US 12,266,254 B2
Corroborating device-detected anomalous behavior
Kevin W. Brew, Niskayuna, NY (US); Michael S. Gordon, Chappaqua, NY (US); Mattias Fitzpatrick, Mount Kisco, NY (US); and Brian Paul Gaucher, Brookfield, NY (US)
Assigned to International Business Machines Corporation, Armonk, NY (US)
Filed by INTERNATIONAL BUSINESS MACHINES CORPORATION, Armonk, NY (US)
Filed on Sep. 15, 2022, as Appl. No. 17/932,494.
Prior Publication US 2024/0096191 A1, Mar. 21, 2024
Int. Cl. G08B 21/10 (2006.01); G16Y 20/10 (2020.01)
CPC G08B 21/10 (2013.01) [G16Y 20/10 (2020.01)] 20 Claims
OG exemplary drawing
 
1. A computer-implemented method comprising:
training a first behavior classifier hosted on a first device included in an Internet of Things (IOT) mesh network to independently identify occurrences of anomalous behavior in a first human environment proximate to the first device, the anomalous behavior resulting from one or more non-computer network events occurring within the first human environment;
training a second behavior classifier hosted on a second device included in the IoT mesh network to independently identify occurrences of anomalous behavior in a second human environment proximate to the second device, the anomalous behavior resulting from one or more non-computer network events occurring within the second human environment;
receiving event data from the first and second devices corresponding to a time window, wherein the event data reports occurrences of at least one type of anomalous behavior in the first and second human environments;
corroborating the at least one type of anomalous behavior to determine that the occurrences of the at least one type of anomalous behavior indicate an anomalous event that meets a reporting threshold for providing notice of the anomalous event; and
generating a notification regarding the anomalous event.