DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Status of Claims
Claims 1-20 are pending and examined on the merits.
Information Disclosure Statement
The information disclosure statements filed 2022-12-05 and 2023-07-21 are acknowledged. A signed copy of the corresponding 1449 form has been included with this Office action.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea and law of nature without significantly more.
Step 2A, Prong 1
In accordance with MPEP § 2106, claims found to recite statutory subject matter (claim 1-18 are drawn to a method; claim 19 is drawn to a system and claim 20 is drawn to an apparatus) (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). In the instant application, the claims recite the following limitations that equate to an abstract idea:
Claim 1, 19 and 20 recite:
“…execute/executing, via the cloud computing platform, the plurality of instructions for completing the selected transform…”, which is a mental process, i.e., the steps can be completed with pen and paper,
Claim 3 recites:
“comparing regions of the genome to at least a portion of the plurality of nucleic acid reads to identify differences and similarities”, mental step.
Claim 9 recites: “determining if the plurality of nucleic acid reads are available to be read;”, mental step.
Claim 10 recites:
“…creating a catalog of transforms for deriving genomic biomarkers…”, mental step
“…determining if there is a problem with each transform…”, mental step
“…returning an error message including an indication of the problem…”, mental step
“…determining if there is a problem with the at least one transform…”, mental step.
“…returning an error message including an indication of the problem with the at least one transform…”, mental step
“…selecting the transforms from the created catalog of transforms…”, mental step
Claim 11 recites: “the notification of the operational status of the data source includes an orchestration error, an execution error, or a timeout error.” Which further limits claim 1.
Claim 12 recites:
“…determining if the transform should be placed in a high priority queue or a low priority queue…”, mental step
“…depending on the determination, placing the order in either the high priority queue or low priority queue…”, mental step
“…executing instructions in the high priority queue before executing instructions in the low priority queue…”, mental step
Claim 13 recites: “…the configuration is cloud computing platform agnostic…”, which further limits claim 1.
Claim 14 recites: “…predicting, based on the stored genomic biomarker, a likelihood of a patient being at a high-risk of one or more of an oncological event, neurological disorder, autoimmune condition, cardiovascular disease, infectious disease, or endocrinological disease…”, mental step.
Claims 15-18 recite: “predicting, based on the stored genomic biomarker, one or more of…”, mental step.
The claims recite an abstract idea of analyzing and modifying genomic sequences (See MPEP 2106.07(a)).
These recitations are similar to the concepts of collecting information, analyzing it and displaying certain results of the collection and analysis in Electric Power Group, LLC, v. Alstom (830 F.3d 1350, 119 USPQ2d 1739 (Fed. Cir. 2016)), organizing and manipulating information through mathematical correlations in Digitech Image Techs., LLC v Electronics for Imaging, Inc. (758 F.3d 1344, 111 U.S.P.Q.2d 1717 (Fed. Cir. 2014)) and comparing information regarding a sample or test to a control or target data in Univ. of Utah Research Found. v. Ambry Genetics Corp. (774 F.3d 755, 113 U.S.P.Q.2d 1241 (Fed. Cir. 2014)) and Association for Molecular Pathology v. USPTO (689 F.3d 1303, 103 U.S.P.Q.2d 1681 (Fed. Cir. 2012)) that the courts have identified as concepts that can be practically performed in the human mind or mathematical relationships. Therefore, these limitations fall under the “Mental process” and “Mathematical concepts” groupings of abstract ideas.
While claim 19 recite performing some aspects of the analysis using a “system”, there are no additional limitations that indicate that this model requires anything other than carrying out the recited mental process or mathematical concept in a generic computer environment. Merely reciting that a mental process is being performed in a generic computer environment does not preclude the steps from being performed practically in the human mind or with pen and paper as claimed. If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components, then if falls within the “Mental processes” grouping of abstract ideas. As such, claim(s) 1-20 recite(s) an abstract idea/law of nature/natural phenomenon (Step 2A, Prong 1: YES).
Step 2A, Prong 2
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 an additional element that reflects an improvement to technology or applies or uses the recited judicial exception to affect a particular treatment for a condition. Rather, the instant claims recite additional elements that amount to mere instructions to implement the abstract idea in a generic computing environment or mere instructions to apply the recited judicial exception via a generic treatment. Specifically, the claims recite the following additional elements:
Claim 1 recites: “a cloud computing platform”, “…associate/associating the selected transform with a cloud computing platform based at least in part on the configuration…”, and “providing, to the cloud computing platform, the transform image”, which further limits claim 1
Claim 4 recites:
“accessing, with the transform, an input directory, wherein the input directory is separate from the data source”, which further limits claim 1
“writing, with the transform, to an output directory, wherein the output directory is separate from the data source”, which further limits claim 4
Claim 7 recites: “the transforms are associated with the cloud computing platforms based on compute requirements of the order to transform the plurality of nucleic acid reads.”, which further limits claim 1.
Claim 8 recites:
“the transforms are associated with the cloud computing platforms based on an available virtual machine (VM) memory size, an available central processing unit (CPU) performance, and a resource quota; and the resource quota comprises a constraint on total compute resources available to: (i) a group of transforms, (ii) a cloud computing system, and/or, (iii) a portion of a cloud computing system.”, which further limits claim 1.
Claim 10 recites:
“…receiving a plurality of transforms for deriving genomic biomarkers”, which further limits claim 1.
“…receiving an update to the at least one transform…” which further limits claim 1.
Claims 19 recites:
“…A computer system …”
“…processors…”
loading, via a communication interface, the plurality of nucleic acid reads into a first storage location indicated by the plurality of indications of storage locations;
communicate, via the communication interface, at least one communication from the execution between the selected transform and the data source, the at least one communication comprising at least an operational status of the selected transform;
store, via the communication interface, the genomic biomarker output from the selected transform into a second storage location indicated by the plurality of indications of storage locations; and
provide a notification, to the data source via the communication interface, a final operational status of the selected transform based at least in part on the storing the genomic biomarker output from the selected transform.
Claim 20 recites:
“…A computing device …”
“one or more processors; and one or more memories coupled to the one or more processors;”
providing, to the cloud computing platform, the transform image;
loading, via a communication interface, the plurality of nucleic acid reads into a first storage location indicated by the plurality of indications of storage locations;
communicating, via the communication interface, at least one communication from the execution between the selected transform and the data source, the at least one communication comprising at least an operational status of the selected transform;
storing, via the communication interface, the genomic biomarker output from the selected transform into a second storage location indicated by the plurality of indications of storage locations; and
providing a notification, to the data source via the communication interface, of a final operational status of the selected transform based at least in part on the storing the genomic biomarker output from the selected transform.
There are no limitations that indicate that the claimed analysis engine or the formats of the provided data 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. As such, claims 1-20 are directed to an abstract idea (Step 2A, Prong 2: NO).
Step 2B
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 equate to mere instructions to apply the recited exception in a generic way or in a generic computing environment. The instant claims recite the following additional elements:
Claim 1, 19 and 20 recite:
“…receive/receiving, from a data source, an order to transform…”, and “…the plurality of nucleic acid reads are derived from next generation sequencing of a specimen…”,
“…receive/receiving a selection of a transform…”, “…the transform comprises a configuration, a transform image comprising a plurality of indications of storage locations and a plurality of instructions for completing the transform…”,
“…associate/associating the selected transform with a cloud computing platform based at least in part on the configuration…”, and “providing, to the cloud computing platform, the transform image”,
“…load/loading, via a communication interface, the plurality of nucleic acid reads into a first storage location indicated by the plurality of indications of storage locations…”
Claim 4 recites:
“accessing, with the transform, an input directory, wherein the input directory is separate from the data source”,
“writing, with the transform, to an output directory, wherein the output directory is separate from the data source”,
Claim 7 recites: “the transforms are associated with the cloud computing platforms based on compute requirements of the order to transform the plurality of nucleic acid reads.”,
Claim 8 recites:
“the transforms are associated with the cloud computing platforms based on an available virtual machine (VM) memory size, an available central processing unit (CPU) performance, and a resource quota; and the resource quota comprises a constraint on total compute resources available to: (i) a group of transforms, (ii) a cloud computing system, and/or, (iii) a portion of a cloud computing system.”,
Claim 10 recites:
“…receiving a plurality of transforms for deriving genomic biomarkers”,
“…receiving an update to the at least one transform…”
Claim 11 recites: “the notification of the operational status of the data source includes an orchestration error, an execution error, or a timeout error.”
Claim 13 recites: “…the configuration is cloud computing platform agnostic…”,
Regarding claims 1, 2, 4-8, 10, 11, 13, 19, and 20, the steps of outputting sequencing data, aggregating/storing sequencing data, and the receiving of a request (which amounts to input of information) do not integrate the abstract idea into a practical application and constitutes an insignificant extra-solution activity (i.e., data gathering and presentation), which does not impose a meaningful limit on the abstract idea (see MPEP 2106.05 (g)).
Additionally, as discussed above, there are no additional limitations to indicate that the claimed analysis requires anything other than generic computer components in order to carry out the recited abstract idea in the claims. Claims that amount to nothing more than an instruction to apply the abstract idea using a generic computer do not render an abstract idea eligible. Alice Corp., 573 U.S. at 223, 110 USPQ2d at 1983. See also 573 U.S. at 224, 110 USPQ2d at 1984. MPEP 2106.05(f) discloses that mere instructions to apply the judicial exception cannot provide an inventive concept to the claims.
The additional elements do not 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 1-20 is/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, 4-7, 9-11, 19 and 20 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Nagasaki et al. (DNA Research, Volume 20, Issue 4, August 2013, Pages 383–390). The limitations of the instant claim are italicized below.
In regards to claims 1, 19, and 20, Nagasaki et al. teaches a cloud computing-based pipeline for high-throughput analysis of next-generation sequencing data. As disclosed in the methods, Nagasaki et al. additionally receives sequencing data which is aligned to a reference genome (Abstract, pg. 386-388) and a user may annotate said data, effectively transforming said data (re: clms. 1, 19, 20, receiv[e][ing], from a data source, an order to transform the plurality of nucleic acid reads to the one or more genomic biomarkers, wherein the plurality of nucleic acid reads are derived from next generation sequencing of a specimen…).
Nagasaki et al. informs and/or communicates to a user of storage locations while transforming genomic data via the web interface as depicted in Fig. 1, pg. 386, and Fig. 2, pg. 387 (re: clms. 1, 19, 20, receiving a selection of a transform for the order, wherein the transform comprises a configuration, a transform image comprising a plurality of indications of storage locations and a plurality of instructions for completing the transform…associating the selected transform with a cloud computing platform based at least in part on the configuration, the association comprising: providing, to the cloud computing platform, the transform image; executing, via the cloud computing platform, the plurality of instructions for completing the selected transform; and loading, via a communication interface, the plurality of nucleic acid reads into a first storage location indicated by the plurality of indications of storage locations; communicating, via the communication interface, at least one communication from the execution between the selected transform and the data source, the at least one communication comprising at least an operational status of the selected transform; storing, via the communication interface, the genomic biomarker output from the selected transform into a second storage location indicated by the plurality of indications of storage locations; and providing a notification, to the data source via the communication interface, of a final operational status of the selected transform based at least in part on the storing the genomic biomarker output from the selected transform.)
Nagasaki et al. discloses on pg. 387, section 3.2 in a description of the pipeline:
“In the basic analysis, the DDBJ Pipeline provides the following useful functions: (i) data transfer: at the start of analysis, users can specify three methods for query data: FTP uploading, secure copy from DRA if the data have been pre-registered to DRA, or HTTP uploading.” (re: …receiving a selection of a transform for the order, wherein the transform comprises a configuration…loading, via a communication interface, the plurality of nucleic acid reads into a first storage location indicated by the plurality of indications of storage locations …)
Nagasaki et al. continues, stating:
“During pre-processing, mapping, or de novo assembly on the supercomputer, users can confirm the status of their operation through a web browser (Fig. 2). The user’s jobs are listed along with their status (‘running’, ‘complete’, ‘error’, etc.) and elapsed times.” (re: … at least one communication from the execution between the selected transform and the data source, the at least one communication comprising at least an operational status of the selected transform…and providing a notification, to the data source via the communication interface, of a final operational status of the selected transform based at least in part on the storing the genomic biomarker output from the selected transform.)
Nagasaki et al. further discloses in section 2.1 that “Mapping and de novo assembly are performed on NIG supercomputers using 704 8-core 2.60-GHz Intel Sandy Bridge CPUs with 64GB RAM and 1.6TB storage, and 96 8-core 2.66 GHzIntel Xenon CPUs with 10 TBRAM, respectively.” Therefore, Nagasaki et al. informs and/or communicates to a user of storage locations while transforming genomic data via the web interface as depicted in Fig. 1, pg. 386, and Fig. 2, pg. 387 (…a transform image comprising a plurality of indications of storage locations and a plurality of instructions for completing the transform…associating the selected transform with a cloud computing platform based at least in part on the configuration…storing, via the communication interface, the genomic biomarker output from the selected transform into a second storage location indicated by the plurality of indications of storage locations…)
As Nagasaki et al. teaches a cloud computing-based pipeline for high-throughput analysis of next-generation sequencing data, Nagasaki et al. anticipates claims 1, 19, and 20.
In regards to claim 2, Nagasaki et al. teaches variant characterization as a genomic biomarker, stating, “For SNP analysis, a figure showing the frequency distribution of SNPs over the entire genome
can be produced. ”(pg. 385, Results, re: clm. 2, The method of claim 1, wherein the one or more genomic biomarkers are selected from: …a variant characterization...). Nagasaki et al. teaching a variant characterization from a genomic biomarker anticipates claim 2.
In regards to claim 4, Nagasaki et al. teaches an input and output directory (pg. 384, “Output files from all processing stages including SAM-formatted files, if supported by the tool, can be downloaded from an FTP server.”, methods, re: clm. 4, he method of claim 1, further comprising: accessing, with the transform, an input directory, wherein the input directory is separate from the data source; and writing, with the transform, to an output directory, wherein the output directory is separate from the data source.). Nagasaki et al. teaching an input and output directory anticipates claim 4.
In regards to claim 5, Nagasaki et al. teaches FASTQ formatted nucleic acid reads (paragraph 1 of the methods, re: clm. 5, The method of claim 1, wherein the plurality of nucleic acid reads are in a FASTQ format...), and therefore anticipates claim 5.
In regards to claim 6, Nagasaki et al. teaches reads aligned to a reference genome, stating “The basic analysis receives transferred reads and maps them to reference genomes or assembles them,”, and therefore anticipates claim 5 (pg. 385, Results, re: clm. 6, The method of claim 1, wherein the plurality of nucleic acid reads are aligned to a common reference genome.).
In regards to claim 7, Nagasaki et al. teaches next generation sequencing (NGS) data associated with a cloud computing platform which is inherently limited by the compute requirements (stated in section 2.1) associated with the cloud computing platform in regards to processing said data (pg. 384, 385, Methods, re: clm. 7, The method of claim 1, wherein the transforms are associated with the cloud computing platforms based on compute requirements of the order to transform the plurality of nucleic acid reads.). Nagasaki et al. teaching cloud-platform-limited NGS sequencing data anticipates claim 7.
In regards to claim 9, Nagasaki et al. teaches status codes on pg. 386 which inherently disclose if nucleic acid reads are available to be read by the pipeline and logically and conditionally continues or halts processing (re: clm. 9 The method of claim 1, further comprising: in response to receiving the order to transform the plurality of nucleic acid reads to the one or more genomic biomarkers, determining if the plurality of nucleic acid reads are available to be read; and wherein the selecting of the transforms occurs in response to a determination that the plurality of nucleic acid reads are in available to be read.). Nagasaki et al. monitoring the status of user-input nucleic acid data derived from a sequencer anticipates claim 9.
In regard to claim 10, Nagasaki et al. teaches status codes on pg. 386 which inherently disclose if nucleic acid reads are available to be read by the pipeline and logically and conditionally continues or halts processing (re: clm. 10, … validating each transform of the received plurality of transforms by determining if there is a problem with each transform; if there is a problem with a transform, returning an error message including an indication of the problem… validating the at least one transform by determining if there is a problem with the at least one transform; and if there is a problem with the at least one transform, returning an error message including an indication of the problem with the at least one transform…). Additionally, Nagasaki et al. teaches a catalogue of sequence analysis jobs which a user may derive genomic biomarkers from, and which are updated via status codes and temporal process tracking genomic biomarkers by: receiving a plurality of transforms for deriving genomic biomarkers… updating at least one transform of the plurality of transforms by: receiving an update to the at least one transform… wherein the selecting of the transforms comprises selecting the transforms from the created catalog of transforms.). Nagasaki et al. teaches this limitation via a catalog of sequence analysis “Jobs”, stating on pg. 386:
“the DDBJ Pipeline communicates with web applications to analyses the NGS data using DDBJ supercomputers and currently supports mapping or de novo assembly software packages (Table 1).”
And in the Figure 2 legend:
“Job status list in basic analysis of the DDBJ Pipeline. Jobs executed in the DDBJ Pipeline are shown in lists, and users may manage the jobs, for example, by downloading results or by halting the jobs. The bars at the right end of the list indicate elapsed times.”
Nagasaki et al. therefore teaches a catalogue of sequence analysis jobs which a user may derive genomic biomarkers from, and which are updated via status codes and temporal process tracking, and therefore anticipates claim 10.
In regards to claim 11, Nagasaki et al. teaches status codes on pg. 386 which inherently disclose if nucleic acid reads are available to be read by the pipeline and logically and conditionally continues or halts processing (re: clm. 11, The method of claim 1, wherein the notification of the operational status of the data source includes an orchestration error, an execution error, or a timeout error.). Nagasaki’s teaching of status codes anticipates claim 11.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 3, 14, and 15 are rejected under 35 U.S.C. 103 as being unpatentable over Nagasaki et al., as applied to claims 1, 2, 4-7, 9-11, 19 and 20 above, in view of Aly Azeem Khan et al. (US20200118644A1).
Nagasaki et al. is applied to claims 1, 2, 4-7, 9-11, 19 and 20 above.
Nagasaki et al. does not teach a genomic biomarker comprising MSI.
Nagasaki, et al does not teach predicting, based on a stored genomic biomarker:
a likelihood of a patient being at a high-risk of one or more of an oncological event, neurological disorder, autoimmune condition, cardiovascular disease, infectious disease, or endocrinological disease (regarding claim 14).
an onset of an oncological disease state; an onset of cancer; a response to a cancer therapy; a suitability for a cancer therapy; a suitability for a cancer clinical trial; a progression free cancer survival; a progression of cancer; a metastasis of cancer; and/or an origin of a metastasized tumor (regarding claim 15).
In regard to claims 3, 14, and 15, Khan et al. teaches a microsatellite instability (MSI) determination system utilizing next generation sequencing (NGS) (clm. 1, “for each locus in a plurality of microsatellite instability (MSI) loci: mapping a first plurality of genomic sequencing reads from a tumor specimen to the locus…re: clm. 3, …wherein the genomic biomarker comprises the MSI… comparing regions of the genome to at least a portion of the plurality of nucleic acid reads to identify differences and similarities).
Khan et al. states on paragraph 0006 of the specification:
“The techniques include an MSI assay that may employ a support vector machine (SVM) classifier to assess MSI. The techniques provide an automated process for MSI testing and MSI status prediction via a supervised machine learning process.“ (re: clm. 15, ….predicting, based on the stored genomic biomarker…)
Khan et al. further states on paragraph 0054 of their specification:
“With the MSI classification, in some examples, the techniques herein further include therapy matching based on the MSI classification. That is, the outcome of the techniques described herein is useful, for example, for determining appropriate treatment regimens for cancer patients.” (re: clm. 15, …, one or more of: …a suitability for a cancer therapy…)
Khan et al. additionally states that MSI is tied to cancer prognosis, stating in paragraph 0004 of the spec:
“MSI is a type of genomic instability that occurs in repetitive DNA regions and results from defects in DNA mismatch repair. MSI occurs in a variety of cancers.” (re: clm. 14, … predicting, based on the stored genomic biomarker, a likelihood of a patient being at a high-risk of one or more of an oncological event…)
Therefore, Khan et al. teaches both NGS to determine microsatellite instability (MSI) status, a prediction method, based on the stored genomic biomarker, a likelihood of a patient being at a high-risk of one or more of an oncological event, and a suitability for a cancer therapy (re: clm. 3, The method of claim 2, wherein the genomic biomarker comprises the MSI, the method further comprising: comparing regions of the genome to at least a portion of the plurality of nucleic acid reads to identify differences and similarities; and reporting the MSI, wherein the MSI comprises a ratio of the identified differences to similarities; clm. 14, The method of claim 1, further comprising predicting, based on the stored genomic biomarker, a likelihood of a patient being at a high-risk of one or more of an oncological event…; clm. 15, The method of claim 1, further comprising predicting, based on the stored genomic biomarker, one or more of… a response to a cancer therapy; a suitability for a cancer therapy).
In KSR Int 'l v. Teleflex, the Supreme Court, in rejecting the rigid application of the teaching, suggestion, and motivation test by the Federal Circuit, indicated that “The principles underlying [earlier] cases are instructive when the question is whether a patent claiming the combination of elements of prior art is obvious. When a work is available in one field of endeavor, design incentives and other market forces can prompt variations of it, either in the same field or a different one. If a person of ordinary skill can implement a predictable variation, § 103 likely bars its patentability.” KSR Int'l v. Teleflex lnc., 127 S. Ct. 1727, 1740 (2007).
Applying the KSR standard to Nagasaki et al. and Khan et, al., the examiner concludes that the combination of the system of cloud-backed NGS data analysis as taught by Nagasaki et al. with the MSI instability determination system using NGS as disclosed by Khan et, al. represents Some teaching, suggestion, or motivation in the prior art that would have led one of ordinary skill to modify the prior art reference or to combine prior art reference teachings to arrive at the claimed invention, with no more than a predictable outcome of a cloud-based NGS analysis pipeline comprising MSI instability analysis of genomic biomarkers, and the ability to determine an oncological event and a suitability for cancer therapy.
One would have been motivated to combine the MSI analysis system of Khan et al. with the cloud based NGS analysis system of Nagasaki et al. as Nagasaki et al.’s system would become more efficient, in that it would produce a superior degree of data depth from Khan et al.’s system.
One of ordinary skill in the art would have had a reasonable expectation of success at applying the system of cloud-backed NGS data analysis of Nagasaki et al. to the MSI analysis system of Khan et al., as Khan et al. is similarly a computational genomic sequencing read analysis pipeline which provides all the necessary instructions or elements in specification and claims to lead to an improved product if utilized in combination with another method. Khan et al. specifically states on paragraph 0032 of the specification that embodiments of the art include a sequencing data pre-processing process and an MSI status calling process, establishing a pathway for a user to include sequencing data processing in a potential combination of Nagasaki et al. and Khan et al.’s systems. Therefore, the invention would have been prima facie obvious to one of skill in the art at the time of filing of the application, absent evidence to the contrary.
Claims 8 and 13 are rejected under 35 U.S.C. 103 as being unpatentable over Nagasaki et al., as applied to claims 1-7, 9-11, 14, 15 and 19 above and in view of Yakneen et al. (Nature Biotechnology volume 38, pages288–292 (2020)).
Nagasaki et al. is applied to claims to claims 1-7, 9-11, 14, 15 and 19 above.
Nagasaki et al. does not teach use of a virtual machine (VM) (but discloses a desire for future incorporation on pg. 388).
Nagasaki et al. is not cloud computing platform agnostic as it specifically relies on the DDBJ.
In regard to claims 8 and 13, Yakneen et al. teaches a computational tool (“Butler”) that facilitates large-scale genomic analyses (using next generation sequencing (NGS)) on public and academic clouds (re: clm. 8, The method of claim 1, wherein: the transforms are associated with the cloud computing platforms based on an available virtual machine (VM) memory size, an available central processing unit (CPU) performance, and a resource quota; and the resource quota comprises a constraint on total compute resources available to: (i) a group of transforms, (ii) a cloud computing system, and/or, (iii) a portion of a cloud computing system.).
Yakneen et al. states on Figure 1: “The framework consists of several interconnected components, each running on a separate virtual machine (VM).” Yakneen et al.’s methods also state:
“The db-server is responsible for hosting most of the databases used by Butler. This VM runs an instance of PostgreSQL Server and hosts the Run Tracking DB, Airflow DB and Sample Tracking DB. The 1-TB block storage volume serves as the backing storage mechanism.” Therefore, Yakneen et al. teaches a genomic sequencing platform based on cloud computing and VM size limited by CPU, compute, and inherently teaches resource quotas. Additionally, Yankeen et al. states in the methods:
“Butler is a highly general workflow framework, built on top of generic open source components that in principle can work with any data in any scientific domain, deploy onto over 20 cloud types, and work on any operating system, and it comprises a rich set of tools for installing and configuring software. Adapting Butler to a new application is straightforward. This process is described below.”
Yankeen et al. goes on to describe, on pg. 295, workflows on handling genomic data on cloud infrastructure (re: clm. 13, The method of claim 1, wherein the configuration is cloud computing platform agnostic.).
Yankeen et al. does not explicitly teach receiving an order to generate a genomic biomarker from a sequence and communicating said biomarker.
Applying the KSR standard to Yakneen et al. and Nagasaki et al., the examiner concludes that the combination of the system of cloud-backed NGS data analysis as taught by Nagasaki et al. with the cloud-based human genomics analysis system as disclosed by Yakneen et al. represents some teaching, suggestion, or motivation in the prior art that would have led one of ordinary skill to modify the prior art reference or to combine prior art reference teachings to arrive at the claimed invention of platform agnostic, cloud-based NGS analysis pipeline comprising separate virtual machines. One skilled in the art of bioinformatics would have been motivated to combine the cloud genomics analysis system of Yakneen et al. with the cloud based NGS analysis system of Nagasaki et al. as the combination would result in a more efficient NGS data analysis pipeline system, in that it would obtain an additional layer of flexibility in processing data via Yakneen et al.’s teaching. In support of this motivation, Nagasaki et al.’s system explicitly states a desire for cloud iterations in section 3.6.
One of ordinary skill in the art of bioinformatics and computational biology would have had a reasonable expectation of success at applying the system of cloud-backed NGS data analysis of Yakneen et al. to the system of cloud-backed NGS data analysis of Nagasaki et, al., as both arts are computational genomic sequencing read analysis pipelines which provide all the necessary instructions or elements for adaptation in the methods. In support of this expectation, Nagasaki et al. explicitly states a desire to include VMs in future developments; therefore, Nagasaki et al. could easily incorporate Yakneen et al.’s teachings of VMs in a genomics analysis system. Therefore, the invention would have been prima facie obvious to one of skill in the art at the time of filing of the application, absent evidence to the contrary.
Claim 18 is rejected under 35 U.S.C. 103 as being unpatentable over Nagasaki et al. as applied to claims to claims 1-11, 13, 14, 15 and 19 above in view of Eric J. Topol et al. (US10597722B2).
Nagasaki et al. is applied to claims 1-11, 13, 14, 15 and 19 above.
Nagasaki et al does not teach predicting, based on a stored genomic biomarker:
an onset of a cardiovascular disease state; an onset of an arrhythmia; an onset of cardiac arrest; an onset of stroke; an onset of atrial fibrillation; an onset of aortic stenosis; an onset of amyloidosis; a response to a cardiovascular therapy; a suitability for a cardiovascular therapy; a progression of a cardiovascular disease state; and/or a suitability for a cardiovascular clinical trial (regarding claim 18).
In regard to claim 18, Topol et al. teaches a predictive analysis for myocardial infarction (MI), wherein the risk for MI is determined in part by calculating a risk score based upon an increase in the weighted expression level of the mRNA transcripts (title, clm. 8, re: clm. 18, The method of claim 1, further comprising predicting, based on the stored genomic biomarker, one or more of: an onset of a cardiovascular disease state…).
Topol et al. further discloses “ In some embodiments, a software module executed by a computer-processing device compares the level of gene expression in the blood sample to the control.” And “In some embodiments, the risk score is calculated using a suitably programmed computer, which can include other electronic devices. In some embodiments, that or another suitably programmed computer compares the risk score to a reference risk score for purposes of determining a likelihood that the individual will experience a cardiovascular event (e.g., a myocardial infarction). “ in the specification, noting a computational modality to calculate MI risk (re: clm. 18, … an onset of a cardiovascular disease state; an onset of an arrhythmia; an onset of cardiac arrest…).
Applying the KSR standard to Nagasaki et al. and Topol et al. , the examiner concludes that the combination of the system of cloud-backed NGS data analysis as taught by Nagasaki et al. with the atrial fibrillation prediction system as disclosed by Topol et al. represents a finding that there was some teaching, suggestion, or motivation, either in the references themselves or in the knowledge generally available to one of ordinary skill in the art, to modify the reference or to combine reference teachings, with no more than a predictable outcome of a cloud-based NGS analysis pipeline comprising myocardial infarction prediction. One of ordinary skill in the art would be motivated to combine the aforementioned arts as the combination would result in a stronger NGS data analysis system inclusive of highly detailed cardiovascular event predictions. In support of this motivation, Nagasaki et al. describes transcriptome analysis in section 2.2 as part of a wider High level analysis of sequencing data which would become stronger with the mRNA transcripts and risk score calculations listed in clm. 1 of Topol et al.
One of ordinary skill in the art of bioinformatics and computational biology would have had a reasonable expectation of success at applying the system of cloud-backed NGS data analysis of Nagasaki et al. to the atrial fibrillation system of Topol et al. , as Nagasaki et al.’s art is fit to receive additional data sources for annotation purposes, stating: “The DDBJ Pipeline supports not only basic analyses, such as mapping, but also high-level analyses via the Galaxy interface, which has the advantage of modifiability and easy maintenance.”
The technique of MI prediction as taught by Topol et al. would have predictably resulted in a usable gene expression-based MI risk calculation system readily available for Nagasaki et al.’s sequencing-based system, which may be adapted to produce a method to predict, based on a given stored genomic biomarker, an onset of a cardiovascular disease state. Therefore, the invention would have been prima facie obvious to one of skill in the art at the time of filing of the application, absent evidence to the contrary.
Claim(s) 16 and 17 are rejected under 35 U.S.C. 103 as being unpatentable over Nagasaki et al. as applied to claims 1-11, 13, 14, 15 and 19 above in view of Jackson et al (Ann Hum Genet. 2020 January ; 84(1): 1–10).
Nagasaki et al. is applied to claims 1-11, 14, 15, 18 and 19 above.
Nagasaki, et al does not teach predicting, based on a stored genomic biomarker:
an onset of an endocrinological disease state; an onset of diabetes; an onset of thyroidism; an onset of an autoimmune disease state; a response to an endocrinological therapy; a suitability for an endocrinological therapy; a progression of an endocrinological disease state; and/or a suitability for an endocrinological clinical trial (in regards to claim 16).
an onset of a mental health disease state; an onset of depression; an onset of a mental disorder; an onset of a behavioral disorder; an onset of a personality disorder; a response to a neurological therapy; a suitability for a neurological therapy; a progression of a mental health disease state; and/or a suitability for a neurological clinical trial (in regards to claim 17).
In regard to claims 16 and 17, Jackson et al. teaches a genetic model of multiple sclerosis severity used to predict future accumulation of disability, in which Jackson, et al. uses patient data, candidate SNPs associated with multiple sclerosis (MS), and a MS Disease Severity Scale (MS-DSS) that accounts for immunomodulatory treatment efficacy as inputs to a machine learning model to predict future MS-related disability accumulation (Abstract, Introduction, Methods, pg. 3-5, re: clm. 16, The method of claim 1, further comprising predicting, based on the stored genomic biomarker, one or more of… an onset of an autoimmune disease state…, clm. 17, The method of claim 1, further comprising predicting, based on the stored genomic biomarker, one or more of… a response to a neurological therapy.).
Jackson, et al. does not explicitly teach cloud platform utilization.
Applying the KSR standard to Nagasaki et al. and Jackson et al., the examiner concludes that the combination of the system of cloud-backed NGS data analysis as taught by Nagasaki et al. with the MS severity prediction system as disclosed by Jackson et al. represents some teaching, suggestion, or motivation in the prior art that would have led one of ordinary skill to modify the prior art reference or to combine prior art reference teachings to arrive at the claimed invention, with no more than a predictable outcome of a cloud-based NGS analysis pipeline comprising a genetic variant-based MS prediction system and analysis of genomic biomarkers.
One of ordinary skill in the art of bioinformatics would have been motivated to combine the system of cloud-backed NGS data analysis as taught by Nagasaki et al. with the MS severity prediction system as disclosed by Jackson et al. as the combination of Nagasaki et al.’s teaching and Jackson et al.’s teaching would result in a more efficient system capable of producing another layer of annotation capable of associating genomic biomarkers with a prediction of neurological disease onset, given the teachings of Jackson et al.’s system.
One of ordinary skill in the art of bioinformatics and computational biology would have had a reasonable expectation of success at applying the system of cloud-backed NGS data analysis of Nagasaki et al. to the MS severity prediction system as disclosed by Jackson et al., as Jackson et al. is a genetic model which processes and analyzes sequencing-derived SNPs, as disclosed on page 3 of the methods, with disclosed data pre-processing methods allowing one of ordinary skill in bioinformatics an avenue to adapt Jackson et al.’s data and MS-DSS-based prediction structure for use with Nagasaki et al.’s cloud platform. Therefore, the invention would have been prima facie obvious to one of skill in the art at the time of filing of the application, absent evidence to the contrary.
Claim 12 is rejected under 35 U.S.C. 103 as being unpatentable over Nagasaki et al. as applied to claims 1-11, 13-19 above in view of Prasadi et al (18th International Conference on Advances in ICT for Emerging Regions (ICTer), Colombo, Sri Lanka, 2018, pp. 400-406).
Nagasaki et al. is applied to claims 1-11, and 13-19 above.
Nagasaki et al. does not teach priority queues associated with the generation or processing of genomic biomarkers as disclosed in claim 12.
In regards to claim 12, Prasadi et al. teaches scheduling algorithms for bioinformatics analysis platforms using cloud technology (Abstract, pg. 400, pg. 403-405). On page 402, Prasadi et al. states:
“We describe the architecture of the proposed scheduling in the microservices platform to support optimized bioinformatics workflow design and modelling. It utilizes multicore processor powered service containers to support optimized parallel execution of algorithms. The proposed solution provides the end users with an API, which exposes algorithmic functionality in a language-agnostic manner using JSON message passing.”
Prasadi et al. additionally teaches comparisons of scheduling algorithms applied to a sequence alignment tool (pg. 403, “Methodology”). Therefore, Prasadi et al. teaches prioritizing the processing of a genomic biomarker (re: clm. 12, The method of claim 1, further comprising: upon receiving the order to transform the plurality of nucleic acid reads to the one or more genomic biomarkers, determining if the transform should be placed in a high priority queue or a low priority queue; and depending on the determination, placing the order in either the high priority queue or low priority queue; and wherein the executing the plurality of instructions for completing the selected transform occurs by executing instructions in the high priority queue before executing instructions in the low priority queue.).
Prasadi et al. does not explicitly teach transforming a plurality of nucleic acid reads to one or more genomic biomarkers.
Applying the KSR standard to Nagasaki et al. and Prasadi et al., the examiner concludes that the combination of the system of cloud-backed NGS data analysis as taught by Nagasaki et al. with the teaching of scheduling algorithms for bioinformatics analysis platforms as disclosed by Prasadi et al. represents some teaching, suggestion, or motivation in the prior art that would have led one of ordinary skill to modify the prior art reference or to combine prior art reference teachings to arrive at the claimed invention, with no more than a predictable outcome of a cloud-based NGS analysis pipeline comprising a prioritized genomic biomarker processing.
One would have been motivated to combine the system of cloud-backed NGS data analysis as taught by Nagasaki et al. with the teaching of scheduling algorithms for bioinformatics analysis platforms as disclosed by Prasadi et al. as Nagasaki et al.’s system would become more efficient, in that it would be capable of ordering genome analysis jobs based upon a chosen schema described in table III of Prasadi et al.
One of ordinary skill in the art of bioinformatics and computational biology would have had a reasonable expectation of success at the system of cloud-backed NGS data analysis as taught by Nagasaki et al. with the teaching of scheduling algorithms for bioinformatics analysis platforms as disclosed by Prasadi et al., as Prasadi et al. teaches optimizations of the analysis of genomic sequences, as disclosed on page 404, and compares said optimizations in table III on page 403. The disclosed bioinformatics system optimization methods could provide one of ordinary skill in bioinformatics an avenue to adapt Nagasaki et al. to adopt the methods disclosed by Prasadi et al. Therefore, the invention would have been prima facie obvious to one of skill in the art at the time of filing of the application, absent evidence to the contrary.
Conclusion
No claims are allowed.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to JOHN T STUBBS whose telephone number is (571)272-0340. The examiner can normally be reached M-F 8-5 EST.
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/J.T.S./Examiner, Art Unit 1686
/LARRY D RIGGS II/Supervisory Patent Examiner, Art Unit 1686
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