Content Delivery System Evaluating Consumption Patterns
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Solution Overview
Problem
Current content delivery systems lack the ability to automatically evaluate user content consumption patterns across multiple devices and venues, leading to inefficient delivery of content that aligns with user preferences in terms of type, device, and timing.
Innovation Solution
A content delivery system that evaluates user consumption patterns across various devices and venues, identifying preferred content, devices, and times to deliver targeted content items by analyzing historical consumption data and sensor information, using a content delivery engine with modules for device association, content consumption evaluation, and content staging.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If content delivery systems manually track user consumption patterns, then delivery accuracy improves, but system complexity and time consumption increase
Solution Approach 1:
The system automatically evaluates content consumption patterns by having user devices self-report consumption data to the content delivery system. The evaluation module processes this data autonomously to determine user preferences for content types, devices, and timing without manual intervention, resolving the contradiction between measurement precision and system complexity.
Solution Approach 2:
The system implements a feedback loop where consumption data is continuously collected from multiple devices, evaluated to update user preference profiles, and used to improve future content delivery decisions. This automated feedback mechanism maintains high measurement precision while reducing system complexity through iterative learning.
2Adaptability or versatility
If content delivery systems monitor multiple devices and venues, then content delivery relevance improves, but data processing complexity increases
Solution Approach 1:
The content delivery system implements a universal evaluation module that handles multiple device types and consumption venues through a single integrated processing framework. This module universally processes consumption data regardless of device type (mobile, desktop, TV) or venue (home, work, commute), improving content delivery relevance while managing data processing complexity through standardized handling procedures.
3Measurement precision
If content delivery systems analyze historical consumption data, then user preference accuracy improves, but processing time increases
Solution Approach 1:
The system performs preliminary evaluation of consumption patterns by continuously analyzing historical data in the background and pre-determining user preferences before content delivery decisions are needed. This preliminary action maintains high user preference accuracy while reducing processing time during actual content delivery by having preferences pre-computed and stored.
Data Source
AI summary
A system includes at least one hardware processor and a memory storing instructions that, when executed by the at least one hardware processor, cause the at least one hardware processor to perform operations including receiving consumption data associated with a user consuming content on one or more user computing devices, determining a preference associated with content consumption of the user based on the received consumption data, the preference including one or more of a delivery time, a computing device of the one or more user computing devices, and a venue, receiving a new content item, determining one or more of a target delivery time, a target computing device of the one or more user computing devices, and a target venue based on the determined preference, and transmitting the new content item to the target computing device for presentation to the user.