US 12,093,860 B2
Systems and methods for determining event schedules
Andysheh Tabrizi, Boston, MA (US); and Evan Charles Smith, Lexington, MA (US)
Assigned to RECENTIVE ANALYTICS, INC., Boston, MA (US)
Filed by Recentive Analytics, Inc., Boston, MA (US)
Filed on Oct. 26, 2022, as Appl. No. 17/974,253.
Application 17/974,253 is a continuation of application No. 17/857,597, filed on Jul. 5, 2022, granted, now 11,537,960.
Application 17/857,597 is a continuation of application No. 17/332,144, filed on May 27, 2021, granted, now 11,386,367, issued on Jul. 12, 2022.
Prior Publication US 2023/0112145 A1, Apr. 13, 2023
This patent is subject to a terminal disclaimer.
Int. Cl. G06Q 10/0631 (2023.01); G06N 20/00 (2019.01)
CPC G06Q 10/063116 (2013.01) [G06N 20/00 (2019.01)] 20 Claims
OG exemplary drawing
 
1. A computer-implemented method of dynamically generating an event schedule, the method comprising:
providing one or more event parameters for series of live events and one or more event target features associated with the series of live events to a machine learning (ML) model, wherein the ML model is at least one of a neural network ML model and a support vector ML model;
iteratively training the ML model to identify relationships between the one or more event parameters and the one or more event target features using historical data corresponding to one or more previous series of live events, wherein such iterative training improves the accuracy of the ML model;
receiving, from a user, one or more user-specific event parameters for a future series of live events, the user-specific event parameters including scheduling information for one or more future performances associated with the user;
receiving, from the user, one or more user-specific event schedule weights representing a combination of prioritized event target features that are of interest to the user for the future series of live events, each user-specific event schedule weight having a value that is scaled based on the user's level of interest in pursuing a corresponding prioritized event target feature for an aggregate of the future series of live events;
providing the one or more user-specific event parameters and the one or more user-specific event schedule weights to the trained ML model;
generating, via the trained ML model, a schedule for the future series of live events by scheduling the future series of live events in an arrangement that is optimized to achieve the combination of prioritized event target features defined by the one or more user-specific event schedule weights;
displaying the schedule via a graphical user interface (GUI), the GUI including at least one of an optimal button, a reset button, a comma-separated values (CSV) button, a save button, and a load button;
detecting a real-time change to the one or more user-specific event parameters;
providing the real-time change to the trained ML model to improve the accuracy of the trained ML model; and
updating, via the trained ML model, the arrangement of the future series of live events such that the schedule remains optimized to achieve the combination of prioritized event target features defined by the one or more user-specific event schedule weights in view of the real-time change to the one or more user-specific event parameters.