System and method to optimize workflow

Abstract

The present disclosure describes a system and method to reduce the overall time taken to complete distributed process workflows. Each workflow can include multiple actions that are completed by or at different client devices. The actions of a workflow can be dependent on prior actions in the workflow. For example, a second client device may not be able to complete a second action until a first client device completes a first action in the workflow. The system can predict time periods and the geolocations where client devices are most likely to complete an assigned action. Using the selected time periods and geolocations, the system can transmit notifications to the client devices when the action is most likely to be completed.

What is claimed: 
     
         1 . A method comprising:
 identifying, by one or more processors, a plurality of time windows and a plurality of locations for each of one or more actions of a workflow;   identifying, by the one or more processors, one or more features of the one or more actions;   determining, by the one or more processors based at least on the one or more features, a confidence level of the likelihood of the one or more actions being completed during each of the plurality of time windows at the plurality of locations; and   scheduling, by the one or more processers based at least on the confidence level, a time window from the plurality of time windows and a location from the plurality of locations for which to have the one or more actions performed at device of a user.   
     
     
         2 . The method of  claim 1 , further comprising determining the one or more features using historical data associated with one or more actions of a same type. 
     
     
         3 . The method of  claim 1 , further comprising determining the one or more features using historical data associated with one or more actions of one of a user or a client device. 
     
     
         4 . The method of  claim 1 , further comprising determining, by the one or more processors, the confidence level using a machine learning model or algorithm. 
     
     
         5 . The method of  claim 1 , further comprising selecting, by the one or more processors, the time window from the plurality of time windows and the location from the plurality of locations having a highest confidence level. 
     
     
         6 . The method of  claim 1 , further comprising scheduling, by the one or more processors, a message to transmit to the device of the user at the location based at least on the time window. 
     
     
         7 . The method of  claim 1 , wherein the one or more features of the action comprises one or more of the following: location information, action duration, historical usage, a time zone and user designation. 
     
     
         8 . A system comprising:
 one or more processors, coupled to memory and configured to:   identify a plurality of time windows and a plurality of locations for each of one or more actions of a workflow;   identify one or more features of the one or more actions;   determine, based at least on the one or more features, a confidence level of the likelihood of the one or more actions being completed during each of the plurality of time windows at the plurality of locations; and   schedule, based at least on the confidence level, a time window from the plurality of time windows and a location from the plurality of locations for which to have the one or more actions performed at device of a user.   
     
     
         9 . The system of  claim 8 , wherein the one or more processors are further configured to determine the one or more features using historical data associated with one or more actions of a same type. 
     
     
         10 . The system of  claim 8 , wherein the one or more processors are further configured to determine the one or more features using historical data associated with one or more actions of one of a user or a client device. 
     
     
         11 . The system of  claim 8 , wherein the one or more processors are further configured to determine the confidence level using a machine learning model or algorithm. 
     
     
         12 . The system of  claim 8 , wherein the one or more processors are further configured to select the time window from the plurality of time windows and the location from the plurality of locations having a highest confidence level. 
     
     
         13 . The system of  claim 8 , wherein the one or more processors are further configured to schedule a message to transmit to the device of the user at the location based at least on the time window. 
     
     
         14 . The system of  claim 8 , wherein the one or more features of the action comprises one or more of the following: location information, action duration, historical usage, a time zone and user designation. 
     
     
         15 . A non-transitory computer readable medium storing program instructions for causing one or more processors to:
 identify a plurality of time windows and a plurality of locations for each of one or more actions of a workflow;   identify one or more features of the one or more actions;   determine, based at least on the one or more features, a confidence level of the likelihood of the one or more actions being completed during each of the plurality of time windows at the plurality of locations; and   schedule, based at least on the confidence level, a time window from the plurality of time windows and a location from the plurality of locations for which to have the one or more actions performed at device of a user.   
     
     
         16 . The non-transitory computer readable medium of  claim 15 , wherein the program instructions further cause the one or more processors to determine the one or more features using historical data associated with one or more actions of a same type. 
     
     
         17 . The non-transitory computer readable medium of  claim 15 , wherein the program instructions further cause the one or more processors to select the time window from the plurality of time windows and the location from the plurality of locations having a highest confidence level 
     
     
         18 . The non-transitory computer readable medium of  claim 15 , wherein the program instructions further cause the one or more processors to schedule a message to transmit to the device of the user at the location based at least on the time window. 
     
     
         19 . The non-transitory computer readable medium of  claim 15 , wherein the program instructions further cause the one or more processors to determine the one or more features using historical data associated with one or more actions of one of a user or a client device. 
     
     
         20 . The non-transitory computer readable medium of  claim 15 , wherein the one or more features of the action comprises one or more of the following: location information, action duration, historical usage, a time zone and user designation.