Google llc (20240338925). Systems and Methods for Extracting Temporal Information from Animated Media Content Items Using Machine Learning simplified abstract
Systems and Methods for Extracting Temporal Information from Animated Media Content Items Using Machine Learning
Organization Name
Inventor(s)
David Mcintosh of San Francisco CA (US)
Erick Hachenburg of San Francisco CA (US)
Peter Chi Hao Huang of Pacifica CA (US)
Systems and Methods for Extracting Temporal Information from Animated Media Content Items Using Machine Learning - A simplified explanation of the abstract
This abstract first appeared for US patent application 20240338925 titled 'Systems and Methods for Extracting Temporal Information from Animated Media Content Items Using Machine Learning
Simplified Explanation:
This patent application describes a computer-implemented method for analyzing temporal information associated with sequentially viewing image frames of a media content item.
- The computing system receives data describing the media content item with multiple image frames.
- The data is input into a machine-learned temporal analysis model.
- The model outputs temporal analysis data describing the temporal information of viewing the image frames.
Key Features and Innovation:
- Utilizes a machine-learned temporal analysis model to analyze temporal information of media content.
- Provides insights into the sequential display of image frames in a media content item.
Potential Applications:
- Video editing software for optimizing the sequence of image frames.
- Content recommendation systems for personalized viewing experiences.
Problems Solved:
- Enhances the understanding of temporal relationships between image frames in media content.
- Improves the viewing experience by analyzing the optimal sequence of image frames.
Benefits:
- Enhanced user engagement with media content.
- Improved content organization and presentation.
Commercial Applications:
Optimizing video editing processes in the entertainment industry for seamless viewing experiences.
Questions about the Technology: 1. How does the machine-learned temporal analysis model improve the analysis of temporal information in media content? 2. What are the potential implications of this technology for content creators and distributors?
Frequently Updated Research:
Stay updated on advancements in machine learning algorithms for temporal analysis in media content.
By focusing on the optimization of sequential image frames in media content, this technology offers a novel approach to enhancing the viewing experience and content organization.
Original Abstract Submitted
1. a computer-implemented method can include receiving, by a computing system including one or more computing devices, data describing a media content item that includes a plurality of image frames for sequential display. the method can include inputting, by the computing system, the data describing the media content item into a machine-learned temporal analysis model that is configured to receive the data describing the media content item, and in response to receiving the data describing the media content item, output temporal analysis data that describes temporal information associated with sequentially viewing the plurality of image frames of the media content item. the method can include receiving, by the computing system and as an output of the machine-learned temporal analysis model, the temporal analysis data.
