Granted Patent B2

US 10,803,472 · App. 13/664,104 · Granted Oct 13, 2020

Inventor: Kelly Joseph Wical (Arlington Heights, IL)

Assignee: TRANSFORM SR BRANDS LLC

G06Q30/02G06Q50/01

Abstract

Claims (62)

1. A method, comprising:

in a system having at least one server that is operable to handle retail data and to communicate electronic messages with at least one customer device, the at least one server comprising memory configured to maintain a hyper-graph structure,

receiving content from a first source of data, the content including a plurality of components of customer data and product data, wherein one or more of the components comprise data in one or more undocumented fields;

storing, 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, 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;

configuring the interconnections to dynamically connect at various levels of granularity and interpretation;

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, based on 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 wherein 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 based on 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 based on 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 based on the performed vertical analysis.

2. The method of claim 1 , wherein the recommendation includes one or more of a product, an article, an image, a catalog, a recipe, a question, an answer, and a video, or wherein the selected portion of the hyper-graph structure corresponds to a cohort of customers that includes the particular customer.

3. The method of claim 1 , comprising filtering the recommendation based on 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 based on business parameters stored in the at least one server, the business parameters including 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, wherein the plurality of customers are characterized by multiple personas, wherein each persona is based on characteristics and data from the plurality of customers, wherein the system determines which of the multiple personas is prevailing at a particular time or for a particular interaction for the first customer, and wherein the prevailing persona is based on characteristics and data from the plurality of customers including the first customer.

6. The method of claim 1 , wherein the customer data or product data includes 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 includes the recommendation; and

tracking an interaction of the particular customer with the recommendation in the electronic message.

9. A method, comprising:

in a system having at least one server that is operable to handle retail data and to communicate electronic messages with at least one customer device, the at least one server comprising memory configured to maintain a hyper-graph structure,

receiving content from a first source of data, the content including a plurality of components of customer data and product data, wherein 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;

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;

configuring the interconnections to dynamically connect at various levels of granularity and interpretation;

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, based on 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 wherein 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 based on 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 based on 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 based on the performed vertical analysis.

10. The method of claim 9 , wherein the personal catalog includes historical interaction information of the particular customer.

11. The method of claim 9 , wherein the personal catalog includes a recommendation that includes one or more of a product, an article, an image, a catalog, a recipe, a question, an answer, and a video.

12. The method of claim 11 , comprising:

determining one or more items related to the recommendation; and

providing the one or more items in the personal catalog.

13. The method of claim 9 , comprising linking the personal catalog to one or more additional catalogs corresponding to the particular customer.

14. The method of claim 9 , wherein the selected portion of the hyper-graph structure corresponds to a cohort of customers that includes the particular customer.

15. A method, comprising:

in a system having at least one server that is operable to handle retail data and to communicate electronic messages with at least one customer device, the at least one server comprising memory configured to maintain a hyper-graph structure,

receiving content from a first source of data, the content including a plurality of components of customer data and product data, wherein one or more of the components comprise data in one or more undocumented fields;

storing, 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, 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;

configuring the interconnections to dynamically connect at various levels of granularity and interpretation;

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, based on an amount of intersection of other components of the data of the first customer and the second customer, wherein the known values are highly weighted to indicate that the filled-in components are characteristic of the first customer, and wherein the known values are lowly weighted to indicate that the filled-in components are not characteristic of the first customer;

selecting a portion of the hyper-graph structure based on 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;

comparing data stored in the at least one server corresponding to one commercial entity with data stored in the at least one server corresponding to another commercial entity, the data being compared including product data;

constructing a personal catalog from at least one recommendation for the particular customer based on the content in the selected portion of the hyper-graph structure and the comparison, 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 based on the performed vertical analysis.

16. The method of claim 15 , wherein the comparison is based on one or both of product pricing and product quality.

17. The method of claim 15 , comprising comparing data stored in the at least one server corresponding to one commercial entity with data stored in the at least one server corresponding to another commercial entity, the data being compared including one or more of product data, a review, a blog, a video, a picture, an activity, a question, and an answer.

18. The method of claim 15 , comprising:

generating an electronic message that includes the recommendation; and

tracking an interaction of the particular customer with the recommendation in the electronic message.

19. The method of claim 15 , wherein the selected portion of the hyper-graph structure corresponds to a cohort of customers that includes the particular customer.

20. The method of claim 15 , wherein each of the customer data and the product data includes data of different formats, the method comprising:

normalizing the customer data and the product data through abstraction and semantic generalization; and

after normalization, storing the customer data and the product data into the hyper-graph structure.

21. The method of claim 1 , wherein the vertical analysis of the consumable products provides, in the personal catalog, recommendations for tools that can be used in connection with the consumable products.

22. The method of claim 9 , wherein the at least one recommendation is based on at least two characteristics of the data of the content in the selected portion of the hyper-graph structure: a similarity connection and a semantic connection, wherein the semantic connection is not based on similarity.

23. The method of claim 15 , wherein the customer data components comprise one or more of an interest, a probable activity, a behavior, and a specific attribute of a customer, and the product data comprises one or more of a property, an attribute, and a characteristic of a product item.

Assignments (14)