IP Library › Granted Patent US 11,922,354
Granted Patent B2
US 11,922,354 · App. 16/951,597 · Granted Mar 5, 2024

Systems and methods for analyzing data sources for evaluating organizational efficiency

Inventors: Shawn Wesley Alexander (Pearland, TX); Daniel D. McQuiston (West Chester, PA); Eric Han Kai Chang (Wilmington, DE); James P. White, III (Middletown, DE); Erin Michelle Perry (Townsend, DE)
Assignee: JPMORGAN CHASE BANK, N.A.
G06Q10/06375G06N5/041G06N20/00G06Q10/06315
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Quick Facts
Patent No.
US 11,922,354
App. No.
16/951,597
Granted
Mar 5, 2024
Kind
B2
Abstract

Systems and methods for determining the efficiency of an organization by analyzing data feeds from various applications are disclosed. According to one embodiment, in an information processing apparatus comprising at least one computer processor a method for measuring efficiency may include: (1) receiving, from an enterprise software program, raw data related to an entity's performance; (2) calculating a plurality of efficiency metrics based on the raw data; (3) identifying a recommended action based on one of the efficiency metrics; and (4) automatically implementing the recommended action.

Claims (47)

1. A method for measuring efficiency, comprising:

in an information processing apparatus comprising at least one computer processor:

receiving, from an enterprise software program, raw data related to an entity's performance;

determining, 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;

calculating 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;

training a machine learning model using historical data for the entity;

identifying 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;

monitoring 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 training the machine learning model based on the monitored change in the efficiency metric and the recommended action.

2. The method of claim 1 , wherein the efficiency metric comprises 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.

3. The method of claim 1 , further comprising:

graphically presenting at least one of the efficiency metrics with a trend indicator based on at least a prior efficiency metric.

4. The method of claim 3 , wherein the trend indicator comprises an arrow.

5. The method of claim 1 , further comprising:

graphically presenting at least one of the efficiency metrics with a comparison to an efficiency metric for a similarly situated entity.

6. The method of claim 1 , wherein the raw data comprises at least one of a number of direct reports for managers, a number of approvers in an approval chain, an average tenure for a manager, an amount of personnel change, an age of hardware infrastructure, and an amount of time spent on internal meetings.

7. The method of claim 1 , wherein the recommended action further comprises limiting a number of meetings.

8. The method of claim 1 , wherein the recommended action further comprises updating hardware infrastructure.

9. The method of claim 1 , wherein the recommended action further comprises changing an email filtering parameter.

10. The method of claim 1 , further comprising:

receiving, from the enterprise software program, updated raw data related to the entity's performance following the implementation of the recommended action;

re-calculating the efficiency metric; and

updating a recommended action database based on the recommended action and the calculated and re-calculated efficiency metric.

11. The method of claim 1 , wherein the enterprise software program comprises an approval system, a human resources (HR) system, an email system, a document storage system, and a calendar or scheduler system.

12. The method of claim 1 , further comprising:

generating hypothetical raw data for a hypothetical change;

calculating a hypothetical efficiency metric based on the hypothetical change; and

graphically presenting the efficiency metric and the hypothetical efficiency metric.

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.

14. The system of claim 13 , wherein the computer program outputs a graphical representation of the efficiency metric with a trend indicator based on at least a prior efficiency metric.

15. The system of claim 13 , wherein the enterprise software program comprises an approval system, a human resources (HR) system, an email system, a document storage system, and a calendar or scheduler system.

Assignments (1)
ASSIGNMENT OF ASSIGNOR'S INTEREST Recorded Aug 21, 2023
From: ALEXANDER, SHAWN WESLEY; MCQUISTON, DANIEL D.; CHANG, ERIC HAN KAI; WHITE, III, JAMES P.; PERRY, ERIN MICHELLE
To: JPMORGAN CHASE BANK, N.A.
Reel/Frame 064648/0669 →
Continuity (2)
Provisional Application 62939365 · Nov 22, 2019
Related Publication 20210158258A1 · May 27, 2021