DETAILED OFFICE 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 Objections
Claims 4, 5, 16 and 17 are objected to because of the following informalities: Perhaps, their dependency should be claims 3 and 15, not claims 2 and 14. Appropriate correction is required.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e., abstract idea – mental processes) without significantly more.
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Claim 1 is used as an example. Claim 1 recites:
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With regard to Step (1), the instant claims recite a method and an apparatus, therefore the answer is “yes”.
With regard to Step (2A), Prong One: Yes. When viewed under the broadest most reasonable interpretation, the instant claims are directed to a Judicial Exception – an abstract idea belong to the group of mental process. The limitations a), c) and d) of detecting, extracting and determining are generically recited because there is no description of how they are accomplished. It can be interpreted as a person merely selecting a region in a pair of images displayed/printed, placing a circle/box over an area of interest in the images and then further identifying/extracting some features in the images for comparison. Hence, limitations a), c) and d) are interpreted as a mental step by having a person who is managing a screen/display of a system to identify a certain area of interest for particular feature comparison and manual quality assessment.
As for the “obtaining a label” step in claim 9, it is also directed to a Judicial Exception – an abstract idea belong to the group of mental process. It can be interpreted as a person merely marking/labeling a desired image after the comparison. Claim 8 explicitly recites a person labeling the image upon the comparison.
With regard to Step (2A), Prong Two: No.
The limitation b) is all considered to be additional elements. The limitation b) is considered to be insignificant extra-solution activity. The additional element is no more than insignificant extra-solution activity that is recited in high level of generality of converting a high-quality image into a set of low quality images. There are no specific on how the high quality image is degraded into a set of low quality images. Upon generating the low quality images, they then could be presented to a user for image analysis which is then can be performed mentally as stated above.
Other than reciting “processors”, “the deep neural network” in claims 1, 9 and 13, nothing in the claim elements precludes the step from practically being performed in the human mind by an operator (i.e., the user manually selecting a bounding box/circle and identifying a certain region for comparison). Additionally, the mere nominal recitation of a generic processor does not take the claim limitation out of the mental processes grouping. The processor is a generic tool to perform the steps recited in the claims, one can do these steps using a generic computer, as recited. There is nothing in the claim that is recited that integrally requires a specific processor to perform such steps. Even when viewed in combination, these additional elements in the claim do no more than automate the mental process that a person used to perform. Therefore, they do not integrate the recited judicial exception into a practical application.
With regard to Step (2B): No.
The pending claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As explained above in Prong Two, the additional elements of b) and “training the deep neural network” in the claim amount to no more than mere instructions to apply the exception using a generic computer component. These additional elements are also, as explained previously, insignificant extra-solution activity which are well understood, routine and conventional in the image processing field. Especially, a recitation of generating a low quality image from a high quality image without any details as to how they are done is recited at a high level of generality. These limitations therefore remain insignificant extra-solution activity/data gathering even upon reconsideration. Thus, these limitations do not amount to significantly more. Even when considered in combination, these additional elements represent mere instructions to apply an exception and insignificant extra-solution activity, which do not provide an inventive concept. The claim is not eligible.
With regard to all dependent claims similar analysis is applied and they therefore do not integrate the judicial exception into a practical application and further do not amount to significant more. These claims are therefore rejected for the same reasons.
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.
Claims 1-7 and 13-20 are rejected under 35 U.S.C. 103 as being unpatentable over Viswanathan et al. USP 12646142 (hereinafter Viswanathan) in view of Zhu et al. USPGPUB 2021/0168376 (hereinafter Zhu).
With respect to claim 1, Viswanathan teaches a method of automated image quality (IQ) assessment using a deep neural network (fig. 4 & col. 10, lines 23-33), comprising:
obtaining an image pair comprises a low-quality image and a high-quality image (col. 21, lines 4-8);
generating a set of ranking images by applying degradation to the high-quality image (a set/plurality of low quality images are generated from the high resolution image until threshold is met in col. 21, line 51 ~ col. 22, line 29);
extracting a first feature set from the low-quality image and a feature set from each ranking image in the set of ranking images using the deep neural network (a certain feature set must be extracted for the comparison in col. 22, lines 1-29); and
determining a selected ranking image in the set of ranking images having the feature set corresponding to the first feature set (determining if the degraded image matches or is substantially equivalent in col. 22, lines 1-29).
Viswanathan, however, does not teach that applying the degradation and determining the ranking image are performed on a selected region of interest.
Zhu, the same field of endeavor aerial image quality control assessment, teaches the method of selecting/detecting a region of interest in an image for further image processing (paragraphs 4, 55, and 57).
It would have been obvious to a person of ordinary skill in the art, before the effective filing date, to modify the image comparison process of Viswanathan to incorporate the teaching of applying a ROI as taught by Zhu.
The suggestion/motivation for doing so would have been to reduce the processing speed/time by only performing the degradation and comparison on a selected portion rather than the entire image as it is widely known in the image processing field.
With respect to claim 2, Viswanathan teaches the method of claim 1, further comprising outputting an image quality score for the low-quality image according to the selected ranking image (the offset difference/degree is construed as the claimed “score” in col. 10, lines 56-57 & col. 22, line 1-29).
With respect to claim 3, Viswanathan teaches the method of claim 2, wherein the set of ranking images are arranged by a set of indexes and the set of indexes represent texture scores and noise scores (adding Gaussian noise and/or other noise, decreasing a number of pixels and/or degrading the high resolution image indicates a various degradation using a different parameters, thus a set of indexes, are used in col. 21, lines 51-67).
With respect to claim 4, Viswanathan teaches the method of claim 2, wherein the texture scores are associated with resolution, dynamic range, Gaussian blur, motion blur, bilateral blur, box blur and/or radial blur (decreasing a number of pixels in col. 21, lines 51-67).
With respect to claim 5, Viswanathan teaches the method of claim 2, wherein the noise scores are associated with Gaussian noise, Poisson noise, shot noise and/or impulsive noise, low-frequency noise (applying Gaussian noise in col. 21, lines 51-67).
With respect to claim 6, Viswanathan teaches the method of claim 1, wherein the low-quality image is captured by a first sensor device or at a first field of view, and the high-quality image is captured by a second sensor device or at a second field of view (col. 21, lines 31-43).
With respect to claim 7, Zhu teaches, wherein detecting the ROI of the image pair is based on a detection algorithm comprising another deep neural network, a segmentation algorithm and/or a computer vision algorithm (algorithm used in identifying an ROI is construed as either segmentation or computer vision algorithm in paragraph 56).
With respect to claim 13, arguments analogous to those presented for claim 1, are applicable.
With respect to claim 14, arguments analogous to those presented for claim 2, are applicable.
With respect to claim 15, arguments analogous to those presented for claim 3, are applicable.
With respect to claim 16, arguments analogous to those presented for claim 4, are applicable.
With respect to claim 17, arguments analogous to those presented for claim 5, are applicable.
With respect to claim 18, arguments analogous to those presented for claim 6, are applicable.
With respect to claim 19, arguments analogous to those presented for claim 7, are applicable.
With respect to claim 20, see col. 21, lines 31-43 of Viswanathan.
Claim 8 is rejected under 35 U.S.C. 103 as being unpatentable over the combination of Viswanathan and Zhu as applied to claim 1 above, and further in view of Zhang et al. USP 10600171 (hereinafter Zhang).
With respect to claim 8, the combination of Viswanathan and Zhu teaches the method of claim 1, but it does not teach the steps of obtaining a human label indicating which ranking image from the set of ranking images is most similar to the low-quality ROI image; and training the deep neural network using the low-quality ROI image, the set of ranking images, and the human label.
Zhang, the same field of endeavor of image quality assessment, teaches the step of obtaining a human label indicating which image from the set of images is most similar to a reference image (col. 5, line 62 ~ col. 6, line 20) and training the deep neural network using the reference image, the set of images, and the human label (col. 5, lines 45-61).
It would have been obvious to a person of ordinary skill in the art, before the effective filing date, to modify the image comparison process of Viswanathan to incorporate a user feedback system as taught by Zhang.
The suggestion/motivation for doing so would have been to provide a tailored user specific training data set to the quality assessment system by directly receiving a user specified label for each image.
Claims 9-12 are rejected under 35 U.S.C. 103 as being unpatentable over Viswanathan in view of Zhu and in further in view of Zhang.
With respect to claim 9, Viswanathan teaches a method of training a deep neural network for automated image quality assessment, comprising:
obtaining an image pair comprising a low-quality image and a high-quality image (col. 21, lines 4-8);
generating a set of ranking images by applying degradation to the high-quality image (a set/plurality of low quality images are generated from the high resolution image until threshold is met in col. 21, line 51 ~ col. 22, line 29);
obtaining a score indicating which ranking image from the set of ranking images corresponding to the low-quality image (the offset difference/degree is construed as the claimed “score” in col. 10, lines 56-57 & col. 22, line 1-29); and
training the deep neural network using the low-quality ROI image, the set of ranking images, and the score (fig. 4 & col. 10, lines 23-33).
Viswanathan, however, does not teach that applying the degradation and determining the ranking image are performed on a selected region of interest.
Zhu, the same field of endeavor aerial image quality control assessment, teaches the method of selecting/detecting a region of interest in an image for further image processing (paragraphs 4, 55, and 57).
It would have been obvious to a person of ordinary skill in the art, before the effective filing date, to modify the image comparison process of Viswanathan to incorporate the teaching of applying a ROI as taught by Zhu.
The suggestion/motivation for doing so would have been to reduce the processing speed/time by only performing the degradation and comparison on a selected portion rather than the entire image as it is widely known in the image processing field.
The combination of Viswanathan and Zhu, however, does not teach the steps of obtaining a label indicating which ranking image from the set of ranking images is most similar to the low-quality ROI image.
Zhang, the same field of endeavor of image quality assessment, teaches the step of obtaining a human label indicating which image from the set of images is most similar to a reference image (col. 5, line 62 ~ col. 6, line 20) and training the deep neural network using the reference image, the set of images, and the label (col. 5, lines 45-61).
It would have been obvious to a person of ordinary skill in the art, before the effective filing date, to modify the image comparison process of Viswanathan to incorporate a user feedback system as taught by Zhang.
The suggestion/motivation for doing so would have been to provide a tailored user specific training data set to the quality assessment system by directly receiving a user specified label for each image.
With respect to claim 10, Viswanathan teaches the method of claim 9, wherein the low-quality image is captured by a first sensor device or at a first field of view, and the high-quality image captured by a second sensor device or at a second field of view (col. 21, lines 31-43).
With respect to claim 11, Viswanathan teaches the method of claim 9, wherein training the deep neural network using the low-quality ROI image, the set of ranking images, and the labels comprises: extracting a first feature set from the low-quality ROI image and a feature set from each of the set of ranking images (a certain feature set must be extracted for the comparison in col. 22, lines 1-29); and minimizing a distance between the first feature set and a corresponding feature set of each ranking image (since the loop of fig. 4 is repeated until the threshold is met, the distance is said to be minimized).
With respect to claim 12, Viswanathan teaches the method of claim 9, wherein detecting the ROI of each image pair is based on a detection algorithm comprising another deep neural network, a segmentation algorithm and/or a computer vision algorithm (algorithm used in identifying an ROI is construed as either segmentation or computer vision algorithm in paragraph 56).
Conclusion
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/CHAN S PARK/Supervisory Patent Examiner, Art Unit 2669
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