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 .
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. The limitations, under their broadest reasonable interpretation, cover mental process (concept performed in a human mind, including as observation, evaluation, judgment, opinion, prediction, organizing human activity and mathematical problem solving and calculations). The claims recite “compare the first set of perturbation classification scores with the second set of perturbation classification scores to determine a plurality of similarity measures; and identify, based on the plurality of similarity measures, pairs of data samples across the first data modality and the second data modality for a multi-modal machine learning model learning process utilizing the pairs of data samples.”. This judicial exception is not integrated into a practical application because the steps do not add meaningful limitations to be considered specifically applied to a particular technological problem to be solved .The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the steps of the claimed invention can be done mentally using paper/pencil, solving mathematical problem and no additional features in the claims would preclude them from being performed as such except for the generic computer elements and generic multimodal machine-learning model recited at high level of generality (i.e., processor, memory and machine-learning) .
According to the USPTO guidelines, a claim is directed to non-statutory subject matter if:
STEP 1: the claim does not fall within one of the four statutory categories of invention (process, machine, manufacture or composition of matter), or
STEP 2: the claim recites a judicial exception, e.g. an abstract idea, without reciting additional elements that amount to significantly more than the judicial exception, as determined using the following analysis:
STEP 2A (PRONG 1): Does the claim recite an abstract idea, law of nature, or natural phenomenon?
STEP 2A (PRONG 2): Does the claim recite additional elements that integrate the judicial exception into a practical application?
STEP 2B: Does the claim recite additional elements that amount to significantly more than the judicial exception?
Using the two-step inquiry, it is clear that claims 1, 5 and 6 are directed to an abstract idea as shown below:
Regarding claims 1, 9 and 17
STEP 1: Do the claims fall within one of the statutory categories?
YES.
Claim(s) 1, 10 and 17 are directed to a computer implemented method, a system and a non-transitory computer readable storing instructions i.e. process, system and manufacture.
STEP 2A (PRONG 1): Is the claim directed to a law of nature, a natural phenomenon or an abstract idea?
YES.
The claims are directed toward a mental process and solving mathematical problem (i.e. abstract idea).
With regard to STEP 2A (PRONG 1), the guidelines provide three groupings of subject matter that are considered abstract ideas:
Mathematical concepts – mathematical relationships, mathematical formulas or equations, mathematical calculations;
Certain methods of organizing human activity – fundamental economic principles or practices (including hedging, insurance, mitigating risk); commercial or legal interactions (including agreements in the form of contracts; legal obligations; advertising, marketing or sales activities or behaviors; business relations); managing personal behavior or relationships or interactions between people (including social activities, teaching, and following rules or instructions); and
Mental processes – concepts that are practicably performed in the human mind (including an observation, evaluation, judgment, opinion).
Claims 1, 10 and 17 comprise a mental process that can be practicably performed in the human mind and solving mathematical problem using paper/pencil (or generic computers or components configured to perform the process and generic machine-learning model) and, therefore, an abstract idea.
Regarding claims 1, 10 and 17 (representative claim 10):
A system comprising:
at least one processor; and at least one non-transitory computer-readable storage medium storing instructions that, when executed by the at least one processor, cause the system to (generic computer hardware/software component):
generate, utilizing a first classification model, a first set of perturbation classification scores from a first data modality (collecting first data which is insignificant extra solution activity and solving the mathematical problem based on mathematical function formulation using paper/pencil and human intelligence),
generate, utilizing a second classification model, a second set of perturbation classification scores from a second data modality (collecting second data which is insignificant extra solution activity and solving the mathematical problem based on mathematical function formulation using paper/pencil and human intelligence),
compare the first set of perturbation classification scores with the second set of perturbation classification scores to determine a plurality of similarity measures (comparing/matching the results of first and second mathematical function formulation based on observation, evaluation, judgment, opinion and prediction human intelligence i.e. mental process), and
identify, based on the plurality of similarity measures, pairs of data samples across the first data modality and the second data modality for a multi-modal machine learning model learning process utilizing the pairs of data samples (identifying similarity between pair of two the results of first and second mathematical function formulation based on observation, evaluation, judgment, opinion and prediction using paper/pencil and human intelligence i.e. mental process).
The above limitations, as drafted, is a simple process that, under their broadest reasonable interpretation, covers performance of the limitations in the mind or by a human intelligence and solving mathematical problem. Furthermore limitations, “at least one processor; and at least one non-transitory computer-readable storage medium storing instructions that, when executed by the at least one processor, cause the system to (generic computer hardware/software component): generate, utilizing a first classification model, a first set of perturbation classification scores from a first data modality (collecting first data which is insignificant extra solution activity and solving the mathematical problem based on mathematical function formulation using paper/pencil and human intelligence), generate, utilizing a second classification model, a second set of perturbation classification scores from a second data modality (collecting second data which is insignificant extra solution activity and solving the mathematical problem based on mathematical function formulation using paper/pencil and human intelligence), compare the first set of perturbation classification scores with the second set of perturbation classification scores to determine a plurality of similarity measures (comparing/matching the results of first and second mathematical function formulation based on observation, evaluation, judgment, opinion and prediction human intelligence i.e. mental process), and identify, based on the plurality of similarity measures, pairs of data samples across the first data modality and the second data modality for a multi-modal machine learning model learning process utilizing the pairs of data samples (identifying similarity between pair of two the results of first and second mathematical function formulation based on observation, evaluation, judgment, opinion and prediction using paper/pencil and human intelligence i.e. mental process)” are insignificant.
The Examiner notes that under MPEP 2106.04(A) (2) (III), the courts consider a mental process (thinking, human intelligence) that can be performed in the mind/intelligence using a paper and pencil to be an abstract idea. CyberSource Corp. v. Retail Decisions, Inc., 654 F.3d 1366, 1372, 99 USPQ2d 1690, 1695 (Fed. Cir. 2011). As the Federal Circuit explained, "methods which can be performed mentally, or which are the equivalent of human mental work, are unpatentable abstract ideas the ‘basic tools of scientific and technological work’ that are open to all.’" 654 F.3d at 1371, 99 USPQ2d at 1694 (citing Gottschalk v. Benson, 409 U.S. 63, 175 USPQ 673 (1972)). See also Mayo Collaborative Servs. v. Prometheus Labs. Inc., 566 U.S. 66, 71, 101 USPQ2d 1961, 1965 ("‘[Mental processes and abstract intellectual concepts are not patentable, as they are the basic tools of scientific and technological work’" (quoting Benson, 409 U.S. at 67, 175 USPQ at 675)); Parker v. Flook, 437 U.S. 584, 589, 198 USPQ 193, 197 (1978).
Furthermore the Examiner also notes that even if you combined the math with the mental process, a combination of abstract ideas don't make a claim eligible. See MPEP 2106.04(II)(A)(2): Because a judicial exception is not eligible subject matter, Bilski, 561 U.S. at 601, 95 USPQ2d at 1005-06 (quoting Chakrabarty, 447 U.S. at 309, 206 USPQ at 197 (1980)), if there are no additional claim elements besides the judicial exception, or if the additional claim elements merely recite another judicial exception, that is insufficient to integrate the judicial exception into a practical application. See, e.g., RecogniCorp, LLC v. Nintendo Co., 855 F.3d 1322, 1327, 122 USPQ2d 1377 (Fed. Cir. 2017) ("Adding one abstract idea (math) to another abstract idea (encoding and decoding) does not render the claim non-abstract").
Other than generic and well-known computer hardware/software and generic machine-learning model recited in the independent claims 1, 10 and 17 disclosed in the specification, nothing in the claims 1, 10 and 17 elements preclude the processing from being performed as mental process, or merely based on the observations, evaluation, judgement, thought process using paper/pencil and solving mathematical function. Limitations “compare the first set of perturbation classification scores with the second set of perturbation classification scores to determine a plurality of similarity measures; and identify, based on the plurality of similarity measures, pairs of data samples across the first data modality and the second data modality for a multi-modal machine learning model learning process utilizing the pairs of data samples”, recited in independent claims 1, 10 and 17 is a mere idea of a solution without details per MPEP 2106.05( f ) or the idea of a technological environment without detail per MPEP 2106.05 ( h ). The generic computing hardware/software and generic machine-learning model are recited as just to automate the mental process of mathematical problem solving(Step 2A, prong 1 Test Abstract idea = Yes).
STEP 2A (PRONG 2): Does the claim recite additional elements that integrate the judicial exception into a practical application?
[YES/NO].
The claims do not recite additional elements that integrate the judicial exception into a practical application.
With regard to STEP 2A (prong 2), whether the claim recites additional elements that integrate the judicial exception into a practical application, the guidelines provide the following exemplary considerations that are indicative that an additional element (or combination of elements) may have integrated the judicial exception into a practical application:
an additional element reflects an improvement in the functioning of a computer, or an improvement to other technology or technical field;
an additional element that applies or uses a judicial exception to affect a particular treatment or prophylaxis for a disease or medical condition;
an additional element implements a judicial exception with, or uses a judicial exception in conjunction with, a particular machine or manufacture that is integral to the claim;
an additional element effects a transformation or reduction of a particular article to a different state or thing; and
an additional element applies or uses the judicial exception in some other meaningful way beyond generally linking the use of the judicial exception to a particular technological environment, such that the claim as a whole is more than a drafting effort designed to monopolize the exception.
While the guidelines further state that the exemplary considerations are not an exhaustive list and that there may be other examples of integrating the exception into a practical application, the guidelines also list examples in which a judicial exception has not been integrated into a practical application:
an additional element merely recites the words “apply it” (or an equivalent) with the judicial exception, or merely includes instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea;
an additional element adds insignificant extra-solution activity to the judicial exception; and
an additional element does no more than generally link the use of a judicial exception to a particular technological environment or field of use.
Claim(s) 1, 10 and 17 do not recite any of the exemplary considerations that are indicative of an abstract idea having been integrated into a practical application.
Claim(s) 1, 10 and 17 recite: (representative claim 10):
A system comprising:
at least one processor; and at least one non-transitory computer-readable storage medium storing instructions that, when executed by the at least one processor, cause the system to (generic computer hardware/software component):
generate, utilizing a first classification model, a first set of perturbation classification scores from a first data modality (collecting first data which is insignificant extra solution activity and solving the mathematical problem based on mathematical function formulation using paper/pencil and human intelligence),
generate, utilizing a second classification model, a second set of perturbation classification scores from a second data modality (collecting second data which is insignificant extra solution activity and solving the mathematical problem based on mathematical function formulation using paper/pencil and human intelligence),
compare the first set of perturbation classification scores with the second set of perturbation classification scores to determine a plurality of similarity measures (comparing/matching the results of first and second mathematical function formulation based on observation, evaluation, judgment, opinion and prediction human intelligence i.e. mental process), and
identify, based on the plurality of similarity measures, pairs of data samples across the first data modality and the second data modality for a multi-modal machine learning model learning process utilizing the pairs of data samples (identifying similarity between pair of two the results of first and second mathematical function formulation based on observation, evaluation, judgment, opinion and prediction using paper/pencil and human intelligence i.e. mental process).
These limitations are recited at a high level of generality (i.e. as a general action or calculation being taken based on the results of the acquiring step) and amounts to mere post solution actions, which is a form of insignificant extra-solution activity without further detail. Further, the claims 1, 9 and 17 are claimed generically and are operating in their ordinary capacity such that they do not use the judicial exception in a manner that imposes a meaningful limit on the judicial exception. Accordingly, even in combination, these additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea.
As stated above, other than generic and well-known computer hardware/software and generic machine-learning model recited in the independent claims 1, 10 and 17 disclosed in the specification, nothing in the claims 1, 10 and 17 elements preclude the processing from being performed as mental process, or merely based on the observations, evaluation, judgement, thought process using paper/pencil and solving mathematical function. Limitations “compare the first set of perturbation classification scores with the second set of perturbation classification scores to determine a plurality of similarity measures; and identify, based on the plurality of similarity measures, pairs of data samples across the first data modality and the second data modality for a multi-modal machine learning model learning process utilizing the pairs of data samples”, recited in independent claims 1, 10 and 17 is a mere idea of a solution without details per MPEP 2106.05( f ) or the idea of a technological environment without detail per MPEP 2106.05 ( h ). The generic computing hardware/software and generic machine-learning model are recited as just to automate the mental process of mathematical problem solving (Step 2A, prong 2 Test Abstract idea = Yes).
STEP 2B: Does the claim recite additional elements that amount to significantly more than the judicial exception?
NO.
The claims do not recite additional elements that amount to significantly more than the judicial exception.
With regard to STEP 2B, whether the claims recite additional elements that provide significantly more than the recited judicial exception, the guidelines specify that the pre-guideline procedure is still in effect. Specifically, that examiners should continue to consider whether an additional element or combination of elements:
adds a specific limitation or combination of limitations that are not well-understood, routine, conventional activity in the field, which is indicative that an inventive concept may be present; or
simply appends well-understood, routine, conventional activities previously known to the industry, specified at a high level of generality, to the judicial exception, which is indicative that an inventive concept may not be present.
As stated above, other than generic and well-known computer hardware/software and generic machine-learning model recited in the independent claims 1, 10 and 17 disclosed in the specification, nothing in the claims 1, 10 and 17 elements preclude the processing from being performed as mental process, or merely based on the observations, evaluation, judgement, thought process using paper/pencil and solving mathematical function. Limitations “compare the first set of perturbation classification scores with the second set of perturbation classification scores to determine a plurality of similarity measures; and identify, based on the plurality of similarity measures, pairs of data samples across the first data modality and the second data modality for a multi-modal machine learning model learning process utilizing the pairs of data samples”, recited in independent claims 1, 10 and 17 is a mere idea of a solution without details per MPEP 2106.05( f ) or the idea of a technological environment without detail per MPEP 2106.05 ( h ). The generic computing hardware/software and generic machine-learning model are recited as just to automate the mental process of mathematical problem solving
Thus, since Claim(s) 1, 10 and 17 are: (a) directed toward an abstract idea and mathematical function problem solving using human intelligence, (b) do not recite additional elements that integrate the judicial exception into a practical application, and (c) do not recite additional elements that amount to significantly more than the judicial exception, it is clear that Claim(s) 1, 10 and 17 are not eligible subject matter under 35 U.S.C 101 (Step 2B, Test Abstract idea = Yes).
Regarding dependent claims: 2-9, 11-16 and 18-20 the limitations further limit the abstract idea of independent claims 1, 10 and 17 of solving mathematical problem using human intelligence using paper/pencil i.e. mental process , the additional limitations do not integrate the mental process into practical application or add significantly more to the mental process. The limitations of dependent claims 2-9, 11-16, 18-20 fall under (mental process including observation and evaluation, and judgement and mathematical problem solving which can be done mentally in the human mind using paper/pencil) OR (insignificant pre/post-solution extra activity of generating/gathering data, performing mathematical calculation) OR (generic computers or components configured to perform the process), the generic machine learning model recited in the dependent claims 2-9, 11-16 and 18-20 is a mere idea of a solution without details per MPEP 2106.05( f ) or the idea of a technological environment without detail per MPEP 2106.05 ( h ).
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 1-7 and 10-20 are rejected under 35 USC 103 as being unpatentable over UHLER (Machine Learning Approaches to Sing-Cell Data Integration and Translation, 2022 IEEE 00189219, pages 557-575)
Regarding claims 1, 10 and 17 UHLER disclose computer implemented method, system and non-transitory computer readable medium storing instructions (UHLER ABSTRACT, section II MACHINE LEARNING APPROSACHES FOR SINGLE CELL BIOLOGY, page 559, Figs 2-4 and 6) comprising:
at least one processor; and at least one non-transitory computer-readable storage medium storing instructions that, when executed by the at least one processor, cause the system to (UHLER ABSTRACT, section II MACHINE LEARNING APPROSACHES FOR SINGLE CELL BIOLOGY, page 559, Figs 2-4 and 6):
generate, utilizing a first classification model, a first set of perturbation classification scores from a first data modality (UHLER, section I INTRODUCTION, page 559, right-column, lines 20-22 UHLER states how can we predict the effect that one perturbation has a new cell type and line 30 states transferring between perturbation -cell type pair, Fig. 2 shows PERTURBATIONS for EXPRESSION in disease 1 & 2 and single cell images, Fig. 4 shows perturbation for sing-cell for cell types 1 and 2, section V. FROM SINGLE CELL-CELL PERTURBATIONS TO GENE REGULATION AND DRUG DISCOVERY USING CAUAL INFERRENCE, page 570, right-column UHLER states Predicting the effect of an intervention requires taking a causal approach. Given that performing large-scale perturbations is possible in biology but the space of domains (cell types/states) and perturbations is huge (see Section II-C), the question then often becomes to transfer the effect of a perturbation from one domain to another [e.g., given expression data from a large-scale drug screen in cancer such as CMap [97], predict the effect of the screened drugs on SARS-CoV-2 infected cells to identify drugs that could be repurposed against COVID-19, see Fig. 2(c)] or predict the effect of an unseen intervention from the observed ones in a fixed domain [e.g., given imaging data from a large-scale drug screen in cancer such as cell painting [30], [31], predict the effect of new molecules on cancer cells to identify new drugs, see Fig. 2(d)], page 571 right column, UHLER states the effect of a drug is represented by a vector in the latent space of a GAN or an autoencoder (machine learning models) and is seen as a “style” that can be applied to a new cell type by appending this vector to the cell type in the latent space to generate the effect of a drug on this cell type [see Fig. 6(a)] i.e., effect of drug in cell in cell type represented by corresponds perturbation score. . This obviously corresponds to generate, utilizing a first classification model, a first set of perturbation classification scores from a first data modality such ad single cell image or expression),
generate, utilizing a second classification model, a second set of perturbation classification scores from a second data modality (UHLER, section I INTRODUCTION, page 559, right-column, lines 20-22 UHLER states how can we predict the effect that one perturbation has a new cell type and line 30 states transferring between perturbation -cell type pair, Fig. 2 shows PERTURBATIONS for EXPRESSION in disease 1 & 2 and single cell images, Fig. 4 shows perturbation for sing-cell for cell types 1 and 2, section V. FROM SINGLE CELL-CELL PERTURBATIONS TO GENE REGULATION AND DRUG DISCOVERY USING CAUAL INFERRENCE, page 570, right-column UHLER states Predicting the effect of an intervention requires taking a causal approach. Given that performing large-scale perturbations is possible in biology but the space of domains (cell types/states) and perturbations is huge (see Section II-C), the question then often becomes to transfer the effect of a perturbation from one domain to another [e.g., given expression data from a large-scale drug screen in cancer such as CMap [97], predict the effect of the screened drugs on SARS-CoV-2 infected cells to identify drugs that could be repurposed against COVID-19, see Fig. 2(c)] or predict the effect of an unseen intervention from the observed ones in a fixed domain [e.g., given imaging data from a large-scale drug screen in cancer such as cell painting [30], [31], predict the effect of new molecules on cancer cells to identify new drugs, see Fig. 2(d)], page 571 right column, UHLER states the effect of a drug is represented by a vector in the latent space of a GAN or an autoencoder (machine learning models) and is seen as a “style” that can be applied to a new cell type by appending this vector to the cell type in the latent space to generate the effect of a drug on this cell type [see Fig. 6(a)] i.e., effect of drug in cell in cell type represented by corresponds perturbation score. This obviously corresponds to generate, utilizing a second classification model, a first set of perturbation classification scores from a first data modality such ad single cell image or expression),
compare the first set of perturbation classification scores with the second set of perturbation classification scores to determine a plurality of similarity measures (UHLER Fig. 6 (a) shows from single-cell perturbations to gene regulation and drug discovery using causal inference. (a) Overparameterized autoencoder framework for aligning the effect of a perturbation (perturbation classification score ) across different cell types. overparameterized autoencoder shows better alignment of drugs across different cell types, page 571 left column, UHLER states A low-rank matrix completion approach as well as a weighted nearest neighbor scheme for predicting missing drug/cell-type combinations were considered . At a high level, the goal is to develop methods that can use the given observations to identify similarities/structure between drugs as well as between cell types to fill in the missing entries. This was done explicitly in [106] by taking an approach akin to semi-supervised learning and encoding the similarities between drug-cell-type pairs and page 571 right column, UHLER states the effect of a drug is represented by a vector in the latent space i.e., [perturbation score] of a GAN or an autoencoder (machine learning models) and is seen as a “style” that can be applied to a new cell type by appending this vector to the cell type in the latent space to generate the effect of a drug on this cell type [see Fig. 6(a)]. Note that such an approach only works if the effect of a drug on different cell types is aligned in the latent space. Given our recent work describing various benefits of using auto-encoders to learn a latent representation of the data. This obviously corresponds to compare the first set of perturbation classification scores with the second set of perturbation classification scores to determine a plurality of similarity measures ), and
identify, based on the plurality of similarity measures, pairs of data samples across the first data modality and the second data modality for a multi-modal machine learning model learning process utilizing the pairs of data samples (UHLER Fig. 6 (a) shows from single-cell perturbations to gene regulation and drug discovery using causal inference. (a) Overparameterized autoencoder framework for aligning the effect of a perturbation (e.g., drug) across different cell types. (b) Overparameterized autoencoder shows better alignment of drugs across different cell types. page 571 left column, UHLER states A low-rank matrix completion approach as well as a weighted nearest neighbor scheme for predicting missing drug/cell-type combinations were considered . At a high level, the goal is to develop methods that can use the given observations to identify similarities/structure between drugs as well as between cell types to fill in the missing entries. This was done explicitly in [106] by taking an approach akin to semi-supervised learning and encoding the similarities between drug-cell-type pairs and page 571 right column, UHLER states the effect of a drug is represented by a vector in the latent space [of a GAN or an autoencoder (machine learning models) and is seen as a “style” that can be applied to a new cell type by appending this vector to the cell type in the latent space to generate the effect of a drug on this cell type [see Fig. 6(a)]. Note that such an approach only works if the effect of a drug on different cell types is aligned in the latent space. Given our recent work describing various benefits of using auto-encoders to learn a latent representation of the data and also Fig. 4, section III MULTIDOMAIN INTEGRATION AND TRANSLATION USING AUTO-ENCODERS, PAGES 567-568. This obviously corresponds to identify, based on the plurality of similarity measures, pairs of data samples across the first data modality and the second data modality for a multi-modal machine learning model learning process utilizing the pairs of data samples).
Therefore it would have been obvious to one of ordinary skill in the art, before the claimed invention was filed to generate, utilizing a first classification model, a first set of perturbation classification scores from a first data modality, generate utilizing a second classification model a second set of perturbation classification scores from a second data modality, compare the first set of perturbation classification scores with the second set of perturbation classification scores to determine a plurality of similarity measures and identify, based on the plurality of similarity measures pairs of data samples across the first data modality and the second data modality for a multi-modal machine learning model learning process utilizing the pairs of data samples as shown UHLER because such a process and system provides automated system for profiling sing-cell in functional context to understand how genes interact in order to form all the different cell types of human body as stated by UHLER page 572, section CONCLUSION.
Regarding claims 2, 11 and 18 UHLER disclose the first data modality comprises phenomic digital images and generating the first set of perturbation classification scores from the first data modality comprises generating, utilizing a phenomic image classification model, the first set of perturbation classification scores from a phenomic digital image of a cell exposed to a perturbation treatment (UHLER, section I INTRODUCTION, page 559, right-column, lines 20-22 UHLER states how can we predict the effect that one perturbation has a new cell type and line 30 states transferring between perturbation -cell type pair, Fig. 2 shows PERTURBATIONS for EXPRESSION in disease 1 & 2 and single cell images, Fig. 5 shows perturbation for sing-cell for cell types 1 and 2, section V. FROM SINGLE CELL-CELL PERTURBATIONS TO GENE REGULATION AND DRUG DISCOVERY USING CAUAL INFERRENCE, page 570, right-column UHLER states Predicting the effect of an intervention requires taking a causal approach. Given that performing large-scale perturbations is possible in biology but the space of domains (cell types/states) and perturbations is huge (see Section II-C), the question then often becomes to transfer the effect of a perturbation from one domain to another [e.g., given expression data from a large-scale drug screen in cancer such as CMap [97], predict the effect of the screened drugs on SARS-CoV-2 infected cells to identify drugs that could be repurposed against COVID-19, see Fig. 2(c)] or predict the effect of an unseen intervention from the observed ones in a fixed domain [e.g., given imaging data from a large-scale drug screen in cancer such as cell painting [30], [31], predict the effect of new molecules on cancer cells to identify new drugs, see Fig. 2(d)], page 571 right column, UHLER states the effect of a drug is represented by a vector in the latent space of a GAN or an autoencoder (machine learning models) and is seen as a “style” that can be applied to a new cell type by appending this vector to the cell type in the latent space to generate the effect of a drug on this cell type [see Fig. 6(a)).
Regarding claims 3, 12 and 19 UHLER disclose he second data modality comprises protein expression data and generating the second set of perturbation classification scores from the second data modality comprises generating, utilizing a protein expression classification model, the second set of perturbation classification scores from a protein expression measurement of a cell exposed to a perturbation treatment (UHLER, section I INTRODUCTION, page 559, right-column, lines 20-22 UHLER states how can we predict the effect that one perturbation has a new cell type and line 30 states transferring between perturbation -cell type pair, Fig. 2 shows PERTURBATIONS for EXPRESSION in disease 1 & 2 and single cell images, Fig. 5 shows perturbation for sing-cell for cell types 1 and 2, section V. FROM SINGLE CELL-CELL PERTURBATIONS TO GENE REGULATION AND DRUG DISCOVERY USING CAUAL INFERRENCE, page 570, right-column UHLER states Predicting the effect of an intervention requires taking a causal approach. Given that performing large-scale perturbations is possible in biology but the space of domains (cell types/states) and perturbations is huge (see Section II-C), the question then often becomes to transfer the effect of a perturbation from one domain to another [e.g., given expression data from a large-scale drug screen in cancer such as CMap [97], predict the effect of the screened drugs on SARS-CoV-2 infected cells to identify drugs that could be repurposed against COVID-19, see Fig. 2(c)] or predict the effect of an unseen intervention from the observed ones in a fixed domain [e.g., given imaging data from a large-scale drug screen in cancer such as cell painting [30], [31], predict the effect of new molecules on cancer cells to identify new drugs, see Fig. 2(d)], page 571 right column, UHLER states the effect of a drug is represented by a vector in the latent space of a GAN or an autoencoder (machine learning models) and is seen as a “style” that can be applied to a new cell type by appending this vector to the cell type in the latent space to generate the effect of a drug on this cell type [see Fig. 6(a)).
Regarding claims 4 and 13 UHLER disclose Regarding claims 3, 12 and 19 UHLER disclose determining the plurality of similarity measures comprises determining a cross-modality distance within a feature space between a first perturbation of the first data modality and a second perturbation of the second data modality; and identifying, utilizing a matching algorithm, the pairs of data samples across the first data modality and the second data modality based on the cross-modality distance (UHLER Fig. 6 (a) shows from single-cell perturbations to gene regulation and drug discovery using causal inference. (a) Overparameterized autoencoder framework for aligning the effect of a perturbation (e.g., drug) across different cell types. (b) Overparameterized autoencoder shows better alignment of drugs across different cell types. page 571 left column, UHLER states A low-rank matrix completion approach as well as a weighted nearest neighbor scheme for predicting missing drug/cell-type combinations were considered . At a high level, the goal is to develop methods that can use the given observations to identify similarities/structure between drugs as well as between cell types to fill in the missing entries. This was done explicitly in [106] by taking an approach akin to semi-supervised learning and encoding the similarities between drug-cell-type pairs and page 571 right column, UHLER states the effect of a drug is represented by a vector in the latent space [of a GAN or an autoencoder (machine learning models) and is seen as a “style” that can be applied to a new cell type by appending this vector to the cell type in the latent space to generate the effect of a drug on this cell type [see Fig. 6(a)]. Note that such an approach only works if the effect of a drug on different cell types is aligned in the latent space. Given our recent work describing various benefits of using auto-encoders to learn a latent representation of the data and also Fig. 4, section III MULTIDOMAIN INTEGRATION AND TRANSLATION USING AUTO-ENCODERS, PAGES 567-568 and note page 567 right column and page 578 left column discriminative loss identifying joint embedding and matching the two modalities).
Regarding claims 5, 14 and 20 UHLER disclose generating, utilizing a multi-modal machine learning model from the multi-modal machine learning model learning process, multi-modal predictions for the pairs of data samples; and modifying parameters of the multi-modal machine learning model utilizing a measure of loss determined based on the pairs of data samples (UHLER Fig. 6 (a) shows from single-cell perturbations to gene regulation and drug discovery using causal inference. (a) Overparameterized autoencoder framework for aligning the effect of a perturbation (e.g., drug) across different cell types. (b) Overparameterized autoencoder shows better alignment of drugs across different cell types. page 571 left column, UHLER states A low-rank matrix completion approach as well as a weighted nearest neighbor scheme for predicting missing drug/cell-type combinations were considered . At a high level, the goal is to develop methods that can use the given observations to identify similarities/structure between drugs as well as between cell types to fill in the missing entries. This was done explicitly in [106] by taking an approach akin to semi-supervised learning and encoding the similarities between drug-cell-type pairs and page 571 right column, UHLER states the effect of a drug is represented by a vector in the latent space [of a GAN or an autoencoder (machine learning models) and is seen as a “style” that can be applied to a new cell type by appending this vector to the cell type in the latent space to generate the effect of a drug on this cell type [see Fig. 6(a)]. Note that such an approach only works if the effect of a drug on different cell types is aligned in the latent space. Given our recent work describing various benefits of using auto-encoders to learn a latent representation of the data and also Fig. 4, section III MULTIDOMAIN INTEGRATION AND TRANSLATION USING AUTO-ENCODERS, PAGES 567-568 and note page 567 right column and page 578 left column discriminative loss identifying joint embedding and matching the two modalities).
Regarding claims 6 and 15 UHLER utilizing the multi-modal machine learning model to: generate protein expression data from phenomic digital images; or generate phenomic digital images from protein expression data (UHLER Fig. 3 shows Data integration and translation in sing cell between different modalities, section III MULTIDOMAIN INTEGRATION AND TRANSLATION USING AUTOMATIC AUTOENCODER, pages 567-568 and Fig. 4 disclose Multidomain data integration and translation using autoencoders. (a) Schematic showing how different data modalities are mapped to an integrated latent space via autoencoders that are specialized to each data modality. (b) Translation between different data modalities is achieved using the encoder from one modality and the decoder from another modality. (c) Autoencoder framework applied to single-cell images and RNA-seq data in T-cells. (d), This obviously corresponds to utilizing the multi-modal machine learning model to: generate protein expression data from phenomic digital images; or generate phenomic digital images from protein expression data).
Regarding claim 7 and 16 UHLER initiating the multi-modal machine learning model learning process utilizing the pairs of data samples by generating a matrix comprising entries that indicate probabilities of samples from the second data modality matching samples from the first data modality (UHLER, section V. FROM SINGLE CELL PERTURBATION TO GENE REGULATION AND DRUG CASUAL INTFERECE page 570 right column thru page 571 left-column UHLER disclose Predicting the effect of an intervention requires taking a causal approach. Given that performing large-scale perturbations is possible in biology but the space of domains (cell types/states) and perturbations is huge (see Section II-C), the question then often becomes to transfer the effect of a perturbation from one domain to another [e.g., given expression data from a large-scale drug screen in cancer such as CMap [97], predict the effect of the screened drugs on SARS-CoV-2 infected cells to identify drugs that could be repurposed against COVID-19, see Fig. 2(c)] or predict the effect of an unseen intervention from the observed ones in a fixed domain [e.g., given imaging data from a large-scale drug screen in cancer such as cell painting [30], [31], predict the effect of new molecules on cancer cells to identify new drugs, see Fig. 2(d)], Viewing large-scale perturbation screens as a partially observed matrix of perturbations× domains, both of these transfer questions are causal imputation problems, where the former asks to complete across columns (domains) and the latter across rows (perturbations). Various prior works have considered this causal imputation problem as a matrix completion task. In particular, the large-scale perturbation screen, CMap [97], motivated various computational approaches for matrix completion: A low-rank matrix completion approach as well as a weighted nearest neighbor scheme for predicting missing drug/cell-type combinations were considered in [141]. At a high level, the goal is to develop methods that can use the given observations to identify similarities/structure between drugs as well as between cell types to fill in the missing entries).
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ISHRAT I. SHERALI
Examiner
Art Unit 2667
/ISHRAT I SHERALI/Primary Examiner, Art Unit 2667
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