US 12,242,981 B1
Contextualized human machine systems and methods of use
Adam Fouse, Medford, MA (US); and Ryan Mullins, Somerville, MA (US)
Assigned to Aptima, Inc., Woburn, MA (US)
Filed by Aptima, Inc., Woburn, MA (US)
Filed on Jan. 6, 2021, as Appl. No. 17/143,152.
Claims priority of provisional application 62/985,123, filed on Mar. 4, 2020.
Int. Cl. G06N 5/02 (2023.01); G06F 40/30 (2020.01); G06N 5/04 (2023.01)
CPC G06N 5/04 (2013.01) [G06F 40/30 (2020.01); G06N 5/02 (2013.01)] 15 Claims
OG exemplary drawing
 
1. A contextualized human machine system configured to automatically determine a context-aware recommendation to a user, the system comprising:
a context platform configured to receive an input data from an analyst workstation;
the context platform configured to automatically define, from the input data, a first property value of a first node corresponding to a multi-layer knowledge graph;
the context platform configured to define a second property value of a second node of the multi-layer knowledge graph;
the second node comprising a content node comprising a work product;
the first node and the second node comprising a node pairing;
the context platform defining a relationship property value of a relationship type between the first node and the second node;
a recommendation engine configured to execute a recommendation algorithm to automatically determine a context-aware recommendation of a third node based on a connection strength measure and a similarity measure;
communicating the second node and the third node to the analyst workstation as an augmented work product; and
wherein the recommendation algorithm comprises a graph traversal algorithm configured to:
(a) identify one or more additional node pairing of the first node connected by any relationship type to another node in a graph layer of the multi-layered knowledge graph;
(b) calculate a connection strength measure of the relationship type for each additional node pairing and associate the connection strength measure to each of the nodes in the additional node pairing;
(c) calculate a similarity measure of the nodes in each additional node pairing and associate the similarity measure to each of the nodes in the additional node pairing;
(d) iterate steps (a)-(c) for a next step out of the graph layer for subsequent node pairings connected by any relationships type until a threshold traversal depth of steps;
(e) define each of the nodes in the each of the additional node pairings and the subsequent node pairings as a plurality of related nodes;
(f) filter the plurality of related nodes to define a plurality of filtered nodes as a plurality of potential recommendations;
(g) determine a weighted value of each of the plurality of filtered nodes as a function of the connection strength measure and the similarity measure; and
(h) select the filtered node with the greatest weighted value as the context-aware recommendation.