Gracenote, Inc. (20240233747). System and Method for Podcast Repetitive Content Detection simplified abstract
Contents
System and Method for Podcast Repetitive Content Detection
Organization Name
Inventor(s)
Amanmeet Garg of Sunnyvale CA (US)
Aneesh Vartakavi of Emeryville CA (US)
System and Method for Podcast Repetitive Content Detection - A simplified explanation of the abstract
This abstract first appeared for US patent application 20240233747 titled 'System and Method for Podcast Repetitive Content Detection
The method described in the patent application involves detecting fingerprint matches, feature matches, and text matches to identify repetitive content within podcast episodes.
- Detect fingerprint match between query and reference fingerprint data
- Detect feature match across multiple time-windows of podcast content
- Detect text match between query text sentences and reference text sentences
- Generate sets of labels identifying potential repetitive content
- Select consolidated set of labels identifying repetitive content segments
- Perform an action based on the selected labels
Potential Applications: - Content recommendation systems for podcasts - Copyright infringement detection in podcast content
Problems Solved: - Identifying repetitive content within podcast episodes - Improving content curation and recommendation algorithms
Benefits: - Enhances user experience by providing relevant and diverse podcast recommendations - Helps content creators protect their original work from unauthorized use
Commercial Applications: Title: Podcast Content Analysis and Recommendation System This technology can be used by podcast platforms to enhance user engagement and satisfaction by offering personalized content recommendations. It can also be utilized by copyright enforcement agencies to identify and address instances of content plagiarism in podcasts.
Questions about Podcast Content Analysis and Recommendation System: 1. How does this technology impact the podcast industry? This technology revolutionizes content curation and recommendation in podcasts, leading to a more personalized and engaging listening experience for users.
2. What are the potential challenges in implementing this system on a large scale? Implementing this system on a large scale may require significant computational resources and efficient data processing capabilities to handle the vast amount of podcast content available online.
Original Abstract Submitted
in one aspect, a method includes detecting a fingerprint match between query fingerprint data representing at least one audio segment within podcast content and reference fingerprint data representing known repetitive content within other podcast content, detecting a feature match between a set of audio features across multiple time-windows of the podcast content, and detecting a text match between at least one query text sentences from a transcript of the podcast content and reference text sentences, the reference text sentences comprising text sentences from the known repetitive content within the other podcast content. the method also includes responsive to the detections, generating sets of labels identifying potential repetitive content within the podcast content. the method also includes selecting, from the sets of labels, a consolidated set of labels identifying segments of repetitive content within the podcast content, and responsive to selecting the consolidated set of labels, performing an action.
