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 .
Response to Arguments
The reply filed on 17 March 2026 has been entered. Applicant’s arguments with respect to claims 1-20 have been considered but are moot in view of new ground(s) of rejection caused by the amendments.
Claims 1-20 are pending in this application and have been considered below.
Information Disclosure Statement
The IDS dated 14 December 2023 that has been previously considered remains placed in the application file.
Specification - Drawings
Acknowledgement is made of the color drawings submitted 14 December 2023 in this application. Applicants are reminded that, absent a successful petition, the black and white drawings submitted on 14 December 2023 will be used. No petition is currently on file.
Claim Interpretation
Claims 8 and 11 have been amended. The interpretation of claims 8 and 11 under 35 USC 112(f) is withdrawn.
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.
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 1-20 (all claims) are rejected under 35 U.S.C. 103 as obvious over US Patent Publication 2024 0233104 A1, (Kozma et al.) in view of US Patent Publication 2024 0331362 A1, (Ouyang et al.) in view of Great Britain Patent Publication GB 2614763 A, (Elliot) (Published 19 July 2023). The references are listed in a PTO-892 from the Office Action in which they are first used.
PNG
media_image1.png
394
582
media_image1.png
Greyscale
Claim 1
Regarding Claim 1, Kozma et al. teach a method ("a method for determining whether an input image is sufficiently sharp," paragraph [0001])comprising:
[AltContent: textbox (Kozma et al. Fig. 8, showing a chart with blur parameter results.)]obtaining, using at least one processor of an electronic device, multiple input image frames generated during a multi-frame capture operation, each input image frame exhibiting an amount of blur ("the input images 10 can be recorded by a camera of a self-driving car in a nighttime or in bad weather conditions," paragraph [0033] );
determining, using the at least one processor , a blurriness score for each of the input image frames ("In said first scenario, the blur score threshold value is a predetermined value, and in said second scenario, the blur score threshold value is determined by further steps based on the input image 10 itself," paragraph [0029]);
a first neural network ("neural network system can remove inhomogenous spreading or warping effects from images, thus mitigating image degradation effects, such as blurring," paragraph [0008]) that receives the input image frames as input ("the method further comprises globally and locally aligning the plurality of images," paragraph [0012]); and
wherein generating the final sharp image comprises: selecting, as a base frame, the input image frame that exhibits a least amount of blur based on the blurriness scores ("Based on the decision, input images 10 having an insufficient sharpness can be labelled (flagged) accordingly or even disregarded for certain applications, or a warning signal can be generated. Furthermore, the level of sufficient sharpness can be set for each application independently," paragraph [0028] where labeling teaches selecting a frame with a maximum amount of blur).
[AltContent: textbox (Ouyang et al. Fig. 3, showing a system for sharpening images.)]
PNG
media_image2.png
516
658
media_image2.png
Greyscale
Kozma et al. is not relied upon to explicitly teach all of generating sharp frames.
However, Ouyang et al. teach generating, using the at least one processor, sharp denoised frames using the input image frames ("the exemplary network can produce images that are sharper and have less noise than the image restored by the Lucky Imaging technique and TWGAN," paragraph [0126]) and
generating, using the at least one processor , a final sharp image based on the sharp denoised frames and the blurriness scores of the input image frames ("These visual observations validate that the exemplary method can be used to correct images degraded by turbulence and provide perceptually better results than the lucky region technique and the TSR-WGAN network," paragraph [0127]).
[AltContent: textbox (Elliot Fig. 11, showing a system for sharpening images using residuals.)]
PNG
media_image3.png
643
522
media_image3.png
Greyscale
Kozma et al. and Ouyang et al. are not relied upon to explicitly teach all of determining a residual to add to the base frame.
However, Elliot teach generating the final sharp image using a second neural network that receives the sharp denoised frames as input and determines a residual to add to the base frame ("upscaling the decoded first component set, and takes new residual data as further inputs in order to obtain output data to be up-sampled in a possible following step," page 12, lines 1-3, and "The trainable upsampler 1105 may be implemented as a convolutional neural network with one or more filters, each filter having a set of trainable filter parameters, page 32, lines 6-8 and "Owing to the lower resolution initial echelon index image and the up-sampling process, the predicted image typically corresponds to a smoothed or blurred picture, page 12, lines 15-17).
Therefore, taking the teachings of Kozma et al., Ouyang et al. and Elliot as a whole, it would have been obvious to a person having ordinary skill in the art before the time of the effective filing date of the claimed invention of the instant application to modify “Method, Data Processing System, Computer Program Product and computer Readable Medium for Determining Image Sharpness” as taught by Kozma et al. to use “AI systema and Method to Enhance Images acquired through Random Medium” as taught by Ouyang et al. and “Upsampling Filter for applying a predicted average modification” by Elliot The suggestion/motivation for doing so would have been that, “an artificial neural network is disclosed, along with the associated neural network system, that can learn or account for characteristics associated with spatial domain loss component and a frequency domain loss component, e.g., via Fourier space-loss function” as noted by the Ouyang et al. disclosure in paragraph [0005], and “Owing to the lower resolution initial echelon index image and the up-sampling process, the predicted image typically corresponds to a smoothed or blurred picture” on page 12, lines 15-17 of Elliot, which also motivates combination because the combination would predictably have a higher efficiency as there is a reasonable expectation that sharpness deficits will need to be corrected and pre-modified neural networks are faster and more efficient; and/or because doing so merely combines prior art elements according to known methods to yield predictable results.
The rejection of method claim 1 above applies mutatis mutandis to the corresponding limitations of apparatus claim 8 and system claim 15 while noting that the rejection above cites to both device and method disclosures. Claims 8 and 15 are mapped below for clarity of the record and to specify any new limitations not included in claim 1.
Claim 2
Regarding claim 2, Kozma et al. teach the method of Claim 1, wherein determining the blurriness score for each of the input image frames comprises:
determining an average of Fourier weights of the input image frame in a spatial frequency domain ("The image frames are transformed by a Fast Fourier Transform (FFT), and a ratio between an accumulated mid to high frequency amplitude and an accumulated low frequency amplitude is determined. This ratio is used to determine the image sharpness as low ratios indicate that more low frequency components are present in the image, thus the image may appear blurry," paragraph [0004]); and
assigning the blurriness score to the input image frame based on the determined average ("the histogram values are generated from the logarithm of the respective two dimensional amplitude spectrum by radial averaging, i.e., for each frequency bins the corresponding histogram value is an average amplitude (intensity) within the frequency range (concentric ring)," paragraph [0052]).
Claim 3
Regarding claim 3, Kozma et al. teach the method of Claim 1, wherein
the first neural network is trained using (i) multiple training images that exhibit blur and (ii) one or more first loss functions ("adjusting, by a processor, a weighting parameter of the artificial neural network based on the loss function to generate a trained neural network, wherein the trained neural network is configured to enhance actual images taken in a turbulent medium," paragraph [0010]).
Claim 4
Regarding claim 4, Kozma et al. teach the method of Claim 3, as noted above.
Kozma et al. is not relied upon to explicitly teach all of residual networks.
However, Ouyang et al. teach wherein the second neural network comprises a residual network having an encoder-decoder architecture ("A second iteration (326) of the single-step DFT may be performed on the localized patches (324) to minimize misalignments to the temporal average. The locally aligned patches (328) may then be used as the input of a convolutional neural network (e.g., 308, shown as 308') to a set of weight maps (330) that corresponded to the images in the sequence," paragraph [0075] and "This may be followed by an encoding convolution layer that downsamples the images and doubles the number of channels. This is performed by using 64 convolution filters with a kernel size of 3x3x3 and a stride of lx2x2," paragraph [0094], and "A decoder convolution layer (collectively shown as 344) may then then used to upsample the image and halve the number of channels," paragraph [0096]).
Kozma et al., Ouyang et al. and Elliot are combined as per claim 1.
Claim 5
Regarding claim 5, Kozma et al. teach the method of Claim 4, as noted above.
Kozma et al. is not relied upon to explicitly teach all of loss functions.
However, Ouyang et al. teach wherein the second neural network is trained using (i) training frames processed by the first neural network and (ii) one or more second loss functions ("adjusting, by a processor, a weighting parameter of the artificial neural network based on the loss function to generate a trained neural network, wherein the trained neural network is configured to enhance actual images taken in a turbulent medium," paragraph [0010]).
Kozma et al., Ouyang et al. and Elliot are combined as per claim 1.
Claim 6
Regarding claim 6, Kozma et al. teach the method of Claim 5, wherein each of the training frames is blurred using a random jitter before being processed by the first neural network ("Preferably, a blurred image 20 (see FIG. 2 for an example) is generated from the input image 10, preferably by a Gaussian blurring, wherein the Gaussian function has a standard deviation in the range of0.01-2," paragraph [0043] where gaussian functions produce jitter).
Claim 7
Regarding claim 7, Kozma et al. teach the method of Claim 1, as noted above.
Kozma et al. is not relied upon to explicitly teach all of demosaicing operations.
However, Ouyang et al. teach further comprising:
performing demosaicing and registration operations on the input image frames before the blurriness score for each of the input image frames is determined ("employed to fuse input image sequences that are aligned together. Multi-frame data 304 can provide additional scene detail than a single image," paragraph [0073] where registration is an alignment operation).
Kozma et al., Ouyang et al. and Elliot are combined as per claim 1.
Claim 8
Regarding claim 8, Kozma et al. teach an electronic device comprising:
at least one processor ("a data processing system comprising means for carrying out the steps of the method according to the invention," paragraph [0064]) configured to: obtain multiple input image frames generated during a multi-frame capture operation, each input image frame exhibiting an amount of blur ("the input images 10 can be recorded by a camera of a self-driving car in a nighttime or in bad weather conditions," paragraph [0033] );
determine a blurriness score for each of the input image frames ("In said first scenario, the blur score threshold value is a predetermined value, and in said second scenario, the blur score threshold value is determined by further steps based on the input image 10 itself," paragraph [0029]);
a first neural network ("neural network system can remove inhomogenous spreading or warping effects from images, thus mitigating image degradation effects, such as blurring," paragraph [0008]) configured to receive the input image frames as input ("the method further comprises globally and locally aligning the plurality of images," paragraph [0012]); and
wherein to generate the final sharp image the at least one processor is configured to:
select, as a base frame, the input image frame that exhibits a least amount of blur based on the blurriness scores ("Based on the decision, input images 10 having an insufficient sharpness can be labelled (flagged) accordingly or even disregarded for certain applications, or a warning signal can be generated. Furthermore, the level of sufficient sharpness can be set for each application independently," paragraph [0028] where labeling teaches selecting a frame with a maximum amount of blur).
Kozma et al. is not relied upon to explicitly teach all of generate sharp denoised frames.
However, Ouyang et al. teach generate sharp denoised frames using the input image frames ("the exemplary network can produce images that are sharper and have less noise than the image restored by the Lucky Imaging technique and TWGAN," paragraph [0126]) and
generate a final sharp image based on the sharp denoised frames and the blurriness scores of the input image frames ("These visual observations validate that the exemplary method can be used to correct images degraded by turbulence and provide perceptually better results than the lucky region technique and the TSR-WGAN network," paragraph [0127]).
Kozma et al. and Ouyang et al. are not relied upon to explicitly teach all of residuals.
However, Elliot teach generate the final sharp image using a second neural network configured to receive the sharp denoised frames as input and determines a residual to add to the base frame ("upscaling the decoded first component set, and takes new residual data as further inputs in order to obtain output data to be up-sampled in a possible following step," page 12, lines 1-3, and "The trainable upsampler 1105 may be implemented as a convolutional neural network with one or more filters, each filter having a set of trainable filter parameters, page 32, lines 6-8 and "Owing to the lower resolution initial echelon index image and the up-sampling process, the predicted image typically corresponds to a smoothed or blurred picture, page 12, lines 15-17).
Kozma et al., Ouyang et al. and Elliot are combined as per claim 1.
Claim 9
Regarding claim 9, Kozma et al. teach the electronic device of Claim 8, wherein, to determine the blurriness score for each of the input image frames, the at least one processor is configured to:
determine an average of Fourier weights of the input image frame in a spatial frequency domain ("The image frames are transformed by a Fast Fourier Transform (FFT), and a ratio between an accumulated mid to high frequency amplitude and an accumulated low frequency amplitude is determined. This ratio is used to determine the image sharpness as low ratios indicate that more low frequency components are present in the image, thus the image may appear blurry," paragraph [0004]); and
assign the blurriness score to the input image frame based on the determined average ("the histogram values are generated from the logarithm of the respective two dimensional amplitude spectrum by radial averaging, i.e., for each frequency bins the corresponding histogram value is an average amplitude (intensity) within the frequency range (concentric ring)," paragraph [0052]).
Claim 10
Regarding claim 10, Kozma et al. teach the electronic device of Claim 8, wherein, the first neural network trained using (i) multiple training images that exhibit blur and (ii) one or more first loss functions ("adjusting, by a processor, a weighting parameter of the artificial neural network based on the loss function to generate a trained neural network, wherein the trained neural network is configured to enhance actual images taken in a turbulent medium," paragraph [0010]).
Claim 11
Regarding claim 11, Kozma et al. teach the electronic device of Claim 10, as noted above.
Kozma et al. is not relied upon to explicitly teach all of neural networks.
However, Ouyang et al. teach wherein,
the second neural network comprises a residual network having an encoder-decoder architecture ("A second iteration (326) of the single-step DFT may be performed on the localized patches (324) to minimize misalignments to the temporal average. The locally aligned patches (328) may then be used as the input of a convolutional neural network (e.g., 308, shown as 308') to a set of weight maps (330) that corresponded to the images in the sequence," paragraph [0075] and "This may be followed by an encoding convolution layer that downsamples the images and doubles the number of channels. This is performed by using 64 convolution filters with a kernel size of 3x3x3 and a stride of lx2x2," paragraph [0094], and "A decoder convolution layer (collectively shown as 344) may then then used to upsample the image and halve the number of channels," paragraph [0096]).
Kozma et al., Ouyang et al. and Elliot are combined as per claim 1.
Claim 12
Regarding claim 12, Kozma et al. teach the electronic device of Claim 11, as noted above.
Kozma et al. is not relied upon to explicitly teach all of neural networks.
However, Ouyang et al. teach wherein the second neural network is trained using (i) training frames processed by the first neural network and (ii) one or more second loss functions ("adjusting, by a processor, a weighting parameter of the artificial neural network based on the loss function to generate a trained neural network, wherein the trained neural network is configured to enhance actual images taken in a turbulent medium," paragraph [0010]).
Kozma et al., Ouyang et al. and Elliot are combined as per claim 1.
Claim 13
Regarding claim 13, Kozma et al. teach the electronic device of Claim 12, wherein each of the training frames is blurred using a random jitter before being processed by the first neural network ("Preferably, a blurred image 20 (see FIG. 2 for an example) is generated from the input image 10, preferably by a Gaussian blurring, wherein the Gaussian function has a standard deviation in the range of0.01-2," paragraph [0043] where gaussian functions produce jitter).
Claim 14
Regarding claim 14, Kozma et al. teach the electronic device of Claim 8, as noted above.
Kozma et al. is not relied upon to explicitly teach all of demosaicing.
However, Ouyang et al. teach wherein the at least one processor is further configured to perform demosaicing and registration operations on the input image frames before determining the blurriness score for each of the input image frames ("employed to fuse input image sequences that are aligned together. Multi-frame data 304 can provide additional scene detail than a single image," paragraph [0073] where registration is an alignment operation).
Kozma et al., Ouyang et al. and Elliot are combined as per claim 1.
Claim 15
Regarding claim 15, Kozma et al. teach a non-transitory machine-readable medium containing instructions that when executed cause at least one processor of an electronic device ("a data processing system comprising means for carrying out the steps of the method according to the invention," paragraph [0064]) to:
obtain multiple input image frames generated during a multi-frame capture operation, each input image frame exhibiting an amount of blur ("the input images 10 can be recorded by a camera of a self-driving car in a nighttime or in bad weather conditions," paragraph [0033] );
determine a blurriness score for each of the input image frames ("In said first scenario, the blur score threshold value is a predetermined value, and in said second scenario, the blur score threshold value is determined by further steps based on the input image 10 itself," paragraph [0029]);
a first neural network ("neural network system can remove inhomogenous spreading or warping effects from images, thus mitigating image degradation effects, such as blurring," paragraph [0008]) configured to receive the input image frames as input ("the method further comprises globally and locally aligning the plurality of images," paragraph [0012]); and
wherein the instructions that when executed cause the at least one processor to generate the final sharp image comprise:
instructions that when executed cause the at least one processor to: select, as a base frame, the input image frame that exhibits a least amount of blur based on the blurriness scores ("Based on the decision, input images 10 having an insufficient sharpness can be labelled (flagged) accordingly or even disregarded for certain applications, or a warning signal can be generated. Furthermore, the level of sufficient sharpness can be set for each application independently," paragraph [0028] where labeling teaches selecting a frame with a maximum amount of blur).
Kozma et al. is not relied upon to explicitly teach all of generate denoised frames.
However, Ouyang et al. teach generate sharp denoised frames using the input image frames ("the exemplary network can produce images that are sharper and have less noise than the image restored by the Lucky Imaging technique and TWGAN," paragraph [0126]) and
generate a final sharp image based on the sharp denoised frames and the blurriness scores of the input image frames ("These visual observations validate that the exemplary method can be used to correct images degraded by turbulence and provide perceptually better results than the lucky region technique and the TSR-WGAN network," paragraph [0127]).
Kozma et al. and Ouyang et al. are not relied upon to explicitly teach all of residuals.
However, Elliot teach generate the final sharp image using a second neural network configured to receive the sharp denoised frames as input and determine a residual to add to the base frame ("upscaling the decoded first component set, and takes new residual data as further inputs in order to obtain output data to be up-sampled in a possible following step," page 12, lines 1-3, and "The trainable upsampler 1105 may be implemented as a convolutional neural network with one or more filters, each filter having a set of trainable filter parameters, page 32, lines 6-8 and "Owing to the lower resolution initial echelon index image and the up-sampling process, the predicted image typically corresponds to a smoothed or blurred picture, page 12, lines 15-17).
Kozma et al., Ouyang et al. and Elliot are combined as per claim 1.
Claim 16
Regarding claim `6, Kozma et al. teach the non-transitory machine-readable medium of Claim 15, wherein the instructions that when executed cause the at least one processor to determine the blurriness score for each of the input image frames comprise:
instructions that when executed cause the at least one processor to:
determine an average of Fourier weights of the input image frame in a spatial frequency domain ("The image frames are transformed by a Fast Fourier Transform (FFT), and a ratio between an accumulated mid to high frequency amplitude and an accumulated low frequency amplitude is determined. This ratio is used to determine the image sharpness as low ratios indicate that more low frequency components are present in the image, thus the image may appear blurry," paragraph [0004]); and
assign the blurriness score to the input image frame based on the determined average ("the histogram values are generated from the logarithm of the respective two dimensional amplitude spectrum by radial averaging, i.e., for each frequency bins the corresponding histogram value is an average amplitude (intensity) within the frequency range (concentric ring)," paragraph [0052]).
Claim 17
Regarding claim 17, Kozma et al. teach the non-transitory machine-readable medium of Claim 15, wherein
the first neural network is trained using (i) multiple training images that exhibit blur and (ii) one or more first loss functions ("adjusting, by a processor, a weighting parameter of the artificial neural network based on the loss function to generate a trained neural network, wherein the trained neural network is configured to enhance actual images taken in a turbulent medium," paragraph [0010]).
Claim 18
Regarding claim 18, Kozma et al. teach the non-transitory machine-readable medium of Claim 17, as noted above.
Kozma et al. is not relied upon to explicitly teach all of neural networks.
However, Ouyang et al. teach wherein the second neural network comprises a residual network having an encoder-decoder architecture ("A second iteration (326) of the single-step DFT may be performed on the localized patches (324) to minimize misalignments to the temporal average. The locally aligned patches (328) may then be used as the input of a convolutional neural network (e.g., 308, shown as 308') to a set of weight maps (330) that corresponded to the images in the sequence," paragraph [0075] and "This may be followed by an encoding convolution layer that downsamples the images and doubles the number of channels. This is performed by using 64 convolution filters with a kernel size of 3x3x3 and a stride of lx2x2," paragraph [0094], and "A decoder convolution layer (collectively shown as 344) may then then used to upsample the image and halve the number of channels," paragraph [0096]).
Kozma et al., Ouyang et al. and Elliot are combined as per claim 1.
Claim 19
Regarding claim 19, Kozma et al. teach the non-transitory machine-readable medium of Claim 18, as noted above.
Kozma et al. is not relied upon to explicitly teach all of neural networks.
However, Ouyang et al. teach wherein the second neural network is trained using (i) training frames processed by the first neural network and (ii) one or more second loss functions ("adjusting, by a processor, a weighting parameter of the artificial neural network based on the loss function to generate a trained neural network, wherein the trained neural network is configured to enhance actual images taken in a turbulent medium," paragraph [0010]).
Kozma et al., Ouyang et al. and Elliot are combined as per claim 1.
Claim 20
Regarding claim 20, Kozma et al. teach the non-transitory machine-readable medium of Claim 15, as noted above.
Kozma et al. is not relied upon to explicitly teach all of demosaicing.
However, Ouyang et al. teach further containing instructions that when executed cause the at least one processor to perform demosaicing and registration operations on the input image frames before determining the blurriness score for each of the input image frames ("employed to fuse input image sequences that are aligned together. Multi-frame data 304 can provide additional scene detail than a single image," paragraph [0073] where registration is an alignment operation).
Kozma et al., Ouyang et al. and Elliot are combined as per claim 1.
Reference Cited
The prior art made of record and not relied upon is considered pertinent to applicant’s disclosure.
US Patent Publication 2025 0086752 A1 to Patel et al. discloses process can include performing a depthwise convolution operation on image data and a depth-wise convolutional filter with pre-determined parameter values to obtain a plurality of color channels for the image data; performing a convolution operation on the plurality of color channels to obtain a processed plurality of color channels; arranging the processed plurality of color channels into a demosaiced image; and outputting the demosaiced image.
US Patent Publication 2024 0281921 A1 to Nossek et al. discloses leveraging neural networks (e.g., convolutional neural networks (CNNs)) for image restoration tasks (e.g., for demosaicing tasks) are described. In certain aspects, Mixture of Experts (MoE) techniques may be employed, where multiple different expert networks are used to divide a problem space (e.g., image reconstruction tasks) into homogenous regions. For example, each MoE module may reconstruct a certain problem in an image, and a gating component may activate certain MoE modules to provide a reconstructed image. In some aspects, training and optimization techniques are described for each expert of the MoE architecture, to increase individual performance (e.g., a sub-task for each expert of an image processing system may be imposed in a residual manner, a gating function may be trained, etc.).
Conclusion
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 HEATH E WELLS whose telephone number is (703)756-4696. The examiner can normally be reached Monday-Friday 8:00-4:00.
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, Ms. Jennifer Mehmood can be reached on 571-272-2976. 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.
/H.E.W/Examiner, Art Unit 2664
Date: 23 April 2026
/JENNIFER MEHMOOD/Supervisory Patent Examiner, Art Unit 2664
Prosecution Insights