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 Interpretation
In regards to limitations “a machine learning logic…”, “first logic”, “second logic” and “third logic”, in claims 1-8 and 15, respectively. The Applicant is not explicit in stating if the application uses logic to mean hardware or something other than hardware: [0182], [0192]-[0194] and [0260]
Merriam-Webster (https://www.merriam-webster.com/dictionary/logic) explicitly defines "logic" as "the arrangement of circuit elements (as in a computer) needed for computation also: the circuits themselves".
Given the context of the claims "logic" is interpreted to be circuitry, and therefore to be a structural keyword (and not invoking 35 USC 112(f)). Interpreting "logic" as a series of steps does not align with the context of the claims, which seem to be declaring "logic" as comprising components, not actions. Given the claim terms their plain language meaning consistent with the Applicant's disclosure, this plain meaning of "logic" as electronic circuit seems to be the reasonable interpretation.
Claim Rejections - 35 USC § 102
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
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.
Claim(s) 15 and 17-18 is/are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Modification of the AdaBoost-based Detector for Partially Occluded Faces to Chen et al., hereinafter “Chen”.
Claim 15. Chen further A weak classifier, Fig. 3 Map each weak classifier to patches
comprising: first logic configured to obtain a plurality of coordinates; Fig. 2b (patches), Fig. 2c Map a weak classifier to patches (coordinates)
second logic configured to obtain a corresponding plurality of values based on the plurality of coordinates; FIG. 2c
and third logic configured to generate a weak classification based on the corresponding plurality of values. Algorithm 1: Map a weak classifier to patches, Step 3
Claim 17. Chen teaches the weak classifier of claim 15, where the plurality of coordinates are associated with a plurality of photosites of a sensor. Fig. 2b, FIG. 2c
Claim 18. Chen teaches the weak classifier of claim 15, where at least two coordinates of the plurality of coordinates overlap. Algorithm 1: Map a weak classifier to patches, Step 1
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
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.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claim(s) 1-6, 9 and 13-14 is/are rejected under 35 U.S.C. 103 as being unpatentable over US 2014/0161364 A1 to Sawai in view of Modification of the AdaBoost-based Detector for Partially Occluded Faces to Chen et al., hereinafter “Chen” and US 2015/0363670 A1 to Sugishita et al., hereinafter, “Sugishita”.
Claim 1. Sawai teaches A machine learning logic, comprising: [0006] an object detection apparatus of an aspect of the present invention is one that detects an object to be detected captured in a determination image according to a feature amount of the object to be detected preliminarily learned by the use of a learning image, FIG. 1
where each detector comprises a plurality of strong classifiers and where each strong classifier comprises a plurality of weak classifiers; [0006] an object detection apparatus of an aspect of the present invention is one that detects an object to be detected… object detection apparatus includes: a plurality of weak classifiers… a plurality of strong classifiers
and third logic configured to generate a weak classification based on a difference of the first value and the second value. [0033-0034] classification result
Sawai fails to explicitly teach obtaining a patch coordinate pair. Chen, in the field of object (face) detection in image data, teaches and where each weak classifier comprises: first logic configured to obtain a patch coordinate pair; Fig. 2b (patches), Fig. 2c Map a weak classifier to patches (coordinates) Fig. 3 Map each weak classifier to patches
second logic configured to obtain a first value based on a first patch coordinate of the patch coordinate pair and a second value based on a second patch coordinate of the patch coordinate pair; FIG. 2c
[page 2] where the x-coordinate denotes the outputs of the all weak classifiers associated to patch, and y-coordinate denotes the number of the features…
[page 1] The feature value is the difference between the sum of the pixels within the white rectangles and the sum of pixels in the grey rectangles
Thus, before the effective filing date of the present application, it would have been obvious to one of ordinary skill in the art to combine the teachings of Sawai with the teachings of Chen [Introduction] to detect partially occluded faces by reasonably modifying the AdaBoost- based face detector.
Sawai and Chen fail to explicitly teach a plurality of detectors. Sugishita, in the same field of object detection in image data, teaches a plurality of detectors, [0218] a plurality of detectors, [0144] the detector is configured of a plurality of weak classifiers cf1 and a strong classifiers cf2
[0057] …using the first detector, [0063] … using the second detector
Thus, before the effective filing date of the present application, it would have been obvious to one of ordinary skill in the art to combine the teachings of Sawai with the teachings of Sugishita [0044] for reducing the effect of such difference between the actual image taken by the camera device and the assumed general environmental conditions or the sample collection, improving robustness.
Claim 2. Chen teaches where the first patch coordinate corresponds to a first photosite and the second patch coordinate corresponds to a second photosite. Fig. 2b (patches), Fig. 2c (corresponding coordinate)
Claim 3. Chen teaches where the first patch coordinate corresponds to a first pixel and the second patch coordinate corresponds to a second pixel. [page 1] The feature value is the difference between the sum of the pixels within the white rectangles and the sum of pixels in the grey rectangles.
Claim 4. Sawai teaches where the first patch coordinate corresponds to a first set of pixels at a first scale and the second patch coordinate corresponds to a second set of pixels at a second scale. [0032] The image area dividing unit 11 has a function of dividing an image area of the determination image into a plurality of small areas (so called sub-window) each having a predetermined size…the image area dividing unit 11 can change the magnification of this sub-window into various sizes.
Claim 5. Sawai teaches where the first patch coordinate and the second patch coordinate are non-contiguous. [0032] The sub-windows can be positioned so as to be overlapped or so as not to be overlapped with one another.
Claim 6. Sawai teaches where the first patch coordinate and the second patch coordinate overlap. [0032] The sub-windows can be positioned so as to be overlapped or so as not to be overlapped with one another.
Claim(s) 7-8 is/are rejected under 35 U.S.C. 103 as being unpatentable over US 2014/0161364 A1 to Sawai in view of Modification of the AdaBoost-based Detector for Partially Occluded Faces to Chen et al., hereinafter “Chen” and US 2015/0363670 A1 to Sugishita et al., hereinafter, “Sugishita” and in further view of US 2018/0261071 A1 to Cao et al., hereinafter, “Cao”.
Claim 7. Sawai fails to explicitly teach the first patch coordinate and the second patch coordinate are anchored to a center coordinate. Cao, in the same field of object detection (surveillance) in image data, teaches where the first patch coordinate and the second patch coordinate are anchored to a center coordinate. [0061] [x.sub.0, y.sub.0, t.sub.0] is the anchor position for patch
Thus, before the effective filing date of the present application, it would have been obvious to one of ordinary skill in the art to combine the teachings of Sawai with the teachings of Cao [0004] it is desired to develop a comprehensive solution that can adapt to various requirements.
Claim 8. Chen teaches where at least one of the first patch coordinate and the second patch coordinate are outside of a detection window associated with the weak classification. [page 2] For the AdaBoost-based cascade classifier, each weak classifier corresponds to a rectangle feature as shown in Fig. 2 (a). In order to determine which patch is present in the input window, we map each weak classifier to patches. Having computed the output of each weak classifier, one can determine which patch appears.
Claim(s) 9 and 13-14 is/are rejected under 35 U.S.C. 103 as being unpatentable over Modification of the AdaBoost-based Detector for Partially Occluded Faces to Chen et al., hereinafter “Chen” in view of US 2014/0161364 A1 to Sawai.
Claim 9. Chen teaches A method, comprising: Algorithm 1: Map a weak classifier to patches
obtaining a patch coordinate pair; Fig. 2b (patches), Fig. 2c Map a weak classifier to patches (coordinates) Fig. 3 Map each weak classifier to patches
obtaining a first value from an image based on a first patch coordinate of the patch coordinate pair and a second value from the image based on a second patch coordinate of the patch coordinate pair; FIG. 2c
[page 2] where the x-coordinate denotes the outputs of the all weak classifiers associated to patch , and y-coordinate denotes the number of the features…
Chen fails to explicitly teach generating a classification result for a detection window based on a difference of the first value and the second value. Sawai in the same field of object detection in image data, teaches and generating a classification result for a detection window based on a difference of the first value and the second value. Sawai [0033-0034] classification result
Thus, before the effective filing date of the present application, it would have been obvious to one of ordinary skill in the art to combine the teachings of Chen with the teachings of Sawai [0005] for achieving higher speed object detection processing.
Claim 13. Chen teaches where the patch coordinate pair is obtained from an offline training process. [page 2] AdaBoost-based cascade classifier. Examiner understand AdaBoost to be an offline learning algorithm.
Claim 14. Chen teaches where the patch coordinate pair is associated with a pair of photosites of a sensor. Fig. 2b, FIG. 2c
Algorithm 1 (step 3)
Claim(s) 10-12 is/are rejected under 35 U.S.C. 103 as being unpatentable over Modification of the AdaBoost-based Detector for Partially Occluded Faces to Chen et al., hereinafter “Chen” in view of US 2014/0161364 A1 to Sawai and in further view of US 2018/0261071 A1 to Cao et al., hereinafter, “Cao”.
Claim 10. Chen fails to teach the patch coordinate pair are anchored to a center coordinate. Cao, in the same field of object detection (surveillance) in image data, teaches where the patch coordinate pair are anchored to a center coordinate. [0061] [x.sub.0, y.sub.0, t.sub.0] is the anchor position for patch
Thus, before the effective filing date of the present application, it would have been obvious to one of ordinary skill in the art to combine the teachings of Chen with the teachings of Cao [0004] it is desired to develop a comprehensive solution that can adapt to various requirements.
Claim 11. Chen teaches where at least one of the first patch coordinate and the second patch coordinate has a negative offset relative to the center coordinate. Fig. 2, boxes 4-15 would be a negative offset from box 0
Claim 12. Chen teaches where at least one of the first patch coordinate and the second patch coordinate is outside the detection window. [page 2] For the AdaBoost-based cascade classifier, each weak classifier corresponds to a rectangle feature as shown in Fig. 2 (a). In order to determine which patch is present in the input window, we map each weak classifier to patches. Having computed the output of each weak classifier, one can determine which patch appears.
Claim(s) 16 is/are rejected under 35 U.S.C. 103 as being unpatentable over Modification of the AdaBoost-based Detector for Partially Occluded Faces to Chen et al., hereinafter “Chen” in view of US 2018/0261071 A1 to Cao et al., hereinafter, “Cao”.
Claim 16. Chen fails to teach the plurality of coordinates are anchored to a center coordinate. Cao, in the same field of object detection (surveillance) in image data, teaches where the plurality of coordinates are anchored to a center coordinate. Cao [0061] [x.sub.0, y.sub.0, t.sub.0] is the anchor position for patch
Thus, before the effective filing date of the present application, it would have been obvious to one of ordinary skill in the art to combine the teachings of Chen with the teachings of Cao [0004] it is desired to develop a comprehensive solution that can adapt to various requirements.
Claim(s) 19 and 20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Modification of the AdaBoost-based Detector for Partially Occluded Faces to Chen et al., hereinafter “Chen” in view of US 2014/0161364 A1 to Sawai.
Claim 19. Chen fails to explicitly teach a first coordinate of the plurality of coordinates is associated with a first scale and a second coordinate of the plurality of coordinates is associated with a second scale. Sawai in the same field of object detection in image data, teaches where a first coordinate of the plurality of coordinates is associated with a first scale and a second coordinate of the plurality of coordinates is associated with a second scale. [0032] The image area dividing unit 11 has a function of dividing an image area of the determination image into a plurality of small areas (so called sub-window) each having a predetermined size…the image area dividing unit 11 can change the magnification of this sub-window into various sizes.
Thus, before the effective filing date of the present application, it would have been obvious to one of ordinary skill in the art to combine the teachings of Chen with the teachings of Sawai [0005] for achieving higher speed object detection processing.
Claim 20. Chen fails to explicitly teach where the plurality of coordinates is further organized in pairs, Fig. 2b (patches), Fig. 2c Map a weak classifier to patches (coordinates) Fig. 3 Map each weak classifier to patches
Sawai teaches and at least one pair of the plurality of coordinates is non-contiguous. [0032] The sub-windows can be positioned so as to be overlapped or so as not to be overlapped with one another.
Conclusion
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/DELOMIA L GILLIARD/Primary Examiner, Art Unit 2661
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