DETAILED ACTION
Claim Rejections - 35 USC § 102
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claim(s) 1-20 is/are rejected under 35 U.S.C. 102(a)(1)as being anticipated by El Saban ( US 20120327172).
Regarding claim 1, El Saban teaches a method comprising:
causing display of an image within a graphical user interface at a client device, the image comprising a background portion, a foreground portion, and a display of an object( FIG. 5A);
receiving a first input that identifies the foreground portion of the image( 530 in FIG. 5A, 570 in FIG 5B);
receiving a second input that identifies the background portion of the image( 530 in FIG. 5A, 570 in FIG 5B);
identifying a boundary of the display of the object based on the foreground portion of the image and the background portion of the image( 530 in FIG. 5A); and
causing display of a visualization of the boundary( 530 in FIG 5A, 630 in Fig. 6A, 670 in Fig. 6B).
Regarding claim 2, El Saban teaches the method of claim 1, further comprising: cropping the object from the image based on the visualization of the boundary( 630 in fig. 6A; 670 in FIG 6B ); and generating a media object based on the cropping the object from the image( 630 in fig. 6A; 670 in FIG 6B ).
Regarding claim 3, El Saban teaches the method of claim 2, further comprising: saving the media object at a memory location associated with a user of the client device( 600 in fig. 6A, 650 in fig 6B).
Regarding claim 4, El Saban teaches the method of claim 2, wherein the image is a first image( Fig. 3B), and the method further comprises: causing display of a second image at the client device ( fig. 5A); receiving a tactile input that identifies a position within the second image( 530 in fig. 5B); and presenting the media object at the position within the second image based on the tactile input that identifies the position within the second image( 537 in fig. 5B).
Regarding claim 5, El Saban teaches the method of claim 1, wherein the first input identifies a set of pixels from within the foreground of the image, and the method further comprises: generating a color model of the set of pixels([0043], CRF techniques include the use of motion probabilities based on background stability and foreground motion and color probabilities); and identifying the foreground of the image based on the color model([0043], CRF techniques include the use of motion probabilities based on background stability and foreground motion and color probabilities).
Regarding claim 6, El Saban teaches the method of claim 1, wherein the first input that identifies the foreground of the image comprises a tactile input that draws a line over the foreground of the image( 537 in fig. 5B).
Regarding claim 7, El Saban teaches the method of claim 1, wherein the first input identifies a first set of pixels within the foreground of the image and the second input identifies a second set of pixels within the background of the image, and the identifying the boundary of the display of the object includes: generating a first color model that represents the foreground portion of the image based on the first set of pixels ( Fig. 4A-4D, [0089], user input is provided to identify a primary face region… used to compute background and foreground color likelihoods); generating a second color model that represents the background portion of the image based on the second set of pixels ( Fig. 4A-4D, [0089], used to compute background and foreground color likelihoods) ; and identifying the boundary of the object depicted in the images based on the first color model and the second color model ( Fig. 12A, [0142], and is assigned a probability value, based on whether the nodes are inside or outside a region defined by the template boundary 1230. Probabilities for color likelihood terms are shown for the nodes in the initial representation of FIG. 12A).
Claims 8-20 recite the system and medium for the method in claims 1-7. Since El Saban also teaches a system and medium ( Fig. 1 and Fig. 2), those claims are also rejected).
Conclusion
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JIANGENG SUN
Examiner
Art Unit 2661
/Jiangeng Sun/Examiner, Art Unit 2671
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