Artificial intelligence (ai)-based multi-level persuasive reference for independent insurance sales agent

Abstract

Methods and systems are provided for AI-based robotic automation for persuasive references. In one novel aspect, a robotic persuasive reference is generated based on a prospect product-service (P_PS) matrix, which is generated based on predictive analysis using DNN model and dynamically obtained feedbacks. In one embodiment, the DNN model is trained with customer personal profiles against associated PS revenues for each customer data set. In one embodiment, the predictive analysis uses a decision tree classifier. In one embodiment, the computer system detects one or more predefined triggering events comprising feedback information for the robotic persuasive reference and one or more predefined lifetime events, updates the P_PS matrix based and the robotic persuasive reference accordingly. In one embodiment, the feedback information is a sentiment analysis on responses from the prospect. In another embodiment, a recency, frequency, and page browsing analysis is performed based on the one or more detected lifetime events.

1 .- 20 . (canceled) 
     
     
         21 . A method, comprising:
 obtaining, by an independent insurance sales agent (USA) Bot computer system with one or more processors coupled with at least one memory unit, one or more prospect input data sets for one or more corresponding prospects, wherein each prospect input data set includes a plurality of predefined prospect attributes;   performing a predictive analysis on the one or more prospect input data sets using a deep neural network (DNN) model, wherein the DNN model is trained by a preexisting Big Data set containing a plurality of customer data sets;   generating a prospect product-service (P_PS) matrix of the prospect based on the predictive analysis and feedback attributes, wherein the feedback attributes are obtained from responses of corresponding prospects; and   generating a personalized robotic persuasive reference identifying one or more matching product and services (PSs) for the prospect based on the P_PS matrix, wherein the personalized robotic persuasive reference provides a guidance for an insurance agent to match the prospect with the identified one or more matching PSs.   
     
     
         22 . The method of  claim 21 , wherein the DNN model is trained with customer personal PS revenues for each customer data set. 
     
     
         23 . The method of  claim 21 , wherein the predictive analysis further uses one or more decision tree classifiers. 
     
     
         24 . The method of  claim 23 , wherein the predictive analysis is further based on a PS knowledgebase and one or more agent profiles. 
     
     
         25 . The method of  claim 23 , wherein one or more prospect input data sets is an augmented data set each including one or more related data sets based on one or more predefined relationship rules. 
     
     
         26 . The method of  claim 21 , wherein the feedback attributes include feedback information for the robotic persuasive reference based on a sentiment analysis on responses from the prospect, and wherein the P_PS matrix is updated based on feedback attributes. 
     
     
         27 . The method of  claim 26 , wherein the sentiment analysis is based on an audio input analysis using a sentiment classifier. 
     
     
         28 . The method of  claim 26 , wherein the sentiment analysis is based on obtained textual inputs from the prospect using a set of predefined sentiment classifiers. 
     
     
         29 . The method of  claim 21 , wherein the feedback attributes include one or more lifetime events based an overt behavior analysis on one or more detected overt actions of the prospect, and wherein the P_PS matrix is updated based on feedback attributes. 
     
     
         30 . The method of  claim 21 , wherein the feedback attributes include a recency and frequency (RF) analysis performed based on one or more predefined triggers comprising one or more detected overt actions, and one or more detected lifetime events. 
     
     
         31 . An independent insurance sales agent (USA) Bot system, comprising:
 one or more network interfaces that connect the system with a network;   a memory; and   one or more processors coupled to one or more memory units, the one or more processors configured to   obtain one or more prospect input data sets for one or more corresponding prospects, wherein each prospect input data set includes a plurality of predefined prospect attributes;   perform a predictive analysis on the one or more prospect input data sets using a deep neural network (DNN) model, wherein the DNN model is trained by a preexisting Big Data set containing a plurality of customer data sets;   generate prospect product-service (P_PS) matrix of the prospect based on the predictive analysis and feedback attributes, wherein the feedback attributes are obtained from responses of corresponding prospects; and   generate a robotic persuasive reference identifying one or more matching product and services (PSs) or the prospect based on the P_PS matrix, wherein the personalized robotic persuasive reference provides a guidance for an insurance agent to match the prospect with the identified one or more matching PSs.   
     
     
         32 . The method of  claim 31 , wherein the DNN model is trained with customer personal PS revenues for each customer data set. 
     
     
         33 . The method of  claim 31 , wherein the predictive analysis further uses one or more decision tree classifiers. 
     
     
         34 . The method of  claim 33 , wherein the predictive analysis is further based on a PS knowledgebase and one or more agent profiles. 
     
     
         35 . The method of  claim 33 , wherein one or more prospect input data sets is an augmented data set each including one or more related data sets based on one or more predefined relationship rules. 
     
     
         36 . The method of  claim 31 , wherein the feedback attributes include feedback information for the robotic persuasive reference based on a sentiment analysis on responses from the prospect, and wherein the P_PS matrix is updated based on feedback attributes. 
     
     
         37 . The method of  claim 36 , wherein the sentiment analysis is based on an audio input analysis using a sentiment classifier. 
     
     
         38 . The method of  claim 36 , wherein the sentiment analysis is based on obtained textual inputs from the prospect using a set of predefined sentiment classifiers. 
     
     
         39 . The method of  claim 31 , wherein the feedback attributes include one or more lifetime events based an overt behavior analysis on one or more detected overt actions of the prospect, and wherein the P_PS matrix is updated based on feedback attributes. 
     
     
         40 . The method of  claim 31 , wherein the feedback attributes include a recency and frequency (RF) analysis performed based on one or more predefined triggers comprising one or more detected overt actions, and one or more detected lifetime events