Editorial Content Engagement Inference for Granular Consumption Measurement
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Solution Overview
Problem
Existing methods are inadequate for accurately measuring the consumption and engagement of online content, as they fail to determine which content is consumed and to what extent, hindering publishers' ability to evaluate and improve their content.
Innovation Solution
A system comprising a script server, analytics engine, and data collection script that analyzes user interactions and metadata to infer user attention and engagement with online content, providing accurate analytics results on content consumption.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If user visit to a web page is used as a surrogate measure for content consumption, then measurement is simple to implement, but measurement precision is poor and cannot detect which content was actually consumed
Solution Approach 1:
The patent segments the web page into multiple content elements (text, images, videos, ads) and tracks user interactions with each segment individually. Instead of treating the entire page visit as a single metric, the system divides content consumption measurement into granular units, allowing precise tracking of which specific content elements were viewed, scrolled, or engaged with.
Solution Approach 2:
The patent introduces JavaScript code as an intermediary between the user's browser and the content elements. This intermediary captures user interactions (mouse movements, scrolling, clicking) and transmits them to a server for analysis, enabling precise measurement of content consumption without requiring changes to the core web page structure or content delivery mechanism.
2Measurement precision
If data collection script is placed on browser to measure content consumption, then measurement precision is improved, but device complexity increases
Solution Approach 1:
The patent implements self-service by having the web page itself generate the measurement data through embedded JavaScript code. The content elements automatically track and report their own consumption metrics (views, scrolls, clicks) without requiring external monitoring tools or manual data collection, reducing the complexity burden on the overall system.
Solution Approach 2:
The patent creates a universal measurement framework that can track multiple types of content elements (text articles, images, videos, advertisements) using the same JavaScript infrastructure. This multi-functional approach allows a single data collection system to handle diverse content types, reducing the need for separate tracking mechanisms for each content type and thereby lowering overall system complexity.
3Ease of manufacture
If existing methods are used to measure content consumption, then implementation is simple, but information loss occurs as no indication of which content was consumed or to what extent
Solution Approach 1:
The patent implements feedback loops where user interactions with content elements are continuously tracked and fed back to the measurement system. The JavaScript code monitors scrolling behavior, mouse movements, and click events, providing real-time feedback about which content elements are being viewed and to what extent, thereby preventing information loss about consumption details.
Solution Approach 2:
The patent applies preliminary action by pre-defining content elements and their associated metrics before user interaction occurs. Each content element is tagged with identification codes and measurement parameters in advance, so that when users interact with the page, the system can immediately capture and record detailed consumption information without requiring post-processing or inference.
Data Source
AI summary
A system for analyzing user consumption of content on web pages or determining user engagement with content comprising a data collection script and an content-engagement-inference engine. The data collection script determines one or more content elements to be measured from a web page and collects description data associated with the one or more content elements. The description data describes information about the one or more content elements. The content-engagement-inference engine creates an inference or analytics result based on the description data. The inference or analytics result describes how the one or more content elements have been consumed by users and then provides the inference or analytics result for users to designated entities.