Web Page Attention Area Determination via Event Stream Aggregation
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Solution Overview
Problem
Existing web-based applications struggle to accurately determine common attention areas on web pages due to coarse-grained click stream data that often lacks user attention focus between events, and only provides insights for single user interactions.
Innovation Solution
A system and method that records and processes user event data across multiple interactions to create attention data sets, aggregating user interest in web page elements and ranking their popularity using an event observer module and event server, which analyzes mouse movements and other events to determine fine-grained user focus.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If click stream data is used to determine user attention areas, then the implementation is simple, but the measurement precision is low due to coarse-grained data and long periods between events
Solution Approach 1:
The patent segments user interaction data into fine-grained event streams including mouse movements, clicks, and hovers at the document object model element level. This segmentation allows tracking of attention at a much more granular level than traditional click stream data, resolving the contradiction by breaking down coarse interactions into multiple precise measurement points.
Solution Approach 2:
The patent implements continuous event streaming that captures user interactions in real-time without gaps. By continuously monitoring and recording events as they occur, the system eliminates the long periods between measurements inherent in traditional click stream analysis, maintaining high measurement precision while managing complexity through continuous data collection.
2Device complexity
If traditional click stream analysis is used, then the system complexity is low, but the quantity of information captured is insufficient for determining common attention areas across multiple users
Solution Approach 1:
The patent merges event streams from multiple users and combines them into aggregate attention data. By consolidating fine-grained interaction data across numerous users, the system captures sufficient quantity of information to determine common attention areas while managing complexity through centralized aggregation and processing.
Solution Approach 2:
The patent adds the dimension of aggregation across multiple users and interactions to the analysis. Instead of analyzing single-user click streams in isolation, the system accumulates event data across many users and interactions, transforming the data from individual interaction records to population-level attention patterns, thereby increasing the quantity of useful information.
3Measurement precision
If fine-grained event tracking is implemented across multiple interactions, then the measurement precision and comprehensiveness improve, but the device complexity and data processing requirements increase
Solution Approach 1:
The patent introduces an event server as an intermediary component that receives, standardizes, and processes fine-grained event streams from multiple sources. This intermediary layer manages the complexity of handling detailed interaction data by providing a centralized processing point that aggregates events and generates attention metrics, thereby enabling high measurement precision while controlling system complexity through modular architecture.
Data Source
AI summary
During an interaction with a web page, user interface events are recorded and augmented with page layout data from the document object model. An event stream is formed with the page layout data and communicated to an event server. The event server processes the event stream to determine a location at which the events were generated and determines attention data for each event stream that indicates where a user's attention was directed during an interaction. The attention data for a plurality of event streams is aggregated to determine common or popular areas of interest of the web page.