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 .
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 06/28/2024 is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
Claim Objections
Claim 3 is objected to because of the following informalities:
Claim 3 recites the limitation “matrixes” in line 3, whereas in other places a plurality of matrices has been recited as “matrices.” The examiner suggests changing the limitation to “matrices” for consistency. Appropriate correction is required.
Claim 3 also recite the limitation “unmixing formula” in line 2. The examiner believes that the limitation should be “the unmixing formula” as recited in other claims. Appropriate correction is required.
Claim Rejections - 35 USC § 102
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.
Claim(s) 1 is/are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Us patent application publication no. 2016/0313184 to Owechko.
For claim 1, Owechko as applied discloses a method for performance of spectrally unmixing comprising:
receiving a real image representing a target in which endmembers are present in unknown proportions (see, e.g., par. 38, which teaches receiving spectroscopic data representing measured electromagnetic energy that has undergone an interaction with a specimen); and
searching and optimizing an abundance matrix space expressed in an unmixing formula that references together with the abundance matrix space, image information of the real image and an endmember spectral profile matrix that specifies spectral profiles for a set of differentiated reference endmembers (see, e.g., pars. 23, 30-35, 39-45, 55 and FIGS. 2-5, which searching through a spectral library, represented by matrix φ, that specifies spectral profiles for many different endmembers to identify the closest matching node by performing the demixing process at multiple levels, wherein traversing through multiple levels increases the accuracy and hence optimizes the search and the demixing formula references the spectrum vector x with the spectral library matrix φ and the abundance vectors α);
wherein as a result of the searching and optimizing the abundance matrix space, there is identified a set of unmixed real image endmembers and abundances associated to the unmixed real image endmembers (see, e.g., pars. 42-43 and FIG. 5, which teach that as a result of the demixing, the endmembers that closely match the nodes of the spectral library are identified and their abundances are assessed).
Claim Rejections - 35 USC § 103
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.
Claim(s) 2 is/are rejected under 35 U.S.C. 103 as being unpatentable over Owechko in view of us patent application publication no. 2020/0302606 to Bahr et al. (hereinafter Bahr).
For claim 2, while Owechko does not explicitly teach, Bahr in the analogous art teaches that the unmixing formula characterizes real image noise of the real image as being distributed according to a real image Poisson noise distribution so that the set of unmixed real image endmembers and abundances associated to the unmixed real image endmembers are noise reduced in accordance with the real image Poisson noise distribution (see, e.g., par. 251 of Bahr, which teaches determining an unmixing matrix based on Poisson regression/noise model that corrects for noise).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Owechko to denoise with a Poisson noise distribution model as taught by Bahr because doing so would outperform an unmixing matrix determined using least square (see, e.g., par. 251 of Bahr).
Claim(s) 5-6 is/are rejected under 35 U.S.C. 103 as being unpatentable over Owechko in view of an applicant-submitted NPL titled “Simultaneously sparse and low-rank abundance matrix estimation for hyperspectral image unmixing” authored by Giampouras et al., and dated October 2015 (hereinafter Giampouras).
For claim 5, while Owechko does not explicitly teach, Giampouras in the analogous art teaches that the unmixing formula imposes a rank constraint on the abundance matrix space (see, e.g., abstract, 4th and 5th full pars. of Section, 2nd and 3rd full pars. of section II and subsections A and B of section III and FIG. 1 of Giampouras, which teach unmixing algorithms for imposing sparse and low rank constraints on abundance matrices), and wherein the unmixing formula imposes a sparseness constraint on the abundance matrix space (see, e.g., abstract, 4th and 5th full pars. of Section I, 2nd and 3rd full pars. of section II and subsections A and B of section III and FIG. 1 of Giampouras, which teach unmixing algorithms for imposing sparse and low rank constraints on abundance matrices).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Owechko to impose the constraints as taught by Giampouras because doing so would improve the effectiveness of the unmixing over other available stat of the art unmixing schemes (see section V of Giampouras).
For claim 6, while Owechko does not explicitly teach, Giampouras in the analogous art teaches that the unmixing formula imposes a rank constraint on the abundance matrix space, and wherein according to the rank constraint, weights are applied to candidate matrices in a manner to reduce a rank of a subset of candidate matrices evaluated by the searching and optimizing (see, e.g., abstract, 4th and 5th full pars. of Section I, 5th and 6th full pars. of section II and subsections A-C of section III of Giampouras, which teach imposing sparse and low rank constraints on abundance matrix that penalizes using the weighted l1 norm and the weighted trace norm of the abundance matrices).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Owechko to impose the constraints as taught by Giampouras because doing so would improve the effectiveness of the unmixing over other available stat of the art unmixing schemes (see section V of Giampouras).
Claim(s) 9 and 11 is/are rejected under 35 U.S.C. 103 as being unpatentable over Owechko in view of Us patent no. 8805083 to Sieracki.
For claim 9, while Owechko as applied does not explicitly teach, Sieracki in the analogous art teaches that the method includes obtaining a plurality of multipixel reference images (see, e.g., lines 4-27 and 46-67 in col. 3, lines 1-21 in col. 4 and lines 42-52 in col. 14 and FIG. 2 of Sieracki, which teach electronically capturing images formed by a group of image pixel data vectors), wherein respective ones of the multipixel reference images are collected with a certain reference endmember of known identity present throughout a reference target (see, e.g., lines 4-27 in col. 3, lines 49-67 in col. 4, lines 1-7 in col. 5, lines 36-59 in col. 13, and lines 4-17 and 42-52 in col. 14 and FIGS. 1A-B of Sieracki, which teach that the captured images, i.e., the target underlying spectra, are referenced with a general, pre-established dictionary as a collection of known spectra);
extracting, for respective ones of the plurality of multipixel reference images, endmember information that includes a spectral profile of a certain reference image endmember (see, e.g., lines 4-27 in col. 3, lines 49-67 in col. 4, lines 1-7 in col. 5, lines 7-59 in col. 13, lines 42-67 in col. 14, lines 1-20 in col. 15 and FIG. 2 of Sieracki, which teach extracting spectral signatures from the image pixel data vectors of the captured images),
wherein the extracting endmember information includes searching and optimizing an endmember spectral profile vector space expressed in an endmember extraction formula that references together with the endmember spectral profile vector space, image information of a respective multipixel reference image (see, e.g., lines 4-27 in col. 3, lines 7-59 in col. 13, lines 53-67 in col. 14, lines 1-8 in col. 15, lines 46-67 in col. 16, and lines 1-17 in col. 17 and FIG. 2 of Sieracki, which teach searching for the spectral signatures by referencing the image vectors with the known spectral signatures in the general dictionary),
wherein as a result of performing the extracting endmember information for the respective multipixel reference images, there is produced the endmember spectral profile matrix that specifies spectral profiles for the set of reference endmembers (see, e.g., lines 4-27 in col. 3, lines 1-46 in col. 7, lines 8-67 in col. 13, lines 1-40 in col. 14, lines 9-20 in col. 15, and lines 46-67 in col. 16, and lines 1-17 in col. 17 and FIG. 2 of Sieracki, which teach storing the core spectral signatures in a refined signature dictionary).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Owechko to obtain its library/dictionary as taught by Sieracki because doing so would yield accurate detection and discrimination of the constituent features from the composite image data (see lines 41-45 in col.3 of Sieracki).
For claim 11, Owechko as applied teaches a method for performance of spectrally unmixing comprising:
receiving a real image representing a target in which real image endmembers are present in unknown proportions (see, e.g., par. 38, which teaches receiving spectroscopic data representing measured electromagnetic energy that has undergone an interaction with a specimen); and
searching and optimizing an abundance matrix space expressed in an unmixing formula that references together with the abundance matrix space, image information of the real image and the reference image endmember spectral profile matrix that specifies spectral profiles for the set of differentiated reference image endmembers (see, e.g., pars. 23, 30-35, 39-45, 55 and FIGS. 2-5, which searching through a spectral library, represented by matrix φ, that specifies spectral profiles for many different endmembers to identify the closest matching node by performing the demixing process at multiple levels, wherein traversing through multiple levels increases the accuracy and hence optimizes the search and the demixing formula references the spectrum vector x with the spectral library matrix φ and the abundance vectors α);
wherein as a result of the searching and optimizing the abundance matrix space, there is identified a set of unmixed real image endmembers and abundances associated to the real image endmembers (see, e.g., pars. 42-43 and FIG. 5, which teach that as a result of the demixing, the endmembers that closely match the nodes of the spectral library are identified and their abundances are assessed).
While Owechko as applied does not explicitly teach, Sieracki in the analogous art teaches:
obtaining a plurality of multipixel reference images (see, e.g., lines 4-27 and 46-67 in col. 3, lines 1-21 in col. 4 and lines 42-52 in col. 14 and FIG. 2, which teach electronically capturing images formed by a group of image pixel data vectors), wherein respective ones of the multipixel reference images are collected with a certain reference endmember of known identity present throughout a reference target (see, e.g., lines 4-27 in col. 3, lines 49-67 in col. 4, lines 1-7 in col. 5, lines 36-59 in col. 13, and lines 4-17 and 42-52 in col. 14 and FIGS. 1A-B of Sieracki, which teach that the captured images, i.e., the target underlying spectra, are referenced with a general, pre-established dictionary as a collection of known spectra);
for respective ones of the plurality of multipixel reference images, extracting endmember information that includes a spectral profile of a certain reference image endmember (see, e.g., lines 4-27 in col. 3, lines 49-67 in col. 4, lines 1-7 in col. 5, lines 7-59 in col. 13, lines 42-67 in col. 14, lines 1-20 in col. 15 and FIG. 2 of Sieracki, which teach extracting spectral signatures from the image pixel data vectors of the captured images), wherein the extracting endmember information includes searching and optimizing an endmember spectral profile vector space expressed in an endmember extraction formula that references together with the endmember spectral profile vector space, image information of a respective multipixel reference image (see, e.g., lines 4-27 in col. 3, lines 7-59 in col. 13, lines 53-67 in col. 14, lines 1-8 in col. 15, lines 46-67 in col. 16, and lines 1-17 in col. 17 and FIG. 2 of Sieracki, which teach searching for the spectral signatures by referencing the image vectors with the known spectral signatures in the general dictionary);
wherein as a result of performing the extracting endmember information for the respective multipixel reference images, there is produced a reference image endmember spectral profile matrix that specifies spectral profiles for a set of differentiated reference image endmembers (see, e.g., lines 4-27 in col. 3, lines 1-46 in col. 7, lines 8-67 in col. 13, lines 1-40 in col. 14, lines 9-20 in col. 15, and lines 46-67 in col. 16, and lines 1-17 in col. 17 and FIG. 2 of Sieracki, which teach storing the core spectral signatures in a refined signature dictionary).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Owechko to obtain its library/dictionary as taught by Sieracki because doing so would yield accurate detection and discrimination of the constituent features from the composite image data (see lines 41-45 in col.3 of Sieracki).
Claim(s) 12 is/are rejected under 35 U.S.C. 103 as being unpatentable over Owechko in view of Sieracki and further in view of us patent no. 12,339,229 to Deissler et al. (hereinafter Deissler).
For claim 12, Owechko as applied teaches:
a microscope that includes multiple spectral detectors (see, e.g., par. 38, which teaches acquiring, using a microscope, spectroscopic data containing multiple spectra),
wherein the apparatus is further operative for performing the receiving, with use of the microscope that includes multiple spectral detectors, the real image representing the target, in which real image endmembers are present in unknown proportions (see, e.g., par. 38, which teach acquiring, using a microscope, spectroscopic data representing measured electromagnetic energy that has undergone an interaction with a specimen);
wherein the apparatus is further operative for performing the searching and optimizing the abundance matrix space expressed in an unmixing formula that references together with the abundance matrix space, image information of the real image and the reference image endmember spectral profile matrix that specifies spectral profiles for the set of differentiated reference image endmembers (see, e.g., pars. 23, 30-35, 39-45, 55 and FIGS. 2-5, which searching through a spectral library, represented by matrix φ, that specifies spectral profiles for many different endmembers to identify the closest matching node by performing the demixing process at multiple levels, wherein traversing through multiple levels increases the accuracy and hence optimizes the search and the demixing formula references the spectrum vector x with the spectral library matrix φ and the abundance vectors α).
While Owechko as applied does not explicitly teach, Sieracki in the analogous art teaches that:
the apparatus is operative for performing the obtaining, with use of the microscope, the plurality of multipixel reference images (see, e.g., lines 4-27 and 46-67 in col. 3, lines 1-21 in col. 4 and lines 42-52 in col. 14 and FIG. 2, which teach electronically capturing images formed by a group of image pixel data vectors), wherein respective ones of the multipixel reference images are collected with a certain reference endmember of known identity present throughout the reference target (see, e.g., lines 4-27 in col. 3, lines 49-67 in col. 4, lines 1-7 in col. 5, lines 36-59 in col. 13, and lines 4-17 and 42-52 in col. 14 and FIGS. 1A-B of Sieracki, which teach that the captured images, i.e., the target underlying spectra, are referenced with a general, pre-established dictionary as a collection of known spectra);
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Owechko to obtain its library/dictionary as taught by Sieracki because doing so would yield accurate detection and discrimination of the constituent features from the composite image data (see lines 41-45 in col.3 of Sieracki).
Oweko in view of Sieracki as applied in claim 11 teaches that the apparatus is operative for performing the method of claim 11.
While Owechko in view of Sieracki teach accounting for variations in illumination type and conditions (see, e.g., lines 32-46 in col. 7 of Sieracki), it does not explicitly teach that identical acquisition settings for the microscope characterize the obtaining and the receiving. Deissler in the analogous art teaches using the same image acquisition settings for the microscope in a sequence of images (see, e.g., lines 57-67 in col. 3 and lines 1-16 in col. 4, lines 66-67 in col. 10, lines 1-18 in col. 11, and lines 11-48 in col. 12 of Deissler).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Owechko in view of Sieracki to use the stored image acquisition settings as taught by Deissler because doing so would allow the previously determine optimum setting values to be used again (see lines 33-43 in col.12 of Deissler).
Claim(s) 13-14 is/are rejected under 35 U.S.C. 103 as being unpatentable over Owechko in view of Sieracki and further in view of Giampouras.
For claim 13, while Owechko in view of Sieracki does not explicitly teach, Giampouras in the analogous art teaches that the unmixing formula imposes a rank constraint on the abundance matrix space (see, e.g., abstract, 4th and 5th full pars. of Section, 2nd and 3rd full pars. of section II and subsections A and B of section III and FIG. 1 of Giampouras, which teach unmixing algorithms for imposing sparse and low rank constraints on abundance matrices).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Owechko to impose the constraints as taught by Giampouras because doing so would improve the effectiveness of the unmixing over other available stat of the art unmixing schemes (see section V of Giampouras).
For claim 14, while Owechko in view of Sieracki does not explicitly teach, Giampouras in the analogous art teaches that the unmixing formula imposes a sparseness constraint on the abundance matrix space (see, e.g., abstract, 4th and 5th full pars. of Section I, 2nd and 3rd full pars. of section II and subsections A and B of section III and FIG. 1 of Giampouras, which teach unmixing algorithms for imposing sparse and low rank constraints on abundance matrices).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Owechko to impose the constraints as taught by Giampouras because doing so would improve the effectiveness of the unmixing over other available stat of the art unmixing schemes (see section V of Giampouras).
Claim(s) 15-17 and 20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Owechko in view of Sieracki and further in view of Bahr.
For claim 15, 16, while Owechko in view of Sieracki does not explicitly teach, Bahr in the analogous art teaches that the endmember extraction formula characterizes noise of respective ones of the multipixel reference images such that an endmember vector of the reference image endmember spectral profile matrix is noise reduced (see, e.g., par. 251 of Bahr, which teaches determining an unmixing formula based on Poisson regression/noise model that corrects for noise).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Owechko to denoise with a Poisson noise distribution model as taught by Bahr because doing so would outperform an unmixing matrix determined using least square (see, e.g., par. 251 of Bahr).
For claim 16, while Owechko in view of Sieracki does not explicitly teach, Bahr in the analogous art teaches that the endmember extraction formula characterizes noise of respective ones of the multipixel reference images according to a reference image Poisson noise distribution pattern so that an endmember vector of the reference image endmember spectral profile matrix is noise reduced according to the reference image Poisson noise distribution pattern (see, e.g., par. 251 of Bahr, which teaches determining an unmixing formula based on Poisson regression/noise model that corrects for noise).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Owechko to denoise with a Poisson noise distribution model as taught by Bahr because doing so would outperform an unmixing matrix determined using least square (see, e.g., par. 251 of Bahr).
For claim 17, while Owechko in view of Sieracki does not explicitly teach, Bahr in the analogous art teaches that the unmixing formula characterizes noise of the real image according to a real image Poisson noise distribution pattern so that the identified set of unmixed real image endmembers and abundances are noise reduced according to the Poisson noise distribution pattern (see, e.g., par. 251 of Bahr, which teaches determining an unmixing formula based on Poisson regression/noise model that corrects for noise).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Owechko to denoise with a Poisson noise distribution model as taught by Bahr because doing so would outperform an unmixing matrix determined using least square (see, e.g., par. 251 of Bahr).
For claim 20, Owechko as applied teaches
receiving a real image representing a target in which endmembers are present in unknown proportions (see, e.g., pars. 38, which teach receiving spectroscopic data representing measured electromagnetic energy that has undergone an interaction with a specimen); and
unmixing the real image representing the target in dependence on the reference image endmember spectral profile matrix (see, e.g., pars. 23, 30-35, 39-45, 55 and FIGS. 2-5, which searching through a spectral library, represented by matrix φ, that specifies spectral profiles for many different endmembers to identify the closest matching node by performing the demixing process at multiple levels).
While Owechko as applied does not explicitly teach, Sieracki in the analogous art teaches a method for performance of spectrally unmixing comprising:
obtaining a plurality of multipixel reference images (see, e.g., lines 4-27 and 46-67 in col. 3, lines 1-21 in col. 4 and lines 42-52 in col. 14 and FIG. 2, which teach electronically capturing images formed by a group of image pixel data vectors), wherein respective ones of the multipixel reference images are collected with a certain reference endmember of known identity present throughout a reference target (see, e.g., lines 4-27 in col. 3, lines 49-67 in col. 4, lines 1-7 in col. 5, lines 36-59 in col. 13, and lines 4-17 and 42-52 in col. 14 and FIGS. 1A-B of Sieracki, which teach that the captured images, i.e., the target underlying spectra, are referenced with a general, pre-established dictionary as a collection of known spectra);
for respective ones of the plurality of multipixel reference images, extracting endmember information that includes a spectral profile of a certain endmember (see, e.g., lines 4-27 in col. 3, lines 49-67 in col. 4, lines 1-7 in col. 5, lines 7-59 in col. 13, lines 42-67 in col. 14, lines 1-20 in col. 15 and FIG. 2 of Sieracki, which teach extracting spectral signatures from the image pixel data vectors of the captured images), wherein the extracting endmember information includes searching and optimizing an endmember spectral profile vector space expressed in an endmember extraction formula that references together with the endmember spectral profile vector space, image information of a respective multipixel reference image (see, e.g., lines 4-27 in col. 3, lines 7-59 in col. 13, lines 53-67 in col. 14, lines 1-8 in col. 15, lines 46-67 in col. 16, and lines 1-17 in col. 17 and FIG. 2 of Sieracki, which teach searching for the spectral signatures by referencing the image vectors with the known spectral signatures in the general dictionary);
wherein as a result of performing the extracting endmember information for the respective multipixel reference images, there is produced a reference image endmember spectral profile matrix that specifies spectral profiles for a set of differentiated reference image endmembers(see, e.g., lines 4-27 in col. 3, lines 1-46 in col. 7, lines 8-67 in col. 13, lines 1-40 in col. 14, lines 9-20 in col. 15, and lines 46-67 in col. 16, and lines 1-17 in col. 17 and FIG. 2 of Sieracki, which teach storing the core spectral signatures in a refined signature dictionary),
While Owechko in view of Seracki does not explicitly teach, Bahr in the analogous art teaches that the unmixing formula characterizes real image noise of the real image as being distributed according to a real image Poisson noise distribution so that the set of unmixed real image endmembers and abundances associated to the unmixed real image endmembers are noise reduced in accordance with the real image Poisson noise distribution (see, e.g., par. 251 of Bahr, which teaches determining an unmixing matrix based on Poisson regression/noise model that corrects for noise).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Owechko in view of Sieracki to denoise with a Poisson noise distribution model as taught by Bahr because doing so would outperform an unmixing matrix determined using least square (see, e.g., par. 251 of Bahr).
Claim(s) 18 is/are rejected under 35 U.S.C. 103 as being unpatentable over Owechko in view of Sieracki and further in view of Bahr and Deissler.
For claim 18, while Owechko in view of Sieracki does not explicitly teach, Bahr in the analogous art teaches that the endmember extraction formula characterizes noise of the respective ones of the multipixel referenced images as being distributed according to a reference image Poisson noise distribution so that the reference image endmember spectral profile matrix produced as a result of performing the extracting endmember information for the respective multipixel reference images is noise reduced according to the reference image Poisson noise distribution (see, e.g., par. 251 of Bahr, which teaches determining an unmixing formula based on Poisson regression/noise model that corrects for noise),
wherein the unmixing formula characterizes real image noise of the real image as being distributed according to a real image Poisson noise distribution so that the set of unmixed real image endmembers and abundances associated to the unmixed real image endmembers are noise reduced in accordance with the real image Poisson noise distribution (see, e.g., par. 251 of Bahr, which teaches determining an unmixing formula based on Poisson regression/noise model that corrects for noise).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Owechko to denoise with a Poisson noise distribution model as taught by Bahr because doing so would outperform an unmixing matrix determined using least square (see, e.g., par. 251 of Bahr).
The examiner notes that due to the similarity between the endmember extraction and the unmixing, e.g., referencing and identifying spectral signature/component using known spectral signatures, the cited teaching of Bahr may be applicable and obvious in both instances.
Owechko in view of Sieracki and Bahr does not explicitly teach that the certain reference endmember of known identity is a fluorophore reference endmember. In the analogous art, Deissler as applied teaches identifying a fluorophore reference endmember (see, e.g., abstract of Deissler).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Owechko in view of Sieracki and Bahr to identify a fluorophore reference endmember as taught by Deissler because doing so would yield predictable results of using the unmixing processing to solve the problems in fluorescence microscopy (see lines 51-67 in col. 6 and lines 1-22 in col. 7 of Deissler).
Allowable Subject Matter
Claims 3-4, 7-8, 10, and 19 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.
In regard to claim 3, when considered as a whole, prior art of record fails to disclose or render obvious, alone or in combination:
“the abundance matrix space is defined by the row=fluorophores x column=pixels matrix space, and wherein unmixing formula includes a constraint that penalizes searched for candidate matrixes in favor of candidate matrices featuring a specified level of sparseness in the fluorophores dimension.”
In regard to claim 4, when considered as a whole, prior art of record fails to disclose or render obvious, alone or in combination:
“the abundance matrix space is defined by the row=fluorophores x column=pixels matrix space A, and wherein the unmixing formula applies a sparseness constraint among the rows of A, wherein the sparseness constraint is provided by the l2,1 norm ||A||2,1.”
In regard to claim 7, when considered as a whole, prior art of record fails to disclose or render obvious, alone or in combination:
“the unmixing formula imposes a rank constraint on the abundance matrix space in favor of candidate matrices having respective ranks less than or equal to a specified rank, and wherein the rank constraint is expressed as a nuclear norm.”
In regard to claim 8, when considered as a whole, prior art of record fails to disclose or render obvious, alone or in combination:
“the unmixing formula references a sliding window matrix that defines the image information of the real image, wherein the sliding window matrix is a row=channel, column=pixels matrix, wherein the pixels dimension comprises a limited number of pixels of the real image, wherein the method includes performing iterations of searching and optimizing, and changing a location of the sliding window intermediate of iterations of the searching and optimizing.”
In regard to claim 10, when considered as a whole, prior art of record fails to disclose or render obvious, alone or in combination:
“…
wherein the abundance matrix space is defined by the row=fluorophores x column=pixels matrix space A, and wherein the unmixing formula applies a sparseness constraint among the rows of A, wherein the sparseness constraint is provided by the 12,1 norm IAll2,1, and wherein the unmixing formula references a sliding window matrix that defines the image information of the real image, wherein the sliding window matrix is a row=channel, column=pixels matrix, wherein the pixels dimension comprises a limited number of pixels of the real image, wherein the method includes performing iterations of searching and optimizing, and changing a location of the sliding window intermediate of iterations of the searching and optimizing.”
In regard to claim 19, when considered as a whole, prior art of record fails to disclose or render obvious, alone or in combination:
“…
wherein the abundance matrix space is defined by the row=fluorophores x column=pixels matrix space A, and wherein the unmixing formula applies a sparseness constraint among the rows of A, wherein the sparseness constraint is provided by the t2,1 norm |Ail2,1, and wherein the unmixing formula references a sliding window matrix that defines the image information of the real image, wherein the sliding window matrix is a row=channel, column=pixels matrix, wherein the pixels dimension comprises a limited number of pixels of the real image, wherein the method includes performing iterations of searching and optimizing, and changing a location of the sliding window intermediate of iterations of the searching and optimizing ….”
Additional Citations
The following table lists several references that are relevant to the subject matter claimed and disclosed in this Application. The references are not relied on by the Examiner, but are provided to assist the Applicant in responding to this Office action.
Citation
Relevance
Bamford et al. (us pat. pub. No. 2016/0035100)
Describes spectral unmixing in fluorescence microscopy. In one embodiment, processing of images acquired via fluorescence microscopy by identifying broadband and other undesired signals from the component signals of a scanned image, and processing selected regions of the image that are known to contain signals of interest, thereby extracting or identifying desired signals while subtracting undesired signals. One or more broadband signals are recognized by their unique signature and ubiquitous dispersion through the image. Regions of the scanned image may be tagged as consisting of predominantly broadband signals and are ignored during a spectral unmixing process. The remaining regions of the image, or selected regions of the image known to contain desired signals, may be unmixed, and the plurality of reference spectra subtracted from the components to extract or identify the target signals. The set of target signals may be refined by eliminating known or obvious sources of noise by, for instance, being compared to known or ideal sets of signals from similar materials.
Janiczek et al. (us pat. pub. No. 2021/0396580)
Describes various embodiments of a system and associated method for hyperspectral unmixing of measured spectra for quantifying pure materials in a mixture. Spectral variation in the mixture is identified by a dispersion model and incorporated into an end-to-end spectral unmixing system via differentiable programming that iteratively optimizes the dispersion model and identifies an abundance of each material in the mixture. A dispersion model is introduced into the unmixing system to simulate realistic spectral variation. Then, this dispersion model is utilized as a generative model within the spectral unmixing system utilizing an analysis-by-synthesis method to quantify materials in an observed mixture and optimizes parameters of the dispersion model. Further, a technique for inverse rendering using a convolutional neural network to predict parameters of the generative model is introduced to enhance performance and speed when training data is available. State-of-the-art results were achieved on both infrared and visible-to-near-infrared (VNIR) datasets as compared to baselines, and show promise for the synergy between physics-based dispersion models and deep learning in hyperspectral unmixing in the future.
Hoffman et al. (us pat. pub. No. 2024/0305314)
Describes techniques for spectral unmixing and decoding for in situ analysis. In one embodiment, input hypercube data comprising voxel data, raw channel data, and decoding round data are obtained, and an initial iteration of codeword data determination is performed. Performing the initial iteration includes generating initial feature data and initial unmixed fluorescence data, generating initial uncorrected codeword data, and generating initial corrected codeword data. One or more subsequent iterations of codeword data determination are then performed based on the input hypercube data and based on feature data, unmixed fluorescence data, uncorrected codeword data, and corrected codeword data from one or more previous iterations. When it is determined that one or more convergence conditions have been satisfied, output comprising an optimized estimate of fluorescence data, and an optimized estimate of uncorrected codeword data, and an optimized estimate of corrected codeword data is generated.
Table 1
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. See Table 1 and form 892.
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