Patent ID: 11922354
Assignee: JPMORGAN CHASE BANK, N.A.
Field: Computer technology (Electrical engineering)
Classification: CPC G | IPC G

Claim 12:
13. A system for measuring efficiency, comprising:
at least one computer including a processor and executing a computer program, wherein when executed by the processor, the computer program causes the computer to:
receive, from an enterprise software program, raw data related to an entity's performance;
determine, from the raw data a first numeric parameter and a second numeric parameter, wherein:
the first numeric parameter is derived from a metric measured by an organization;
the first numeric parameter measures the metric with respect to the entity;
the second numeric parameter is derived from the metric, and;
the second numeric parameter measures a median value of the metric across a plurality of entities of the organization;

calculate an efficiency metric based on the first numeric parameter, the second numeric parameter, and a metric weight, wherein the calculating includes dividing the second numeric parameter into the first numeric parameter to arrive at a first quotient, and multiplying the first quotient by the metric weight, wherein the efficiency metrics comprise at least one of a disempowerment efficiency metric, a bloat efficiency metric, a politics efficiency metric, an inertia efficiency metric, a risk aversion efficiency metric, an insularity efficiency metric, and a friction efficiency metric;
train a machine learning model using historical data for the entity;
identify a recommended action based on the efficiency metric via the machine learning model, wherein the recommended action comprises updating system software and the enterprise software program is configured to automatically update the system software;
monitor a change in the efficiency metric in response to the implementation, the change based on a relative success or failure of the implementation; and
further train the machine learning model based on the monitored change in the efficiency metric and the recommended action.