Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Response to Amendment
This action is in response to the amendment filed on February 11th, 2026. Claims 1, and 7 have been amended. The amended claims limitations have been fully considered, but are not persuasive.
Response to Arguments
Applicant's arguments filed February 11th, 2026 have been fully considered but they are not persuasive.
Regarding claim 1, applicant argues that the prior art of record fails to read on the newly recited claim language. Examiner disagrees. Examiner asserts that Tao teaches:
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Tao discloses at (Tao; Background, describes the PSF of a microscopic imaging system is an Airy disc and, step 1.8, describes using a Bessel function to model the PSF. Expressing the Airy disc PSF using the first order Bezier function parameter would have been an obvious substitution of known mathematical principles to the PSF model taught by Tao.)
(Tao, ¶1.6, s42, describes the full width at half maximum of the PSF and uses it to define the extent of the PSF.)
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is the central coordinate of the point diffusion model, (Tao; s42, describes local extreme points centered on the PSF. This teaches a central coordinate of the point diffusion model.) (x,y) is an object of coordinates of the point diffusion model in which the object is to be detected(Tao; 1.5, describes
pixel-by-pixel comparison across image coordinates where fluorescent molecules (objects) are to be detected.) Therefore, this argument is not persuasive and therefore claim 1 stands rejected as further detailed below.
Applicant argues that the dependent claims are allowable based on their dependence on an independent claim. However, as the independent claims stand rejected as discussed above, the dependent claims also stand rejected as further detailed below.
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.
Claims 1 and 8 are rejected under 35 U.S.C. 103 as being unpatentable over Langlois (US20210118110A1) and Tao (CN114820449A).
Regarding claim 1, Langlois teaches an image enhancement method for widefield fluorescence imaging system comprising a widefield fluorescence microscope and a computer equipment connected to the widefield fluorescence microscope (ABST describes structured illumination microscopy (SIM), which uses widefield fluorescent microscopes, and computing resources (computer equipment) for enhanced images), the method comprising: arranging fluorescent microspheres on an image plane of the widefield fluorescence microscope, and collecting, by the widefield fluorescence microscope, the microsphere image of the fluorescent microspheres (¶0195 obtaining images of distributed (arranged) fluorescent microspheres) and collecting a sample image by the widefield fluorescence microscope (ABST capturing images using the microscope and ¶0193 the acquired sample image), and then processing, by the computer equipment, the sample image based on the point diffusion model to obtain an enhanced image (¶0193 describes for a sample (sample image), constructing and using a point spread function, which represents how much blur the system introduces (point diffusion model), converting the PSF to an OTF (optical transfer function) and ¶0195 imaging the fluorescent microspheres to obtain the point spread function (PSF) and then OTF which is then used to apply the estimation technique to the subtiles, which are used to obtain an enhanced image).
However, although Langlois teaches determining a PSF from the fluorescent microspheres, Langlois does not explicitly teach, processing, by the computer equipment, the microsphere image, to obtain the half-peak full width value and constructing a point diffusion model based on the half-peak full width value nor the specific formula for the point diffusion model.
Tao teaches (¶1.6) that the half-peak full width (full width half max or FWHM) is the distance between the points where the value equals half of the peak and to search for (obtain) the full width at half maximum of the point spread function. (¶S42) “the other local extremum points are the point spread function centered on the local extremum point Full width at half maximum”. The full width half maximum value is used to define the extent of the PSF (point diffusion model).
While Tao describes the fluorescent point sources as fluorescent molecules rather than fluorescent microspheres, both show up as small intensity peaks in the image that reflect how the microscope spreads light from a point source (PSF), and so determining the FWHM of the fluorescence intensity peaks would read as processing the microsphere image to obtain the half-peak full width value. wherein the point diffusion model comprises:
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(Tao; Background, describes the PSF of a microscopic imaging system is an Airy disc and, step 1.8, describes using a Bessel function to model the PSF. Expressing the Airy disc PSF using the first order Bezier function parameter would have been an obvious substitution of known mathematical principles to the PSF model taught by Tao.) wherein in the formula, h(x, y) is the point diffusion model, J1, is a first type of first order Bezier function (Examiner interprets “Bezier function” as “Bessel function” based on the
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notation and the Airy disc context. Tao; step 1.8, describes using a Bessel function to model the PSF.)
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(Tao, ¶1.6, s42, describes the full width at half maximum of the PSF and uses it to define the extent of the PSF.)
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is the central coordinate of the point diffusion model, (Tao; s42, describes local extreme points centered on the PSF. This teaches a central coordinate of the point diffusion model.) (x,y) is an object of coordinates of the point diffusion model in which the object is to be detected(Tao; 1.5, describes pixel-by-pixel comparison across image coordinates where fluorescent molecules (objects) are to be detected.)
It would have been obvious to one of ordinary skill, before the effective filing date, to modify the method of Langlois with the full-width-half-max-based PSF of Tao as the PSF of Langlois reasonably includes width, and Tao teaches FWHM is well-known and routinely used for width and substitution of one known element for another, and to express Airy disc PSF using this, providing the benefit of increase accuracy when modeling.
Regarding claim 8, Langlois in view of Tao teaches a widefield fluorescence imaging system applying the image enhancement method according to claim 1, and Langlois further teaches it comprising: the widefield fluorescence microscope for collecting the sample image (Langlois ¶0087, describes the SIM hardware, which uses widefield fluorescent microscopes and ¶0354 describes specifically wide-field fluorescence microscopy) and the computer equipment connected to the widefield fluorescence microscope, the computer equipment receives the sample image and processes the sample image based on the image enhancement method to obtain the enhanced image (¶0477, describes the methods (of claim 1) being used in a computer implemented system).
Regarding claim 9, Langlois in view of Tao teach the widefield fluorescence imaging system according to claim 8, and Langlois further teaches wherein the computer device comprises: a model building module, the model building module constructs the point diffusion model according to the microsphere image collected by the widefield fluorescence microscope (Langlois ¶0507, describes that the computer equipment includes software modules that implement the methods, including the method of constructing the point diffusion model from microsphere images as previous taught in claim 1) and the image enhancement module, the image enhancement module is connected to the model building module, and the image enhancement module processes the sample image according to the point diffusion model to obtain the enhanced image (¶0507 describing that the computer system includes modules implementing the functionality of the methods, including the image enhancement method of claim 1. The modules are stored in the same storage system (connected) and executed by the processor).
Claim 2 is rejected under 35 U.S.C. 103 as being unpatentable over Langlois (US20210118110A1), Tao (CN114820449A), and Hupfel (Hüpfel, M., Yu. Kobitski, A., Zhang, W. and Nienhaus, G.U., 2021. Wavelet-based background and noise subtraction for fluorescence microscopy images. Biomedical Optics Express, 12(2), pp.969-980.)
Regarding claim 2, Langlois in view of Tao fails to teach, but Hupfel teaches wherein before arranging the fluorescent microspheres, the widefield fluorescence microscope collects a background image on the image plane;
(¶3.2 DSLM image acquisition, describes acquiring an image, before the fluorescent beads, with the illumination switched off) and after collecting, by the widefield fluorescence microscope, the microsphere image, the computer equipment outputs the microsphere image after noise removal based on the background image(¶3.2 the background image is subtracted from the slices to remove dark camera background (denoise)). It would have been obvious to one of ordinary skill in the art, before the effective filing date, to apply and/or modify the method as taught by Langlois in view of Tao with the background noise removal technique of Hupfel because removing camera background noise through background image subtraction is a well-known technique in fluorescence microscopy imaging that improves image quality and accuracy by removing background noise.
Claims 3, 4, and 6 are rejected under 35 U.S.C. 103 as being unpatentable over Langlois (US20210118110A1), Tao (CN114820449A), and Lal (Lal, A., Shan, C. and Xi, P., 2016. Structured illumination microscopy image reconstruction algorithm. IEEE Journal of Selected Topics in Quantum Electronics, 22(4), pp.50-63.)
Regarding claim 3, Langlois in view of Tao fails to teach, but Lal teaches the image enhancement method according to claim 1, wherein in the process of collecting microsphere image by the widefield fluorescence microscope, the widefield fluorescence microscope collects a plurality of fluorescence images on the fluorescent microspheres in turn (pg. 8 B. Experimental Results ¶2, obtain several images of 100nm fluorescent microspheres) the computer equipment receives each of the fluorescence images in turn (pg. 8 B. Experimental Results ¶3, describes the computer equipment receives the raw images acquired by the microscope for preprocessing using Matlab) and the computer equipment averages all of the fluorescence images and outputs them to function as the microsphere image (pg. 8 B. Experimental Results ¶2, describes the computer equipment averages the intensity distributions from the multiple microsphere images to obtain the PSF, which acts as the microsphere image for the system). It would have been obvious to one of ordinary skill in the art, before the effective filing date, to apply and/or modify the method as taught by Langlois in view of Tao with the image averaging technique of Lal because averaging multiple microsphere images to determine the PSF is a known technique in fluorescence microscopy imaging that improves the resolution of image capture systems as stated by Langlois in ¶0050.
Regarding claim 4, Langlois in view of Tao teaches the image enhancement method according to claim 1, as discussed in claim 1, including generating, by the computer equipment, the half-peak full width value according to the image data of the target microspheres (Tao teaches, as discussed in claim 1, that the half-peak full width (or full width half max) is obtained/searched for from point spread functions (¶1.6, S42). When applied to the target microspheres selected from the preprocessed images, as taught by Lal, this reads on generating the half-peak full width value of the image data of the target microspheres).
However, Langlois in view of Tao fails to teach, but Lal teaches wherein the process of processing the microsphere image by the computer equipment further comprises: performing morphological processing, by the computer equipment, on the microsphere image to obtain a preprocessed image; (Lal pg. 8 section B. Experimental Results ¶3, describes performing morphological processing using the imopen function on the images to preprocess them, which reads on performing morphological preprocessing on the microsphere image to obtain a preprocessed image) and screening, by the computer equipment, the fluorescent microspheres from the preprocessed image, to obtain target microspheres (pg. 8 B. Experimental Results ¶2, the computer equipment identifies and selects specific microspheres (more than 100 microspheres) from the images for analysis, which reads on screening the fluorescent microspheres from the preprocessed image to obtain target microspheres). It would have been obvious to one of ordinary skill in the art, before the effective filing date, to apply and/or modify the method as taught by Langlois in view of the morphological preprocessing and microsphere screening of Lal. Langlois cites Lal (¶0050) for SIM processing techniques, and the combination of morphological preprocessing and microsphere screening yields the predictable result of improved PSF and FWHM determination accuracy.
Regarding claim 6, Langlois in view of Tao and in further view of Lal teaches the image enhancement method according to claim 3, wherein the process of generating the half-peak full width value by the computer equipment according to the image data comprises: performing, by the computer equipment, a nonlinear least squares fitting to the image data of each of the target microspheres to obtain the half-peak full width value of the fluorescent microspheres (Lal pg. 13 Appendix B Effective PSF Determination, teaches using least squares fitting to analyze image data and determine PSF and (pg. 8 B. Experimental Results ¶2) applying this to the microsphere data, which would show that the least squares fitting is applied to the image data of the (target) microspheres., Tao teaches obtaining the half-peak full width as previously discussed in claim 1. The combination of Lal’s least squares fitting applied to microsphere image data with Tao’s teaching of obtaining half-peak full width reads on performing least squares fitting to the image data of each target microsphere to obtain the half-peak full width value.) and averaging, by the computer equipment, all of the half-peak full width value of the fluorescent microspheres to obtain the half-peak full width value. Lal teaches (pg. 13 Appendix B Effective PSF Determination) computing the mean (average) of multiple determinations and (pg. 8 B. Experimental Results ¶2) averaging data from more than 100 microspheres, which reads on averaging all of the half-peak full width values to obtain the final half-peak full width value. It would have been obvious to one of ordinary skill in the art, before the effective filing date, to apply and/or modify the method as taught by Langlois in view of Tao and Lal to use least squares fitting to determine PSF parameters from microsphere image data and to average the half-peak full width values obtained from multiple microspheres because least squares fitting is a well-known mathematical technique for parameter estimation from image data with the predictable result of obtaining accurate half-peak full width values by averaging across multiple microspheres to reduce variability.
Claim 5 is rejected under 35 U.S.C. 103 as being unpatentable over Langlois (US20210118110A1), Tao (CN114820449A), Lal (Lal, A., Shan, C. and Xi, P., 2016. Structured illumination microscopy image reconstruction algorithm. IEEE Journal of Selected Topics in Quantum Electronics, 22(4), pp.50-63.), and Jose (Jose, A.J., Wong, L.S., Merrington, J. and Bradley, M., 2005. Automated image analysis of polymer beads and size distribution. Industrial & engineering chemistry research, 44(23), pp.8659-8662.)
Regarding claim 5, Langlois in view of Tao and Lal fails to teach, but Jose teaches the image enhancement method according to claim 4, wherein the process of screening, by the computer equipment, the fluorescent microspheres further comprises: performing, by the computer equipment, a detection of connectivity domain on the preprocessed image to obtain a plurality of connectivity domains, and removing, according to the connectivity domain, the adjacent fluorescent microspheres (Jose pg. 8660, Results and Discussion ¶1, describes identifying and locating individual beads (microspheres) while excluding aggregates and debris. This reads on detecting connectivity domains to identify individual beads and removing adjacent fluorescent microspheres by excluding aggregates), generating, by the computer equipment, a roundness of each of the fluorescent microspheres after screening (pg. 8660 ¶1, a roundness index using the perimeter and software automatically identifying the perimeter of the particles, which reads on calculating a roundness value for each bead after screening), comparing, by the computer equipment, the roundnesses with a preset roundness threshold to remove the fluorescent microspheres whose roundness is less than the roundness threshold (pg. 8660 ¶1, applying a second roundness filter to exclude (remove) particles with a roundness index greater than 1.2 (threshold)) and outputting, by the computer equipment, the screened fluorescent microspheres to function as the target microspheres (pg. 8660 ¶2, the selected (screened) beads are counted and the area and diameter are calculated by the software. The selected beads that pass the filtering step are output as the target beads for analysis). It would have been obvious to one of ordinary skill in the art, before the effective filing date, to apply and/or modify the method as taught by Langlois in view of Tao and Lal with the automated bead screening techniques of Jose because detection of connectivity domains, roundness calculation, and threshold filtering are well known image processing techniques of using isolated and correctly sized/formed microspheres to more accurately determine the half-peak full width and PSF.
Claims 7 and 10 are rejected under 35 U.S.C. 103 as being unpatentable over Langlois (US20210118110A1), Tao (CN114820449A), and Diaz-Zamboni (Diaz-Zamboni, J.E., Paravani, E.V., Adur, J.F. and Casco, V.H., 2007. Implementation of an iterative deconvolution algorithm and its evaluation on three-dimensional images of fluorescence microscopy. Acta Microsc, 16, pp.8-15.)
Regarding claim 7, Langlois in view of Tao fails to teach, but Diaz-Zamboni teaches the image enhancement method according to claim 1, wherein the process of processing the sample image by the computer equipment comprises: processing, by the computer equipment, the sample image based on the point diffusion model to obtain an intermediate image of this iteration (pg. 9, Introduction ¶2, obtaining the point spread function or PSF of the system (point diffusion model) and that (pg. 8, Summary) iterative algorithms work on an estimator, where Equation 4 (pg. 10)
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shows the iterative process where ô(k+1)(x,y,z) is the estimator (intermediate image) at k+1 (iteration) and using the PSF (x,y,z). The sample image i(x,y,z) is processed based on the PSF to obtain an intermediate estimate at each iteration.), generating, by the computer equipment, an evaluation result of this iteration according to the intermediate image, the point spread function, and the sample image (pg. 9-10, Equation 3
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, where R(k) is the calculated estimation error in each k cycle. The R(k) result is generated using ô(k) (intermediate image), s( x,y,z) (PSF), and i(x,y,z) (sample image)), determining, by the computer equipment, whether the intermediate image meets a preconfigured iteration condition (pg. 9 equation 3, teaches evaluating convergence by calculating the estimation error R(k) in each k cycle and (pg. 10-11 Software) stopping using a counter that stops after a determined amount of iterations (preconfigured iteration condition)), if yes, outputting, by the computer equipment, the intermediate image to function as the enhanced image (pg. 11-12 Algorithm Evaluation, when iterations the final estimator is output and the deconvolved image is output as the enhanced result (Fig. 1-6 comparing raw and deconvolved images)), If not, processing, by the computer equipment, the intermediate image based on the point diffusion model to obtain an intermediate image of next iteration (as described above, pg. 10 Equation 4, the iterative loop using ô(k+1)(x,y,z) shows that if the preconfigured iteration condition is not met, ô(k) (intermediate image) is iterated again, ô(k+1), using (x,y,z) (PSF)). It would have been obvious to one of ordinary skill in the art, before the effective filing date, to apply and/or modify the method as taught by Langlois in view of Tao with the iterative deconvolution technique of Diaz-Zamboni because iterative deconvolution algorithms are well known techniques in fluorescence microscopy for image reconstruction that improve image quality though iterative refinement cycles to enhanced resolution/focus.
However, Diaz-Zamboni does not explicitly teach:
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Diaz-Zamboni teaches an additive correction method, whereas the claimed sub-steps use a multiplicative correction method. Both are well-known iterative deconvolution approaches in fluorescence microscopy. It would have been obvious to one of ordinary skill in the art, before the effective filing date to substitute the additive correction od Diaz Zamboni with the multiplicative correction of the claimed method, as both are well known iterative deconvolution methods in fluorescence microscopy and substituting one for the other yields the predictable result of iteratively enhancing image quality using the PSF.
Regarding claim 10, Langlois in view of Tao and in further view of Diaz-Zamboni teaches the widefield fluorescence imaging system according to claim 9, wherein the image enhancement module is provided with an iterative submodule, the iterative submodule iterates the sample image according to the point diffusion model, to obtain the enhanced image (Diaz-Zamboni teaches the iterative method as discussed in claim 7, where the sample image ( i(x,y,z) ) is iteratively processed (Equation 4) according to the PSF (point diffusion model), (x,y,z) , to obtain an enhanced image (see claim 7).) As discussed in claim 9, Langlois teaches (¶0507) implementing functions as modules. Applying Diaz-Zamboni’s iterative deconvolution technique within Langlois’ modular architecture teaches an image enhancement module with an iterative submodule that iterates the sample image according to the point diffusion model to obtain an enhanced image. It would have been obvious to one of ordinary skill in the art, before the effective filing date, to apply and/or modify the iterative deconvolution technique of Diaz-Zamboni in the modular system architecture as taught by Langlois in view of Tao because iterative deconvolution algorithms are well known techniques in fluorescence microscopy for PSF-based image enhancement to enhanced image quality through successive refinements that reduce blur and improve resolution.
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to DAN F KALHORI whose telephone number is (571)272-5475. The examiner can normally be reached Mon-Fri 8:30-5:30 ET.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, DEVONA E FAULK can be reached at (571) 272-7515. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/DAN F KALHORI/Examiner, Art Unit 2618
/DEVONA E FAULK/Supervisory Patent Examiner, Art Unit 2618
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