# US 2020/0401985 A1 · System and method for the adaptive scheduling of hourly staff to optimize labor cost

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- Number: US 2020/0401985 A1
- Document ID: US20200401985A1
- Status: Published application
- Kind: A1
- Application number: 16/905,015
- Filed: 2020-06-18
- Published: 2020-12-24
- Earliest claimed: 2019-06-18
- Assignees: Branch Messenger Inc.
- Applicants: Branch Messenger Inc.
- Inventors: DIVYDEEP AGARWAL; HIMANSHU MISRA
- Web page: https://patentgrep.com/patent/US20200401985A1

## CPC

- G06Q 10/04: Forecasting or optimisation specially adapted for administrative or management purposes, e.g. linear programming or "cutting stock problem" (market predictions or forecasting for commercial activities G06Q30/0202)
- G06F 18/214: Generating training patterns; Bootstrap methods, e.g. bagging or boosting
- G06N 3/086: using evolutionary algorithms, e.g. genetic algorithms or genetic programming
- G06Q 10/063116: {Schedule adjustment for a person or group}
- G06Q 10/063118: {Staff planning in a project environment}
- G06Q 10/063119: {Staff planning or adjustment in a physical establishment based on arrival predictions or arrival tracking}
- 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
- G06F: ELECTRIC DIGITAL DATA PROCESSING (computer systems based on specific computational models G06N)
- G06N: COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS

## Abstract

A system and method for the adaptive scheduling of hourly labor cost through the use of an electronic device to predict a preferred schedule of a workforce. In particular, the computer implemented method of the present disclosure allows for the input of data in the form of employees and demand and utilizes computer processing and machine learning to model a predictive schedule based upon the input data in the form of a generated schedule. By implementing machine learning in the comparison of the generated schedule and a manager generated manager schedule, the computer processing system and method steps can predict a more efficient generated schedule over time.

## Claims

### Claim 1

A computer implemented system and method for the adaptive scheduling of labor comprising:
providing input data in the form of employees and demand;
using a computer processing system to model a predictive schedule based upon the input data in the form of a generated schedule;
allowing a manager to edit the generated schedule to create a manager schedule;
using a computer processing system to compare the manager schedule to the generated schedule for use in learning future generated schedules; and
using a computer processing system to quantify the learning to predict a more efficient generated schedule.

### Claim 2 (depends on 1)

The computer implemented method of claim 1, wherein the predictive schedule is generated through mixed integer programming.

### Claim 3 (depends on 1)

The computer implemented method of claim 1, wherein the computer processing system is multi-threaded application as a microservice and hosted on a backend system.

### Claim 4 (depends on 1)

The computer implemented method of claim 1, wherein the computer processing system is hosted on a cloud.

### Claim 5 (depends on 1)

The computer implemented method of claim 1, wherein the quantified learning includes implicit manager preferences.

### Claim 6 (depends on 1)

The computer implemented method of claim 1, wherein the learning is conducted on a neural network utilized to learn and translate the generated schedule to the manager schedule.

### Claim 7 (depends on 6)

The computer implemented method of claim 6, wherein the neural network is trained to using stochastic gradient descent.

### Claim 8 (depends on 6)

The computer implemented method of claim 6, wherein the neural network is trained using an evolutionary based algorithm.

### Claim 9

A method for the adaptive scheduling of hourly labor cost, the method comprising:
providing input data in the form of employees and demand in the form of hours and shifts in a defined constraint;
using a computer processing system to model a predictive schedule based upon the input data and the defined constraints in the form of a generated schedule;
allowing a manager to edit the generated schedule to create a manager schedule;
using a computer processing system to compare the manager schedule to the generated schedule for use in future generated schedules by learning implicit manager preferences; and
using a computer processing system to quantify the learning to predict a more efficient generated schedule.

### Claim 10 (depends on 9)

The method of claim 9, wherein the predictive schedule is generated through mixed integer programming.

### Claim 11 (depends on 9)

The method of claim 9, wherein the computer processing system is multi-threaded application as a microservice and hosted on a backend system.

### Claim 12 (depends on 9)

The method of claim 9, wherein the computer processing system is hosted on a cloud.

### Claim 13 (depends on 9)

The method of claim 9, wherein the learning is conducted on a neural network utilized to learn and translate the generated schedule to the manager schedule.

### Claim 14 (depends on 13)

The method of claim 13, wherein the neural network is trained to using stochastic gradient descent.

### Claim 15 (depends on 13)

The method of claim 13, wherein the neural network is trained using an evolutionary based algorithm.

### Claim 16

A computer implemented method for the adaptive scheduling of hourly employees to optimize cost and scheduling efficiency, the method comprising the steps of:
providing input data in the form of an employee and shift and time constraints for the employee;
using a computer processing system to model a predictive schedule based upon the input data in the form of a generated schedule;
allowing a manager to edit the generated schedule to create a manager schedule;
using a computer processing system to compare the manager schedule to the generated schedule;
using a computer processing system with a neural network to learn and translate based upon the comparison of the generated schedule and manager schedule in future generated schedules; and
using the neural network to quantify the learning to predict a more efficient generated schedule.

### Claim 17 (depends on 16)

The computer implemented method of claim 16, wherein the predictive schedule is generated through mixed integer programming.

### Claim 18 (depends on 16)

The computer implemented method of claim 16, wherein the computer processing system is multi-threaded application as a microservice and hosted on a backend system.

### Claim 19 (depends on 16)

The computer implemented method of claim 16, wherein the computer processing system is hosted on a cloud.

### Claim 20 (depends on 1)

The computer implemented method of claim 1, wherein the quantified learning includes implicit manager preferences.