Indoor Place Prediction Platform for Context‑Aware Location Intelligence (Indoor Place Prediction, Tech ID: 23‑011)
Technology Overview: This technology provides an indoor place prediction system that infers user location and contextual place (e.g., room type or functional area) within indoor environments. By fusing signals such as device sensors, network data, and learned spatial patterns, the platform delivers accurate, privacy‑aware indoor localization without requiring extensive new infrastructure. The approach supports real‑time inference and adapts to changing indoor layouts, enabling smarter experiences across campuses, enterprise buildings, and public venues.
Industry Pain Point: GPS is unreliable indoors, and existing solutions require costly beacons or manual calibration.
NJIT Solution: Infrastructure‑light, data‑driven indoor place prediction with robust accuracy and scalability.
Key Features & Advantages
- Works without dense beacon deployment
- Context‑aware place inference (not just coordinates)
- Privacy‑conscious and adaptable to layout changes
- Real‑time operation
Development Stage: TRL 3–4 – Algorithmic validation completed.
Target Markets
- Smart buildings & campuses
- Enterprise analytics
- Indoor navigation platforms
Market Opportunity
- Indoor location services (2026): ~$18B • CAGR: ~15–18% • 2035: ~$70–80B
Commercial & IP Details
Inventors: Manoop Talasila, Qiong Wu, Wen‑Ling Hsu, Xiaopeng Jiang, Cristian Borcea, Pritam Sen