DETAILED ACTION
Notice of 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 .
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 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.
Status of the Claims
Claim 9 is canceled.
Claims 1-8 and 10-21 are pending.
Claims 4, 7, 13 and 20 are objected to.
Claims 1-8 and 10-21 are rejected.
Priority
This US Application 18/042,285 (02/20/2023) is a 371 of PCT/EP2021/073184 (08/20/2021) which claims benefit of Foreign Application No. GB 2013035.7 (08/20/2020), as reflected in the filing receipt mailed on 07/06/2023. Receipt is acknowledged of certified copies of papers required by 37 CFR 1.55. The claims to the benefit of priority are acknowledged; and the effective filing date of claims 1-8 and 10-21 is 08/20/2020.
Information Disclosure Statement
The information disclosure statements (IDS) submitted on 02/20/2023 was considered by the examiner.
Claim objections
Claims 4, 7, 13 and 20 are objected to because of the following informalities. Appropriate correction is required.
Claims 4 and 7 recite and extra space between "and" and "/" and between "/" and "or" as it appears in the recited "amplification curve data and / or the input data."
Claims 13 and 20 recite an extra line before the wherein clause and improper indentation of claim elements. As set forth in 37 CPR 1.75, each element or step of the claim should be separated by a line indentation (608.01(m) Form of Claims). Sub-steps / elements should be indented from their parent step / element. As it appears in the claims, only the last claim element is indented. If Applicant intends to indent claim elements then all claim elements in claim should be indented consistently.
Claim Rejections - 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.
Claims 1-8 and 10-21 are rejected under 35 U.S.C. 112(b)as being indefinite for failing to particularly point out and distinctly claim the subject matter the invention. Dependent claims are rejected similarly, unless otherwise noted below. The following issues cause the respective claims to be rejected under 112(b) as indefinite:
The following recitations require but lack antecedent basis, rendering their claims indefinite because there is no previous recitations of the followings terms as written:
claims 1 and 21, "the presence of any of a plurality of prospective target nucleic acids"
claim 18, "the presence of a plurality of different nucleic acids"
claims 1, 19 and 21, "the degree of amplification"
claim 4 " the duration of the amplification reaction"
claim 10, " the volume"
In claim 4, the recited "the majority" is a term of relative or vague degree or form of association, neither defined in the specification nor having a well-known and sufficiently particular definition in the art and in the instant context. The disclosure at pg. 2 line 11 is not interpreted as a definition. MPEP 2173.05(b) pertains. Although claims are interpreted in light of the specification, examples from the specification are not imported into the claims as limitations absent a clearly limiting definition in the specification. MPEP 2173.05(b) pertains.
In claim 12, the recited "most likely" is a term of relative or vague degree or form of association, neither defined in the specification nor having a well-known and sufficiently particular definition in the art and in the instant context. The disclosure at pg. 2 line 31 is not interpreted as a definition. MPEP 2173.05(b) pertains. Although claims are interpreted in light of the specification, examples from the specification are not imported into the claims as limitations absent a clearly limiting definition in the specification. MPEP 2173.05(b) pertains.
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 1-8 and 10-21 are rejected under 35 USC § 101 because the claimed inventions are directed to one or more Judicial Exceptions (JEs) without significantly more. Regarding JEs, "Claims directed to nothing more than abstract ideas..., natural phenomena, and laws of nature are not eligible for patent protection" (MPEP 2106.04 §I). Abstract ideas include mathematical concepts and procedures for evaluating, analyzing or organizing information, which are a type of mental process (MPEP 2106.04(a)(2)).
101 background
MPEP 2106 organizes JE analysis into Steps 1, 2A (Prong One & Prong Two), and 2B as analyzed below. MPEP 2106 and the following USPTO website provide further explanation and case law citations: uspto.gov/patent/laws-and-regulations/examination-policy/examination-guidance-and-training-materials.
Step 1: Are the claims directed to a process, machine, manufacture, or composition of matter (MPEP 2106.03)?
Step 2A, Prong One: Do the claims recite a judicially recognized exception, i.e., a law of nature, a natural phenomenon, or an abstract idea (MPEP 2106.04(a-c))?
Step 2A, Prong Two: If the claims recite a judicial exception under Prong One, then is the judicial exception integrated into a practical application by an additional element (MPEP 2106.04(d))?
Step 2B: Do the claims recite a non-conventional arrangement of elements in addition to any identified judicial exception(s) (MPEP 2106.05)?
Analysis of instant claims
Step 1: Are the claims directed to a 101 process, machine, manufacture, or composition of matter (MPEP 2106.03)?
The instant claims are directed to a method (claims 1-8 and 10-20); which falls within one of the categories of statutory subject matter. [Step 1: claims 1-8 and 10-20: Yes] [Step 1: claim 21: No]
Claims 21 is rejected under 35 U.S.C. 101 because the claimed inventions are directed to non-statutory subject matter.
Claim 21 is referring to a "computer readable medium comprising computer executable instructions" which is not, in all embodiments within a BRI, interpreted as belonging to any claim type listed in 101. In a BRI, the claim reads on data and/or software comprising no structure other than data and/or software. The claim is not recited as a process, and the claim is not limited to any particular structure as a 101 machine or manufacture. The claim reads on transitory propagating signals which are not proper patentable subject matter because it does not fit within any of the four statutory categories of invention (In re Nuijten, Federal. Circuit, 2006). For compact examination, the claims will be further analyzed under the remaining steps of the Alice/Mayo test.
Step 2A, Prong One: Do the claims recite a judicially recognized exception, i.e., a law of nature, a natural phenomenon, or an abstract idea (MPEP 2106.04(a-c))?
Background
With respect to Step 2A, Prong One, the claims recite judicial exceptions in the form of abstract ideas. MPEP § 2106.04(a)(2) further explains that abstract ideas are defined as:
• mathematical concepts (mathematical formulas or equations, mathematical relationships
and mathematical calculations) (MPEP 2106.04(a)(2)(I));
• certain methods of organizing human activity (fundamental economic principles or practices, managing personal behavior or relationships or interactions between people) (MPEP 2106.04(a)(2)(II)); and/or
• mental processes (concepts practically performed in the human mind, including observations, evaluations, judgments, and opinions) (MPEP 2106.04(a)(2)(III)).
Analysis of instant claims
With respect to the instant claims, under the Step 2A, Prong One evaluation, the claims are found to recite abstract ideas that fall into the grouping of mathematical concepts (in particular mathematical relationships and formulas) and mental processes (in particular procedures for observing, analyzing and organizing information) are as follows.
Mathematical concepts (in particular mathematical relationships and formulas) include:
• "processing the received data, wherein the processing comprises inputting input data into a machine learning model trained to identify any of the plurality of prospective target nucleic acids, wherein the input data is based on the amplification curve data and is indicative of the degree of amplification of the at least one unknown nucleic acid over time during the amplification reaction" (independent claims 1 and 21);
• "processing the received data, wherein the processing comprises inputting input data into a machine learning model to generate a prediction as to whether the known nucleic acid is one of the plurality of prospective target nucleic acids, wherein the input data is based on the amplification curve data, is indicative of the degree of amplification of the at least one known nucleic acid over time, and is labelled according to the known nucleic acid (independent claim 19);
• "pre-processing the amplification curve data to generate the input data, wherein pre-processing comprises any of background subtraction, normalization, and artificially increasing the volume of real-time amplification data and / or melting curve data using data augmentation techniques" (claim 10);
• "inputting first input data into the first machine learning model, the first input data being based on the received amplification curve data and the first machine learning model being trained to identify any of the plurality of prospective target nucleic acids based on the first input data" (claim 17);
• "inputting second input data into the second machine learning model, the second input data being based on the received melting curve data and the second machine learning model being trained to identify any of the plurality of prospective target nucleic acids based on the second input data" (claim 17);
• "generating the combined input data based on outputs from the first and second machine learning models; and inputting the combined input data into the concluding machine learning model, the concluding machine learning model being trained to identify any of the plurality of prospective target nucleic acids based on the combined input data" (claim 17); and
• "wherein the input data is further based on the melting curve data (claim 20).
The claims identified above read on math. The abstract ideas recited in the claims are evaluated under the Broadest Reasonable Interpretation and determined each element performed either in the mind and/or by mathematical operation. Without further detail as to the methodology involved in "executing machine learning algorithms using probabilities to identify target nucleic acids", under the BRI, one may simply, for example, use pen and paper to perform mathematical steps to arrive at the described steps. Further support for the mathematical techniques used in the claims is provided in the specification at pg. 11 para. 1, which discloses a supervised machine learning model trained to combine the predictions of amplification and melting curve analysis and output probabilities for the amplification event belonging to each target of interest. Thus, the recited terms correspond to verbal equivalents of mathematical concepts because they constitute actions executed by a group of mathematical steps in a form of a mathematical algorithm; thus mathematical concepts (MPEP 2106.04(a)(2)). A mathematical concept need not be expressed in mathematical symbols, because "words used in a claim operating on data to solve a problem can serve the same purpose as a formula." In re Grams, 888 F.2d 835, 837 and n.1, 12 USPQ2d 1824, 1826 and n.1 (Fed. Cir. 1989). MPEP 2106.04(a)(2) pertains.
Mental processes, defined as concepts or steps practically performed in the human mind such as steps of observations, evaluations, judgments, analysis, opinions or organizing information include:
• "based on the processing, determining that the at least one unknown nucleic acid is one of the plurality of prospective nucleic acids, and thereby identifying the presence of at least one of the plurality of target nucleic acids in the solution" (independent claims 1 and 21);
• "determining, based on the processing, which of the plurality of prospective target nucleic acids the unknown nucleic acid is most likely to be" (claim 12) and
• "determining that each of the plurality of unknown nucleic acids is a member of the plurality of prospective nucleic acids, and thereby identifying the presence of a plurality of different nucleic acids present in the solution" (claim 18).
Under the BRI, the recited limitations are mental processes because a human mind is also sufficiently capable of determining the presence of a nucleic acid in the solution based on the data evaluated, determining the likelihood of an unknown nucleic acid based on data evaluation and determining that each of the plurality of unknown nucleic acids is a member of the plurality of prospective nucleic acids.
Dependent claims 3, 11 and 14 recite further steps that limit the judicial exceptions in independent claim 1 and, as such, also are directed to those abstract ideas. For example, claim 3 recites further details about the processing step; claims 11 and 14 recite further details about the machine learning model.
[Step 2A Prong One: claims 1-8 and 10-21: Yes ]
Step 2A, Prong Two: If the claims recite a judicial exception under Prong One, then is the judicial exception integrated into a practical application by an additional element (MPEP 2106.04(d))?
Background
MPEP 2106.04(d).I lists the following example considerations for evaluating whether a judicial exception is integrated into a practical application:
An improvement in the functioning of a computer or an improvement to other technology or another technical field, as discussed in MPEP §§ 2106.04(d)(1) and 2106.05(a);
Applying or using a judicial exception to effect a particular treatment or prophylaxis for a disease or medical condition, as discussed in MPEP § 2106.04(d)(2);
Implementing a judicial exception with, or using a judicial exception in conjunction with, a particular machine or manufacture that is integral to the claim, as discussed in MPEP § 2106.05(b);
Effecting a transformation or reduction of a particular article to a different state or thing, as discussed in MPEP § 2106.05(c); and
Applying or using the judicial exception in some other meaningful way beyond generally linking the use of the judicial exception to a particular technological environment, such that the claim as a whole is more than a drafting effort designed to monopolize the exception, as discussed in MPEP § 2106.05(e).
Analysis of instant claims
Instant claims 1, 13 and 19-21 recite additional elements that are not abstract ideas:
• "computer-implemented" (independent claims 1, 19 and 21);
• "receiving amplification curve data indicative of an amplification reaction associated with at least one unknown nucleic acid present in the solution" (independent claims 1 and 21);
• "receiving melting curve data associated with the at least one unknown nucleic acid, the melting curve data being indicative of a degree of dissociation of the at least one unknown nucleic acid with increasing temperature; and wherein the input data is further based on the melting curve data" (claim 13);
• "receiving amplification curve data indicative of an amplification reaction associated with at least one known nucleic acid, the known nucleic acid being one of the plurality of prospective target nucleic acids" (claim 19) and
• "receiving melting curve data associated with the at least one known nucleic acid, the melting curve data being indicative of a degree of dissociation of the at least one known nucleic acid with increasing temperature" (claim 20).
Dependent claims 2-8 and 16 recite further details about the amplification curve data received. Dependent claim 15 recites further details about the melting curve data receiving."
Considerations under Step 2A, Prong Two
The recited limitations in claims 1-8 and 10-21 are interpreted as requiring the use of a computer. Hence, the claims explicitly recite steps executed by computers and therefore can be described as computer functions or instructions to implement on a generic computer. Further steps directed to additional non-abstract elements of a computing device/computer do not describe any specific computational steps by which the "computer parts" perform or carry out the judicial exceptions, nor do they provide any details of how specific structures of the computer are used to implement these functions. The claims state nothing more than a generic computer which performs the functions that constitute the judicial exceptions.
The judicial exceptions in the claims are considered to perform the claimed abstract idea with a computer, which is not sufficient to integrate an abstract idea into a practical application (see MPEP 2106.05(f)); since steps that can be performed mentally and merely performing the mental process in a computer environment do not negate the fact that something that can be carried out in the human mind. See MPEP 2106.04(a)(2).III.C.
Claims directed to "receiving" read on receiving or transmitting data over a network -Symantec, 838 F.3d at 1321 - MPEP 2106.05(a) pertains; which constitutes just necessary data gathering and therefore correspond to insignificant extra-solution activity.
There are no additional limitations to indicate details of exactly how the judicial exception is being integrated by the additional elements. There are no additional limitations to indicate that the claimed computer, processor, or computer readable medium require anything other than generic computer components in order to carry out the recited abstract idea in the claims. Claims that amount to 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(b). The recited "real-time" aspect in claim 3 also read on "apply it" because it relates to how the computer executes the judicial exceptions (i.e. math).
Hence, these are mere instructions to apply the abstract idea using a computer and insignificant extra-solution activity and therefore the claims do not integrate that abstract idea into a practical application (see MPEP 2106.04(d) § I; 2106.05(f); and 2106.05(g)).
In Step 2A, Prong One above, claim steps and/or elements were identified as part of one or more judicial exceptions (JEs).
In this Step 2A, Prong Two immediately above claim steps and/or elements were identified as part of one or more additional elements. Additional elements are further discussed in Step 2B below.
Here in Step 2A, Prong Two, no additional step or element clearly demonstrates integration of the JE(s) into a practical application.
[Step 2A Prong Two: claims 1-8 and 10-21: No]
Step 2B: Do the claims recite a non-conventional arrangement of elements in addition to any identified judicial exception(s) (MPEP 2106.05)?
According to analysis so far, the additional elements described above do not provide significantly more than the judicial exception. A determination of whether additional elements provide significantly more also rests on whether the additional elements or a combination of elements represents other than what is well-understood, routine, and conventional. Conventionality is a question of fact and may be evidenced as: a citation to an express statement in the specification or to a statement made by an applicant during examination that demonstrates a well-understood, routine or conventional nature of the additional element(s); a citation to one or more of the court decisions as discussed in MPEP 2106(d)(II) as noting the well-understood, routine, conventional nature of the additional element(s); a citation to a publication that demonstrates the well-understood, routine, conventional nature of the additional element(s); and/or a statement that the examiner is taking official notice with respect to the well-understood, routine, conventional nature of the additional element(s).
Claims 1-8 and 10-21 recite a computer or computer functions, interpreted as instructions to apply the abstract idea using a computer, where the computer does not impose meaningful limitations on the judicial exceptions; which can be performed without the use of a computer (MPEP 2106.04(d) § I; and MPEP 2106.05(f)).
The computer-related elements or the general purpose computer and the machine learning model do not rise to the level of significantly more than the judicial exception. The claims state a generic computer which performs the functions that constitute the judicial exceptions. Hence, these are mere instructions to apply the judicial exceptions using a computer, which the courts have found to not provide significantly more when recited in a claim with a judicial exception (Alice Corp., 573 U.S. at225-26, 110 USPQ2d at 1984; see MPEP 2106.05(A)).
Further, the courts have found that receiving data is a well-understood, routine, and conventional functions of a computer when claimed in a generic manner or as insignificant extra-solution activity (see Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information), 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), Versa ta Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1334, 115 USPQ2d 1681, 1701 (Fed. Cir. 2015), and OIP Techs., 788 F.3d at 1363, 115 USPQ2d at 1092-93, as discussed in MPEP 2106.05(d)(Il)(i)).
When the claims are considered as a whole, they do not integrate the abstract idea into a practical application; they do not confine the use of the abstract idea to a particular technology; they do not solve a problem rooted in or arising from the use of a particular technology; they do not improve a technology by allowing the technology to perform a function that it previously was not capable of performing; and they do not provide any limitations beyond generally linking the use of the abstract idea to a broad technological environment. See MPEP 2106.05(a) and 2106.05(h).
The instant claims constitute insignificant extra solution activity, and when considered individually, are insufficient to constitute inventive concepts that would render the claims significantly more than an abstract idea (see MPEP 2106.05(g)). Hence, these elements, when considered individually, are insufficient to constitute inventive concepts that would render the claims significantly more than an abstract idea (see MPEP 2106.05(d)).
[Step 2B: claims 1-8 and 10-21: No]
Conclusion: Instant claims are directed to non-statutory subject matter
For the reasons above, the claims in this instant application, when the limitations are considered individually and as a whole, are directed to an abstract idea and lack an inventive concept not clearly anything significantly more.
Claim Rejections - 35 USC § 103
The following is a quotation of pre-AIA 35 U.S.C. 103(a) which forms the basis for all obviousness rejections set forth in this Office action:
(a) A patent may not be obtained though the invention is not identically disclosed or described as set forth in section 102, if the differences between the subject matter sought to be patented and the prior art are such that the subject matter as a whole would have been obvious at the time the invention was made to a person having ordinary skill in the art to which said subject matter 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 pre-AIA 35 U.S.C. 103(a) 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.
A. Claims 1-8, 10-16 and 18-21 are rejected under 35 U.S.C. 103(a) as being unpatentable over Moniri ("A framework for analysis of real-time nucleic acid amplification data using novel multidimensional standard curves." bioRxiv 379180 (2018)) in view of Velez ("Massively parallel digital high resolution melt for rapid and absolutely quantitative sequence profiling." Scientific Reports 7(1):42326 (2017)), as cited on the attached Form PTO-892.
Claim 1 recites a method comprising steps. Claim 21 recites a computer readable medium comprising computer executable instructions which, when performed by a processor, cause the processor to perform the a method of identifying the presence of any of a plurality of prospective target nucleic acids in a solution containing a biological sample, the method comprising said steps.
The prior art to Moniri discloses a method for quantification of nucleic via the analysis of real-time nucleic acid amplification data using novel multidimensional standard curves (pg. 1 Abstract); wherein statistical analyses were performed in MATLAB software (i.e. CRM) (pg. 5 col. 2 para. 3).
The steps performed by the method of claim 1 and a computer readable medium of claim 21 comprise:
receiving amplification curve data indicative of an amplification reaction associated with at least one unknown nucleic acid present in the solution;
processing the received data, wherein the processing comprises…, wherein the input data is based on the amplification curve data and is indicative of the degree of amplification of the at least one unknown nucleic acid over time during the amplification reaction; and
based on the processing, determining that the at least one unknown nucleic acid is one of the plurality of prospective nucleic acids, and thereby identifying the presence of at least one of the plurality of target nucleic acids in the solution
• Moniri teaches a method for quantification of nucleic via the analysis of real-time nucleic acid amplification data using novel multidimensional standard curves (i.e. degree of amplification of the at least one unknown nucleic acid over time during the amplification reaction) (pg. 1 Abstract) and using the cycle-threshold method for the quantification of a specific target sequence (i.e. amplification curve data indicative of an amplification reaction associated with at least one unknown nucleic acid) (pg. 1 col. 1 para. 2); wherein quantification comprised computed features and curve-fitting parameters for each amplification curve grouped by concentration (i.e. computer implemented method for identifying nucleic acids) (pg. 6 col. 1 para. 3); wherein raw amplification data for several known concentrations of the target are pre-processed and fitted with an appropriate curve and multiple features can be extracted in order to construct a multidimensional standard curve to achieve enhanced quantification via multidimensional analyses (i.e. receiving amplification input data is based on the amplification curve data and is indicative of the degree of amplification of the at least one unknown nucleic acid over time during the amplification reaction) (pg. 2 Fig. 1); wherein once training is complete, unknown samples are quantified for an absolute quantification based on the quantification curve (pg. 3 col. 1 para. 3) of DNA solutions (pg. 6 col. 1 para. 1) (i.e. based on the processing, determining that the at least one unknown nucleic acid is one of the plurality of prospective nucleic acids, and thereby identifying the presence of at least one of the plurality of target nucleic acids in the solution).
inputting input data into a machine learning model trained to identify any of the plurality of prospective target nucleic acids
• Moniri does not teach the recitation above. However, Velez teaches a system that incorporates a microfluidic chip and instrumentation to accomplish universal PCR amplification, High Resolution Melting (HRM), and machine learning within 20,000 picoliter scale reactions, simultaneously (i.e. computer implemented method for identifying nucleic acids) (pg. 1 para. 1); wherein amplification followed by HRM sequence fingerprinting in all reactions resulting in bacteria-specific melt curves identified by Support Vector Machine learning (pg. 1 para. 1) in clinical blood samples (pg. 12 para. 5).
Claim 2 recites:
wherein the amplification curve data is received from a thermocycler or a device configured to perform an amplification reaction
• Moniri teaches oligonucleotides fluorescence datasets acquisition comprised a thermocycling step for amplification reactions (pg. 5 col. 2 para. 5).
Claim 3 recites:
wherein the receiving and the processing occurs in real-time as the amplification reaction is ongoing
• Moniri teaches oligonucleotides fluorescence datasets acquisition comprised a real-time PCR amplifications (pg. 5 col. 2 para. 5).
Claim 4 recites:
wherein the amplification curve data and / or the input data comprises a time series depicting the degree of amplification over time throughout a majority of the duration of the amplification reaction
Claim 5 recites:
wherein the time series depicts the degree of amplification throughout the entirety of the duration of the amplification reaction
• Moniri teaches oligonucleotides fluorescence datasets acquisition comprised a real-time PCR amplifications (pg. 5 col. 2 para. 5); wherein curve-fitted real-time amplification curves shows fluorescence data points at different time points (i.e. wherein the amplification curve data and / or the input data comprises a time series) (pg. 2 Fig. 2).
• Moniri does not teach a degree of amplification. However, Velez teaches that a typical dPCR cycle number is kept to ~35 cycles (i.e. data depicting the degree of amplification over time throughout a majority of the duration of the amplification reaction as in claim 4), but we find that 70 cycles ensures full endpoint amplification from single molecules (i.e. data depicting the degree of amplification throughout the entirety of the duration of the amplification reaction as in claim 5) (pg. 10 para. 1). Here, the full endpoint amplification reads on entirety of the duration of the amplification reaction, and the typical dPCR cycle number of about 35 cycles reads on the majority of the duration of the amplification reaction).
Claim 6 recites:
wherein the amplification curve data and / or the input data comprises a time series depicting the degree of amplification over time from an initial phase in which no amplification is occurring until at least a saturation phase
• Moniri does not teach the recitation above. However, Velez teaches that reactions having no amplification, i.e. no melt curve, were classified as true negatives and make up the remainder of the 20,000 total reactions per universal digital high resolution melt namely U-dHRM chip (pg. 8 Table 1). Here, reactions having no amplification at all read on no amplification from an initial phase until at least a saturation phase.
Claim 7 recites:
wherein the amplification curve data and/or the input data is representative of an entire amplification curve
• Moniri does not teach the recitation above. However, Velez teaches that a typical dPCR cycle number is kept to ~35 cycles but we find that 70 cycles ensures full endpoint amplification from single molecules (i.e. amplification curve data and / or the input data is representative of an entire amplification curve) (pg. 10 para. 1).
Claim 8 recites:
wherein the amplification curve data is real-time PCR data
• Moniri teaches oligonucleotides fluorescence datasets acquisition comprised a real-time PCR amplifications (pg. 5 col. 2 para. 5).
Claim 10 recites:
further comprising pre-processing the amplification curve data to generate the input data, wherein pre-processing comprises any of background subtraction, normalization, and artificially increasing the volume of real-time amplification data and / or melting curve data using data augmentation techniques
• Moniri teaches raw amplification data for several known concentrations of the target pre-processed and fitted with an appropriate curve and multiple features can be extracted in order to construct a multidimensional standard curve to achieve enhanced quantification via multidimensional analyses (i.e. pre-processing the amplification curve data to generate the input data) (pg. 2 Fig. 1); wherein pre-processing performed background subtraction (pg. 4 col. 1 para. 2).
Claim 11 recites:
wherein the machine learning model has been trained using labelled amplification curve data, the labelled amplification curve data comprising respective data subsets each associated with a different one of the plurality of prospective target nucleic acids
• Moniri teaches that training involved processed and curve fitted real-time nucleic acid amplification curves obtained by serially diluting the known target template (i.e. respective data subsets each associated with a different one of the plurality of prospective target nucleic acids) (pg. 2 col.1 para. 3).
• Moniri does not teach "a machine learning model." However, Velez teaches a system that incorporates a microfluidic chip and instrumentation to accomplish universal PCR amplification, High Resolution Melting (HRM), and machine learning within 20,000 picoliter scale reactions, simultaneously (pg. 1 para. 1); wherein amplification followed by HRM sequence fingerprinting in all reactions resulting in bacteria-specific melt curves identified by Support Vector Machine learning (pg. 1 para. 1) in clinical blood samples (pg. 12 para. 5).
Claim 12 recites:
further comprising determining, based on the processing, which of the plurality of prospective target nucleic acids the unknown nucleic acid is most likely to be
• Moniri teaches that once training is complete, unknown samples are quantified for an absolute quantification based on the quantification curve (pg. 3 col. 1 para. 3) of DNA solutions (pg. 6 col. 1 para. 1) (i.e. determining, based on the processing, which of the plurality of prospective target nucleic acids the unknown nucleic acid is most likely to be).
Claim 13 recites:
further comprising receiving melting curve data associated with the at least one unknown nucleic acid, the melting curve data being indicative of a degree of dissociation of the at least one unknown nucleic acid with increasing temperature; and wherein the input data is further based on the melting curve data
• Moniri teaches a method for quantification of nucleic via the analysis of real-time nucleic acid amplification data using novel multidimensional standard curves (pg. 1 Abstract); wherein all the runs were completed with a melting curve analysis to confirm the specificity of amplification (i.e. comprising receiving melting curve data associated with the at least one unknown nucleic acid) (pg. 6 col. 1 para. 1).
• Moniri does not teach "the melting curve data being indicative of a degree of dissociation of the at least one unknown nucleic acid with increasing temperature." However, Velez teaches an integrative U-dHRM platform for the absolute quantification and identification of multiple genotypes in heterogeneous samples at clinically relevant concentrations (pg. 9 para. 1); wherein Fig. 5 depicts the identification by melting curves of L. monocytogenes bacterial DNA in mock blood sample (i.e. degree of dissociation of the at least one unknown nucleic acid with increasing temperature) (pg. 9 Fig. 5).
Claim 14 recites:
wherein the machine learning model has been trained using labelled melting curve data, the labelled melting curve data comprising respective data subsets each associated with a different one of the plurality of prospective target nucleic acids
• Moniri does not teach the recitation above. However, Velez teaches the application of One Support Vector Machines (OVO SVM) to automatically identify sequences by their melt curve signatures despite inherent experimental variability (pg. 2 para. 2); wherein Table 2 shows U-dHRM data of different proportions (ratios) of S. pneumoniae DNA to L. monocytogenes DNA polymicrobial samples were loaded to DPCR chips (i.e. labelled melting curve data, the labelled melting curve data comprising respective data subsets each associated with a different one of the plurality of prospective target nucleic acids) (pg. 8 Table 2).
Claim 15 recites:
wherein the degree of dissociation of the at least one unknown nucleic acid is determined via monitoring the fluorescence of the solution
• Moniri teaches oligonucleotides fluorescence datasets acquisition comprised a real-time PCR amplifications (pg. 5 col. 2 para. 5); wherein once training is complete, unknown samples are quantified for an absolute quantification based on the quantification curve (pg. 3 col. 1 para. 3) of DNA solutions (pg. 6 col. 1 para. 1).
Claim 16 recites:
wherein the solution contains an intercalating dye
• Moniri does not teach the recitation above. However, Velez teaches that 37 clinically relevant organisms could be distinguished by general intercalating dye-based melt curves (pg. 9 para. 2).
Claim 18 recites:
wherein the at least one unknown nucleic acid is a plurality of unknown nucleic acids, and the method further comprises determining that each of the plurality of unknown nucleic acids is a member of the plurality of prospective nucleic acids, and thereby identifying the presence of a plurality of different nucleic acids present in the solution
• Moniri does not teach the recitation above. However, Velez teaches the application of One Support Vector Machines (OVO SVM) to automatically identify sequences by their melt curve signatures despite inherent experimental variability (pg. 2 para. 2); wherein the targeted mixture ratios were created based on absorbance measurements of individual bacterial DNA concentrations and then analyzed by U-dHRM and OVO SVM classification of mixed genomic DNA samples (i.e. identifying the presence of a plurality of different nucleic acids present in the solution) (pg. 8 Table 2) for detection and quantification of microbial DNA in clinical samples (pg. 8 para. 2).
Claim 19 recites:
receiving amplification curve data indicative of an amplification reaction associated with at least one known nucleic acid, the known nucleic acid being one of the plurality of prospective target nucleic acids;
• Moniri teaches a method for quantification of nucleic via the analysis of real-time nucleic acid amplification data using novel multidimensional standard curves (pg. 1 Abstract) and the cycle-threshold method for the quantification of a specific target sequence (i.e. amplification curve data indicative of an amplification reaction associated with at least one unknown nucleic acid) (pg. 1 col. 1 para. 2); wherein raw amplification data for several known concentrations of the target are pre-processed and fitted with an appropriate curve and multiple features can be extracted in order to construct a multidimensional standard curve to achieve enhanced quantification via multidimensional analyses (i.e. receiving amplification curve data indicative of an amplification reaction associated with at least one known nucleic acid, the known nucleic acid being one of the plurality of prospective target nucleic acids) (pg. 2 Fig. 1); wherein once training is complete, unknown samples are quantified for an absolute quantification based on the quantification curve (pg. 3 col. 1 para. 3) of DNA solutions (pg. 6 col. 1 para. 1)
processing the received data, wherein the processing comprises inputting input data into a machine learning model to generate a prediction as to whether the known nucleic acid is one of the plurality of prospective target nucleic acids, wherein the input data is based on the amplification curve data, is indicative of the degree of amplification of the at least one known nucleic acid over time, and is labelled according to the known nucleic acid; and based on the generated prediction, training the machine learning model to identify any of the plurality of prospective target nucleic acids
• Moniri does not teach the recitation above. However, Velez teaches the application of One Support Vector Machines (OVO SVM) to automatically identify sequences by their melt curve signatures despite inherent experimental variability (i.e. based on the generated prediction, training the machine learning model to identify any of the plurality of prospective target nucleic acids) (pg. 2 para. 2); wherein the targeted mixture ratios were created based on absorbance measurements of individual bacterial DNA concentrations using an Eppendorf Biospectrometer and then analyzed by U-dHRM and OVO SVM classification of mixed genomic DNA samples (pg. 8 Table 2) for detection and quantification of microbial DNA in clinical samples (i.e. processing the received data, wherein the processing comprises inputting input data into a machine learning model to generate a prediction as to whether the known nucleic acid is one of the plurality of prospective target nucleic acids) (pg. 8 para. 2).
Claim 20 recites:
further comprising receiving melting curve data associated with the at least one known nucleic acid, the melting curve data being indicative of a degree of dissociation of the at least one known nucleic acid with increasing temperature; and wherein the input data is further based on the melting curve data
• Moniri teaches a method for quantification of nucleic via the analysis of real-time nucleic acid amplification data using novel multidimensional standard curves (pg. 1 Abstract); wherein all the runs were completed with a melting curve analysis to confirm the specificity of amplification (i.e. comprising receiving melting curve data associated with the at least one unknown nucleic acid) (pg. 6 col. 1 para. 1)
Rationale for combining (MPEP §2142-2143)
Regarding claims 1-8, 10-16 and 18-21, it would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine, in the course of routine experimentation and with a reasonable expectation of success, the methods of Moniri in view of Velez because all references disclose methods for identifying nucleic acids based on amplification curve data and melting curve data. The motivation would have been to incorporate rapid and accurate profiling of genotypes in complex samples (pg. 1 para. 2 Velez).
Therefore it would have been obvious to one of ordinary skill in the art to substitute the identifying nucleic acids based on amplification curve data and melting curve data method of Moniri to the methods by Velez because such a substitution is no more than the simple substitution of one known element for another. One of ordinary skill in the art would be able to motivated to combine the teachings in these references with a reasonable expectation of success since the described teachings pertain to methods for identifying nucleic acids based on amplification curve data and melting curve data.
B. Claim 17 is rejected under 35 U.S.C. 103(a) as being unpatentable over Moniri and Velez as applied to claim 1 above further in view of Li ("A review on machine learning principles for multi-view biological data integration." Briefings in bioinformatics 19(2):325-340 (2018)), as cited on the attached Form PTO-892.
Claim 17 recites:
wherein the input data is combined input data, and wherein the machine learning model is a concluding machine learning model in a system of machine learning models comprising a first, a second, and the concluding machine learning model;
wherein processing the received data further comprises:
inputting first input data into the first machine learning model, the first input data being based on the received amplification curve data and the first machine learning model being trained to identify any of the plurality of prospective target nucleic acids based on the first input data;
inputting second input data into the second machine learning model, the second input data being based on the received melting curve data and the second machine learning model being trained to identify any of the plurality of prospective target nucleic acids based on the second input data;
generating the combined input data based on outputs from the first and second machine learning models; and inputting the combined input data into the concluding machine learning model,
the concluding machine learning model being trained to identify any of the plurality of prospective target nucleic acids based on the combined input data
• Neither Moniri or Velez teach the recitation above. However, Li teaches that, given multiple neural networks (i.e. subset of machine learning) with identical nodes but different edges (i.e. reading on first and second machine learning models), an established idea is to fuse these networks in a final network (i.e. the concluding machine learning model) reflecting common and view specific connections; which a typical example of this principle is shown by similarity network fusion approach integrates mRNA expression, DNA methylation and miRNA of cohort cancer patients for tumor subtyping and survival prediction (i.e. data comprising plurality of prospective target nucleic acids)(pg. 330 col. 2 para. 2); wherein integrative analyses are conducted by machine learning methods on four types of multi-view data (pg. 327 Table 1); wherein here are three ways to integrate data by ensemble learning (i.e. reading on inputting data): the first way is to use the concatenated features as input of random forest; the second way is to build multiple trees for each data view, and then use all learned trees of all views to vote for the final decision and third, granularity of features can be carefully considered in the step of sampling features (pg. 330 col. 1 para. 1).
Rationale for combining (MPEP §2142-2143)
Regarding claim 17, it would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine, in the course of routine experimentation and with a reasonable expectation of success, the methods of Moniri and Velez in view of Li because all references disclose methods for biological data processing using computational analysis. The motivation would have been to integrate in multi-modal learning for capturing the complex mechanism of biological systems (pg. 325 Abstract Li).
Therefore it would have been obvious to one of ordinary skill in the art to substitute the method for biological data processing using computational analysis of Moniri and Velez to the methods by Li because such a substitution is no more than the simple substitution of one known element for another. One of ordinary skill in the art would be able to motivated to combine the teachings in these references with a reasonable expectation of success since the described teachings pertain to methods for biological data processing using computational analysis.
Conclusion
No claims are allowed.
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/F.F.L./Examiner, Art Unit 1685
/JANNA NICOLE SCHULTZHAUS/Examiner, Art Unit 1685
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