Patent ID: 11922346
Assignee: PAYACTIV, INC.
Field: Computer technology (Electrical engineering)
Classification: CPC G | IPC G

Claim 0:
1. A computer-implemented method to improve shift coverage, the method comprising:
receiving a shift schedule template;
identifying a plurality of problem shifts in the shift schedule template, wherein identifying a plurality of problem shifts comprises:
obtaining a plurality of published shift schedules, each published shift schedule associated with a respective shift of a respective employer of one or more employers, wherein each published shift schedule includes a location attribute, industry code attribute, and week indicator attribute;

for each published shift schedule,
obtaining a corresponding historical time and attendance record; programmatically analyzing the published shift schedule and the corresponding historical time and attendance record to determine one or more unscheduled shifts; and, adding unscheduled shift data associated with the one or more unscheduled shifts to a first training corpus, wherein the unscheduled shift data includes two or more of an employer identifier, a location identifier, an industry identifier, an employee identifier, a job type identifier; and
applying a machine learning (ML) model to the first training corpus to identify the plurality of problem shifts by identifying shifts that lie at a threshold Hamming distance from a centroid of a cluster of problem shifts;
determining, using the machine learning model, a randomized incentive offer for each problem shift;
instantiating a graphical user interface (GUI) portion on a plurality of user devices;
displaying the randomized incentive offer for each of the identified problem shifts via the GUI;
receiving shift bids from each of a set of users via one of more user devices of the plurality of user devices;
measuring a lift value associated with the randomized incentive offer;
adjusting a shift schedule based on the received shift bids to generate a published shift schedule;
determining, based on the adjusted shift schedule, a shift compliance metric;
updating feature vectors based on the shift compliance metric;
creating a second training corpus based on the updated feature vectors; and
retraining the ML model using the second training corpus until a threshold level of shift compliance is reached.