DETAILED ACTION
Contents
Notice of Pre-AIA or AIA Status 2
Claim Rejections - 35 USC § 101 2
Claim Rejections - 35 USC § 102 3
Claim Rejections - 35 USC § 103 6
Allowable Subject Matter 11
Conclusion 13
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 .
This action is responsive to applicant’s claim set received on 3/15/24. Claims 1-15 are currently pending.
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 1-3, 7, 12, 15 are rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter as follows. Regarding claims 1, 15 the claims are directed to an abstract idea since the limitations focus on processing image data by mathematically comparing pixel values and computing parameters used to transform pixels. The claims do not integrate the abstract idea into a practical application but rather use generic image processing steps and rely on conventional filtering and comparison operations. Lastly, the claims do not recite an inventive concept to transform the abstract idea, rather the claims utilize elements that are well-understood, routine and conventional. Regarding claim 2, the claim is an abstract idea and does not have a practical application and does not invoke a inventive concept. Regarding claim 3, again an abstract idea manipulating operations; organizes data spatially and does not invoke an inventive concept. Regarding claim 7, the claim is an abstract idea that propagates data values with no inventive concept. Regarding claim 12, the claim is an abstract idea with no improvement to computer operation and with no inventive concept. Thus, all of the claims listed are considered non-statutory subject matter.
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 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.(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.
Claims 1, 3, 15 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Lin et al (US 8,655,109 B2). Regarding claim 1, Lin discloses an image processing method, applied by an image processing device, comprising: obtaining a first image, and down-sampling the first image into a second image (see col. 16, lines 50-67; A set of natural images is downscaled to generate a set of low-resolution images), wherein the first image comprises a plurality of first pixels, and the second image comprises a plurality of second pixels (see col. 4, lines 1-30; Image patches are subdivisions of an image);
obtaining a first resolution parameter combination of each of the second pixels in the second image (col. 14, lines 40-67; The first order regression model is learned by solving a least square problem.);
selecting a target pixel among the first pixels (see col. 16, lines 10-25; For each low-resolution image patch y of Y at location (x', y') a search for its best match y.sub.0 in a small local neighborhood of (x'=s; y'=s) in Y.sub.0 is performed (block 918). A regression model is used to estimate the high-resolution image patch x from Y, Y.sub.0 and X.sub.0 (block 920). Blocks 918-920 are repeated for overlapping patches of Y (block 922). Overlapping pixels of the estimated high-resolution image patches are averaged, which, in some embodiments, results in denoising (block 924)), and obtaining a plurality of second pixels corresponding to the target pixel among the second pixels accordingly (see col. 16, lines 10-25; For each low-resolution image patch y of Y at location (x', y') a search for its best match y.sub.0 in a small local neighborhood of (x'=s; y'=s) in Y.sub.0 is performed (block 918).); determining a comparison result between the target pixel and each of the second pixels corresponding to the target pixel (see col. 5, lines 25-67; identifying best matches includes find k nearest neighbor patches), and
selecting at least one candidate pixel among the second pixels corresponding to the target pixel accordingly (see col. 13, lines 1-65; Instead of finding only one best match in the local neighborhood of (x'=s.+-.1, y'=s.+-.1) for y 445 at location (x, y) 430 (in Y 440), embodiments keep track of T nearest neighbors, perform regression for each of them, and obtain a final result by a weighted linear combination of all the T regression results. Suppose the T matched example pairs for y 445 are {y.sub.0.sup.i, x.sub.0.sup.i}.sub.i=1.sup.T. Some embodiments exploit the observation that);
obtaining a second resolution parameter combination of the target pixel based on the first resolution parameter combination of each of the at least one candidate pixel (see col. 13, lines 1-67; perform regression for each of them, and obtain a final result by a weighted linear combination of all the T regression results); and
converting the target pixel into an output pixel based on the second resolution parameter combination (see col. 12, lines 5-20; embodiments employ a regression model to estimate 320 the high-resolution image patch x 330. Embodiments repeat the procedure for overlapping patches of Y 340, and the final image X 325 is obtained by averaging those overlapping pixels of the estimated high-resolution image patches).
Regarding claim 3, Lin discloses obtaining a specific pixel, among the second pixels, formed by down-sampling the target pixel; and obtaining a plurality of second pixels surrounding the specific pixel, and assigning the surrounding second pixels as a plurality of reference pixels corresponding to the target pixel (see col. 12, lines 1-67).
Regarding claim 15, Lin discloses an image processing device, comprising: a non-transitory storage circuit, storing a program code; and a processor, coupled to the non-transitory storage circuit (see col. 3, lines 1-67; a memory of a specific apparatus or special purpose computing device or platform. In the context of this particular specification, the term specific apparatus or the like includes a general purpose computer once it is programmed to perform particular functions pursuant to instructions from program software), accessing the program code to implement the limitations of claim 1 (see rejection of claim 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 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 claimedinvention is not identically disclosed as set forth in section 102 of this title, 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.
Claim 2 is rejected under 35 U.S.C. 103 as being unpatentable over Lin et al (US 8,655,109 B2) in view of Branbandere et al (ML: “Dynamic Filter Networks”).
Regarding claim 2, Lin teaches all elements as mentioned above in claim 1. Lin does not teach expressly inputting the second image input into a neural network, wherein the neural network outputs the first resolution parameter combination of each of the second pixels in response to the second image.
Branbandere, in the same field of endeavor, teaches inputting the second image input into a neural network, wherein the neural network outputs the first resolution parameter combination of each of the second pixels in response to the second image (see section 3.1, 3.2, abstract, 3, 2; The filter-generating network takes an input IA ∈ R h×w×cA , where h, w and cA are height, width and number of channels of the input A respectively……. The filter-generating network dynamically generates sample-specific filter parameters conditioned on the network’s input… Dynamic local filtering layer. An extension of the dynamic convolution layer that proves interesting, as we show in the experiments, is the dynamic local filtering layer. In this layer the filtering operation is not translation invariant anymore. Instead, different filters are applied to different positions of the input IB similarly to the traditional locally connected layer: for each position (i, j) of the input IB, a specific local filter Fθ (i,j) is applied to the region centered around IB(i, j): G(i, j) = Fθ (i,j) (IB(i, j)) (2) The filters used in this layer are not only sample specific but also position specific. Note that dynamic convolution as discussed in the previous section is a special case of local dynamic filtering where the local filters are shared over the image’s spatial dimensions. The dynamic local filtering layer is shown schematically in Figure 2b. If the generated filters are again constrained with a softmax function so that each filter only contains one non-zero element, then the dynamic local filtering layer replaces each element of the input IB by an element selected from a local neighbourhood around it. This offers a natural way to model local spatial deformations conditioned on another input IA. The dynamic local filtering layer can perform not only a single transformation like the dynamic convolutional layer, but also position-specific transformations like local deformation. Before or after applying the dynamic local filtering operation we can add a dynamic pixel-wise bias to each element of the input IB to address situations like photometric changes. This dynamic bias can be produced by the same filter-generating network that generates the filters for the local filtering…… In this section we describe our dynamic filter framework. A dynamic filter module consists of a filtergenerating network that produces filters conditioned on an input, and a dynamic filtering layer that applies the generated filters to another input. Both components of the dynamic filter module are differentiable).
It would have been obvious (before the effective filing date of the claimed invention) or (at the time the invention was made) to one of ordinary skill in the art to modify Lin to utilize the cited limitations as suggested by Branbandere. The suggestion/motivation for doing so would have been to reach state-of-the-art performance (see abstract). Furthermore, the prior art collectively includes each element claimed (though not all in the same reference), and one of ordinary skill in the art could have combined the elements in the manner explained above using known engineering design, interface and/or programming techniques, without changing a “fundamental” operating principle of Lin, while the teaching of Branbandere continues to perform the same function as originally taught prior to being combined, in order to produce the repeatable and predictable result. It is for at least the aforementioned reasons that the examiner has reached a conclusion of obviousness with respect to the claim in question.
Claim 7 is rejected under 35 U.S.C. 103 as being unpatentable over Lin et al (US 8,655,109 B2) in view of Rukundo et al (IJACSA: “Nearest Neighbor Value Interpolation”).
Regarding claim 7, Lin teaches all elements as mentioned above in claim 1. Lin does not teach expressly assigning the first resolution parameter combination of the at least one candidate pixel as the second resolution parameter combination of the target pixel when the at least one candidate pixel among the second pixels corresponding to the target pixel is obtained and the at least one candidate pixel comprises a second pixel with a largest numerical value or a second pixel with a smallest numerical value among the second pixels.
Rukundo, in the same field of endeavor, teaches assigning the first resolution parameter combination of the at least one candidate pixel as the second resolution parameter combination of the target pixel when the at least one candidate pixel among the second pixels corresponding to the target pixel is obtained and the at least one candidate pixel comprises a second pixel with a largest numerical value or a second pixel with a smallest numerical value among the second pixels (see abstract, section 3-B, 3-C).
It would have been obvious (before the effective filing date of the claimed invention) or (at the time the invention was made) to one of ordinary skill in the art to modify Lin to utilize the cited limitations as suggested by Rukundo. The suggestion/motivation for doing so would have been to demonstrate higher performance (see abstract). Furthermore, the prior art collectively includes each element claimed (though not all in the same reference), and one of ordinary skill in the art could have combined the elements in the manner explained above using known engineering design, interface and/or programming techniques, without changing a “fundamental” operating principle of Lin, while the teaching of Rukundo continues to perform the same function as originally taught prior to being combined, in order to produce the repeatable and predictable result. It is for at least the aforementioned reasons that the examiner has reached a conclusion of obviousness with respect to the claim in question.
Claim 12 is rejected under 35 U.S.C. 103 as being unpatentable over Lin et al (US 8,655,109 B2) in view of Getreuer, Pascal (IPOL: “Roussos–Maragos Tensor-Driven Diffusion for Image Interpolation”).
Regarding claim 12, Lin teaches all elements as mentioned above in claim 1. Lin does not teach expressly performing a pre-smoothing process on the first resolution parameter combination with a first filter, wherein after obtaining the second resolution parameter combination of the target pixel, the method further comprises: performing a post-smoothing process on the second resolution parameter combination with a second filter, wherein the first filter is different from the second filter.
Getreuer, in the same field of endeavor, teaches performing a pre-smoothing process on the first resolution parameter combination with a first filter, wherein after obtaining the second resolution parameter combination of the target pixel, the method further comprises: performing a post-smoothing process on the second resolution parameter combination with a second filter, wherein the first filter is different from the second filter (see section 2).
It would have been obvious (before the effective filing date of the claimed invention) or (at the time the invention was made) to one of ordinary skill in the art to modify Lin to utilize the cited limitations as suggested by Getreuer. The suggestion/motivation for doing so would have been to enhance performance (see section 6). Furthermore, the prior art collectively includes each element claimed (though not all in the same reference), and one of ordinary skill in the art could have combined the elements in the manner explained above using known engineering design, interface and/or programming techniques, without changing a “fundamental” operating principle of Lin, while the teaching of Getreuer continues to perform the same function as originally taught prior to being combined, in order to produce the repeatable and predictable result. It is for at least the aforementioned reasons that the examiner has reached a conclusion of obviousness with respect to the claim in question.
Allowable Subject Matter
Claims 4-6, 8-10, 13-14 are objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims.
Regarding claims 4-6, none of the references of record alone or in combination suggest or fairly teach wherein determining the comparison result between the target pixel and each of the second pixels corresponding to the target pixel, and accordingly selecting the at least one candidate pixel among the second pixels corresponding to the target pixel comprises: in response to determining that numerical values of the second pixels corresponding to the target pixel are all greater than a numerical value of the target pixel, selecting a second pixel with a smallest numerical value among the second pixels corresponding to the target pixel as the at least one candidate pixel; in response to determining that the numerical values of the second pixels corresponding to the target pixel are all smaller than the numerical value of the target pixel, selecting a second pixel with a largest numerical value among the second pixels corresponding to the target pixel as the at least one candidate pixel; and in response to determining that the numerical values of the second pixels corresponding to the target pixel are not all smaller than the numerical value of the target pixel and are not all greater than the numerical value of the target pixel, selecting two second pixels among the second pixels corresponding to the target pixel as the at least one candidate pixel.
Regarding claim 8-10, none of the references of record alone or in combination suggest or fairly teach wherein obtaining the second resolution parameter combination of the target pixel based on the first resolution parameter combination of each of the at least one candidate pixel comprises: when the at least one candidate pixel obtained from the second pixels corresponding to the target pixel comprises a first candidate pixel and a second candidate pixel, combining a first resolution parameter combination of the first candidate pixel and a first resolution parameter combination of the second candidate pixel into the second resolution parameter combination of the target pixel, wherein a numerical value of the first candidate pixel is greater than a numerical value of the target pixel, and a numerical value of the second candidate pixel is smaller than the numerical value of the target pixel.
Regarding claim 11, none of the references of record alone or in combination suggest or fairly teach wherein the target pixel is characterized by INi,jHR, the second resolution parameter combination of the target pixel is characterized by (Ai,jHR,bi,jHR), and the output pixel corresponding to the target pixel is characterized by OUTi,jHR, wherein OUTi,jHR=Ai,jHR∙INi,jHR+bi,jHR.
Regarding claim 13-14, none of the references of record alone or in combination suggest or fairly teach wherein the first filter and the second filter are with a size of 3×3, the first filter has a plurality of weight values, the second filter has a plurality of weight values, and at least one of the weight values of the first filter is different from the weight values of the second filter and
wherein the first filter with a size of 3×3 and the second filter with a size of 3×3 have nine weight values respectively; wherein in the first filter, the weight value located at a center of the first filter is a first weight value; the weight values located at four corners of the first filter are same, each being a second weight value; rest of the weight values are same, each being a third weight value; wherein in the second filter, the weight value located at a center of the second filter is a fourth weight value; the weight values located at four corners of the second filter are same, each being a fifth weight value; and rest of the weight values are the same, each being a sixth weight value.
Conclusion
Claims 1-3, 7, 12, 15 are rejected. Claims 4-6, 8-10, 13-14 are objected to as being dependent upon a rejected base claim.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to EDWARD PARK. The examiner’s contact information is as follows:
Telephone: (571)270-1576 | Fax: 571.270.2576 | Edward.Park@uspto.gov
For email communications, please notate MPEP 502.03, which outlines procedures pertaining to communications via the internet and authorization. A sample authorization form is cited within MPEP 502.03, section II.
The examiner can normally be reached on M-F 9-6 CST.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Andrew Moyer, can be reached on (571) 272-9523. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/EDWARD PARK/
Primary Examiner, Art Unit 2666