US 11,106,871 B2Granted

Systems and methods for configurable messaging response-action engine

AssigneeConversica, Inc.

InventorsGeorge Alexis Terry, Werner Koepf, James D. Harriger, Joseph M. Silverbears, William Dominic Webb-Purkis, Macgregor S. Gainor, Ryan Francis Ginstrom, Siddhartha Reddy Jonnalagadda

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Abstract

Systems and methods for a configurable response-action engine are provided. Actions are generated for a conversation when an insight is received from a natural language processing system. Industry, segment, client specific instructions, third party data, a state for the lead and lead historical patterns are also received. A decision making action model is tuned using this information. An objective for the conversation may be extracted from the state information for the lead. The tuned model is then applied to the insight and objective to output an action. A response message may be generated for the action. The action is directed to cause a state transition of the lead to a preferred state. In another embodiment, systems and methods are presented for feature extraction from one or more messages. In yet other embodiments, systems and methods for message cadence optimization are provided.

Claims10

Dependent claims

A computer implemented method for generating actions to a message comprising:

receiving at least one insight regarding a message from a natural language processing machine learning model;
receiving a plurality of objectives;
receiving a set of completed objectives;
generating a set of outstanding objectives by comparing the plurality of objectives with the set of completed objectives;
receiving client specific instructions, and lead historical patterns, wherein the client specific instructions indicates how aggressive the language to be used is, and where the client specific instructions includes message wait length, and order of objective completion, and wherein the lead historical patterns includes response time and effective persuasion techniques;
tuning a decision making action model using the client specific instructions and lead historical patterns to modify at least one of response time, message content and order of completing the set of outstanding objectives
applying the tuned model to the insight to generate an action.

4 more elements

Dependencies are inferred from the claim text.

Facts

Application: 16/168,737Filed 23 Oct 2018 · continuation in part
Priority: 23 Jan 2015
Published: US 2019/0121856 A125 Apr 2019
Granted: 31 Aug 2021
Term ends: Not estimatedThe source records don’t support an estimate.

Classification11 codes

G06F 40/30
Handling natural language data: semantic analysis
G06N 3/006
Artificial life, i.e. computing arrangements simulating life: based on simulated virtual individual or collective life forms, e.g. social…
G06N 3/0464
Architecture, e.g. interconnection topology: convolutional networks [CNN, ConvNet]
G06N 3/09
Learning methods: supervised learning

Family

Application14/604,610Parent of this continuation-in-part · filed 23 Jan 2015
Application14/604,602Parent of this continuation-in-part · filed 23 Jan 2015
Application14/604,594Parent of this continuation-in-part · filed 23 Jan 2015
Application62/577,635Us provisional application · filed 26 Oct 2017
Application16/019,382Parent of this continuation-in-part · filed 26 Jun 2018
US 10,026,037 B2Parent of this continuation-in-part · filed 17 Jul 2018
US 2019/0121856 A1Pre-grant publication · published 25 Apr 2019
US 11,042,910 B2Parent of this continuation-in-part
US 10,803,479 B2Parent of this continuation-in-part
US 11,301,632 B2Related patent
US 2016/0217391 A1Published application
US 2016/0217500 A1Published application
US 2016/0217501 A1Published application
US 2018/0373696 A1Published application
US 11,106,871 B2This patent

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