DYNAMIC LIST CREATION

• US 20150149298A1
• Filed: 10/31/2014
• Published: 05/28/2015
• Est. Priority Date: 11/22/2013
• Status: Abandoned Application

• Current Assignee: PayPal, Inc. (PayPal Holdings, Inc.)
• Original Assignee: PayPal, Inc. (PayPal Holdings, Inc.)
• Inventors: Tapley, John
• Application Number:
  US14/530,458
• Publication Number:
  US 20150149298A1
• Time in Patent Office: Days
• US Class Current: 705/14.66
• CPC Class Codes: G06Q 30/0269 based on user profile or attribute
• CPC Class Codes: G06Q 30/0633 Lists, e.g. purchase orders, compilation or processing

Abstract

In various example embodiments, a system and method for dynamically creating an aggregate list are presented. For one embodiment, sensor data associated with a first data source type is received from a network. The sensor data represents at least one item to be added to the aggregate list from the first data source type representing a connected appliance. The aggregate list is associated with at least one user. The sensor data is processed based on predictive modeling associated with consumption of the at least one time to be added to the list to automatically generate learning data. The learning data is associated with a second data source type and representing at least one item to be added to the aggregate list from the second data source type. The non-sensor data associated with a third data source type is received from a network. The non-sensor data represents at least one item to be added to the aggregate list from the third data source type. An aggregate list is generated including a list of items from each of the first data source type, the second data source type and the third data source type.

Claims

1. A method comprising:

receiving, from a network, sensor data associated with a first data source type, the sensor data representing at least one item to be added to an aggregate list from the first data source type, the aggregate list associated with at least one user, the first data source type representing a connected appliance;

processing, using at least one processor, the sensor data based on predictive modeling associated with a consumption of the at least one item to be added to the aggregate list from the first data source type to automatically generate learning data, the learning data associated with a second data source type and representing at least one item to be added to the aggregate list from the second data source type;

receiving, from the network, non-sensor data associated with a third data source type, the non-sensor data representing at least one item to be added to the aggregate list from the third data source type;

generating the aggregate list of items representing at least one item added to the aggregate list from each of the first data source type, the second data source type, and the third data source type.

2. The method of claim 1, further comprising:

receiving, from the network, condition input data and condition criteria, the condition input data associated with a fourth data source type;

processing, using at least one processor, the condition input data to determine whether the condition input data satisfies the condition criteria;

automatically generating, using at least one processor, condition data representing at least one item to be added to the aggregate list from the fourth data source type.

3. The method of claim 2, wherein the learning data for the at least one item to be added to the aggregate list may be overridden by condition data.

4. The method of claim 2, wherein generating the aggregate list further comprises:

generating the aggregate list of items representing at least one item added to the aggregate list from each of the first data source type, the second data source type, the third data source type, and the fourth data source type.

5. The method of claim 1, wherein the third data source type includes one or more persons associated with the at least one user;

and

wherein the non-sensor data includes user specified data representing the at least one item to be added to the aggregate list from the one or more persons.

6. The method of claim 1, wherein the sensor data includes the at least one item to be added to the aggregate list and associated product identification information.

7. The method of claim 6, wherein the product identification information includes a stock keeping unit (SKU) number of the at least one item on the aggregate list.

8. The method of claim 7, wherein the SKU number is used by a merchant inventory system associated with a network of affiliated merchants to determine whether one or more affiliated merchants has available inventory of the at least one item on the aggregate list.

9. The method of claim 6, further comprising:

determining, based on the product identification information, whether at least one merchant from the network of affiliated merchants has an exact match with inventory for one item on the aggregate list;

if the exact match is not successfully determined, determining which of the at least one merchant from the network of affiliated merchants has a nearest match with inventory for the one item on the aggregate list; and

if the nearest match is not successful, determining whether at least one merchant from the network of affiliated merchants has a generic product having a same product category as the one item on the aggregate list.

10. The method of claim 1, wherein the receiving, from the network, the non-sensor data further comprises:

retrieving the non-sensor data from a cloud computing environment, the cloud computing environment hosting a list application accessible by a client device, the non-sensor data received by the list application through the client device.

11. The method of claim 1, further comprising:

identifying available inventory for the at least one item on the aggregate list from one or more merchants within a network of affiliated merchants.

12. The method of claim 11, further comprising:

identifying available advertising discounts associated with the at least one item on the aggregate list offered by one or more merchants within the network of affiliated merchants.

13. The method of claim 1, wherein the second data source type represents a learning machine.

14. The method of claim 1, wherein the third data source type represents a list application.

15. The method of claim 1,

further comprising:

receiving, from the network, non-sensor data associated with a fifth data source type, the non-sensor data representing at least one item to be added to the aggregate list from the fifth data source type, the fifth data source type representing a recipe application; and

wherein generating the aggregate list further comprises;

generating an aggregate list of items representing at least one item added from each of the first data source type, the second data source type, the third data source type, and the fifth data source type.

16. A system to manage system resources, comprising:

at least one processor configured to perform operations for processor-implemented modules including;

an inventory management system configured to;

receive sensor data associated with a first data source type, the sensor data representing at least one item to be added to an aggregate list from the first data source type, the aggregate list associated with at least one user, the first data source type representing a connected appliance; and

non-sensor data associated with a third data source type, the non-sensor data representing at least one item to be added to the aggregate list from the third data source type;

a learning machine configured to process the sensor data based on predictive modeling associated with consumption of the at least one item to be added to the aggregate list from the first data source type to automatically generate learning data, the learning data associated with a second data source type and representing at least one item to be added to the aggregate list from the second data source type; and

an aggregate list generation system configured to generate the aggregate list of items representing at least one item added to the aggregate list from each of the first data source type, the second data source type, and the third data source type.

17. The system of claim 16, further comprising:

a condition system configured to;

receive condition input data and condition criteria, the condition input data associated with a fourth data source type;

process the condition input data to determine whether the condition input data satisfies the condition criteria; and

automatically generate condition data representing at least one item to be added to the aggregate list from the fourth data source type.

18. The system of claim 16, further comprising:

a merchant inventory system configured to identify available inventory for the at least one item on the aggregate list from one or more merchants within the network of affiliated merchants.

19. The system of claim 18, further comprising:

an advertising generation module configured to identify available advertising discounts associated with the at least one item on the aggregate list offered by one or more merchants within the network of affiliated merchants.

20. A non-transitory machine readable medium storing instructions that, when executed by at least one processor of a machine, cause the machine to perform operations comprising:

receiving sensor data associated with a first data source type, the sensor data representing at least one item to be added to an aggregate list from the first data source type, the aggregate list associated with at least one user, the first data source type representing a connected appliance;

processing the sensor data based on predictive modeling associated with consumption of the at least one item to be added to the aggregate list from the first data source type to automatically generate learning data, the learning data associated with a second data source type and representing at least one item to be added to the aggregate list from the second data source type;

receiving non-sensor data associated with a third data source type, the non-sensor data representing at least one item to be added to the aggregate list from the third data source type; and

generating the aggregate list of items representing at least one item added to the aggregate list from each of the first data source type, the second data source type, and the third data source type.