E-Form Analytics via Interaction Sensor Clustering
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Solution Overview
Problem
Digital channel owners face challenges in preventing premature termination of interactions with electronic forms and detecting fraudulent submissions, as existing solutions lack specific insights into friction points and often result in high false positives/negatives due to reliance on external factors.
Innovation Solution
A method and system that generate analytics by tracking interaction sensor signals from users with embedded scripts, clustering similar signals to identify friction points and fraudulent activity, providing insight scores to determine user behavior and detect anomalies in real-time.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If existing solutions use only external data (form completion rates, user locations, interaction durations) to analyze e-form interactions, then the solution is simple to implement, but it cannot identify specific friction points or provide insights into why users terminate interactions prematurely
Solution Approach 1:
The patent segments the e-form into individual fields and tracks interactions at the field level rather than treating the form as a whole. This segmentation enables identification of specific friction points by analyzing user behavior on individual fields, such as time spent, number of edits, and abandonment patterns at each field level.
Solution Approach 2:
The patent adds a new dimension of analysis by implementing real-time tracking of user interactions within the e-form interface. This goes beyond traditional external metrics by capturing internal form interaction data such as field focus events, input changes, and navigation patterns, creating a multi-dimensional view of user behavior.
2Measurement precision
If existing solutions indicate only the time of termination or the last field being completed, then the analytics are simple to generate, but they may introduce errors when abandonment is due to cumulative effort across multiple fields rather than a single field
Solution Approach 1:
The patent implements feedback mechanisms that continuously monitor user interactions with e-form fields and provide real-time analytics on friction points. The system collects data on user behavior patterns across multiple fields and provides feedback to form creators about which specific fields or sequences of fields are causing user abandonment, enabling precise identification of friction points.
Solution Approach 2:
The patent performs preliminary analysis of user interaction patterns by tracking and storing detailed field-level interaction data as users complete forms. This preliminary collection of granular interaction data enables later analysis to identify friction points without requiring complex real-time processing during form completion, as the data is already captured and organized by field.
3Reliability
If fraud detection relies on subjective evaluation of handwriting, syntax, or speech patterns, then traditional methods can be applied to paper forms, but the determinations are highly subjective and vary between employees
Solution Approach 1:
The patent replaces subjective human evaluation of handwriting, syntax, and speech patterns with automated computational analysis systems. The system uses algorithms to objectively analyze form completion patterns, interaction timing, and behavioral metrics to detect fraudulent submissions, eliminating variability between human employees and providing consistent, reproducible fraud detection decisions.
Solution Approach 2:
The patent changes the parameters used for fraud detection from subjective qualitative assessments (handwriting appearance, speech patterns) to objective quantitative parameters (interaction timing, field completion sequences, pause durations, navigation patterns). This parameter transformation enables automated, consistent analysis that can be processed computationally without human subjectivity.
4Productivity
If digital channels use automated bot submissions to complete survey e-forms, then survey completion volume increases, but the results are skewed and benefits intended for multiple people are accrued by a single person
Solution Approach 1:
The patent implements feedback mechanisms that monitor survey completion patterns and identify bot-generated submissions. The system tracks interaction patterns such as completion speed, navigation behavior, and temporal patterns to detect automated submissions, providing feedback to filter out invalid responses and ensure only genuine human participants contribute to survey results.
Data Source
AI summary
A system and method for generating analytics based on interactions through digital channels. The method includes determining a plurality of interaction sensor signals based on interactions with an electronic form (e-form); clustering at least one set of similar interaction sensor signals of the determined plurality of interaction sensor signals, wherein each set of similar interaction sensor signals includes signals determined based on interactions with the same portion of the e-form; and generating at least one analytic based on each clustered set of interaction sensor signals.