US 11,841,886 B2
Recommendation system for change management in a quality management system
Jason Mckibbin, Carmel, IN (US); and Tyler Foxworthy, Indianapolis, IN (US)
Assigned to Soladoc, LLC, Indianapolis, IN (US)
Filed by Soladoc, LLC, Indianapolis, IN (US)
Filed on Sep. 13, 2021, as Appl. No. 17/473,779.
Claims priority of provisional application 63/076,994, filed on Sep. 11, 2020.
Prior Publication US 2022/0083578 A1, Mar. 17, 2022
Int. Cl. G06F 16/33 (2019.01); G06F 16/338 (2019.01)
CPC G06F 16/3344 (2019.01) [G06F 16/338 (2019.01)] 17 Claims
OG exemplary drawing
 
1. A change management system for Quality Management System (QMS) dependency visualization, comprising:
a server, coupled to a processor, and configured to execute instructions that:
receive an input from a user, the input comprising information relating to a proposed change to one or more objects (QMS objects) in a QMS (QMS network) via a graphical user interface (GUI), comprising a written description and justification for the change.
apply natural language processing techniques to extract structured metadata from the one or more QMS objects; and
generate an impact score corresponding to aggregate risk associated with a change in a first QMS object towards all other QMS objects in the QMS, wherein risk is defined as a statistical measure of the likelihood that a change in one or more interdependent QMS objects may contradict or invalidate the one or more policies of another of the QMS objects,
wherein the impact of a change may be determined in a bi-directional manner, such that for any pair of objects (a,b) the risks of a directed impact (a impacting b, a->b, or b impacting a, b->a) are potentially asymmetrical |R(a->b)-R(b->a)|>=0,
wherein the QMS network is an intranet, an extranet, a virtual private network (VPN), a non-TCP/IP based network, or any LAN,
wherein the metadata features comprise latent references to other objects within the QMS, either by name or unique identifier, and
wherein the metadata features comprising one or more keywords and/or vectorized topic representations.