Google llc (20240248927). QUERY RESPONSE USING MEDIA CONSUMPTION HISTORY simplified abstract
Contents
QUERY RESPONSE USING MEDIA CONSUMPTION HISTORY
Organization Name
Inventor(s)
Matthew Sharifi of Kilchberg (CH)
QUERY RESPONSE USING MEDIA CONSUMPTION HISTORY - A simplified explanation of the abstract
This abstract first appeared for US patent application 20240248927 titled 'QUERY RESPONSE USING MEDIA CONSUMPTION HISTORY
The patent application describes methods, systems, and apparatus for receiving a natural language query from a user, along with environmental data, to identify a media item and determine an entity type based on the query. It then selects an entity associated with the media item that matches the entity type, and retrieves one or more media items from a database that have been consumed by the user and are associated with the selected entity to provide a response to the query.
- Natural language query processing
- Environmental data analysis
- Entity type identification
- Media item selection based on entity type
- Retrieval of consumed media items associated with selected entity
- Response generation based on consumed media items
Potential Applications: - Personalized content recommendations - Enhanced user experience in media consumption platforms - Improved search functionality for media libraries
Problems Solved: - Efficient matching of user queries with consumed media items - Enhanced relevance of search results for users - Personalized content delivery based on user preferences
Benefits: - Increased user engagement with media content - Enhanced user satisfaction with search results - Improved user retention on media platforms
Commercial Applications: Title: Personalized Media Content Recommendation System This technology can be utilized in streaming services, online media platforms, and digital libraries to enhance user experience and increase user engagement with personalized content recommendations.
Prior Art: Readers interested in prior art related to this technology can explore research papers on natural language processing, content recommendation systems, and user behavior analysis in media consumption platforms.
Frequently Updated Research: Researchers are constantly exploring new algorithms and techniques to improve the accuracy and efficiency of personalized content recommendation systems in various applications.
Questions about Personalized Media Content Recommendation System:
1. How does this technology improve user satisfaction with search results? This technology enhances user satisfaction by providing personalized content recommendations based on the user's consumption history and preferences.
2. What are the key features that differentiate this system from traditional content recommendation systems? This system integrates environmental data and entity type identification to tailor content recommendations specifically to the user's preferences and consumption patterns.
Original Abstract Submitted
methods, systems, and apparatus for receiving a natural language query of a user, and environmental data, identifying a media item based on the environmental data, determining an entity type based on the natural language query, selecting an entity associated with the media item that matches the entity type, selecting, from a media consumption database that identifies media items that have been indicated as consumed by the user, one or more media items that have been indicated as consumed by the user and that are associated with the selected entity, and providing a response to the query based on selecting the one or more media items that have been indicated as consumed by the user and that are associated with the selected entity.
