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 .
DETAILED ACTION
This Office Action is in response to the Amendment After Non-Final Rejection filed 08/17/2026. Claims 1-20 are pending and have been examined.
Response to Arguments
Applicant’s arguments with respect to claims 1-20 have been considered but are moot because the new ground of rejection does not rely on any reference applied in the prior rejection of record for any teaching or matter specifically challenged in the argument.
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.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claims 1, 3, 5-8, 10, 12-15 and 17-20 are rejected under 35 U.S.C. 103 as being unpatentable over Chen et al. (US 2022/0198607), herein Chen, in view of Thompson et al. (US 2017/0113611), herein Thompson.
Consider claim 1, Chen clearly teaches a computer-implemented method, (Fig. 3) the method comprising:
obtaining images; (Fig. 3: Source image 308 is obtained from video source 152, [0039], [0098].)
determining, for a first image, an interpolation sequence to interpolate the first image according to a fractional interpolation value, wherein the interpolation sequence comprises one or more combinations of respective integer interpolations and integer downsamplings; (Fig. 3: Based on the downsampling factor 124(1) a resampling factor numerator 212(1) and resampling factor denominator 214(1) are determined wherein the upsampling residual block 310 upsamples the source image 308 by the numerator and the downsampling residual block 360 downsamples upsampled image 318 by the denominator, [0097]-[0118].) and
interpolating the first image according to the interpolation sequence. (Fig. 3: Trained downsampling CNN 140(1) interpolates source image 308 into downsampled representation 248, [0098].)
However, Chen does not explicitly teach obtaining images from a plurality of image sensors positioned about a vehicle, wherein at least a subset of the image sensors operates at different resolutions; determining a common resolution based on the different resolutions; a first image from a first image sensor of the image sensors; and causing the first image to be at the common resolution.
In an analogous art, Thompson, which discloses a system for image processing, clearly teaches obtaining images from a plurality of image sensors positioned about a vehicle, (Figs. 1: Automotive vehicle 200 includes first camera 12 and second camera 14, wherein the resolution of the first camera 16 is higher than the resolution of the second camera 20, [0020]-[0024].) wherein at least a subset of the image sensors operates at different resolutions; determining a common resolution based on the different resolutions; a first image from a first image sensor of the image sensors; and causing the first image to be at the common resolution. (Fig. 6: The first camera resolution 16 is determined, e.g. 1080p, and second processing segment 28 processes frame 34 obtained from the second camera 14 to match the resolution of a frame captured by the first camera 12, [0024], [0045], [0049].)
Therefore, before the effective filing date of the claimed invention, it would have been obvious to one with ordinary skill in the art to modify the system of Chen by obtaining images from a plurality of image sensors positioned about a vehicle, wherein at least a subset of the image sensors operates at different resolutions; determining a common resolution based on the different resolutions; a first image from a first image sensor of the image sensors; and causing the first image to be at the common resolution, as taught by Thompson, for the benefit of capturing images of the environment surrounding a vehicle.
Consider claim 3, Chen combined with Thompson clearly teaches interpolation is performed via a convolutional engine or convolutional processor. (The down sampling CNN 140(1) is executed by general purpose processors, special-purpose processors, application-specific processors, or field-programmable gate arrays, [0181] Chen.)
Consider claim 5, Chen combined with Thompson clearly teaches interpolation of the first image comprises interpolating the first image and downsampling the first image using convolutional processing. (Fig. 3: Based on the downsampling factor 124(1) a resampling factor numerator 212(1) and resampling factor denominator 214(1) are determined wherein the upsampling residual block 310 upsamples the source image 308 by the numerator and the downsampling residual block 360 downsamples upsampled image 318 by the denominator, [0097]-[0118] Chen.)
Consider claim 6, Chen combined with Thompson clearly teaches values of the integer interpolations multiply to equal a numerator of the fractional interpolation value. (Resampling factor numerator 212(1), [0097] Chen.)
Consider claim 7, Chen combined with Thompson clearly teaches values of the integer downsamplings multiply to equal a denominator of the fractional interpolation value. (Resampling factor denominator 214(1), [0097] Chen.)
Consider claim 8, Chen clearly teaches a processor system, (Fig. 3) wherein the processor system is configured to:
obtain images; (Fig. 3: Source image 308 is obtained from video source 152, [0039], [0098].)
determine, for a first image, an interpolation sequence to int image according to a fractional interpolation value, wherein the interpolation sequence comprises one or more combinations of respective integer interpolations and integer downsamplings; (Fig. 3: Based on the downsampling factor 124(1) a resampling factor numerator 212(1) and resampling factor denominator 214(1) are determined wherein the upsampling residual block 310 upsamples the source image 308 by the numerator and the downsampling residual block 360 downsamples upsampled image 318 by the denominator, [0097]-[0118].) and
interpolate, using a convolutional engine of the vehicle processor system, (The down sampling CNN 140(1) is executed by general purpose processors, special-purpose processors, application-specific processors, or field-programmable gate arrays, [0181].) the first image according to the interpolation sequence. (Fig. 3: Trained downsampling CNN 140(1) interpolates source image 308 into downsampled representation 248, [0098].)
However, Chen does not explicitly teach obtaining images from a plurality of image sensors positioned about a vehicle, wherein at least a subset of the image sensors operates at different resolutions; determining a common resolution based on the different resolutions; a first image from a first image sensor of the image sensors; and causing the first image to be at the common resolution.
In an analogous art, Thompson, which discloses a system for image processing, clearly teaches obtaining images from a plurality of image sensors positioned about a vehicle, (Figs. 1: Automotive vehicle 200 includes first camera 12 and second camera 14, wherein the resolution of the first camera 16 is higher than the resolution of the second camera 20, [0020]-[0024].) wherein at least a subset of the image sensors operates at different resolutions; determining a common resolution based on the different resolutions; a first image from a first image sensor of the image sensors; and causing the first image to be at the common resolution. (Fig. 6: The first camera resolution 16 is determined, e.g. 1080p, and second processing segment 28 processes frame 34 obtained from the second camera 14 to match the resolution of a frame captured by the first camera 12, [0024], [0045], [0049].)
Therefore, before the effective filing date of the claimed invention, it would have been obvious to one with ordinary skill in the art to modify the system of Chen by obtaining images from a plurality of image sensors positioned about a vehicle, wherein at least a subset of the image sensors operates at different resolutions; determining a common resolution based on the different resolutions; a first image from a first image sensor of the image sensors; and causing the first image to be at the common resolution, as taught by Thompson, for the benefit of capturing images of the environment surrounding a vehicle.
Consider claim 10, Chen combined with Thompson clearly teaches interpolation is performed via a convolutional engine or convolutional processor. (The down sampling CNN 140(1) is executed by general purpose processors, special-purpose processors, application-specific processors, or field-programmable gate arrays, [0181] Chen.)
Consider claim 12, Chen combined with Thompson clearly teaches interpolation of the first image comprises interpolating the first image and downsampling the first image using convolutional processing. (Fig. 3: Based on the downsampling factor 124(1) a resampling factor numerator 212(1) and resampling factor denominator 214(1) are determined wherein the upsampling residual block 310 upsamples the source image 308 by the numerator and the downsampling residual block 360 downsamples upsampled image 318 by the denominator, [0097]-[0118] Chen.)
Consider claim 13, Chen combined with Thompson clearly teaches values of the integer interpolations multiply to equal a numerator of the fractional interpolation value. (Resampling factor numerator 212(1), [0097] Chen.)
Consider claim 14, Chen combined with Thompson clearly teaches values of the integer downsamplings multiply to equal a denominator of the fractional interpolation value. (Resampling factor denominator 214(1), [0097] Chen.)
Consider claim 15, Chen clearly teaches a non-transitory computer storage media storing instructions that when executed by a system of one or more processors, cause the one or more processors ([0180], [0181]) to:
obtain images; (Fig. 3: Source image 308 is obtained from video source 152, [0039], [0098].)
determine, for a first image, an interpolation sequence to interpolate the first image according to a fractional interpolation value, wherein the interpolation sequence comprises one or more combinations of respective integer interpolations and integer downsamplings; (Fig. 3: Based on the downsampling factor 124(1) a resampling factor numerator 212(1) and resampling factor denominator 214(1) are determined wherein the upsampling residual block 310 upsamples the source image 308 by the numerator and the downsampling residual block 360 downsamples upsampled image 318 by the denominator, [0097]-[0118].) and
interpolate, using a convolutional engine of the one or more processors, (The down sampling CNN 140(1) is executed by general purpose processors, special-purpose processors, application-specific processors, or field-programmable gate arrays, [0181].) first image according to the interpolation sequence. (Fig. 3: Trained downsampling CNN 140(1) interpolates source image 308 into downsampled representation 248, [0098].)
However, Chen does not explicitly teach obtaining images from a plurality of image sensors positioned about a vehicle, wherein at least a subset of the image sensors operates at different resolutions; determining a common resolution based on the different resolutions; a first image from a first image sensor of the image sensors; and causing the first image to be at the common resolution.
In an analogous art, Thompson, which discloses a system for image processing, clearly teaches obtaining images from a plurality of image sensors positioned about a vehicle, (Figs. 1: Automotive vehicle 200 includes first camera 12 and second camera 14, wherein the resolution of the first camera 16 is higher than the resolution of the second camera 20, [0020]-[0024].) wherein at least a subset of the image sensors operates at different resolutions; determining a common resolution based on the different resolutions; a first image from a first image sensor of the image sensors; and causing the first image to be at the common resolution. (Fig. 6: The first camera resolution 16 is determined, e.g. 1080p, and second processing segment 28 processes frame 34 obtained from the second camera 14 to match the resolution of a frame captured by the first camera 12, [0024], [0045], [0049].)
Therefore, before the effective filing date of the claimed invention, it would have been obvious to one with ordinary skill in the art to modify the system of Chen by obtaining images from a plurality of image sensors positioned about a vehicle, wherein at least a subset of the image sensors operates at different resolutions; determining a common resolution based on the different resolutions; a first image from a first image sensor of the image sensors; and causing the first image to be at the common resolution, as taught by Thompson, for the benefit of capturing images of the environment surrounding a vehicle.
Consider claim 17, Chen combined with Thompson clearly teaches interpolation is performed via a convolutional engine or convolutional processor. (The down sampling CNN 140(1) is executed by general purpose processors, special-purpose processors, application-specific processors, or field-programmable gate arrays, [0181] Chen.)
Consider claim 18, Chen combined with Thompson clearly teaches interpolation the first image comprises interpolating the first image and downsampling the first image using convolutional processing. (Fig. 3: Based on the downsampling factor 124(1) a resampling factor numerator 212(1) and resampling factor denominator 214(1) are determined wherein the upsampling residual block 310 upsamples the source image 308 by the numerator and the downsampling residual block 360 downsamples upsampled image 318 by the denominator, [0097]-[0118] Chen.)
Consider claim 19, Chen combined with Thompson clearly teaches values of the integer interpolations multiply to equal a numerator of the fractional interpolation value. (Resampling factor numerator 212(1), [0097] Chen.)
Consider claim 20, Chen combined with Thompson clearly teaches values of the integer downsamplings multiply to equal a denominator of the fractional interpolation value. (Resampling factor denominator 214(1), [0097] Chen.)
Claims 2, 9 and 16 are rejected under 35 U.S.C. 103 as being unpatentable over Chen et al. (US 2022/0198607) in view of Thompson et al. (US 2017/0113611) in view of Smirnov et al. (US 2020/0334787), herein Smirnov.
Consider claim 2, Chen combined with Thompson clearly teaches the interpolation sequence comprises a first combination including a first interpolation followed by a first downsampling. (Fig. 3, Chen)
However, Chen combined with Thompson does not explicitly teach the interpolation sequence comprises a first combination including a first interpolation followed by a first downsampling, and further comprises a second combination which receives output from the first combination, wherein the second combination comprises a second interpolation followed by a second downsampling.
In an analogous art, Smirnov, which discloses a system for image processing, clearly teaches the interpolation sequence comprises a first combination including a first interpolation followed by a first downsampling, and further comprises a second combination which receives output from the first combination, wherein the second combination comprises a second interpolation followed by a second downsampling. (Fig. 5, 7: Horizontal interpolation and horizontal decimation is performed then vertical interpolation and vertical decimation are performed, [0079]-[0083], [0102].)
Therefore, before the effective filing date of the claimed invention, it would have been obvious to one with ordinary skill in the art to modify the system of Chen combined with Thompson by the interpolation sequence comprises a first combination including a first interpolation followed by a first downsampling, and further comprises a second combination which receives output from the first combination, wherein the second combination comprises a second interpolation followed by a second downsampling, as taught by Smirnov, to achieve the predictable result of downsampling the image.
Consider claim 9, Chen combined with Thompson and Smirnov clearly teaches the interpolation sequence comprises a first combination including a first interpolation followed by a first downsampling, and further comprises a second combination which receives output from the first combination, wherein the second combination comprises a second interpolation followed by a second downsampling. (Fig. 5, 7: Horizontal interpolation and horizontal decimation is performed then vertical interpolation and vertical decimation are performed, [0079]-[0083], [0102] Smirnov.)
Consider claim 16, Chen combined with Thompson and Smirnov clearly teaches the interpolation sequence comprises a first combination including a first interpolation followed by a first downsampling, and further comprises a second combination which receives output from the first combination, wherein the second combination comprises a second interpolation followed by a second downsampling. (Fig. 5, 7: Horizontal interpolation and horizontal decimation is performed then vertical interpolation and vertical decimation are performed, [0079]-[0083], [0102] Smirnov.)
Claims 4 and 11 are rejected under 35 U.S.C. 103 as being unpatentable over Chen et al. (US 2022/0198607) in view of Thompson et al. (US 2017/0113611) in view of Liu et al. (US 2023/0108629), herein Liu.
Consider claim 4, Chen combined with Thompson clearly teaches the convolutional engine or convolutional processor. ([0181] Chen)
However, Chen combined with Thompson does not explicitly teach the convolutional engine or convolutional processor comprises a matrix of computation elements, and wherein the matrix of computational elements is non-systolic.
In an analogous art, Liu, which discloses a convolutional neural network system, clearly teaches the convolutional engine or convolutional processor comprises a matrix of computation elements, and wherein the matrix of computational elements is non-systolic. (Fig. 8A: Array 650 may be non-systolic, [0194].)
Therefore, before the effective filing date of the claimed invention, it would have been obvious to one with ordinary skill in the art to modify the system of Chen combined with Thompson by the convolutional engine or convolutional processor comprises a matrix of computation elements, and wherein the matrix of computational elements is non-systolic, wherein the second combination comprises a second interpolation followed by a second downsampling, as taught by Liu, for the benefit of accelerating matrix multiplication.
Consider claim 11, Chen combined with Thompson and Liu clearly teaches the convolutional engine or convolutional processor comprises a matrix of computation elements, and wherein the matrix of computational elements is non-systolic. (Fig. 8A: Array 650 may be non-systolic, [0194] Liu.)
Conclusion
In the case of amending the claimed invention, applicant is respectfully requested to indicate the portion(s) of the specification which dictate(s) the structure relied on for proper interpretation and also to verify and ascertain the metes and bounds of the claimed invention.
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to JOHN R SCHNURR whose telephone number is (571)270-1458. The examiner can normally be reached M-F 6a-4p.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Brian Pendleton can be reached at (571)272-7527. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/JOHN R SCHNURR/ Primary Examiner, Art Unit 2425
Prosecution Insights