Autonomous journaling response using data model LUTS

Abstract

Matching journal entries to appropriate context responses can be a daunting problem, especially when there are no salient keyword matches between the entry and the proposed library of appropriate responses. We examine a real-world application for matching interactive journaling requests for guidance to an a priori established archive of sufficient multimedia responses. We show the analysis required to enable a Data Model based algorithm to group journaling entries according to intrinsic context information and type. We demonstrate a new lookup table (LUT) classifier that exploits all available data in LUT form