Systems and methods for intelligently providing supporting information using machine-learning

Abstract

Systems and methods for intelligently providing users with supporting information based on big-data analyses of a data set. Machine-learning algorithms may be executed using the data set to identify correlations between data objects of the data set. The correlations can be used to recommend supporting information to a user. A user interface can be provided to enable a user to initiate a process associated with an event. In response to receiving the input, the system can identify variables associated with the request. Based on these variables, the system can retrieve output data of the machine-learning algorithms to identify the supporting information for the user.

What is claimed is: 
     
         1 . A computer-implemented method comprising:
 detecting a first communication transmitted from a computing device, the first communication being associated with a user of the computing device and corresponding to a request to initiate a process associated with a particular event, wherein the process corresponds to a request to define a future event or a request for reimbursement of an expense associated with a past event;   in response to detecting the first communication, determining one or more variables from the request, each variable of the one or more variables being included in the first communication and representing a characteristic of the particular event;   mapping each variable of the one or more variables to at least one node of a plurality of nodes based on a trained machine-learning model, the trained machine-learning model having been trained with a data set including one or more past events;   identifying one or more nodes of the plurality of nodes corresponding to the one or more variables based at least in part on the mapping, wherein the one or more nodes are identified based on one or more correlations between at least two nodes of the plurality of nodes;   retrieving one or more values associated with each node of the identified one or more nodes; and   transmitting a second communication to the computing device, the second communication being responsive to the first communication and including supporting information for the particular event, the supporting information determined based on the retrieved one or more values.   
     
     
         2 . The computer-implemented method of  claim 1 , further comprising:
 identifying a client variable from the request, the client variable being one of the variables determined from the request;   in response to identifying the client variable, accessing one or more rules associated with the client variable;   restricting a set of nodes identified using the one or more correlations, the set of nodes being restricted to a subset of nodes, the restriction being based on the one or more rules;   retrieving a value for each node of the subset of nodes; and   transmitting the second communication to the computing device, the second communication including the retrieved one or more values.   
     
     
         3 . The computer-implemented method of  claim 1 , wherein collecting the data set is continuously performed, such that when a new event has occurred, the new event is included in the data set. 
     
     
         4 . The computer-implemented method of  claim 3 , further comprising:
 updating the trained machine-learning model when the new event is included in the data set, such that at least one weight that corresponds to a node of the plurality of nodes is updated.   
     
     
         5 . The computer-implemented method of  claim 1 , wherein the retrieved one or more values correspond to one or more recommended values provided as a recommendation associated with the particular event. 
     
     
         6 . The computer-implemented method of  claim 1 , further comprising:
 detecting a third communication from an additional computing device, wherein the third communication corresponds to another request to initiate another process associated with the particular event, wherein the third communication is received after the first communication is received and before the particular event occurs;   identifying that the first communication and the third communication each correspond to the particular event; and   transmitting an alert message to the additional computing device, the alert message including a notification that the user associated with the first communication is also associated with the particular event.   
     
     
         7 . The computer-implemented method of  claim 6 , further comprising:
 transmitting a fourth communication to the additional computing device, the fourth communication being responsive with the third communication and including at least one of the retrieved one or more values.   
     
     
         8 . The computer-implemented method of  claim 6 , wherein the user associated with the first communication and a different user associated with the third communication are each associated with a same entity. 
     
     
         9 . The computer-implemented method of  claim 1 , further comprising:
 detecting that a particular node of the one or more nodes is associated with a predicted occurrence, the predicted occurrence corresponding to an event parameter exceeding a defined threshold; and   accessing a workflow associated with the predicted occurrence, the workflow including an identification of one or more documents associated with the predicted occurrence, the one or more documents identifying a procedure for obtaining an offset associated with the particular node.   
     
     
         10 . The computer-implemented method of  claim 1 , wherein at least one node of the one or more nodes corresponds to a workflow for identifying one or more documents that identify a procedure for obtaining an offset associated with the particular event. 
     
     
         11 . A system, comprising:
 one or more data processors; and   a non-transitory computer-readable storage medium containing instructions which, when executed on the one or more data processors, cause the one or more data processors to perform operations including:
 detecting a first communication transmitted from a computing device, the first communication being associated with a user of the computing device and corresponding to a request to initiate a process associated with a particular event, wherein the process corresponds to a request to define a future event or a request for reimbursement of an expense associated with a past event; 
 in response to detecting the first communication, determining one or more variables from the request, each variable of the one or more variables being included in the first communication and representing a characteristic of the particular event; 
 mapping each variable of the one or more variables to at least one of a plurality of nodes based on a trained machine-learning model, the trained machine-learning model having been trained with a data set including one or more past events; 
 identifying one or more nodes of the plurality of nodes corresponding to the one or more variables based at least in part on the mapping, wherein the one or more nodes are identified based on one or more correlations between at least two nodes of the plurality of nodes; 
 retrieving one or more values associated with each node of the identified one or more nodes; and 
 transmitting a second communication to the computing device, the second communication being responsive to the first communication and including supporting information for the particular event, the supporting information determined based on of the retrieved one or more values. 
   
     
     
         12 . The system of  claim 11 , wherein the operations further comprise:
 identifying a client variable from the request, the client variable being one of the variables determined from the request;   in response to identifying the client variable, accessing one or more rules associated with the client variable;   restricting a set of nodes identified using the one or more correlations, the set of nodes being restricted to a subset of nodes, the restriction being based on the one or more rules;   retrieving a value for each node of the subset of nodes; and   transmitting the second communication to the computing device, the second communication including the retrieved one or more values.   
     
     
         13 . The system of  claim 11 , wherein collecting the data set is continuously performed, such that when a new event has occurred, the new event is included in the data set. 
     
     
         14 . The system of  claim 13 , wherein the operations further comprise:
 updating the trained machine-learning model when the new event is included in the data set, such that at least one weight that corresponds to a node of the plurality of nodes is updated.   
     
     
         15 . The system of  claim 11 , wherein the retrieved one or more values correspond to one or more recommended values provided as a recommendation associated with the particular event. 
     
     
         16 . The system of  claim 11 , wherein the operations further comprise:
 detecting a third communication from an additional computing device, wherein the third communication corresponds to another request to initiate another process associated with the particular event, wherein the third communication is received after the first communication is received and before the particular event occurs;   identifying that the first communication and the third communication each correspond to the particular event; and   transmitting an alert message to the additional computing device, the alert message including a notification that the user associated with the first communication is also associated with the particular event.   
     
     
         17 . The system of  claim 16 , wherein the operations further comprise:
 transmitting a fourth communication to the additional computing device, the fourth communication being responsive with the third communication and including at least one of the retrieved one or more values.   
     
     
         18 . The system of  claim 16 , wherein the user associated with the first communication and a different user associated with the third communication are each associated with a same entity. 
     
     
         19 . The system of  claim 11 , wherein the operations further comprise:
 detecting that a particular node of the one or more nodes is associated with a predicted occurrence, the predicted occurrence corresponding to an event parameter exceeding a defined threshold; and   accessing a workflow associated with the predicted occurrence, the workflow including an identification of one or more documents associated with the predicted occurrence, the one or more documents identifying a procedure for obtaining an offset associated with the particular node.   
     
     
         20 . The system of  claim 11 , wherein at least one node of the one or more nodes corresponds to a workflow for identifying one or more documents that identify a procedure for obtaining an offset associated with the particular event.