US 11,817,212 B2
Maintenance method for a laboratory system
Maik Hadorn, Lucern (CH); Markus Bornhoeft, Kuessnacht am Rigi (CH); and Herbert Schurtenberger, Goldau (CH)
Assigned to Roche Diagnostics Operations, Inc., Indianapolis, IN (US)
Filed by Roche Diagnostics Operations, Inc., Indianapolis, IN (US)
Filed on Jun. 22, 2020, as Appl. No. 16/907,605.
Claims priority of application No. 19182503 (EP), filed on Jun. 26, 2019.
Prior Publication US 2020/0411176 A1, Dec. 31, 2020
Int. Cl. G16H 40/40 (2018.01); G16H 40/67 (2018.01); G16H 10/40 (2018.01); G06F 11/00 (2006.01); G06N 7/01 (2023.01)
CPC G16H 40/67 (2018.01) [G16H 40/40 (2018.01); G06F 11/004 (2013.01); G06N 7/01 (2023.01); G16H 10/40 (2018.01)] 15 Claims
OG exemplary drawing
 
1. A maintenance method for a laboratory system, wherein the laboratory system comprises a first group and a second group of laboratory instruments for processing biological samples, a plurality of data collection components communicatively connected to the first group and second group of laboratory instruments, and a remote maintenance system communicatively connected to the data collection components, wherein the first group of laboratory instruments is connected to a first data collection component while the second group of laboratory instruments is connected to a second data collection component, the method comprising:
collecting operational data from the laboratory instruments by the data collection components, the operational data being indicative of one or more operational parameters of the respective laboratory instruments;
detecting an anomaly related to one or more of the plurality of laboratory instruments of the first group by the first of the plurality of data collection components based on the collected operational data;
transmitting context data by the first of the plurality of data collection components to the remote maintenance system upon detection of an anomaly, the context data comprising operational data and data indicative of the anomaly;
determining one or more correlation(s) between the operational data and the anomaly(s) at the remote maintenance system;
validating the one or more correlation(s) at the remote maintenance system;
determining at the remote maintenance system one or more predictive rules corresponding to validated correlations;
transmitting the one or more predictive rule(s) by the remote maintenance system to the data collection components; and
predicting occurrence of an anomaly of one or more of the plurality of laboratory instruments based on the one or more predictive rule(s) by one or more of the plurality of data collection components.