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 .
Status of Claims
Claims 14, 15, 17, 25 – 29 remain pending.
Claims 14, 25 and 28 are Amended
Claims 1 – 13 have been canceled
Response to Amendment
The amendment filed 01/21/2026 overcomes the following objections/rejections.
112 (d) Rejection
Response to Arguments
Applicant's arguments filed January 21, 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 Freytag Patent Application Publication No. WO-2021198252-A1 (hereinafter Freytag).
Regarding claim 14, Navid discloses an 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”)
Navid doesn’t disclose about the limitation as indicated via strike-through above.
Freytag discloses without spatial co-registration of different imaginq modalities (Freytag in [Page – 4, Paragraph – 1] discloses, “training the cycle generative adversarial network may be performed without a registration of the training imaging data and the reference images on a global scale. Alternatively, or in addition, the training may also be performed free of a registration on a local scale”).
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 Freytag into the system of Navid because it would make the computation of the system more faster by reducing the computational burden.
Summary of Citations (Freytag)
[Page – 4, Paragraph – 1]; “training the cycle generative adversarial network may be performed without a registration of the training imaging data and the reference images on a global scale. Alternatively, or in addition, the training may also be performed free of a registration on a local scale”.
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]; “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”.
Regarding claims 26 and 27, the grounds of rejection from the last Office Action with respect to Navid in view of Freytag apply here.
Claims 15, 28 and 29 are rejected under 35 U.S.C 103 as being unpatentable over Navid in view of Freytag and further in view of Nelson Patent Application Publication No. WO-2017146813-A1 (hereinafter Nelson).
Regarding claim 15, the ground of rejection based on Nelson from previous non-final Office Action of 10/21/2025 applies in here.
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 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”);, 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 claim14.
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”).
Freytag further discloses without spatial co- registration of different imaqinq modalities (Freytag in [Page – 4, Paragraph – 1] discloses, “training the cycle generative adversarial network may be performed without a registration of the training imaging data and the reference images on a global scale. Alternatively, or in addition, the training may also be performed free of a registration on a local scale”).
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]; “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 (Freytag)
[Page – 4, Paragraph – 1]; “training the cycle generative adversarial network may be performed without a registration of the training imaging data and the reference images on a global scale. Alternatively, or in addition, the training may also be performed free of a registration on a local scale”.
Summary of Citations (Nelson)
Paragraph [0034]; “The system obtains training data for the stained cell neural network (step 302). The training data includes multiple training examples, with each 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”.
Regarding claim 29, the ground of rejection based on Nelson from previous non-final Office Action of 10/21/2025 applies in here.
Claim 17 is rejected under 35 U.S.C 103 as being unpatentable over Navid in view of Freytag 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).
The ground of rejection based on Sue, Wendel and Lefkofsky from previous non-final Office Action of 10/21/2025 applies in here.
Claims 25 are rejected under 35 U.S.C 103 as being unpatentable over Navid in view of Freytag 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, Navid in the combination discloses the method as claimed in claim 14 further comprising training the system with a training architecture that comprises a generator-discriminator network (Zhaoyang in [Section – 1.2] discloses, “A significant amount of investigations have been made to explore the potential of GAN in natural images related tasks like image synthesizing, image super-resolution, and style transfer”).
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 teaching of Zhaoyang into the system of Navid to improve original network and to improve translation accuracy of cancer diagnosis (Abstract).
Summary of Citations (Zhaoyang)
[Section – 1.2]; “A significant amount of investigations have been made to explore the potential of GAN in natural images related tasks like image synthesizing, image super-resolution, and style transfer”.
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.
Contact
Any inquiry concerning this communication or earlier communications from the examiner should be directed to ZAID MUHAMMAD SALEH whose telephone number is (703)756-1684. The examiner can normally be reached M-F 8 am - 5 pm ET.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Vu Le can be reached on (571)272-7332. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/ZAID MUHAMMAD SALEH/
Examiner, Art Unit 2668
2/05/2026
/VU LE/Supervisory Patent Examiner, Art Unit 2668