Computer-implemented systems configured for automated electronic calendar item predictions for calendar item rescheduling and methods of use thereof

Abstract

In order to facilitate automatic electronic calendar rescheduling in response to out-of-office statuses, systems and methods are described including receiving, by processors, an out-of-office notification associated with meeting attendees. The processors identify a need-to-reschedule meeting data item of respective need-to-reschedule meetings. The processors utilize a meeting scheduling machine learning model to predict a plurality of parameters of a meeting room object representing respective candidate rescheduled meetings based at least in part on schedule information and location information associated with the at least one need-to-reschedule meeting data items. The processors cause to display an indication of the respective candidate rescheduled meetings in response to the out-of-office notification on a screen of a computing device associated with the respective attendees. The processors receive a selection of the at least one respective candidate rescheduled meeting from the at least one respective attendee and dynamically secure the respective candidate rescheduled meetings.

What is claimed is: 
     
         1 . A method comprising:
 detecting, by at least one processor, a data record notification associated with an automatic notification setting of a software application;
 wherein the data record notification comprises an indication of:
 a plurality of parameters of at least one resource associated with at least one electronic calendar object of an electronic calendar, and 
 a user selection of at least one modification to the at least one electronic calendar object from at least one user; 
 
   creating, by the at least one processor, a training pair comprising the plurality of parameters of the at least one resource and the at least one modification;   training, by the at least one processor, a time period machine learning model using the training pair to update the time period machine learning model;
 wherein the time period machine learning model is configured to predict the plurality of parameters based at least in part on software application data associated with the at least one user and location information associated with the at least one user; 
 wherein the software application data comprises:
 an availability data identifying at least one open time period associated with the at least one user, 
 a data record history associated with the at least one user, 
 wherein the data record history comprises:
 cancellation data identifying cancelled data records, and 
 modified data identifying modified data records; 
 
 
   applying, by the at least one processor, the time period machine learning model to predict a plurality of subsequent parameters of at least one subsequent resource associated with the at least one electronic calendar object; and   dynamically securing, by the at least one processor, the at least one subsequent resource within the at least one electronic calendar object of the electronic calendar associated with the at least one user according to the plurality of subsequent parameters.   
     
     
         2 . The method of  claim 1 , wherein the location information further comprises meeting room needs associated with at least one additional data record;
 wherein the meeting room needs comprise:
 meeting room resources, and 
 a meeting room size. 
   
     
     
         3 . The method of  claim 2 , wherein the location information associated with the at least one user comprises a real-time location based on tracking an employee badge. 
     
     
         4 . The method of  claim 2 , wherein the location information associated with the at least one user comprises a real-time location based on global positioning (GPS) data associated with an user mobile device. 
     
     
         5 . The method of  claim 1 , further comprising determining, by the at least one processor, traffic data identifying a traffic delay for a transit time associated a transit from each user location to each resource location. 
     
     
         6 . The method of  claim 1 , further comprising determining, by the at least one processor, a cancellation prediction using the time period machine learning model based at least in part on the cancellation data associate with each of the at least one respective user. 
     
     
         7 . The method of  claim 1 , wherein the time period machine learning model is further utilized to predict an user prioritization parameter to prioritize an availability associated with the at least one user according to each respective hierarchical position associated with the at least one user;
 wherein the user prioritization parameter comprises:
 a prioritization of schedule information associated with the at least one user, and 
 the location information associated with the at least one user; 
   wherein the hierarchical position of each of the at least one user is based on an organization chart.   
     
     
         8 . The method of  claim 1 , further comprising training, by the at least one processor, the time period machine learning model based on a meeting result. 
     
     
         9 . The method of  claim 8 , wherein the meeting result comprises meeting disposition data identifying a completed meeting according to the plurality of parameters. 
     
     
         10 . The method of  claim 9 , wherein the meeting disposition data comprises one of selection comprising a cancellation indication and a reschedule indication;
 wherein the cancellation indication identifies:
 a cancelling of the location parameter, and 
 a cancelling of the time parameter; 
   wherein the reschedule indication identifies:
 a rescheduling of the location parameter, and 
 a rescheduling of the time parameter. 
   
     
     
         11 . A system comprising:
 at least one processor configured to:
 detect a data record notification associated with an automatic notification setting of a software application;
 wherein the data record notification comprises an indication of:
 a plurality of parameters of at least one resource associated with at least one electronic calendar object of an electronic calendar, and 
 a user selection of at least one modification to the at least one electronic calendar object from the at least one user; 
 
 
 create a training pair comprising the plurality of parameters of the at least one resource and the at least one modification; 
 train a time period machine learning model using the training pair to update the time period machine learning model;
 wherein the time period machine learning model is configured to predict the plurality of parameters based at least in part on software application data associated with the at least one user and location information associated with the at least one user; 
 wherein the software application data comprises:
 an availability data identifying at least one open time period associated with the at least one user, 
 a data record history associated with the at least one user, 
 wherein the data record history comprises: 
  cancellation data identifying cancelled data records, and 
  modified data identifying modified data records; 
 
 
 apply the time period machine learning model to predict a plurality of subsequent parameters of at least one subsequent resource associated with the at least one electronic calendar object; and 
 dynamically secure the at least one subsequent resource within the at least one electronic calendar object of the electronic calendar associated with the at least one user according to the plurality of subsequent parameters. 
   
     
     
         12 . The system of  claim 11 , wherein the location information further comprises meeting room needs associated with at least one additional data record;
 wherein the meeting room needs comprise:
 meeting room resources, and 
 a meeting room size. 
   
     
     
         13 . The system of  claim 12 , wherein the location information associated with the at least one user comprises a real-time location based on tracking an employee badge. 
     
     
         14 . The system of  claim 12 , wherein the location information associated with the at least one user comprises a real-time location based on global positioning (GPS) data associated with an user mobile device. 
     
     
         15 . The system of  claim 11 , wherein the at least one processor is further configured to determine traffic data identifying a traffic delay for a transit time associated a transit from each user location to each resource location. 
     
     
         16 . The system of  claim 11 , wherein the at least one processor is further configured to determine a cancellation prediction using the time period machine learning model based at least in part on the cancellation data associate with each of the at least one respective user. 
     
     
         17 . The system of  claim 11 , wherein the time period machine learning model is further utilized to predict an user prioritization parameter to prioritize an availability associated with the at least one user according to each respective hierarchical position associated with the at least one user;
 wherein the user prioritization parameter comprises:
 a prioritization of schedule information associated with the at least one user, and 
 the location information associated with the at least one user; 
   wherein the hierarchical position of each of the at least one user is based on an organization chart.   
     
     
         18 . The system of  claim 11 , wherein the at least one processor is further configured to train the time period machine learning model based on a meeting result. 
     
     
         19 . The system of  claim 18 , wherein the meeting result comprises meeting disposition data identifying a completed meeting according to the plurality of parameters. 
     
     
         20 . The system of  claim 19 , wherein the meeting disposition data comprises one of selection comprising a cancellation indication and a reschedule indication;
 wherein the cancellation indication identifies:
 a cancelling of the location parameter, and 
 a cancelling of the time parameter; 
   wherein the reschedule indication identifies:
 a rescheduling of the location parameter, and 
 a rescheduling of the time parameter.