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 .
Drawings
The drawings are objected to as failing to comply with 37 CFR 1.84(p)(5) because they do not include the following reference sign(s) mentioned in the description: “method 500” (see Fig. 5 and [0045]). Corrected drawing sheets in compliance with 37 CFR 1.121(d) are required in reply to the Office action to avoid abandonment of the application. Any amended replacement drawing sheet should include all of the figures appearing on the immediate prior version of the sheet, even if only one figure is being amended. Each drawing sheet submitted after the filing date of an application must be labeled in the top margin as either “Replacement Sheet” or “New Sheet” pursuant to 37 CFR 1.121(d). If the changes are not accepted by the examiner, the applicant will be notified and informed of any required corrective action in the next Office action. The objection to the drawings will not be held in abeyance.
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 1-9 and 11-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to the judicial exception of mathematical concepts type abstract idea which performs mathematical calculations without significantly more.
Independent claims 1, 12 and 20, recites the subject matter, “compute one or more frequency domain predictions based at least on one or more first adjustments applied to one or more sub-band images derived from at least one wavelet frequency representation of a resampled image input; compute a spatial domain prediction based at least on applying one or more second adjustments to the resampled image input; compute a corrected wavelet frequency representation of the spatial domain prediction based at least on the one or more frequency domain predictions; and generate a reconstructed image based at least on the corrected wavelet frequency representation; and produce a resampled image prediction output using the reconstructed image” (claim 1);
“compute a corrected wavelet frequency representation for a resampled image input based at least on one or more frequency domain predictions individually computed based at least on one or more first adjustments applied to one or more sub-band images derived from at least one initial wavelet frequency representation of the resampled image input; and generate a reconstructed image based at least on the corrected wavelet frequency representation to produce a resampled image prediction output” (claim 12); and
“generating image data representing resampled image data based at least on one or more non-linear adjustments applied to one or more sub-band images derived from at least one wavelet frequency representation of the resampled image data to produce a corrected set of one or more sub-band images, and generating a reconstructed image from the corrected set of one or more sub-band images based at least on an inverse wavelet frequency operation” (claim 20).
The noted subject matter of claims 1, 12 and 20 refers to performing a series of computations to calculate frequency domain predictions based on adjustments to computed wavelet sub-band images, spatial domain predictions based on adjustments to a resampled image input, and corrected wavelet frequency representations of the spatial domain predictions based on frequency domain predictions to generate a reconstructed image and to produce a resampled image prediction output; which given the broadest reasonable interpretation of the claim in light of the specification, encompasses merely performing mathematical calculations. Thus, the broadest reasonable interpretation, in light of the specification, of the claimed subject matter directs to a judicial exception of mathematical concepts type abstract idea of performing mathematical calculations. See MPEP 2106.04(a)(2) I. C.
The judicial exception of claims 1 and 12 are further not integrated into a practical application because the additional claim limitations of, “one or more processors comprising processing circuitry” (claim 1) and “one or more processors” (claim 12) describe the use of generic computing elements to implement the noted abstract idea with a high level of generality, such that the claims amounts merely implementing the abstract idea on generic computing elements. See MPEP 2106.04(d), MPEP 2106.05(b), and MPEP 2106.05(f).
Claims 1 and 12 do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the above noted additional claim limitations, similarly as discussed above, continue to merely encompass the use of generic computing elements to implement the noted abstract idea with a high level of generality, such that the claims amounts to merely implementing the abstract idea on generic computing elements, and are insufficient to provide significantly more than the noted judicial exceptions. See MPEP 2106.05(b) and MPEP 2106.05(f). Furthermore, in consideration of the claims 1 and 12 additional elements as a combination, the additional elements continue to merely implement the abstract idea on generic computing elements, and thus do not provide significantly more than the noted judicial exception.
Claims 2, 4, 6-9, and 13-18 do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the claims recite additional subject matter of,
“compute the at least one wavelet frequency representation of the resampled image input based at least on a multi-level decomposition of the resampled image input” (claims 2 and 15),
“compute the one or more frequency domain predictions based at least on a discrete wavelet transform, wherein the one or more frequency domain predictions comprise at least one of: a low-low sub-band prediction, a high-high sub-band prediction, a low-high sub-band prediction, and a high-low sub-band prediction” (claims 4 and 18),
“wherein the one or more frequency domain predictions are individually derived based at least on one or more non-linear corrections computed by one or more residual block-based frameworks of a frequency domain path of a machine learning model” (claims 6 and 16),
“wherein the spatial domain prediction is derived based at least on one or more non-linear corrections computed using a residual block-based framework of a spatial domain path of a neural network model” (claims 7 and 14),
“wherein the one or more first adjustments and the one or more second adjustments are based at least on two-dimensional convolution operations” (claim 8)
“compute the one or more frequency domain predictions based at least on a wavelet frequency decomposition algorithm comprising a discrete wavelet transform (DWT); and generate the reconstructed image based at least on applying the corrected wavelet frequency representation to an inverse DWT” (claims 9 and 17), and
“compute a spatial domain prediction based at least on applying one or more second adjustments to the resampled image input; and wherein the corrected wavelet frequency representation is based at least on a correction of the spatial domain prediction based at least on the one or more frequency domain predictions” (claim 13),
which describe further steps of the noted mathematical calculations type abstract idea, which given the broadest reasonable interpretation of the claims in light of the specification, encompasses merely performing additional mathematical calculations. See MPEP 2106.04(a)(2) I. C.
Claims 2, 4, 6-9, and 13-18 do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the above noted additional claim limitations, similarly as discussed above, continue to merely describe further steps of the noted mathematical calculations type abstract idea, and are insufficient to provide significantly more than the noted judicial exceptions. See MPEP 2106.04(a)(2) I. C. Furthermore, in consideration of the claims 2, 4, 6-9, and 13-18 additional elements as a combination, the additional elements continue to merely describe performing additional mathematical calculations, and thus do not provide significantly more than the noted judicial exception.
Claims 3 and 5 do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the additional claimed subject matter of
“wherein the resampled image input comprises an upsampled image or a downsampled image, based at least on an image data input” (claim 3), and
“wherein the one or more frequency domain predictions include at least the low-low sub-band prediction and the high-high sub-band prediction” (claim 5),
describe performing additional insignificant extra solution activity related to selecting a particular data source or type of data to be manipulated by the mathematical calculations of the noted abstract idea. See MPEP 2106.05(g).
Claims 3 and 5 do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the above noted additional claim limitations, similarly as discussed above, continue to merely encompass performing additional insignificant extra solution activity related to selecting a particular data source or type of data to be manipulated by the mathematical calculations, and are insufficient to provide significantly more than the noted judicial exceptions. See MPEP 2106.05(g). Furthermore, in consideration of the claims 3 and 5 additional elements as a combination, the additional elements continue to merely perform respective ional insignificant extra solution activity, and do not provide significantly more than the noted judicial exception.
Claims 11 and 19 do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the additional claimed subject matter of
“comprised in at least one of: a control system for an autonomous or semi-autonomous machine; a perception system for an autonomous or semi-autonomous machine; a system for performing simulation operations; a system for performing digital twin operations; a system for performing light transport simulation; a system for performing collaborative content creation for three-dimensional assets; a system for performing deep learning operations; a system for performing remote operations; a system for performing real-time streaming; a system for generating or presenting one or more of augmented reality content, virtual reality content, or mixed reality content; a system implemented using an edge device; a system implemented using a robot; a system for performing conversational AI operations; a system implementing one or more language models; a system implementing one or more large language models (LLMs); a system implementing one or more vision language models (VLMs); a system for generating synthetic data; a system for generating synthetic data using AI; a system incorporating one or more virtual machines (VMs); a system implemented at least partially in a data center; or a system implemented at least partially using cloud computing resources” (claims 11 and 19) describe generally linking the use of the judicial exception to a particular technological environment or field of use, and thus merely indicating a field of use or technological environment in which to apply the judicial exception. See MPEP 2106.05(h).
Claims 11 and 19 do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the above noted additional claim limitations, similarly as discussed above, continue to generally link the use of the judicial exception to a particular technological environment or field of use, and are insufficient to provide significantly more than the noted judicial exceptions. See MPEP 2106.05(g). Furthermore, in consideration of the claims 11 and 19 additional elements as a combination, the additional elements continue to merely indicate a field of use or technological environment in which to apply the judicial exception, and do not provide significantly more than the noted judicial exception.
Examiner notes that claim 10 recite additional features of “wherein the one or more frequency domain predictions and the spatial domain prediction are generated based at least on a machine learning model trained based at least on a loss function comprising at least a first loss component for a frequency sub-band prediction loss, and at least a second loss component for a resampled image prediction loss”, which would add specific limitation other than what is well-understood, routine, conventional activity in the field, or adding unconventional steps that confine the claim to a particular useful application, such that the claim as a whole is more than a drafting effort designed to monopolize the exception. See MPEP 2106.04(d)
Thus, claim 10 is directed to statutory eligible subject matter.
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.
Claims 1-20 are rejected under 35 U.S.C. 103 as being unpatentable over Qin et al. (“Deep ResNet Based Remote Sensing Image Super-Resolution Reconstruction in Discrete Wavelet Domain”), herein Qin, in view of Yang et al. (“An Effective and Comprehensive Image Super Resolution Algorithm Combined With a Novel Convolutional Neural Network and Wavelet Transform”), herein Yang.
Regarding claim 1, Qin discloses one or more processors comprising processing circuitry (see Qin sect. 4.2. Comparisons with State of the Art Methods, where the experimetns are performed in Matlab using a GeForce 720M GPU with 8G memory) to:
compute one or more frequency domain predictions based at least on one or more first adjustments applied to one or more sub-band images derived from at least one wavelet frequency representation of a resampled image input (see Qin Fig. 1 and sect. 3. PROPOSED ALGORITHM, where an input image is decomposed into discrete and stationary wavelet transform to respectively get four sub-bands and are fed into the Deep Learning Residual Network to predict corresponding residual images); and
generate a reconstructed image based at least on the corrected wavelet frequency representation (see Qin Fig. 1 and sect. 3. PROPOSED ALGORITHM, where the four fused sub-band images and residual images are added as the new sub-bands of 2D stationary wavelet transform to perform reconstruction); and
produce a resampled image prediction output using the reconstructed image (see Qin Fig. 1 and sect. 3. PROPOSED ALGORITHM, where the inverse 2D Stationary wavelet transform is performed to get the final output Super Resolution HR image).
Qin does not explicitly disclose
compute a spatial domain prediction based at least on applying one or more second adjustments to the resampled image input;
compute a corrected wavelet frequency representation of the spatial domain prediction based at least on the one or more frequency domain predictions
Yang teaches in a related and pertinent image super resolution algorithm combining deep learning and wavelet transform (see Yang Abstract), where the proposed method is based on spatial domain to wavelet domain to reconstruct the super-resolution image, where the LR images are taken as the input of the Feature extraction network, composed of several residual blocks and the output of the feature extraction network is input to the inference network, the output of the inference network and upsampled LR image are added and wavelet transform is performed as input to the reconstruction network, and by using inverse wavelet transform, a series of wavelet images are generated into corresponding SR image (see Yang Fig. 3 and sect. III. B. Network Architecture).
At the time of filing, one of ordinary skill in the art would have found it obvious to apply the teachings of Yang to the teachings of Qin, such that the input LR images are also input into feature extraction network and inference network as taught by Yang to extract features from the LR images and infer wavelet coefficients for improved super resolution wavelet reconstruction.
This modification is rationalized as an application of a known technique to a known device ready for improvement to yield predictable results.
In this instance, Qin disclose a base super resolution method for remote sensing images based on a LR input image decomposed into discrete and stationary wavelet transform to respectively get four sub-bands images which are fed into the Deep Learning Residual Network to predict corresponding residual images to reconstruct the super resolution image
Yang teaches a known technique for an image super resolution algorithm, where the LR images are taken as the input of the Feature extraction network, composed of several residual blocks and the output of the feature extraction network is input to the inference network, the output of the inference network and upsampled LR image are added and wavelet transform is performed as input to the reconstruction network, and by using inverse wavelet transform, a series of wavelet images are generated into corresponding SR image.
One of ordinary skill in the art would have recognized that by applying Yang’s technique would allow for the device of Qin to also input the LR images into feature extraction network and inference network as taught by Yang to extract features from the LR images and infer wavelet coefficients for improved super resolution wavelet reconstruction, predictably leading to an improved super-resolution method.
Regarding claim 2, please see the above rejection of claim 1. Qin and Yang disclose the one or more processors of claim 1, wherein the one or more processors are further to compute the at least one wavelet frequency representation of the resampled image input based at least on a multi-level decomposition of the resampled image input (see Qin Fig. 1, Fig. 2, and sect. 3. PROPOSED ALGORITHM, where the wavelet transform decomposes the input image into subbands; see Yang Fig. 1 and sect. III. A. WAVELET TRANSFORM, where multi level wavelet decomposition of an input image is known).
Regarding claim 3, please see the above rejection of claim 1. Qin and Yang disclose the one or more processors of claim 1, wherein the resampled image input comprises an upsampled image or a downsampled image, based at least on an image data input (see Yang Fig. 3 and sect. III. B. Network Architecture, where the LR image is upsampled and added to the output of the inference network for the wavelet transform as input to the reconstruction network).
Regarding claim 4, please see the above rejection of claim 1. Qin and Yang disclose the one or more processors of claim 1, wherein the one or more processors are further to compute the one or more frequency domain predictions based at least on a discrete wavelet transform, wherein the one or more frequency domain predictions comprise at least one of: a low-low sub-band prediction, a high-high sub-band prediction, a low-high sub-band prediction, and a high-low sub-band prediction (see Qin Fig. 1 and sect. 3. PROPOSED ALGORITHM, where the residual images corresponding to the LL, LH, HL, and HH sub-bands are predicted).
Regarding claim 5, please see the above rejection of claim 4. Qin and Yang disclose the one or more processors of claim 4, wherein the one or more frequency domain predictions include at least the low-low sub-band prediction and the high-high sub-band prediction (see Qin Fig. 1 and sect. 3. PROPOSED ALGORITHM, where the residual images corresponding to the LL sub-band and HH sub-band are predicted).
Regarding claim 6, please see the above rejection of claim 1. Qin and Yang disclose the one or more processors of claim 1, wherein the one or more frequency domain predictions are individually derived based at least on one or more non-linear corrections computed by one or more residual block-based frameworks of a frequency domain path of a machine learning model (see Qin Fig. 1 and sect. 3. PROPOSED ALGORITHM, where the deep learning residual network blocks are used to predict the corresponding residual images of the fused sub-band images).
Regarding claim 7, please see the above rejection of claim 1. Qin and Yang disclose the one or more processors of claim 1, wherein the spatial domain prediction is derived based at least on one or more non-linear corrections computed using a residual block-based framework of a spatial domain path of a neural network model (see Yang sect. 1) FEATURE EXTRACTION NETWORK, where the LR image is processed by the feature extraction network, composed of residual blocks, and the inference network).
Regarding claim 8, please see the above rejection of claim 1. Qin and Yang disclose the one or more processors of claim 1, wherein the one or more first adjustments and the one or more second adjustments are based at least on two-dimensional convolution operations (see Qin Fig. 1 and sect. 3. PROPOSED ALGORITHM, where the deep learning residual network blocks are based on convolution operations and used to process two dimensional sub-band images; see Yang sect. 1) FEATURE EXTRACTION NETWORK, where the residual blocks are composed of convolution blocks which process the two dimensional LR images).
Regarding claim 9, please see the above rejection of claim 1. Qin and Yang disclose the one or more processors of claim 1, wherein the one or more processors compute the one or more frequency domain predictions based at least on a wavelet frequency decomposition algorithm comprising a discrete wavelet transform (DWT) (see Qin Fig. 1 and sect. 3. PROPOSED ALGORITHM, where discrete wavelet transform is used to decompose the input images into four subbands); and
generate the reconstructed image based at least on applying the corrected wavelet frequency representation to an inverse DWT (see Yang sect. 3) RECONSTRUCTION NETWORK, (2) Image space loss where the inverse discrete wavelet transform (idwt) to obtain the learned residual image and the final super resolution image can be obtained).
Regarding claim 10, please see the above rejection of claim 1. Qin and Yang disclose the one or more processors of claim 1, wherein the one or more frequency domain predictions and the spatial domain prediction are generated based at least on a machine learning model trained based at least on a loss function comprising at least a first loss component for a frequency sub-band prediction loss, and at least a second loss component for a resampled image prediction loss (see Yang sect. 3) RECONSTRUCTION NETWORK and Eq. (1), where the network is optimized by a total loss function consisting of wavelet coefficient loss and image space pixel loss).
Regarding claim 11, please see the above rejection of claim 1. Qin and Yang disclose the one or more processors of claim 1, wherein the processing circuitry is comprised in at least one of: a control system for an autonomous or semi-autonomous machine; a perception system for an autonomous or semi-autonomous machine; a system for performing simulation operations; a system for performing digital twin operations; a system for performing light transport simulation; a system for performing collaborative content creation for three-dimensional assets; a system for performing deep learning operations; a system for performing remote operations (see Qin Abstract, where the super resolution method is used for remote sensing images); a system for performing real-time streaming; a system for generating or presenting one or more of augmented reality content, virtual reality content, or mixed reality content; a system implemented using an edge device; a system implemented using a robot; a system for performing conversational AI operations; a system implementing one or more language models; a system implementing one or more large language models (LLMs); a system implementing one or more vision language models (VLMs); a system for generating synthetic data; a system for generating synthetic data using AI; a system incorporating one or more virtual machines (VMs); a system implemented at least partially in a data center; or a system implemented at least partially using cloud computing resources.
Regarding claim 12, Qin and Yang disclose a system comprising one or more processors (see Qin sect. 4.2. Comparisons with State of the Art Methods, where the experimetns are performed in Matlab using a GeForce 720M GPU with 8G memory) to:
compute a corrected wavelet frequency representation for a resampled image input based at least on one or more frequency domain predictions individually computed based at least on one or more first adjustments applied to one or more sub-band images derived from at least one initial wavelet frequency representation of the resampled image input (see Qin Fig. 1 and sect. 3. PROPOSED ALGORITHM, where an input image is decomposed into discrete and stationary wavelet transform to respectively get four sub-bands and are fed into the Deep Learning Residual Network to predict corresponding residual images; see Yang Fig. 3 and sect. III. B. Network Architecture, where the LR images are taken as the input of the Feature extraction network, composed of several residual blocks and the output of the feature extraction network is input to the inference network, the output of the inference network and upsampled LR image are added and wavelet transform is performed as input to the reconstruction network, and by using inverse wavelet transform, a series of wavelet images are generated into corresponding SR image); and
generate a reconstructed image based at least on the corrected wavelet frequency representation to produce a resampled image prediction output (see Qin Fig. 1 and sect. 3. PROPOSED ALGORITHM, where the four fused sub-band images and residual images are added as the new sub-bands of 2D stationary wavelet transform to perform reconstruction; see Yang Fig. 3 and sect. III. B. Network Architecture, by using inverse wavelet transform, a series of wavelet images are generated into corresponding SR image ).
Please see the above rejection for claim 1, as the rationale to combine the teachings of Qin and Yang are similar, mutatis mutandis.
Regarding claim 13, please see the above rejection of claim 12. Qin and Yang disclose the system of claim 12, the one or more processors further to: compute a spatial domain prediction based at least on applying one or more second adjustments to the resampled image input; and wherein the corrected wavelet frequency representation is based at least on a correction of the spatial domain prediction based at least on the one or more frequency domain predictions (see Yang Fig. 3 and sect. III. B. Network Architecture, where the LR images are taken as the input of the Feature extraction network, composed of several residual blocks and the output of the feature extraction network is input to the inference network, the output of the inference network and upsampled LR image are added and wavelet transform is performed as input to the reconstruction network, and by using inverse wavelet transform, a series of wavelet images are generated into corresponding SR image).
Regarding claim 14, please see the above rejection of claim 13. Qin and Yang disclose the system of claim 13, wherein the spatial domain prediction is derived based at least on one or more non-linear corrections computed using a residual block-based framework of a spatial domain path of a neural network model (see Yang sect. 1) FEATURE EXTRACTION NETWORK, where the LR image is processed by the feature extraction network, composed of residual blocks, and the inference network).
Regarding claim 15, see above rejection for claim 12. It is a system claim reciting similar subject matter as claim 2. Please see above claim 2 for detailed claim analysis as the limitations of claim 15 are similarly rejected.
Regarding claim 16, see above rejection for claim 12. It is a system claim reciting similar subject matter as claim 6. Please see above claim 6 for detailed claim analysis as the limitations of claim 16 are similarly rejected.
Regarding claim 17, see above rejection for claim 12. It is a system claim reciting similar subject matter as claim 9. Please see above claim 9 for detailed claim analysis as the limitations of claim 17 are similarly rejected.
Regarding claim 18, see above rejection for claim 12. It is a system claim reciting similar subject matter as claim 4. Please see above claim 4 for detailed claim analysis as the limitations of claim 18 are similarly rejected.
Regarding claim 19, see above rejection for claim 12. It is a system claim reciting similar subject matter as claim 11. Please see above claim 11 for detailed claim analysis as the limitations of claim 19 are similarly rejected.
Regarding claim 20, Qin and Yang disclose a method comprising:
generating image data representing resampled image data based at least on one or more non-linear adjustments applied to one or more sub-band images derived from at least one wavelet frequency representation of the resampled image data to produce a corrected set of one or more sub-band images (see Qin Fig. 1 and sect. 3. PROPOSED ALGORITHM, where an input image is decomposed into discrete and stationary wavelet transform to respectively get four sub-bands and are fed into the Deep Learning Residual Network to predict corresponding residual images; see Yang Fig. 3 and sect. III. B. Network Architecture, where the LR images are taken as the input of the Feature extraction network, composed of several residual blocks and the output of the feature extraction network is input to the inference network, the output of the inference network and upsampled LR image are added and wavelet transform is performed as input to the reconstruction network, and by using inverse wavelet transform, a series of wavelet images are generated into corresponding SR image; see Yang sect. 1) FEATURE EXTRACTION NETWORK, where the LR image is processed by the feature extraction network, composed of residual blocks, and the inference network), and
generating a reconstructed image from the corrected set of one or more sub-band images based at least on an inverse wavelet frequency operation (see Qin Fig. 1 and sect. 3. PROPOSED ALGORITHM, where the four fused sub-band images and residual images are added as the new sub-bands of 2D stationary wavelet transform to perform reconstruction; see Yang Fig. 3 and sect. III. B. Network Architecture, by using inverse wavelet transform, a series of wavelet images are generated into corresponding SR image ).
Please see the above rejection for claim 1, as the rationale to combine the teachings of Qin and Yang are similar, mutatis mutandis.
Conclusion
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/TIMOTHY CHOI/Examiner, Art Unit 2671
/VINCENT RUDOLPH/Supervisory Patent Examiner, Art Unit 2671
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