Content Delivery Scene Definition for Personalization
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Solution Overview
Problem
Current methods for delivering personalized content are inefficient and unfriendly, particularly due to the challenges of navigating vast content options and understanding user preferences, leading to a need for improved systems that can effectively recommend content based on complex user circumstances.
Innovation Solution
A system and method that determine a 'scene' based on various factors such as user preferences, location, and time to deliver personalized content, using a network of servers and client devices to analyze and recommend content consistent with the defined scene, incorporating environmental and spatiotemporal patterns.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If content is delivered based on limited user data (selections, favorites, viewing habits), then the system complexity is reduced, but the personalization accuracy and user experience deteriorate
Solution Approach 1:
The patent transforms the personalization approach by changing from limited explicit parameters (favorites, selections) to comprehensive implicit parameters (spatiotemporal patterns, environmental context, device information, user behavior patterns). This parameter expansion enables higher personalization accuracy while managing system complexity through automated scene determination algorithms.
Solution Approach 2:
The system implements self-service by automatically determining user scenes and recommending content without requiring active user input. The system autonomously collects data from multiple sources, analyzes patterns, and generates content recommendations, reducing the burden on users while improving personalization accuracy.
2Ease of operation
If users search for content through traditional interfaces (program guides), then content delivery is straightforward, but the time consumption and operational efficiency worsen
Solution Approach 1:
The system performs preliminary action by pre-determining user scenes and pre-selecting relevant content before users initiate any search. Content recommendations are prepared in advance based on analyzed user patterns and current context, eliminating the need for users to manually search through guides and significantly reducing content discovery time.
Solution Approach 2:
The system implements feedback loops that continuously monitor user interactions, viewing habits, and environmental changes to refine scene determination and content recommendations. This adaptive feedback mechanism improves recommendation accuracy over time while maintaining simple interface interaction for users.
3Adaptability or versatility
If the content library expands dramatically, then content variety increases, but the difficulty of locating and delivering relevant content worsens
Solution Approach 1:
The patent introduces 'scene' as an intermediary concept that bridges the gap between diverse content and user needs. By determining user scenes from multiple data sources and matching scenes to content, the system effectively organizes vast content libraries without requiring users to navigate complex categories, making content location as easy as identifying user context.
Data Source
AI summary
Systems and methods are described that relate to delivering content to users. In one aspect, a primary client device and/or servers may communicate with secondary client devices to determine the identity of one or more users requesting content at the primary client device. Once the users and other attributes are identified, the primary client device may determine user characteristics which may be analyzed in conjunction with one or more spatiotemporal factors related to the content request. This analysis may lead to definition of a scene associated with the content request and to recommendation of content.