Mouse Trajectory Analysis for User Intent Classification
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Solution Overview
Problem
Advertisers face challenges in understanding user intent during online searches, as click-through rates do not provide insights into why users click or ignore ads, making it difficult to target advertising effectively.
Innovation Solution
Tracking mouse movements to infer user intent through client-side instrumentation and classification engines, which analyze mouse trajectories to distinguish between navigational, informational, commercial, and non-commercial queries, and adapt the user interface for more relevant content presentation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If click-through rates are used to measure advertising success, then advertising campaign performance can be quantified, but user intent and reasons for clicking or ignoring ads cannot be understood
Solution Approach 1:
The patent introduces mouse movement tracking as an intermediary data source between user interaction and intent analysis. By capturing mouse trajectories, hover patterns, and movement speed, the system gains access to behavioral indicators that reveal user intent without requiring users to explicitly state their intentions or provide additional information beyond natural browsing behavior.
2Measurement precision
If mouse movement tracking is implemented to infer user intent, then user intent classification accuracy improves, but system complexity and data processing requirements increase
Solution Approach 1:
The patent extracts only the essential features from mouse movement data that are most indicative of user intent, such as trajectory patterns, hover duration, and movement speed. By focusing on these key extracted features rather than processing all raw mouse movement data, the system achieves accurate intent classification while keeping computational requirements manageable.
Solution Approach 2:
The patent segments mouse movement data into distinct behavioral patterns and phases (e.g., initial positioning, hovering, clicking, scrolling). This segmentation allows the system to analyze different aspects of user interaction separately and combine them to infer overall intent, reducing the complexity of analyzing continuous raw data streams.
3Loss of information
If detailed mouse trajectory analysis is performed to understand user behavior, then diagnostic information about user cognitive abilities can be obtained, but data collection and processing time increase
Solution Approach 1:
The patent performs preliminary processing of mouse movement data by continuously tracking and storing trajectory information during natural user interaction. This preliminary data collection occurs in the background without requiring additional user actions or interrupting the user experience, so that when intent analysis is needed, the data is already prepared and readily available for quick processing.
Data Source
AI summary
User intent may be inferred from mouse movements made within a user interface. Client-side instrumentation may be provided that collects mouse movement data that is provided to a classification engine. The classification engine receives the mouse movement data and creates a mouse trajectory. The mouse trajectory may be split into segments, and features associated with each segment may be determined. Features representing the context of the search, that is, content of the search result page, previous queries submitted, and interaction features such as scrolling, may be included. By examining the features associated with the mouse trajectories within the context of a search session, the user intent may be classified into categories using machine learning classification techniques. By inferring user intent, Web search engines may be able to predict whether a user's intent is commercial and tailor advertising accordingly.