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 .
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 06/09/2026 has been entered.
Status of Claims
Claims 14, 15, 17 and 25 – 29 remain pending.
Claims 14 and 28 are Amended.
Claims 16 and 18 – 24 have been withdrawn.
Response to Arguments
Applicant's arguments filed June 09, 2026 with respect to claims 14, 15, 17, 25 – 29
have been considered but are moot because the new grounds of rejection do not rely on any reference applied in the prior rejection of record for any teaching or matter specifically challenged in the argument.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 14, 26 and 27 are rejected under 35 U.S.C 103 as being unpatentable over Navid “Digital staining through the application of deep neural networks to multi -modal multi-photon microscopy” (hereinafter Navid) in view of Godavarty Patent Application Publication No. US-20150190061-A1 (hereinafter Godavarty).
Regarding claim 14, Navid discloses imaging method for generating a digitally stained image of a biological tissue probe from a physical image of an unstained biological tissue probe (Navid in [Abstract] discloses, “Deep neural networks have been used to map multi-modal, multi-photon microscopy measurements of a label-free tissue sample to its corresponding histologically stained brightfield microscope colour image”), the method comprising:G1) obtaining a physical image of an unstained biological tissue probe by optical microscopy (Navid in [Section – 2.1] discloses, “It was first observed with an integrated multi-modal microscope capable of recording spatially co-registered TPEF, FLIM, SHG, and optical coherence tomography (OCT) modalities”), G2) generating a digitally stained image from the physical image by using an artificial intelligence system (Navid in [Section – 1; Last paragraph] discloses, “These DNNs were used to produce qualitatively accurate visual reconstructions of the stained images from label-free observations using two different MPM techniques”), wherein the system is trained to predict a digitally stained image obtainable by staining the probe in a physical staining method (Navid in [Section – 1; Last paragraph] discloses, “A combination of TPEF and FLIM was used as the source dataset to train the DNNs. An H&E-stained brightfield microscope image of the same tissue sample was used as the target dataset”), wherein step G1) comprises obtaining the physical image of the unstained probe by simultaneous multi-modal microscopy (Navid in [Section – 2.2.1] discloses, “TPEF and FLIM modes were already co-registered since they were recorded simultaneously on the same imaging instrument”) in which the different imaging modalities are obtained through at least one common scan component (Navid in [Section – 2.1, Paragraph – 2] discloses, “Two computer-controlled galvanometer mirrors (Micromax 671, Cambridge Technology) were then used to raster scan the focused spot across the tissue section to construct the different 2-dimensional MPM images” wherein galvanometer mirrors is the common scan component).
Navid doesn’t disclose about the limitation as indicated via strike-through above.
Godavarty discloses without spatial co-registration of different imaging modalities (Godavarty in [0151] discloses, “a user may utilize system 100 without co-registering the images, without applying quantum efficiency compensation (e.g., if only one NIR wavelength is used), without sorting the NIR image data by wavelength (e.g., if only one NIR wavelength is used), etc”. Godavarty in [0029] discloses dual-wavelength light source equates to different imaging modalities).
It would have been obvious to one with one having an ordinary skill in art before the effective filling date of the claimed invention to integrate the technique of Godavarty into the system of Navid because it would make the computation of the system more faster by reducing the computational burden.
Summary of Citations (Navid)
[Abstract]; “Deep neural networks have been used to map multi-modal, multi-photon microscopy measurements of a label-free tissue sample to its corresponding histologically stained brightfield microscope colour image”.
[Section – 1; Last Paragraph]; “These DNNs were used to produce qualitatively accurate visual reconstructions of the stained images from label-free observations using two different MPM techniques. A combination of TPEF and FLIM was used as the source dataset to train the DNNs. An H&E-stained brightfield microscope image of the same tissue sample was used as the target dataset”.
[Section – 2.1, Paragraph – 2]; “Two computer-controlled galvanometer mirrors (Micromax 671, Cambridge Technology) were then used to raster scan the focused spot across the tissue section to construct the different 2-dimensional MPM images”.
[Section – 2.1]; “The tissue section was a 10 μm thick slice of ex vivo label-free fixed rat liver tissue mounted on a glass microscope slide. It comprised hepatic cells to which capillaries deliver blood. It was first observed with an integrated multi-modal microscope capable of recording spatially co-registered TPEF, FLIM, SHG, and optical coherence tomography (OCT) modalities”.
[Section – 2.2.1]; “TPEF and FLIM modes were already co-registered since they were recorded simultaneously on the same imaging instrument”.
Summary of Citations (Godavarty)
Paragraph [0029]; “source assembly 180 may be configured as a dual-wavelength light source emitting NIR light having wavelengths between the inclusive ranges of 670-710 nm (e.g., 690 nm) and 810-850 (e.g., 830 nm) to obtain the changes in oxy- (HbO) and deoxy-hemoglobin concentrations (HbR) of the imaged tissue region”.
Paragraph [0151]; “a user may utilize system 100 without co-registering the images, without applying quantum efficiency compensation (e.g., if only one NIR wavelength is used), without sorting the NIR image data by wavelength (e.g., if only one NIR wavelength is used), etc”.
Regarding claim 26 and 27, the combination of Navid and Godavarty as a whole teaches claim 1, and Navid teaches claim 26 for the same grounds of rejection from the Final Office Action of 02/11/2026.
Claims 15, 28 and 29 are rejected under 35 U.S.C 103 as being unpatentable over Navid in view of Godavarty and further in view of Nelson Patent Application Publication No. WO-2017146813-A1 (hereinafter Nelson).
Regarding claim 15, the combination of Navid and Godavarty as a whole teaches claim 1 but fails to teach the further limitations as recited in claim 15. Nelson teaches claim 15 for the same grounds of rejection and motivation established in the Final Office Action of 02/11/2026.
Regarding claim 28, Navid discloses a system for generating a digitally stained image of a biological tissue probe and/or for training an artificial intelligence system, the system comprising (Navid in [Abstract] discloses, “Deep neural networks have been used to map multi-modal, multi-photon microscopy measurements of a label-free tissue sample to its corresponding histologically stained brightfield microscope colour image”):- an optical microscopic system including at least one common scan component for obtaining physical images of biological tissue probes by simultaneous multi- modal microscopy (Navid in [Section – 2.1, Paragraph – 2] discloses, “It was first observed with an integrated multi-modal microscope capable of recording spatially co-registered TPEF, FLIM, SHG, and optical coherence tomography (OCT) modalities”. Navid in [Section – 2.1, Paragraph – 2] discloses, “Two computer-controlled galvanometer mirrors (Micromax 671, Cambridge Technology) were then used to raster scan the focused spot across the tissue section to construct the different 2-dimensional MPM images” wherein galvanometer mirrors is the common scan component); each pair comprising - a physical image of an unstained biological tissue probe obtained by simultaneous multi-modal microscopy (Navid in [Section – 2.1, Paragraph – 1] discloses, “The multi-modal dataset used for this study comprised a 16 spectral channel TPEF mode, a single channel FLIM mode, and a 3 channel stained brightfield microscope image of the same rat liver tissue sample”), - a stained image of said probe obtained in a physical staining method (Navid in [Section – 2.1, Paragraph – 3] discloses, “After measuring its TPF characteristics, the tissue was stained using H&E and observed under a brightfield microscope”); and - a processing unit for performing the imaging method according to claim 14.
Nelson further discloses a data storage for storing a multitude of image pairs (Nelson in [0034] discloses, “training example including one or more images of one or more cells, and, for each training example, one or more corresponding stained images. In some cases, for each training example, the corresponding stained images can depict the cells being stained with a variety of stains”).
Godavarty discloses without spatial co-registration of different imaging modalities (Godavarty in [0151] discloses, “a user may utilize system 100 without co-registering the images, without applying quantum efficiency compensation (e.g., if only one NIR wavelength is used), without sorting the NIR image data by wavelength (e.g., if only one NIR wavelength is used), etc”. Godavarty in [0029] discloses dual-wavelength light source equates to different imaging modalities).
Regarding claim 29, the combination of Navid and Godavarty as a whole teaches claim 14 but fails to teach the further limitations as recited in claim 29. Nelson teaches claim 29 for the same grounds of rejection and motivation established in the Final Office Action of 02/11/2026.
Claim 17 is rejected under 35 U.S.C 103 as being unpatentable over Navid in view of Godavarty and further in view of Sue Patent Publication No. US-11928820-B2 (hereinafter Sue), Wendel Patent Application Publication No. WO-2016149542-A1 (hereinafter Wendel) and Lefkofsky US Patent Application Publication No. US-20210118559-A1 (hereinafter Lefkofsky).
Regarding claim 17, the combination of Navid and Godavarty as a whole teaches claim 14 but fails to teach the further limitations as recited in claim 17. Sue, Wendel and Lefkofsky teaches claim 17 for the same grounds of rejection and motivation established in the Final Office Action of 02/11/2026.
Claims 25 are rejected under 35 U.S.C 103 as being unpatentable over Navid in view of Godavarty and further in view of Zhaoyang “GAN-based Virtual Re-Staining: A Promising Solution for Whole Slide Image Analysis” (hereinafter Zhaoyang), applicant submitted prior art.
Regarding claim 25, the combination of Navid and Godavarty as a whole teaches claim 14 but fails to teach the further limitations as recited in claim 25. Zhaoyang teaches claim 25 for the same grounds of rejection and motivation established in the Final Office Action of 02/11/2026.
Conclusion
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/ZAID MUHAMMAD SALEH/
Examiner, Art Unit 2668
07/06/2026
/VU LE/Supervisory Patent Examiner, Art Unit 2668
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