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 .
Claim Rejections - 35 USC § 102
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 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 the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale or otherwise available to the public before the effective filing date of the claimed invention.
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claims 1-5, 7-9, 11-14, 16-18 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Tretter (US 6,463,173)
As for claims 1, 11, Tretter teaches
A system for producing a digital image by automatically correcting for scattering and/or absorption effects in an original digital image, the system comprising:
a processor;
memory containing a computer program that when executed will cause the processor to:
receive radiance spectra of a digital image (Fig 5 el 109, 501, the RGB or YCrCb representation of an input image); and
execute a Gaussian machine learning model to:
generate a reflectance parameter by predicting a ground reflectance value based on a Gaussian probability distribution of radiance spectra (Fig 5 el 509 histogram, Fig 7 col 9 ln 4-5 “parameters of the Gaussian distribution”);
solve for a conversion coefficient based on the reflectance parameter (Fig 5 el 515 histogram equalization col 11 ln 40-50 remapped histogram, i.e. “conversion coefficient”); and
produce an altered digital image by at least one or more of removing, filtering, and/or altering data for at least one pixel of a digital image based on the conversion coefficient (Fig 5 el 517 pixel remapping)
As for claims 2, 12, Tretter teaches
the computer program will cause the processor to receive a digital image and extract radiance spectra therefrom (Fig 5 el 515 conversion to YcrCb; Fig 3 el 305)
As for claims 3, 13, Tretter teaches
the computer program will cause the processor to receive a digital image from an imaging device (Fig 3 el 303, 305)
As for claim 4, Tretter teaches
in combination with the imaging device (Fig 3 el 303)
As for claims 5, 14, Tretter teaches
generate plural reflectance parameters and solve for plural conversion coefficients based on the plural reflectance parameters (Fig 7 col 8 ln 60-70 multiple clusters can be Gaussian, each with a different set of Gaussian parameters)
As for claims 7, 16, Tretter teaches
generate the altered digital image by at least one or more of removing, filtering, and/or altering data pertaining to a scattering and/or absorption effect on reflectance of a digital image (“absorption effect on reflectance” can be broadly understood as brightness of pixels, which represent light reflected from scene surfaces; therefore altering the RGB matrix of pixels in Fig 5 517 can be called “altering the digital image .. pertaining to an absorption effect on reflectance”)
As for claims 8, 17, Tretter teaches
generate the altered digital image by generating at least one or more offset values and applying the at least one or more offset values to at least one pixel of a digital image (Fig 5 el 515-517 histogram-equalized remapping)
As for claims 9, 18, Tretter teaches
generate the altered digital image by generating an offset value and applying the offset value to each pixel of a digital image (Fig 5 el 515-517 histogram-equalized remapping)
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 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 of this title, 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 6, 15 are rejected under 35 U.S.C. 103 as being unpatentable over Tretter over Scikit (extracted from https://scikit-learn.org/stable/modules/linear_model.html available 2012)
As for claims 6, 15, Tretter does not teach, Scikit however teaches
predict a ground reflectance value by generating one or more approximate ground reflectance output vectors from one or more radiance spectra input vectors in a Bayesian framework (Scikit, p 13, ch 1.1.10, teaches a Bayesian regression for curve fitting)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention, to modify the histogram-based image contrast enhancement method of Tretter to include the Bayesian technique of curve fitting. The suggestion/motivation for doing so would have been to enhance and/or provide an alternative in the calculation of Gaussian curve parameters of Tretter.
Claims 6, 15 are rejected under 35 U.S.C. 103 as being unpatentable over Tretter over Doblis (“Denoising Autoencoders”, Towards Data Science, April 2022)
As for claims 10, 19, Tretter does not teach, Doblis however teaches
execute a denoising autoencoder machine learning model (p 8) to:
encode digital image data by passing digital image data through plural layers of a deep learning neural network that decrease in size leading to a dimensionally smaller representation of the data (p 6, 7 progressively smaller layers prior to Embedded Information layer);
decode digital image data by passing encoded digital image data through a symmetric series of layers until the reaching a data shape before being encoded (p 6, 7 progressively larger layers post Embedded Information ); and
learn a representation of data in a reduced dimensional space to facilitate removal of noise during encoding and recovery of data in original space during decoding (p 6, 7 Output Layer)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention, to modify the histogram-based image contrast enhancement method of Tretter to include the denoising technique taught by Doblis. The suggestion/motivation for doing so would have been to enhance the image by removing unwanted noise, either before or after Tretter’s contrast enhancement.
Contact Information
Any inquiry concerning this communication or earlier communications from the examiner should be directed to MARK ROZ whose telephone number is (571)270-3382. The examiner can normally be reached on 9AM-5PM M-F.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Andrew Moyer can be reached on (571)272-9523. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.Information regarding the status of an application may be obtained from the Patent Application Information Retrieval (PAIR) system. Status information for published applications may be obtained from either Private PAIR or Public PAIR. Status information for unpublished applications is available through Private PAIR only. For more information about the PAIR system, see http://pair-direct.uspto.gov. Should you have questions on access to the Private PAIR system, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative or access to the automated information system, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/MARK ROZ/
Primary Examiner, Art Unit 2669