IP Library Granted Patent US 10,289,937
Granted Patent B2
US 10,289,937 · App. 15/815,606 · Granted May 14, 2019

Selective image backup using trained image classifier

View Patent ↗
Loading inventors, assignments & file history…
Monitor This Case
Get email alerts when status or documents change.
Order Certified Copies
Most orders are placed with the USPTO same day — all within 24 business hours.
Order via The Patent Place →
Pre-filled with this patent's details
Quick Facts
Patent No.
US 10,289,937
App. No.
15/815,606
Granted
May 14, 2019
Kind
B2
Abstract

Image backup using a trained image classifier is disclosed. In various embodiments, an image classifier is applied to a plurality of images to identify one or more images to be included in a save set of images. The save set of images are selectively stored to a second location according to one or more policies.

Claims (40)

1. A system, comprising:

a processor configured to:

train an image classifier to identify an image in a search space, wherein the search space comprises a plurality of storage locations across a plurality of devices associated with a user, wherein the plurality of storage locations store multiple instances of the image;

apply the trained image classifier to a plurality of images stored across the plurality of devices associated with the user to identify one or more images to be included in a save set of images; and

selectively store the save set of images to a second location according to one or more policies, wherein the save set of images is deduplicated at the second location that is different than the plurality of storage locations, wherein the one or more policies indicate at least one of a frequency that the save set of images is selectively stored to the second location, a number of remote storage locations to store the save set of images, a type of storage that is to be used to store the save set of images, and/or a length of time in which the save set of images are to be stored at the second location; and

a memory coupled to the processor and configured to provide the processor with instructions.

2. The system of claim 1 , wherein the processor is further configured to receive an image selection criteria comprising an object or pattern within the image.

3. The system of claim 1 , wherein the processor is further configured to receive an image selection criteria comprising an object or pattern within the image, wherein to train the image classifier further includes to receive a set of positive images that correspond to the image selection criteria and a set of negative images that do not correspond to the image selection criteria.

4. The system of claim 1 , wherein the processor is further configured to receive an image selection criteria comprising an object or pattern within the image, wherein to train the image classifier further includes to receive a set of positive images that correspond to the image selection criteria and a set of negative images that do not correspond to the image selection criteria, wherein the set of positive images are extracted from frames of one or more videos containing the object or pattern.

5. The system of claim 1 , wherein the processor is further configured to:

provide a user interface to allow a user to review the save set of images; and

receive an indication from the user that one or more images of the save set of images is incorrectly identified as satisfying an image selection criteria.

6. The system of claim 1 , wherein the processor is further configured to:

provide a user interface to allow a user to review the save set of images;

receive an indication from the user that one or more images of the save set of images is incorrectly identified as satisfying an image selection criteria; and

retrain the image classifier based at least in part on the image selection criteria and the indication.

7. The system of claim 1 , wherein the one or more images of the save set of images are identified with a tag or other metadata associated with an image selection criteria.

8. The system of claim 1 , wherein the stored save set of images are searchable using at least one of an index, a tag, or other metadata.

9. The system of claim 1 , wherein the processor is further configured to:

receive an indication to apply the image classifier to the plurality of storage locations to return a second save set of images; and

store the second save set of images to the second location according to the one or more policies, wherein the second save set of images does not include images from the save set of images.

10. The system of claim 1 , wherein a set of positive images and a set of negative images used to train the image classifier are selected by a user.

11. A method comprising:

training an image classifier to identify an image in a search space, wherein the search space comprises a plurality of storage locations across a plurality of devices associated with a user, wherein the plurality of storage locations store multiple instances of the image;

applying the trained image classifier to a plurality of images stored across the plurality of devices associated with the user to identify one or more images to be included in a save set of images; and

selectively storing the save set of images to a second location according to one or more policies, wherein the save set of images is deduplicated at the second location that is different than the plurality of storage locations, wherein the one or more policies indicate at least one of a frequency that the save set of images is selectively stored to the second location, a number of remote storage locations to store the save set of images, a type of storage that is to be used to store the save set of images, and/or a length of time in which the save set of images are to be stored at the second location.

12. The method of claim 11 , further comprising receiving an image selection criteria comprising an object or pattern within the image.

13. The method of claim 11 , further comprising receiving an image selection criteria comprising an object or pattern within the image, wherein training the image classifier further includes receiving a set of positive images that correspond to the image selection criteria and a set of negative images that do not correspond to the image selection criteria.

14. The method of claim 11 , further comprising receiving an image selection criteria comprising an object or pattern within the image, wherein training the image classifier further includes receiving a set of positive images that correspond to the image selection criteria and a set of negative images that do not correspond to the image selection criteria, wherein the set of positive images are extracted from frames of one or more videos containing the object or pattern.

15. The method of claim 11 , further comprising:

providing a user interface that allows a user to review the save set of images; and

receiving an indication from the user that one or more images of the save set of images are incorrectly identified as satisfying an image selection criteria.

16. The method of claim 11 , further comprising:

providing a user interface that allows a user to review the save set of images;

receiving an indication from the user that one or more images of the save set of images are incorrectly identified as satisfying an image selection criteria; and

retraining the image classifier based at least in part on the image selection criteria and the indication.

17. A computer program product, the computer program product being embodied in a tangible non-transitory computer readable storage medium and comprising computer instructions for:

training an image classifier to identify an image in a search space, wherein the search space comprises a plurality of storage locations across a plurality of devices associated with a user, wherein the plurality of storage locations store multiple instances of the image;

applying the trained image classifier to a plurality of images stored across the plurality of devices associated with the user to identify one or more images to be included in a save set of images; and

selectively storing the save set of images to a second location according to one or more policies, wherein the save set of images is deduplicated at the second location that is different than the plurality of storage locations, wherein the one or more policies indicate at least one of a frequency that the save set of images is selectively stored to the second location, a number of remote storage locations to store the save set of images, a type of storage that is to be used to store the save set of images, and/or a length of time in which the save set of images are to be stored at the second location.

Assignments (7)
RELEASE OF SECURITY INTEREST IN PATENTS PREVIOUSLY RECORDED AT REEL/FRAME (045482/0131) Recorded May 20, 2022
From: THE BANK OF NEW YORK MELLON TRUST COMPANY, N.A., AS NOTES COLLATERAL AGENT
To: DELL PRODUCTS L.P.; EMC CORPORATION; EMC IP HOLDING COMPANY LLC; DELL MARKETING CORPORATION (SUCCESSOR-IN-INTEREST TO WYSE TECHNOLOGY L.L.C.)
Reel/Frame 061749/0924 →
RELEASE OF SECURITY INTEREST AT REEL 045482 FRAME 0395 Recorded Nov 2, 2021
From: CREDIT SUISSE AG, CAYMAN ISLANDS BRANCH
To: DELL PRODUCTS L.P.; EMC CORPORATION; EMC IP HOLDING COMPANY LLC; WYSE TECHNOLOGY L.L.C.
Reel/Frame 058298/0314 →
SECURITY AGREEMENT Recorded Mar 21, 2019
From: CREDANT TECHNOLOGIES, INC.; DELL INTERNATIONAL L.L.C.; DELL MARKETING L.P.; DELL PRODUCTS L.P.; DELL USA L.P.; EMC CORPORATION; FORCE10 NETWORKS, INC.; WYSE TECHNOLOGY L.L.C.; EMC IP HOLDING COMPANY LLC
To: THE BANK OF NEW YORK MELLON TRUST COMPANY, N.A.
Reel/Frame 049452/0223 →
PATENT SECURITY AGREEMENT (NOTES) Recorded Mar 1, 2018
From: DELL PRODUCTS L.P.; EMC CORPORATION; EMC IP HOLDING COMPANY LLC; WYSE TECHNOLOGY L.L.C.
To: THE BANK OF NEW YORK MELLON TRUST COMPANY, N.A., AS COLLATERAL AGENT
Reel/Frame 045482/0131 →
PATENT SECURITY AGREEMENT (CREDIT) Recorded Mar 1, 2018
From: DELL PRODUCTS L.P.; EMC CORPORATION; EMC IP HOLDING COMPANY LLC; WYSE TECHNOLOGY L.L.C.
To: CREDIT SUISSE AG, CAYMAN ISLANDS BRANCH, AS COLLATERAL AGENT
Reel/Frame 045482/0395 →
ASSIGNMENT OF ASSIGNOR'S INTEREST Recorded Nov 16, 2017
From: PRASAD, RAGHUPRASAD; AMBALJERI, MAHANTESH M.; MEHTA, AMITABH
To: EMC CORPORATION
Reel/Frame 044156/0899 →
ASSIGNMENT OF ASSIGNOR'S INTEREST Recorded Nov 16, 2017
From: EMC CORPORATION
To: EMC IP HOLDING COMPANY LLC
Reel/Frame 044935/0196 →
Cited By (1)
US 12,235,731