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
Applicant’s arguments with respect to claim(s) 1, 9, 21 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.
Claim(s) 1-13, 21-27 is/are rejected under 35 U.S.C. 103 as being unpatentable over Xiao (US 2021/0366082) in view of QIN (US 2021/0266496).
As to claim 1, Xiao discloses a method, comprising:
obtaining lower resolution (LR) images (Fig. 4A, para. 0025, 0039);
generating, with a first neural network, one or more neural network parameters based, at least in part, on image data associated with the LR images at a first frame rate (Figs. 4A, 4B, para. 0041, 0044, 0047, 0029, 0030, 0033);
providing the one or more neural network parameters to a second neural network different from the first neural network (Figs. 4A, 4B, para. 0044, 0047); and
generating, with the second neural network, high resolution (HR) images based, at least in part, on the LR images (Figs. 4A, 4B, para. 0049).
Although Xiao describes multiple well-known techniques for real-time rendering with different rates (paragraphs 0027, 0028), Xiao is silent regarding generating, with the second neural network at a different second frame rate, high resolution (HR) images based, at least in part, on the LR images.
QIN teaches generating, with the second neural network at a different second frame rate, high resolution (HR) images based, at least in part, on the LR images (Fig. 3, item 036, para. 0030, 0031).
It would have been obvious to one of ordinary skill in the art to incorporate QIN’s teaching of generating, with the second neural network at a different second frame rate, high resolution (HR) images based, at least in part, on the LR images into Xiao since doing so would merely combine prior art elements according to known methods to yield predictable results, and greatly speed the processing frame rates for producing video super-resolution video as suggested by QIN, at paragraph 0031.
As to claim 2, the combination of Xiao and QIN discloses the method of claim 1, wherein the one or more neural network parameters are generated separately from generating the HR images with neural network (Xiao, Figs. 4A, 4B, para. 0044, 0047).
As to claim 3, the combination of Xiao and QIN discloses the method of claim 2, wherein at least two HR images are generated at the different second frame rate that is different than the first frame rate associated with one or more frames of a sequence that are used to generate the one or more neural network parameters (Xiao, para. 0029, 0030, 0033; QIN, Fig. 3, item 036, para. 0030, 0031).
As to claim 4, the combination of Xiao and QIN discloses the method of claim 1, further comprising: obtaining a video sequence of the LR images, and wherein the one or more neural network parameters are updated at a predetermined image interval along the video sequence (Xiao, para. 0029, 0030, 0032, 0044, 0047).
As to claim 5, the combination of Xiao and QIN discloses the method of claim 1, wherein at least one of the one or more neural network parameters are generated to be used for a target image and enabled to be used on one or more images near the target image along a video sequence of the images (Xiao, para. 0029, 0030, 0032, 0044, 0047).
As to claim 6, the combination of Xiao and QIN discloses the method of claim 1, further comprising: inputting image data based, at least in part, on one or more of the LR images into a neural network to generate the one or more neural network parameters (Xiao, Figs. 4A, 4B, para. 0025, 0039, 0041, 0044, 0047).
As to claim 7, the combination of Xiao and QIN discloses the method of claim 6, wherein the image data is at least associated with one or more sample locations in the LR images (Xiao, Figs. 2, 3, 4A, para.0036-0038, 0043).
As to claim 8, the combination of Xiao and QIN discloses the method of claim 1, wherein at least one of the one or more neural network parameters is generated using the image data to generate weights (Xiao, para. 0047).
As to claim 9, Xiao discloses a system for image processing, comprising:
at least one processor (Fig. 13, para. 0073); and
at least one memory (Fig. 13, para. 0073) communicatively coupled to the at least one processor and storing a video sequence of low resolution (LR) images (para. 0029, 0030, 0032), the at least one processor being configured to operate by:
generating higher resolution (HR) images comprising inputting image data of the LR images into a second neural network (Figs. 4A, 4B, para. 0049);
generating, with a first neural network different from the second neural network, one or more neural network parameters based, at least in part on image data associated with the LR images at a first frame rate (Figs. 4A, 4B, para. 0041, 0044, 0047, 0029, 0030, 0033); and
providing the one or more neural network parameters to the second neural network to be used to generate the HR images (Figs. 4A, 4B, para. 0044, 0047).
Although Xiao describes multiple well-known techniques for real-time rendering with different rates (paragraphs 0027, 0028), Xiao is silent regarding generating, with the second neural network at a different second frame rate, high resolution (HR) images based, at least in part, on the LR images.
QIN teaches generating, with the second neural network at a different second frame rate, high resolution (HR) images based, at least in part, on the LR images (Fig. 3, item 036, para. 0030, 0031).
It would have been obvious to one of ordinary skill in the art to incorporate QIN’s teaching of generating, with the second neural network at a different second frame rate, high resolution (HR) images based, at least in part, on the LR images into Xiao since doing so would merely combine prior art elements according to known methods to yield predictable results, and greatly speed the processing frame rates for producing video super-resolution video as suggested by QIN, at paragraph 0031.
As to claim 10, the combination of Xiao and QIN discloses the system of claim 9, wherein the image data is generated in association with at least one neural network (Xiao, Figs. 4A, 4B, para. 0041, 0044, 0047, 0049);
As to claim 11, the combination of Xiao and QIN discloses the system of claim 9, wherein the image data comprises pixel values representing at least part of an image (Xiao, Figs. 4A, 4B, para. 0041, 0044, 0047, 0049).
As to claim 12, the combination of Xiao and QIN discloses the system of claim 9, wherein the generating the one or more neural network parameters comprises inputting the image data into at least one neural network (Xiao, Figs. 4A, 4B, para. 0041, 0044, 0047, 0049).
As to claim 13, the combination of Xiao and QIN discloses the system of claim 9, wherein the second neural network uses at least one of available one or more neural network parameters rather than waiting for other one or more neural network parameters to be generated (Xiao, para. 0029, 0030, 0032, 0044, 0047).
As to claim 21, Xiao discloses a non-transitory computer-readable medium (Fig. 13, para. 0073) comprising instructions that, when performed by at least one processor of a computing device, cause the computing device to at least:
cause a first neural network to generate one or more neural network parameters based on a video sequence of low resolution (LR) images at a first frame rate (Figs. 4A, 4B, para. 0041, 0044, 0047, 0029, 0030, 0033, e.g., feature re-weighting network);
provide the one or more neural network parameters to a second neural network different from the first neural network, the second neural network to perform high resolution (HR) image generation (Figs. 4A, 4B, para. 0044, 0047); and
cause the second neural network to generate HR images based on the LR images (Figs. 4A, 4B, para. 0049).
Although Xiao describes multiple well-known techniques for real-time rendering with different rates (paragraphs 0027, 0028 , Xiao is silent regarding generating, with the second neural network at a different second frame rate, high resolution (HR) images based, at least in part, on the LR images.
QIN teaches generating, with the second neural network at a different second frame rate, high resolution (HR) images based, at least in part, on the LR images (Fig. 3, item 036, para. 0030, 0031).
It would have been obvious to one of ordinary skill in the art to incorporate QIN’s teaching of generating, with the second neural network at a different second frame rate, high resolution (HR) images based, at least in part, on the LR images into Xiao since doing so would merely combine prior art elements according to known methods to yield predictable results, and greatly speed the processing frame rates for producing video super-resolution video as suggested by QIN, at paragraph 0031.
As to claim 22, the combination of Xiao and QIN discloses the non-transitory computer-readable medium of The non-transitory computer-readable medium of wherein the instructions that, when performed by the at least one processor of the computing device, cause the computing device to further cause the second neural network to generate HR images based on the LR images at the different second frame rate that is different than the first frame rate associated with the video sequence (Xiao, para. 0029, 0030, 0033, QIN, Fig. 3, item 036, para. 0030, 0031).
As to claim 23, the combination of Xiao and QIN discloses the non-transitory computer-readable medium of The non-transitory computer-readable medium of wherein the instructions that, when performed by the at least one processor of the computing device, cause the computing device to generate the one or more neural network parameters separately from generating the HR images with the second neural network (Xiao, Figs. 4A, 4B, para. 0044, 0047).
As to claim 24, the combination of Xiao and QIN discloses the non-transitory computer-readable medium of claim 21, wherein the first neural network comprises a convolutional neural network (Xiao, para. 0047).
As to claim 25, the combination of Xiao and QIN discloses the non-transitory computer-readable medium of claim 21, wherein the one or more neural network parameters comprise one or more weights (Xiao, para. 0047).
As to claim 26, the combination of Xiao and QIN discloses the non-transitory computer-readable medium of The non-transitory computer-readable medium of wherein the instructions that, when performed by the at least one processor of the computing device, cause the computing device to train the second neural network on training images (Xiao, para. 0031, 0054, 0056).
As to claim 27, the combination of Xiao and QIN discloses the non-transitory computer-readable medium of The non-transitory computer-readable medium of wherein the instructions that, when performed by the at least one processor of the computing device, cause the computing device to input image data based, at least in part, on one or the LR images into a neural network to generate the one or more neural network parameters (Xiao, Figs. 4A, 4B, para. 0044, 0047).
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to PHUOC TRAN whose telephone number is (571)272-7399. The examiner can normally be reached 9am-5pm.
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, Vu Le can be reached at 571-272-7332. 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.
/PHUOC TRAN/Primary Examiner, Art Unit 2668