Activity Context-Aware System Architecture for Intelligent Natural Speech Based Interfaces

We propose a reference architecture for intelligent context-aware natural speech-enabled systems delivering complex functionality, with direct access to information, simplifying business processes and activities while providing domain-specific and task-specific depth in interactive banking, insurance, wealth management, finance, clinical, legal, telecom customer service, operations, supply chain, connected living room, and personal assistants. This system understands not just words, but intentions, and context of the interaction. We accomplish this through a marriage of speech recognition with advanced natural language processing techniques, scalable inference and semantic technologies. This architecture is expected to dramatically improve the quality of proactive decision support provided by virtual agents by enabling them to seek explanations, make predictions, generate and test hypothesis and perform what-if-analyses using scalable inference engines. The system can provide extreme personalization (N=1) by inferring user intent, making relevant suggestions, maintaining context, carrying out cost-benefit analysis from multiple perspectives, finding similar cases before they are searched for, finds relevant documents and answers, issues resolved by experts in similar situations. The architecture enables meaningfully correlating, finding and connecting people and information sources through discovery of causal, temporal and spatial relations. We present two examples of demonstrations of concept that we are in the process of building out.