IP Library Granted Patent US 8,917,943
Granted Patent B2
US 8,917,943 · App. 13/469,135 · Granted Dec 23, 2014

Determining image-based product from digital image collection

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Quick Facts
Patent No.
US 8,917,943
App. No.
13/469,135
Granted
Dec 23, 2014
Kind
B2
Abstract

A method of making an image-based product includes providing an image collection having a plurality of digital images, each digital image having an image type; providing one or more image-type distributions, each image-type distribution corresponding to a theme and including a distribution of image types related to the theme; using a processor to automatically compare the image types of the digital images in the image collection to the image types in the image-type distribution; using the processor to automatically determine a match between the image types in the image collection and the image types in the image-type distribution; and selecting a group of digital images from the image collection having a distribution of image types specified by the determined matching image-type distribution. The method further includes assembling the digital images in the selected group of images into an image-based product and causing the construction of the image-based product.

Claims (55)

1. A method of making an image-based product, the method comprising:

comparing, by a processing system, image types of a plurality of digital images to image types of an image-type distribution, wherein the image-type distribution corresponds to a theme and includes a distribution of image types related to the theme;

determining, by the processing system, a match between the image types of the plurality of digital images and the image types of the image-type distribution;

analyzing, by the processing system, the match to determine image types of the image-type distribution that are missing from the plurality of digital images;

communicating, by the processing system, a request for the missing image types;

receiving, by the processing system, digital images having the missing image types in response to the request for the missing image types;

selecting, by the processing system, a group of digital images from the plurality of digital images and from the received digital images having the missing image types, wherein the group of digital images has a distribution of image types specified by the image-type distribution; and

assembling, by the processing system, the group of digital images into an image-based product.

2. The method according to claim 1 , further comprising:

comparing, by the processing system, the image types of the plurality of digital images to image types in each of a plurality of image-type distributions;

determining, by the processing system, a plurality of matches between the image types of the plurality of digital images and the image types for each of the plurality of image-type distributions;

comparing, by the processing system, the plurality of matches; and

selecting, by the processing system, a best match from the plurality of matches, wherein the best match indicates the image-type distribution of the plurality of image-type distributions having image types that best correspond to the image types of the plurality of digital images.

3. The method according to claim 1 , further comprising:

comparing the image types of the plurality of digital images to the image types in each of a plurality of image-type distributions;

determining a plurality of matches between the image types of the plurality of digital images and the image types for each of the plurality of image-type distributions; and

communicating the plurality of matches or themes associated with the respective image-type distributions corresponding to the matches.

4. The method according to claim 3 , further comprising:

comparing the plurality of matches;

ordering the matches of the plurality of matches; and

communicating the ordered matches.

5. The method according to claim 3 , further comprising receiving a selection of a match or theme from the communicated plurality of matches or themes.

6. The method according to claim 1 , further comprising receiving, via a user interface, one or more image-type distributions.

7. The method according to claim 1 , further comprising analyzing the plurality of digital images to determine one or more themes.

8. The method according to claim 1 , further comprising providing one or more image-type distributions associated with an image-based product.

9. The method according to claim 1 , further comprising providing a plurality of image-type distributions associated with one theme.

10. The method according to claim 1 , wherein the image types of the plurality of digital images include one or more of the types: introduction type, character type, person type, object type, action type, and conclusion type.

11. The method according to claim 1 , wherein the digital images of the plurality of digital images each have a temporal association, and wherein the image-type distribution includes an image type time order.

12. The method according to claim 1 , wherein image-type distribution includes a specified distribution of person or character image types.

13. The method according to claim 1 , wherein an image type of the image types of the image-type distribution includes an identified person type.

14. The method according to claim 1 , wherein the image product is a photo-book or a photo-collage.

15. The method according to claim 1 , further comprising analyzing the plurality of digital images to identify themes associated with the plurality of digital images.

16. The method according to claim 1 , wherein the image-based product comprises an electronic image-based product or a printed image-based product, the method further comprising causing construction of the electronic image-based product or the printed image-based product.

17. The method according to claim 2 , wherein the selecting the best match comprises:

determining an image-match metric for each comparison of the plurality of digital images to one of the plurality of image-type distributions; and

selecting a highest scoring image-match metric.

18. The method according to claim 17 , wherein the image-match metric comprises at least one of an overall number of digital images having image types matching an image type of the respective image-type distribution or an average percentage match of image types of the plurality of digital images to image types of the respective image-type distribution.

19. A non-transitory computer-readable medium having instructions stored thereon that, upon execution by a computing device, cause the computing device to perform operations comprising:

comparing image types of a plurality of digital images to image types of an image-type distribution, wherein the image-type distribution corresponds to a theme and includes a distribution of image types related to the theme;

determining a match between the image types of the plurality of digital images and the image types of the image-type distribution;

analyzing the match to determine image types of the image-type distribution that are missing from the plurality of digital images;

communicating a request for the missing image types;

receiving digital images having the missing image types in response to the request for the missing image types;

selecting a group of digital images from the plurality of digital images and from the received digital images having the missing image types. wherein the group of digital images has a distribution of image types specified by the image-type distribution; and

assembling the group of digital images into an image-based product.

20. A device comprising:

a memory configured to store a plurality of digital images and an image-type distribution;

a processing system coupled to the memory and configured to:

compare image types of the plurality of digital images to image types of the image-type distribution, wherein the image-type distribution corresponds to a theme and includes a distribution of image types related to the theme;

determine a match between the image types of the plurality of digital images and the image types of the image-type distribution;

analyze the match to determine image types of the image-type distribution that are missing from the plurality of digital images;

communicate a request for the missing image types;

receive digital images having the missing image types in response to the request for the missing image types;

select a group of digital images from the plurality of digital images and from the received digital images having the missing image types. wherein the group of digital images has a distribution of image types specified by the image-type distribution; and

assemble the group of digital images into an image-based product.

Assignments (5)
RELEASE OF SECURITY INTEREST Recorded Aug 15, 2023
From: INTELLECTUAL VENTURES FUND 83 LLC
To: MONUMENT PEAK VENTURES, LLC
Reel/Frame 064599/0304 →
ASSIGNMENT OF ASSIGNOR'S INTEREST Recorded Mar 9, 2017
From: INTELLECTUAL VENTURES FUND 83 LLC
To: MONUMENT PEAK VENTURES, LLC
Reel/Frame 041941/0079 →
ASSIGNMENT OF ASSIGNOR'S INTEREST Recorded Mar 19, 2013
From: EASTMAN KODAK COMPANY
To: INTELLECTUAL VENTURES FUND 83 LLC
Reel/Frame 030042/0857 →
PATENT RELEASE Recorded Feb 1, 2013
From: CITICORP NORTH AMERICA, INC.; WILMINGTON TRUST, NATIONAL ASSOCIATION
To: EASTMAN KODAK COMPANY; EASTMAN KODAK INTERNATIONAL CAPITAL COMPANY, INC.; FAR EAST DEVELOPMENT LTD.; KODAK (NEAR EAST), INC.; KODAK AMERICAS, LTD.; KODAK PORTUGUESA LIMITED; KODAK REALTY, INC.; LASER-PACIFIC MEDIA CORPORATION; KODAK AVIATION LEASING LLC; KODAK PHILIPPINES, LTD.; NPEC INC.; FPC INC.; KODAK IMAGING NETWORK, INC.; PAKON, INC.; QUALEX INC.; CREO MANUFACTURING AMERICA LLC
Reel/Frame 029913/0001 →
ASSIGNMENT OF ASSIGNOR'S INTEREST Recorded May 11, 2012
From: COK, RONALD STEVEN
To: EASTMAN KODAK COMPANY
Reel/Frame 028192/0388 →