DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claim(s) 1-8 are rejected under 35 U.S.C. 103 as being unpatentable over “Motion-based skin region of interest detection with real-time connected component labeling algorithm” by Song et al. in view of “Real-Time Detection, Tracking and Classification of Multiple Moving Objects in UAV Videos” by Baykara et al. (hereinafter ‘Baykara’).
In regards to claim 1, Song teaches a method of detecting colored moving elements in a video file, comprising: (See Song Abstract, Song teaches detecting motion based regions of interest.)
receiving a video file in which a target colored moving element is to be located; (See Song Section 1, Song teaches receiving video sequences the comprise skin regions.)
extracting video frames, from the video file, into images; pre-processing the images including: detecting a desired motion pattern and generating a desired motion pattern region of interest where the desired motion pattern is detected; (See Song Section 3.2 and Figure 1, Song teaches motion detection of skin regions.)
detecting a desired color signature and generating a desired color signature region of interest where the desired color signature is detected; (See Song Section 3.1 and Figure 1, Song teaches determining areas with skin color.)
detecting an intersection between the desired motion pattern region of interest and the desired color signature region of interest; digital zooming on the intersection, by screening and considering only a selected number of pixels from a full image scope to generate a new image in a reduced scope. (See Song Section 3.2 and Figure 1, Song teaches determining regions that have both motion and skin color and isolating that region as shown in Figure 1.)
However, Song does not expressly teach inferring, at the intersection, by using a convolutional neural network (CNN), the target colored moving element in the images and notify identification of the target colored moving element.
Baykara teaches inferring, at the intersection, by using a convolutional neural network (CNN), the target colored moving element in the images and notify identification of the target colored moving element. (See Baykara Figure 1 and Section II(e), Baykara teaches using CNN to classify moving objects.)
It would have been obvious before the effective filing date of the claimed invention to one of ordinary skill in the art to Song to include the CNN of Baykara. The determination of obviousness is predicated upon the following findings: One skilled in the art would have been motivated to modify Song in this manner because/in order to be able to accurately and speedily identify moving objects in videos.
Further, one skilled in the art could have combined the elements as described above by known method with no change in their respective functions, and the combination would have yielded nothing more than predictable results.
Therefore, it would have been obvious to combine Song with Baykara to obtain the invention as specified in claim 1.
In regards to claim 2, Song and Baykara teach all the limitations of claim 1. Song also teaches wherein based on no detection of the desired motion pattern, returning to the extracting of the video frames from the video file. (See Song Section 3, Song teaches working a sequence of video frames.)
In regards to claim 3, Song and Baykara teach all the limitations of claim 1. Song also teaches wherein based on no detection of the desired color signature, returning to the extracting of the video frames from the video file. (See Song Section 3, Song teaches working a sequence of video frames.)
In regards to claim 4, Song and Baykara teach all the limitations of claim 1. Song also teaches wherein based on no detection of the intersection between the desired motion pattern region of interest and the desired color signature region of interest, returning to the extracting of the video frames from the video file. (See Song Section 3, Song teaches working a sequence of video frames.)
In regards to claim 5, Song and Baykara teach all the limitations of claim 1. Song also teaches wherein the digital zooming extracts from the video frames, considering an area of the intersection as a central point, (See Song Figure 1, Song teaches area of intersection.)
a selection of pixels used as input of a model of the CNN according to model input layer specification. (See Baykara Section II(e), Baykara teaches CNN for classifying objects.)
In regards to claim 6, Song and Baykara teach all the limitations of claim 1. Baykara also teaches wherein based on the inferring not detecting the target colored moving element, returning to the extracting of the video frames from the video file. (See Baykara Figure 1.)
In regards to claim 7, Song and Baykara teach all the limitations of claim 1. Song also teaches wherein the detection of the desired motion pattern and the generation of the desired motion pattern region of interest is done by means of background subtraction “BGS”. (See Song Section 2).
In regards to claim 8, Song and Baykara teach all the limitations of claim 1. Song also teaches wherein detecting the desired color signature and generating a desired color signature region of interest is done by means of color space signature detection “CSSD”. (See Song Section 3.1).
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to UTPAL D SHAH whose telephone number is (571)272-5729. The examiner can normally be reached M-F: 7:30-5:30.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Vu Le can be reached at (571) 272-7332. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/UTPAL D SHAH/Primary Examiner, Art Unit 2668