ARTIFICIAL INTELLIGENCE-BASED RECOMMENDER AND SELF-PROVISIONER

• US 20120150642A1
• Filed: 12/10/2010
• Published: 06/14/2012
• Est. Priority Date: 12/10/2010
• Status: Active Grant

• Current Assignee: Verizon Patent and Licensing Incorporated (Verizon Communications Inc.)
• Original Assignee: Verizon Patent and Licensing Incorporated (Verizon Communications Inc.)
• Inventors: Kandanala, Venket, Srinivasan, Karthik, Joshi, Nicky Pinakin, Varughese, Mathew P., Sankaran, Anand N., Gunda, Siddhartha X., Mahajan, Pankul X., Parambath, Jubish C., Ramalingam, Paranjothi X., Caputo, Lisa A., Varadharajan, Sundarajan
• Granted Patent: US 8,626,587 B2
• Time in Patent Office: Days
• US Class Current: 705/14.53
• CPC Class Codes: G06Q 30/02 Marketing; Price estimation or determination; Fundraising

Abstract

A method including providing at least one of a phone service, an Internet access service, or a television service to customers; identifying a customer when the customer uses the at least one of the phone service, the Internet access service, or the television service; selecting products or services for which the customer qualifies based on the identifying; gathering customer-specific data for the customer; generating recommendations that pertain to the at least one of the phone service, the Internet access service, or the television service for the customer based on the products or the services for which the customer qualifies, the customer-specific data, and other factors, wherein the other factors include at least one of a location of the customer or a time in which the recommendations is to be provided to the customer; and providing the recommendations to the customer via the at least service.

Claims

1. A method comprising:

providing at least one of a phone service, an Internet access service, or a television service to customers;

identifying a customer when the customer uses the at least one of the phone service, the Internet access service, or the television service;

selecting products or services for which the customer qualifies based on the identifying;

gathering customer-specific data for the customer;

generating recommendations that pertain to the at least one of the phone service, the Internet access service, or the television service for the customer based on the products or the services for which the customer qualifies, the customer-specific data, and other factors, wherein the other factors include at least one of a location of the customer or a time in which the recommendations is to be provided to the customer; and

providing the recommendations to the customer via the at least one of the phone service, the Internet access service, or the television service.

2. The method of claim 1, further comprising:

receiving a selection of a recommendation from the customer; and

provisioning the at least one service based on the selection of the recommendation.

3. The method of claim 1, further comprising:

assigning a weight, which corresponds to a likelihood of the customer selecting the recommendation, to each of the recommendations; and

sorting the recommendations in an order based on the weights associated the recommendations.

5. The method of claim 1, wherein the generating comprises:

using rules that include criteria for generating the recommendations, wherein the rules pertain to one or more of customer migration history, customer affinity to a recommendation based on customer'"'"'s current subscription, or customer usage.

6. The method of claim 5, wherein when the recommendations pertain to the television service in which television content is being recommended, the rules include whether the customer has previously viewed a particular television package being recommended.

7. The method of claim 1, wherein the location of the customer corresponds to a ZIP code associated with the customer.

8. The method of claim 1, wherein the selecting further comprises:

accessing customer profile information based on the identifying;

determining products used by the customer or services subscribed to by the customer based on the accessing;

determining products or services associated with a marketing campaign;

comparing the products or the services based on the accessing with the products or the services associated with the marketing campaign; and

selecting products or services for which the customer qualifies.

4. The method of claim 4, wherein the providing comprises:

generating a user interface that allows the customer to select from the recommendations and provides the recommendations in the order that is from the most likely to the least likely that the customer will select a particular recommendation.

9. A device comprising logic configured to:

obtain customer-specific data for customers receiving at least one of phone service, Internet access service, or television service;

obtain a list of products or services for which a customer qualifies;

obtain rules that include criteria for selecting recommendations that pertain to the at least one of the phone service, the Internet access service, or the television service; and

generate recommendations that pertain to the at least one of the phone service, the Internet access service, or the television service for a customer based on the customer-specific data, the rules, the list of products or the services, and other factors, wherein the other factors include a location of the customer in which the customer receives the least one of the phone service, the Internet access service, or the television service.

10. The device of claim 9, wherein the other factors include at least one of a time in which the recommendations are being provided to the customer, or a state of the economy.

11. The device of claim 9, wherein the logic is further configured to:

assign a weight that corresponds to a likelihood of the customer selecting the recommendation to each of the recommendations; and

sort the recommendations in an order based on the weights associated the recommendations.

12. The device of claim 9, wherein the logic is further configured to:

increase the weight or decrease the weight based on a measurement of success of the recommendation with other customers.

13. The device of claim 9, wherein the rules include criteria pertaining to one or more of customer migration history, customer affinity to a recommendation based on customer'"'"'s current subscription, or customer usage.

14. The device of claim 9, wherein the recommendations pertain to a television content package or a television content bundle associated with sports or movies.

15. The device of claim 14, wherein the rules include criteria pertaining to one or more of a time period between a last purchase date and a recommendation date or whether the customer has previously viewed the television content package or the television content bundle.

16. The device of claim 9, wherein the logic is further configured to:

generate the recommendations on a customer basis or a customer device-level basis.

17. A device comprising logic configured to:

receive recommendations that pertain to at least one of phone service, Internet access service, or television service, wherein the recommendations are customer-specific and have an order corresponding to a likelihood of the customer selecting the recommendations;

generate a user interface, which allows the customer to which the recommendations are directed, to self-provision a recommendation;

provide the user interface to the customer via the least one of the phone service, the Internet access service, or the television service; and

receive a selection of at least one of the recommendations from the customer.

18. The device of claim 17, wherein the recommendations pertain to television content.

19. The device of claim 17, wherein the user interface corresponds to a graphical user interface, and when generating, the logic is further configured to:

select a graphical user interface that corresponds to each recommendation; and

sorting the graphical user interfaces based on the order.

20. The device of claim 17, wherein the recommendation pertains to customer equipment.