PREDICTIVE CUSTOMER BEHAVIOR MODELING SYSTEM BASED ON IOT SENSOR DATA AND METHOD THEREOF

Abstract :
The present invention relates to a predictive customer behavior modeling system and method that leverages real-time multimodal data collected
from a distributed network of Internet-of-Things (IoT) sensors deployed in physical environments such as retail stores, airports, and public
venues. The system comprises a combination of sensor modalities including motion detectors, facial expression analyzers, biometric signal
processors, and environmental monitors, all integrated with an edge-based computing device referred to as the Behavioral Intelligence Node
(BIN). The BIN is configured to perform on-device preprocessing, feature extraction, and short-term behavior inference using deep learning
models, while a cloud-based orchestration engine performs long-term pattern analysis and federated model updates. The invention enables
dynamic, context-aware prediction of customer intent and decision-making behavior by unifying diverse sensor data streams into a cohesive
behavior modeling framework. Additionally, it incorporates a privacy-preserving learning protocol to ensure compliance with data protection
standards while maintaining high model adaptability.