User Attention Area Detection via DOM Event Stream Analysis
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Solution Overview
Problem
Existing methods for determining a user's attention focus during web interactions, such as click stream data, are coarse-grained and lack precision, particularly between mouse click events, making it difficult to accurately assess user attention over time.
Innovation Solution
A system and method that records and processes document object model elements and user interface events within a web browser, using an event observer module and layout module to generate event streams, which are then analyzed to predict the user's area of focus, incorporating event capturing and bubbling phases, and viewport and DOM element layout information.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If click stream data is used to determine user attention, then implementation is simple, but measurement precision is poor due to coarse-grained data and long periods between events
Solution Approach 1:
The patent segments user interaction data into multiple granular event types (mouse movements, clicks, hovers, keyboard inputs) and temporal phases (event capturing, event bubbling). This segmentation transforms coarse click stream data into fine-grained attention indicators, resolving the contradiction between implementation simplicity and measurement precision by breaking down the measurement into manageable event components.
Solution Approach 2:
The patent adds temporal and spatial dimensions to traditional click stream data by recording event timestamps, cursor coordinates, and event sequences. This dimensional expansion converts one-dimensional click data into multi-dimensional interaction trajectories, enabling precise attention tracking while maintaining computational feasibility through structured event processing.
2Measurement precision
If fine-grained event tracking is implemented, then measurement precision improves, but device complexity increases due to multiple modules and processing phases
Solution Approach 1:
The patent merges event capturing, event bubbling, and attention calculation into an integrated processing pipeline. The event observer module combines multiple event types and phases into unified event streams, which are then processed together to determine attention areas. This merging reduces system complexity by consolidating functions while maintaining fine-grained measurement precision.
Solution Approach 2:
The event observer module serves multiple functions simultaneously: it captures various event types (mouse, keyboard, touch), processes different event phases (capturing, bubbling), and generates attention metrics. This multi-functionality reduces overall system complexity by using a single versatile module instead of separate specialized components for each function.
3Speed
If traditional click stream analysis is used, then processing speed is fast, but loss of information occurs due to long periods between events where attention is unknown
Solution Approach 1:
The patent implements continuous event monitoring by capturing multiple event types (mouse movements, hovers, clicks, keyboard inputs) that occur continuously during user interaction. This continuous event stream fills the information gaps between traditional click events, maintaining both high processing speed and complete attention information without loss.
Solution Approach 2:
The event capturing phase records all user interactions before processing, preserving complete interaction data including preliminary actions like mouse movements and hovers that occur before actual clicks. This preliminary recording ensures no attention information is lost while maintaining processing efficiency through batch processing of captured events.
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, which can be used to predict an area of a user's attention.