Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Information Disclosure Statement
The non-patent literatures No. B93, and B102 cited on the information disclosure statement (IDS) filed on 03/19/2025 by Applicant, have not been considered for not complying with the MPEP: 37 CFR 1.98 (b) (5)) for missing the dates of publication. "Each publication listed in an information disclosure statement must be identified by publisher, author (if any), title, relevant pages of the publication, date, and place of publication" (MPEP: 37 CFR 1.98 (b) (5)).
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 1-2, 8-11, and 16-20 are rejected under 35 U.S.C. 103 as being unpatentable over Shao et al, ("scDeepSort: a pre-trained cell-type annotation method for single-cell transcriptomics using deep learning with a weighted graph neural network", Nucleic acids research 2021, Vol. 49, No. 21; September 9, 2021) in view of Chris et al, (“Predicting single-cell gene expression profiles of imaging flow cytometry data with machine learning”, Nucleic Acids Research, 2020, Vol. 48, No. 20 PP 11335-11346, October 20, 2020).
In regards to claim 1, Shao et al discloses a computer-implemented method
to determine an omics profile of a cell using microscopy imaging data, (see at least: Abstract, “a pre-trained cell-type annotation tool for single-cell transcriptomics that uses a deep learning model with a weighted graph neural network (GNN)”), comprising:
b. determining, by the at least one computing device, a cells from a particular tissue of a set of target genes from the dataset using a first machine learning model, the target genes identifying a cell type or cell state of interest, (see at least: Page 4, left-hand-side, second paragraph, the trained cell-gene graph and the gene expression of new cells will be added to the edge between the new cell node and the gene node as the weight; and from Page 4, left-hand-side, second paragraph, last paragraph through right-hand-column, first paragraph, CellMatch. MCA or HCL were used as the reference datasets, “i.e., cells from particular tissue”. Using cells from a particular tissue to train learning model for cell-type prediction on the test cells that originated from the same tissue, [i.e., determining a targeted expression profile of a set of target genes from the dataset, using a first machine learning model, “using cells from a particular tissue, such as cell-gene graph and the gene expression of new cells from dataset”, the target genes identifying a cell type or cell state of interest, “cell-type prediction”]);
c. determining, by the at least one computing device, a single-cell omics profile for the cell or population of cells using a second machine learning algorithm model, wherein the cells from a particular tissue and a reference single-cell RNA-seq data set are used as input data for the second machine learning model, (see at least: Fig. 2, and Page 5, right-hand-column, first paragraph, an undirected and weighted graph containing cell nodes and gene nodes was constructed from an adjacent weighted matrix by taking the gene expression as the weighted edges between cells and genes to model the intrinsic geometric information, which constitutes scDeepSort's first embedding layer (Figure 2), [i.e., determining a single-cell omics profile for the cell or population of cells, “intrinsic geometric information”, using a second machine learning algorithm model, “the weighted GNN model”]. Further, from Page 4, left-hand-column, last paragraph, the reference datasets for reference-dependent methods. To compare the performance of scDeepSort with other methods on annotating cell types of single-cell transcriptomics data, only the cell types that existed in both cell marker database (Cell Match) and RNA-seq profiles (MCA and HCL), were selected, [i.e., wherein the targeted expression profile, “cells from a particular tissue” and a reference single-cell RNA-seq data set, “RNA-seq profiles (MCA and HCL)”, are used as input data for the second machine learning model, “the weighted GNN model, as shown in Fig. 2”]).
Shao et al does not expressly disclose receiving, by at least one computing device, microscopy imaging data of a cell or a population of cells; and the determining a targeted expression profile.
Chris discloses receiving, by at least one computing device, microscopy imaging data of a cell or a population of cells, (see at least: Page 1, under introduction, “one of the most prominent single-cell profiling methods is the fluorescence microscopy (1), which allows for the acquisition of information-rich imaging data, “implicit the receiving of microscopy imaging data of cell data”); and determining, a targeted expression profile, (see at least: Abstract, using machine learning for predicting the expression profile of every cell in an imaging flow cytometry experiment, “i.e., determining targeted expression profile”).
Shao and Chris are combinable because they are both concerned with microscopy imaging analysis. Therefore, it would have been obvious to a person of ordinary skill in the art, to modify Shao, to machine learning methodology as though by Chris, in order to predict gene expression directly from brightfield images in a label-free manner, (Chris, Abstract)
In regards to claim 2, the combine teaching Shao and Chris as whole discloses the limitations of claim 1.
Chris further discloses wherein the targeted expression profile is targeted spatial expression profile, (see at least: Page 16, first paragraph, “spatially resolved transcriptional information in tissues”).
In regards to claim 8, the combine teaching Shao and Chris as whole discloses the limitations of claim 1.
Shao further discloses wherein the gene expression data is sequencing based omics data, imaging-based omics data or spatial omics data, (see at least: Fig. 1, under data preparation, where the cell transcriptomics Atlas represents the sequencing-based omics data)
In regards to claim 9, the combine teaching Shao and Chris as whole discloses the limitations of claim 1.
Shao further discloses wherein the first machine learning model comprises gradient boosting; and/or the second machine learning model comprises neural networks, (see at least: Abstract, and Page 2, left-hand-column, line 5, “graph neural network”).
Regarding claim 10, claim 10 recites substantially similar limitations as set forth in claim 1. As such, claim 10 is rejected for at least similar rational.
The Examiner further acknowledged the following additional limitation(s): “system to determine an omics profile of a cell using microscopy imaging data”. However, Shao discloses the “system to determine an omics profile of a cell using microscopy imaging data”, (Page 2, under section data preprocessing, implicitly using a system or device for perform data were preprocessed).
Regarding claim 11, claim 11 recites substantially similar limitations as set forth in claim 2. As such, claim 11 is rejected for at least similar rational.
Regarding claim 16, claim 16 recites substantially similar limitations as set forth in claim 8. As such, claim 16 is rejected for at least similar rational.
Regarding claim 17, claim 17 recites substantially similar limitations as set forth in claim 9. As such, claim 17 is rejected for at least similar rational.
Regarding claim 18, claim 18 recites substantially similar limitations as set forth in claim 1. As such, claim 18 is rejected for at least similar rational.
The Examiner further acknowledged the following additional limitation(s): “computer program product, comprising: a non-transitory computer-readable storage device having computer-executable program instructions embodied thereon”. However, Shao discloses the “computer program product, comprising: a non-transitory computer-readable storage device having computer-executable program instructions embodied thereon”, (see at least: Page 2, under section “scDeepSort algorithm”, which the algorithm implicit using computer program product …).
Regarding claim 19, claim 19 recites substantially similar limitations as set forth in claim 2. As such, claim 19 is rejected for at least similar rational.
Regarding claim 20, claim 20 recites substantially similar limitations as set forth in claim 8. As such, claim 20 is rejected for at least similar rational.
Claims 3-4, and 12 are rejected under 35 U.S.C. 103 as being unpatentable over Shao and Chris, as applied to claim 1 above; and further in view of Cremer et al, (US-PGPUB 20090263002)
In regards to claim 3, the combine teaching Shao and Chris as whole discloses the limitations of claim 1.
The combine teaching Shao and Chris as whole does not expressly disclose wherein the microscopy imaging data is obtained from a label-free microscopy method or an in vivo imaging method.
Cremer discloses wherein the microscopy imaging data is obtained from a label-free microscopy method or an in vivo imaging method, (see at least: Par. 0021, obtaining microscopic images; and from Par. 0225, 0227, in-vivo measurements implicit the in vivo imaging method).
Shao, Chris, and Cremer are combinable because they are all concerned with microscopy imaging analysis. Therefore, it would have been obvious to a person of ordinary skill in the art, to modify the combine teaching Shao and Chris, to use the in-vivo measurements, as though by Cremer, in order to enable the "real-time" observation of e.g. physiological processes, (Cremer, Par. 0186).
The following prior art made of record and not relied upon is considered
pertinent to claim 3
-- Mao (US-PGPUB 20230257587) discloses an in vivo imaging for an early detection, screening, diagnosis, image-guided surgical intervention, and treatment of various diseases, (Par. 0190).
In regards to claim 4, the combine teaching Shao and Chris as whole discloses the limitations of claim 1.
The combine teaching Shao and Chris as whole does not expressly disclose wherein the cell or population of cells are live or fixed
Cremer discloses wherein the cell or population of cells are live or fixed, (see at least: Par. 0186, live cell imaging enables the "real-time" observation of e.g. physiological processes, “i.e., the cell or population of cells are live cells”).
Shao, Chris, and Cremer are combinable because they are all concerned with microscopy imaging analysis. Therefore, it would have been obvious to a person of ordinary skill in the art, to modify the combine teaching Shao and Chris, to use the in-vivo measurements, as though by Cremer, in order to enable the "real-time" observation of e.g. physiological processes, (Cremer, Par. 0186).
Regarding claim 12, claim 12 recites substantially similar limitations as set forth in claim 3. As such, claim 12 is rejected for at least similar rational.
Claims 5 and 13 are rejected under 35 U.S.C. 103 as being unpatentable over Shao and Chris, as applied to claim 1 above; and further in view of Meade et al, (US-PGPUB 20230026291)
In regards to claim 5, the combine teaching Shao and Chris as whole discloses the limitations of claim 1.
The combine teaching Shao and Chris as whole does not expressly disclose wherein the microscopy imaging data is vibrational hyperspectral imaging data
Meade discloses wherein the microscopy imaging data is vibrational hyperspectral imaging data, (see at least: Par. 0118, implicit by hyperspectral or chemical images acquisition based on inserting the sample into the focus of a Fourier-transform infrared microscope. Note that the Fourier-transform infrared implicitly comprises the frequencies of the modes of vibration).
Shao, Chris, and Meade are combinable because they are all concerned with microscopy imaging analysis. Therefore, it would have been obvious to a person of ordinary skill in the art, to modify the combine teaching Shao and Chris, to inserting the sample into the focus of a Fourier-transform infrared microscope, as though by Meade, in order to acquire images of the sample, (Meade, Par. 0118).
Regarding claim 13, claim 13 recites substantially similar limitations as set forth in claim 5. As such, claim 13 is rejected for at least similar rational.
Claims 6 and 14 are rejected under 35 U.S.C. 103 as being unpatentable over Shao and Chris, as applied to claim 1 above; and further in view of Koller et al, (US-PGPUB 20210366577)
In regards to claim 6, the combine teaching Shao and Chris as whole discloses the limitations of claim 1.
The combine teaching Shao and Chris as whole does not expressly disclose wherein the microscopy imaging data comprises Cell Painting or Cell Profiler.
Koller discloses wherein the microscopy imaging data comprises Cell Painting or Cell Profiler, (see at least: Par. 0245, preparation of cells can involve the use of cell painting using images acquired by confocal imaging and two-photon microscopy).
Shao, Chris, and Koller are combinable because they are all concerned with microscopy imaging analysis. Therefore, it would have been obvious to a person of ordinary skill in the art, to modify the combine teaching Shao and Chris, to use the cell painting, as though by Koller, in order to identify biological targets (e.g., genes) that, if perturbed, can modulate the disease, (Chris, Par. 0003)
Regarding claim 14, claim 14 recites substantially similar limitations as set forth in claim 6. As such, claim 14 is rejected for at least similar rational.
Allowable Subject Matter
Claims 7 and 15 are objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims.
With respect to claim 1, the prior art of record, alone or in reasonable combination, does not teach or suggest, the following limitation(s), (in consideration of the claim as a whole):
“training the first machine learning model using Raman imaging spectra obtained from a sample cell or population of cells as training inputs, and gene expression data obtained for the set of target genes as ground truths)
The prior art of record, Chris discloses receiving, by at least one computing device, microscopy imaging data of a cell or a population of cells, (see at least: Page 1, under introduction, “one of the most prominent single-cell profiling methods is the fluorescence microscopy (1), which allows for the acquisition of information-rich imaging data, “implicit the receiving of microscopy imaging data of cell data”); and determining, a targeted expression profile, (see at least: Abstract, using machine learning for predicting the expression profile of every cell in an imaging flow cytometry experiment, “i.e., determining targeted expression profile”). Chris further discloses gene expression data obtained for the set of target genes, (see at least: Abstract, predicting gene expression directly from brightfield images, “obtaining gene expression data”).
While disclosing predicting gene expression directly from brightfield images; Chris fails to teach or suggest, either alone or in combination with the other cited references, using the expression data obtained for the set of target genes as ground truths.
A further prior art of record, Tsalik et al, (US-PGPUB 20180245154) discloses developing gene expression-based classifiers that can be used to identify and characterize the etiology of an ARI in a subject with a high degree of accuracy, (Par. 0074). Tsalik further discloses direct detection and measurement of RNA molecules, involving the use traditional northern blotting and surface-enhanced Raman spectroscopy (SERS), (Par. 0085); but fails to teach or suggest, either alone or in combination with the other cited references, using the expression data obtained for the set of target genes as ground truths.
Regarding claim 15, claim 15 recites substantially similar limitations as set forth in claim 7. As such, claim 15 is in condition for allowance, for at least similar reasons, as stated above.
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/AMARA ABDI/Primary Examiner, Art Unit 2668 02/21/2026