US 10,706,359 B2Granted

Method and system for generating predictive models for scoring and prioritizing leads

AssigneeServiceNow, Inc.

InventorsBaskar Jayaraman, Debashish Chatterjee, Kannan Govindarajan, Ganesh Rajan

Life of this patent

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Abstract

A computer implemented system for automating the generation of an analytic model includes a processor configured to process a plurality of data sets. Each data set includes values for a plurality of variables. A time-stamping module is configured to derive values for a plurality of elapsed-time variables for each data set, and the plurality of variables and plurality of elapsed-time variables are included in a plurality of model variables. A model generator is configured to create a plurality of comparison analytic models each based on a different subset of model variables. Each comparison analytic model is configured to operate on new data sets associated with current leads, and to output a likelihood of successfully closing an associated transaction. A model testing module is configured to select an operational analytic model from among the comparison analytic models based on a quality metric.

Claims24

Dependent claims

A computer implemented system comprising:

a processor;
and
a memory communicatively coupled to the processor, the memory storing:
a customer relationship management (CRM) database configured to store a first data set from a plurality of data sets, wherein the first data set comprises a plurality of variables;
and
instructions that, when executed by the processor, cause the processor to perform operations comprising:
identifying a first time variable for the first data set and a second time variable for the first data set;
calculating a difference between the first time variable and the second time variable to produce an elapsed time associated with the first data set;
adding the elapsed time to the plurality of variables of the CRM database;
extracting a keyword from the first data set;
assigning a weight to the keyword to produce structured data for the first data set;
adding the structured data to the plurality of variables of the CRM database;
creating a first comparison analytic model for the first data set using the plurality of variables, wherein the first comparison analytic model is associated with a first model type, and wherein the first comparison analytic model is configured to operate on a new data set associated with a first sales lead;
and
outputting a first representation of a first likelihood of successfully closing a first transaction associated with the first sales lead.

12 more elements

A method comprising:

identifying a first time variable for a first data set and a second time variable for the first data set;
calculating a difference between the first time variable and the second time variable to produce an elapsed time associated with the first data set;
adding the elapsed time to a plurality of variables of a customer relationship management (CRM) database;
extracting a keyword from the first data set;
assigning a weight to the keyword to produce structured data for the first data set;
adding the structured data to the plurality of variables of the CRM database;
creating a first comparison analytic model for the first data set using the plurality of variables, wherein the first comparison analytic model is associated with a first model type, and wherein the first comparison analytic model is configured to operate on a new data set associated with a first sales lead;
and
outputting a first representation of a first likelihood of successfully closing a first transaction associated with the first sales lead.

6 more elements

A non-transitory computer-readable medium having computer-executable that, when executed by a processor, cause the processor to perform actions comprising:

identifying a first time variable for a first data set and a second time variable for the first data set;
calculating a difference between the first time variable and the second time variable to produce an elapsed time associated with the first data set;
adding the elapsed time to a plurality of variables of a customer relationship management (CRM) database;
extracting a keyword from the first data set;
assigning a weight to the keyword to produce structured data for the first data set;
adding the structured data to the plurality of variables of the ERNI database;
creating a first comparison analytic model for the first data set using the plurality of variables, wherein the first comparison analytic model is associated with a first model type, and wherein the first comparison analytic model is configured to operate on a new data set associated with a first sales lead;
and outputting a first representation of a first likelihood of successfully closing a first transaction associated with the first sales lead.

5 more elements

Dependencies are inferred from the claim text.

Facts

Application: 15/405,076Filed 12 Jan 2017 · continuation in part
Priority: 30 Nov 2012
Published: US 2017/0124459 A14 May 2017
Granted: 7 Jul 2020
Term ends: Not estimatedThe source records don’t support an estimate.

Classification5 codes

G06N 5/02
Computing arrangements using knowledge-based models: knowledge representation; Symbolic representation
G06F 16/22
Of structured data, e.g. relational data: indexing; Data structures therefor; Storage structures
G06N 20/00
Machine learning
G06N
AI and machine learning

Family

Application61/731,595Us provisional application · filed 30 Nov 2012
Application14/093,403Parent of this continuation · filed 29 Nov 2013
Application15/009,042Parent of this continuation-in-part · filed 28 Jan 2016
US 9,280,739 B2Parent of this continuation · filed 8 Mar 2016
US 2017/0124459 A1Pre-grant publication · published 4 May 2017
US 9,582,759 B2Parent of this continuation-in-part
Application15/009,042Parent of this continuation
US 2014/0156581 A1Published application
US 2016/0148094 A1Published application
US 10,706,359 B2This patent

From the US filing history. Foreign family members aren’t listed.