Device, method, and medium for packing recommendations based on container volume and contextual information

• US 10,460,464 B1
• Filed: 12/19/2014
• Issued: 10/29/2019
• Est. Priority Date: 12/19/2014
• Status: Active Grant

• Current Assignee: Amazon Technologies, Inc. (Amazon.com, Inc.)
• Original Assignee: Amazon Technologies, Inc. (Amazon.com, Inc.)
• Inventors: Hasan, Shiblee Imtiaz
• Primary Examiner(s): Smith, Jeffrey A.
• Assistant Examiner(s): Duraisamygurusamy, Lalith M
• Application Number:
  US14/577,327
• Time in Patent Office: 1,775 Days
• Field of Search: 705 267
• CPC Class Codes: G06F 16/5838 using colour
• CPC Class Codes: G06F 16/951 Indexing; Web crawling techniques
• CPC Class Codes: G06Q 30/0631 Item recommendations
• CPC Class Codes: G06T 2207/10004 Still image; Photographic image
• CPC Class Codes: G06T 7/62 of area, perimeter, diameter or volume

Abstract

A packing service for packing objects into containers is disclosed. An image-capture device may capture an image of a container and the image may be used to determine the volume of the container. Contextual information associated with the container may be determined, such as objects already in the container, a destination for the container, or objects to be placed into the container, for example. The contextual information may be obtained from the image or from any of various services or systems. Items to be placed into the container are determined, based on the contextual information, for example. Recommendations to purchase items to pack into the container may be made. Recommendations of how to pack or arrange the items into the container may also be made. Additional images of the container may be captured and analyzed to determine whether the packing recommendations were followed or to determine additional recommendations.

Claims

1. A device, comprising:

an image capture device for capturing image data;

a display for displaying content; and

one or more computer hardware processors in communication with the image capture device and the display, the one or more computer hardware processors configured to at least;

recognize, based on application of one or more computer-based object recognition techniques to image data obtained via the image capture device, a container to be packed with one or more items;

identify, based on computer-based analysis of the image data, context data for a packing operation associated with the container;

based at least in part on automated analysis of the obtained image data and the identified context data,

determine an item packing recommendation for the container, wherein the automated analysis of the obtained image data comprises computer-based analysis of the obtained image data; and

instruct presentation of the item packing recommendation via the display.

2. The device recited in claim 1, wherein the one or more processors are further configured to at least:

obtain updated image data of the container via the image capture device;

determine feedback based at least in part on the updated image data, wherein determining the feedback is based at least in part on recognition of a packed item in the updated image data using automated analysis of the updated image data; and

instruct presentation, on the display, of a message based at least in part on the feedback.

3. The device recited in claim 1, wherein the one or more processors are further configured to at least determine an available volume of the container for packing based at least in part on the image data of the container, and wherein the item packing recommendation is determined based at least in part on the available volume.

4. The device recited in claim 1, wherein the context data comprises at least one of travel information, destination information, user item packing preference information, purchase history information, or available item information, and wherein the context data is used to determine that at least one of a purchase-based recommendation to purchase an item or a spatial-based recommendation to pack an item in a particular location of the container, should be made.

5. The device recited in claim 1, wherein the computer-based analysis of the obtained image data comprises at least one of machine vision-based analysis or three-dimensional modeling.

6. A computer-implemented method comprising:

receiving, by one or more computer processors, container data derived from container image data captured by an image capture device;

determining, by the one or more computer processors, an available volume for a packing operation based at least in part on volume data for the container;

obtaining, by the one or more computer processors, context data for the packing operation, wherein context data comprises at least one of travel information, destination information, user item packing preference information, purchase history information, or available item information;

determining, by the one or more computer processors, an item packing recommendation for the packing operation based at least in part on the available volume for the packing operation and the context data for the packing operation; and

generating instructions to display the item packing recommendation.

7. The computer-implemented method as recited in claim 6,

wherein the item packing recommendation indicates a recommended item for packing;

and

wherein said determining the item packing recommendation for the packing operation comprises;

determining a volume of the recommended item for packing; and

generating a spatial-based packing recommendation based at least in part on analysis of the volume of the container and the volume of the recommended item for packing, wherein the spatial-based recommendation indicates a placement of the recommended item in the container, and wherein the item packing recommendation includes the spatial-based packing recommendation.

8. The computer-implemented method as recited in claim 6, wherein the context data comprises travel information, wherein the travel information is obtained from a travel service, and wherein the travel information comprises at least one of a travel date, a number of travelers, or a form of travel transportation.

9. The computer-implemented method as recited in claim 8,

wherein the travel information comprises the travel date;

and

wherein the item packing recommendation comprises a recommendation of an item for purchase based at least in part on determining, from item availability and delivery information, that the item can be delivered prior to the travel date.

10. The computer-implemented method as recited in claim 6, wherein the context data comprises travel destination information, wherein the travel destination information is obtained from a travel service, and wherein the travel destination information comprises at least one of weather information for the destination, length of stay at the destination or services available at the destination.

11. The computer-implemented method as recited in claim 6, wherein the context data comprises user item packing preference information, wherein the user item packing preference information is obtained from an electronic user profile, and wherein the user item packing preference information comprises at least one of characteristics of a preferred suitcase, a checklist of items to pack, a number of items to pack, types of items to pack, or preferred characteristics of items to be packed.

12. The computer-implemented method as recited in claim 6, wherein the context data comprises purchase history information, wherein the purchase history information is obtained from a transaction history system, and wherein the purchase history information comprises historical purchase transaction information for one or more travelers.

13. The computer-implemented method as recited in claim 6, wherein the context data comprises available item information, wherein the available item information is obtained from an e-commerce site, and wherein the available item information comprises information for an item that is available for delivery to at least one of:

a location of the container prior to travel, or

a travel destination during the travel period.

14. The computer-implemented method as recited in claim 6, wherein the computer-implemented method further comprises determining, based at least in part on the context data, that the item packing recommendation is at least one of a purchase-based recommendation to purchase an item for packing in the container or a spatial-based recommendation for packing an item in a particular location of the container.

15. The computer-implemented method as recited in claim 6,

wherein the item packing recommendation indicates a recommended item for packing;

and

wherein generating instructions to display the item packing recommendation further comprises generating instructions to display a selectable user interface element that when selected generates instructions to order the recommended item.

16. The computer-implemented method as recited in claim 6, further comprising:

obtaining updated image data of the container;

determining feedback based at least in part on the updated image data, wherein determining the feedback comprises recognizing a packed item in the updated image data using machine vision analysis; and

generating instructions to display a message based at least in part on the feedback.

17. The computer-implemented method as recited in claim 16, further comprising:

determining a volume of the recognized packed item;

updating the available volume based at least in part on the volume of the recognized packed item; and

generating instructions to display another item packing recommendation, based at least in part on the updated available volume.

18. The computer-implemented method as recited in claim 6, wherein the received container data comprises a volume of the container, wherein the computer-implemented method further comprises obtaining a volume associated with one or more items in the container, and wherein determining the available volume for the packing operation based at least in part on volume data for the container comprises determining the difference between the volume of the container and the volume associated with the one or more items in the container.

19. The computer-implemented method as recited in claim 6, wherein

determining the item packing recommendation for the packing operation based at least in part on the available volume for the packing operation and the context data for the packing operation comprises:

determining one or more items to be packed into the container based at least in part on information about a travel destination;

instructing a search on a network-based site for the one or more items to be packed in the container, wherein the instruction includes a limitation restricting results of the search based at least in part on the available volume for the packing operation; and

determining the item packing recommendation for the packing operation based at least in part on the results of the search.

20. A non-transitory computer readable medium storing program instructions that, when executed by one or more processors of a computing device, perform operations comprising:

recognizing, based on applying one or more computer-based object recognition techniques to image data, a container, the image data captured by an image capture device;

identifying, based on computer-based analysis of the image data, context data for a packing operation associated with the container;

based at least in part on the image data and the context data, determining an item packing recommendation for the container; and

generating instructions to present the item packing recommendation via a display.

21. The non-transitory computer readable medium as recited in claim 20, wherein the operations further comprise calculating a volume of the container based at least in part on the image data of the container, and wherein determining the item packing recommendation is based at least in part on the volume of the container that is calculated.

22. The non-transitory computer readable medium as recited in claim 20, wherein the context data is obtained from a memory of the computing device.

23. The non-transitory computer readable medium as recited in claim 20, wherein determining the item packing recommendation for the container further comprises:

sending the image data of the container to another computing device, wherein the other computing device is configured to calculate an available volume of the container based at least in part on the image data of the container;

receiving from the other computing device and in response sending the image data, the available volume of the container; and

determining the item packing recommendation based at least in part on the available volume of the container received from the other computing device.

24. The non-transitory computer readable medium as recited in claim 20, wherein determining the item packing recommendation for the container further comprises:

sending the image data of the container to another computing device, wherein the other computing device is configured to determine the item packing recommendation based at least in part on the image data; and

receive from the other computing device and in response sending the image data, the item packing recommendation.

25. The non-transitory computer readable medium as recited in claim 20, wherein determining an item packing recommendation for the container further comprises determining, based at least in part on the context data, that the item packing recommendation is at least one of a purchase-based recommendation to purchase an item or a spatial-based recommendation to pack an item in a particular location of the container.