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 .
Priority
This application discloses and claims only subject matter disclosed in prior application, and names the inventor or at least one joint inventor named in the prior application. Accordingly, this application may constitute a continuation or divisional. Should applicant desire to claim the benefit of the filing date of the prior application, attention is directed to 35 U.S.C. 120, 37 CFR 1.78, and MPEP § 211 et seq. The presentation of a benefit claim may result in an additional fee under 37 CFR 1.17(w)(1) or (2) being required, if the earliest filing date for which benefit is claimed under 35 U.S.C. 120, 121, 365(c), or 386(c) and 1.78(d) in the application is more than six years before the actual filing date of the application.
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 06/03/2025 submission is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
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 1, 11 and 19 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.
The term “realism factor” needs further clarification as to what constitutes “realism” as it relates to the instant claim language. - Appropriate clarification is required.
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.
Claims 1-11, 15-17, 19, 21 and 23 are rejected under 35 U.S.C. 103 as being unpatentable over Carmel et al. (US20180091814) (hereinafter Carmel) in view of Won et al. (US20190089987) (hereinafter Won).
Regarding claim 1, Carmel discloses a method, comprising:
obtaining a bitstream that includes an encoded representation of a source image [Figs. 9-16, 0078-0095, 0191-0206, 0543-0554, 0568; obtaining and storing image data from a bitstream].
[Figs. 9-16, 0078-0095, 0191-0206, 0220-0222, 0543-0554, 0568; image quality parameter related to a quantitative similarity measure between the output and input image].
inputting the realism factor and the encoded representation to a decoder to obtain the reconstructed image of the source image [Figs. 9-16, 0078-0095, 0191-0206, 0220-0222, 0543-0554, 0568; image quality parameter related to a quantitative similarity measure between the output and input image].
storing or displaying the reconstructed image [Figs. 9-16, 0078-0095, 0191-0206, 0543-0554, 0568; obtaining and storing image data from a bitstream].
Carmel discloses the limitations of the claim. However, Carmel does not explicitly disclose receiving a realism factor indicative of an amount of synthesized content in a reconstructed image of the source image.
Won more explicitly discloses receiving a realism factor indicative of an amount of synthesized content in a reconstructed image of the source image [Figs. 6-9, 0087-0090, 0117-0126; performing coding on multiple images and reconstructing image with parameters for improving image quality].
It would have been obvious to one of ordinary skill in the art before the effective filing date to incorporate the teachings of Carmel with the teachings of Won as stated above. By incorporating the teachings as such dynamic improvement of image quality is achieved (see Won 0006-0019).
Regarding claim 2, Carmel discloses wherein the encoded representation comprises a latent space representation of the source image and is created by an encoder [0011-0020; performing coding on compressed image data].
Regarding claim 3, Carmel discloses wherein the decoder is a generator of a Generative Adversarial Network (GAN) [0274-0277, 0503-0509; learning process for obtaining image information].
Regarding claim 4, Carmel discloses wherein the realism factor is obtained as input from a user [0022-0025; manual implementation of quality metrics by user].
Regarding claim 5, Carmel discloses the limitations of the claim. However, Carmel does not explicitly disclose wherein the generator includes a plurality of convolution layers, and wherein the realism factor is injected into at least some of the convolution layers.
Won more explicitly discloses wherein the generator includes a plurality of convolution layers, and wherein the realism factor is injected into at least some of the convolution layers [Figs. 6-9, 0087-0090, 0117-0126; performing coding on divided images and reconstructing image with parameters for improving image quality].
It would have been obvious to one of ordinary skill in the art before the effective filing date to incorporate the teachings of Carmel with the teachings of Won for the same reasons as stated above].
Regarding claim 6, Carmel discloses wherein the realism factor is a value in a range that includes a first value and a second value and other values between the first value and the second value, wherein the first value indicates that the reconstructed image includes no synthesized content and the second value indicates that the reconstructed image does not include synthesized content [Figs. 9-16, 0078-0095, 0191-0206, 0220-0222, 0543-0554, 0568; image quality parameter related to a quantitative similarity measure between the output and input image].
Regarding claim 7, Carmel discloses wherein a level of synthesized content in the reconstructed image is based on the realism factor [Figs. 9-16, 0078-0095, 0191-0206, 0220-0222, 0543-0554, 0568; image quality parameter related to output and input image].
Regarding claim 8, Carmel discloses wherein the bitstream is obtained via a compression process that includes a hyper-prior-based autoencoder [0274-0277, 0503-0509; coding process utilizing learning process for obtaining image information].
Regarding claim 9, Carmel discloses applying a machine-learning entropy model to the bitstream prior to obtaining the encoded representation [0274-0277, 0503-0509; coding process utilizing learning process for obtaining image information].
Regarding claim 10, Carmel discloses wherein the realism factor is derived from a range of values indicative of desired perceptual qualities in the reconstructed image [Figs. 9-16, 0078-0095, 0191-0206, 0220-0222, 0543-0554, 0568; image quality parameter related to output and input image].
Regarding claim 11, Carmel discloses A method, comprising:
receiving a source image [Figs. 9-16, 0078-0095, 0191-0206, 0543-0554, 0568; obtaining and storing image data from a bitstream].
encoding the source image using an autoencoder to generate an encoded representation, wherein the autoencoder comprises a hyperprior-based architecture [0274-0277, 0503-0509; coding process utilizing learning process for obtaining image information].
conditioning a generator on a realism factor to produce a reconstructed image from the encoded representation [Figs. 9-16, 0078-0095, 0191-0206, 0220-0222, 0543-0554, 0568; image quality parameter related to a quantitative similarity measure between the output and input image].
storing or transmitting the encoded representation [Figs. 9-16, 0078-0095, 0191-0206, 0543-0554, 0568; obtaining and storing image data from a bitstream].
Carmel discloses the limitations of the claim. However, Carmel does not explicitly disclose wherein the realism factor is adjustable to control a level of synthesized content in the reconstructed image.
Won more explicitly discloses wherein the realism factor is adjustable to control a level of synthesized content in the reconstructed image [Figs. 6-9, 0087-0090, 0117-0126; performing coding on multiple images and reconstructing image with parameters for improving image quality].
It would have been obvious to one of ordinary skill in the art before the effective filing date to incorporate the teachings of Carmel with the teachings of Won as stated above. By incorporating the teachings as such dynamic improvement of image quality is achieved (see Won 0006-0019).
Regarding claim 15, Carmel discloses wherein the generator is part of a Generative Adversarial Network (GAN), and wherein the reconstructed image is evaluated against a real image by a discriminator within the GAN. [0274-0277, 0503-0509; learning process for obtaining image information].
Regarding claim 16, Carmel discloses the limitations of the claim. However, Carmel does not explicitly disclose wherein the generator is trained using a loss function that includes a rate-distortion component and a realism component.
Won discloses wherein the generator is trained using a loss function that includes a rate-distortion component and a realism component [Figs. 6-9, 0054, 0087-0090, 0117-0126; performing coding on divided images and reconstructing image with parameters for improving image quality including rate-distortion cost].
It would have been obvious to one of ordinary skill in the art before the effective filing date to incorporate the teachings of Carmel with the teachings of Won for the same reasons as stated above].
Regarding claim 17, Carmel discloses a device, comprising: a processor that is configured to perform the method of claim 1 [0071-0076; device and processor].
Regarding claim 19, Carmel discloses a non-transitory computer-readable storage medium, comprising executable instructions that, when executed by a processor, cause performance of operations that perform a method comprising:
obtaining a bitstream that includes an encoded representation of a source image [Figs. 9-16, 0078-0095, 0191-0206, 0543-0554, 0568; obtaining and storing image data from a bitstream].
inputting the realism factor and the encoded representation to a decoder to obtain the reconstructed image of the source image [Figs. 9-16, 0078-0095, 0191-0206, 0220-0222, 0543-0554, 0568; image quality parameter related to a quantitative similarity measure between the output and input image].
storing or displaying the reconstructed image [Figs. 9-16, 0078-0095, 0191-0206, 0543-0554, 0568; obtaining and storing image data from a bitstream].
Carmel discloses the limitations of the claim. However, Carmel does not explicitly disclose receiving a realism factor indicative of an amount of synthesized content in a reconstructed image of the source image.
Won more explicitly discloses receiving a realism factor indicative of an amount of synthesized content in a reconstructed image of the source image [Figs. 6-9, 0087-0090, 0117-0126; performing coding on multiple images and reconstructing image with parameters for improving image quality].
It would have been obvious to one of ordinary skill in the art before the effective filing date to incorporate the teachings of Carmel with the teachings of Won as stated above. By incorporating the teachings as such dynamic improvement of image quality is achieved (see Won 0006-0019).
Regarding claim 21, Carmel discloses A device, comprising: a memory; and a processor, the processor configured to execute instructions stored in the memory to perform the method of claim 11 [0071-0076; device and processor].
Regarding claim 23, Carmel discloses wherein the decoder is a generator of a Generative Adversarial Network (GAN) [0274-0277, 0503-0509; learning process for obtaining image information].
Allowable Subject Matter
Claims 12-14 are objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims including the resolution of any and all 35 USC 101/112 matters.
The prior arts of record individually nor in combination do not explicitly discloses processing the realism factor through a feature-generator to obtain a set of realism- factor features and inputting the realism-factor features to the generator and processing the realism factor through a multilayer perceptron (MLP) to generate the realism-factor features, when taken in the environment of the independent claims.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to TALHA M NAWAZ whose telephone number is (571)270-5439. The examiner can normally be reached Flex, M-R 6:30am-3:30pm; F 8:30am-12:30pm.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Joe G Ustaris can be reached at 571-272-7383. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/TALHA M NAWAZ/Primary Examiner, Art Unit 2483
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