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 .
Priority
This application filed 12/14/2022 is a Continuation of 16011753, filed 06/19/2018, which claims priority from Provisional Application 62640432, filed 03/08/2018, and from Provisional Application 62521759, filed 06/19/2017. The claims are therefore examined as filed on 06/19/2017, the effective filing date. In future actions, the effective filing date of one or more claims may change, due to amendments to the claims, or further review of the priority application(s).
Claim Status
Claims 138-157 are pending.
Claims 1-137 are cancelled.
Claims 138-157 are examined.
Claims 138-157 are rejected.
Information Disclosure Statement
The Information Disclosure Statements are in compliance with the provisions of 37 CFR 1.97. Accordingly, all references have been considered.
Claim Rejections - 35 USC § 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 138-157 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 applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Claims 138, 149, and 157 are unclear regarding the limitation of training the model using the known association, “wherein the known association is based on a plurality of dependent features assayed using a functional assay, the functional assay generating a molecular measurement or a derivative of the molecular measurement for each molecular variant in the Truth Set…” The metes and bounds of the claims are unclear, as it is unclear whether performing a functional assay is intended to be an active step performed before the other claim steps (before the receiving of data for analysis), or if the claim limitation is satisfied by obtaining whatever data would result from a functional assay (a product-by process claim, as described in MPEP 2113). As such, this claim is indefinite due to lack of clarity.
Claims 139-148 and 150-156 are also indefinite as they depend from claims 138 and 149, and do not resolve the issues above.
Claim 145 is similarly unclear as it describes how the derivates of the molecular measurements obtained by the functional assays of claim 138 are generated using artificial neural networks. As above, it is unclear if/when generating the derivatives of the molecular measurements are actively performed to satisfy the claim limitations, or if the metes and bounds of the claims are satisfied by if the derivative is obtained as data. As such, this claim is also indefinite due to lack of clarity.
Claim 139 is also unclear with respect to “synthetic compartments” – it is unclear what is meant by this phrase, as there is no definition for a synthetic compartment in the specification, and it is unclear what characteristics would differentiate a synthetic compartment from the cellular compartments or subcellular compartments of the claim. Therefore, this claim is indefinite due to lack of clarity.
Claim 140 is also unclear with respect to the phrase “other clinically valuable genes.” The term “clinically valuable” is not defined by the claim, the specification does not provide a standard for ascertaining the requisite degree, and one of ordinary skill in the art would not be reasonably apprised of the scope of the invention. It is unclear what characteristics a gene would be to possess to be considered “clinically valuable.” Further, it is unclear what “other” in the phrase “other clinically valuable” refers to, as the only other reference to a gene/genes in this claim and independent claim 138 refer to variants within genes, with no mention of their clinical value. Therefore, there is no clear antecedent basis for this phrase. For both of these reasons, this claim is indefinite due to lack of clarity.
Claims 147-148 and 155-156 are also unclear as they are “use claims” as described by MPEP 2173.05(q), which recite uses for the method and system without reciting any active steps to achieve the use. Attempts to claim a process without setting forth any steps involved in the process generally raises an issue of indefiniteness. Therefore, these claims are indefinite for failing to recite any active steps. The Examiner recommends amending claim 147 to read “wherein the method further comprises informing a test subject's lifetime risk of developing cancer…” and claim 148 to read “wherein the method further comprises identifying significantly mutated regions and significantly mutated networks…”, as this changes the “use” of these claims into active steps that are performed. Similar changes should be made to claims 155-156 so that the system actively performs the actions.
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 138-157 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea of mental processes and mathematical concepts, without significantly more.
The MPEP at MPEP 2106 sets forth steps for identifying eligible subject matter:
(1) Are the claims directed to a process, machine, manufacture or composition of matter?
(2A)(1) Do the claims recite a judicially recognized exception, i.e. a law of nature, a natural phenomenon, or an abstract idea?
(2A)(2) Do the claims recite additional elements that integrate the judicial exception into a practical application?
(2B) If the claims recite a judicial exception and do not integrate the judicial exception, do the claims recite additional elements that provide an inventive concept and amount to significantly more than the judicial exception?
With regard to step (1) (Are the claims directed to a process, machine, manufacture or composition of matter?): Yes. The claims are directed to one of the statutory classes. Claims 138-148 are directed to a process (a method), claims 149-156 are directed to a product (a system comprising a processor and non-transitory computer readable storage medium), and claim 157 is also directed to a product (a non-transitory computer readable storage medium).
With regard to step (2A)(1) (Do the claims recite a judicially recognized exception?): Yes. The claims recite the abstract ideas of processing data using mental steps and mathematical processes. Claims that recite nothing more than abstract ideas, natural phenomena, or laws of nature are not eligible for patent protection (see MPEP 2106.04).
Abstract ideas include mathematical concepts, (mathematical formulas or equations, mathematical relationships and mathematical calculations), certain methods of organizing human activity, and mental processes (including procedures for collecting, observing, evaluating, and organizing information (See MPEP 2106.04(a)(2)). In particular, these abstract ideas include but are not limited to:
Determining the phenotypic impact of the target molecular variant based on a known association between the molecular variants and their known phenotypic impacts (mental process; the human mind is capable of applying a known association between parameters to new target data in order to make a prediction; claims 138, 149, 157)
Reducing dimensionality and removing noise from an association corresponding to the molecular measurement, wherein the derivative of the molecular measurement is used to determine the phenotypic impact of the target molecular variant (mental process/mathematical concept; the human mind is capable of reducing the dimensionality of data with algorithms and removing noise, and reducing dimensionality is a mathematical process; claim 145, 153)
Dependent claims 140-143, 146-148, 150-151, and 154-156 further limit the abstract ideas recited in the independent claims, and do not change their characterization as abstract ideas.
Therefore, the claims recite elements that constitute one or more judicial exceptions.
With regard to step (2A)(2) (Do the claims recite additional elements that integrate the judicial exception into a practical application?): No. The claims recite the limitations of training and using a machine learning model to perform the abstract ideas, with claims 144 and 152 further describing the model as a supervised learning model. Claim 149 and its dependents also recite the additional element of a system, comprising at least one computer hardware processor and at least one non-transitory computer-readable storage medium, storing instructions” that cause the processor to perform the claimed steps. Claim 157 similarly recites at least one non-transitory computer readable storage medium storing processor executable instructions for implementing the claimed steps. Claim 138 and its dependents also recite the additional elements of receiving a plurality of samples (interpreted as sample data rather than physical samples, based on context), wherein the plurality of samples comprises a plurality of molecular variants and each sample comprises a variant in a gene, and wherein the plurality of molecular variants is divided into two groups, and claim 139 further describes the samples. Claims 145 and 153 also recite the additional element wherein the derivative of the molecular measurement is generated using a plurality of Artificial Neural Networks.
While the claims recite the additional element of receiving data, such steps that only amount to necessary data gathering , without any technical details of how the data is obtained that integrate the judicial exception, are insignificant extrasolution activities that do not add a meaningful limitation to the claims (see MPEP 2106.05(g)). As a result, the judicial exception is not integrated into a practical application. In addition, while the claims recite additional elements related to the use of computers, they do not provide any specific details by which the system, processors or computer-readable storage media perform or carry out the judicial exception listed in step (2A)(1), nor do they provide any details of how specific structures of the computer are used to implement these functions. The judicial exception is therefore not integrated into a practical application because the generically recited computer elements do not add a meaningful limitation to the abstract idea, as they amount to simply implementing the abstract idea on a computer (see MPEP 2106.05(f)). This also applies to the use of training and using machine learning models to process data, as such models, without recited physical structure outside of general computer components, is also analogous to implementing an abstract idea of data analysis on a computer. Because the claims do not recite any additional elements that integrate the judicial exception into a practical application, the claims as a whole are directed to an abstract idea.
With regard to step (2B) (Do the claims recite additional elements that provide an inventive concept and amount to significantly more than the judicial exception?): No. The claims recite an abstract idea with additional elements; however, these additional elements are general computer elements added to abstract ideas, and non-particular instructions to apply the abstract idea by linking it to a field of use or extrasolution activity (see MPEP 2106.05(f-h)). The general computer elements and/or the use of a trained machine learning model do not provide an inventive concept, and similarly, non-particular instructions to gather or produce data do not provide an inventive concept. Non-particular instructions to gather or receive data using general computer elements are also considered well-understood, routine and conventional activities (see MPEP 2106.05(d), which indicates that limitations such as “Receiving or transmitting data over a network” from Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362, and “Storing and retrieving information in memory” from Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1334, 115 USPQ2d 1681, 1701 (Fed. Cir. 2015); OIP Techs., 788 F.3d at 1363, 115 USPQ2d at 1092-93, are recognized as conventional activities). The claims therefore do not include additional elements that are sufficient to amount to significantly more than the judicial exception. As a result, the claims as a whole do not provide an inventive concept.
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.
Claim Rejection
Claims 138-144, 146-147, 149-152, 154-155, and 157 are rejected under 35 U.S.C. 103 as being unpatentable over SHIHAB 2015 “An integrative approach to predicting the functional effects of non-coding and coding sequence variation” in view of GUIDUGLI 2014 “Functional Assays for Analysis of Variants of Uncertain Significance in BRCA2.”
Claim Interpretation and Scope and Contents of Prior Art
Claim 138, 149, and 157 recite a method, system, and computer-readable storage medium for determining a phenotypic impact of a target molecular variant, where the method comprises the step of receiving a plurality of samples, wherein the plurality of samples comprises a plurality of molecular variants and each sample comprises a variant in a gene, and wherein the plurality of molecular variants is divided into two groups: a. a Truth Set comprising molecular variants with known phenotypic impacts, and b. a Target Set comprising molecular variants with unknown phenotypic impacts, wherein the Target Set comprises the target molecular variant.
With respect to these limitations, SHIHAB teaches computer and web-based methods for predicting functional effects of variants using a machine learning model (Abstract), involving receiving variant data with variant-effect associations from multiple sources, including the Human Gene Mutation Database, the 1000 Genomes Project, and ENCODE database (pg 1537), and dividing the variants into training and testing sets (analogous to a Truth set and Target set, pg 1538 col 1) and further evaluating the model against a set of variants with effects unknown to the model (pg 1541 col 1 par 2).
Claims 138, 149, and 157 also recite the limitations of training a machine learning model using a known association between the molecular variants in the Truth Set and the known phenotypic impacts, wherein the known association is based on a plurality of dependent features assayed using a functional assay, the functional assay generating a molecular measurement or a derivative of the molecular measurement for each molecular variant in the Truth Set; and determining the phenotypic impact of the target molecular variant using the trained machine learning model.
With respect to this limitation, SHIHAB teaches training a machine learning model using the known association between the variants and known phenotypic impacts, where the known associations are based on dependent features (pg 1537 col 2), but does not specify that the dependent features are assayed using a functional assay. However, the databases in SHIBAB that provide the variants and their known impacts are known for containing data associated with published literature and experiments obtained using functional assays, and GUIDUGLI contains examples of different known functional assays specifically for analysis of variants (pg 154-159); it would be obvious to one of ordinary skill in the art, therefore, to use such functional assays to provide the association data in the databases used to train the model.
Claim 139 recites the limitation wherein the plurality of samples comprises single cells, cellular compartments, subcellular compartments, or synthetic compartments. SHIBAB does not describe the samples that the variants are obtained from. However, GUIDUGLI explains that the functional assays used to determine variants are performed using both cell samples and cellular/subcellular compartments (pg 154-159)
Claim 140 recites the limitation wherein the plurality of molecular variants comprises coding or non-coding variants within previously identified mutational hotspots of functional elements, genes, and pathways associated with other clinically valuable genes, mutational hotspots of functional elements, genes, and pathways associated with Mendelian disorders, pathways associated with known cancer drivers, or pathways associated with variation in drug response. With respect to this limitation, SHIBAB teaches the variant data can include at least coding or non-coding regions, and genes that are clinically valuable as they relate to disease (pg 1537 col 1).
Claim 141 recites the limitation wherein the plurality of molecular variants is derived based on clinical databases, phenotype databases, population databases, molecular annotation databases, or functional databases of variants, subjects, or populations or produced using a mutagenesis assay. With respect to this limitation, SHIBAB teaches that the variants are derived from multiple sources, including the Human Gene Mutation Database (clinical database), the 1000 Genomes Project (molecular annotation database), and the ENCODE database (functional database with data from mutagenesis assays; pg 1537).
Claims 142 and 150 recite the limitations wherein the known phenotypic impacts of the molecular variants in the Truth Set and the unknown phenotypic impacts of the target molecular variants in the Target Set measure pathogenicity, functionality, or relative effect of the molecular variant. With respect to these limitations, SHIBAB teaches that the impacts of the molecular variants measure functional impact (pg 1537 col 1).
Claims 143 and 151 recite the limitation wherein the molecular measurement further comprises locus-specific measurements of gene expression, protein expression, chromatin accessibility, epigenetic modification, regulatory activity, post-transcriptional processing, post- translational modification, mutation status, mutation burden, or mutation rate of molecules within each sample in the plurality of samples. With respect to this limitation, SHIBAB teaches that the molecular measurement comprise chromatin accessibility (Open Chromatin), epigenetic modification (histone modifications) and regulatory activity (transcription factor binding sites; pg 1537 col 2).
Claims 144 and 152 recite the limitation wherein the machine learning model is a supervised learning model. With respect to this limitation, SHIBAB teaches that their machine learning model is a supervised learning model, as it is trained on a labelled/known dataset (pg 1537).
Claim 146 and 154 recite the limitation wherein the known association is based on a plurality of independent features that are not assayed for each molecular variant in the Truth Set and wherein the plurality of independent features comprises one or more of evolutionary, population, annotation-based, structural, dynamical, physicochemical features associated with variants, genomic coordinates, transcript coordinates, translated coordinates, and amino acids.
With respect to this limitation, SHIBAB teaches that the known association is based on independent features not assayed in the training/truth set, including at least evolutionary features (sequence conservation), and annotations (footprints, pg 1537 col 2).
Claims 147 and 155 recite the limitation wherein the method is used to inform a test subject's lifetime risk of developing cancer, wherein the test subject has the target molecular variant. With respect to this limitation, SHIBAB teaches that the method can be used to predict variants in specific disease contexts, such as cancer (pg 1542 col 2). It would be obvious to one of ordinary skill in the art then, that a test subject with a variant related to a cancer could be at risk of developing that cancer.
Resolving Ordinary Skill in the Art and Obviousness Rationale
A teaching, suggestion, or motivation in the prior art would have led one of ordinary skill in the art to modify or combine the prior art to arrive at the claimed invention. Specifically, a person of ordinary skill in variant-phenotype associations and databases of associations would have been motivated to combine the teachings of SHIHAB with the teachings of GUIDUGLI, in order to achieve the claimed invention, because the use of functional assays are well-known methods of determining the impact of a variant on function (Abstract), and would provide the variant-phenotype association data contained in the databases used in SHIHAB. A person of ordinary skill would reasonably expect success from combining these teachings, as both SHIHAB and GUIDUGLI teach methods of determining or predicting variant impacts on phenotype/function, and the methods of GUIDUGLI could be used to provide the data for the model of SHIHAB. Therefore, the claims at issue would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention as there is both a reason to modify or combine the prior art, and a reasonable expectation of success (see MPEP 2143.02 (I)).
Claim Rejection
Claims 145 and 153 are rejected under 35 U.S.C. 103 as being unpatentable over SHIBAB in view of GUIDUGLI as applied to claims 138-144, 146-147, 149-152, 154-155, and 157 above, and further in view of RITCHIE 2015 “Methods of integrating data to uncover genotype–phenotype interactions.”
Claim Interpretation and Scope and Contents of Prior Art
SHIBAB in view of GUIDUGLI teaches the limitations of claims 138-144, 146-147, 149-152, 154-155, and 157 above.
Claims 145 and 153 recite the limitations wherein the derivative of the molecular measurement is generated using a plurality of Artificial Neural Networks (ANNs), wherein the plurality of ANNs comprises: a. a first ANN to generate a database of molecular measurements for the Truth Set, b. a second ANN to generate a plurality of associations between each of the molecular measurements in the database and one or more from the group consisting of molecular states, phenotypes, and genomics metrics using statistical methods, and c. a third ANN to generate the derivative of the molecular measurement by reducing dimensionality and removing noise from an association corresponding to the molecular measurement, wherein the derivative of the molecular measurement is used to determine the phenotypic impact of the target molecular variant.
With respect to these limitations, SHIBAB teaches that derivates of molecular measurements are used to determine the phenotypic impact of molecular variants in the test set (pg 1537 col 2) but does not specify that the derivative of the molecular measurement is generated using a plurality of Artificial Neural Networks. However, RITCHIE teaches methods of using artificial neural networks to integrate data (generate a database) and generate associations between measurements such as copy number alterations, methylation, microRNA and gene expression with phenotypes (pg 93) and perform data reduction through filtering of noise and dimensionality reduction using principal components analysis (pg 88 col 1, 89 col 1).
Resolving Ordinary Skill in the Art and Obviousness Rationale
A teaching, suggestion, or motivation in the prior art would have led one of ordinary skill in the art to modify or combine the prior art to arrive at the claimed invention. Specifically, a person of ordinary skill in variant-phenotype associations would have been motivated to combine the teachings of SHIHAB in view of GUIDUGLI with the teachings of RITCHIE, in order to achieve the claimed invention, because using a combination of neural networks approaches in integrating data, generating associations, and filtering/dimensionality reduction can achieve a more thorough and informative interrogation of genotype–phenotype associations, and also compensate for missing or unreliable data (pg 85 col 2). A person of ordinary skill would reasonably expect success from combining these teachings, as SHIHAB in view of GUIDUGLI and RITCHIE teach methods of determining or predicting variant impacts on phenotype/function, and the methods of RITCHIE could be used to provide the data or inform the model of SHIHAB. Therefore, the claims at issue would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention as there is both a reason to modify or combine the prior art, and a reasonable expectation of success (see MPEP 2143.02 (I)).
Claim Rejection
Claims 148 and 156 are rejected under 35 U.S.C. 103 as being unpatentable over SHIBAB in view of GUIDUGLI as applied to claims 138-144, 146-147, 149-152, 154-155, and 157 above, and further in view of ARAYA 2015 “Systematic identification of significantly mutated regions reveals a rich landscape of functional molecular alterations across cancer genomes.”
Claim Interpretation and Scope and Contents of Prior Art
SHIBAB in view of GUIDUGLI teaches the limitations of claims 138-144, 146-147, 149-152, 154-155, and 157 above.
Claims 148 and 156 recite the limitation wherein the method is used to identify significantly mutated regions and significantly mutated networks by identifying phenotype- associated mutation density.
SHIBAB does not teach identifying significantly mutated regions and significantly mutated networks by identifying phenotype- associated mutation density. However, ARAYA teaches using identified variants and their functional impacts to identify significantly mutated regions by identifying cancer-associated mutation density (Abstract, pg 3).
Resolving Ordinary Skill in the Art and Obviousness Rationale
A teaching, suggestion, or motivation in the prior art would have led one of ordinary skill in the art to modify or combine the prior art to arrive at the claimed invention. Specifically, a person of ordinary skill in variant-phenotype associations would have been motivated to combine the teachings of SHIHAB in view of GUIDUGLI with the teachings of ARAYA, in order to achieve the claimed invention, because identifying significantly mutated regions or networks can reveal disease related domains and molecular interfaces, identifying drivers for the disease (Abstract, pg 3). A person of ordinary skill would reasonably expect success from combining these teachings, as SHIBAB in view of GUIDUGLI and ARAYA teach methods associating variants/mutations with phenotypic outcomes, and because it would be obvious to one of ordinary skill that the method of SHIBAB in view of GUIDUGLI could be used to identify the variants and functional impacts that can then be used in the method of ARAYA to identify mutated regions/networks associated with disease. Therefore, the claims at issue would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention as there is both a reason to modify or combine the prior art, and a reasonable expectation of success (see MPEP 2143.02 (I)).
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to MARY C LEVERETT whose telephone number is (571)272-5494. The examiner can normally be reached 8:00am - 5:00pm M-Th.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Karlheinz R. Skowronek can be reached at (571) 272-9047. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/MARY C LEVERETT/Examiner, Art Unit 1687
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