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 .
Status of Application
This action is in reply to the correspondence received March 6, 2026.
Claims 15-17 are withdrawn.
Claims 1-14 are pending and have been examined.
Election/Restrictions
Claims 15-17 withdrawn from further consideration pursuant to 37 CFR 1.142(b) as being drawn to a nonelected group, there being no allowable generic or linking claim. Election was made without traverse in the reply filed on March 6, 2026.
Information Disclosure Statements
The information disclosure statements submitted March 9, 2026 and their contents have been considered.
Claim Rejections - 35 U.S.C. § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 1-14 are rejected under 35 U.S.C. § 112(b) or 35 U.S.C. § 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor, or for pre-AIA the applicant regards as the invention.
Claims 1, 2, 5, 6, 8, 11, and 13: The terms “CATCH” and “snRNAseq” in these claims are undefined acronyms that render the claims indefinite, and one of ordinary skill in the art would not be reasonably apprised of the scope of the invention.
Claims 2-14 are rejected for incorporating the deficiencies of the rejected claims on which they respectively depend.
Claim Rejections - 35 U.S.C. § 102
The following is a quotation of the appropriate paragraphs of 35 U.S.C. § 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale or otherwise available to the public before the effective filing date of the claimed invention.
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claims 1, 2, 4, 6, 7, 9, and 10 are rejected under 35 U.S.C. § 102(a)(2) as being anticipated by Rajwa et al (U.S. Pub. No 2021/0364499 A1) (hereinafter “Rajwa”).
Claim 1: Rajwa, as shown, discloses the following limitations:
a non-transitory computer-readable medium with instructions stored thereon (see at least ¶ [0173]: Sys1. A system for evaluating/comparing biological datasets, comprising a non-transitory computer readable storage medium storing a computer program that, when executed on a computer, causes the computer to perform any of the foregoing or following methods; see also at least ¶¶ [0174]-[0179] and [0189]), which when executed by a processor perform steps comprising:
collecting a quantity of cellular data (see at least ¶ [0281]: cytometric multi-parametric data can be expressed as tensors and the comparisons between controls and tested samples can be described by compound fingerprint tensors. A tensor is a multidimensional array and can be considered as a generalization of a matrix. A first-order (or one-way) tensor is a vector; a second-order (two-way) tensor is a matrix. Tensors of order three (three-way) or higher are called higher-order tensors; see also at least ¶ [0282]: biological measurements performed in a single-cell system individually for every cell in a population form a distribution. A distance between a distribution of measurements performed on cells exposed to a presence of a compound, and a distribution of measurements performed on cells not exposed to the compound can be expressed by a single number (scalar value). The cells may be exposed to a number of different drug concentrations, and a biological measurement can be performed for each of these exposure levels. Such an experiment produces a series of values that can be expressed as a vector (e.g., a one-way tensor). If multiple biological parameters are measured, the results can be arranged in a two-way tensor (or a matrix), in which every column contains a different measured parameter and every row describes a different concentration of the compound; see also at least ¶¶ [0259] and [0283]);
providing a CATCH toolkit, wherein the CATCH toolkit comprises a set of topologically inspired machine learning tools to identify, characterize and compare populations of cells across the cellular hierarchy (see at least ¶ [0316]: the decomposed form of the tensor can be further used for tensor-to-tensor comparison, as well as an input for supervised machine learning methods, such as, for example, to support vector machine learning; see also at least ¶ [0319]: an embodiment provides for the use of model driven automatic gating (although, the use of gating algorithms is optional). Herein, state-of-art techniques of mixture modeling with or without proprietary additions may be added to the algorithm. The system may rely on an iterative approach to improve efficiency of the assay; see also at least ¶ [0335]: the fingerprints representing compounds belonging to various groups defined by function, response type, chemical structure, etc., are used to create a training library with multiple classes. The training library can subsequently be used to train a classifier (such as a neural-network classifier, support-vector machine, or another type of machine-learning system) that will categorize previously unknown fingerprints into classes defined by known compounds; see also at least ¶¶ [0326] and [0339]);
providing the cellular data to the CATCH toolkit (see at least ¶¶ [0316], [0319], [0326], [0335], and [0339] and the analysis above; see also at least ¶¶ [0242]-[0243] and [0342]-[0343]); and
calculating the level of at least one cellular population with the CATCH toolkit from the cellular data (see at least ¶ [0242]: illustrative embodiments of the present invention provide automated, observer-independent, robust, reproducible, and generic methods to collect, compile, represent, and mine complex population based information, particularly, for instance, cytometry-based information, as for example for quantifying and comparing physiological responses of cells exposed to chemical compounds, such as drugs. Various embodiments provide methods for characterizes responses by response tensors. Illustrative embodiments provide for the use of various statistical measures of distances between distributions in one or more dimensions, and measures of dissimilarity between response vectors grouped into multiway tensors. In various embodiments the differences in cells responses to two (or more) chemical compounds is characterized as the difference between two response tensors (“fingerprints”) that represent said compounds. Embodiments provide methods for generating said fingerprints, and methods to manipulate, process, store, classify and use them; see also at least ¶ [0243]: biological datasets are analyzed to determine matches between them, often between test datasets and control, or between test datasets and profile datasets. Comparisons may be made between two or more datasets, where a typical dataset comprises readouts from multiple cellular parameters, such as those resulting from exposure of cells to biological factors in the absence or presence of a candidate agent, where the agent may be, for instance, a genetic agent, e.g., expressed coding sequence; or a chemical agent, e.g. drug candidate; or an environmental toxin. In various embodiments, measurements are performed using cytometry, e.g., flow cytometry; see also at least ¶ [0342]: the present invention provides for methods for assaying cellular states using a plurality of cell types, e.g., two or more cell lines (from tissue culture) in a single assay; see also at least ¶ [0343]: the responding cell line in cell mixtures can be identified using either DNA content (some cell lines are diploid; others are aneuploid with different abnormal DNA content), or biological characteristics (cell surface markers), or cells can be “barcoded” (G. Nolan et al.). Finally, signaling assays can include cell cycle analysis (e.g. DNA content) to allow correlation of signal transduction pathway responses with cell physiology in response to the same drugs).
Claim 2: Rajwa discloses the limitations as shown in the rejections above. Further, Rajwa, as shown, discloses the following limitations:
wherein the CATCH toolkit comprises a set of machine learning tools for:
a) determination of persistent homology (see at least ¶ [0007]: the methods involve exposing cells to drugs and assessing the effect of altering the cellular environment by monitoring multiple output parameters. Two different environments, such as those with different compounds present in the environment, can be directly compared to determine similarities and differences. Based on these comparisons, the compounds can be characterized at a functional level, allowing identification of the pathways and prediction of side effects of the compounds. Berg also discloses a representation of the measured data in the form of a “biomap,” which is a very simplified heatmap showing graphically all the measured cellular parameters. Berg is related to measuring biological signaling pathways, rather than physiological responses to stress; see also at least ¶ [0245]: fluorescence measurements can be performed either using either “intrinsic” fluorophores naturally present in cells (such as, for example, porphyrins, flavins, lipofuscins, NADPH), fluorophores genetically engineered for specific expression (e.g., GFP, RFP, etc.), or fluorescent reporters which target specific epitopes or structures in or on various cell types (e.g., fluorophore conjugated antibodies, aptamers, phage display, or peptides, or reporters that are converted from non-fluorescent to fluorescent states by specific enzymes in or on cells));
b) a topologically-inspired approach to understand the multigranular structure of single cells based on their inherent manifold geometry (see at least ¶ [0278]: some labels are encoded by two related signals (for instance, JC-1, the mitochondrial membrane potential label that emits fluorescence in two separate channels). In this case, a 2-D dissimilarity measure between distributions is computed. Finally, it may be preferable to compute 2-D or 3-D dissimilarity measures by utilizing multidimensional distributions based on morphology-related measurements (obtained via light scatter) and an abundance (computed from the fluorescence signal). A variety of distances or dissimilarity measures, assuming that they are easily generalizable to multiple dimensions, may be used. For instance, routine methods based on the Wasserstein metric or the QFD may be used in this context, but not the Kolmogorov metric; see also at least ¶ [0245]: fluorescence measurements can be performed either using either “intrinsic” fluorophores naturally present in cells (such as, for example, porphyrins, flavins, lipofuscins, NADPH), fluorophores genetically engineered for specific expression (e.g., GFP, RFP, etc.), or fluorescent reporters which target specific epitopes or structures in or on various cell types (e.g., fluorophore conjugated antibodies, aptamers, phage display, or peptides, or reporters that are converted from non-fluorescent to fluorescent states by specific enzymes in or on cells); see also at least ¶ [0378]: cells were fixed and permeabilized, using a combination of chemical fixatives, which prevent degradation of cellular proteins, lipids, carbohydrates as well as cellular structure. Following fixation, cytoplasmic and nuclear membranes are permeabilized using routine detergents, which process allows probe molecules (e.g. antibody-conjugates) to access intracellular compartments);
c) diffusion condensation (see at least ¶ [0359]: CALCEIN AM is a nonfluorescent compound that passes through the cytoplasmic membrane, and in living cells, the compound is converted into a strongly green fluorescent compound which cannot pass through the cytoplasmic membrane; if cells subsequently loose cytoplasmic membrane integrity, the fluorescent compound leaves the cell by passive diffusion; see also at least ¶ [0262]); and
d) differential expression analysis via approximation of Wasserstein earth mover's distance (see at least ¶ [0040]: changes in multidimensional data point-clouds are calculated as distances by any one or more of a Wasserstein metric, a metric defined as a solution to the Kantorovich-Rubinstein transportation problem, a quadratic-form distance, a quadratic chi-distance, Kullback-Leibler divergence, a Jensen-Shannon divergence, Kolmogorov metric, a Csiszáφ-divergence, a Burbea and Rao divergence, and Bregman divergence; see also at least ¶ [0277]: various measures of dissimilarity or distance can be applied, including (but not limited to): Wasserstein metric, quadratic-form distance (QFD), quadratic chi-distance, Kolmogorov metric, (symmetrized) Kullback-Leibler divergence, etc. In the preferred implementation, the methods and algorithms of the instant invention use Wasserstein metric or quadratic chi-distance; see also at least ¶¶ [0278]).
Claim 4: Rajwa discloses the limitations as shown in the rejections above. Further, Rajwa, as shown, discloses the following limitations:
wherein the cellular data is single cell datasets (see at least ¶ [0282]: biological measurements performed in a single-cell system individually for every cell in a population form a distribution. A distance between a distribution of measurements performed on cells exposed to a presence of a compound, and a distribution of measurements performed on cells not exposed to the compound can be expressed by a single number (scalar value). The cells may be exposed to a number of different drug concentrations, and a biological measurement can be performed for each of these exposure levels. Such an experiment produces a series of values that can be expressed as a vector (e.g., a one-way tensor). If multiple biological parameters are measured, the results can be arranged in a two-way tensor (or a matrix), in which every column contains a different measured parameter and every row describes a different concentration of the compound; see also at least ¶ [0010]).
Claim 6: Rajwa discloses the limitations as shown in the rejections above. Further, Rajwa, as shown, discloses the following limitations:
An assay for detecting at least one cellular population in a sample (see at least ¶ [0342]: the present invention provides for methods for assaying cellular states using a plurality of cell types, e.g., two or more cell lines (from tissue culture) in a single assay. One advantage of this approach is it allows analyses of DNA damage/responses. An additional advantage is that it allows studies of both constitutive and inducible signaling pathways in the same assay (using one cell line with constitutive expression and another that can activate the same pathway using an appropriate agonist). Using two (or more) cell lines simultaneously, it will be possible to cover multiple signaling pathways in one assay; see also at least ¶¶ [0262] and [0319]), the method comprising:
a) obtaining cellular data from a sample (see at least ¶ [0281]: cytometric multi-parametric data can be expressed as tensors and the comparisons between controls and tested samples can be described by compound fingerprint tensors. A tensor is a multidimensional array and can be considered as a generalization of a matrix. A first-order (or one-way) tensor is a vector; a second-order (two-way) tensor is a matrix. Tensors of order three (three-way) or higher are called higher-order tensors; see also at least ¶ [0282]: biological measurements performed in a single-cell system individually for every cell in a population form a distribution. A distance between a distribution of measurements performed on cells exposed to a presence of a compound, and a distribution of measurements performed on cells not exposed to the compound can be expressed by a single number (scalar value). The cells may be exposed to a number of different drug concentrations, and a biological measurement can be performed for each of these exposure levels. Such an experiment produces a series of values that can be expressed as a vector (e.g., a one-way tensor). If multiple biological parameters are measured, the results can be arranged in a two-way tensor (or a matrix), in which every column contains a different measured parameter and every row describes a different concentration of the compound; see also at least ¶¶ [0259] and [0283]);
b) applying the cellular data to the system of claim 1 (see the rejections of claim 1), wherein the system comprises a CATCH toolkit, wherein the CATCH toolkit comprises a set of topologically inspired machine learning tools to identify, characterize and compare populations of cells across the cellular hierarchy (see at least ¶ [0316]: the decomposed form of the tensor can be further used for tensor-to-tensor comparison, as well as an input for supervised machine learning methods, such as, for example, to support vector machine learning; see also at least ¶ [0319]: an embodiment provides for the use of model driven automatic gating (although, the use of gating algorithms is optional). Herein, state-of-art techniques of mixture modeling with or without proprietary additions may be added to the algorithm. The system may rely on an iterative approach to improve efficiency of the assay; see also at least ¶ [0335]: the fingerprints representing compounds belonging to various groups defined by function, response type, chemical structure, etc., are used to create a training library with multiple classes. The training library can subsequently be used to train a classifier (such as a neural-network classifier, support-vector machine, or another type of machine-learning system) that will categorize previously unknown fingerprints into classes defined by known compounds; see also at least ¶¶ [0326] and [0339]); and
c) calculating the level of at least one cellular population with the CATCH toolkit from the cellular data (see at least ¶ [0242]: illustrative embodiments of the present invention provide automated, observer-independent, robust, reproducible, and generic methods to collect, compile, represent, and mine complex population based information, particularly, for instance, cytometry-based information, as for example for quantifying and comparing physiological responses of cells exposed to chemical compounds, such as drugs. Various embodiments provide methods for characterizes responses by response tensors. Illustrative embodiments provide for the use of various statistical measures of distances between distributions in one or more dimensions, and measures of dissimilarity between response vectors grouped into multiway tensors. In various embodiments the differences in cells responses to two (or more) chemical compounds is characterized as the difference between two response tensors (“fingerprints”) that represent said compounds. Embodiments provide methods for generating said fingerprints, and methods to manipulate, process, store, classify and use them; see also at least ¶ [0243]: biological datasets are analyzed to determine matches between them, often between test datasets and control, or between test datasets and profile datasets. Comparisons may be made between two or more datasets, where a typical dataset comprises readouts from multiple cellular parameters, such as those resulting from exposure of cells to biological factors in the absence or presence of a candidate agent, where the agent may be, for instance, a genetic agent, e.g., expressed coding sequence; or a chemical agent, e.g. drug candidate; or an environmental toxin. In various embodiments, measurements are performed using cytometry, e.g., flow cytometry; see also at least ¶ [0342]: the present invention provides for methods for assaying cellular states using a plurality of cell types, e.g., two or more cell lines (from tissue culture) in a single assay; see also at least ¶ [0343]: the responding cell line in cell mixtures can be identified using either DNA content (some cell lines are diploid; others are aneuploid with different abnormal DNA content), or biological characteristics (cell surface markers), or cells can be “barcoded” (G. Nolan et al.). Finally, signaling assays can include cell cycle analysis (e.g. DNA content) to allow correlation of signal transduction pathway responses with cell physiology in response to the same drugs).
Claim 7: Rajwa discloses the limitations as shown in the rejections above. Further, Rajwa, as shown, discloses the following limitations:
wherein the cellular data is single cell data (see at least ¶ [0282]: biological measurements performed in a single-cell system individually for every cell in a population form a distribution. A distance between a distribution of measurements performed on cells exposed to a presence of a compound, and a distribution of measurements performed on cells not exposed to the compound can be expressed by a single number (scalar value). The cells may be exposed to a number of different drug concentrations, and a biological measurement can be performed for each of these exposure levels. Such an experiment produces a series of values that can be expressed as a vector (e.g., a one-way tensor). If multiple biological parameters are measured, the results can be arranged in a two-way tensor (or a matrix), in which every column contains a different measured parameter and every row describes a different concentration of the compound; see also at least ¶ [0010]).
Claim 9: Rajwa discloses the limitations as shown in the rejections above. Further, Rajwa, as shown, discloses the following limitations:
wherein the sample is a biological sample (see at least ¶ [0411]: following this operation a series of distances for the biological samples are calculated in an analogous fashion; see also at least ¶¶ [0342] and [0404]).
Claim 10: Rajwa discloses the limitations as shown in the rejections above. Further, Rajwa, as shown, discloses the following limitations:
wherein the sample is a patient sample (see at least ¶ [0343]: for example, using human myeloid cell lines (derived from patients with myeloid leukemia), one cell line responsive to LPS will activate NF-κB and PI3 Kinase pathways, while another responsive to TNF-α will activate multiple MAP kinase pathways; see also at least ¶¶ [0329]).
Claim Rejections - 35 U.S.C. § 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.
The factual inquiries set forth in Graham v. John Deere Co., 383 U.S. 1, 148 USPQ 459 (1966), that are applied 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.
Claims 5 and 8 are rejected under AIA 35 U.S.C. § 103 as being unpatentable over Rajwa et al (U.S. Pub. No 2021/0364499 A1) (hereinafter “Rajwa”) in view of Blanchard et al. (U.S. Pub. No. 2022/0288104 A1) (hereinafter “Blanchard”).
Claims 5 and 8: Rajwa discloses the limitations as shown in the rejections above.
Rajwa does not explicitly disclose, but Blanchard, as shown, teaches the following limitations:
wherein the cellular data is snRNAseq data (see at least ¶ [0075]: insight into APOE4-mediated pathogenesis is complicated by the fact that APOE is differentially expressed across nearly all cell-types of the human brain and leads to widespread cell-autonomous and non-autonomous dysregulation of biological processes28-33. To resolve this complexity, the prefrontal cortex (BA10) was profiled from APOE4-carriers and non-carriers using single-nucleus RNA-sequencing, generating a comprehensive reference of the biological processes dysregulated in the post-mortem APOE4 brain. This transcriptomic approach was complemented with phenotypic analysis of isogenic (iPSC) models and humanized APOE knock-in mouse studies. Through this integrated computational and genetic-experimental approach, key molecular and cellular pathways affected by APOE4 in the human brain were discovered and validated).
It would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to combine the cellular analysis techniques taught by Blanchard with the cellular analysis systems disclosed by Rajwa, because Blanchard teaches at ¶ [0075] that by using single-nucleus RNA-sequencing, complexity is resolved and “[t]hrough this integrated computational and genetic-experimental approach, key molecular and cellular pathways affected by APOE4 in the human brain were discovered and validated.” See M.P.E.P. § 2143(I)(G).
Moreover, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to combine the cellular analysis techniques taught by Blanchard with the cellular analysis systems disclosed by Rajwa, because the claimed invention is merely a combination of old elements (the cellular analysis techniques taught by Blanchard and the cellular analysis systems disclosed by Rajwa), in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable. See M.P.E.P. § 2143(I)(A).
Statement Regarding the Prior Art
Claim 3 recite features for implementing diffusion operator landmarking, weighted random walks and data merging to efficiently scale to thousands of cells and implementing diffusion condensation with alpha decay kernel for automated cluster characterization and efficient computation of differentially expression genes with condensed transport.
Claim 11 recites features for applying the cellular data to the system of claim 1, wherein the system comprises a CATCH toolkit, wherein the CATCH toolkit comprises a set of topologically inspired machine learning tools to identify, characterize and compare populations of cells across the cellular hierarchy, calculating the level of at least one rare cell population with the CATCH toolkit from the cellular data , comparing the level of at least one rare cell population detected in the patient sample to a comparator control level of the rare cell population, and diagnosing the subject as having or at risk of a disease or disorder when the level of at least one rare cell population detected in the patient sample is significantly increased or decreased relative to a predetermined cut-off or comparator control level of the rare cell population. Claim 13 recites similar features as claim 11.
The relevant of Rajwa and Blanchard to the claims is discussed above.
Kimmerling et al. (U.S. Pub. No. 2023/0119020 A1) discloses a treatment response assessment using normalized single cell measurements, such as potential drug candidate, by comparing normalized single-cell measurements of cellular properties without the need of a calibration step. In Kimmerling, any single cell measurement modality measuring a cellular property, such as mass, volume, diameter, impedance, capacitance, resistance, optical properties, fluorescence intensity, density, stiffness, surface friction, deformation, cell-cycle state, viability, differentiation state, activation state, fluorescent properties and others, which is or are altered by a treatment or stimulus applied to the cell, can be used. However, Kimmerling does not disclose the features recited in the claims identified above.
Schiebinger et al. (U.S. Pub. No. 2020/0224172 A1) discloses techniques for reconstruction of developmental landscapes by optimal transport analysis. In Schiebinger, a process for how to compute probabilistic flows from a time series of single cell gene expression profiles by using optimal transport (S1) is provided. The embodiments therein show how to compute an optimal coupling of adjacent time points by solving a convex optimization problem. However, Schiebinger does not disclose the features recited in the claims identified above.
The prior art made of record and not relied upon is considered pertinent to applicant’s disclosure. The following references have been cited to further show the state of the art with respect to single cell analyses.
Bhaskar et al. (“Topological data analysis of collective and individual epithelial cells using persistent homology of loops.” Soft matter 17.17 (2021): 4653-4664).
The closest art of record, including the combination of references discussed above, fails to teach, suggest, or render obvious each and every element of the claims as arranged in the claims. Further, one of ordinary skill in the art at the time of invention would not look to combine these references, or the other art of record, to arrive at the present claims.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Christopher Tokarczyk, whose telephone number is 571-272-9594. The examiner can normally be reached Monday-Thursday between 6:00 AM and 4:00 PM Eastern.
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/CHRISTOPHER B TOKARCZYK/Primary Examiner, Art Unit 3687
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