MUMBAI, India, Sept. 14 -- Intellectual Property India has published a patent application (202641105413 A) filed by Sr University on September 01, 2026, for Ai-Enabled Adaptive Workplace Sustainability And Work-Life Balance Management System For Dynamic Technology-Based Work Environments.
Inventors include B Pushpalatha; and Dr. Karra.
The application for the patent was published on September 11, 2026, under issue no. 37/2026.
Abstract: The present invention relates to an AI-enabled adaptive workplace sustainability and work-life balance management system for dynamically managing technology- enabled work environments. The system integrates technology workload, employee work-life boundary conditions, and digital environmental sustainability within a unified computational architecture. A workplace data acquisition layer receives authorized metadata and event-level information from enterprise resource planning systems, customer relationship management systems, project-management platforms, communication tools, calendars, collaboration platforms, cloud-computing systems, workstation telemetry, and organizational energy-management systems. An intelligent workplace state engine converts the acquired information into normalized indicators including task intensity, task duration, communication frequency, meeting density, after-hours activity, interruption frequency, task urgency, deadline proximity, availability status, and digital workload concentration. The system generates a Technology Workload Index, Boundary Interference Index, Digital Sustainability Index, and Workplace Adaptability Index. An AI-based adaptive decision engine analyzes the combined workplace state and dynamically determines suitable interventions, including delaying non-critical notifications, merging communications, rescheduling computing-intensive workloads, modifying meeting schedules, enabling protected non-work periods, and shifting suitable workloads to lower-impact computing periods or resources. A sustainability computation layer evaluates the environmental impact of digital workloads, while a feedback-learning layer uses intervention outcomes and subsequent workplace-state information to improve future decisions. The system further supports privacy-preserving operation by processing aggregated or pseudonymized metadata without requiring access to the substantive content of employee communications or documents.
Disclaimer: Curated by HT Syndication.
