Method and system for emergent data processing

Patent metadata

Field	Value
Publication number	US-12579550-B2
Application number	US-202418753617-A
Country	US
Kind code	B2
Filing date	Jun 25, 2024
Priority date	Oct 30, 2012
Publication date	Mar 17, 2026
Grant date	Mar 17, 2026

Abstract

A method and system for emergent data processing are described. A system having one or more servers operable to handle retail data can receive content including customer data and product data. The content can be normalized and stored into a hyper-graph structure in the servers. The system can be used to select a portion of the hyper-graph structure based on a particular customer and to generate a recommendation for the particular customer based on the content in that portion of the hyper-graph structure. The system can also generate personal catalogs based on the information in the hyper-graph structure. The system can perform competitive analysis between products from different sources and include the results in the recommendations. Moreover, the system can perform a vertical analysis of consumable products to provide recommendations for tools or products that can be used in connection with the consumable products.

First claim

What is claimed is:

1 . A method, comprising:
in a system comprising at least one server operable to handle retail data and communicate electronic messages with at least one customer device:
maintaining a hyper-graph structure;
receiving content from a first source of data, wherein:
the content comprises a plurality of components of customer data and product data, and
one or more of the components comprise data in one or more undocumented fields;

storing, in the memory, the plurality of components of the received content to establish interconnections in the hyper-graph structure of the received data with a plurality of components of content for related customer data and product data from other sources, wherein:
the data in the one or more undocumented fields are placed unaltered in the hyper-graph structure, and
the data from the one or more undocumented fields are understood via their interconnections in the hyper-graph structure, the interconnections being dynamically generated by mapping each undocumented field into a typed node or edge using semantic similarity measures, such that heterogeneous data formats are normalized into a unified graph representation without requiring schema modification, thereby reducing computational overhead and improving data integration performance;

filling in particular components missing from data of a first customer in the hyper-graph structure using known values of the particular components in data of a second customer in the hyper-graph structure, according to an amount of intersection of other components of the data of the first customer and the second customer, wherein:
the known values are weighted to indicate that the filled-in components are characteristic of the first customer, and
the known values are weighted to indicate that the filled-in components are not characteristic of the first customer;

selecting a portion of the hyper-graph structure according to similarity of components of data for a particular customer to components of customer data present in the hypergraph structure;
performing a vertical analysis of consumable products of the selected portion of the hyper-graph structure;
constructing a personal catalog from at least one recommendation for the particular customer according to the content in the selected portion of the hyper- graph structure, for transmission to a device of the particular customer for viewing; and
providing a graphical user interface that comprises one or more graphical elements that can be selected to organize and display the personal catalog according to the performed vertical analysis.

2 . The method of claim 1 , wherein:
the recommendation comprises one or more of a product, an article, an image, a catalog, a recipe, a question, an answer, and a video.

3 . The method of claim 1 , comprising filtering the recommendation according to one or both of a merchant black-listing by theme and sentiment information about the particular customer.

4 . The method of claim 1 , comprising generating the recommendations for the particular customer according to business parameters stored in the at least one server, the business parameters comprising one or more of margin, revenue, competitive positioning, costumer acquisition, customer retention, and customer activity.

5 . The method of claim 1 , wherein:
the first customer is one of a plurality of customers, the plurality of customers are characterized by multiple personas, each persona is associated with characteristics and data from the plurality of customers, the system determines which of the multiple personas is prevailing at a particular time or for a particular interaction for the first customer, and the prevailing persona is associated with characteristics and data from the plurality of customers comprising the first customer.

6 . The method of claim 1 , wherein the customer data or product data comprises data of different formats, the method comprising:
normalizing the customer data or the product data through abstraction and semantic generalization; and after normalization, storing the customer data or the product data into the hyper- graph structure.

7 . The method of claim 1 , wherein one or both of the first customer and the second customer can be defined in an emergent data processing system using dynamic data dimensions.

8 . The method of claim 1 , comprising:
generating an electronic message that comprises the recommendation; and tracking an interaction of the particular customer with the recommendation in the electronic message.

9 . The method of claim 1 , comprising:
dynamically connecting the interconnections at various levels of granularity and interpretation.

10 . A method, comprising:
in a system comprising at least one server that is operable to handle retail data and to communicate electronic messages with at least one customer device,
maintaining a hyper-graph structure;
receiving content from a first source of data, wherein:
the content comprises a plurality of components of customer data and product data, and
one or more of the components comprise data in one or more undocumented fields;

storing the plurality of components of the received content to establish interconnections in the hyper-graph structure of the received data with a plurality of components of content for related customer data and product data from other sources, wherein:
the data in the one or more undocumented fields are placed unaltered in the hyper-graph structure, and
the data from the one or more undocumented fields are understood via their interconnections in the hyper-graph structure, the interconnections being dynamically generated by mapping each undocumented field into a typed node or edge using semantic similarity measures, such that heterogeneous data formats are normalized into a unified graph representation without requiring schema modification, thereby reducing computational overhead and improving data integration performance;

normalizing, in the memory of the at least one server, the plurality of components of the received content to establish interconnections in the hyper-graph structure of the received data with a plurality of components of content for related customer data and product data from other sources;
filling in particular components missing from data of a first customer in the hyper-graph structure using known values of the particular components in data of a second customer in the hyper-graph structure, according to an amount of intersection of other components of the data of the first customer and the second customer, wherein:
the known values are weighted to indicate that the filled-in components are true for the first customer, and
the known values are weighted to indicate that the filled-in components are not true for the first customer;

selecting a portion of the hyper-graph structure according to similarity of components of data for a particular customer to components of customer data present in the hypergraph structure;
performing a vertical analysis of consumable products of the selected portion of the hyper-graph structure;
constructing a personal catalog from at least one recommendation for the particular customer according to the content in the selected portion of the hyper-graph structure, for transmission to a device of the particular customer for viewing; and
providing a graphical user interface that comprises one or more graphical elements that can be selected to organize and display the personal catalog according to the performed vertical analysis.

11 . The method of claim 10 , wherein the personal catalog comprises historical interaction information of the par

Assignees

• Transf Sr Brands Llc

Inventors

• Wical Kelly Joseph

Classifications

• G06Q10/48

  using social graphs · CPC title

• G06Q10/42

  Determination of affinities or common interests between users · CPC title

• G06Q10/40

  Business processes related to social networking or social networking services · CPC title

• G06Q30/02 Primary

  Marketing; Price estimation or determination; Fundraising · CPC title

• G06Q50/01

  Physics · mapped topic