Abstract
Field Computer-implemented systems for detecting employee knowledge deficits by analyzing clickstream and workflow interaction patterns within enterprise software applications, without processing any natural language communications or administering formal assessments. Background Employees who lack knowledge in a particular area exhibit distinctive behavioral patterns when interacting with enterprise software: they dwell longer on form fields they are uncertain about, access help documentation more frequently, avoid features they do not understand, and show higher randomness in their navigation. These patterns are observable from standard UI event telemetry. Technical Description The system captures UI events from instrumented enterprise applications. Each event contains: timestamp, employee_id, application_id, event_type (click, hover, scroll, field_focus, field_blur, help_open, navigation, feature_access), target_element, and duration_ms. Events are collected continuously during normal work activity. Four behavioral signals are computed per employee per knowledge domain per rolling time window (default: 14 days). Signal 1 (Dwell Anomaly): For each form field or UI element mapped to a knowledge domain, compute the employee's average dwell time. Flag elements where average dwell exceeds the peer cohort mean by more than 2 standard deviations. Signal 2 (Help Access Frequency): Count the number of help documentation access events per knowledge domain. Compare against peer cohort baseline. A rate exceeding 1.5 times the peer baseline triggers this signal. Signal 3 (Feature Avoidance): For each feature mapped to a knowledge domain, count the employee's interaction events. A zero-interaction count on a feature that peer cohort members use at least once per evaluation window triggers this signal. Signal 4 (Navigation Entropy): Compute Shannon entropy over the employee's click-event sequences within each workflow. Persistent elevation above peer baseline indicates uncertainty. A composite deficit score is computed from the four signals using configurable weights (defaults: dwell 0.30, help_access 0.25, avoidance 0.25, entropy 0.20). When the composite exceeds a threshold for two or more consecutive evaluation windows, the system generates a gap record containing: employee_id, knowledge_domain_id, composite_score, individual_signal_scores, contributing_event_ids, and evaluation_window_timestamps. Distinguishing Characteristics This system operates on clickstream telemetry from enterprise software, not on natural language communications. No NLP pipeline processes any input. No intent classification, entity extraction, or taxonomy mapping from text occurs. The gap signal comes from behavioral patterns (dwelling, help-seeking, avoidance, navigation randomness), not from what the employee says or how they score on assessments
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This work is licensed under a Creative Commons Attribution 4.0 License.
Recommended Citation
Davis, Kenneth, "Clickstream Behavioral Pattern Analysis for Employee Knowledge Gap Inference in Enterprise Software", Technical Disclosure Commons, (March 25, 2026)
https://www.tdcommons.org/dpubs_series/9639