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 .
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.
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 1-3, 6-17 and 19-20 are rejected under 35 U.S.C. 103 as being unpatentable over Shrestha et al (10,546,197) in view of Chan et al (11,234,666)
Regarding claims 1 and 15 Shrestha discloses,
Process an image of the one or more images to generate a feature map associated with spatio-channel data of the image (note fig. 2, block S230 and col. 13 lines 57 – col. 14 lines 11);
Chan discloses generate, using a first encoder, a first feature weight map and a second feature weight map based on the spatio-channel data of the feature map (note fig. 11, block 212, apply weights)
Chan discloses apply a noise filter to the first feature weight map to generate a first downsampled feature weight map (note col. 4 lines 33-38);
Chan discloses performing a selective pooling downsample of the second feature weight map to generate a second downsampled feature weight map (note col. 11 lines 10-20); and
Shrestha generate, based on the first downsampled feature weight map and the second downsampled feature weight map, a reduced resolution representation of the image (note col. 14 lines 5-10, reducing complexity of image data). Shrestha and Chan are combinable because they are from the same field. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to include generating weight map, noise filtering and perform downsampling in the system of Shrestha as evidence by Chan. The suggestion/motivation for doing so provides denoising methods that are robust to variations in the noise level of the reconstructed images. It would have been obvious to combine Chan with Shrestha to obtain the invention as specified by claims 1 and 15.
Regarding claims 2 and 16 Shrestha and Chan discloses,
Apply the noise filter to the first feature weight map using a plurality of dilated convolutions and a convolutional range-gaussian filter to reduce outlier noise in the first feature weight map (Chan note col. 4 lines 33-38, noise suppression).
Regarding claim 3 and 17 Shrestha discloses,
Generate the feature map using a transformation to shift pixel arrangements of the image across channels as patches, wherein the feature map is based on the patches (note fig. 2, block S230 and col. 13 lines 57 – col. 14 lines 11);
Regarding claims 6 and 19 Shrestha and Chan discloses,
Perform the selective pooling downsample using an adaptive threshold on frequency components of the second feature weight map (note col. 11 lines 12-20).
Regarding claims 7 and 20 Shrestha discloses,
Provide the reduced resolution representation of the image to a machine learning model to perform one or more tasks associated with objects represented in the reduced resolution representation (note col. 14 lines 5-10, reducing complexity of image data) .
Regarding claim 8 Shrestha discloses,
Wherein the machine learning model is a deep neural network (note col. 13 lines 1-7, deep neural network).
Regarding claim 9 Shrestha discloses,
Wherein the machine learning model is trained using on-device training (note col. 6 lines 65- col. 7 lines 13).
Regarding claim 10 Shrestha discloses,
Determine to downsample the image based on a power saving mode of the apparatus.
Regarding claim 11 Shrestha discloses,
Wherein the feature map is a hyperspace map (note col. 9 lines 2-8).
Regarding claim 12 Shrestha discloses,
Adapt parameters of the first encoder based on a target machine learning model (note col. 6 lines 63 – col. 7 lines 13, collected data based on machine learning model).
Regarding claim 13 Shrestha disclose,
Wherein the apparatus is a sub-component of a system, and wherein the system comprises a camera system, a display system, or a video coding system (note fig. 1, block 130, user interface, col. 10 lines 26-42, user interface camera, display and processor).
Regarding claim 14 Shrestha discloses,
One or more cameras configured to capture the one or more images (note fig. 1 block 110, image data source and col. 8 lines 8-10, capturing device capturing images).
Allowable Subject Matter
Claims 4-5 and 18 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.
The following is a statement of reasons for the indication of allowable subject matter for dependent claims 4 and 18. Prior art could not be found for the features assign, using a second encoder, scores to a first plurality of features of the first feature weight map and a second plurality of features of the second feature weight map; and remove features from the first plurality of features and the second plurality of features based on the scores. These features in combination with other features could not be found in the prior art. Claim 5 depend on claim 4. Therefore are also objected.
Related Prior Art
Pyati (11,176,589) Process an image of the one or more images to generate a feature map associated with spatio-channel data of the image (note fig. 1, block 112, image feature extractor).
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to GREGORY M DESIRE whose telephone number is (571)272-7449. The examiner can normally be reached Monday-Friday 6:30am-3:00pm.
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G.D.
June 21, 2026
/GREGORY M DESIRE/Primary Examiner, Art Unit 2676
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