SBIR Phase II: Pushing the Boundaries of Intelligent Assistants for Financial Services

The broader impact/commercial potential of this Small Business Innovation Research (SBIR) Phase II project is in providing state-of-the-art tools allowing anyone to build and deploy domain specific commercial intelligent virtual assistant (IVA) solutions. These tools allow others to understand how IVAs should be architected and integrate IVA technology into their offerings. IVAs have shown promise in numerous commercial domains including financial services, healthcare, education, law enforcement, and retail, to name a few, reducing the barrier to knowledge access within domains by providing a medium for people to converse naturally with sophisticated computer and information systems. This Small Business Innovation Research (SBIR) Phase II project will address the significant technological challenges involved when scaling the domains and capabilities of an Intelligent Virtual Assistant (IVA). This project will innovate in designing scalable artificial intelligence models capable of learning and identifying hundreds or thousands of learned concepts, and designing the accompanying system architecture to support the growing compute demand of sophisticated algorithms. Specifically, the project aims at achieving: (1) Scalable Intelligence: the ability to handle hundreds or thousands of competencies and extractable semantic concepts, allowing users to interact with the system with unbounded, unconstrained language; (2) Customizable Intelligence: the ability to allow customers to (semi-) automatically train and (re)train and customize the intelligence on demand (adding new competencies, identifying new slot-value pairs, modify responses); (3) Conversational Complexity: support multi-turn conversations, where the context from prior utterances is used to refine and understand what the end-user is trying to accomplish; and (4) Scalable System Infrastructure: enhance open source IVA software infrastructure to seamlessly scale up and down the computational resources allocated for each intelligence engine based on load.