DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Claim Status
Claims 1-20 are currently pending and under examination herein.
Claims 1-20 are rejected.
Priority
The instant application claims priority to US Provisional Application 63285187 filed 02 December 2021. In this action, claims 1-20 are examined as though they had an effective filing date of 02 December 2021. In future actions, the effective filing date of one or more claims may change, due to amendments to the claims, or further analysis of the disclosure(s) of the priority application(s).
Information Disclosure Statement
The information disclosure statement(s) (IDS) submitted on 23 March 2023 and 08 June 2023 are in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
Drawings
The drawings filed on 28 November 2022 are accepted.
Nucleotide and/or Amino Acid Sequence Disclosures
Nucleic acid sequences (≥ 10 NA) without seq IDs were found in the drawings (Figure 4) and in the specification. A petition granted on 15 April 2026 to waive requiring sequence listings is noted.
Claim Rejections - 35 USC § 112
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 13 is rejected under 35 U.S.C. 112(b) as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor regards as the invention.
Claim 13 recites “on a sequencing machine of the system”. The system, which is described in Claim 12, which claim 13 is dependent on, does not describe having a “sequencing machine”. The metes and bounds of a sequencing machine of the system are unclear, rendering the claim indefinite. The rejection can be overcome by adding a sequencing machine to the system of claim 12 or claim 13.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. In accordance with MPEP § 2106, claims found to recite statutory subject matter (Step 1: YES) are then analyzed to determine if the claims recite any concepts that equate to an abstract idea or natural law (Step 2A, Prong 1). Claims 1-17 are directed to systems and Claims 18-20 are directed to a method. In the instant application, the claims recite the following limitations that equate to an abstract idea:
Claim 1 recites the limitation - identify, for a cluster of oligonucleotides, a read position following an error-inducing sequence within one or more nucleotide-fragment reads; detect a signal from labeled nucleotide bases within the cluster of oligonucleotides during a cycle corresponding to the read position; and determine a nucleotide-base call for the read position corresponding to the cluster of oligonucleotides based on the adjusted signal. Based on the broadest reasonable interpretation, identifying a read position, detecting a signal, and determining a base could practically be done by the human mind. This draws the limitation to a mental process, which classifies the limitation as an abstract idea. Claim 1 also recites determine, for the cluster of oligonucleotides, a cluster-specific-phasing correction to correct the signal for estimated phasing and estimated pre-phasing; and adjust the signal based on the cluster-specific-phasing correction. Based on the broadest reasonable interpretation, determine a cluster-specific-phasing correction (i.e. a mathematical function) and adjust the signal based on the function could include equations and could practically be done by the human mind. This draws the limitation to a mathematical concept and a mental process, which classifies the limitation as an abstract idea.
Claim 2 recites the limitation - wherein the error-inducing sequence comprises a sequence of one or more repeated nucleotide bases, a sequence motif, or a trigger sequence identified by a sequence recognition model. This limitation specifies the possible types of sequences identified in the judicial exception of claim 1. The refined identification indicated by this limitation still represents a judicial expectation.
Claim 3 recites the limitation - wherein the sequence of one or more repeated nucleotide bases or the sequence motif comprise a homopolymer of a same nucleotide base, a near-homopolymer, a guanine quadruplex, a variable number tandem repeat (VNTR), a dinucleotide-repeat sequence, a trinucleotide-repeat sequence, an inverted-repeat sequence, a minisatellite sequence, a microsatellite sequence, or a palindromic sequence. This limitation specifies the possible types of sequences identified in the judicial exception of claim 1. The refined identification indicated by this limitation still represents a judicial expectation.
Claim 4 recites the limitation - determining, for the cluster of oligonucleotides, a cluster-specific-phasing coefficient corresponding to a nucleotide base for a previous cycle and a cluster-specific-pre-phasing coefficient corresponding to a nucleotide base for a subsequent cycle. Based on the broadest reasonable interpretation, the determining could practically be done by the human mind. This draws the limitation to a mental process, which classifies the limitation as an abstract idea. Claim 4 also recites determining the cluster-specific-phasing correction based on the cluster-specific-phasing coefficient and the cluster-specific-pre-phasing coefficient. Based on the broadest reasonable interpretation, the determining a function could include equations and could practically be done by the human mind. This draws the limitation to a mathematical concept and a mental process, which classifies the limitation as an abstract idea.
Claim 5 recites the limitation - generating a previous-cycle weight estimating a phasing effect of the nucleotide base for the previous cycle based on the cluster-specific-phasing coefficient; and generating a subsequent-cycle weight estimating a pre-phasing effect of the nucleotide base for the subsequent cycle based on the cluster-specific-pre-phasing coefficient. Based on the broadest reasonable interpretation, generating the weights could practically be done by the human mind. This draws the limitation to a mental process, which classifies the limitation as an abstract idea. Claim 5 also recites generating a current-cycle weight estimating the phasing effect and the pre-phasing effect for the cycle based on the cluster-specific-phasing coefficient and the cluster-specific-pre-phasing coefficient; and determining the cluster-specific-phasing correction based on the previous-cycle weight, the subsequent-cycle weight, and the current-cycle weight. Based on the broadest reasonable interpretation, generating the wights based on values and determining the correction could include equations and could practically be done by the human mind. This draws the limitation to a mathematical concept and a mental process, which classifies the limitation as an abstract idea.
Claim 6 recites the limitation - determine the cluster-specific-phasing correction further based on a signal intensity corresponding to the previous cycle, a signal intensity corresponding to the cycle, and a signal intensity corresponding to the subsequent cycle. Based on the broadest reasonable interpretation, the determining a function could include equations and could practically be done by the human mind. This draws the limitation to a mathematical concept and a mental process, which classifies the limitation as an abstract idea.
Claim 7 recites the limitation - determining, for the cluster of oligonucleotides, a set of cluster-specific-phasing coefficients corresponding to a set of nucleotide bases for a set of previous cycles; and determining, for the cluster of oligonucleotides, a set of cluster-specific-pre-phasing coefficients corresponding to a set of nucleotide bases for a set of subsequent cycles. Based on the broadest reasonable interpretation, determining the coefficients could practically be done by the human mind. This draws the limitation to a mental process, which classifies the limitation as an abstract idea. Claim 7 also recites determining the cluster-specific-phasing correction based on the set of cluster-specific-phasing coefficients and the set of cluster-specific-pre-phasing coefficients. Based on the broadest reasonable interpretation, determining the correction could include equations and could practically be done by the human mind. This draws the limitation to a mathematical concept and a mental process, which classifies the limitation as an abstract idea.
Claim 8 recites the limitation - determine, for a set of clusters of oligonucleotides, a multi-cluster-phasing correction to correct signals from the set of clusters for estimated phasing and estimated pre-phasing; and adjust the signal based on the cluster-specific-phasing correction or the multi-cluster-phasing correction. Based on the broadest reasonable interpretation, the determining a function and adjusting using a function could include equations and could practically be done by the human mind. This draws the limitation to a mathematical concept and a mental process, which classifies the limitation as an abstract idea.
Claim 9 recites the limitation - determine, for the cluster of oligonucleotides and a subsequent read position, a different cluster-specific-phasing correction to correct a signal for a subsequent cycle from the cluster of oligonucleotides for phasing and pre-phasing of the signal for the subsequent cycle. Based on the broadest reasonable interpretation, the determining could include equations and could practically be done by the human mind. This draws the limitation to a mathematical concept and a mental process, which classifies the limitation as an abstract idea.
Claim 10 recites the limitation - identify, for an additional cluster of oligonucleotides, a different read position preceding the error-inducing sequence within a different nucleotide-fragment read; and detect an additional signal from labeled nucleotide bases within the additional cluster of oligonucleotides during a cycle corresponding to the different read position. Based on the broadest reasonable interpretation, the identifying and detecting could practically be done by the human mind. This draws the limitation to a mental process, which classifies the limitation as an abstract idea. Claim 10 also recites the limitation adjust the additional signal based on a multi-cluster-phasing correction without a cluster-specific-phasing correction for the additional cluster of oligonucleotides. Based on the broadest reasonable interpretation, adjusting the signal with a function could include equations and could practically be done by the human mind. This draws the limitation to a mathematical concept and a mental process, which classifies the limitation as an abstract idea.
Claim 11 recites the limitation - determine the cluster-specific-phasing correction. Based on the broadest reasonable interpretation, determining a function could include equations and could practically be done by the human mind. This draws the limitation to a mathematical concept and a mental process, which classifies the limitation as an abstract idea.
Claim 12 recites the limitation - identify, for a cluster of oligonucleotides, a read position following an error-inducing sequence within one or more nucleotide-fragment reads; detect a signal from labeled nucleotide bases within the cluster of oligonucleotides during a cycle corresponding to the read position; determine, for the cluster of oligonucleotides, a cluster-specific-phasing coefficient corresponding to a nucleotide base for a previous cycle and a cluster-specific-pre-phasing coefficient corresponding to a nucleotide base for a subsequent cycle; adjust the signal based on the cluster-specific-phasing coefficient and the cluster-specific-pre-phasing coefficient; and determine a nucleotide-base call for the read position corresponding to the cluster of oligonucleotides based on the adjusted signal. Based on the broadest reasonable interpretation, the identifying, detecting, determining, and adjusting could practically be done by the human mind. This draws the limitation a mental process, which classifies the limitation as an abstract idea.
Claim 13 recites the limitation determine the cluster-specific-phasing coefficient and the cluster-specific-pre-phasing coefficient utilizing a Linear Equalizer, Decision Feedback Equalizer, Maximum Likelihood Sequence Estimator, forward-backward model, or machine learning model. Based on the broadest reasonable interpretation, determining using equations could include equations and could practically be done by the human mind. This draws the limitation to a mathematical concept and a mental process, which classifies the limitation as an abstract idea.
Claim 14 recites the limitation - determine the cluster-specific-phasing coefficient and the cluster-specific-pre-phasing coefficient after a sequencing run. This limitation specifies when the determining takes place in the judicial exception of claim 12. The refined determination indicated by this limitation still represents a judicial expectation.
Claim 15 recites the limitation - determine, for a set of clusters of oligonucleotides, one or more of a multi-cluster-phasing coefficient for estimated phasing or a multi-cluster-pre-phasing coefficient for estimated pre-phasing; and adjust the signal based on one or more of the multi-cluster-phasing coefficient, the cluster-specific-phasing coefficient, the multi-cluster-pre-phasing coefficient, or the cluster-specific-pre-phasing coefficient. Based on the broadest reasonable interpretation, determining and adjusting could practically be done by the human mind. This draws the limitation to a mental process, which classifies the limitation as an abstract idea.
Claim 16 recites the limitation - determining, for the cluster of oligonucleotides, an additional cluster-specific-phasing coefficient corresponding to an additional nucleotide base for an additional previous cycle; and determining, for the cluster of oligonucleotides, an additional cluster-specific-pre-phasing coefficient corresponding to an additional nucleotide base for an additional subsequent cycle. Based on the broadest reasonable interpretation, the determinings could practically be done by the human mind. This draws the limitation to a mental process, which classifies the limitation as an abstract idea. Claim 16 also recites determining a cluster-specific-phasing correction based on the cluster-specific-phasing coefficient, the additional cluster-specific-phasing coefficient, the cluster-specific-pre-phasing coefficient, and the additional cluster-specific-pre-phasing coefficient. Based on the broadest reasonable interpretation, determining a function could include equations and could practically be done by the human mind. This draws the limitation to a mathematical concept and a mental process, which classifies the limitation as an abstract idea.
Claim 17 recites the limitation - generating a previous-cycle weight estimating a phasing effect of the nucleotide base for the previous cycle based on the cluster-specific-phasing coefficient; generating a subsequent-cycle weight estimating a pre-phasing effect of the nucleotide base for the subsequent cycle based on the cluster-specific-pre-phasing coefficient; and generating a current-cycle weight estimating the phasing effect and the pre-phasing effect for the cycle based on the cluster-specific-phasing coefficient and the cluster-specific-pre-phasing coefficient. Based on the broadest reasonable interpretation, generating weights could practically be done by the human mind. This draws the limitation to a mental process, which classifies the limitation as an abstract idea. Claim 17 also recites determining a cluster-specific-phasing correction based on the previous-cycle weight, the subsequent-cycle weight, and the current-cycle weight; and applying the cluster-specific-phasing correction to the signal. Based on the broadest reasonable interpretation, determining and applying functions could include equations and could practically be done by the human mind. This draws the limitation to a mathematical concept and a mental process, which classifies the limitation as an abstract idea.
Claim 18 recites the limitation - identifying, for a cluster of oligonucleotides, a read position following an error-inducing sequence within one or more nucleotide-fragment reads; detecting a signal from labeled nucleotide bases within the cluster of oligonucleotides during a cycle corresponding to the read position; and determining a nucleotide-base call for the read position corresponding to the cluster of oligonucleotides based on the adjusted signal. Based on the broadest reasonable interpretation, identifying, detecting and determining could practically be done by the human mind. This draws the limitation to a mental process, which classifies the limitation as an abstract idea. Claim 18 also recites determining, for the cluster of oligonucleotides, a cluster-specific-phasing correction to correct the signal for phasing and pre-phasing; and adjusting the signal based on the cluster-specific-phasing correction. Based on the broadest reasonable interpretation, determining functions and adjusting based on functions could include equations and could practically be done by the human mind. This draws the limitation to a mathematical concept and a mental process, which classifies the limitation as an abstract idea.
Claim 19 recites wherein the error-inducing sequence comprises a sequence of one or more repeated nucleotide bases or a direction-specific sequence motif. This limits the Judicial exception of claim 18, which still represents a judicial exception.
Claim 20 recites the limitation - determining, for the cluster of oligonucleotides, a cluster-specific-phasing coefficient corresponding to a nucleotide base for a previous cycle immediately preceding the cycle and a cluster-specific-pre-phasing coefficient corresponding to a nucleotide base for a subsequent cycle immediately following the cycle. Based on the broadest reasonable interpretation, determining could practically be done by the human mind. This draws the limitation to a mental process, which classifies the limitation as an abstract idea. Claim 20 also recites determining the cluster-specific-phasing correction based on the cluster-specific-phasing coefficient and the cluster-specific-pre-phasing coefficient. Based on the broadest reasonable interpretation, determining could include equations and could practically be done by the human mind. This draws the limitation to a mathematical concept and a mental process, which classifies the limitation as an abstract idea.
These limitations recite concepts of determining, identifying, generating, adjusting, and applying information and functions that are so generically recited that they can be practically performed in the human mind as claimed, which falls under the “Mental processes” and “Mathematical concepts” grouping of abstract ideas. As such, claims 1-20 recite an abstract idea (Step 2A, Prong 1: YES).
Claims found to recite a judicial exception under Step 2A, Prong 1 are then further analyzed to determine if the claims as a whole integrate the recited judicial exception into a practical application or not (Step 2A, Prong 2). These judicial exceptions are not integrated into a practical application because the claims do not recite an additional element that reflects an improvement to technology (MPEP § 2106.04(d)(1)). Rather, the claims provide insignificant extra-solution activity (MPEP § 2106.05(g)) and provide mere instructions to apply a judicial exception (MPEP § 2106.05(f)). Specifically, the claims recite the following additional elements:
Claim 1 recites a non-transitory computer readable storage medium comprising instructions executed by at least one processor.
Claim 11 recites utilizing a processor of a sequencing device.
Claim 12 recites a system comprising: at least one processor; and a non-transitory computer readable medium comprising instructions that, when executed by the at least one processor.
Claim 13 recites a sequencing machine of the system.
There are no limitations that indicate that the claimed determining, identifying, generating, adjusting, and applying information and functions require anything other than generic computing systems. As such, these limitations equate to mere instructions to implement the abstract idea on a generic computer that the courts have stated does not render an abstract idea eligible in Alice Corp., 573 U.S. at 223, 110 USPQ2d at 1983. See also 573 U.S. at 224, 110 USPQ2d at 1984. There is no indication that these steps are affected by the judicial exception in any way and thus do not integrate the recited judicial exception into a practical application. As such, claims 1-20 are directed to an abstract idea (Step 2A, Prong 2: NO).
Claims found to be directed to a judicial exception are then further evaluated to determine if the claims recite an inventive concept that provides significantly more than the judicial exception itself (Step 2B). The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the claims recite conventional additional elements that equate to mere instructions to apply the recited exception in a generic way or in a generic computing environment. The claims also recite conventional additional elements that represent insignificant extra-solution activities. Claims that amount to nothing more than an instruction to apply the abstract idea using a generic computer do not render an abstract idea or natural law eligible. MPEP 2106.05(f) discloses that mere instructions to apply the judicial exception cannot provide an inventive concept to the claims. As specified in MPEP 2106.05(g), extra-solution activities can be understood as incidental to the primary process or product that are merely a nominal or tangential addition to the claim. Insignificant extra-solution activities include mere data gathering, selecting a particular data source or type of data to be manipulated, and displaying information. Additionally, Wong et al. (2019, ACM Computing Surveys, Vol. 52, No. 5: 1-30) teach the use of computers, which inherently include computer readable media, and sequencing machines to process sequencing data, including related to improve data quality, is well understood, routine, and conventional (Page 6, Paragraph 1: A shortcoming of SOLiD is the high cost of its computational infrastructure; Page 15, Paragraph 1: a comprehensive parallel analysis toolkit for sequencing data, such as GATK, can be used to locally realign the sequences around INDELs and recalibrate the base quality scores using empirical error rates).
The additional elements do not comprise an inventive concept when considered individually or as an ordered combination that transforms the claimed judicial exception into a patent-eligible application of the judicial exception. Therefore, the claims do not amount to significantly more than the judicial exception itself (Step 2B: No). As such, Claims 1-20 are not patent eligible.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claims 1-4, 7-12, 14-16, and 18-20 are rejected under 35 U.S.C. 103 as being unpatentable over Belitz et al. (US 20180274023 A1). Italicized text from reference art. Text cited from the specification of the instant application utilizes the paragraph numbers of the published disclosure (US 20230343415 A1).
Regarding Claims 1 and 18, Belitz et al. teach (Claim 1.i) identify, for a cluster of oligonucleotides, a read position following an error-inducing sequence within one or more nucleotide-fragment reads (Page 4, Paragraph 0042: calculating a set of predictor values for the base call; using the predictor values to look up a quality score in a quality table. suitable predictor values can include, for example: motif accumulation; approximate homopolymer; penultimate chastity; signal overlap with background (SOWB). Any suitable combination of the above predictor values can be used in the methods presented herein; Page 5, Paragraph 0051: As used herein, “approximate homopolymer” refers to a calculation of the number of consecutive identical base calls preceding a base call). This this interpreted as describing associating specific sequences with error inducing sequences, such as homopolymers. Base calls are associated with particular positions (Paragraph 0036: the signal color is different for each of the four different images, thereby producing a cycle of four color images that corresponds to the four possible nucleotides present at a particular position in the nucleic acid). Additionally, detecting a specific sequence within a DNA sequence is inherent to the sequencing process (Page 1, Paragraph 0003: A valuable technique for detecting specific nucleic acid sequences in a biological sample is nucleic acid sequencing). Belitz et al. also teach (Claim 1.ii) detect a signal from labeled nucleotide bases within the cluster during a cycle corresponding to the read position (Page 1, Paragraph 0008: performing a plurality of cycles of a sequencing by synthesis reaction such that, at each cycle, a signal is generated indicative of incorporation of the same nucleotide into a plurality of identical polynucleotides). The detection of signals is how the base calls are generated (i.e. each determined base of a sequence represents a signal detected). Belitz et al. also teach (Claim 1.iii) determine, for the cluster of oligonucleotides, a cluster-specific-phasing correction to correct the signal for phasing and pre-phasing (Page 10, Paragraph 0098: the method comprises a first order phasing correction for a given cycle, where I represents intensity and X and Y represent the phasing and prephasing weights calculated for this cycle). A first order phasing correction is interpreted as equivalent to a cluster-specific-phasing correction. This can be considered cluster specific because this is applied to DNA fused to plates (sequencing by synthesis) and specific clusters of DNA (Page 11, Paragraph 100: in some embodiments, a separate phasing correction is calculated for every individual lane of an imaged surface, such as an individual flow cell lane). Cluster-specific-phasing correction is defined by the specification as a process or function that, when applied, adjusts a signal from labeled nucleotides bases within a particular cluster of oligonucleotides to correct for estimated phasing or pre-phasing (Paragraph 0043). Belitz et al. teach (Claim 1.iv) adjust the signal based on the cluster-specific-phasing correction (Page 10, Paragraph 0093: a phasing estimation is performed to adjust the observed intensities). An intensity is synonymous with a signal as applied to using a light to determine a base call. An adjusted signal is also produced by the correction described in Claim 1.iii. Belitz et al. teach (Claim 1.v) determine a base call for the read position corresponding to the cluster based on the adjusted signal (Page 2, Paragraph 0009: a program for generating a phasing-corrected intensity value, the program comprising instructions for: performing a plurality of cycles of a sequencing by synthesis reaction such that, at each cycle, a signal is generated indicative of incorporation of the same nucleotide into a plurality of identical polynucleotides). The nucleotide bases are determined by intensity values including those that get corrected. A determined nucleotide is synonymous with a base call. Additionally, Belitz et al. teach methods are performed by a non-transitory computer readable storage medium comprising instructions that are executed by a processor (Paragraph 241: the system controller includes one or more processors/modules configured to process and, optionally, analyze data in accordance with one or more methods set forth herein (e.g., instructions stored on a tangible and/or non-transitory computer readable storage medium, excluding signals)). Claim 18 recites the limitations of claim 1 directed to a method.
Regarding Claim 2, Belitz et al. teach wherein the error-inducing sequence comprises a sequence of one or more repeated nucleotide bases, a sequence motif, or a trigger sequence identified by a sequence recognition model (Page 4, Paragraph 0042: calculating a set of predictor values for the base call; using the predictor values to look up a quality score in a quality table. suitable predictor values can include, for example: motif accumulation; approximate homopolymer. Any suitable combination of the above predictor values can be used in the methods presented herein; Page 5, Paragraph 0051: As used herein, “approximate homopolymer” refers to a calculation of the number of consecutive identical base calls preceding a base call).
Regarding Claim 3, Belitz et al. teach wherein the sequence of one or more repeated nucleotide bases or the sequence motif comprise a homopolymer of a same nucleotide base, a near-homopolymer, a guanine quadruplex, a variable number tandem repeat (VNTR), a dinucleotide-repeat sequence, a trinucleotide-repeat sequence, an inverted-repeat sequence, a minisatellite sequence, a microsatellite sequence, or a palindromic sequence (Page 4, Paragraph 0042: calculating a set of predictor values for the base call; using the predictor values to look up a quality score in a quality table. suitable predictor values can include, for example: approximate homopolymer. Any suitable combination of the above predictor values can be used in the methods presented herein; Page 5, Paragraph 0051: As used herein, “approximate homopolymer” refers to a calculation of the number of consecutive identical base calls preceding a base call).
Regarding Claim 4, Belitz et al. teach determining, for the cluster, a cluster-specific-phasing coefficient corresponding to a nucleotide base for a previous cycle and a cluster-specific-pre-phasing coefficient corresponding to a nucleotide base for a subsequent cycle; and determining the cluster-specific-phasing correction based on the coefficients (Page 10, Paragraph 0098: the method comprises a first order phasing correction for a given cycle, where I represents intensity and X and Y represent the phasing and prephasing weights calculated for this cycle). The phasing correction is synonymous with cluster-specific-phasing correction (see Claim 1.iii). The terms phasing and prephasing weights are considered to be values equivalent to cluster-specific-phasing coefficient and the cluster-specific-pre-phasing coefficient, respectively. A cluster-specific-phasing coefficient and a cluster-specific-pre-phasing coefficient are defined by the specification as a factor or value that estimates or measures cluster-specific phasing on a signal for a cluster and a factor or value that estimates or measures cluster-specific pre-phasing on a signal for a cluster, respectively (Paragraphs 0045-0046).
Regarding Claim 7, Belitz et al. teach (Claim 7.i) determining, for the cluster, a set of cluster-specific-phasing coefficients corresponding to a set of nucleotide bases for a set of previous cycles (Page 11, Paragraph 0103: the method comprises a second order phasing correction as defined by the following: I(cycle) = −a*I(cycle−2) −A*I(cycle−1) +I(cycle) −B*I(cycle+1) −b*I(cycle+2); where I represents intensity and a, A, B, and b represent the first and second order terms to the phasing correction). In the equation above, a and A are interpreted as a set of cluster-specific-phasing coefficients based on previous cycles (i.e. cycle -1, cycle -2). Belitz et al. also teach (Claim 7.ii) determining, for the cluster, a set of cluster-specific-pre-phasing coefficients corresponding to a set of nucleotide bases for a set of subsequent cycles (Page 11, Paragraph 0103: the method comprises a second order phasing correction as defined by the following: I(cycle) = −a*I(cycle−2) −A*I(cycle−1) +I(cycle) −B*I(cycle+1) −b*I(cycle+2); where I represents intensity and a, A, B, and b represent the first and second order terms to the phasing correction). In the equation above, b and B are interpreted as a set of cluster-specific-phasing coefficients based on subsequent cycles (i.e. cycle +1, cycle +2). Belitz et al. teach (Claim 7.iii) determining the cluster-specific-phasing correction based on the set of cluster-specific-phasing coefficients and the set of cluster-specific-pre-phasing coefficients (Page 11, Paragraph 0103: the method comprises a second order phasing correction as defined by the following: I(cycle) = −a*I(cycle−2) −A*I(cycle−1) +I(cycle) −B*I(cycle+1) −b*I(cycle+2); where I represents intensity and a, A, B, and b represent the first and second order terms to the phasing correction). The entire equation with the phasing and prephasing coefficients is interpreted as the determined cluster-specific-phasing correction, which is defined to be a function (see Regarding Claim 1).
Regarding Claim 8, Belitz et al. teach determine, for a set of clusters, a multi-cluster-phasing correction to correct signals from the set of clusters for estimated phasing and estimated pre-phasing; and adjust the signal based on the cluster-specific-phasing correction or the multi-cluster-phasing correction (Page 11, Paragraph 102: FIG. 1 A shows raw intensities for a particular tile and a particular cycle in a two-channel system where the C nucleotide is represented by signal in channel 1 only, A nucleotide is represented by signal in channel 2 only, T nucleotide is represented by signal in both channels 1 and 2, and G nucleotide is “dark.” FIG. 1B shows phasing corrected intensities of the same data using the above-described phasing correction). Each channel within a 2 channel system is interpreted as a set of nucleotides that the correction is applied to (i.e. a multi-cluster-phasing correction is interpreted a correction based on multiple clusters, in this case multiple channels). See above for Belitz et al. teachings of estimated phasing and estimated pre-phasing (i.e. coefficients). No limiting definition of a multi-cluster-phasing correction was found in the specification.
Regarding Claim 9, Belitz et al. teach determine, for the cluster and a subsequent read position, a different cluster-specific-phasing correction to correct a signal for a subsequent cycle from the cluster of oligonucleotides for phasing and pre-phasing of the signal for the subsequent cycle (Page 11, Paragraph 0099: In some embodiments, a phasing correction is calculated at every cycle during a sequencing run). If a phasing correction is calculated at every cycle using previous and subsequent cycles, there will be many corrections calculated which is interpreted to include a different cluster-specific-phasing correction. A subsequent read position is interpreted as equivalent to a read position following an error-inducing sequence and no limiting definition of a subsequent read position was found in the specification.
Regarding Claim 10, Belitz et al. teach (Claim 10.i) identify, for an additional cluster, a different read position preceding the error-inducing sequence within a different nucleotide-fragment read. It is explained how Belitz et al. teach identifying a sequence subsequent to an error inducing sequence in Claim 1.i. It is explained how the teachings apply to multiple clusters in Claim 8. Belitz et al. also teach the corrections for phasing and prephasing in regards to features, which can be used to identify any part of the sequence, which make the interpretation as the sequence before or after obvious (Page 3, Paragraph 0032: As used herein, a “feature” is an area of interest within a specimen or field of view; Page 10, Paragraph 0092: As used herein, “phased”, “phasing” and like terms refer to the situation where a molecule at a feature falls at least one base behind other molecules at the same feature as a result of the feature being sequenced at a particular cycle. As used herein, “pre-phased”, “pre-phasing” and like terms refer to the situation where a molecule at a feature jumps at least one base ahead of other molecules at the same feature as a result of the feature being sequenced at a particular cycle). This is also interpreted as indicating the sequence could be a different nucleotide-fragment read than considered in claim 1. Belitz et al. also teach (Claim 10.ii) detect an additional signal from labeled nucleotide bases within the additional cluster of oligonucleotides during a cycle corresponding to the different read position; and adjust the additional signal based on a multi-cluster-phasing correction without a cluster-specific-phasing correction for the additional cluster of oligonucleotides. A signal is detected from each base that is sequenced as part of the sequencing process. Regarding claim 8 teaches incorporating signals from multiple clusters (i.e. multiple channel sequencing) as part of the phasing correction that is interpreted as a multi cluster correction, which can be done at each cycle (see regarding Claim 9). See claim 10.i for why it would be obvious to apply the methods to any part of the sequence within a sequence as long it is related to a particular type of sequence, such as error inducing.
Regarding Claim 11, Belitz et al. teach determine the cluster-specific-phasing correction utilizing a processor of a sequencing device (Page 2, Paragraph 0009: The systems can comprise: a processor; a storage capacity; and a program for generating a phasing-corrected intensity value). Belitz et al. teaches determine the cluster-specific-phasing correction (See regarding Claim 1) using a system that contains a processor that is interpreted as a sequencing device. No limiting definition of a sequencing device was found within the specification (i.e. a sequencing device could be a device that only analyzes raw sequence data).
Regarding Claim 12, Belitz et al. (Claim 12.i) identify, for a cluster of oligonucleotides, a read position following an error-inducing sequence within one or more nucleotide-fragment reads (Page 4, Paragraph 0042: calculating a set of predictor values for the base call; using the predictor values to look up a quality score in a quality table. suitable predictor values can include, for example: motif accumulation; approximate homopolymer; penultimate chastity; signal overlap with background (SOWB). Any suitable combination of the above predictor values can be used in the methods presented herein; Page 5, Paragraph 0051: As used herein, “approximate homopolymer” refers to a calculation of the number of consecutive identical base calls preceding a base call). This this interpreted as describing associating specific sequences with error inducing sequences, such as homopolymers. Base calls are associated with particular positions (Paragraph 0036: the signal color is different for each of the four different images, thereby producing a cycle of four color images that corresponds to the four possible nucleotides present at a particular position in the nucleic acid). Additionally, detecting a specific sequence within a DNA sequence is inherent to the sequencing process (Page 1, Paragraph 0003: A valuable technique for detecting specific nucleic acid sequences in a biological sample is nucleic acid sequencing). Belitz et al. also teach (Claim 12.ii) detect a signal from labeled nucleotide bases within the cluster of oligonucleotides during a cycle corresponding to the read position (Page 1, Paragraph 0008: performing a plurality of cycles of a sequencing by synthesis reaction such that, at each cycle, a signal is generated indicative of incorporation of the same nucleotide into a plurality of identical polynucleotides). The detection of signals is how the base calls are generated. Belitz et al. also teach (Claim 12.iii) determine, for the cluster, a cluster-specific-phasing coefficient corresponding to a nucleotide base for a previous cycle and a cluster-specific-pre-phasing coefficient corresponding to a nucleotide base for a subsequent cycle; and adjust the signal based on the coefficients; (Page 10, Paragraph 0098: the method comprises a first order phasing correction for a given cycle, where I represents intensity and X and Y represent the phasing and prephasing weights calculated for this cycle). The phasing correction is synonymous with cluster-specific-phasing correction (see Claim 1.iii). The terms phasing and prephasing weights are considered to be values equivalent to cluster-specific-phasing coefficient and the cluster-specific-pre-phasing coefficient, respectively. The calculated I based on X and Y is interpreted as the adjusted signal based on the coefficients. Belitz et al. also teach (Claim 12.iv) determine a base call for the read position corresponding to the cluster based on the adjusted signal (Page 2, Paragraph 0009: a program for generating a phasing-corrected intensity value, the program comprising instructions for: performing a plurality of cycles of a sequencing by synthesis reaction such that, at each cycle, a signal is generated indicative of incorporation of the same nucleotide into a plurality of identical polynucleotides). The nucleotide bases are determined by intensity values including those that get corrected. Additionally, Belitz et al. teach the methods are performed by a system containing a non-transitory computer readable storage medium comprising instructions that are executed by at least one processor (Paragraph 241: the system controller includes one or more processors/modules configured to process and, optionally, analyze data in accordance with one or more methods set forth herein (e.g., instructions stored on a tangible and/or non-transitory computer readable storage medium, excluding signals)).
Regarding Claim 14, Belitz et al. teach determine the cluster-specific-phasing coefficient and the cluster-specific-pre-phasing coefficient after a sequencing run (Page 11, Paragraph 0099: In some embodiments, a phasing correction is calculated at every cycle during a sequencing run). Calculated at every cycle is interpreted is included for the last run which means it would occur after the run. No limiting definition for a sequencing run was found in the specification. See above for how Belitz et al. teach the phasing correction incorporates the phasing and prephasing coefficients.
Regarding Claim 15, Belitz et al. teach determine, for a set of clusters, a multi-cluster-phasing coefficient for estimated phasing or a multi-cluster-pre-phasing coefficient for estimated pre-phasing; and adjust the signal based on one or more of the multi-cluster-phasing coefficient, the cluster-specific-phasing coefficient, the multi-cluster-pre-phasing coefficient, or the cluster-specific-pre-phasing coefficient (Page 11, Paragraph 102: FIG. 1 A shows raw intensities for a particular tile and a particular cycle in a two-channel system where the C nucleotide is represented by signal in channel 1 only, A nucleotide is represented by signal in channel 2 only, T nucleotide is represented by signal in both channels 1 and 2, and G nucleotide is “dark.” FIG. 1B shows phasing corrected intensities of the same data using the above-described phasing correction). Each channel within a 2 channel system is interpreted as a set of nucleotides that the correction is applied to (i.e. a multi-cluster-phasing correction is interpreted a correction based on multiple clusters, in this case multiple channels). Calculating the coefficients would therefore utilize multiple clusters for phasing and prephasing (i.e. generates multi-cluster-phasing coefficient and multi-cluster-pre-phasing coefficient). See above for Belitz et al. teachings of estimated phasing and estimated pre-phasing (i.e. coefficients) and how these coefficients are used to adjust the signal. The specification does not explicitly define multi-cluster-phasing coefficient or multi-cluster-pre-phasing coefficient.
Regarding Claim 16, Belitz et al. teach (Claim 16.i) determining, for the cluster, an additional cluster-specific-phasing coefficient corresponding to an additional nucleotide base for an additional previous cycle (Page 11, Paragraph 0103: a second order empirical phasing correction can be calculated. For example, in some embodiments, the method comprises a second order phasing correction as defined by the following: I(cycle) = −a*I(cycle−2) −A*I(cycle−1) +I(cycle) −B*I(cycle+1) −b*I(cycle+2)). The equation adds another phasing coefficient from a base from another cycle (a*I(cycle−2)) compared to the original phasing coefficient used previously (A*I(cycle−1)). Belitz et al. also teach (Claim 16.ii) determining, for the cluster of oligonucleotides, an additional cluster-specific-pre-phasing coefficient corresponding to an additional nucleotide base for an additional subsequent cycle (Page 11, Paragraph 0103: a second order empirical phasing correction can be calculated. For example, in some embodiments, the method comprises a second order phasing correction as defined by the following: I(cycle) = −a*I(cycle−2) −A*I(cycle−1) +I(cycle) −B*I(cycle+1) −b*I(cycle+2)). The equation adds another prephasing coefficient from a base from another cycle (b*I(cycle+2)) compared to the original phasing coefficient used previously (B*I(cycle+1)). Belitz et al. also teach (Claim 16.iii) determining a cluster-specific-phasing correction based on the cluster-specific-phasing coefficient, the additional cluster-specific-phasing coefficient, the cluster-specific-pre-phasing coefficient, and the additional cluster-specific-pre-phasing coefficient (Page 11, Paragraph 0103: a second order empirical phasing correction can be calculated. For example, in some embodiments, the method comprises a second order phasing correction as defined by the following: I(cycle) = −a*I(cycle−2) −A*I(cycle−1) +I(cycle) −B*I(cycle+1) −b*I(cycle+2)). The entire equation is interpreted as the correction determined and contain cluster-specific-phasing coefficient (A*I(cycle−1)), the additional cluster-specific-phasing coefficient (a*I(cycle−2)), the cluster-specific-pre-phasing coefficient (B*I(cycle+1)), and the additional cluster-specific-pre-phasing coefficient (b*I(cycle+2)).
Regarding Claim 19, Belitz et al. teach the error-inducing sequence comprises a sequence of one or more repeated nucleotide bases or a direction-specific sequence motif (Page 4, Paragraph 0042: calculating a set of predictor values for the base call; using the predictor values to look up a quality score in a quality table. suitable predictor values can include, for example: motif accumulation; endiness; approximate homopolymer. Any suitable combination of the above predictor values can be used in the methods presented herein; Page 5, Paragraph 0051: As used herein, “approximate homopolymer” refers to a calculation of the number of consecutive identical base calls preceding a base call). The predictor in this case is interpreted as related to identifying an error inducing sequence.
Regarding Claim 20, (Claim 20.i) Belitz et al. teach determining, for the cluster of oligonucleotides, a cluster-specific-phasing coefficient corresponding to a nucleotide base for a previous cycle immediately preceding the cycle and a cluster-specific-pre-phasing coefficient corresponding to a nucleotide base for a subsequent cycle immediately following the cycle; and determining the cluster-specific-phasing correction based on the cluster-specific-phasing coefficient and the cluster-specific-pre-phasing coefficient (Page 10, Paragraph 0098: the method comprises a first order phasing correction for a given cycle, where I represents intensity and X and Y represent the phasing and prephasing weights calculated for this cycle; I(cycle) = I(cycle) −X*I(cycle−1) −Y*I(cycle+1)). The phasing correction is synonymous with cluster-specific-phasing correction (see Claim 1.iii). The terms phasing and prephasing weights are considered to be values equivalent to cluster-specific-phasing coefficient and the cluster-specific-pre-phasing coefficient, respectively. Cycle−1 is interpreted as a previous cycle immediately preceding the cycle and cycle+1 is interpreted as a subsequent cycle immediately following the cycle.
The teachings of Belitz et al. relied on obviousness in some instances, which is the reason for the use of one reference in a 35 USC 103 rejection over a 35 USC 102 Rejection.
.
Claims 1-20 are rejected under 35 U.S.C. 103 as being unpatentable over Belitz et al., as applied to Claims 1-4, 7-12, 14-16, and 18-20 above, in view of Kircher et al. (2009, Genome Biology, Vol.10, No. 8: 1-9). Italicized text from reference art. Text cited from the specification of the instant application utilizes the paragraph numbers of the published disclosure (US 20230343415 A1).
Regarding Claims 1-4, 7-12, 14-16, and 18-20, the limitations are taught by Belitz et al. as indicated above.
Regarding Claim 5, Belitz et al. teach (Claim 5.i) generating a previous-cycle weight estimating a phasing effect of the nucleotide base for the previous cycle based on the cluster-specific-phasing coefficient (Page 10, Paragraph 0098: the method comprises a first order phasing correction for a given cycle, where I represents intensity and X and Y represent the phasing and prephasing weights calculated for this cycle). Belitz et al. also teach (Claim 5.ii) generating a subsequent-cycle weight estimating a pre-phasing effect of the nucleotide base for the subsequent cycle based on the cluster-specific-pre-phasing coefficient (Page 10, Paragraph 0098: the method comprises a first order phasing correction for a given cycle, where I represents intensity and X and Y represent the phasing and prephasing weights calculated for this cycle). In Claim 5.i and 5.ii the weights and coefficients are interpreted to be equivalent. Additionally, Kircher et al. teach applying a machine learning model to the phasing and prephasing values which generate weights/coefficients applied to these terms as part of the model training process (see Kircher et al. teaching of Claim 5.iii below).
Regarding Claim 6, Belitz et al. teach determine the cluster-specific-phasing correction further based on a signal intensity corresponding to the previous cycle, a signal intensity corresponding to the cycle, and a signal intensity corresponding to the subsequent cycle (Page 10, Paragraph 0098: I(cycle) = I(cycle) − X*I(cycle−1) − Y*I(cycle+1)). I(cycle) is the signal of the current cycle. I(cycle−1) is the phased cycle. I(cycle+1) is the prephased cycle.
Regarding Claim 13, Belitz et al. teach determine, on a sequencing machine of the system, the cluster-specific-phasing coefficient and the cluster-specific-pre-phasing coefficient utilizing a Linear Equalizer, Decision Feedback Equalizer, Maximum Likelihood Sequence Estimator, forward-backward model, or machine learning model (Page 2, Paragraph 0009: The systems can comprise: a processor; a storage capacity; and a program for generating a phasing-corrected intensity value; Page 10, Paragraph 0098: For example, it is possible to numerically optimize via a pattern search over X and Y to maximize the mean chastity). Belitz et al. teach determine the cluster-specific-phasing correction (See regarding Claim 1) using a system that contains a processor that is interpreted as a sequencing device. No limiting definition of a sequencing device was found within the specification. Optimizing the values of X and Y as part of calculating the correction describes a machine learning process.
Regarding Claim 17, Belitz et al. teach (Claim 17.i) generating a previous-cycle weight estimating a phasing effect of the nucleotide base for the previous cycle based on the cluster-specific-phasing coefficient (Page 10, Paragraph 0098: the method comprises a first order phasing correction for a given cycle, where I represents intensity and X and Y represent the phasing and prephasing weights calculated for this cycle). Belitz et al. also teach (Claim 17.ii) generating a subsequent-cycle weight estimating a pre-phasing effect of the nucleotide base for the subsequent cycle based on the cluster-specific-pre-phasing coefficient (Page 10, Paragraph 0098: the method comprises a first order phasing correction for a given cycle, where I represents intensity and X and Y represent the phasing and prephasing weights calculated for this cycle). In Claim 5.i and 5.ii the wights and coefficients are interpreted to be equivalent. Additionally, Kircher et al. teach applying a machine learning model to the phasing and prephasing values which will generate weights/coefficients applied to these terms as part of the model training process (see Kircher et al. teaching of Claim 5.iii below). Belitz et al. also teach (Claim 17.iv) applying the cluster-specific-phasing correction to the signal (Page 10, Paragraph 0098: the method comprises a first order phasing correction for a given cycle, where I represents intensity and X and Y represent the phasing and prephasing weights calculated for this cycle). The equation represents the correction. The I (I = intensity = signal) that is calculated is interpreted as applying the cluster-specific-phasing correction to the signal.
Belitz et al. do not teach generating a current-cycle weight estimating the phasing effect and the pre-phasing effect for the cycle based on the cluster-specific-phasing coefficient and the cluster-specific-pre-phasing coefficient; and determining the cluster-specific-phasing correction based on the previous-cycle weight, the subsequent-cycle weight, and the current-cycle weight (Claim 5.iii). Belitz et al. also do not teach generating a current-cycle weight estimating the phasing effect and the pre-phasing effect for the cycle based on the cluster-specific-phasing coefficient and the cluster-specific-pre-phasing coefficient; and determining a cluster-specific-phasing correction based on the previous-cycle weight, the subsequent-cycle weight, and the current-cycle weight (Claim 17.iii)
Regarding Claim 5, Kircher et al. teach (Claim 5.iii) generating a current-cycle weight estimating the phasing effect and the pre-phasing effect for the cycle based on the cluster-specific-phasing coefficient and the cluster-specific-pre-phasing coefficient; and determining the cluster-specific-phasing correction based on the previous-cycle weight, the subsequent-cycle weight, and the current-cycle weight (Page 5, Column 1, Paragraphs 1-2: From this simulation, we conclude that most of the signal to be captured by a statistical learner is contained in the raw intensities of the previous, the current and the next cycle. We therefore implemented a base caller with SVM classifiers for each cycle that have the intensity values of the current cycle and its two neighbors as input). Parameters (i.e. weights) are applied to each terms as part of the model training (Page 5, Column 1, Paragraph 3: We verify the result of the training by using the test data set with the trained models and comparing the predicted labels with the ones obtained from the reference sequence). The application of the trained model as a whole is interpreted as the application of the correction .
Regarding Claim 13, Kircher et al. determine the cluster-specific-phasing coefficient and the cluster-specific-pre-phasing coefficient utilizing a Linear Equalizer, Decision Feedback Equalizer, Maximum Likelihood Sequence Estimator, forward-backward model, or machine learning model (Page 5, Column 1, Paragraphs 1-2: From this simulation, we conclude that most of the signal to be captured by a statistical learner is contained in the raw intensities of the previous, the current and the next cycle. We therefore implemented a base caller with SVM classifiers for each cycle that have the intensity values of the current cycle and its two neighbors as input). Parameters (i.e. weights) are applied to each terms as part of the model training (Page 5, Column 1, Paragraph 3: We verify the result of the training by using the test data set with the trained models and comparing the predicted labels with the ones obtained from the reference sequence).
Regarding Claim 17, Kircher et al. teach (Claim 17.iii) generating a current-cycle weight estimating the phasing effect and the pre-phasing effect for the cycle based on the cluster-specific-phasing coefficient and the cluster-specific-pre-phasing coefficient; and determining a cluster-specific-phasing correction based on the previous-cycle weight, the subsequent-cycle weight, and the current-cycle weight; and (Page 5, Column 1, Paragraphs 1-2: From this simulation, we conclude that most of the signal to be captured by a statistical learner is contained in the raw intensities of the previous, the current and the next cycle. We therefore implemented a base caller with SVM classifiers for each cycle that have the intensity values of the current cycle and its two neighbors as input). Parameters (i.e. weights) are applied to each terms as part of the model training (Page 5, Column 1, Paragraph 3: We verify the result of the training by using the test data set with the trained models and comparing the predicted labels with the ones obtained from the reference sequence). The application of the trained model as a whole is interpreted as the application of the correction .
It would have been obvious to one of ordinary skill in the art at the time of the effective filing date to combine Belitz et al. with Kircher et al. because Kircher et al. teach methods of correcting base calls based on phasing with a focus on technical performance (Page 2, Column 2, Paragraph 2: We present Ibis (Improved base identification system), an accurate, fast and easy-to-use base caller for the Illumina sequencing system, which aims to significantly reduce the error rate and increase the output of usable reads; Page 7, Column 1, Paragraph 2: To compare the computational resources required for base calling In other words, using Ibis one has to invest three times more time for base calling, for Rolexa 21-fold more time and for AltaCyclic 73-fold more time compared to Bustard). Technical performance is major focus of the established methods of Belitz et al. (Page 3 Paragraph 0031: presented herein are methods and systems which reduce the computational burden of processing data in the face of rapidly increasing data output. Some embodiments of the methods and systems presented herein can reduce the time, hardware, networking, and laboratory infrastructure requirements needed to produce usable sequence data; Page 10, Paragraph 0092: The methods and systems presented herein provide a computational solution which surprisingly yield improved base calling over extended sequencing cycles compared to traditional phasing correction methods). Therefore, it would have been obvious to someone of ordinary skill in the art at the time of the effective filing date to combine the methods from the references indicated above. Furthermore, one of ordinary skill in the art would predict that the methods taught by Kircher et al. could be readily added to the method of Belitz et al. with a reasonable expectation of success because both are within the same technical field - methods that utilize the same input data which consists of intensity values across multiple sequencing cycles to improve base calling. Accordingly, Claims 1-20 taken as a whole would have been prima facie obvious before the effective filing date and are rejected under 35 U.S.C. 103.
Double Patenting
No double patenting was identified.
Conclusion
No claims are allowed.
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/B.H.E./Examiner, Art Unit 1687
/Karlheinz R. Skowronek/Supervisory Patent Examiner, Art Unit 1687
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