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
Receipt is acknowledged that application is a National Stage application of PCT PCT/EP2023/057947. Priority to EP 22170392.9 with a priority date of 04/27/2022 is acknowledged under 35 USC 119(e) and 37 CFR 1.78.
Information Disclosure Statement
The IDS(s) dated 10/24/2024 has been considered and placed in the application file.
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.
Claim(s) 1-4, 6-10, 13-16, and 18-20 is/are rejected under 35 U.S.C. 103 as obvious over Hong et al (US 20200382715 A1, hereafter referred to as Hong) in view of Paxman et al (Paxman, R. G., Schulz, T. J., & Fienup, J. R. (1992). Joint estimation of object and aberrations by using phase diversity. Journal of the Optical Society of America A, 9(7), 1072-1085, hereafter referred to as Paxman), further in view of Simonov et al (WO 2009108050 A1, hereafter referred to as Simonov).
Claim 1
Regarding Claim 1, Hong teaches A system for improving image quality, the system comprising:
a controller including circuitry configured to cause the system to perform (Hong in ¶6 discloses “a computer implemented system” where “the image acquirer operates on a processor”):
obtaining a plurality of images of an area of a sample (Hong in ¶6 discloses that the image acquirer “receives a plurality of images of the sample from the image sensor”; ¶20 discloses that “a first image of the sample is acquired … a second image of the sample is acquired … and additional images of the sample are acquired”).
Hong does not explicitly teach all of determining via a phase diversity analysis; a plurality of focus-related values, wherein each focus-related value of the plurality of focus-related values is associated with each image of the plurality of images; a maximum likelihood estimate (MLE) of the plurality of images; generating a focus-corrected image of the area based on the determined plurality of focus-related values and the determined MLE.
However, Paxman teaches determining via a phase diversity analysis (Paxman in Abstract discloses “joint estimation of an object and the aberrations of an incoherent imaging system from multiple images incorporating phase diversity”; page 1072 left column discloses “a technique known as phase diversity can also be used to infer phase aberrations from image data”):
a plurality of focus-related values, wherein each focus-related value of the plurality of focus-related values is associated with each image of the plurality of images (Paxman in page 1072 right column discloses that additional images are formed by perturbing unknown aberrations “in some known fashion”, including imagery degraded with “a known amount of defocus”; page 1073 discloses “θk is a known phase function associated with the kth diversity image, and … Phase diversity can be created, for example, by intentionally defocusing the system by known amounts.”);
a maximum likelihood estimate (MLE) of the plurality of images (Paxman in ¶ discloses “Maximum-likelihood estimation is considered under additive Gaussian and Poisson noise models” for “multiple images incorporating phase diversity”; page 1073 right column discloses “Given the set of K detected diversity images {dk}, the corresponding set of phase-diversity functions {θk}, and the binary pupil functions {|Hk|}, estimate the object f and the aberration parameters α”; page 1073 left column discloses an objective function that “yields the maximum-likelihood estimate (MLE) for the aberration parameters when maximized”; page 1074 left column discloses “The MLE is the estimate that is most likely to I produced a specific measurement” and is found by maximizing the likelihood function “with respect to f and α”; page 1075 left column discloses that once the MLE for the aberration parameter is found, it can be used “to find the MLE for the object parameter”); and
generating a focus-corrected image of the area based on the determined plurality of focus-related values and the determined MLE (Paxman in page 11072 right column discloses that once aberration estimates are known, “an estimate of the system optical transfer function (OTF) can be constructed, and the object can be restored by using, for example, Wiener filtering”; page 1076 discloses “Once an aberration function that maximizes LM is found, the corresponding OTF's can be constructed, and the MLE of the object is easily computed with Eq. (19)”).
Therefore, it would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to modify the invention of Hong by incorporating maximum-likelihood phase-diversity estimation from multiple diversity images that is taught by Paxman, since both reference are analogous art in the field of computational image-quality improvement using multiple images acquired at different focus/phase-diversity conditions; thus, one of ordinary skilled in the art would be motivated to combine the references since Hong’s focus-correction framework with Paxman’s maximum-likelihood phase-diversity estimation yields the predictable result of improving the determination of focus/aberration information from multiple images, thereby improving the accuracy of the focus-corrected image.
Hong in view of Paxman does not explicitly teach all of generating a focus-corrected image of the area based on the determined plurality of focus-related values and the determined MLE.
However, Simonov teaches generating a focus-corrected image of the area based on the determined plurality of focus-related values and the determined MLE (Simonov in page 2 discloses “image reconstruction resulting in a corrected in-focus image”; page 6 discloses “digitally reconstruct a final in-focus image of the object based on digital processing of multiple intermediate defocused images”; page 7 discloses “a focused final image of an object is derived, by digital reconstruction, from at least two, defocused intermediate images … having a precisely known degree of defocus”).
Therefore, it would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to modify the invention of Hong in view of Paxman by incorporating reconstruction of a final in-focus image from phase-diverse defocused intermediate images having known relative defocus that is taught by Simonov, since both reference are analogous art in the field of computational focus correction and image reconstruction; thus, one of ordinary skilled in the art would be motivated to combine the references since Hong in view of Paxman’s focus-corrected imaging and MLE phase-diversity estimation framework with Simonov’s phase-diverse in-focus reconstruction technique yields the predictable result of generating a focus-corrected/in-focus image from multiple defocused images using known focus/defocus relationships, thereby improving image quality and reducing defocus-related blur.
Thus, the claimed subject matter would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention.
Claim 2
Regarding Claim 2, Hong in view of Paxman in view of Simonov teaches The system of claim 1, wherein each focus-related value of the plurality of focus-related values comprises a defocus value (Paxman in page 1072 right column discloses that additional images are formed by perturbing unknown aberrations “in some known fashion”, including imagery degraded with “a known amount of defocus”; page 1073 discloses “θk is a known phase function associated with the kth diversity image, and … Phase diversity can be created, for example, by intentionally defocusing the system by known amounts.”).
Claim 3
Regarding Claim 3, Hong in view of Paxman in view of Simonov teaches The system of claim 1, wherein each image of the plurality of images has a different associated focus-related value (Paxman in page 1073 discloses a known phase function associated with the kth diversity image and intentional defocusing by known amounts).
Claim 4
Regarding Claim 4, Hong in view of Paxman in view of Simonov teaches The system of claim 1, wherein the plurality of focus-related values comprise a range of focus-related values (Hong in ¶17 discloses “the objective lens is positioned at a predetermined minimum distance” and continuing until the distance is “at least a first predetermined maximum distance”; ¶20 discloses imaging from a “second minimum distance” until reaching “at least a second maximum distance from the focus plane of the sample”; ¶54 discloses “Z′min, Z′max, and ΔZ” values for generating the focus-corrected image).
Claim 6
Regarding Claim 6, Hong in view of Paxman in view of Simonov teaches The system of claim 1, wherein the area is within a field of view of the sample (Hong in ¶6 discloses that the image acquirer “receives a plurality of images of the sample from the image sensor”; ¶20 discloses that “a first image of the sample is acquired … a second image of the sample is acquired … and additional images of the sample are acquired”).
Claim 7
Regarding Claim 7, Hong in view of Paxman in view of Simonov teaches The system of claim 1, wherein the MLE is determined based on the determined plurality of focus-related values (Paxman in page 1073 right column discloses “Given the set of K detected diversity images {dk}, the corresponding set of phase-diversity functions {θk}, and the binary pupil functions {|Hk|}, estimate the object f and the aberration parameters α”; page 1073 discloses “θk is a known phase function associated with the kth diversity image; page 1074 left column discloses “The MLE is the estimate that is most likely to I produced a specific measurement” and is found by maximizing the likelihood function “with respect to f and α”; page 1075 left column discloses that once the MLE for the aberration parameter is found, it can be used “to find the MLE for the object parameter”).
Claim 8
Regarding Claim 8, Hong in view of Paxman in view of Simonov teaches The system of claim 1, wherein:
the area comprises a first area of the sample and a second area of the sample (Paxman in page 1072 discloses that phase diversity accommodates “extended objects or even scenes” and that “the ability to operate on image subframes successfully, which is a fundamental requirement in the application of phase diversity to scenes, has also been demonstrated”);
the plurality of images comprises a first set of images of the first area of the sample and a second set of images of the second area of the sample (Paxman in page 1072 discloses applying phase diversity to image subframes in scenes and collecting multiple phase-diversity images for an area. Applying the same process to a first subframe and a second subframe yields a first set of images for the first area and a second set of images for the second area);
the phase diversity analysis comprises a first phase diversity analysis corresponding to the first set of images and a second phase diversity analysis corresponding to the second set of images (Paxman in page 1072 discloses phase-diversity estimation from multiple images and successful operation on image subframes. Applying Paxman’s phase-diversity estimation to each subframe/image set yields first and second phase-diversity analyses);
the plurality of focus-related values comprises a first set of focus-related values corresponding to the first phase diversity analysis and a second set of focus-related values corresponding to the second phase diversity analysis (Paxman in page 1073 discloses a known phase function associated with the kth diversity image and intentional defocusing by known amounts. Applying those values to each subframe set gives first and second sets of focus-related values);
the MLE comprises a first MLE of the first set of images and a second MLE of the
second set of images (Paxman in page 1072 discloses MLE phase-diversity estimation from multiple diversity images. Paxman’s subframe disclosure supports applying the MLE process separately to first and second image sets); and
the focus-corrected image comprises a first focus-corrected image of the first area and a second focus-corrected image of the second area (Simonov in page 7 discloses deriving a focused final image from defocused intermediate images having known defocus relationships; page 11 discloses reconstructing a final in-focus image from phase-diverse intermediate images. Applying the reconstruction separately to first and second subframe/area image sets yields first and second focus-corrected images).
Claim 9
Regarding Claim 9, Hong in view of Paxman in view of Simonov teaches The system of claim 8, wherein the circuitry is configured to cause the system to further perform:
determining, via the first phase diversity analysis:
the first set of focus-related values, wherein each focus-related value of the first set of focus-related values is associated with each image of the first set of images (Paxman in page 1073 discloses a known phase function associated with the kth diversity image and intentional defocusing by known amounts. Applying those values to each subframe set gives first and second sets of focus-related values);
the first MLE of the first set of images (Paxman in page 1072 discloses MLE phase-diversity estimation from multiple diversity images. Paxman’s subframe disclosure supports applying the MLE process separately to first and second image sets); and
generating the first focus-corrected image of the first area based on the determined first plurality of focus-related values and the determined first MLE (Simonov in page 7 discloses deriving a focused final image from defocused intermediate images having known defocus relationships; page 11 discloses reconstructing a final in-focus image from phase-diverse intermediate images. Applying the reconstruction separately to first and second subframe/area image sets yields first and second focus-corrected images).
Claim 10
Regarding Claim 10, Hong in view of Paxman in view of Simonov teaches The system of claim 8, wherein the circuitry is configured to cause the system to further perform:
determining, via the second phase diversity analysis:
the second set of focus-related values, wherein each focus-related value of the second set of focus-related values is associated with each image of the second set of images (Paxman in page 1073 discloses a known phase function associated with the kth diversity image and intentional defocusing by known amounts. Applying those values to each subframe set gives first and second sets of focus-related values);
the second MLE of the second set of images (Paxman in page 1072 discloses MLE phase-diversity estimation from multiple diversity images. Paxman’s subframe disclosure supports applying the MLE process separately to first and second image sets); and
generating the second focus-corrected image of the second area based on the determined second plurality of focus-related values and the determined second MLE (Simonov in page 7 discloses deriving a focused final image from defocused intermediate images having known defocus relationships; page 11 discloses reconstructing a final in-focus image from phase-diverse intermediate images. Applying the reconstruction separately to first and second subframe/area image sets yields first and second focus-corrected images).
Claim 13
Regarding Claim 13, Hong in view of Paxman in view of Simonov teaches A method of improving image quality, the method comprising:
obtaining a plurality of images of an area of a sample (Hong in ¶6 discloses that the image acquirer “receives a plurality of images of the sample from the image sensor”; ¶20 discloses that “a first image of the sample is acquired … a second image of the sample is acquired … and additional images of the sample are acquired”).
Hong does not explicitly teach all of determining via a phase diversity analysis; a plurality of focus-related values, wherein each focus-related value of the plurality of focus-related values is associated with each image of the plurality of images; a maximum likelihood estimate (MLE) of the plurality of images; generating a focus-corrected image of the area based on the determined plurality of focus-related values and the determined MLE.
However, Paxman teaches determining via a phase diversity analysis (Paxman in Abstract discloses “joint estimation of an object and the aberrations of an incoherent imaging system from multiple images incorporating phase diversity”; page 1072 left column discloses “a technique known as phase diversity can also be used to infer phase aberrations from image data”):
a plurality of focus-related values, wherein each focus-related value of the plurality of focus-related values is associated with each image of the plurality of images (Paxman in page 1072 right column discloses that additional images are formed by perturbing unknown aberrations “in some known fashion”, including imagery degraded with “a known amount of defocus”; page 1073 discloses “θk is a known phase function associated with the kth diversity image, and … Phase diversity can be created, for example, by intentionally defocusing the system by known amounts.”);
a maximum likelihood estimate (MLE) of the plurality of images (Paxman in ¶ discloses “Maximum-likelihood estimation is considered under additive Gaussian and Poisson noise models” for “multiple images incorporating phase diversity”; page 1073 right column discloses “Given the set of K detected diversity images {dk}, the corresponding set of phase-diversity functions {θk}, and the binary pupil functions {|Hk|}, estimate the object f and the aberration parameters α”; page 1073 left column discloses an objective function that “yields the maximum-likelihood estimate (MLE) for the aberration parameters when maximized”; page 1074 left column discloses “The MLE is the estimate that is most likely to I produced a specific measurement” and is found by maximizing the likelihood function “with respect to f and α”; page 1075 left column discloses that once the MLE for the aberration parameter is found, it can be used “to find the MLE for the object parameter”); and
generating a focus-corrected image of the area based on the determined plurality of focus-related values and the determined MLE (Paxman in page 11072 right column discloses that once aberration estimates are known, “an estimate of the system optical transfer function (OTF) can be constructed, and the object can be restored by using, for example, Wiener filtering”; page 1076 discloses “Once an aberration function that maximizes LM is found, the corresponding OTF's can be constructed, and the MLE of the object is easily computed with Eq. (19)”).
Therefore, it would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to modify the invention of Hong by incorporating maximum-likelihood phase-diversity estimation from multiple diversity images that is taught by Paxman, since both reference are analogous art in the field of computational image-quality improvement using multiple images acquired at different focus/phase-diversity conditions; thus, one of ordinary skilled in the art would be motivated to combine the references since Hong’s focus-correction framework with Paxman’s maximum-likelihood phase-diversity estimation yields the predictable result of improving the determination of focus/aberration information from multiple images, thereby improving the accuracy of the focus-corrected image.
Hong in view of Paxman does not explicitly teach all of generating a focus-corrected image of the area based on the determined plurality of focus-related values and the determined MLE.
However, Simonov teaches generating a focus-corrected image of the area based on the determined plurality of focus-related values and the determined MLE (Simonov in page 2 discloses “image reconstruction resulting in a corrected in-focus image”; page 6 discloses “digitally reconstruct a final in-focus image of the object based on digital processing of multiple intermediate defocused images”; page 7 discloses “a focused final image of an object is derived, by digital reconstruction, from at least two, defocused intermediate images … having a precisely known degree of defocus”).
Therefore, it would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to modify the invention of Hong in view of Paxman by incorporating reconstruction of a final in-focus image from phase-diverse defocused intermediate images having known relative defocus that is taught by Simonov, since both reference are analogous art in the field of computational focus correction and image reconstruction; thus, one of ordinary skilled in the art would be motivated to combine the references since Hong in view of Paxman’s focus-corrected imaging and MLE phase-diversity estimation framework with Simonov’s phase-diverse in-focus reconstruction technique yields the predictable result of generating a focus-corrected/in-focus image from multiple defocused images using known focus/defocus relationships, thereby improving image quality and reducing defocus-related blur.
Thus, the claimed subject matter would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention.
Claim 14
Regarding Claim 14, Hong in view of Paxman in view of Simonov teaches The method of claim 13, wherein each focus-related value of the plurality of focus-related values comprises a defocus value (Paxman in page 1072 right column discloses that additional images are formed by perturbing unknown aberrations “in some known fashion”, including imagery degraded with “a known amount of defocus”; page 1073 discloses “θk is a known phase function associated with the kth diversity image, and … Phase diversity can be created, for example, by intentionally defocusing the system by known amounts.”).
Claim 15
Regarding Claim 15, Hong in view of Paxman in view of Simonov teaches The method of claim 13, wherein each image of the plurality of images has a different associated focus-related value (Paxman in page 1073 discloses a known phase function associated with the kth diversity image and intentional defocusing by known amounts).
Claim 16
Regarding Claim 16, Hong in view of Paxman in view of Simonov teaches The method of claim 13, wherein the plurality of focus-related values comprise a range of focus-related values (Hong in ¶17 discloses “the objective lens is positioned at a predetermined minimum distance” and continuing until the distance is “at least a first predetermined maximum distance”; ¶20 discloses imaging from a “second minimum distance” until reaching “at least a second maximum distance from the focus plane of the sample”; ¶54 discloses “Z′min, Z′max, and ΔZ” values for generating the focus-corrected image).
Claim 18
Regarding Claim 18, Hong in view of Paxman in view of Simonov teaches The method of claim 13, wherein the area is within a field of view of the sample (Hong in ¶6 discloses that the image acquirer “receives a plurality of images of the sample from the image sensor”; ¶20 discloses that “a first image of the sample is acquired … a second image of the sample is acquired … and additional images of the sample are acquired”).
Claim 19
Regarding Claim 19, Hong in view of Paxman in view of Simonov teaches The method of claim 13, wherein the MLE is determined based on the determined plurality of focus-related values (Paxman in page 1073 right column discloses “Given the set of K detected diversity images {dk}, the corresponding set of phase-diversity functions {θk}, and the binary pupil functions {|Hk|}, estimate the object f and the aberration parameters α”; page 1073 discloses “θk is a known phase function associated with the kth diversity image; page 1074 left column discloses “The MLE is the estimate that is most likely to I produced a specific measurement” and is found by maximizing the likelihood function “with respect to f and α”; page 1075 left column discloses that once the MLE for the aberration parameter is found, it can be used “to find the MLE for the object parameter”).
Claim 20
Regarding Claim 20, Hong in view of Paxman in view of Simonov teaches A non-transitory computer readable medium that stores a set of instructions that is executable by at least one processor of a computing device to cause the computing device to perform operations for improving image quality, the operations comprising:
obtaining a plurality of images of an area of a sample (Hong in ¶6 discloses that the image acquirer “receives a plurality of images of the sample from the image sensor”; ¶20 discloses that “a first image of the sample is acquired … a second image of the sample is acquired … and additional images of the sample are acquired”).
Hong does not explicitly teach all of determining via a phase diversity analysis; a plurality of focus-related values, wherein each focus-related value of the plurality of focus-related values is associated with each image of the plurality of images; a maximum likelihood estimate (MLE) of the plurality of images; generating a focus-corrected image of the area based on the determined plurality of focus-related values and the determined MLE.
However, Paxman teaches determining via a phase diversity analysis (Paxman in Abstract discloses “joint estimation of an object and the aberrations of an incoherent imaging system from multiple images incorporating phase diversity”; page 1072 left column discloses “a technique known as phase diversity can also be used to infer phase aberrations from image data”):
a plurality of focus-related values, wherein each focus-related value of the plurality of focus-related values is associated with each image of the plurality of images (Paxman in page 1072 right column discloses that additional images are formed by perturbing unknown aberrations “in some known fashion”, including imagery degraded with “a known amount of defocus”; page 1073 discloses “θk is a known phase function associated with the kth diversity image, and … Phase diversity can be created, for example, by intentionally defocusing the system by known amounts.”);
a maximum likelihood estimate (MLE) of the plurality of images (Paxman in ¶ discloses “Maximum-likelihood estimation is considered under additive Gaussian and Poisson noise models” for “multiple images incorporating phase diversity”; page 1073 right column discloses “Given the set of K detected diversity images {dk}, the corresponding set of phase-diversity functions {θk}, and the binary pupil functions {|Hk|}, estimate the object f and the aberration parameters α”; page 1073 left column discloses an objective function that “yields the maximum-likelihood estimate (MLE) for the aberration parameters when maximized”; page 1074 left column discloses “The MLE is the estimate that is most likely to I produced a specific measurement” and is found by maximizing the likelihood function “with respect to f and α”; page 1075 left column discloses that once the MLE for the aberration parameter is found, it can be used “to find the MLE for the object parameter”); and
generating a focus-corrected image of the area based on the determined plurality of focus-related values and the determined MLE (Paxman in page 11072 right column discloses that once aberration estimates are known, “an estimate of the system optical transfer function (OTF) can be constructed, and the object can be restored by using, for example, Wiener filtering”; page 1076 discloses “Once an aberration function that maximizes LM is found, the corresponding OTF's can be constructed, and the MLE of the object is easily computed with Eq. (19)”).
Therefore, it would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to modify the invention of Hong by incorporating maximum-likelihood phase-diversity estimation from multiple diversity images that is taught by Paxman, since both reference are analogous art in the field of computational image-quality improvement using multiple images acquired at different focus/phase-diversity conditions; thus, one of ordinary skilled in the art would be motivated to combine the references since Hong’s focus-correction framework with Paxman’s maximum-likelihood phase-diversity estimation yields the predictable result of improving the determination of focus/aberration information from multiple images, thereby improving the accuracy of the focus-corrected image.
Hong in view of Paxman does not explicitly teach all of generating a focus-corrected image of the area based on the determined plurality of focus-related values and the determined MLE.
However, Simonov teaches generating a focus-corrected image of the area based on the determined plurality of focus-related values and the determined MLE (Simonov in page 2 discloses “image reconstruction resulting in a corrected in-focus image”; page 6 discloses “digitally reconstruct a final in-focus image of the object based on digital processing of multiple intermediate defocused images”; page 7 discloses “a focused final image of an object is derived, by digital reconstruction, from at least two, defocused intermediate images … having a precisely known degree of defocus”).
Therefore, it would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to modify the invention of Hong in view of Paxman by incorporating reconstruction of a final in-focus image from phase-diverse defocused intermediate images having known relative defocus that is taught by Simonov, since both reference are analogous art in the field of computational focus correction and image reconstruction; thus, one of ordinary skilled in the art would be motivated to combine the references since Hong in view of Paxman’s focus-corrected imaging and MLE phase-diversity estimation framework with Simonov’s phase-diverse in-focus reconstruction technique yields the predictable result of generating a focus-corrected/in-focus image from multiple defocused images using known focus/defocus relationships, thereby improving image quality and reducing defocus-related blur.
Thus, the claimed subject matter would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention.
Claim(s) 5 and 17 is/are rejected under 35 U.S.C. 103 as obvious over Hong et al (US 20200382715 A1, hereafter referred to as Hong) in view of Paxman et al (Paxman, R. G., Schulz, T. J., & Fienup, J. R. (1992). Joint estimation of object and aberrations by using phase diversity. Journal of the Optical Society of America A, 9(7), 1072-1085, hereafter referred to as Paxman), further in view of Simonov et al (WO 2009108050 A1, hereafter referred to as Simonov), further in view of Shemesh et al (X, hereafter referred to as Shemesh).
Claim 5
Regarding Claim 5, Hong in view of Paxman in view of Simonov teaches The system of claim 1, wherein a range of the plurality of focus-related values (Hong in ¶52 discloses a range of focus-related positions by using a minimum distance Z’min and maximum distance Z’max between the objective lens and the focus plane of the sample over which images are captured)
Hong in view of Paxman in view of Simonov does not explicitly teach all of corresponds to a range of voltages associated with an objective lens.
However, Shemesh teaches corresponds to a range of voltages associated with an objective lens (Shemesh in ¶30 discloses that changing the energy of the charged-particle beam includes changes of potentials in the charged-particle beam column that influence charged velocity within the objective lens, that an accelerating voltage may be varied, and that any other voltage or current modification that affects the focusing properties of the objective lens can be used. The variation in energy produces defocusing and may be conducted as a “single linear ramp”; ¶61 discloses a high-voltage supply unit, an objective-lens supply unit, and a specimen-voltage supply unit controlled by a parameter-adjustment unit).
Therefore, it would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to modify the invention of Hong in view of Paxman in view of Simonov by incorporating voltage/current-based focus or defocus control associated with an objective lens that is taught by Shemesh, since both reference are analogous art in the field of controlling focus/defocus conditions in imaging systems; thus, one of ordinary skilled in the art would be motivated to combine the references since Hong in view of Paxman in view of Simonov’s focus/phase-diversity image-correction system with Shemesh’s voltage/current-based focus-control technique yields the predictable result of electronically generating a range of focus/defocus values corresponding to voltage changes, thereby enabling faster and more controllable focus variation.
Thus, the claimed subject matter would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention.
Claim 17
Regarding Claim 17, Hong in view of Paxman in view of Simonov teaches The method of claim 13, wherein a range of the plurality of focus-related values (Hong in ¶52 discloses a range of focus-related positions by using a minimum distance Z’min and maximum distance Z’max between the objective lens and the focus plane of the sample over which images are captured)
Hong in view of Paxman in view of Simonov does not explicitly teach all of corresponds to a range of voltages associated with an objective lens.
However, Shemesh teaches corresponds to a range of voltages associated with an objective lens (Shemesh in ¶30 discloses that changing the energy of the charged-particle beam includes changes of potentials in the charged-particle beam column that influence charged velocity within the objective lens, that an accelerating voltage may be varied, and that any other voltage or current modification that affects the focusing properties of the objective lens can be used. The variation in energy produces defocusing and may be conducted as a “single linear ramp”; ¶61 discloses a high-voltage supply unit, an objective-lens supply unit, and a specimen-voltage supply unit controlled by a parameter-adjustment unit).
Therefore, it would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to modify the invention of Hong in view of Paxman in view of Simonov by incorporating voltage/current-based focus or defocus control associated with an objective lens that is taught by Shemesh, since both reference are analogous art in the field of controlling focus/defocus conditions in imaging systems; thus, one of ordinary skilled in the art would be motivated to combine the references since Hong in view of Paxman in view of Simonov’s focus/phase-diversity image-correction system with Shemesh’s voltage/current-based focus-control technique yields the predictable result of electronically generating a range of focus/defocus values corresponding to voltage changes, thereby enabling faster and more controllable focus variation.
Thus, the claimed subject matter would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention.
Claim(s) 11-12 is/are rejected under 35 U.S.C. 103 as obvious over Hong et al (US 20200382715 A1, hereafter referred to as Hong) in view of Paxman et al (Paxman, R. G., Schulz, T. J., & Fienup, J. R. (1992). Joint estimation of object and aberrations by using phase diversity. Journal of the Optical Society of America A, 9(7), 1072-1085, hereafter referred to as Paxman), further in view of Simonov et al (WO 2009108050 A1, hereafter referred to as Simonov), further in view of Makitalo et al (X, hereafter referred to as Makitalo).
Claim 11
Regarding Claim 11, Hong in view of Paxman in view of Simonov teaches The system of claim 1.
Hong in view of Paxman in view of Simonov does not explicitly teach all of wherein the circuitry is configured to cause the system to further perform: denoising noise in the obtained plurality of images, wherein the noise is modeled as a Poisson distribution; and modeling the denoised noise in the obtained plurality of images on a Gaussian distribution
However, Makitalo teaches wherein the circuitry is configured to cause the system to further perform:
denoising noise in the obtained plurality of images (Makitalo in Abstract discloses denoising for errors of digital imaging devices modeled as Poisson-Gaussian noise and states that the noise removal can be approached through variance stabilization; Section 4 discloses denoising each image using a three-step variance-stabilization approach and using BM3D or BLS-GSM as Gaussian denoising algorithms),
wherein the noise is modeled as a Poisson distribution (Makitalo in Section 2.1 discloses modeling each observed pixel value as an independent random Poisson variable p with an underlying mean value, scaled and corrupted by additive Gaussian noise, where p is distributed according to a function; Section 2.2 discloses the pure Poisson case of the Anscombe transformation for data corrupted by Poisson noise); and
modeling the denoised noise in the obtained plurality of images on a Gaussian
distribution (Makitalo in Section 1 discloses a three-step process in which noisy data is first modified by a variance-stabilizing transformation (VST), the resulting data can be treated as Gaussian with unitary variance and denoised with an algorithm designed for Gaussian noise, and an inverse VST is applied to the denoised data; Section 4 discloses denoising each image using BM3D or BLS-GSM as the Gaussian denoising algorithm).
Therefore, it would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to modify the invention of Hong in view of Paxman in view of Simonov by incorporating a Poisson/Poisson-Gaussian image-noise denoising and Gaussian-modeling technique that is taught by Makitalo, since both reference are analogous art in the field of digital image processing for improving reconstructed image quality from noisy data; thus, one of ordinary skilled in the art would be motivated to combine the references since Hong in view of Paxman in view of Simonov’s focus/phase-diversity reconstruction system with Makitalo’s denoising and Gaussian-modeling technique yields the predictable result of reducing Poisson/photon-limited image noise before or during phase-diversity reconstruction, thereby improving the accuracy and quality of the resulting focus-corrected image.
Thus, the claimed subject matter would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention.
Claim 12
Regarding Claim 12, Hong in view of Paxman in view of Simonov, further in view of Makitalo teaches The system of claim 11,
wherein each focus-related value of the plurality of focus-related values is associated with each denoised image of the plurality of images (Paxman in page 1073 discloses that a known phase function is associated with the kth diversity image and that phase diversity can be created by intentionally defocusing the system by known amounts; Makitalo in Abstract discloses denoising each image using the three-step variance-stabilization approach)
modeled on the Gaussian distribution (Makitalo in Section 1 discloses a three-step process in which noisy data is first modified by a variance-stabilizing transformation (VST), the resulting data can be treated as Gaussian with unitary variance and denoised with an algorithm designed for Gaussian noise, and an inverse VST is applied to the denoised data; Section 4 discloses denoising each image using BM3D or BLS-GSM as the Gaussian denoising algorithm).
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to JUSTIN P CASCAIS whose telephone number is (703) 756-5576. 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, Mr. O'Neal Mistry can be reached on (313) 446-4912. 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.
/J.P.C./Examiner, Art Unit 2674
/ONEAL R MISTRY/Supervisory Patent Examiner, Art Unit 2674
Date: 7/8/2026
Prosecution Insights