Systems and methods for generating a targeted communication based on life events

Abstract

In an aspect of the disclosure, a method, a computer-readable medium, and an apparatus are provided. The apparatus may receive digital data associated with digital media communications. In certain aspects, the digital data may include textual data from the digital media communications. The apparatus may input the textual data into a natural language processing (NLP) model. The apparatus may obtain a life event indication as an output of the NLP model. In certain aspects, the life event indication may include a classification tag and intent information associated with an item. The apparatus may determine whether the intent information meets a likelihood threshold. The apparatus may output, to an external device, the digital data and the life event indication upon determining that the intent information meets a likelihood threshold.

What is claimed is: 
     
         1 . A method of user journey generation, comprising:
 receiving digital data associated with digital media communications, the digital data including textual data from the digital media communications;   inputting the textual data into a natural language processing (NLP) model;   obtaining a life event indication as an output of the NLP model, the life event indication including a classification tag and intent information associated with an item;   determining whether the intent information meets a likelihood threshold; and   outputting, to an external device, the digital data and the life event indication upon determining that the intent information meets a likelihood threshold.   
     
     
         2 . The method of  claim 1 , wherein the digital data further includes image data, the method further comprising:
 inputting the image data into an image model; and   updating the life event indication using the output of the image model.   
     
     
         3 . The method of  claim 2 , further comprising:
 applying a first filter to the textual data and a second filter to the image data,
 wherein the first filter extracts a set of words from the textual data and the second filter, and 
 wherein the second filter extracts a set of images from the image data. 
   
     
     
         4 . The method of  claim 2 , wherein the receiving the digital data associated with the digital media communications comprises:
 continuously scanning, using an application programming interface (API), digital media communications associated with a user; and   extracting the digital data upon detecting a new digital media communication.   
     
     
         5 . The method of  claim 1 , further comprising:
 determining whether the external device sends a notification to a customer in response to the outputting of the digital data and the life event indication to the external device.   
     
     
         6 . The method of  claim 5 , further comprising:
 maintaining information associated with communications between the customer and the external device upon determining that the external device sends the notification to the customer.   
     
     
         7 . The method of  claim 6 , further comprising:
 updating the NLP model based at least in part on the textual data upon determining that the external device sends the notification to the customer in response to the digital data and the life event indication to the external device.   
     
     
         8 . The method of  claim 7 , further comprising:
 updating the image model based at least in part on the image data upon determining that the external device sends the notification to the customer in response to the digital data and the life event indication to the external device.   
     
     
         9 . The method of  claim 1 , wherein:
 the digital data further includes purchasing information associated with a user, and   the intent information is output based at least in part on the purchasing information.   
     
     
         10 . An apparatus for user journey generation, comprising:
 a memory; and   at least one processor coupled to the memory and configured to:
 receive digital data associated with digital media communications, the digital data including textual data from the digital media communications; 
 input the textual data into a natural language processing (NLP) model; 
 obtain a life event indication as an output of the NLP model, the life event indication including a classification tag and intent information associated with an item; 
 determine whether the intent information meets a likelihood threshold; and 
 output, to an external device, the digital data and the life event indication upon determining that the intent information meets a likelihood threshold. 
   
     
     
         11 . The apparatus of  claim 10 , wherein the digital data further includes image data, the at least one processor being further configured to:
 input the image data into an image model; and   update the life event indication using the output of the image model.   
     
     
         12 . The apparatus of  claim 11 , wherein the at least one processor is further configured to:
 apply a first filter to the textual data and a second filter to the image data,
 wherein the first filter extracts a set of words from the textual data and the second filter, and 
 wherein the second filter extracts a set of images from the image data. 
   
     
     
         13 . The apparatus of  claim 11 , wherein the at least one processor is configured to receive the digital data associated with the digital media communications by:
 continuously scanning, using an application programming interface (API), digital media communications associated with a user; and   extracting the digital data upon detecting a new digital media communication.   
     
     
         14 . The apparatus of  claim 10 , wherein the at least one processor is further configured to:
 determine whether the external device sends a notification to a customer in response to the outputting of the digital data and the life event indication to the external device.   
     
     
         15 . The apparatus of  claim 14 , wherein the at least one processor is further configured to:
 maintain information associated with communications between the customer and the external device upon determining that the external device sends the notification to the customer.   
     
     
         16 . The apparatus of  claim 15 , wherein the at least one processor is further configured to:
 update the NLP model based at least in part on the textual data upon determining that the external device sends the notification to the customer in response to the digital data and the life event indication to the external device.   
     
     
         17 . The apparatus of  claim 16 , wherein the at least one processor is further configured to:
 update the image model based at least in part on the image data upon determining that the external device sends the notification to the customer in response to the digital data and the life event indication to the external device.   
     
     
         18 . The apparatus of  claim 10 , wherein:
 the digital data further includes purchasing information associated with a user, and   the intent information is output based at least in part on the purchasing information.   
     
     
         19 . A computer-readable medium storing computer executable code, comprising code to:
 receive digital data associated with digital media communications, the digital data including textual data from the digital media communications;   input the textual data into a natural language processing (NLP) model;   obtain a life event indication as an output of the NLP model, the life event indication including a classification tag and intent information associated with an item;   determine whether the intent information meets a likelihood threshold; and   output, to an external device, the digital data and the life event indication upon determining that the intent information meets a likelihood threshold.   
     
     
         20 . The computer-readable medium of  claim 19 , wherein the digital data further includes image data, and wherein the executable code is further configured to:
 input the image data into an image model; and   update the life event indication using the output of the image model.