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
Claims 1-30 are pending.
Claims 16, 20-21, 23, 25, and 29 are objected to.
Claims 1-30 are rejected.
Priority
This US Application 17/876,528 (07/28/2022) claims benefit of US Application 63/228,954 (08/03/2021), as reflected in the filing receipt mailed on 11/30/2023. The claims to the benefit of priority are acknowledged; and the effective filing date of claims 1-30 is 08/03/2021.
Information Disclosure Statement
The information disclosure statements (IDS) submitted on 09/06/2022 and 12/05/2022 were considered.
Specification
The lengthy specification has not been checked to the extent necessary to determine the presence of all possible minor errors. Applicant’s cooperation is requested in correcting any errors of which applicant may become aware in the specification.
Claim objections
Claims 16, 20-21, 23, 25, and 29 are objected to because of the following informalities. Appropriate correction is required.
In claim 16, the recited "generating the final classification information comprise" should read "generating the final classification information comprises", or similar, for proper grammar. In contrast, claim 7 does not present the same issue.
Claims 20-21 repeat the issue above.
In claim 20, 2nd limitation, the recited "a second called base that is same as the first called base" should read "a second called base that is the same as the first called base" for proper grammar, or similar.
In claim 21, final limitation, the recited " a final called base that one of (i) the first called base, (ii) the second called base, or (iii) is marked as inconclusive" should read "a final called base selected from (i) the first called base, (ii) the second called base, or (iii) a called base marked as inconclusive", or similar, to improve readability in the claim.
In claim 23, the recited "placing first/second/third/fourth weight" should read "placing a first/second/third/fourth weight", or similar, for proper grammar.
Claim 25 repeats the issue above.
In claim 29, the commas at the end of the 2nd and 3rd limitations should be replaced by semicolons.
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.
Claim 19 is 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:
In claim 19, the relationship is unclear between "a specific base to be called" and the previous "a specific base to be called" in claim 16, from which claim 19 ultimately depends on. It is unclear if both instances are related to the same element or not. In contrast, the same issue does not exist for claims 20-21, which depend on claim 1.
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 1-30 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 1-29) and a CRM (claim 30); each of which falls within one of the categories of statutory subject matter.
[Step 1: claims 1-30: Yes]
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)).
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 mental processes (in particular procedures for observing, analyzing and organizing information) and mathematical concepts (in particular mathematical relationships and formulas) as well as a law of nature or a natural phenomenon are as follows:
• "executing at least a first base caller and a second base caller on sensor data generated for sensing cycles in a series of sensing cycles" (independent claims 1 and 30);
• "generating, by the first base caller, first classification information associated with the sensor data, based on executing the first base caller on the sensor data; generating, by the second base caller, second classification information associated with the sensor data, based on executing the second base caller on the sensor data; and based on the first classification information and the second classification information, generating a final classification information, the final classification information including one or more base calls for the sensor data" (independent claims 1 and 30);
• "executing at least a first base caller and a second base caller on at least corresponding portions of the sensor data, and selectively switching execution of the first and second base callers, based on context information associated with the sensor data, wherein the first base caller is different from the second base caller" (independent claim 29);
• "generating, by the first base caller and the second base caller, first classification information and second classification information, respectively, generating base calls, based on one or both of the first classification information and the second classification information" (independent claim 29);
• "detecting, from the sensor data, presence of one or more bubbles in at least one cluster of a tile of a flow cell" (dependent claim 27);
• "detecting that the at least one image is an out of focus image" (dependent claim 28).
Dependent claims 2, 4-7, 13-14, and 16-28 recite further steps that limit the judicial exceptions in independent claims 1 and 29-30 and, as such, also are directed to those abstract ideas. For example, claim 2 further limits the at least one of the first base caller and the second base caller to implementing a non-linear function and to being part linear; claims 4-5 and 16-17 further limit the classification information generated by the base callers to comprising a plurality of scores indicative of a probability of the base to be called being one of A, C, T, or G; claim 6 further limits the at least one of the first base caller and the second base caller to using a softmax function to generate the corresponding plurality of scores; claim 7 further limits the step of generating the final classification information to selectively combining the first classification information and the second classification information; claim 13 further limits a cluster to be classified as an edge cluster if the cluster is estimated to be located within a threshold distance from an edge of the tile; claim 14 further limits a cluster to be classified as a non-edge cluster if the cluster is estimated to be located more than a threshold distance from any edge of the tile; claim 18 further limits the first/second/third/fourth final scores to being an average, a normalized weighted average, a minimum, or a maximum of a list of scores; claims 19-25, and 27-28 further limit the first/second/final classification information, with claims 23-25, and 27-28 further reciting details about "placing weights" on classification information; and claim 26 further limits the one or more clusters as being edge clusters that are disposed within a threshold distance from one or more edges of the tile of the flow cell; and non-edge clusters that are disposed beyond the threshold distance from the one or more edges of the tile of the flow cell.
The abstract ideas recited in the claims are evaluated under the Broadest Reasonable Interpretation (BRI) and determined to each cover performance either in the mind and/or by mathematical operation. The human mind is sufficiently capable of combining information, detecting the presence of abnormal data such as bubbles or out of focus images, and picking one algorithm to be used over another. Without further detail as to the methodology involved in "executing a base caller to generate classification information", under the BRI, one may simply, for example, use pen and paper to perform mathematical steps to arrive at a classification for a called base. Further support for the mathematical techniques used in the claims is provided in the specification at [0149], which describes mathematical models used for base calling purposes. 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.
[Step 2A Prong One: claims 1-30: 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, 3, 24 and 29-30 recite additional elements that are not abstract ideas:
• "computer implemented" (independent claims 1 and 29);
• " at least one of the first base caller and the second base caller implements a neural network model, and at least another of the first base caller and the second base caller does not include a neural network model" (dependent claim 3)
• "the first base caller implements a neural network model, and the second base caller does not include a neural network model" (dependent claim 24)
• "generating sensor data for sensing cycles in the series of sensing cycles" (independent claim 29);
• "computer program" (independent claim 30); and
• "processor" (independent claim 30).
Dependent claims 8-12, and 15 recite further details about the context information associated with the sensor data.
Considerations under Step 2A, Prong Two
The recited limitations in claims 1 and 29-30 are interpreted as requiring the use of a computer. The use of a computer is broadly interpreted and not actually described in the claims or specification. 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 recited "generating sensor data" (claim 29) reads on data gathering activities; not amounting to a practical application. The type of data doesn’t change that it is mere data gathering or conventional computer receiving means. The sensor data generated is used as input for the subsequent mathematical calculations in the judicial exception.
With respect to claims 3 and 24, the computer-related elements or the general purpose computer and the recited neural network model do not rise to the level of significantly more than the judicial exception. The claims state nothing more than 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)). The specification as published also notes that computer processors and systems, as example, are known and widely used examples of neural networks that may be used without limitation [0137]. The additional elements are set forth at such a high level of generality that they can be met by a general purpose computer. Therefore, the computer components constitute no more than a general link to a technological environment, which is insufficient to constitute an inventive concept that would render the claims significantly more than the judicial exceptions (see MPEP 2106.05(b)I-III). 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).
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)). None of the dependent claims recite any additional non-abstract elements; they are all directed to further aspects of the information being analyzed, the manner in which that analysis is performed, or the mathematical operations performed on the information.
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.
At this point in examination it is not yet the case that any of the Step 2A, Prong Two considerations enumerated above clearly demonstrates integration of the identified JE(s) into a practical application. Referring to the considerations above, none of 1. an improvement, 2. treatment, 3. a particular machine or 4. a transformation is clear in the record.
[Step 2A Prong Two: claims 1-30: 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 and 29-30 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 and outputting data are 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).
As explained above, 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 1-30: 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 § 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)(l) 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.
Claims 1-7, 16-17, and 21 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Silvestre-Ryan "Pair consensus decoding improves accuracy of neural network base callers for nanopore sequencing." Genome biology 22(1):38 (2021), as cited on the attached Form PTO-892.
Silvestre-Ryan discloses a pairwise dynamic programming approach (pg. 4 para. 1) via a base calling software that finds the consensus of two neural networks (pg. 1 para. 1). Bullet points indicate the teachings of the instant features over the prior art. Instantly claimed elements which are considered to be equivalent to the prior art teachings are described in bold for all claims.
Claim 1 recites:
executing at least a first base caller and a second base caller on sensor data generated for sensing cycles in a series of sensing cycles; generating, by the first base caller, first classification information associated with the sensor data, based on executing the first base caller on the sensor data; generating, by the second base caller, second classification information associated with the sensor data, based on executing the second base caller on the sensor data; and based on the first classification information and the second classification information, generating a final classification information, the final classification information including one or more base calls for the sensor data
• Silvestre-Ryan teaches a pairwise dynamic programming approach (pg. 4 para. 1) applied to nanopore base calling maps signal to sequence (i.e. sensor data) (pg. 2 Fig. 1) via a base calling software (i.e. computer implemented method for base calling) that finds the consensus of two neural networks (i.e. at least two base callers) by aligning their probability profiles (pg. 1 para. 1); wherein to base call a single read, the time series of current signal is fed into a neural network base caller (i.e. first and second base caller) which outputs for each measurement the probabilities of each base (i.e. first and second classification information) and the final probability profile is then decoded to find the most likely base called sequence (i.e. final classification based on the first and second classification information) (pg. 2 Fig. 1).
Claim 2 recites:
wherein at least one of the first base caller and the second base caller implements a non-linear function, and wherein at least another of the first base caller and the second base caller is at least in part linear
• Silvestre-Ryan teaches that the neural network output can thus be interpreted as a linear hidden Markov model (i.e. linear aspect) with the softmax probability function (i.e. by definition a softmax probability function is a nonlinear function) (pg. 1 para. 1 SI).
Claim 3 recites:
wherein at least one of the first base caller and the second base caller implements a neural network model, and at least another of the first base caller and the second base caller does not include a neural network model
• Silvestre-Ryan teaches that a Connectionist Temporal Classification algorithm (i.e. reading on a portion of the method that does not include a neural network model) is applied to the output of the neural network (i.e. reading on a portion of the method that includes a neural network model) to define a probability distribution over all possible labeling of the input (pg. 1 para. 1 SI).
Claim 4 recites:
wherein the first classification information generated by the first base caller comprises, for each base calling cycle, (i) a first plurality of scores, each score of the first plurality of scores indicative of a probability of the base to be called being one of A, C, T, or G, and (ii) a first called base; and the second classification information generated by the second base caller comprises, for each base calling cycle, (i) a second plurality of scores, each score of the second plurality of scores indicative of a probability of the base to be called being one of A, C, T, or G, and (ii) a second called base
• Silvestre-Ryan teaches that the model output specifies the probability of emitting each base where the bases can be any of {A,C,G,T}; wherein base calling then yields multiple probabilities profiles (i.e. comprising first and second classification information) while the task is to find the single sequence that maximizes probability (i.e. final classification) (pg. 2 para. 3); wherein base called sequences are shown (i.e. at least first, second and third bases called) (pg. 2Fig. 1).
Claim 5 recites:
wherein the final classification information comprises, for each base calling cycle, (i) a third plurality of scores, each score of the third plurality of scores indicative of a probability of the base to be called being one of A, C, T, or G, and (ii) a final called base
• Silvestre-Ryan teaches that the model output specifies the probability of emitting each base where the bases can be any of {A,C,G,T}; wherein base calling then yields multiple probabilities profiles (i.e. comprising a plurality of scores indicative of a probability) while the task is to find the single sequence that maximizes probability (i.e. final classification) (pg. 2 para. 3).
Claim 6 recites:
wherein at least one of the first base caller and the second base caller uses a softmax function to generate the corresponding plurality of scores
• Silvestre-Ryan teaches that the neural network output can thus be interpreted as a linear hidden Markov model with the softmax probability function (pg. 1 para. 1 SI).
Claim 7 recites:
wherein generating the final classification information comprises: generating the final classification information, by selectively combining the first classification information and the second classification information, based on context information associated with the sensor data
• Silvestre-Ryan teaches that to base call a single read, the time series of current signal is fed into a neural network base caller (i.e. first and second base caller) which outputs for each measurement the probabilities of each base (i.e. first and second classification information) and the final probability profile is then decoded to find the most likely base called sequence (i.e. final classification based on the first and second classification information) (pg. 2 Fig. 1B).
Claim 16 recites:
wherein for a specific base to be called, the first classification information comprises a first score, a second score, a third score, and a fourth score indicating probabilities of the base to be called is A, C, T, and G, respectively; for the specific base to be called, the second classification information comprises a fifth score, a sixth score, a seventh score, and an eighth score indicating probabilities of the base to be called is A, C, T, and G, respectively; and generating the final classification information comprise: generating, for the specific base to be called, the final classification information based on the first score, the second score, the third score, the fourth score, the fifth score, the sixth score, the seventh score, and the eighth score
• Silvestre-Ryan teaches that the model output specifies the probability of emitting each base where the bases can be any of {A,C,G,T} wherein for each iteration {1..T} (i.e. multiple scores depending on the selection) the probability is updated (i.e. score indicating probability) (pg. 2 para. 3); wherein base calling then yields multiple probabilities profiles (i.e. multiple scores indicating probabilities) while the task is to find the single sequence that maximizes probability (i.e. final classification - corresponding score that is highest among all scores) (pg. 2 para. 3); wherein base called sequences are shown (i.e. final classification information based on all scores) (pg. 2Fig. 1); wherein the output comprises a probability distribution over all possible labeling of the input (pg. 1 para. 1 SI).
Claim 17 recites:
wherein: the final score comprises a first final score that is a function of the first score and the fifth score, the first final score indicating a probability of the base to be called is A; the final score comprises a second final score that is a function of the second score and the sixth score, the second final score indicating a probability of the base to be called is C; the final score comprises a third final score that is a function of the third score and the seventh score, the third final score indicating a probability of the base to be called is T; and the final score comprises a fourth final score that is a function of the fourth score and the eighth score, the fourth final score indicating a probability of the base to be called is G
• Silvestre-Ryan teaches that to base call a single read, the time series of current signal is fed into a neural network base caller (i.e. first and second base caller) which outputs for each measurement the probabilities of each base (i.e. first and second classification information) and the final probability profile is then decoded to find the most likely base called sequence (i.e. final classification based on the first and second classification information) (pg. 2 Fig. 1B); wherein the comparison of the two reads together is based on the probability of the called bases as a function of the score profiling (pg. 2 para. 3); wherein the model output specifies the probability of emitting each base where the bases can be any of {A,C,G,T} wherein for each iteration {1..T} (i.e. multiple scores depending on the selection) the probability is updated (i.e. score indicating probability) (pg. 2 para. 3).
Claim 21 recites:
wherein for a specific base to be called, the first classification information comprises a first called base that is one of A, C, T, and G; for the specific base to be called, the second classification information comprises a second called base that is another of A, C, T, and G, such that the second called base does not match with the first called base; and generating the final classification information comprise: generating, for the specific base to be called, the final classification information, such that the final classification information includes a final called base that one of (i) the first called base, (ii) the second called base, or (iii) is marked as inconclusive
• Silvestre-Ryan teaches a pairwise dynamic programming approach (pg. 4 para. 1); wherein the model output specifies the probability of emitting each base where the bases can be any of {A,C,G,T} (pg. 2 para. 3); wherein to base call a single read, the time series of current signal is fed into a neural network base caller (i.e. first and second base caller) which outputs each read as depicted (i.e. sequence of called bases) (pg. 2 Fig. 1); wherein using the probabilities of each base the final probability profile (i.e. final classification) is then decoded to find the most likely base called sequence (i.e. final called base) (pg. 2 Fig. 1).
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 8-12 and 15 are rejected under 35 U.S.C. 103(a) as being unpatentable over Silvestre-Ryan as applied to claims 1 and 7 in the 102 rejection above further in view of Whiteford ("Swift: primary data analysis for the Illumina Solexa sequencing platform." Bioinformatics 25(17):2194-2199 (2009)) in view of Bushnell ("Introducing FilterByTile: Remove Low-Quality Reads Without Adding Bias" https://www.seqanswers.com/forum/bioinformatics/bioinformatics-aa/60180-introducing-filterbytile-remove-low-quality-reads-without-adding-bias (2016)), as cited on the attached Form PTO-892.
Claim 8 recites:
wherein the context information associated with the sensor data comprises temporal context information, spatial context information, base sequence context information, and other context information
• Silvestre-Ryan does not teach the limitation above. However, Whiteford teaches a tool for analysis of second generation DNA sequencing data via image analysis and base calling (pg. 2194 col. 2 para. 2); wherein the flow cell is moved under the camera in order to image each tile in each cycle and runs are typically around 37 cycles (i.e. temporal context information) (pg. 2195 col. 1 para. 1); wherein image analysis yields an intensity vector for each cluster (i.e. other context information) (pg. 2195 col. 1 para. 2); wherein processing steps are required to process primary data from images to base calls, which yield a base sequence (i.e. base sequence context information) (pg. 2196 Fig. 5). Whiteford does not teach "temporal context information." Furthermore, Bushnell teaches an algorithm, namely FilterByTitle – BBMap tool, to filter low quality reads of flow cell data based on positional information (pg. 1 para. 3); wherein the data processed contain information about each cluster's lane, tile and X, Y coordinates (i.e. spatial context information) (pg. 2 para. 2).
Claim 9 recites:
wherein the context information associated with the sensor data comprises temporal context information that is indicative of one or more base calling cycle numbers associated with the sensor data
• Whiteford teaches that the flow cell is moved under the camera in order to image each tile in each cycle and runs are typically around 37 cycles (i.e. temporal context information that is indicative of one or more base calling cycle numbers) (pg. 2195 col. 1 para. 1).
Claim 10 recites:
wherein the context information associated with the sensor data comprises spatial context information that is indicative of location of one or more tiles within the flow cell that generate the sensor data
• Bushnell teaches an algorithm, namely FilterByTitle – BBMap tool, to filter low quality reads of flow cell data based on positional information (pg. 1 para. 3); wherein the data processed contain information about each cluster's lane, tile and X, Y coordinates (i.e. location of one or more tiles within the flow cell) (pg. 2 para. 2).
Claim 11 recites:
wherein the context information associated with the sensor data comprises spatial context information that is indicative of location of one or more clusters within a tile of the flow cell that generates the sensor data
• Bushnell teaches an algorithm, namely FilterByTitle – BBMap tool, to filter low quality reads of flow cell data based on positional information (pg. 1 para. 3); wherein the data processed contain information about each cluster's lane, tile and X, Y coordinates (i.e. location of one or more clusters within a tile of the flow cell) (pg. 2 para. 2).
Claim 12 recites:
wherein the spatial context information is indicative of whether the one or more clusters within the tile of the flow cell, which generates the sensor data, are edge clusters or non-edge clusters
• Bushnell teaches that FilterByTitle eliminates spikes during cluster imaging corresponding to low-quality locations on the flow cell such as flow cell edges (i.e. indicating non-edge clusters and filtering them out) (pg. 2 para. 1).
Claim 15 recites:
wherein the context information associated with the sensor data comprises base sequence context information that is indicative of a base sequence being called for the sensor data
• Whiteford teaches processing steps are required to process primary data from images to base calls, which yield a base sequence (i.e. base sequence context information) (pg. 2196 Fig. 5).
Rationale for combining (MPEP §2142-2143)
Regarding claims 8-12 and 15, 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 Silvestre-Ryan in view of Whiteford and Bushnell because all references disclose methods for the investigation of sensor data. The motivation would have been to:
• enable the user to understand the primary data for the optimization and validity of their scientific work (pg. 2194 col. 1 para. 1 Whiteford) and
• increase the quality of data libraries without incurring bias (pg. 1 para. 1 Bushnell).
Therefore it would have been obvious to one of ordinary skill in the art to substitute the allelic imbalance analysis method of Silvestre-Ryan to the methods by Whiteford and Bushnell 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 investigation of sensor data.
B. Claims 13-14 are rejected under 35 U.S.C. 103(a) as being unpatentable over Silvestre-Ryan as applied to claims 1 and 7 in the 102 rejection and over Whiteford and Bushnell as applied to claim 11 above further in view of Dinh ("A clustering based method for edge detection in hyperspectral images." Scandinavian Conference on Image Analysis. Berlin, Heidelberg: Springer Berlin Heidelberg, 2009)), as cited on the attached Form PTO-892.
Claim 13 recites:
wherein a cluster is classified as an edge cluster if the cluster is estimated to be located within a threshold distance from an edge of the tile
• Dinh teaches an algorithm for edge detection for hyperspectral images; wherein a pixel in the cluster is considered as an edge pixel if it satisfies two criteria: its confidence to the non-edge cluster is in a range between two thresholds and it has a spatial connection (i.e. distance threshold) with an already established edge pixel, while all remaining pixels are considered as non-edge pixels (pg. 583 para. 1)
Claim 14 recites:
wherein a cluster is classified as a non-edge cluster if the cluster is estimated to be located more than a threshold distance from any edge of the tile
• Dinh teaches an algorithm for edge detection for hyperspectral images; wherein a pixel in the cluster is considered as an edge pixel if it satisfies two criteria: its confidence to the non-edge cluster is in a range between two thresholds and it has a spatial connection (i.e. distance threshold) with an already established edge pixel, while all remaining pixels are considered as non-edge pixels (pg. 583 para. 1)
Rationale for combining (MPEP §2142-2143)
Regarding claims 13-14, 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 Silvestre-Ryan, Whiteford and Bushnell in view of Dinh because all references disclose methods for the investigation of sensor data. The motivation would have been to reduce the effect of noise and preserves more edge information in the images (pg. 586 para. 3 Dinh).
Therefore it would have been obvious to one of ordinary skill in the art to substitute the allelic imbalance analysis method of Silvestre-Ryan, Whiteford and Bushnell to the methods by Dinh 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 investigation of sensor data.
No prior art has been applied to the following claims
Claims 18-20 and 22-28 are free of the analogous art at least because close art, e.g. Silvestre-Ryan, Whiteford, Bushnell and Dinh, as cited on the attached Form PTO-892, either individually or in obvious combination, does not teach the recited combination of the following limitations:
• "the first/second/third/fourth final score is an average, a normalized weighted average, a minimum, or a maximum of …scores" (claim 18)
• "the final classification information, such that the final classification information includes a final called base that matches with the first called base and the second called base" (claim 20)
• "placing… first/second/third/fourth weight on the first/second classification information … wherein weights are different" (claims 22-23 and 27-28)
• "the first weight is lower than the second weight, … second classification information from the second base caller is emphasized more than the first classification information … third weight is higher than the fourth weight, … the first classification information from the first base caller is emphasized more than the second classification information" (claim 24)
• "placing first/second/third/fourth weight on the first classification information from the first one or more clusters" (claim 25)
Conclusion
No claims are allowed.
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/F.F.L./Examiner, Art Unit 1685
/JANNA NICOLE SCHULTZHAUS/Examiner, Art Unit 1685