Behavioral Analytics Platform Using Mouse Data for Survey Accuracy
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Solution Overview
Problem
Traditional surveys rely solely on self-reported data, which is prone to inaccuracies due to lack of standardization, untruthful responses, and implicit biases, as evident in the 2016 and 2020 U.S. presidential election polls, necessitating a system to capture behavioral data for enhanced analysis.
Innovation Solution
A behavioral analytics platform utilizing machine and deep learning models to analyze user behavior, including mouse movements and response times, to establish a behavioral baseline for respondents, enabling precise analysis of emotional states and sentiment through continuous data capture and real-time adjustments.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If self-reported data is collected in standard surveys, then data collection is simple and quick, but accuracy and reliability deteriorate due to untruthful responses, lack of standardization, and implicit biases
Solution Approach 1:
The patent segments the data collection process into multiple independent components: self-reported answers are separated from behavioral data (mouse movements, response times, page scrolling). This segmentation allows each data type to be collected and analyzed independently, with behavioral data providing objective verification of self-reported responses, thereby improving overall accuracy without requiring a complete system overhaul
Solution Approach 2:
The patent introduces behavioral data as an intermediary element that mediates between the respondent's self-reported answers and the final analysis. This intermediary behavioral data (such as mouse movement patterns, response timing, and page engagement) serves as an objective indicator that validates or challenges self-reported responses, improving measurement precision while adding only moderate system complexity through standard web analytics capabilities
2Measurement precision
If behavioral data such as mouse movements and response times is captured, then analysis precision improves, but data collection complexity increases
Solution Approach 1:
The patent implements self-service by leveraging the respondent's own device and natural interactions to collect behavioral data. The system automatically captures mouse movements, response times, and page engagement metrics through standard web analytics code without requiring additional sensors, hardware modifications, or complex collection infrastructure. The respondent's device serves itself to gather the data, minimizing system complexity while maximizing analysis precision
Solution Approach 2:
The patent changes the parameters being measured from subjective self-reported opinions to objective behavioral parameters (mouse movement velocity, response time intervals, page scroll depth). These parameter changes enable precise behavioral analysis using standard computational methods, improving measurement precision while keeping system complexity manageable through algorithmic processing of naturally occurring digital traces
3Reliability
If end-to-end user journey data is captured to understand decision-making process, then reliability of results improves, but loss of time in processing and analyzing data increases
Solution Approach 1:
The patent applies preliminary action by collecting and organizing behavioral data continuously throughout the survey-taking process, rather than attempting to analyze it after data collection. Behavioral metrics such as response times and mouse movements are captured in real-time and pre-processed into analyzable formats during the user journey. This preliminary organization of data significantly reduces processing time while maintaining high reliability, as the data is ready for immediate analysis without requiring post-collection reconstruction of user behavior patterns
Solution Approach 2:
The patent replaces manual or complex mechanical analysis methods with automated computational algorithms that process behavioral data. Machine learning models and statistical algorithms automatically analyze mouse movement patterns, response time variations, and page engagement metrics to derive reliability indicators. This substitution of mechanical analysis with automated computational processing dramatically reduces analysis time while improving reliability through consistent, objective evaluation of behavioral patterns
Data Source
AI summary
A behavioral analysis platform is a comprehensive system designed to enhance the accuracy of polls and surveys. It utilizes a online survey optimized through payload management and employs machine and deep learning models to analyze behavioral, haptic, or biometric data. The platform captures user behavior, including mouse movements, response times, and other haptic data, using custom APIs. It features a payload manager subsystem for campaign planning and execution, optimizing survey elements for behavioral analytics. The system establishes a behavioral baseline for each respondent, enabling precise analysis of survey responses in terms of conviction, veracity, and sentiment. Real-time adjustments to survey elements and continuous data capture contribute to improved predictive capabilities.