Generating and utilizing personalized predictive models are provided. When an electronic input is received, a generic predictive model is used to predict a user response to the input. After a prescribed period of time, an analysis is performed to determine the user's actual response to the input, as well as, the user's actual responses to other inputs of the same type. Training is performed on the generic predictive model to generate a new and personalized predictive model based on the user's actual responses to the analyzed inputs. The personalized predictive model is then utilized for predicting user response to future inputs of the same type. At a prescribed frequency, the generated personalized predictive model is updated by analyzing actual user responses to predictions provided by the personalized predictive model.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. A system for generating a personalized predictive model; comprising: a processor; and a memory including instructions, which when executed by the processor, cause the system to operate: a generic predictive modeling module operative to generate a generic predictive model for predicting a user decision on a received electronic input; a usage monitoring module operative to compare an actual user decision on the received electronic input with the predicted user decision for determining an accuracy of the predicted user decision; a personalized predictive model training module operative to update one or more features of the generic predictive model for personalizing the generic predictive model as a personalized predictive model for a user of the received electronic input; and a personalized predictive modeling module operative to generate the personalized predictive model based on the one or more updated features for predicting a decision of the user on one or more future electronic inputs. 2. The system of claim 1, the usage monitoring module being further operative to compare a plurality of actual user decisions with a plurality of corresponding predicted user decisions, the actual user decisions and the corresponding predicted user decisions accumulated over a prescribed period of time. 3. The system of claim 2, the generic predictive modeling module being further operative to utilize a generic precision/recall curve for obtaining a user decision threshold for determining a percentage of received electronic inputs to be decided according to a prescribed decision by the user. 4. The system of claim 3, the personalized predictive model training module being further operative to compute an updated precision/recall curve that is personalized to the receiving user based on the plurality of actual user decisions and corresponding predicted user decisions accumulated over the prescribed period of time. 5. The system of claim 4, the personalized predictive model training module being further operative to determine a plurality of user decision thresholds that will improve accuracy of future predicted user decisions on future electronic inputs. 6. The system of claim 5, the personalized predictive model training module being further operative to compute an optimum user decision threshold for use in the personalized predictive model for the future predicted user decisions. 7. The system of claim 6, the personalized predictive module being further operative to generate an updated personalized predictive model based on the optimum user decision threshold for use in predicting future user decisions on future electronic inputs. 8. The system of claim 7, the personalized prediction modeling module being further operative to update the updated personalized predictive model based on an updated optimum user decision threshold generated in response to an analysis of predicted future user decisions compared with actual user decisions on the future electronic inputs. 9. The system of claim 1, wherein the system for generating a personalized predictive model is operative to communicate the personalized predictive model to a software application for causing the software application to act on one or more future electronic inputs received by a user of the software application. 10. The system of claim 9, wherein one or more decisions by the user of the software application on the one or more future electronic inputs are monitored by the usage monitoring module for determining an accuracy of predictions made by the personalized predictive model for responses to the one or more future electronic inputs by the user of the software application. 11. The system of claim 10, wherein the personalized predictive modeling module is further operative to update the generated personalized predictive model based on the one or more decisions by the user of the software application on the one or more future electronic inputs. 12. A method for improving the functionality of a computing device on which is operated a software application with functionality requiring user action on received electronic inputs, comprising: predicting a user decision on a received electronic input via a generic predictive model operated by the computing device; comparing an actual user decision on the received electronic input with the predicted user decision for determining an accuracy of the predicted user decision; updating one or more features of the generic predictive model for personalizing the generic predictive model as a personalized predictive model for a user of the received electronic input; and generating the personalized predictive model based on the one or more updated features for predicting a decision of the user on one or more future electronic inputs. 13. The method of claim 12, further comprising comparing a plurality of actual user decisions with a plurality of corresponding predicted user decisions, the actual user decisions and the corresponding predicted user decisions accumulated over a prescribed period of time. 14. The method of claim 13, further comprising utilizing a generic precision/recall curve for obtaining a user decision threshold for determining a percentage of received electronic inputs to be decided according to a prescribed decision by the user. 15. The method of claim 14, further comprising computing an updated precision/recall curve that is personalized to the receiving user based on the plurality of actual user decisions and corresponding predicted user decisions accumulated over the prescribed period of time. 16. The method of claim 15, further comprising determining a plurality of user decision thresholds that will improve accuracy of future predicted user decision on future electronic inputs. 17. The method of claim 16, further comprising computing an optimum user decision threshold for use in the personalized predictive model for the future predicted user decisions. 18. The method of claim 17, further comprising generating an updated personalized predictive model based on the optimum user decision threshold for use in predicting future user decisions on future electronic inputs. 19. A method for predicting a user's decisions on received electronic inputs; comprising: receiving via a software application an electronic input requiring a decision by a receiving user; using a generic predictive model, predicting a user decision on a received electronic input; comparing an actual user decision on the received electronic input with the predicted user decision for determining an accuracy of the predicted user decision; generating a personalized predictive model by updating one or more features of the generic predictive model for personalizing the generic predictive model as a personalized predictive model for a user of the received electronic input; using the personalized predictive model for predicting a user decision for each of a plurality of future electronic inputs; after a prescribed duration of time, comparing an actual user decision for each of the plurality of future electronic inputs with corresponding predicted user decisions for each of the plurality of future electronic inputs; and updating the personalized predictive model for more accurately predicting a user decision for each of a plurality of additional future electronic inputs. 20. The method of claim 19, wherein updating the personalized predictive model includes: computing an updated precision/recall curve that is personalized to the receiving user based on the plurality of actual user decisions and corresponding predicted user decisions accumulated over the prescribed duration of time; determining a plurality of user decision thresholds that will improve accuracy of future predicted user decision on future electronic inputs; computing an optimum user decision threshold for use in the personalized predictive model for the future predicted user decisions; and utilizing the optimum user decision threshold for predicting future user decisions on the plurality of additional future electronic inputs.