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 .
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's After-Final Amendment filed on March 19, 2026 has been entered by way of the Request for Continued Examination filed April 24, 2026.
Response to Arguments
Applicant’s arguments and amendments filed March 19, 2026 (herein “Amendment”), with respect to the rejection of independent claims 1, 11, 21 and 26, and various claims dependent therefrom under 35 U.S.C. 103 have been fully considered and are persuasive in part. Therefore, the rejection has been withdrawn. However, upon further consideration, a new ground(s) of rejection is made in view of Woo et al.,"Ghost-Free Deep High-Dynamic-Range Imaging Using Focus Pixels for Complex Motion Scenes," in IEEE Transactions on Image Processing, vol. 30, pp. 5001-5016, 2021, doi: 10.1109/TIP.2021.3077137. Specifically, the amendments to the claims have clarified that the target image format corresponds to “spatially-fixed binned image data” and the target image format [resolution]—presumed although the informality of the omitted word resolution is noted below in an objection—is higher than the second resolution. While secondary reference Duenyas does at least teach that the target image format corresponds to spatially fixed binned image data (see col. 9, ll. 47–58 teaching that regions of interest allocated different binning factors are output to a stitching block and stitched together and converted to the same resolution to present in a full frame video). Duenyas merely does not teach as well, where newly cited Woo teaches the target image format resolution is higher than the second resolution. For this reliance upon newly cited Woo, the previous rejection is withdrawn and a new grounds of rejection in view of Kodukula, Duenyas and Woo is set forth.
Claim Objections
Claims 1, 11, 21 and 26, and therefore all claims depending therefrom are objected to because of the following informalities: all four claims recite “the target image format is higher” but should instead recite “a resolution of the target image format is higher.” Appropriate correction is required.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 36, 39, 42 and 45 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention. Specifically, claims 36, 39, 42 and 45 recite the limitation "the non-binned image data.” There is insufficient antecedent basis for this limitation in the claim.
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.
Claims 1–2, 4, 11–12, 14, 21, 26, 28, 30–31, 33, 36, 39, 42 and 45 are rejected under 35 U.S.C. 103 as being unpatentable over Kodukula et al., "Rhythmic pixel regions: multi-resolution visual sensing system towards high-precision visual computing at low power," ASPLOS '21: Proceedings of the 26th ACM International Conference on Architectural Support for Programming Languages and Operating Systems, Pages 573 - 586, April 17, 2021 (herein “Kodukula”) in view of Duenyas et al., US Patent No. US 11,570,384 B2 (herein “Duenyas”) in view of Woo et al., "Ghost-Free Deep High-Dynamic-Range Imaging Using Focus Pixels for Complex Motion Scenes," in IEEE Transactions on Image Processing, vol. 30, pp. 5001-5016, 2021, doi: 10.1109/TIP.2021.3077137 (herein “Woo”).
Regarding claims 1, 11 and 21, with claim 1 as exemplary, substantive differences between the claims noted in curly brackets {}, and with deficiencies of Kodukula indicated in square brackets [], Kodukula teaches {a method comprising (Kodukula Abstract, operations and process of a visual sensing pipeline architecture): - claim 1 / an apparatus, comprising (Kodukula Abstract, a visual sensing pipeline architecture including the components as shown in fig. 4): a memory storing processor-readable code; and at least one processor coupled to the memory, the at least one processor configured to execute the processor-readable code to cause the at least one processor to perform operations including (Kodukula figs. 4 and 6, page 580, section 5.1, Rhythmic Pixel Encoder/Decoder shown in fig. 4 as being coupled to the image sensor (camera icon), and designed as a software decoder using C++ and Open CV libraries, with memory mapped interfaces on the input and output for integration with controllers and processing units, the software executed on a processing subsystem comprising a CPU and GPU (at least one processor), which would inherently be coupled to the memory storing the software decoder that the CPU/GPU executes): - claim 11/ a non-transitory computer-readable medium storing instructions that, when executed by a processor, cause the processor to perform operations comprising (Kodukula figs. 4 and 6, page 580, section 5.1, Rhythmic Pixel Encoder/Decoder shown in fig. 4 as being coupled to the image sensor (camera icon), and designed as a software decoder using C++ and Open CV libraries, with memory mapped interfaces on the input and output for integration with controllers and processing units, the software executed on a processing subsystem comprising a CPU and GPU (at least one processor), which would inherently be coupled to the memory storing the software decoder that the CPU/GPU executes): - claim 21}
receiving image data comprising at least a portion of an image frame (Kodukula fig. 2, fig. 4, pages 576–577, an original frame from a camera is input into a sensor interface, then an image signal processor (ISP), and then received by a rhythmic pixel encoder);
receiving metadata (Kodukula figs. 4 and 6, page 577, section 4.1, and page 579, left column, encoder generates metadata and stores it in DRAM which forwards on the metadata to a metadata scratchpad (thus transmits as a second interface), which forwards the metadata to the pixel memory management unit which processes via a decoding, the pixel data (image data) with the metadata and thus is an image signal processor) comprising a first indication of a first binning pattern for a first portion of the image data and a second indication of a second binning pattern different from the first binning pattern for a second portion of the image data (Kodukula fig. 6, pages 576–577, section 3.3, metadata including EncMasks, and per-row offset, where the EncMask indicates how a pixel is sampled in space and time (indication of a binning pattern), where EncMask has multiple values, thus first and second indications and portions), the first portion having a first resolution and the second portion having a second resolution (Kodukula pages 577–578, section 4.1, fig. 5, a pixel in a region of interest is forwarded/sampled and the others are skipped/binned, resulting a binning pattern, as shown in fig. 2a’s encoded frame, for pixels within a designated region of interest, where page 573 Abstract and fig. 1 teaches that with the disclosed rhythmic pixel regions, different regions are captured at different spatio-temporal resolutions, where relevant pixels in a scene of a fast moving object with high detail (first portion) has a higher resolution than a low detail slow or static object (second portion), thus second resolution lower/different than first);
coding the first portion of the image data according to the first indication (Kodukula pages 577–578, section 4.1, fig. 5, page 576, section 3.2 first paragraph, region labels determine encoding of an image frame, the row and pixel location for an incoming pixel stream is tracked, and a pixel in a region of interest matching the stride and x-index is forwarded/sampled and the others are skipped/binned, resulting a binning pattern indicated by EncMask, as shown in fig. 2a’s encoded frame, for pixels within a designated region of interest) [wherein the coding comprises remosaicing the first portion of the image data to a target image format using a first interpolation algorithm based on the first indication]; and
[coding] the second portion of the image data according to the second indication (Kodukula pages 577–578, section 4.1, fig. 5, region labels determine encoding of an image frame, the row and pixel location for an incoming pixel stream is tracked, and a pixel with a y-value not in the y-range, or not even in the x-range of any region is not forwarded (i.e. skipped/binned)), [wherein the coding comprises remosaicing the second portion of the image data to the target image format using a second interpolation algorithm based on the second indication],
[wherein the target image format corresponds to spatially-fixed binned image data including the remosaiced first and second portions of image data, and the target image format is higher than the second resolution.]
While Kodukula teaches that the encoder intercepts the incoming pixel stream and only forwards pixels that are in a region of interest, thus having a binning pattern for ROI, and that the other pixels are handled differently, Kodukula does not explicitly teach that there is a “coding” of the second portion, as claimed, along with the other deficiencies noted above in square brackets.
Duenyas teaches wherein the coding comprises remosaicing the first portion of the image data to a target image format (Duenyas fig. 4, col. 8, ll. 3–12, and 22–37, and col. 8, l. 64–col. 9, l. 6, ROIs (portions including a first portion), of the same type with the same binning factor, and a same resolution are processed in a single pipeline, where the pipeline processing includes digital processing including remosaicing, where the C region indicated by 454 are high resolution ROIs that are displayed in their respective high resolution format) using a first interpolation algorithm based on the first indication (Duenyas fig. 6A, col. 9, ll. 41–51, and col. 8, ll. 34–37, high resolution processing block processes the ROIs identified as high resolution (first indication) including remosaicing executing an image transformation algorithm).
Duenyas further teaches coding the second portion of the image data (Duenyas col. 7, ll. 1–40, regions of interest (ROIs) are identified and binned with a binning factor resulting in a higher resolution in the ROI than the binning factor used in the remaining regions (second portion)).
Duenyas yet further teaches wherein the coding comprises remosaicing the second portion of the image data to the target image format (Duenyas fig. 4, col. 8, ll. 3–12, and 22–37, and col. 8, l. 64–col. 9, l. 6, ROIs (portions including a first portion), of the same type with the same binning factor, and a same resolution are processed in a single pipeline, where the pipeline processing includes digital processing including remosaicing, where the B region indicated by 452 are high resolution ROIs that are displayed in their respective high resolution format) using a second interpolation algorithm based on the second indication (Duenyas fig. 6A, col. 9, ll. 41–51, and col. 8, ll. 34–37, high resolution processing block processes the ROI region B identified as high resolution (second indication) including remosaicing executing an image transformation algorithm), wherein the target image format corresponds to spatially-fixed binned image data including the remosaiced first and second portions of image data (Duenyas col. 9, ll. 47–58, regions of interest allocated different binning factors (first and second portions of image data) are output to a stitching block and stitched together and converted to the same resolution (spatially-fixed binned image data) to present in a full frame video).
Woo teaches and the target image format is higher than the second resolution (Woo page 5002, left column, low-resolution focus pixel image (second portion with second resolution – a low-resolution), is super-resolved (resolution is changed to be higher) and deeply merged to result in a high-resolution HDR image (target image format higher than the second resolution)).
Therefore, taking the teachings of Kodukula and Duenyas together as a whole, it would have been obvious to a person having ordinary skill in the art (herein “PHOSITA”) before the effective filing date of the claimed invention to have modified the operations of the visual sensing pipeline architecture of Kodukula to include the different binning patterns for different regions of an image as taught by Duenyas at least because doing so would reduce the size of data to be processed in an image sensor (Duenyas col. 1, ll. 42–44).
Further, taking the teachings of Kodukula as modified by Duenyas and Woo together as a whole, it would have been obvious to a PHOSITA before the effective filing date of the claimed invention to have modified the operations of the visual sensing pipeline architecture of Kodukula to include the deep merging of a lower resolution portion to be that of the higher resolution target/output image as taught in Woo at least because doing so would provide a ghost-free natural-looking result and a high-quality image (Woo page 5002, upper right column).
Regarding claims 2, 12, 28 and 31, Kodukula teaches wherein the first portion corresponds to a first region in an area of interest of the image frame, and wherein the second portion corresponds to a second region of the image frame outside the area of interest (Kodukula page 576, section 3.2, pixels within the designated regions (first region in an area of interest) are stored in original raster order, while omitting pixels that do not fall into the rection (second region outside area of interest)).
Regarding claims 4, 14 and 33, with claim 4 as exemplary, Kodukula teaches wherein the second resolution is lower than the first resolution (Kodukula pages 577–578, section 4.1, fig. 5, a pixel in a region of interest is forwarded/sampled and the others are skipped/binned, resulting a binning pattern, as shown in fig. 2a’s encoded frame, for pixels within a designated region of interest, where page 573 Abstract and fig. 1 teaches that with the disclosed rhythmic pixel regions, different regions are captured at different spatio-temporal resolutions, where relevant pixels in a scene of a fast moving object with high detail (first portion) has a higher resolution than a low detail slow or static object (second portion), thus second resolution lower than first).
Regarding claim 26, with deficiencies of Kodukula noted in square brackets [], Kodukula teaches an image capture device comprising (Kodukula Abstract, a visual sensing pipeline architecture including the components as shown in fig. 4): an image sensor [comprising a binning module to] output image data comprising at least a portion of an image frame (Kodukula fig. 4, page 577, section 4.1, camera and sensor interface outputting a pixel stream from an image sensor pipeline) and [to output] metadata comprising a first indication of a first binning pattern for a first portion of the image data and a second indication of a second binning pattern different from the first binning pattern for a second portion of the image data (Kodukula pages 576–577, section 3.3, encoder generates metadata including EncMasks, and per-row offset, where the EncMask indicates how a pixel is sampled in space and time (indication of a binning pattern), where EncMask has multiple values, thus first and second indications and different binning patterns and outputs it to DRAM), the first portion having a first resolution and the second portion having a second resolution (Kodukula pages 577–578, section 4.1, fig. 5, a pixel in a region of interest is forwarded/sampled and the others are skipped/binned, resulting a binning pattern, as shown in fig. 2a’s encoded frame, for pixels within a designated region of interest, where page 573 Abstract and fig. 1 teaches that with the disclosed rhythmic pixel regions, different regions are captured at different spatio-temporal resolutions, where relevant pixels in a scene of a fast moving object with high detail (first portion) has a higher resolution than a low detail slow or static object (second portion), thus second resolution lower/different than first);
a memory storing processor-readable code; and at least one processor coupled to the memory and to the image sensor, the at least one processor configured to execute the processor-readable code to cause the at least one processor to perform operations comprising (Kodukula figs. 4 and 6, page 580, section 5.1, Rhythmic Pixel Encoder/Decoder shown in fig. 4 as being coupled to the image sensor (camera icon), and designed as a software decoder using C++ and Open CV libraries, with memory mapped interfaces on the input and output for integration with controllers and processing units, the software executed on a processing subsystem comprising a CPU and GPU (at least one processor), which would inherently be coupled to the memory storing the software decoder that the CPU/GPU executes)
receiving, from the image sensor over a data bus (Kodukula figs. 4 and 6, page 578, section 4.1.2, data received by the decoder originates upstream from the image sensor, and is transmitted through a DRAM which is written to via burst DMA (data bus)), the image data and the metadata (Kodukula figs. 4 and 6, page 577, section 4.1, and page 579, left column, encoder generates metadata and stores it in DRAM which forwards on the metadata to a metadata scratchpad (thus transmits as a second interface), which forwards the metadata to the pixel memory management unit which processes via a decoding, the pixel data (image data) with the metadata and thus is an image signal processor);
decoding the first portion according to the first indication, [wherein the decoding comprises remosaicing the first portion to a target image format using a first interpolation algorithm based on the first indication]; decoding the second portion according to the second indication, [wherein the decoding comprises remosaicing the second portion to the target image format using a second interpolation algorithm based on the second indication] (Kodukula fig. 6, pages 578–579, section 4.2, decoder uses the metadata and encoded frames to interpolate (decode) according to spatial stride or temporal skip situations (first and second binning indications) according to the metadata EncMask value (thus different for portions with different EncMask values – first and second portions)),
[wherein the target image format corresponds to spatially-fixed binned image data including the remosaiced first and second portions of image data, and the target image format is higher than the second resolution.]
While Kodukula teaches an image sensor and a pixel encoder that outputs metadata, Kodukula does not explicitly teach that the image sensor comprises a binning module, as claimed, along with the other deficiencies noted above in square brackets.
Duenyas teaches image sensor comprises a binning module (Duenyas fig. 2, col. 5, ll. 20–22 and 44–52, image sensor including binning controller, and CTL signals output from an image processor that is part of the image sensor, where the CTL signals indicate boundaries of ROIs used for applying respective binning factors).
Duenyas further teaches wherein the decoding comprises remosaicing the first portion of the image data to a target image format (Duenyas fig. 4, col. 8, ll. 3–12, and 22–37, and col. 8, l. 64–col. 9, l. 6, ROIs (portions including a first portion), of the same type with the same binning factor, and a same resolution are processed in a single pipeline, where the pipeline processing includes digital processing including remosaicing, where the C region indicated by 454 are high resolution ROIs that are displayed in their respective high resolution format) using a first interpolation algorithm based on the first indication (Duenyas fig. 6A, col. 9, ll. 41–51, and col. 8, ll. 34–37, high resolution processing block processes the ROIs identified as high resolution (first indication) including remosaicing executing an image transformation algorithm).
Duenyas yet further teaches wherein the coding comprises remosaicing the second portion of the image data to the target image format (Duenyas fig. 4, col. 8, ll. 3–12, and 22–37, and col. 8, l. 64–col. 9, l. 6, ROIs (portions including a first portion), of the same type with the same binning factor, and a same resolution are processed in a single pipeline, where the pipeline processing includes digital processing including remosaicing, where the B region indicated by 452 are high resolution ROIs that are displayed in their respective high resolution format) using a second interpolation algorithm based on the second indication (Duenyas fig. 6A, col. 9, ll. 41–51, and col. 8, ll. 34–37, high resolution processing block processes the ROI region B identified as high resolution (second indication) including remosaicing executing an image transformation algorithm), wherein a resolution of the target image format is higher than the second resolution (Duenyas col. 9, ll. 1–26, fig. 5, digitally zoomed region C based on high resolution data (target image format) shown with ROI region B in low or “lower-than-high” intermediate resolution (second resolution)).
Duenyas further teaches wherein the target image format corresponds to spatially-fixed binned image data including the remosaiced first and second portions of image data (Duenyas col. 9, ll. 47–58, regions of interest allocated different binning factors (first and second portions of image data) are output to a stitching block and stitched together and converted to the same resolution (spatially-fixed binned image data) to present in a full frame video).
Woo teaches and the target image format is higher than the second resolution (Woo page 5002, left column, low-resolution focus pixel image (second portion with second resolution – a low-resolution), is super-resolved (resolution is changed to be higher) and deeply merged to result in a high-resolution HDR image (target image format higher than the second resolution)).
Therefore, taking the teachings of Kodukula and Duenyas together as a whole, it would have been obvious to a PHOSITA before the effective filing date of the claimed invention to have modified the operations of the visual sensing pipeline architecture of Kodukula to include the image sensor including ROI specific binning and transmission circuitry as taught by Duenyas at least because doing so would reduce the size of data to be processed in an image sensor (Duenyas col. 1, ll. 42–44).
Further, taking the teachings of Kodukula as modified by Duenyas and Woo together as a whole, it would have been obvious to a PHOSITA before the effective filing date of the claimed invention to have modified the operations of the visual sensing pipeline architecture of Kodukula to include the deep merging of a lower resolution portion to be that of the higher resolution target/output image as taught in Woo at least because doing so would provide a ghost-free natural-looking result and a high-quality image (Woo page 5002, upper right column).
Regarding claim 30, Kodukula teaches wherein the second resolution is lower than the first resolution (Kodukula pages 577–578, section 4.1, fig. 5, a pixel in a region of interest is forwarded/sampled and the others are skipped/binned, resulting a binning pattern, as shown in fig. 2a’s encoded frame, for pixels within a designated region of interest, where page 573 Abstract and fig. 1 teaches that with the disclosed rhythmic pixel regions, different regions are captured at different spatio-temporal resolutions, where relevant pixels in a scene of a fast moving object with high detail (first portion) has a higher resolution than a low detail slow or static object (second portion), thus second resolution lower than first, and where page 578, section 4.2 teaches the decoding is based on the metadata establishing the resolution respective to each section (ROI or not ROI)).
Regarding claims 36, 39, 42 and 45, with claim 36 as exemplary, Kodukula teaches generating an output image frame by processing the non-binned image data (Kodukula page 578, fig. 6, section 4.2, the decoder returns (generates) the pixel value that is sampled without interpolation by the spatial stride or temporal skip (thus non-binned) if the pixel is not specified to be within any of the specified region labels, and the returned pixel value is forwarded to the return path to return pixels to the vision application (output image frame)), but does not explicitly teach, where Duenyas teaches wherein the method further comprises, after remosaicing the first portion of the image data and the second portion of the image data (Duenyas col. 8, ll. 22–37, multiple pipeline processing respective to different ROIs with respective resolutions, thus at least a first and second portion of the image data, where each pipeline is digitally processed including respective remosaicing): and outputting, to a display device, the output image frame (Duenyas col. 9, ll. 6–11, PIP frame output for display on display 32).
Therefore, taking the teachings of Kodukula and Duenyas together as a whole, it would have been obvious to a PHOSITA before the effective filing date of the claimed invention to have modified the operations of the visual sensing pipeline architecture of Kodukula to include remosaicing digital processing, and frame determination and output as taught by Duenyas at least because doing so would reduce the size of data to be processed in an image sensor (Duenyas col. 1, ll. 42–44).
Claims 3, 13, 29 and 32 are rejected under 35 U.S.C. 103 as being unpatentable over Kodukula in view of Duenyas in view of Woo, as set forth above regarding claim 2 from which claim 3 depends, regarding claim 12 from which claim 13 depends, and regarding claim 28 from which claim 29 depends, and regarding claim 31 from which claim 32 depends, further in view of Zhou et al., US Patent No. 6,057,539 (herein “Zhou”).
Regarding claims 3, 13, 29 and 32, with claim 3 as illustrative, Kodukula as modified above does not explicitly teach, but Zhou teaches wherein the area of interest is identified (claims 3, 13 and 32)/the binning module identifies the area of interest (claim 29 only) based on a comparison between a first pixel intensity of image regions located within the area of interest and a second pixel intensity of image regions located outside the area of interest (Zhou col. 4, ll 31–57, brightness of pixels (pixel intensity) is determined using thresholding to separate pixels above a threshold with a high brightness, and those below a threshold with low brightness, where the resolution is set differently for the set of pixels above the threshold and those below the threshold, and where col. 8, ll. 20–44 teaches that the brighter pixels causing for a higher resolution correspond to a targeted object by the image sensor (region of interest)).
Therefore, taking the teachings of Kodukula as modified by Duenyas, and Zhou together as a whole, it would have been obvious to a “PHOSITA” before the effective filing date of the claimed invention to have modified the operations of the visual sensing pipeline architecture of Kodukula to include the distinction between bright pixels for determining higher resolution to track a targeted object as taught by Zhou at least because doing so would significantly enhance both the signal level and the signal to noise ratio of an image (Zhou col. 2, ll. 18–28).
Claims 34, 37, 40 and 43 are rejected under 35 U.S.C. 103 as being unpatentable over Kodukula in view of Duenyas in view of Woo, as set forth above regarding claims 1, 11, 21 and 26, which claims 34, 37, 40 and 43 respectively depend from, further in view of Yang et al., US Patent Application Publication No. US 2013/0051519 A1 (herein “Yang”).
Regarding claims 34, 37, 40 and 43, with claim 34 as exemplary, while Kodukula teaches wherein the first interpolation algorithm based on the first indication and the second interpolation algorithm based on the second indication (Kodukula fig. 6, pages 576–577, section 3.3, pixels are sampled (first and second interpolation algorithms) based on whether the pixel is in a particular region according to the EncMask having multiple values (first and second indications)), Kodukula does not explicitly teach, but Yang teaches are determined using a machine learning model (Yang ¶¶ 60 and 63, interpolator can be applied in two modes, a low resolution binning mode or a super-resolution second binning mode, the interpolator being trained (machine learning model)).
Therefore, taking the teachings of Kodukula as modified by Duenyas and Yang together as a whole, it would have been obvious to a PHOSITA before the effective filing date of the claimed invention to have modified the interpolation algorithms disclosed in Kodukula to be determined using a machine learning model as disclosed in Yang at least because doing so would increase output resolution of the image. See Yang ¶ 63.
Claims 35, 38, 41 and 44 are rejected under 35 U.S.C. 103 as being unpatentable over Kodukula in view of Duenyas in view of Woo, as set forth above regarding claims 1, 11, 21 and 26, which claims 35, 38, 41 and 44 respectively depend from, further in view of Zeng et al., “Inheriting Bayer's Legacy-Joint Remosaicing and Denoising for Quad Bayer Image Sensor,” arXiv:2303.13571v1 [cs.CV], March 23, 2023, https://doi.org/10.48550/arXiv.2303.13571 (herein “Zeng”).
Regarding claims 35, 38, 41 and 44, with claim 35 as exemplary, Kodukula as modified by Duenyas does not explicitly teach, but Zeng teaches wherein the target image format is a Bayer pattern image format (Zeng Fig. 3, section 4.1, remosaicing and denoising network outputting a target format of a Bayer image).
Therefore, taking the teachings of Kodukula as modified by Duenyas and Zeng together as a whole, it would have been obvious to a PHOSITA before the effective filing date of the claimed invention to have modified the target image format disclosed in Kodukula to be a Bayer pattern image format as disclosed in Zeng at least because doing so would allow for producing high-quality images under low-light conditions. See Zeng section 1.
Conclusion
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MICHELLE M. KOETH
Primary Examiner
Art Unit 2671
/MICHELLE M KOETH/Primary Examiner, Art Unit 2671
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