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 1-20 are currently pending and are herein under examination.
Claims 1-20 are rejected.
Claims 5, 7-8, 15 and 17-18 are objected.
Priority
The instant application does not claim benefit to any prior filed application. As such, the effective filing date for claims 1-20 is 30 March 2023.
Information Disclosure Statement
No IDS has been filed.
Drawings
The drawings are objected to as failing to comply with 37 CFR 1.84(p)(5) because they include the following reference character(s) not mentioned in the description: 701 in FIG. 7.
Corrected drawing sheets in compliance with 37 CFR 1.121(d), or amendment to the specification to add the reference character(s) in the description in compliance with 37 CFR 1.121(b) are required in reply to the Office action to avoid abandonment of the application. Any amended replacement drawing sheet should include all of the figures appearing on the immediate prior version of the sheet, even if only one figure is being amended. Each drawing sheet submitted after the filing date of an application must be labeled in the top margin as either “Replacement Sheet” or “New Sheet” pursuant to 37 CFR 1.121(d). If the changes are not accepted by the examiner, the applicant will be notified and informed of any required corrective action in the next Office action. The objection to the drawings will not be held in abeyance.
The drawings filed 03/30/2023 are objected to because Figures 1, 3 and 5-9 have been uploaded in color. Color photographs and color drawings are not accepted in utility applications unless a petition filed under 37 CFR 1.84(a)(2) is granted. Any such petition must be accompanied by the appropriate fee set forth in 37 CFR 1.17(h), one set of color drawings or color photographs, as appropriate, if submitted via the USPTO patent electronic filing system or three sets of color drawings or color photographs, as appropriate, if not submitted via the via USPTO patent electronic filing system, and, unless already present, an amendment to include the following language as the first paragraph of the brief description of the drawings section of the specification:
The patent or application file contains at least one drawing executed in color. Copies of this patent or patent application publication with color drawing(s) will be provided by the Office upon request and payment of the necessary fee.
Color photographs will be accepted if the conditions for accepting color drawings and black and white photographs have been satisfied. See 37 CFR 1.84(b)(2).
Specification
The disclosure is objected to because it contains an embedded hyperlink and/or other form of browser-executable code. Specification paras. [48] and [50] contain hyperlinks. Applicant is required to delete the embedded hyperlink and/or other form of browser-executable code; references to websites should be limited to the top-level domain name without any prefix such as http:// or other browser-executable code. See MPEP § 608.01.
Claim Objections
Claims 5, 7-8, 15 and 17-18 are objected to because of the following informalities:
Claims 5, 7, 15 and 17 should recite “comprise” instead of “comprising”.
Claim 8, line 1, and claim 18, line 1, should recite “comprise” instead of “comprises”.
Claim 8, lines 2-3, should recite “units, and wherein the predicting further comprises” to correct grammar.
Claim 18, lines 2-3, should recite “units, and wherein the predicting further comprises .
Appropriate correction is required.
Claim Rejections - 35 USC § 112
35 USC 112(b)
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 1-20 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Claims dependent from a rejected claim are also rejected, unless otherwise noted.
A broad range or limitation together with a narrow range or limitation that falls within the broad range or limitation (in the same claim) may be considered indefinite if the resulting claim does not clearly set forth the metes and bounds of the patent protection desired. See MPEP § 2173.05(c). Claim 1 (line 3), claim 11 (line 4), and claim 20 (line 4) recite a broad limitation of “one or more training datasets”. Claim 1 (line 6), claim 11 (line 7), and claim 20 (line 7) then recite “the training datasets”, which is a narrower limitation of the broad limitation, and is being interpreted to mean more than one training dataset. The claims are indefinite because there is a question or doubt as to whether the feature introduced by such narrower language is (a) merely exemplary of the remainder of the claims, and therefore not required, or (b) a required feature of the claims.
Claims 6 and 16 recite “the plurality of training variables” which renders the claims indefinite. It is unclear which plurality is being referenced because claim 1 (lines 2-3) and claim 11 (lines 4-5) recite that each genomic test has a plurality of training variables. Clarify which plurality from which genomic test is being referenced.
Claims 7-8 and 17-18 recite “the training variables” which renders the claims indefinite. It is unclear which training variables are being referenced because claim 1 (lines 2-3) and claim 11 (lines 4-5) recite that each genomic test has a plurality of training variables. Clarify which training variables from which genomic tests are being referenced.
Claims 11, 14 and 18 recite method steps in a system claim because the processors are actively executing instructions to receive, train, receive, predict, and generate. MPEP 2173.05(p) recites “A single claim which claims both an apparatus and the method steps of using the apparatus is indefinite under 35 U.S.C. 112(b) or pre-AIA 35 U.S.C. 112, second paragraph.” This rejection can be overcome by clarifying that the processors are configured to perform these steps.
A broad range or limitation together with a narrow range or limitation that falls within the broad range or limitation (in the same claim) may be considered indefinite if the resulting claim does not clearly set forth the metes and bounds of the patent protection desired. See MPEP § 2173.05(c). Claim 20, line 2, recites a broad limitation of “one or more processors”. Claim 20, line 2, then recites “the processors”, which is a narrower limitation of the broad limitation. The claims are indefinite because there is a question or doubt as to whether the feature introduced by such narrower language is (a) merely exemplary of the remainder of the claim, and therefore not required, or (b) a required feature of the claim.
Claim Rejections - 35 USC § 101
Non-Statutory Subject Matter
Claim 20 is rejected under 35 U.S.C. 101 because it is directed to non-statutory subject matter (Step 1: NO). Claim 20 recites a computer readable medium storing instructions thereon. The broadest reasonable interpretation of storing instructions in memory includes transitory forms of signal transmission or “signals per se” when the memory is not recited as “non-transitory”. Signals per se do not fall within a category of statutory subject matter (MPEP 2106.03.I).
Claim 20 can be amended to recite statutory subject matter by specifying that the medium is non-volatile, as discussed in specification para. [29]. However, this amendment still results in a rejection of claim 20 under 35 U.S.C. 101 for being directed to an abstract idea without significantly more. In the interest of compact prosecution, claim 20 is analyzed below under 35 U.S.C. 101 using the Alice/Mayo test as if it recited statutory subject matter.
Statutory Subject Matter
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1:
Step 1 asks whether the claims recite statutory subject matter. In the instant application, claims 1-10 recite a method, claims 11-19 recite a system, and claim 20 recites a CRM. As such, these claims recite statutory subject matter (Step 1: YES).
Step 2A, Prong 1:
Claims that recite statutory subject matter are analyzed under Step 2A, Prong 1 to determine if they recite any concepts that equate to an abstract idea, law of nature or natural phenomena. The instant claims recite the following limitations that equate to one or more categories of judicial exception:
Claims 1, 11 and 20 recite “receiving one or more training datasets of a genomic pipeline comprising a plurality of training variables for each of a plurality of genomic tests and corresponding results of each of the genomic tests; training a machine learning model using the training datasets; receiving a new genomic workflow pipeline comprising new genomic testing variables; and predicting, using the trained machine learning model and new genomic testing variables, whether the new genomic workflow pipeline will be successfully completed within a first compute environment.”
Claims 2 and 12 recite “wherein the training datasets comprise, for each of a plurality of genomic tests, a corresponding batch size, sample size and queue size”
Claims 3 and 13 recite “wherein the machine learning model comprises a supervised logistic regression model”
Claims 6 and 16 recite “wherein the new genomic testing variables correspond to the plurality of training variables”
Claims 7 and 17 recite “wherein the training variables comprising a corresponding compute environment”
Claims 8 and 18 recite “wherein the training variables comprises an amount of memory and a number of central processing units, the predicting further comprising a recommendation of a new compute environment for executing the new genomic workflow pipeline”
Claims 9 and 19 recite “wherein the recommendation comprises a selection of one of a plurality of pre-configured cloud compute environments, the recommendation based at least in part on a cost of each of the pre-configured cloud compute environments”
Limitations reciting a mental process.
Claims 1, 3, 8-9, 11, 13 and 18-20 contain limitations recited at such a high level of generality that they equate to a mental process because they 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)), which the courts have identified as concepts that can be practically performed in the human mind. The paragraphs below discuss the broadest reasonable interpretation (BRI) of the limitations in these claims that recite a mental process.
Regarding claims 1, 11 and 20, receiving training datasets and a new workflow include collecting information. Training a machine learning model includes performing calculations of a logistic regression, as recited in claims 3 and 13, and a log loss. Predicting pipeline completion includes performing calculations using the trained logistic regression to produce a binary result of yes or no regarding successful completion.
Claims 8-9 and 18-19 include a human generating on pen and paper a recommendation comprising pre-configured cloud compute environments based on cost of each environment.
Limitations reciting a mathematical concept.
Claims 1, 3, 11, 13 and 20 recite limitations that equate to a mathematical concept because they are similar to the concepts of 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)), which the courts have identified as mathematical concepts. Regarding claims 1, 3, 11, 13 and 20, training a model, which may be a logistic regression, includes calculations of a logistic regression, maximum expectation likelihood, and log loss.
MPEP 2106.04(a)(2)(I)(C) recites “A claim that recites a mathematical calculation, when the claim is given its broadest reasonable interpretation in light of the specification, will be considered as falling within the ‘mathematical concepts’ grouping. A mathematical calculation is a mathematical operation (such as multiplication) or an act of calculating using mathematical methods to determine a variable or number … a claim does not have to recite the word ‘calculating’ in order to be considered a mathematical calculation. For example, a step of ‘determining’ a variable or number using mathematical methods or ‘performing’ a mathematical operation may also be considered mathematical calculations when the broadest reasonable interpretation of the claim in light of the specification encompasses a mathematical calculation.”
Limitations included in the recited judicial exception.
Claims 2, 6-7, 12 and 16-17 further limit the training datasets, testing variables, and training variables which are part of the recited judicial exception in claims 1 and 11 of collecting information.
As such, claims 1-20 recite an abstract idea (Step 2A, Prong 1: YES).
Additional Elements:
Once limitations have been identified that recite a judicial exception, the claims are evaluated for additional elements. The additional elements are then analyzed under Step 2A, Prong 2 then Step 2B. The instant claims recite the following additional elements:
Claims 4 and 14 recite “generating a user interface with a plurality of input elements that correspond to the new genomic testing variables”
Claims 5 and 15 recite “wherein the input elements comprising sliders”
Claim 10 recites “further comprising providing an artificial intelligence based chatbot for responding to prompts regarding the pre-configured cloud compute environments.”
Claim 11 recites “A genomic test prediction system comprising one or more processors executing instructions to generate a prediction”
Claims 12-19 recite “the system of”
Claim 20 recites “A computer readable medium having instructions stored thereon that, when executed by one or more processors, cause the processors to predict genomic testing using machine learning”
These above recited additional elements are analyzed below under both Step 2A, Prong 2 and Step 2B:
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). The judicial exception is not integrated into a practical application because the claims do not recite additional elements that reflect an improvement to a computer, technology, or technical field (MPEP § 2106.04(d)(1) and 2106.5(a)), require a particular treatment or prophylaxis for a disease or medical condition (MPEP § 2106.04(d)(2)), implement the recited judicial exception with a particular machine that is integral to the claim (MPEP § 2106.05(b)), effect a transformation or reduction of a particular article to a different state or thing (MPEP § 2106.05(c)), nor provide some other meaningful limitation (MPEP § 2106.05(e)). Rather, the claims include limitations that equate to an equivalent of the words “apply it” and/or to instructions to implement an abstract idea on a computer (MPEP § 2106.05(f)) and to insignificant extra-solution activity (MPEP § 2106.05(g)). The paragraphs below discuss the additional elements recited above in the instant claims.
Claims 11-20 recite a system comprising one or more processors and a CRM. There are no limitations requiring anything other than a generic computer and/or generic computing system. Therefore, these limitations equate to mere instructions to implement an abstract idea on a generic computer, which the courts have established does not render an abstract idea eligible in Alice Corp. 573 U.S. at 223, 110 USPQ2d at 1983. These limitations also invoke a computer as a tool to perform generic functions such as storing, receiving, and transmitting data, which does not provide a practical application (MPEP 2106.05(f)(2)).
Claim 10 recites providing a chatbot. This equates to insignificant, post-solution activity. It is nominally related to the invention of predicting successful pipeline execution (MPEP 2106.05(g)(2)). The limitation of “for responding to prompts regarding the pre-configured cloud compute environments” equates to an intended use and is thus not required by the claim.
Claims 4-5 and 14-15 equate to insignificant extra-solution activity of data outputting because the user interface displays the collected data recited in claims 1 and 11.
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). These claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception because these claims recite additional elements that equate to instructions to apply the recited exception in a generic way and/or in a generic computing environment (MPEP § 2106.05(f)) and to well-understood, routine and conventional (WURC) limitations (MPEP § 2106.05(d)). The paragraphs below discuss the additional elements recited above in the instant claims.
Claims 11-20 recite a system comprising one or more processors and a CRM. There are no limitations requiring anything other than a generic computer and/or generic computing system. Therefore, these limitations equate to instructions to implement an abstract idea on a generic computing environment, which the courts have established does not provide an inventive concept in Intellectual Ventures I LLC v. Capital One Bank (USA), 792 F.3d 1363, 1367, 115 USPQ2d 1636, 1639 (Fed. Cir. 2015). These limitations also invoke a computer as a tool to perform generic functions such as storing, receiving, and transmitting data, which does not provide an inventive concept (MPEP 2106.05(f)(2)).
Claim 10 recites providing a chatbot. This limitation equates to transmitting data over a network, which the courts have established as WURC limitation of a generic computer in buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355, 112 USPQ2d 1093, 1096 (Fed. Cir. 2014). The limitation of “for responding to prompts regarding the pre-configured cloud compute environments” equates to an intended use and is thus not required by the claim.
Claim 20 stores information in a CRM which equates to storing information in memory, which the courts have established as a WURC function of a generic computer in Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1334, 115 USPQ2d 1681, 1701 (Fed. Cir. 2015).
When the additional elements of claims 4-5, 11, 14-15 and 20 of a user interface with input sliders and generic computer functions/components are considered in combination, they equate to WURC limitations as taught by Jia et al. (“Jia”; Briefings in bioinformatics 23, no. 1 (2022): bbab415), Rebeiz et al. (“Rebeiz”; Developmental biology 271, no. 2 (2004): 431-438) and Hussain et al. (“Hussain”; Plant Direct 2, no. 10 (2018): e00091).
Jia discloses interactive biological web applications that use slider text as input selection (pg. 9, col. 1, last para.), and Tables I and II show biological applications that use genomic data. Rebeiz discloses a tool for genome sequence visualization and analysis (abstract), and Figure 4 shows input sliders to select a number of upstream/downstream genes. Hussain discloses an interactive Manhattan plot for longitudinal genome-wide association studies that contains input slider bars (pg. 2, col. 2, last para.) (sec. 3.2) (Figure 1).
When these additional elements are considered individually and in combination, they do not provide an inventive concept because they equate to WURC functions/components of a generic computer in combination with a user interface containing slider input elements as taught above by Jia, Rebeiz and Hussain. Therefore, these additional elements do not transform the claimed judicial exception into a patent-eligible application of the judicial exception and do not amount to significantly more than the judicial exception itself (Step 2B: No).
As such, claims 1-20 are not patent eligible.
Claim Rejections - 35 USC § 102
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 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.
Claims 1, 3, 6-7, 11, 13, 16-17 and 20 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Bala et al. (“Bala”; Expert systems with applications 42, no. 3 (2015): 980-989).
The bold and italicized text below are the limitations of the instant claims, and the italicized text serves to map the prior art onto the instant claims.
Claims 1, 11 and 20:
Claim 1: A method of predicting genomic testing using machine learning, the method comprising:
Claim 11: A genomic test prediction system comprising one or more processors executing instructions to generate a prediction, the generating the prediction comprising:
Claim 20: A computer readable medium having instructions stored thereon that, when executed by one or more processors, cause the processors to predict genomic testing using machine learning, the predicting comprising:
Bala discloses machine learning models for task failure prediction of scientific workflows deployed in cloud services (abstract) (pg. 980, col. 2, para. 1). Methods were performed on a cluster computer (sec. 6).
receiving one or more training datasets of a genomic pipeline comprising a plurality of training variables for each of a plurality of genomic tests and corresponding results of each of the genomic tests; training a machine learning model using the training datasets;
Historical data from workflow executions in WorkflowSim was used as training data to train a logistic regression, artificial neural network, random forest and naïve bayes approach (training a machine learning model using the training dataset) (sec. 3.1) (Figure 2) (sec. 4-4.4) (Table 3) (sec. 6.2). Sipht, a bioinformatic workflow with different programs that searches for small untranslated RNAs, contains multiple tasks and is included in the historical data (a genomic pipeline comprising a plurality … a plurality of genomic tests) (sec. 5.1) (sec. 6.3) (sec. 7). Table 2 shows attributes used as training data to measure task failure of the workflows (training variables for each of a plurality of genomic tasks). Each task is labeled as failure or not failure (corresponding results) (Table 2).
receiving a new genomic workflow pipeline comprising new genomic testing variables; predicting, using the trained machine learning model and new genomic testing variables, whether the new genomic workflow pipeline will be successfully completed within a first compute environment.
Epigenome workflow was used to compare task failure prediction accuracy between the trained models and an existing model called NB1 (sec. 6.4) (Figure 1). Alternatively, workflow datasets including Sipht were split into training and test sets (receiving a new genomic workflow pipeline) (sec. 5.2.1). The models predict whether a task will fail given attributes such as virtual machine type and CPU/RAM utilization percentage (a first compute environment) (Table 2) (Figure 2).
Claims 3 and 13: One of the models is a logistic regression (sec. 4.1).
Claims 6-7 and 16-17: Epigenome was used to compare the trained models to an existing model called NB1 (sec. 6. 4) (Figure 7). The attributes for Epigenome are the same used for the historical data (training data) (pg. 981, col. 2, para. 2) (sec. 6.1) (Table 2). The attributes are part of a compute environment (Table 2).
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 text of those sections of Title 35, U.S. Code not included in this action can be found in a prior Office action.
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.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claims 4-5 and 14-15 are rejected under 35 USC 103 for being unpatentable over Bala et al. (“Bala”; Expert systems with applications 42, no. 3 (2015): 980-989) in view of Howard et al. (“Howard”; US 2008/0082933 A1).
The limitations of claims 1 and 11 have been taught above by Bala in Claim Rejections - 35 USC § 102.
The bold and italicized text below are the limitations of the instant claims, and the italicized text serves to map the prior art onto the instant claims.
Claims 4-5 and 14-15:
Bala discloses CPU/RAM utilization parameters for Epigenome used to predict task failure (Table 2) (Figure 7) (new genomic testing variables). However, Bala does not teach a user interface with a plurality of input sliders.
Howard discloses a graphical user interface (GUI) to control a cloud server (abstract). Figures 3 and 6 show a slide bar used to select a desired number of processors for a computing task that displays resultant cost and time [130] [141].
It would have been prima facie obvious to have modified the method of Bala by incorporating the GUI of Howard because Howard teaches that clouds typically lack user interactivity and users cannot follow task completion progress or identify/correct processing anomalies [2-3]. Thus, one of skill would want a GUI that allows a user to identify cost/time constraints based upon user defined processor allocation. This aligns with Bala who teaches that their models can be useful for resource provisioning and scheduling by predicting time and cost-based parameter (sec. 7.1). Furthermore, one of skill would want to use the task prediction failure of Bala to inform a computing task performed on a cloud server using the GUI of Howard.
There would have been a reasonable expectation of success because Howard states that the GUI is not computer platform or operating system specific and can be implemented within different software environments [106].
Claims 8-9 and 18-19 are rejected under 35 USC 103 for being unpatentable over Bala et al. (“Bala”; Expert systems with applications 42, no. 3 (2015): 980-989) in view of Chang et al. (“Chang”; US 2022/0157414 A1).
The limitations of claims 1, 7, 11 and 17 have been taught above by Bala in Claim Rejections - 35 USC § 102.
The bold and italicized text below are the limitations of the instant claims, and the italicized text serves to map the prior art onto the instant claims.
Claims 8-9 and 18-19:
Bala teaches attribute parameters include CPU/RAM utilization percentage (the training variables comprise an amount of memory and a number of CPUs) (Table 2).
However, Bala does not generate a recommendation comprising a compute environment, wherein the compute environment contains a plurality of pre-configured cloud compute environment based on a cost of each environment.
Chang optimizes a cluster computer for sequencing data using adaptive data parallelization (abstract). A data parallelization configuration is determined based on sequencing data and a pipeline selection (abstract). A recommendation list is generated based on the data parallelization configuration and computing resources of the cluster computer (abstract) [9]. The recommendation list includes a plurality of computing resources indicating estimated processing time and cost [17]. From the recommendation list, the cluster computer selects at least one resource allocation to perform sequencing analysis (abstract).
It would have been prima facie obvious to modify the method of Bala for task failure prediction on cloud computers by incorporating the cluster computer optimization method of Chang that provides a recommendation of cloud environments. Motivation for doing so is taught by Chang who teaches that their method streamlines bioinformatic analysis and reduces task completion turnaround time [3] [38]. This motivation aligns with Bala who aims to increase efficiency of cloud services (sec. 7.2). There would have been a reasonable expectation of success because Chang states that their method can be implemented by cluster computing systems [131], and Bala is directed toward using cloud services for computing (abstract).
Claim 10 is rejected under 35 USC 103 for being unpatentable over Bala et al. (“Bala”; Expert systems with applications 42, no. 3 (2015): 980-989) in view of Chang et al. (“Chang”; US 2022/0157414 A1), as applied to claim 9 above, and in further view of Lubiana et al. (“Lubiana”; arXiv preprint arXiv:2303.16429 (29 March 2023)).
The limitations of claims 1 and 7 have been taught above by Bala in Claim Rejections - 35 USC § 102. The limitations of claims 8-9 have been taught in the rejection above by Bala and Chang.
Claim 10:
Bala predicts task failure of cloud computing tasks (abstract). However, Bala and Chang do not provide a chatbot. The limitation “for responding to prompts regarding the pre-configured cloud compute environments” equates to an intended use and is thus not required by the claim.
Lubiana discloses tips for applying ChatGPT in computational biology (abstract). ChatGPT can optimize workflows and help interpret results (pg. 1, para. 1) (pg. 2, last para.).
It would have been prima facie obvious to have added ChatGPT to the workflow of Bala for predicting pipeline failure in cloud servers because Lubiana teaches that ChatGPT improves interfaces for user-friendly applications, allowing for a user to interact with software (pg. 10, Tip 9). This aligns with the work of Bala of managing cloud services and controlling web applications (pg. 980, col. 2, para. 1). Alternatively, incorporating ChatGPT would allow a user to interpret results of the prediction models as taught by Lubiana (pg. 1, para. 1). Lubiana also teaches that ChatGPT can help optimize workflows (pg. 2, last para.), which would be advantageous for a user to optimize a workflow to be inputted into the prediction model of Bala. There would have been a reasonable expectation of success to use ChatGPT with the prediction models of Bala or as a separate tool because ChatGPT is configurable as either.
Conclusion
No claims are allowed.
Claims 2 and 12 are free from the prior art. The prior art does not fairly teach or suggest predicting whether a genomic workflow pipeline will be successfully completed within a compute environment based on batch size, sample size, and queue size corresponding to each of a plurality of genomic tests in the pipeline. The closest prior art is Bala et al. (“Bala”; Expert systems with applications 42, no. 3 (2015): 980-989).
Notable, but not relied upon, prior art includes: Jassas et al. (Sensors 22, no. 5 (2022): 2035) review of failure prediction (sec. 2.2). Rosa et al. (In 2018 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), pp. 1703-1710. IEEE, 2018) resource and cost prediction for bioinformatic workflows in clouds. Samak et al. (In Proceedings of the 6th workshop on Workflows in support of large-scale science, pp. 107-116. 2011) failure prediction in large scientific workflows. Ahmad et al. (IEEE Access 10 (2022): 77614-77632) fault tolerant workflow management and scheduling system in cloud.
Inquiries
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Noah A. Auger whose telephone number is (703)756-4518. The examiner can normally be reached M-F 7:30-4:30 EST.
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/N.A.A./Examiner, Art Unit 1687
/KAITLYN L MINCHELLA/Primary Examiner, Art Unit 1685
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