# US 12,112,092 B2 · System and method for meeting volume optimizer

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- Number: US 12,112,092 B2
- Document ID: US12112092B2
- Status: Granted
- Kind: B2
- Application number: 17/588,595
- Filed: 2022-01-31
- Published: 2023-08-03
- Granted: 2024-10-08
- Earliest claimed: 2022-01-31
- Assignees: Capital One Services, LLC
- Applicants: Capital One Services, LLC
- Inventors: Michael Anthony Young, Jr.; Christopher Mcdaniel; Matthew Louis Nowak
- Web page: https://patentgrep.com/patent/US12112092B2

## CPC

- G06F 3/165: {Management of the audio stream, e.g. setting of volume, audio stream path}
- G06F 40/20: Natural language analysis (semantic analysis of natural language G06F40/30)
- G06Q 10/063116: {Schedule adjustment for a person or group}
- G06Q 10/06398: {Performance of employee with respect to a job function}
- G06Q 10/1093: Calendar-based scheduling for persons or groups
- G06N 20/00: Machine learning
- G06F: ELECTRIC DIGITAL DATA PROCESSING (computer systems based on specific computational models G06N)
- G06Q: INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06N: COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS

## Abstract

Disclosed herein are system, method, and computer program product embodiments for machine learning systems to predict real-time productivity changes based on prospective scheduling. A productivity predictive model is trained by a machine learning engine with meeting and productivity training data, wherein the productivity predictive model includes one or more algorithms to select a future productivity change based on a future schedule. The system receives a current schedule and current productivity measurements of a user, receives a scheduling request for the user, predicts a future productivity change and displays a graphic to a user revealing the future productivity change of the user based on accepting the scheduling request.

## Claims

### Claim 1

A system comprising:
a machine learning system configured to:
receive first training data as an input to the machine learning system, wherein the first training data includes multiple scheduling instances, and wherein the scheduling instances specify a plurality of scheduling parameters upon which to make a prediction of a future schedule;
receive second training data as an input to the machine learning system, wherein the second training data includes multiple productivity measurement instances, and wherein the productivity measurement instances specify a plurality of productivity parameters upon which to make a prediction of a future productivity, wherein the plurality of productivity parameters are based on work product measurements;
train, by the machine learning system, a productivity predictive model with the first training data and the second training data, wherein the productivity predictive model includes one or more algorithms to predict a current productivity change based on either accepting a new scheduling request or alternatively, rescheduling the new scheduling request;
receive a current schedule and current productivity measurements of a user;
receive a scheduling request for the user, wherein the scheduling request includes one or more proposed scheduling parameters;
predict a future productivity change based on using the productivity predictive model with the current schedule, the current productivity measurements of the user, the one or more proposed scheduling parameters and accepting the scheduling request; and
a graphical user interface configured to:
display a graphic revealing the future productivity change of the user based on accepting the scheduling request.

### Claim 2 (depends on 1)

The system of claim 1, further comprising:
a natural language processor configured to:
analyze text, numbers or speech of the first training data to ascertain the plurality of scheduling parameters from the scheduling instances; and
analyze text, numbers or speech of the second training data to ascertain the plurality of productivity parameters from the productivity measurement instances.

### Claim 3 (depends on 1)

The system of claim 1, wherein the plurality of scheduling parameters comprises any of: time of meeting, length of meeting, attendee travel components, importance of meeting, importance of meeting attendees, associated project, availability, conflicts, work hours, meetings outside of the work hours, time zone, or previous meeting cancelations.

### Claim 4 (depends on 1)

The system of claim 1, wherein the first training data comprises calendar components.

### Claim 5 (depends on 1)

The system of claim 1, wherein the second training data comprises project tracking components.

### Claim 6 (depends on 1)

The system of claim 1, wherein the plurality of productivity parameters comprises any of: work completed by a worker per a selected time frame, a number of hours worked on an assigned project, an assigned work project status, a completion level of work product checklists, a summary of completed work or a number of calls successfully answered per selected time period.

### Claim 7 (depends on 1)

The system of claim 1, wherein the machine learning system is further configured to:
selectively weight the plurality of productivity parameters based on predetermined productivity standards of any of: a company, an industry, a technology, a geography, a project, a team or an individual.

### Claim 8 (depends on 1)

The system of claim 1, wherein the future productivity change is based on any of: meetings attended, calls answered, cancellations, work product completed, project status, or time to be spent on a project associated with the scheduling request.

### Claim 9 (depends on 1)

The system of claim 1, wherein the machine learning system is further configured to:
generate a productivity score;
generate a change to the productivity score; and
wherein the graphic further reflects at least the change to the productivity score.

### Claim 10

A computer implemented method for processing a scheduling request, comprising:
receiving first training data as an input to a machine learning system, wherein the first training data includes multiple scheduling instances, and wherein the scheduling instances specify a plurality of scheduling parameters upon which to make a prediction of a future schedule;
receiving second training data as an input to the machine learning system, wherein the second training data includes multiple productivity measurement instances, and wherein the productivity measurement instances specify a plurality of productivity parameters upon which to make a prediction of a future productivity, wherein the plurality of productivity parameters are based on work product measurements;
training, by the machine learning system, a productivity predictive model with the first training data and the second training data, wherein the productivity predictive model includes one or more algorithms to predict a current productivity change based on either accepting a new scheduling request or alternatively, rescheduling the new scheduling request;
receiving a current schedule and current productivity measurements of a user;
receiving a scheduling request for the user, wherein the scheduling request includes one or more proposed scheduling parameters;
predicting a future productivity change based on using the productivity predictive model with the current schedule, the current productivity measurements of the user, the one or more proposed scheduling parameters and accepting the scheduling request; and
generating a graphic revealing the future productivity change of the user based on accepting the scheduling request.

### Claim 11 (depends on 10)

The method of claim 10, further comprising:
analyzing text, numbers or speech of the first training data to ascertain the plurality of scheduling parameters from the scheduling instances; and
analyzing text, numbers or speech of the second training data to ascertain the plurality of productivity parameters from the productivity measurement instances.

### Claim 12 (depends on 10)

The method of claim 10, wherein the plurality of scheduling parameters comprises any of: time of meeting, length of meeting, attendee travel components, importance of meeting, importance of meeting attendees, associated project, availability, conflicts, work hours, meetings outside of the work hours, time zone, or previous meeting cancelations.

### Claim 13 (depends on 10)

The method of claim 10, wherein the first training data comprises calendar components.

### Claim 14 (depends on 10)

The method of claim 10, wherein the second training data comprises project tracking components.

### Claim 15 (depends on 10)

The method of claim 10, wherein the plurality of productivity parameters comprises any of: work completed by a worker per a selected time frame, a number of hours worked on an assigned project, an assigned work project status, a completion level of work product checklists, a summary of completed work or a number of calls successfully answered per selected time period.

### Claim 16 (depends on 10)

The method of claim 10, further comprising: selectively weighting the plurality of productivity parameters based on predetermined productivity standards of any of: a company, an industry, a technology, a geography, a project, a team or an individual.

### Claim 17 (depends on 10)

The method of claim 10, wherein the future productivity change is based on any of: meetings attended, calls answered, cancellations, work product completion, project status, or time to be spent on a project associated with the scheduling request.

### Claim 18 (depends on 10)

The method of claim 10, further comprising generating at least a productivity score and a change to the productivity score to be included in the graphic.

### Claim 19

A non-transitory computer-readable medium having instructions stored thereon that, when executed by at least one computing device, cause the at least one computing device to perform operations comprising:
receiving first training data as an input to a machine learning system, wherein the first training data includes multiple scheduling instances, and wherein the scheduling instances specify a plurality of scheduling parameters upon which to make a prediction of a future schedule;
receiving second training data as an input to the machine learning system, wherein the second training data includes multiple productivity measurement instances, and wherein the productivity measurement instances specify a plurality of productivity parameters upon which to make a prediction of a future productivity, wherein the plurality of productivity parameters are based on work product measurements;
training, by the machine learning system, a productivity predictive model with the first training data and the second training data, wherein the productivity predictive model includes one or more algorithms to predict a current productivity change based on either accepting a new scheduling request or alternatively, rescheduling the new scheduling request;
receiving a current schedule and current productivity measurements of a user;
receiving a scheduling request for the user, wherein the scheduling request includes one or more proposed scheduling parameters;
predicting a future productivity change based on using the productivity predictive model with the current schedule, the current productivity measurements of the user, the one or more proposed scheduling parameters and accepting the scheduling request; and
generating a graphic revealing the future productivity change of the user based on accepting the scheduling request.

### Claim 20 (depends on 19)

The non-transitory computer-readable medium of claim 19, further configured to perform operations comprising:
selectively weighting the plurality of productivity parameters based on predetermined productivity standards of any of: a company, an industry, a technology, a geography, a selected project, a team or an individual.