Systems and methods for prioritizing local shopping options

• US 10,019,751 B2
• Filed: 07/22/2015
• Issued: 07/10/2018
• Est. Priority Date: 04/18/2012
• Status: Active Grant

• Current Assignee: eBay Inc.
• Original Assignee: eBay Inc.
• Inventors: Tapley, John, Higgins, Krystal Rose, Giger, Matt Lee, Goldman, David W.
• Primary Examiner(s): Allen, William J
• Application Number:
  US14/806,505
• Publication Number:
  US 20150324891A1
• Time in Patent Office: 1,084 Days
• CPC Class Codes: G01C 21/206 specially adapted for indoor navigation
• CPC Class Codes: G01C 21/34 Route searching; Route guidance
• CPC Class Codes: G06Q 10/047 Optimisation of routes or paths, e.g. travelling salesman problem
• CPC Class Codes: G06Q 30/0201 Market modelling; Market analysis; Collecting market data
• CPC Class Codes: G06Q 30/0633 Lists, e.g. purchase orders, compilation or processing
• CPC Class Codes: G06Q 30/0639 Item locations
• CPC Class Codes: G06Q 30/0641 Shopping interfaces
• CPC Class Codes: G06Q 50/01 Social networking
• CPC Class Codes: H04W 4/02 Services making use of location information
• CPC Class Codes: H04W 4/024 Guidance services
• CPC Class Codes: H04W 4/029 Location-based management or tracking services

Abstract

Systems and methods to provide a prioritized shopping system are discussed. For example, a method can include receiving a list of target items, receiving busyness data for a plurality of local merchants, developing a prioritized shopping plan, and communicating the prioritized shopping plan to a mobile device. Each target item in the list of target items can represents a product or service that a user has indicated an interest in purchasing. The prioritized shopping plan is based at least in part on the busyness data and the list of target items. Busyness data provides an indication of traffic levels within at least a portion of the plurality of local merchants.

Claims

1. A method of generating a mobile interface at a mobile device comprising:

receiving, on a shopping service computing device, a list of target items, the list of target items including items from one or more previously established shopping lists maintained by a user within a plurality of shopping sites;

accessing busyness data associated with a local merchant which carries an item from the list of target items, the busyness data including one or more inputs received from external data sources, the busyness data representing a level of traffic within a physical location of the local merchant;

determining a current location of the mobile device by a location determination application, the location determination application executing at a server system separate from the mobile device;

correlating the busyness data to the current location of the mobile device;

generating, by the shopping service device, a map image, the map image including a visual representation of the correlated busyness data; and

causing display of the map image at the mobile device.

2. The method of claim 1, further comprising:

receiving inventory data associated with the local merchant; and

developing a prioritized shopping plan based on at least the busyness data, the list of target items, and the inventory data.

3. The method of claim 2, further comprising:

determining, based on the inventory data of the local merchant, a low inventory level condition of a target item from among the list of target items; and

wherein

the developing the prioritized shopping plan includes prioritizing the target item with the low inventory level condition.

4. The method of claim 1, further comprising:

detecting, based at least in part on the busyness data, a hotspot at the physical location of the local merchant, the hotspot indicating a high traffic level, and the method further comprises;

developing a prioritized shopping plan, wherein the developing the prioritized shopping plan includes avoiding the hotspot.

5. The method of claim 4, wherein the determining the hotspot includes determining a time period during which the hotspot occurs at the physical location of the local merchant.

6. The method of claim 1, wherein the receiving the busyness data includes receiving at least a portion of the data types in the following group of data types:

check-in data;

tweets;

status updates;

security camera feeds;

cell phone usage data;

traffic camera feeds;

point of sale (POS) data; and

sensor grid data.

7. The method of claim 1, wherein the map image includes:

a proposed route to the physical location of the local merchant based on the busyness data.

8. The method of claim 1, wherein the map image includes:

a graphical representation of a first item from the list of target items, at a position within the map image, the position correlating with the physical location of the local merchant.

9. A system configured to generate a mobile interface comprising:

a network; and

a server coupled to the network and including one or more processors to execute a plurality of modules, the plurality of modules including,

a user profile module to manage a list of target items, each target item in the list of target items representing an item a user has indicated an interest in purchasing;

a location module to monitor a current location of the mobile device;

a merchant module to manage busyness data for a local merchants, and correlate the busyness data to the current location of the mobile device, the local merchant selling at least one target item from the list of target items;

a routing module to cause display of a map image at a client device, the map image including a visual representation of the correlated busyness data; and

wherein the server communicates over the network to a mobile device the prioritized shopping plan.

10. The system of claim 9, wherein the merchant module is further configured to:

receive inventory data associated with the local merchant; and

wherein

the routing module is further configured to develop a prioritized shopping plan based on the busyness data, the list of target items, and the inventory data.

11. The system of claim 10, wherein the merchant module is further configured to:

determine, based on the inventory data of the local merchant, a low inventory condition of a target item from among the list of target items; and

wherein

developing the prioritized shopping plan by the routing module includes prioritizing the target item with the low inventory condition.

12. The system of claim 9, wherein the routing module manages is further configured to;

detect, based at least in part on the busyness data, a hotspot within the plurality of local merchants, the hotspot indicating a high traffic level; and

develop a prioritized shopping plan, wherein developing the prioritized shopping plan includes avoiding the hotspot.

13. The system of claim 9, wherein managing the busyness data by the merchant module includes receiving at least a portion of the data types in the following group of data types:

check-in data;

tweets;

status updates;

security camera feeds;

cell phone usage data;

traffic camera feeds;

point of sale (POS) data; and

sensor grid data.

14. The system of claim 9, wherein generating the map image by the routing module includes:

determining a proposed route to the physical location of the local merchant based on the busyness data.

15. The system of claim 9, wherein generating the map image by the routing module includes:

generating a graphical representation of a first item from the list of target items, at a position within the map image, the position correlating with the physical location of the local merchant.

16. A non-transitory machine-readable storage medium including instructions that, when executed by a machine, cause the machine to perform operations to generate a mobile interface comprising:

receiving, on a shopping service computing device, a list of target items, the list of target items including items from one or more previously established shopping lists maintained by a user within a plurality shopping sites;

accessing busyness data associated with a local merchant which carries an item from the list of target items, the busyness data including one or more inputs received from external data sources, the busyness data representing a level of traffic within a physical location of the local merchant;

determining a current location of a mobile device by a location determination application, the location determination application executing at a server system separate from the mobile device;

correlating the busyness data to the current location of the mobile device;

generating, by the shopping service device, a map image, the map image including a visual representation of the correlated busyness data; and

communicating the map image to the mobile device.

17. The non-transitory machine-readable medium of claim 16, further comprising instructions that cause the machine to:

receive inventory data associated with the local merchant; and

develop a prioritized shopping plan based on at least the busyness data, the list of target items, and the inventory data.

18. The non-transitory machine-readable medium of claim 17, further comprising instructions that cause the machine to:

determine, based on the inventory data of the merchant, a low inventory level condition of a target item from among the list of target items; and

wherein

the instructions to develop the prioritized shopping plan includes prioritizing the target item with the low inventory level condition.

19. The non-transitory machine-readable medium of claim 17, further comprising instructions that cause the machine to:

detect, based at least in part on the busyness data, a hotspot within the plurality of local merchants, the hotspot indicating a high traffic level, and wherein;

the developing the prioritized shopping plan includes avoiding the hotspot.

20. The non-transitory machine-readable medium of claim 16, wherein the instructions to receive the busyness data includes receiving at least a portion of the data types in the following group of data types:

check-in data;

tweets;

status updates;

security camera feeds;

cell phone usage data;

traffic camera feeds;

point of sale (POS) data; and

sensor grid data.