Spotify ab (20240134907). SYSTEMS AND METHODS FOR DETECTING MISMATCHED CONTENT simplified abstract
Contents
SYSTEMS AND METHODS FOR DETECTING MISMATCHED CONTENT
Organization Name
Inventor(s)
Nicola Montecchio of Berlin (DE)
SYSTEMS AND METHODS FOR DETECTING MISMATCHED CONTENT - A simplified explanation of the abstract
This abstract first appeared for US patent application 20240134907 titled 'SYSTEMS AND METHODS FOR DETECTING MISMATCHED CONTENT
The abstract describes a patent application for an electronic device that utilizes machine learning to analyze and cluster media items based on their attributes, modifying metadata and displaying them in a user interface accordingly.
- The electronic device gathers a variety of media items and their attributes.
- A machine learning model is used to determine pairwise similarity between the media items.
- An acyclic graph is generated to represent the similarity distances between the media items.
- Nodes in the graph are clustered based on similarity, with each node representing a media item.
- Metadata associated with a media item in a cluster is modified based on the clustering.
- The first media item in a cluster is displayed in a user interface with the modified metadata.
Potential Applications: - Content recommendation systems - Image or video organization tools - Music playlist generation algorithms
Problems Solved: - Efficient organization and display of media items - Personalized user experiences based on content similarity
Benefits: - Enhanced user engagement with media content - Improved content discovery and navigation - Tailored recommendations based on user preferences
Commercial Applications: Title: "Enhanced Media Content Organization and Display Technology" This technology can be utilized in digital media platforms, streaming services, e-commerce websites, and social media platforms to enhance user experience, increase user engagement, and drive content discovery.
Prior Art: There are existing technologies in the field of machine learning and content recommendation systems that analyze media attributes for clustering and personalized recommendations. However, this specific approach of modifying metadata based on clustering results to enhance user interface display is a novel aspect of this patent application.
Frequently Updated Research: Research on machine learning algorithms for content clustering and similarity analysis in media items is continuously evolving, with advancements in deep learning models and data processing techniques contributing to more accurate and efficient systems. Stay updated on the latest research in this field to leverage cutting-edge technologies for content organization and display.
Questions about the technology: Question 1: How does the machine learning model determine pairwise similarity between media items? Answer: The machine learning model uses a set of attributes of each media item to calculate a similarity score between pairs of items, based on which the pairwise similarity is determined.
Question 2: What are the potential challenges in implementing this technology in real-world applications? Answer: Some challenges may include processing large volumes of media items efficiently, ensuring accurate clustering results, and managing user privacy concerns related to data analysis and recommendation algorithms.
Original Abstract Submitted
an electronic device obtains a plurality of media items, including, for each media item in the plurality, a set of attributes of the media item. the device provides the set of attributes for each media item of the plurality of media items to a machine learning model that is trained to determine a pairwise similarity between respective media items in the plurality of media items and generates an acyclic graph of an output of the machine learning model that is trained to determine pairwise similarity distances between respective media items in the plurality of media items. the device clusters nodes of the acyclic graph, each node corresponding to a media item. based on the clustering, the electronic device modifies metadata associated with a first media item in a first cluster and displays a representation of the first media item in a user interface according to the modified metadata.
