Intellectual Property India Publishes Patent Application for Machine Learning-Based System For Early Detection Of Workplace Mental Health Risks And Employee Well-Being Optimization.

MUMBAI, India, July 30 -- Intellectual Property India has published a patent application (202641088798 A) filed by Dr. D. Abraham Pradeep on July 21, 2026, for Machine Learning-Based System For Early Detection Of Workplace Mental Health Risks And Employee Well-Being Optimization..

Inventor includes Dr. D. Abraham Pradeep.

The application for the patent was published on July 24, 2026, under issue no. 30/2026.

Abstract: ABSTRACT [505] Timely and accurate identification of emerging workplace mental health risk factors constitutes a foundational requirement for sustainable organizational productivity and employee retention; however, conventional workplace well-being management approaches such as periodic voluntary surveys, retrospective absenteeism reporting, and manager-observation-based referral remain inherently reactive, imprecise, and slow, thereby exposing organizations to preventable employee burnout, disengagement, and attrition. [510] Existing workplace well-being monitoring systems exhibit critical limitations including reliance on infrequent, self-reported survey data that fails to capture continuous behavioural and workload-pattern indicators of emerging risk, insufficient integration of privacy- preserving analytics pipelines capable of deriving aggregate risk indicators without exposing individually identifiable sensitive information, absence of interpretable, organization-level risk scoring suitable for prioritising well-being resource allocation, and lack of automated, non-diagnostic alerting and recommended-action generation linked to identified organizational risk drivers. [515] The integration of privacy-preserving multi-source workplace data ingestion, anonymized behavioural and linguistic feature extraction, machine learning based aggregate risk-indicator estimation, early warning and alerting mechanisms, and automated well-being recommendation generation presents transformative opportunities for developing a continuously operating, enterprise-deployable workplace mental health risk monitoring capability that is significantly more proactive, privacy-respecting, and actionable than traditional survey-based or retrospective well-being management approaches. [520] The present invention describes a Machine Learning-Based System for Early Detection of Workplace Mental Health Risks and Employee Well-being Optimization comprising a data ingestion and privacy-preserving integration module that consolidates anonymized workload, scheduling, communication-pattern, and voluntary survey data from multiple enterprise sources; a behavioural and linguistic feature extraction module that derives non-identifying engagement, workload- strain, and sentiment-trend indicators from the consolidated data; a machine learning risk prediction module that estimates aggregate, team-level and organization-level mental health risk indicators using trained classification models; an early warning and alerting module that continuously updates organizational risk indicators as new data is recorded and triggers alerts to human resources and well-being personnel upon threshold breach; a well-being recommendation module that generates targeted organizational intervention suggestions linked to identified risk drivers; and an anonymization and ethical safeguard module that enforces data minimisation, aggregation thresholds, and access controls to protect individual employee privacy throughout the system's operation. [525] Comprehensive evaluation of the system across a benchmark dataset of anonymized enterprise workforce records demonstrated organizational risk indicator prediction accuracy of 91.4 ± 1.6 percent, area under the receiver operating characteristic curve of 0.947 ± 0.011, precision of 89.2 ± 1.8 percent, recall of 87.6 ± 1.9 percent, false positive rate of under 6.3 percent, individual re-identification risk of under 0.4 percent following aggregation and anonymisation, and mean end-to-end risk-indicator refresh latency of under 2.8 seconds. [530] These findings confirm that the Machine Learning-Based System for Early Detection of Workplace Mental Health Risks and Employee Well-being Optimization constitutes a significant advancement in workplace well-being management technology, with deployment applicability spanning corporate human resources departments, occupational health service providers, and enterprise well-being platforms, thereby addressing the substantial unmet need for a proactive, privacy- respecting, and actionable workplace mental health risk monitoring solution. [532] The system is expressly configured to generate only aggregate, organization-level and team-level indicators and non-diagnostic recommendations, and does not generate individual clinical diagnoses; identified at-risk situations are directed toward qualified human resources professionals and licensed occupational health practitioners for appropriate follow-up.

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