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 .
Claim Status
Claims 1-3, 8, 10-11, 14, 16, 24, 26-27, 30, 34-35, 41-42, 45, 47, and 52-53 are currently pending and under examination herein.
Claims 1-3, 8, 10-11, 14, 16, 24, 26-27, 30, 34-35, 41-42, 45, 47, and 52-53 are rejected.
Priority
The instant application also claims benefit to U.S. provisional application No. 63/139994 filed on 04/30/2020. Domestic benefit is acknowledged. As such, the effective filing date of claims 1-3, 8, 10-11, 14, 16, 24, 26-27, 30, 34-35, 41-42, 45, 47, and 52-53 is 1/12/2021.
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 7/18/2023 are in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner. A signed copy of a list of references cited from each IDS is included in this Office Action.
Drawings
The drawings submitted on 7/18/2023 are accepted.
Specification
The drawings submitted on 7/18/2023 are accepted.
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 1-3, 8, 10-11, 14, 16, 24, 26-27, 30, 34-35, 41-42, 45, 47, and 52-53 are rejected under 35 U.S.C 101 because the claimed invention is directed to an abstract idea and/or a natural phenomenon without significantly more.
In accordance with MPEP 2106, claims found to recite statutory subject matter (Step 1: YES) are then analyzed to determine if the claims recite any concepts that equate to an abstract idea, law of nature, or natural phenomenon (Step 2A, Prong 1).
Claim 1 recites a method for determining the genetic status of a subject comprising: applying a model to the first dataset, or a plurality of dimensionality reduction components thereof, thereby determining the genetic status of the subject as output of the model.
Claims 1, 52, and 53 recite: determining, for each respective gene in a first set of genes within the first plurality of genes, a corresponding abundance value for each respective RNA boundary element in a respective plurality of boundary elements of the respective gene in the first plurality of nucleic acid sequences.
Claim 2 recites the genetic status of the subject comprises an mRNA isoform status for the first respective gene.
Claim 3 recites the mRNA isoform status for the first respective gene (i) comprises an indication of whether the subject has a particular splicing pattern for the first respective gene, or (ii) is an estimate of the prevalence, in the first plurality of mRNA molecules, of one or more respective mRNA isoform in the plurality of mRNA isoforms, and: the respective plurality of boundary elements further comprises a boundary element for a genomic rearrangement contained entirely within the first respective gene.
Claim 8 recites the subject has a disease or disorder; and a first respective state, in a plurality of states, for the mRNA isoform status for the first respective gene is associated with an improved clinical outcome following treatment of the disease or disorder with a targeted therapy relative to a clinical outcome following treatment of the disease or disorder associated with a second respective state, in the plurality of states, for the mRNA isoform status, with the targeted therapy.
Claim 10 recites the genetic status of the subject comprises an indication of whether the subject carries a gene fusion between the pair of respective genes.
Claim 11 recites the method of claim 10, wherein the respective plurality of boundary elements further comprises a set of gene fusion boundary elements for fusions between the pair of respective genes.
Claim 14 recites the method of claim 10, wherein: the subject has a disease or disorder; and treatment of the disease or disorder with a targeted therapy in a patient carrying a gene fusion between the pair of respective genes is associated with an improved clinical outcome relative to a clinical outcome following treatment of the disease or disorder in a patient that does not carry a gene fusion between the pair of respective genes with the targeted therapy.
Claim 16 recites the genetic status of the subject comprises a disease state for a disease associated with aberrant mRNA splicing, wherein the disease is cancer, a cardiovascular disease, or a neurological disorder, and the disease state comprises a cancer type, a prognosis for the disease, or a severity of the disease.
Claim 30 recites the method of claim 1, wherein the obtaining: B) comprises: identifying, for each respective nucleic acid sequence in the plurality of nucleic acid sequences that maps to a respective gene in the first set of genes, each RNA boundary element in the respective plurality of boundary elements that is present in the respective nucleic acid sequence, and counting, for each respective gene in the first set of genes, the number of occurrences of each respective RNA boundary element in the respective plurality of boundary elements across each respective nucleic acid sequence in the plurality of nucleic acid sequences that maps to a respective gene in the first set of genes, thereby generating a respective abundance value for each respective boundary element in the respective plurality of boundary elements.
Claim 34 recites the method of claim 1, wherein the corresponding abundance values are determined for each of at least 100 respective RNA boundary elements.
Claim 35 recites the method of claim 1, wherein the model is a statistical inference model, a machine learning model, or a regression model, wherein: the statistical inference model is a Bayesian inference model, a likelihood-based inference model, a frequentist inference model, an AIC-based inference model, or a mixture model, and the machine learning model is a support vector regression, a random forest model, an XGBoost model, a Gaussian process model, a deep neural network model, a convolutional neural network model, or a recurrent neural network model.
Claim 41 recites the method of claim 1, wherein the model processes the first data set, or a plurality of dimensionality reduction components thereof, to determine the genetic status of the subject as an output of the model in N- dimensional space in the applying C), wherein N is a positive integer of at least 4.
Claim 42 recites the method of claim 1, further comprising determining a confidence value for the genetic status of the subject, wherein the confidence value is dependent upon (i) a measure of sequencing depth for the first plurality of nucleic acid sequences, or (ii) the presence or absence of orthogonal evidence for the genetic status.
The limitations of a method determining the genetic status of a subject; determining, for each respective nucleic acid sequence in the first plurality of nucleic acid sequences, the respective one or more genes in the plurality of genes corresponding to the respective nucleic acid sequence by mapping the respective nucleic acid sequence to a reference construct representing at least 1 Mb of the genome for the species of the subject, identifying, for each respective nucleic acid sequence in the plurality of nucleic acid sequences that maps to a respective gene in the first set of genes, each RNA boundary element in the respective plurality of boundary elements that is present in the respective nucleic acid sequence, and counting, for each respective gene in the first set of genes, the number of occurrences of each respective RNA boundary element in the respective plurality of boundary elements across each respective nucleic acid sequence in the plurality of nucleic acid sequences that maps to a respective gene in the first set of genes, thereby generating a respective abundance value for each respective boundary element in the respective plurality of boundary elements; and determining, for each respective gene in a first set of genes within the first plurality of genes, a corresponding abundance value for each respective RNA boundary element in a respective plurality of boundary elements of the respective gene in the first plurality of nucleic acid sequences recites a mental process and falls under the “mental process” grouping of ideas. Evaluation based on a set of information can be practically performed in the human mind or with the aid of a pen or paper and therefore is a mental process. In addition, although the limitations of obtaining a data set amount to insignificant extra solution activity as discussed below, the steps of determination of correlation by mapping a respective sequence to a reference, identification of relevant RNA boundary elements, and counting RNA boundary elements to generate a relative abundance value also can be practically performed in the human mind and is therefore a mental process.
The limitations of applying a model to the first dataset, or a plurality of dimensionality reduction components thereof, thereby determining the genetic status of the subject as output of the model; and counting RNA boundary elements to generate a relative abundance value; the method of claim 1, wherein the model is a statistical inference model, a machine learning model, or a regression model, wherein: the statistical inference model is a Bayesian inference model, a likelihood-based inference model, a frequentist inference model, an AIC-based inference model, or a mixture model, and the machine learning model is a support vector regression, a random forest model, an XGBoost model, a Gaussian process model, a deep neural network model, a convolutional neural network model, or a recurrent neural network model; the method of claim 1, wherein the model processes the first data set, or a plurality of dimensionality reduction components thereof, to determine the genetic status of the subject as an output of the model in N- dimensional space in the applying C), wherein N is a positive integer of at least 4; determining a confidence value for the genetic status of the subject, wherein the confidence value is dependent upon (i) a measure of sequencing depth for the first plurality of nucleic acid sequences, or (ii) the presence or absence of orthogonal evidence for the genetic status; and determining, for each respective gene in a first set of genes within the first plurality of genes, a corresponding abundance value for each respective RNA boundary element in a respective plurality of boundary elements of the respective gene in the first plurality of nucleic acid sequences are verbal equivalents of mathematical concepts and therefore fall under the “mathematical concept” grouping of ideas.
The limitations of the genetic status of the subject comprising an mRNA isoform status for the first respective gene; an indication of whether the subject has a particular splicing pattern for the first respective gene, or (ii) is an estimate of the prevalence, in the first plurality of mRNA molecules, of one or more respective mRNA isoform in the plurality of mRNA isoforms, and: the respective plurality of boundary elements further comprises a boundary element for a genomic rearrangement contained entirely within the first respective gene; the subject has a disease or disorder; and a first respective state, in a plurality of states, for the mRNA isoform status for the first respective gene is associated with an improved clinical outcome following treatment of the disease or disorder with a targeted therapy relative to a clinical outcome following treatment of the disease or disorder associated with a second respective state, in the plurality of states, for the mRNA isoform status, with the targeted therapy; the genetic status of the subject comprises an indication of whether the subject carries a gene fusion between the pair of respective genes; the respective plurality of boundary elements further comprises a set of gene fusion boundary elements for fusions between the pair of respective genes; the genetic status of the subject comprises a disease state for a disease associated with aberrant mRNA splicing, wherein the disease is cancer, a cardiovascular disease, or a neurological disorder, and the disease state comprises a cancer type, a prognosis for the disease, or a severity of the disease; merely serve to further limit the recited mental process as stated above.
The limitations reciting the corresponding abundance values are determined for each of at least 100 respective RNA boundary elements merely serve to further limit the recited mathematical computation as stated above.
The limitations reciting that the subject has a disease or disorder; and treatment of the disease or disorder with a targeted therapy in a patient carrying a gene fusion between the pair of respective genes is associated with an improved clinical outcome relative to a clinical outcome following treatment of the disease or disorder in a patient that does not carry a gene fusion between the pair of respective genes with the targeted therapy amounts to natural phenomena (see MPEP 2106.04(b)). The treatment of a disease based on the presence of a particular genotypic configuration is analogous to the natural relationship between a patient’s CYP2D6 metabolizer genotype and the risk that the patient will suffer QTc prolongation after administration of a medication called iloperidone. (see Vanda Pharmaceuticals Inc. v. West-Ward Pharmaceuticals, 887 F.3d 1117, 1135-36, 126 USPQ2d 1266, 1281 (Fed. Cir. 2018)) which the courts have ruled to be ineligible subject matter. As such, claims 1-3, 8, 10-11, 14, 16, 24, 26-27, 30, 34-35, 41-42, 45, 47, and 52-53 recite abstract ideas.
Claims found to recite a judicial exception under Step 2A, Prong 1 are then further analyzed to determine if the claims as a whole integrate the recited judicial exception into a practical application or not (Step 2A, Prong 2). This judicial exception is not integrated into a practical application because the claims do not recite additional elements that reflects an improvement to technology or applies or uses the recited judicial exception in some other meaningful way. Rather, the instant claims recite additional elements that amount to mere instructions to implement the abstract idea in a generic computing environment. Specifically, the claims recite the following additional elements:
Claim 1 recites a computer system having one or more processors, and memory storing one or more programs for execution by the one or more processors.
Claim 52 recites a computer system for determining a genetic status of a subject, the computer system comprising: one or more processors; and memory addressable by the one or more processors, the memory storing at least one program for execution by the one or more processors, the at least one program comprising instructions for performing a method.
Claim 53 recites a non-transitory computer readable storage medium, wherein the non-transitory computer readable storage medium stores instructions, which when executed by a computer system, cause the computer system to perform a method for determining a genetic status of a subject.
Claims 1, 52, and 53 recite obtaining, in electronic form, a first plurality of at least 100,000 nucleic acid sequences for a first plurality of mRNA molecules from a first biological sample of the subject, wherein each mRNA molecule in the first plurality of mRNA molecules corresponds to one or more genes in a plurality of genes; obtaining a first dataset by a process.
Claim 2 recites the first set of genes comprises a first respective gene in the plurality of genes; the respective plurality of boundary elements comprises each exon-exon boundary present in at least one respective mRNA isoform in a plurality of mRNA isoforms for the respective gene.
Claim 8 recites when the output of the model indicates the subject has the first respective state for the mRNA isoform status for the first respective gene, administering a first therapeutic regimen comprising the targeted therapy to the subject, and when the output of the model indicates the subject does not have the first respective state for the mRNA isoform status for the first respective gene, administering a second therapeutic regimen comprising a therapy for the disease or disorder other than the targeted therapy to the subject, wherein the second therapeutic regimen is different than the first therapeutic regimen.
Claim 10 recites the method of claim 1, wherein: the first set of genes comprises a pair of respective genes in the plurality of genes; the respective plurality of boundary elements comprises, for each respective gene in the pair of respective genes, each corresponding exon-exon boundary element present in one or more mRNA isoforms for the respective gene.
Claim 14 recites when the output of the model indicates the subject carries a gene fusion between the pair of respective genes, administering the targeted therapy to the subject, and when the output of the model indicates the subject does not carry a gene fusion between the pair of respective genes, administering a therapy for the disease or disorder other than the targeted therapy to the subject.
Claim 16 recites the method of claim 1, wherein: the respective plurality of boundary elements comprises, for each respective gene in the first set of genes, each exon-exon boundary present in at least one respective mRNA isoform in a plurality of mRNA isoforms for the respective gene.
Claim 24 recites the method of claim 1, wherein the one or more genes is at least 25 genes, or wherein the one or more genes represents a whole transcriptome.
Claim 26 recites the method of claim 1, wherein the first plurality of nucleic acid sequences were obtained by sequencing cDNA generated from the first plurality of mRNA molecules from the first biological sample.
Claim 27 recites the method of claim 1, wherein the first biological sample of the subject is a solid tumor sample from the subject, a non-cancerous tissue sample from the subject, or a saliva sample or a blood sample from the subject.
Claim 30 recites determining, for each respective nucleic acid sequence in the first plurality of nucleic acid sequences, the respective one or more genes in the plurality of genes corresponding to the respective nucleic acid sequence by mapping the respective nucleic acid sequence to a reference construct representing at least 1 Mb of the genome for the species of the subject.
Claim 45 recites the method of claim 1, wherein the first data set further comprises one or more features derived from a second plurality of nucleic acid sequences for a first plurality of DNA molecules from a second biological sample of the subject, and wherein the one or more features derived from the second plurality of nucleic acid sequences comprises support for a genomic rearrangement.
Claim 46 recites the method of claim 1, wherein the first data set further comprises an indication of a personal characteristic of the subject, wherein: the personal characteristic of the subject comprises an age, gender, race, ethnicity, smoking status, diabetes status, personal medical history, familial medical history, or a disease state for the subject comprising a cancer type or cancer stage.
The limitations of obtaining, in electronic form, a first plurality of at least 100,000 nucleic acid sequences for a first plurality of mRNA molecules from a first biological sample of the subject, wherein each mRNA molecule in the first plurality of mRNA molecules corresponds to one or more genes in a plurality of genes; obtaining a first dataset by a process comprising determining, for each respective gene in a first set of genes within the first plurality of genes, a corresponding abundance value for each respective RNA boundary element in a respective plurality of boundary elements of the respective gene in the first plurality of nucleic acid sequences; and determining, for each respective nucleic acid sequence in the first plurality of nucleic acid sequences, the respective one or more genes in the plurality of genes corresponding to the respective nucleic acid sequence by mapping the respective nucleic acid sequence to a reference construct representing at least 1 Mb of the genome for the species of the subject constitute insignificant data gathering to carry out the recited judicial exception. Of note, the courts have ruled in Electric Power Group, LLC V. Alstom S.A., 830 F.3d 1350, 1354-55, 119 USPQ2d 1739, 1742 (Fed. Cir. 2016) that the collection, analysis, and display of data are considered insignificant extra-solution activity and does not integrate the judicial exception into a practical application (see MPEP 2106.05(g)).
The limitations of the first set of genes comprises a first respective gene in the plurality of genes; the respective plurality of boundary elements comprises each exon-exon boundary present in at least one respective mRNA isoform in a plurality of mRNA isoforms for the respective gene; the first set of genes comprises a pair of respective genes in the plurality of genes; the respective plurality of boundary elements comprises, for each respective gene in the pair of respective genes, each corresponding exon-exon boundary element present in one or more mRNA isoforms for the respective gene; the respective plurality of boundary elements further comprises a set of gene fusion boundary elements for fusions between the pair of respective genes; the method of claim 1, wherein: the respective plurality of boundary elements comprises, for each respective gene in the first set of genes, each exon-exon boundary present in at least one respective mRNA isoform in a plurality of mRNA isoforms for the respective gene; the method of claim 1, wherein the one or more genes is at least 25 genes, or wherein the one or more genes represents a whole transcriptome; the method of claim 1, wherein the first plurality of nucleic acid sequences were obtained by sequencing cDNA generated from the first plurality of mRNA molecules from the first biological sample; the method of claim 1, wherein the first biological sample of the subject is a solid tumor sample from the subject, a non-cancerous tissue sample from the subject, or a saliva sample or a blood sample from the subject; the method of claim 1, wherein the first data set further comprises one or more features derived from a second plurality of nucleic acid sequences for a first plurality of DNA molecules from a second biological sample of the subject, and wherein the one or more features derived from the second plurality of nucleic acid sequences comprises support for a genomic rearrangement; merely serves to further limit the insignificant data gathering step and does not integrate into a practical application.
The limitations of administering a first therapeutic regimen comprising the targeted therapy to the subject, and when the output of the model indicates the subject does not have the first respective state for the mRNA isoform status for the first respective gene; administering a second therapeutic regimen comprising a therapy for the disease or disorder other than the targeted therapy to the subject, wherein the second therapeutic regimen is different than the first therapeutic regimen amounts to an insignificant extra-solution activity and a field-of-use limitation (see MPEP 2106.04(d)(2) for “Particular Treatment” considerations and MPEP 2106.05(h)). Namely, the administration of therapeutic regimen based on mRNA isoform status which informs a disease is analogous to a step of administering a drug providing 6-thioguanine to patients with an immune-mediated gastrointestinal disorder, which the courts have ruled merely indicate a field of use or technological environment to apply a judicial exception, because limiting drug administration to this patient population did no more than simply refer to the relevant pre-existing audience of doctors who used thiopurine drugs to treat patients suffering from autoimmune disorders (see Mayo Collaborative Servs. v. Prometheus Labs. Inc., 566 U.S. 66, 78, 101 USPQ2d 1961, 1968 (2012). In addition, the courts have ruled in Parker V. Flook, 437 U.S. 584, 198 USPQ 193 (1978) that limiting an abstract idea to a field of use or adding post solution components does not make the concept patentable. Thus, limitations that amount to merely indicating a field of use or technological environment in which to apply a judicial exception do not amount to significantly more than the exception itself, and cannot integrate a judicial exception into a practical application.
In regards to the recited computer system and non-transitory computer medium, there are no limitations that indicate that the claimed computer, processor, input device or computer-readable medium require anything other than generic computing systems. As such, these limitations equate to mere instructions to implement the abstract idea on a generic computer that the courts have stated does not render an abstract idea eligible in Alice Corp., 573 U.S. at 223, 110 USPQ2d at 1983. See also 573 U.S. at 224, 110 USPQ2d at 1984. As such claims 1-3, 8, 10-11, 14, 16, 24, 26-27, 30, 34-35, 41-42, 45, 47, and 52-53 do not integrate into a practical application.
Claims found to be directed to a judicial exception are then further evaluated to determine if the claims recite an inventive concept that provides significantly more than the judicial exception itself (Step 2B). The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the claims recite additional elements that amount to mere instructions to implement the abstract idea in a generic field-of-use and/or technological environment. The instant claims recite the following additional elements:
Claim 1 recites a computer system having one or more processors, and memory storing one or more programs for execution by the one or more processors.
Claim 52 recites a computer system for determining a genetic status of a subject, the computer system comprising: one or more processors; and memory addressable by the one or more processors, the memory storing at least one program for execution by the one or more processors, the at least one program comprising instructions for performing a method.
Claim 53 recites a non-transitory computer readable storage medium, wherein the non-transitory computer readable storage medium stores instructions, which when executed by a computer system, cause the computer system to perform a method for determining a genetic status of a subject.
Claims 1, 52, and 53 recite obtaining, in electronic form, a first plurality of at least 100,000 nucleic acid sequences for a first plurality of mRNA molecules from a first biological sample of the subject, wherein each mRNA molecule in the first plurality of mRNA molecules corresponds to one or more genes in a plurality of genes; obtaining a first dataset by a process comprising determining, for each respective gene in a first set of genes within the first plurality of genes, a corresponding abundance value for each respective RNA boundary element in a respective plurality of boundary elements of the respective gene in the first plurality of nucleic acid sequences.
Claim 2 recites the first set of genes comprises a first respective gene in the plurality of genes; the respective plurality of boundary elements comprises each exon-exon boundary present in at least one respective mRNA isoform in a plurality of mRNA isoforms for the respective gene.
Claim 8 recites when the output of the model indicates the subject has the first respective state for the mRNA isoform status for the first respective gene, administering a first therapeutic regimen comprising the targeted therapy to the subject, and when the output of the model indicates the subject does not have the first respective state for the mRNA isoform status for the first respective gene, administering a second therapeutic regimen comprising a therapy for the disease or disorder other than the targeted therapy to the subject, wherein the second therapeutic regimen is different than the first therapeutic regimen.
Claim 10 recites the method of claim 1, wherein: the first set of genes comprises a pair of respective genes in the plurality of genes; the respective plurality of boundary elements comprises, for each respective gene in the pair of respective genes, each corresponding exon-exon boundary element present in one or more mRNA isoforms for the respective gene.
Claim 14 recites when the output of the model indicates the subject carries a gene fusion between the pair of respective genes, administering the targeted therapy to the subject, and when the output of the model indicates the subject does not carry a gene fusion between the pair of respective genes, administering a therapy for the disease or disorder other than the targeted therapy to the subject.
Claim 16 recites the method of claim 1, wherein: the respective plurality of boundary elements comprises, for each respective gene in the first set of genes, each exon-exon boundary present in at least one respective mRNA isoform in a plurality of mRNA isoforms for the respective gene.
Claim 24 recites the method of claim 1, wherein the one or more genes is at least 25 genes, or wherein the one or more genes represents a whole transcriptome.
Claim 26 recites the method of claim 1, wherein the first plurality of nucleic acid sequences were obtained by sequencing cDNA generated from the first plurality of mRNA molecules from the first biological sample.
Claim 27 recites the method of claim 1, wherein the first biological sample of the subject is a solid tumor sample from the subject, a non-cancerous tissue sample from the subject, or a saliva sample or a blood sample from the subject.
Claim 30 recites determining, for each respective nucleic acid sequence in the first plurality of nucleic acid sequences, the respective one or more genes in the plurality of genes corresponding to the respective nucleic acid sequence by mapping the respective nucleic acid sequence to a reference construct representing at least 1 Mb of the genome for the species of the subject.
Claim 45 recites the method of claim 1, wherein the first data set further comprises one or more features derived from a second plurality of nucleic acid sequences for a first plurality of DNA molecules from a second biological sample of the subject, and wherein the one or more features derived from the second plurality of nucleic acid sequences comprises support for a genomic rearrangement.
Claim 46 recites the method of claim 1, wherein the first data set further comprises an indication of a personal characteristic of the subject, wherein: the personal characteristic of the subject comprises an age, gender, race, ethnicity, smoking status, diabetes status, personal medical history, familial medical history, or a disease state for the subject comprising a cancer type or cancer stage.
The limitations of a computer system having one or more processors, and memory storing one or more programs for execution by the one or more processors; a computer system for determining a genetic status of a subject, the computer system comprising: one or more processors; and memory addressable by the one or more processors, the memory storing at least one program for execution by the one or more processors, the at least one program comprising instructions for performing a method; and a non-transitory computer readable storage medium, wherein the non-transitory computer readable storage medium stores instructions, which when executed by a computer system, cause the computer system to perform a method for determining a genetic status of a subject are well-understood, routine, and conventional. Specifically, the courts have identified steps of receiving data over a network or storing and retrieving information in memory as conventional computer functions in Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information); TLI Communications LLC V. AV Auto. LLC, 823 F.3d 607, 610, 118 USPQ2d 1744, 1745 (Fed. Cir. 2016) (using a telephone for image transmission); OIP Techs., Inc., V. Amazon.com, Inc., 788
F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015) (sending messages over a network); buySAFE, Inc. V. Google, Inc., 765 F.3d 1350, 1355, 112 USPQ2d 1093, 1096 (Fed. Cir. 2014) (computer receives and sends information over a network); and Versata Dev. Group, Inc. V. SAP Am., Inc., 793 F.3d 1306, 1334, 115 USPQ2d 1681, 1701 (Fed. Cir. 2015).
Additionally, the limitations of obtaining, in electronic form, a first plurality of at least 100,000 nucleic acid sequences for a first plurality of mRNA molecules from a first biological sample of the subject, wherein each mRNA molecule in the first plurality of mRNA molecules corresponds to one or more genes in a plurality of genes; obtaining a first dataset by a process comprising determining, for each respective gene in a first set of genes within the first plurality of genes, a corresponding abundance value for each respective RNA boundary element in a respective plurality of boundary elements of the respective gene in the first plurality of nucleic acid sequences; the method of claim 1, wherein: the first set of genes comprises a pair of respective genes in the plurality of genes; the respective plurality of boundary elements comprises, for each respective gene in the pair of respective genes, each corresponding exon-exon boundary element present in one or more mRNA isoforms for the respective gene; the method of claim 1, wherein: the respective plurality of boundary elements comprises, for each respective gene in the first set of genes, each exon-exon boundary present in at least one respective mRNA isoform in a plurality of mRNA isoforms for the respective gene; the method of claim 1, wherein the one or more genes is at least 25 genes, or wherein the one or more genes represents a whole transcriptome; the method of claim 1, wherein the first plurality of nucleic acid sequences were obtained by sequencing cDNA generated from the first plurality of mRNA molecules from the first biological sample; the method of claim 1, wherein the first biological sample of the subject is a solid tumor sample from the subject, a non-cancerous tissue sample from the subject, or a saliva sample or a blood sample from the subject; and the method of claim 1, wherein the first data set further comprises one or more features derived from a second plurality of nucleic acid sequences for a first plurality of DNA molecules from a second biological sample of the subject, and wherein the one or more features derived from the second plurality of nucleic acid sequences comprises support for a genomic rearrangement; determining, for each respective nucleic acid sequence in the first plurality of nucleic acid sequences, the respective one or more genes in the plurality of genes corresponding to the respective nucleic acid sequence by mapping the respective nucleic acid sequence to a reference construct representing at least 1 Mb of the genome for the species of the subject are well-understood, routine, and conventional. As aforementioned, the courts have identified steps of receiving data over a network or storing and retrieving information in memory as conventional computer functions in Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information); TLI Communications LLC V. AV Auto. LLC, 823 F.3d 607, 610, 118 USPQ2d 1744, 1745 (Fed. Cir. 2016) (using a telephone for image transmission); OIP Techs., Inc., V. Amazon.com, Inc., 788
F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015) (sending messages over a network); buySAFE, Inc. V. Google, Inc., 765 F.3d 1350, 1355, 112 USPQ2d 1093, 1096 (Fed. Cir. 2014) (computer receives and sends information over a network); and Versata Dev. Group, Inc. V. SAP Am., Inc., 793 F.3d 1306, 1334, 115 USPQ2d 1681, 1701 (Fed. Cir. 2015). Of note, specifying the sample, the type of nucleic acid obtained, and nucleic acid features do not preclude it from simply being a data reception step which is well-understood, routine, and conventional.
The limitations of the method of claim 1, wherein the first data set further comprises an indication of a personal characteristic of the subject, wherein: the personal characteristic of the subject comprises an age, gender, race, ethnicity, smoking status, diabetes status, personal medical history, familial medical history, or a disease state for the subject comprising a cancer type or cancer stage are well-understood, routine, and conventional. The courts have ruled that storing and retrieving information in memory (see 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) is a well-understood, routine, and conventional function within computers.
The limitations of when the output of the model indicates the subject has the first respective state for the mRNA isoform status for the first respective gene, administering a first therapeutic regimen comprising the targeted therapy to the subject, and when the output of the model indicates the subject does not have the first respective state for the mRNA isoform status for the first respective gene, administering a second therapeutic regimen comprising a therapy for the disease or disorder other than the targeted therapy to the subject, wherein the second therapeutic regimen is different than the first therapeutic regimen and of when the output of the model indicates the subject carries a gene fusion between the pair of respective genes, administering the targeted therapy to the subject, and when the output of the model indicates the subject does not carry a gene fusion between the pair of respective genes, administering a therapy for the disease or disorder other than the targeted therapy to the subject are well-understood, routine, and conventional. Precision oncology is the practice of tailoring cancer therapy to the unique genomic, epigenetic, and/or transcriptomic profile of an individual’s cancer and is built upon conventional therapeutic regimens as recited by the applicant’s specification in [0003]. The Examiner notes that the applicant has also stated that over time researchers have identified genomic, transcriptomic, and epigenetic markers to improve treatment predictions as well as further evidence that treatment based on isoform status is conventional in paragraphs [0004]-[0006].
There are no additional elements that comprise an inventive concept when considered individually or as an ordered combination that transforms the claimed judicial exception into a patent-eligible application of the judicial exception. Therefore, the claims do not amount to significantly more than the judicial exception itself (Step 2B: No). As such, claims 1-3, 8, 10-11, 14, 16, 24, 26-27, 30, 34-35, 41-42, 45, 47, and 52-53 are not patent eligible.
Claim Rejections - 35 USC § 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.
Claim(s) 1-2 and 52-53 is/are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Shi et al. (SparseIso: a novel Bayesian approach to identify alternatively spliced isoforms from RNA-seq data. Bioinformatics. 2018 Jan 1;34(1):56-63).
Regarding claim 1, Shi teaches:
A method for determining a genetic status of a subject (disclosure identifies and quantifies expressed isoforms from RNA-seq which can be interpreted as the “genetic status” of an organism; see “Abstract” on page 56) comprising: on a computer system (Sparselso algorithm is implemented as a C++ package; see “2.5 Implementation and availability” on page 59) having one or more processors, and memory storing one or more programs for execution by the one or more processors:
A) obtaining, in electronic form, a first plurality of at least 100,000 nucleic acid sequences for a first plurality of mRNA molecules from a first biological sample of the subject, wherein each mRNA molecule in the first plurality of mRNA molecules corresponds to one or more genes in a plurality of genes (current sequencing technology allows for preparation of enormous amounts of sequencing reads as recited in the Introduction which is subsequently used in the disclosure’s pipeline; see “Introduction” on page 56).
B) obtaining a first dataset (processed data yields matrix obtained in “2.3 Mixture of reads from multiple transcripts” on page 58) by a process comprising determining, for each respective gene in a first set of genes within the first plurality of genes, a corresponding abundance value for each respective RNA boundary element in a respective plurality of boundary elements of the respective gene in the first plurality of nucleic acid sequences (explicitly recited as the abundance of isoforms are estimated and sampled in “2. Material and Methods” on page 57; model includes splice junctions to model spliced reads where the segment is defined as the union of exons and splice junctions in “2.2 Read count model”; see page 58)
C) applying a model to the first dataset, or a plurality of dimensionality reduction components thereof, thereby determining the genetic status of the subject as output of the model (see “Results” where the disclosure explicitly discusses the use of a Bayesian method to identify spliced isoforms from RNA-seq data (genetic status under the broadest reasonable interpretation); see page 59).
Regarding claim 2, Shi teaches:
The method of claim 1, wherein:
the first set of genes comprises a first respective gene in the plurality of genes (the entire method disclosure is directed to the analysis of genes in a set of genes; see “Abstract” on page 56)
the respective plurality of boundary elements comprises each exon-exon boundary present in at least one respective mRNA isoform in a plurality of mRNA isoforms for the respective gene (model includes splice junctions to model spliced reads where the segment is defined as the union of exons and splice junctions in “2.2 Read count model”; see page 58); and
the genetic status of the subject comprises an mRNA isoform status for the first respective gene (see “Abstract” where the method is directed to identifying and quantifying mRNA isoforms; page 56).
Regarding claim 52,
A computer system (Sparselso algorithm is implemented as a C++ package; see “2.5 Implementation and availability” on page 59) for determining a genetic status of a subject, the computer system comprising: one or more processors; and memory addressable by the one or more processors, the memory storing at least one program for execution by the one or more processors, the at least one program comprising instructions for performing a method comprising:
A) obtaining, in electronic form, a first plurality of at least 100,000 nucleic acid sequences for a first plurality of mRNA molecules from a first biological sample of the subject, wherein each mRNA molecule in the first plurality of mRNA molecules corresponds to one or more genes in a plurality of genes (current sequencing technology allows for preparation of enormous amounts of sequencing reads as recited in the Introduction which is subsequently used in the disclosure’s pipeline; see “Introduction” on page 56);
B) obtaining a first dataset (processed data yields matrix obtained in “2.3 Mixture of reads from multiple transcripts” on page 58) by a process comprising determining, for each respective gene in a first set of genes within the first plurality of genes, a corresponding abundance value for each respective RNA boundary element in a respective plurality of boundary elements of the respective gene in the first plurality of nucleic acid sequences (explicitly recited as the abundance of isoforms are estimated and sampled in “2. Material and Methods” on page 57; model includes splice junctions to model spliced reads where the segment is defined as the union of exons and splice junctions in “2.2 Read count model”; see page 58); and
C) applying a model to the first dataset, or a plurality of dimensionality reduction components thereof, thereby determining the genetic status of the subject as output of the model (see “Results” where the disclosure explicitly discusses the use of a Bayesian method to identify spliced isoforms from RNA-seq data (genetic status under the broadest reasonable interpretation); see page 59).
Regarding claim 53,
A non-transitory computer readable storage medium (Sparselso algorithm is implemented as a C++ package; see “2.5 Implementation and availability” on page 59), wherein the non-transitory computer readable storage medium stores instructions, which when executed by a computer system, cause the computer system to perform a method for determining a genetic status of a comprising:
A) obtaining, in electronic form, a first plurality of at least 100,000 nucleic acid sequences for a first plurality of mRNA molecules from a first biological sample of the subject, wherein each mRNA molecule in the first plurality of mRNA molecules corresponds to one or more genes in a plurality of genes (current sequencing technology allows for preparation of enormous amounts of sequencing reads as recited in the Introduction which is subsequently used in the disclosure’s pipeline; see “Introduction” on page 56);
B) obtaining a first dataset (processed data yields matrix obtained in “2.3 Mixture of reads from multiple transcripts” on page 58) by a process comprising determining, for each respective gene in a first set of genes within the first plurality of genes, a corresponding abundance value for each respective RNA boundary element in a respective plurality of boundary elements of the respective gene in the first plurality of nucleic acid sequences; and
C) applying a model to the first dataset, or a plurality of dimensionality reduction components thereof, thereby determining the genetic status of the subject as output of the model (see “Results” where the disclosure explicitly discusses the use of a Bayesian method to identify spliced isoforms from RNA-seq data (genetic status under the broadest reasonable interpretation); see page 59).
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.
The factual inquiries 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.
The present rejection(s) reference specific passages from cited prior art. However,
Applicant is advised that the rejections are based on the entirety of each cited prior art. That is,
each cited prior art reference “must be considered in its entirety”. (See MPEP 2141.02(VI))
Therefore, Applicant is advised to review all portions of the cited prior art if traversing a
rejection based on the cited prior art.
Claim(s) 3, 8, 10, 14, 16, 24, 26-27, 30, 34-35, 41-42, 45, and 47 is/are rejected under 35 U.S.C. 103 as being unpatentable over Shi et al. (SparseIso: a novel Bayesian approach to identify alternatively spliced isoforms from RNA-seq data. Bioinformatics. 2018 Jan 1;34(1):56-63), as applied in claim 1-2, in view of Haas et al. (STAR-Fusion: Fast and Accurate Fusion Transcript Detection from RNA-Seq. bioRxiv.) as evidenced by Mills et al. (Multiple treatment comparison meta-analyses: a step forward into complexity. Clin Epidemiol.)
Regarding claim 3, Shi teaches:
The method of claim 2, wherein: the mRNA isoform status for the first respective gene (i) comprises an indication of whether the subject has a particular splicing pattern for the first respective gene, or (ii) is an estimate of the prevalence, in the first plurality of mRNA molecules, of one or more respective mRNA isoform in the plurality of mRNA isoforms (explicitly recited as the abundance of isoforms are estimated and sampled in “2. Material and Methods” on page 57). During patent examination, claims are given their broadest reasonable interpretation consistent with the specification. See MPEP 2111. Therefore, the limitation of "A or B" is interpreted to encompass embodiments comprising A, B, or either alternative, and the prior art need only disclose one of the recited alternatives to satisfy the limitation.
Shi does not teach that the respective plurality of boundary elements further comprises a boundary element for a genomic rearrangement contained entirely within the first respective gene. Haas teaches STAR-Fusion, a fast and accurate fusion transcript detection technique implemented on RNA-Seq data (see description of the STAR-Fusion tool in “Abstract” on page 1). Therefore, it would have been obvious before the effective filing date of the claimed invention to incorporate Haas’s STAR-Fusion algorithm into Shi’s existing pipeline in order to address the need for faster and more accurate methods of fusion detections (see paragraphs 1-2 on page 3). The combination would have been accomplished with reasonable expectation of success as they both operate on the same field of endeavor directed to analyzing RNA-sequencing data.
Regarding claim 8, Haas teaches:
The method of claim 2, wherein: the subject has a disease or disorder; and a first respective state, in a plurality of states, for the mRNA isoform status for the first respective gene is associated with an improved clinical outcome following treatment of the disease or disorder with a targeted therapy relative to a clinical outcome following treatment of the disease or disorder associated with a second respective state, in the plurality of states, for the mRNA isoform status, with the targeted therapy (determination of the driver of a given tumor is explicitly stated to be important to inform the best therapeutic strategy which is what the disclosure is directed to; see “Introduction” on page 2), further comprising:
when the output of the model indicates the subject has the first respective state for the mRNA isoform status for the first respective gene (STAR-Fusion outputs lists of candidate fusion transcripts (e.g. isoforms) as output which were obtained from a sample; see “Methods” on page 2-3),
Haas does not explicitly teach administering a first therapeutic regimen comprising the targeted therapy to the subject, and when the output of the model indicates the subject does not have the first respective state for the mRNA isoform status for the first respective gene. However, it would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to administer the first therapeutic regimen as there is already a recognized need or problem within the art and it would be obvious to try to administer the therapy as evidenced by Haas (determination of the driver of a given tumor is explicitly stated to be important to inform the best therapeutic strategy implies treatment for patients who could benefit from them; see “Introduction” on page 2). The administration would have been accomplished with reasonable expectation of success as both operate in the same field of endeavor.
In addition, although Haas does not explicitly teach administering a second therapeutic regimen comprising a therapy for the disease or disorder other than the targeted therapy to the subject, wherein the second therapeutic regimen is different than the first therapeutic regimen. Of note, according to MPEP 2111.04, the broadest reasonable interpretation of a method (or process) claim having contingent limitations requires only those steps that must be performed and does not include steps that are not required to be performed because the condition(s) precedent is not met.
Regarding claim 10, Haas teaches:
The method of claim 1, wherein: the first set of genes comprises a pair of respective genes (Chromosomal rearrangements leading to the formation of fusion transcripts include the known cancer-driving fusion transcripts as explicitly stated in “Introduction” on page 2 and STAR-Fusion selects candidate gene pairs later in the pipeline on pages 4-5 in “Mapping split and discordant read alignments to reference gene annotations”) in the plurality of genes; the respective plurality of boundary elements comprises, for each respective gene in the pair of respective genes, each corresponding exon-exon boundary element present in one or more mRNA isoforms for the respective gene (explicitly stated in “Mapping split and discordant read alignments to reference gene annotations ” on pages 4-5 where split read alignments reported to STAR are mapped to exons of reference transcript annotations); and the genetic status of the subject comprises an indication of whether the subject carries a gene fusion between the pair of respective genes (STAR-Fusion outputs lists of candidate fusion transcripts (e.g. isoforms) as output which were obtained from a sample; see “Methods” on page 2-3).
Regarding claim 11, Haas teaches:
The method of claim 10, wherein the respective plurality of boundary elements further comprises a set of gene fusion boundary elements for fusions between the pair of respective genes (known in the art and used in the pipeline as evidence supporting predicted fusions is measured by the number of RNA-Seq fragments found as a split (junction) reads that directly overlap the fusion transcript chimeric junction; see page 2).
Regarding claim 14, Haas teaches:
The method of claim 10, wherein: the subject has a disease or disorder; and treatment of the disease or disorder with a targeted therapy in a patient carrying a gene fusion between the pair of respective genes is associated with an improved clinical outcome relative to a clinical outcome following treatment of the disease or disorder in a patient that does not carry a gene fusion between the pair of respective genes with the targeted therapy, further comprising (determination of the driver of a given tumor is explicitly stated to be important to inform the best therapeutic strategy which is what the disclosure is directed to; see “Introduction” on page 2): when the output of the model indicates the subject carries a gene fusion between the pair of respective genes (STAR-Fusion outputs lists of candidate fusion transcripts (e.g. isoforms) as output which were obtained from a sample; see “Methods” on page 2-3), administering the targeted therapy to the subject (determination of the driver of a given tumor is explicitly stated to be important to inform the best therapeutic strategy implies treatment for patients who could benefit from them; see “Introduction” on page 2). Although Hass does not teach when the output of the model indicates the subject does not carry a gene fusion between the pair of respective genes, administering a therapy for the disease or disorder other than the targeted therapy to the subject, similarly to claim 8, although the reference does not explicitly state that there is an alternative therapy besides the one above based on the mRNA isoform status, it is obvious to have a control therapy as a point of reference to show that the first therapeutic regimen is effective (see MPEP 2144.05 and in KSR International Co. v. Teleflex Inc., 550 U.S. 398, 82 USPQ2d 1385 (2007), wherein the Supreme Court held that "obvious to try" was a valid rationale for an obviousness finding, for example, when there is a "design need" or "market demand" and there are a "finite number" of solutions. 550 U.S. at 421, 82 USPQ2d at 1397.). Comparing treatments in clinical trials is well-known within the art and a standard step of clinical analysis as evidenced by Mills et al. (meta-analyses use the effect of an intervention against a control; second paragraph in “Introduction”; page 193).
Regarding claim 16, Hass teaches:
The method of claim 1, wherein: the respective plurality of boundary elements comprises, for each respective gene in the first set of genes, each exon-exon boundary present in at least one respective mRNA isoform in a plurality of mRNA isoforms for the respective gene (used in the pipeline as evidence supporting predicted fusions is measured by the number of RNA-Seq fragments found as a split (junction) reads that directly overlap the fusion transcript chimeric junction; see page 2); and the genetic status of the subject comprises a disease state for a disease associated with aberrant mRNA splicing, wherein the disease is cancer, a cardiovascular disease, or a neurological disorder (see “Introduction” on page 2; where chromosomal rearrangements leading to the formation of fusion transcripts represent one class of genomic aberrations that occurs at high frequencies in certain cancer types and STAR-Fusion is directed towards working with these rearrangements), and the disease state comprises a cancer type, a prognosis for the disease, or a severity of the disease (identification of the cancer-associated fusions would inherently inform future studies (e.g. hallmarks of diverse cancer types and prognositics) which is explicitly stated in “Discussion” on pages 11-12). The Examiner notes that concerning the last limitation, Haas already identifies applications of the STAR-Fusion method and the courts have ruled that an “obvious to try” line of reasoning can support an obvious rejection. (see KSR International Co. v. Teleflex Inc., 550 U.S. 398, 82 USPQ2d 1385 (2007), the Supreme Court held that "obvious to try" was a valid rationale for an obviousness finding, for example, when there is a "design need" or "market demand" and there are a "finite number" of solutions. 550 U.S. at 421, 82 USPQ2d at 1397.).
Regarding claim 24, Hass teaches:
The method of claim 1, wherein the one or more genes is at least 25 genes, or wherein the one or more genes represents a whole transcriptome (RNA-seq data for the method is retrieved via Whole Genome Sequencing (WGS) as explicitly stated in “Introduction”; page 2).
Regarding claim 26, Haas teaches:
The method of claim 1, wherein the first plurality of nucleic acid sequences were obtained by sequencing cDNA generated from the first plurality of mRNA molecules from the first biological sample (see “Compiling a Genome Resource Lib” which states that reference transcript sequences for use in STAR-Fusion uses .cdna files in FASTA format; page 6).
Regarding claim 27, Haas teaches:
The method of claim 1, wherein the first biological sample of the subject is a solid tumor sample from the subject, a non-cancerous tissue sample from the subject, or a saliva sample or a blood sample from the subject (RNA-seq data obtained via WGS is used for STAR-Fusion and is obtained from tumors; see “Introduction” on page 2).
Regarding claim 30, Haas as modified teaches:
The method according wherein the obtaining B) comprises:
determining, for each respective nucleic acid sequence in the first plurality of nucleic acid sequences, the respective one or more genes in the plurality of genes corresponding to the respective nucleic acid sequence by mapping the respective nucleic acid sequence to a reference construct representing at least 1 Mb of the genome for the species of the subject (Disclosure recites to determine the candidate gene pairs of potential fusions, the discordant read pairs and split read alignments reported by STAR are next mapped to exons of reference transcript annotations based on coordinate overlaps, leveraging interval tree data structures as recited in “Mapping split and discordant read alignment to reference gene annotations” on page 4),
identifying, for each respective nucleic acid sequence in the plurality of nucleic acid sequences that maps to a respective gene in the first set of genes, each RNA boundary element in the respective plurality of boundary elements that is present in the respective nucleic acid sequence (used in the pipeline as evidence supporting predicted fusions is measured by the number of RNA-Seq fragments found as a split (junction) reads that directly overlap the fusion transcript chimeric junction; see page 2);,
and counting, for each respective gene in the first set of genes, the number of occurrences of each respective RNA boundary element in the respective plurality of boundary elements across each respective nucleic acid sequence in the plurality of nucleic acid sequences that maps to a respective gene in the first set of genes (STAR-Fusion selects those candidate gene pairs for which the fusion supporting evidence indicates a sense-sense orientation between the fusion pairs and scores them according to the number of split reads supporting the fusion breakpoint and the number of paired-end fragments that span the breakpoint on page 5), thereby generating a respective abundance value for each respective boundary element in the respective plurality of boundary elements (Haas quantifies the fused pairs but does not quantify across each respective boundary element but Shi does. Shi: explicitly recited as the abundance of isoforms are estimated and sampled in “2. Material and Methods” on page 57; model includes splice junctions to model spliced reads where the segment is defined as the union of exons and splice junctions in “2.2 Read count model”; see page 58).
Regarding claim 34, Shi teaches:
The method according to claim 1, wherein the corresponding abundance values are determined for each of at least 100 respective RNA boundary elements (Shi already operates on whole transcriptome simulation data as stated in “3. Results” on page 59 which inherently contains an enormous amount of boundary elements). Accordingly, Whole Transcriptome Sequencing (WSG) necessarily involves 100 respective RNA boundary elements. It is elementary that the mere recitation of a newly discovered function or property, inherently possessed by things in the prior art, does not cause a claim drawn to distinguish of the prior art. Under the principles of inherency, if a prior art device, in its normal and usual operation, would necessarily perform the method claimed, then the method claimed will be considered to be anticipated by the prior art device. Additionally, where the Patent Office has
reason to believe that a functional limitation asserted to be critical for establishing novelty in the
claimed subject matter may, in fact, be an inherent characteristic of the prior art, it possesses
the authority to require the applicant to prove that the subject matter shown to be in the prior art
does not possess the characteristic relied on (see MPEP § 2112).
Regarding claim 35, Shi teaches:
The method of claim 1, wherein the model is a statistical inference model, a machine learning model, or a regression model, wherein: the statistical inference model is a Bayesian inference model, a likelihood-based inference model, a frequentist inference model, an AIC-based inference model, or a mixture model, and the machine learning model is a support vector regression, a random forest model, an XGBoost model, a Gaussian process model, a deep neural network model, a convolutional neural network model, or a recurrent neural network model (Sparselso is a novel Bayesian method as explicitly stated in “Abstract”; on page 1).
Regarding claim 42,
The method of claim 1, further comprising determining a confidence value for the genetic status of the subject, wherein the confidence value is dependent upon (i) a measure of sequencing depth for the first plurality of nucleic acid sequences, or (ii) the presence or absence of orthogonal evidence for the genetic status (see “2. Materials and Methods” on page 57 where confidence is derived from RNA-seq read counts and abundance estimates).
Regarding claim 45,
The method of claim 1, wherein the first data set further comprises one or more features derived from a second plurality of nucleic acid sequences for a first plurality of DNA molecules from a second biological sample of the subject, and wherein the one or more features derived from the second plurality of nucleic acid sequences comprises support for a genomic rearrangement (Chromosomal rearrangements leading to the formation of fusion transcripts include the known cancer-driving fusion transcripts as explicitly stated in “Introduction” on page 2). In addition, disclosed steps from the prior art can be repeated and would still meet the limitations of the claim. The courts have held In re Harza, 274 F.2d 669, 124 USPQ 378 (CCPA 1960), that mere duplication of parts has no patentable significance unless a new and unexpected result is produced.
Regarding claim 47,
The method of claim 1, wherein the first data set further comprises an indication of a personal characteristic of the subject, wherein: the personal characteristic of the subject comprises an age, gender, race, ethnicity, smoking status, diabetes status, personal medical history, familial medical history, or a disease state for the subject comprising a cancer type or cancer stage (STAR-Fusion is directed towards the field of chromosomal rearrangements and discloses that chromosomal rearrangements lead to the formation of fusion transcripts and represent one class of genomic aberrations that occur at high frequencies in certain cancer types, including leukemias and prostate cancer in “Introduction” on page 2). Thus, possession of particular chromosomal rearrangements would inherently inform one of ordinary skill in the art of particular cancers to be incorporated in the data set. Accordingly, particular disease types necessarily involves particular chromosomal rearrangements. It is elementary that the mere recitation of a newly discovered function or property, inherently possessed by things in the prior art, does not cause a claim drawn to distinguish of the prior art. Under the principles of inherency, if a prior art device, in its normal and usual operation, would necessarily perform the method claimed, then the method claimed will be considered to be anticipated by the prior art device. Additionally, where the Patent Office has reason to believe that a functional limitation asserted to be critical for establishing novelty in the claimed subject matter may, in fact, be an inherent characteristic of the prior art, it possesses the authority to require the applicant to prove that the subject matter shown to be in the prior art does not possess the characteristic relied on (see MPEP § 2112). In addition, during patent examination, claims are given their broadest reasonable interpretation consistent with the specification. See MPEP 2111. Therefore, the limitation of "A or B" is interpreted to encompass embodiments comprising A, B, or either alternative, and the prior art need only disclose one of the recited alternatives to satisfy the limitation.
Claim(s) 41 is/are rejected under 35 U.S.C. 103 as being unpatentable over Shi et al. (SparseIso: a novel Bayesian approach to identify alternatively spliced isoforms from RNA-seq data. Bioinformatics. 2018 Jan 1;34(1):56-63), as applied in claim 2, in view of Haas et al. (STAR-Fusion: Fast and Accurate Fusion Transcript Detection from RNA-Seq. bioRxiv. doi:10.1101/120295), as applied in claim 1, further in view of Waller et al. (Deep transcriptome profiling of multiple myeloma with quantitative measures using the SPECTRA approach. medRxiv)
Regarding claim 41,
Shi and Haas do not teach the method of claim 1, wherein the model processes the first data set, or a plurality of dimensionality reduction components thereof, to determine the genetic status of the subject as an output of the model in N- dimensional space in the applying C), wherein N is a positive integer of at least 4. Waller teaches the method of using principal component analysis to provide an optimized representation of variance, particularly, the use of a 5-dimensional PCA space for genome prediction. (Explicitly discloses in his disclosure a method of dimensionality reduction of 50 genes to 5 dimensions on page 4; see also Fig. 2). Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate the dimensionality reduction component Walter’s genome prediction pipeline to Shi as modified’s existing prediction pipeline in order to provide an improved representation of an individual’s tissue to allow identification of expression characteristics as recited by Walter (see first paragraph on page 4). This would have been accomplished with reasonable expectation of success as Shi as modified already makes use of dimensionality reduction/machine learning algorithms in the same field of endeavor.
Conclusion
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/P.N./Examiner, Art Unit 1685
/OLIVIA M. WISE/Supervisory Patent Examiner, Art Unit 1685
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