US-12619999-B2 - Predictive and interventive intelligence

US12619999B2US 12619999 B2US12619999 B2US 12619999B2US-12619999-B2

Abstract

A method includes receiving historical data housed in the one or more computer systems, the historical data including structured data and unstructured data, storing the historical data in a central database, aggregating the historical data in the central database according to subject matter, validating the aggregated historical data, analyzing the validated aggregated historical data using a series of tools, generating normal profiles in the first computer system from the analyzed validated aggregated historical data, storing the generated normal profiles as horizon data sets, receiving real time data housed in the one or more computer systems, comparing the real time data to the horizon data sets to identify normal and out of normal profiles, and generating rules-based, predictive and interventive outcomes for out of normal profiles based on parameters for investigation.

Inventors

Severence M. MacLaughlin

Assignees

DeLorean Artificial Intelligence, Inc.

Dates

Publication Date: 20260505
Application Date: 20200526

Claims (14)

1 . A method of conducting a deal, the method comprising: receiving, by a first computer system, historical deal data housed in one or more interconnected computer systems, the historical deal data comprising historical structured deal data and historical unstructured deal data; storing, by the first computer system, the historical deal data in a central database residing in the first computer system in the network; aggregating, by the first computer system, the historical deal data in the central database in the first computer system according to subject matter to generate aggregated historical deal data; validating, by the first computer system, the aggregated historical deal data in the first computer system to generate validated-aggregated historical deal data; analyzing, by the first computer system, the validated-aggregated historical deal data in the first computer system using a series of tools to generate analyzed-validated-aggregated historical deal data; training, by the first computer system based on the analyzed-validated-aggregated historical deal data, a deal profile model, the training of the deal profile model comprising: determining, by the first computer system based on the historical unstructured deal data, enrichment data, the determining of enrichment data comprising: vectorizing the unstructured electronic documents to generate vectorized historical unstructured deal data; clustering, using unsupervised learning comprising k-means clustering, the vectorized historical unstructured deal data to generate historical unstructured deal data clusters corresponding to clusters of the electronic documents corresponding to stages of the deal; training, by the first computer system using supervised learning and the vectorized historical unstructured deal data, a stage model configured to determine a current phase of a deal based on unstructured data for the deal, the supervised learning comprising: labeling the electronic documents of the vectorized historical unstructured deal data to generate labeled documents data; and training a neural network classifier using the historical unstructured deal data clusters and the labeled data, the stage model; and determining sentiments for the electronic documents of the historical unstructured deal data, the enrichment data comprising the historical unstructured deal data clusters, the stage model and the sentiments for the historical unstructured data; training, by the first computer system, using the historical structured data and the enrichment data, the deal profile model to determine a current deal phase, a predicted deal outcome, and next best action to improve deal outcome based on deal data comprising structured deal data and unstructured deal data; receiving, by the first computer system, real time data housed in the one or more of the interconnected computer systems in the first computer system, the real time data comprising current deal data comprising current structured deal data and current unstructured deal data for a current deal; determining, by the first computer system applying to the deal profile model, the current structured deal data and the current unstructured deal data to determine a deal phase of the current deal, a predicted deal outcome of the current deal, and a next best action to improve deal outcome of the current deal, the next best action comprising a set of deal actions that are weighted based on priority to improve the outcome of the current deal; and conducting, by the first computer system responsive to determining the next best action, the set of deal actions prioritized based on the weighting of the set of deal actions to improve an outcome of the current deal.
2 . The method of claim 1 wherein the series of tools include one or more of data science tools, analytical tools and artificial intelligence tools.
3 . The method of claim 1 wherein receiving historical data further comprises receiving structured and unstructured data from additional computer systems linked to the network.
4 . The method of claim 3 wherein the structured data comprises data that resides in a fixed field within a record or file.
5 . The method of claim 4 wherein the unstructured data comprises data that does not have a pre-defined data model or is not organized in a pre-defined manner.
6 . The method of claim 2 wherein the series tools include one or more of text mining, natural language processing (NLP), natural language utilization (NLU), natural language generation (NLG), machine learning (supervised and unsupervised), genetic programming, evolutionary programming, deep learning, neural networks, computer vision and audio sound recognition.
7 . The method of claim 1 wherein real time data is decision time data.
8 . The method of claim 7 wherein decision time data comprises data that is analyzed and quantified to generate insights and predictions based on a need of an industry.
9 . The method of claim 1 wherein the electronic documents comprise electronic mail documents comprising unstructured text.
10 . A system comprising: a processor; a memory; a data store; the memory comprising instructions that are executable to perform a predictive process, the predictive process comprising: receiving, by a first computer system, historical deal data housed in one or more interconnected computer systems, the historical deal data comprising historical structured deal data and historical unstructured deal data; storing, by the first computer system, the historical deal data in the data store; aggregating, by the first computer system, the historical deal data in the data store according to subject matter to generate aggregated historical deal data; validating, by the first computer system, the aggregated historical deal data to generate validated-aggregated historical deal data; analyzing, by the first computer system, the validated aggregated historical deal data using a series of tools to generate analyzed-validated-aggregated historical deal data; training, by the first computer system, a deal profile model, the training of the deal profile model comprising: determining, based on the historical unstructured deal data, enrichment data, the determining of enrichment data comprising: vectorizing the unstructured electronic documents to generate vectorized historical unstructured deal data; clustering, using unsupervised learning comprising k-means clustering, the vectorized historical unstructured deal data to generate historical unstructured deal data clusters corresponding to clusters of the electronic documents corresponding to stages of the deal; training, by the first computer system using supervised learning and the vectorized historical unstructured deal data, a stage model configured to determine a current phase of a deal based on unstructured data for the deal, the supervised learning comprising: labeling the electronic documents of the vectorized historical unstructured deal data to generate labeled documents data; and training a neural network classifier using the historical unstructured deal data clusters and the labeled data, the stage model; and determining sentiments for the electronic documents of the historical unstructured deal data, the enrichment data comprising the historical unstructured deal data clusters, the stage model and the sentiments for the historical unstructured data; training, by the first computer system, using the historical structured data and the enrichment data, deal profile model to determine a current deal phase, a predicted deal outcome, and next best action to improve deal outcome based on deal data comprising structured deal data and unstructured deal data; receiving, by the first computer system, real time data from the one or more of the interconnected computer systems, the real time data comprising current deal data comprising current structured deal data and current unstructured deal data for a current deal; determining, by the first computer system applying to the deal profile model, the current structured deal data and the current unstructured deal data to determine a deal phase of the current deal, a predicted deal outcome of the current deal, and a next best action to improve deal outcome of the current deal, the next best action comprising a set of deal actions that are weighted based on priority to improve the outcome of the current deal; and conducting, by the first computer system responsive to determining the next best action, the set of deal actions prioritized based on the weighting of the set of deal actions to improve an outcome of the current deal.
11 . The system of claim 10 wherein the series of tools include one or more of data science tools, analytical tools and artificial intelligence tools.
12 . The system of claim 10 wherein the electronic documents comprise electronic mail documents comprising unstructured text.
13 . A method of conducting a deal, the method comprising: obtaining, by a computer system, historical deal data comprising: historical structured deal data; and historical unstructured deal data; training, by the computer system based on the deal data, a deal profile model, the training of the deal profile model comprising: vectorizing, by the computer system, electronic documents of the historical unstructured deal data to generate vectorized historical unstructured deal data; clustering, by the computer system using unsupervised learning comprising k-means clustering, the vectorized historical unstructured deal data to generate historical unstructured deal data clusters corresponding to clusters of the electronic documents corresponding to stages of the deal; training, by the computer system using supervised learning, the vectorized historical unstructured deal data, a stage model configured to determine a current phase of a deal based on unstructured data for the deal, the supervised learning comprising: labeling, by the computer system, the electronic documents of the vectorized historical unstructured deal data to generate labeled documents data; training, by the computer system, a neural network classifier using the historical unstructured deal data clusters and the labeled data, the stage model; and determining, by the computer system, sentiments for the historical unstructured deal data; determining, by the computer system, enrichment data comprising: the historical unstructured deal data clusters; the stage model; and the sentiments for the historical unstructured data; training, by the computer system using the historical structured data and the enrichment data, the deal profile model to determine a current deal phase, a predicted deal outcome, and next best action to improve deal outcome based on deal data comprising structured deal data and unstructured deal data; receiving, by the computer system, current deal data comprising current structured deal data and current unstructured deal data for a current deal; determining, by the computer system applying to the deal profile model, the current structured deal data and the current unstructured deal data to determine a deal phase of the current deal, a predicted deal outcome of the current deal, and a next best action to improve an outcome of the current deal, the next best action comprising a set of deal actions that are weighted based on priority to improve the outcome of the current deal; and conducting, by the computer system responsive to determining the next best action, the set of deal actions prioritized based on the weighting of the set of deal actions to improve an outcome of the current deal.
14 . The method of claim 13 wherein the electronic documents comprise electronic mail documents comprising unstructured text.

Description

BACKGROUND OF THE INVENTION The present invention relates generally to data modeling, and more particularly to predictive and interventive intelligence. In general, businesses today are looking for smarter ways to streamline their existing sales operations process and improve conversion rates. For example, businesses look to chase opportunities that have higher changes of conversion and profitability, shorten sales cycles without compromising conversion odds, and reducing cost of winning deals and ongoing operations. SUMMARY OF THE INVENTION The following presents a simplified summary of the innovation in order to provide a basic understanding of some aspects of the invention. This summary is not an extensive overview of the invention. It is intended to neither identify key or critical elements of the invention nor delineate the scope of the invention. Its sole purpose is to present some concepts of the invention in a simplified form as a prelude to the more detailed description that is presented later. In general, in one aspect, the invention features a method including receiving historical data housed in the one or more interconnected computer systems in a first computer system, the historical data comprising structured data and unstructured data, storing the historical data in a central database residing in the first computer system in the network, aggregating the historical data in the central database in the first computer system according to subject matter, validating the aggregated historical data in the first computer system, analyzing the validated aggregated historical data in the first computer system using a series of tools, generating normal profiles in the first computer system from the analyzed validated aggregated historical data, storing the generated normal profiles as horizon data sets, receiving real time data housed in the one or more of the interconnected computer systems in the first computer system, comparing the real time data to the horizon data sets to identify normal and out of normal profiles, and generating rules-based outcomes for out of normal profiles based on parameters for investigation. In another aspect, the invention features a system including a processor, a memory, a data store, the memory including an operating system and a predictive process, the predictive process including receiving historical data housed in one or more interconnected computer systems, the historical data comprising structured data and unstructured data, storing the historical data in the data store, aggregating the historical data in the data store according to subject matter, validating the aggregated historical data, analyzing the validated aggregated historical data using a series of tools, generating normal profiles from the analyzed validated aggregated historical data, storing the generated normal profiles as horizon data sets, receiving real time data from the one or more of the interconnected computer systems, comparing the real time data to the horizon data sets to identify normal and out of normal profiles, and generating rules-based outcomes for out of normal profiles based on parameters for investigation. These and other features and advantages will be apparent from a reading of the following detailed description and a review of the associated drawings. It is to be understood that both the foregoing general description and the following detailed description are explanatory only and are not restrictive of aspects as claimed. BRIEF DESCRIPTION OF THE DRAWINGS These and other features, aspects, and advantages of the present invention will become better understood with reference to the following description, appended claims, and accompanying drawings where: FIG. 1 is a block diagram of an exemplary network. FIG. 2 is a block diagram of an exemplary server. FIG. 3 is a flow diagram. FIG. 4 is a flow diagram. FIG. 5 illustrates an exemplary CRM framework. FIG. 6 illustrates some exemplary types of data ingested. FIG. 7 illustrates an example of next best action. DETAILED DESCRIPTION The subject innovation is now described with reference to the drawings, wherein like reference numerals are used to refer to like elements throughout. In the following description, for purposes of explanation, numerous specific details are set forth in order to provide a thorough understanding of the present invention. It may be evident, however, that the present invention may be practiced without these specific details. In other instances, well-known structures and devices are shown in block diagram form in order to facilitate describing the present invention. As shown in FIG. 1, an exemplary network 10 includes a local area network (LAN) 12 and a link 14 to network 16 of interconnected computers (e.g., Internet). The LAN 12 includes one of more server systems 18, 20 and 22. Although only three servers 18, 20, 22 are shown, the LAN 12 may include any number of servers. The servers 18, 20, 22 are all linked to e