Network diffusion convergence and divergence analysis system

Patent metadata

Field	Value
Publication number	US-2016247186-A1
Application number	US-201514626725-A
Country	US
Kind code	A1
Filing date	Feb 19, 2015
Priority date	Feb 19, 2015
Publication date	Aug 25, 2016
Grant date	—

Abstract

Embodiments of the invention are directed to a system, method, or computer program product for providing network diffusion convergence and divergence analysis. Thus identifying the convergence of a network diffusion model and subsequent divergence of the diffusion back to normal spend standards. The invention creates a repository of spend data for a period of time and for a specific category of purchases. Using geospatial information identifies a group within the repository and an influencer based on an inferred relationship and degree influence. In this way, the invention provides a means of delivering to the influencer for diffusion throughout a group of individuals. The system subsequent tracks the duration of convergence, initial divergence, and complete divergence. This data is then utilized to determine influencer and indirect delivery effectiveness.

First claim

What is claimed is:

1 . A system for convergence and divergence analysis, the system comprising:
a memory device with non-transitory computer-readable program code stored thereon; a communication device; a processing device operatively coupled to the memory device and the communication device within a distributive network for the convergence and divergence analysis, wherein the processing device is configured to execute the computer-readable program code to:
creating a repository of spend data for a predetermined period of time and for a specific product category;
identifying, using network data feeds from the distributive network, a group of individuals within the repository, wherein the group is identified based on each member of the group purchasing a product within the specific product category, within the predetermined period of time, and based on geospatial recognition;
inference mapping for identification of influencers among the group for the specific product category;
transforming, via the processing device, inference mapping data into degree ranking for influencer;
providing an offer to the influencer for a product within the specific product category, wherein the offer value is based on the degree ranking of the influencer;
identifying convergence of group spend data converging to influencer spend data based on the offer;
tracking and identifying divergence of group spend based on the convergence and record time data associated with divergence; and
transforming convergence data, divergence data, and recorded time data associated with divergence to advertisement diffusion feedback and influencer degree adjustments, whereby providing advertisement and offer effectiveness for the product category.

2 . The system of claim 1 , wherein the operation of identifying the group of individuals within the repository comprises using geographic location determination in connection with transaction history, coincident mapping, or social network mapping to identify the group of individuals geospatially located and associated with the specific product category, wherein transaction history identifies similar transactions for the specific product category, coincided mapping maps likely association of individuals based on the specific category of products, and social networking mapping identifies a network of individuals associated with each other.

3 . The system of claim 1 , wherein inference mapping for identification of influencers among the group for the specific product category further comprises inference mapping based on social network and geospatial data to identify and build degrees of influence among group individuals.

4 . The system of claim 1 , wherein identifying convergence of group spend data further comprises identifying when one or more individuals in the group purchase one or more products in the specific category of products that the influencer has purchased based on the offer.

5 . The system of claim 1 , wherein identifying convergence of group spend data further comprises tracking a duration of time associated with the convergence of group spend data.

6 . The system of claim 1 , wherein the operation of identifying divergence of group spend data further comprises determining an initial time point when one or more individuals of the group initially diverge in spend trends from the influencer back to the one or more individuals spend trend prior to being influenced.

7 . The system of claim 1 , wherein transform convergence data, divergence data, and recorded time data associated with divergence further comprises calculating an influence rate of the influencer based on the transform convergence data, divergence data, and recorded time data associated with divergence to adjust influencer degree for future groups and product categories.

8 . A computer program product for convergence and divergence analysis, the computer program product, within a distributive network for the convergence and divergence analysis, comprising at least one non-transitory computer-readable medium having computer-readable program code portions embodied therein, the computer-readable program code portions comprising:
an executable portion configured for creating a repository of spend data for a predetermined period of time and for a specific product category; an executable portion configured for identifying, using network data feeds from the distributive network, a group of individuals within the repository, wherein the group is identified based on each member of the group purchasing a product within the specific product category, within the predetermined period of time, and based on geospatial recognition; an executable portion configured for inference mapping for identification of influencers among the group for the specific product category; an executable portion configured for transforming inference mapping data into degree ranking for influencer; an executable portion configured for providing an offer to the influencer for a product within the specific product category, wherein the offer value is based on the degree ranking of the influencer; an executable portion configured for identifying convergence of group spend data converging to influencer spend data based on the offer; an executable portion configured for tracking and identifying divergence of group spend based on the convergence and record time data associated with divergence; and an executable portion configured for transforming convergence data, divergence data, and recorded time data associated with divergence to advertisement diffusion feedback and influencer degree adjustments, whereby providing advertisement and offer effectiveness for the product category.

9 . The computer program product of claim 8 , wherein the operation for identifying the group of individuals within the repository comprises using geographic location determination in connection with transaction history, coincident mapping, or social network mapping to identify the group of individuals geospatially located and associated with the specific product category, wherein transaction history identifies similar transactions for the specific product category, coincided mapping maps likely association of individuals based on the specific category of products, and social networking mapping identifies a network of individuals associated with each other.

10 . The computer program product of claim 8 , wherein inference mapping for identification of influencers among the group for the specific product category further comprises inference mapping based on social network and geospatial data to identify and build degrees of influence among group individuals.

11 . The computer program product of claim 8 , wherein identifying convergence of group spend data further comprises identifying when one or more individuals in the group purchase one or more products in the specific category of products that the influencer has purchased based on the offer.

12 . The computer program product of claim 8 , wherein identifying convergence of group spend data further comprises tracking a duration of time associated with the convergence of group spend data.

13 . The computer program product of claim 8 , wherein the operation of identifying divergence of group spend data further comprises determining an initial time point when one or more individuals of the group initially diverge in spend trends from the influencer back to the one or more individuals spend trend prior to being influenced.

14 . The computer program product of claim 8 , wherein transform convergence data, divergence data, and recorded time data associa

Assignees

• Bank Of America

Inventors

• Abbott Robert L
• Blackhurst Jason P
• Hamilton Alfred
• Shahda Frederick A

Classifications

• G06Q30/0244 Primary

  Optimization · CPC title

• G06Q30/0269

  based on user profile or attribute · CPC title