, then users receive comprehensive information about all objects, but computing and networking resources are wasted on scenes users are not interested in
Solution Approach 1:
The system provides supplemental content selectively rather than for every scene. It identifies specific scenes where supplemental content is most valuable based on user interactions and object importance, providing partial coverage that maximizes user benefit while minimizing resource consumption.
Solution Approach 2:
The system pre-identifies objects and potential supplemental content before the user views the media asset. By analyzing the media asset in advance and preparing supplemental content for key objects, the system can quickly retrieve and display relevant information when users query, avoiding real-time processing overhead.
2Ease of manufacture
If the same supplemental content options are provided to all users, then the system is simple to implement, but the content is not tailored to individual user interests
Solution Approach 1:
The system customizes supplemental content delivery based on individual user characteristics and preferences. Each user receives tailored supplemental content recommendations rather than a uniform set, with the content selection adapting to user-specific interests, viewing history, and query patterns.
Solution Approach 2:
The supplemental content system dynamically adapts to user needs and preferences. The content selection changes based on user interactions, query patterns, and contextual information, making the system flexible and responsive rather than static and rigid.
3Loss of information
If users rewatch the media asset or seek information from third-party sources to understand depicted objects, then they can obtain detailed information, but their current viewing experience is disrupted and time is lost
Solution Approach 1:
The system acts as an intermediary between the media asset and the user, providing supplemental content that bridges the information gap without requiring users to leave the viewing experience. The supplemental content is delivered through the media player interface, seamlessly integrating information delivery with content consumption.
Solution Approach 2:
The system pre-processes the media asset to identify key objects and prepare supplemental content in advance. When users query about depicted objects, the system can immediately retrieve and display pre-prepared supplemental information, avoiding the need for users to rewatch scenes or search external sources.
Data Source
AI summary
Systems and methods are described for generating for display a media asset, and receiving a query regarding an object depicted in the media asset at a first time point within a presentation duration of the media asset. The system and methods may, based on receiving the query, determine one or more second presentation points within the presentation duration of the media asset related to the object, identify the one or more second presentation points as supplemental content, and generate for display the supplemental content while the media asset is being generated for display.