US 12,422,792 B1
Individual machine configuration based on overall process performance and latent metrics
Bart Schouwenaars-Harms, Henley-on-Thames (GB)
Assigned to Amazon Technologies, Inc., Seattle, WA (US)
Filed by Amazon Technologies, Inc., Seattle, WA (US)
Filed on May 25, 2021, as Appl. No. 17/330,213.
Int. Cl. G05B 13/04 (2006.01); G05B 13/02 (2006.01); H04L 41/08 (2022.01); H04L 41/0803 (2022.01); H04L 41/0859 (2022.01)
CPC G05B 13/042 (2013.01) [G05B 13/0265 (2013.01); H04L 41/08 (2013.01); H04L 41/0803 (2013.01); H04L 41/0859 (2013.01)] 22 Claims
OG exemplary drawing
 
1. A system, comprising:
one or more storage systems comprising:
a metrics repository for storing individual non-latent metrics including machine-level metrics data for a plurality of machines used together to implement a process that produces physical products and process-level metrics data for the process as-a-whole;
a configuration data repository for storing historical machine configuration setting values for the plurality of machines;
one or more computers comprising respective processors and memory and configured to:
obtain, during a current time period after a production time period during which processing of a physical product is performed and completed, latent metrics data for the completed physical product produced by the processing, wherein the obtained latent metrics data were unavailable during the production time period of the physical product through to immediately subsequent to the production time period and, if the physical product was tested, unavailable through testing of the physical product during and immediately subsequent to the production time period;
access the configuration data repository to determine previous machine configuration setting values for the plurality of machines during the production time period during which the physical product was processed;
provide the machine-level metrics data and the process-level metrics data for the current time period, the latent metrics data for the physical product produced by the process, and the machine configuration setting values during the production time period to one or more machine learning models to determine updated configuration setting values for the plurality of machines; and
transmit the updated configuration setting values, determined via the one or more machine learning models based at least in part on the machine-level metrics data, the process-level metrics data for the current time period, the latent metrics data for the physical product produced by the process during the production time period, and the machine configuration setting values during the production time period, to enable the plurality of machines to implement the updated configuration setting values.