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 .
Applicant’s amendments and arguments filed 12 May 2026 (hereinafter “Amendment”) are acknowledged and entered.
Withdrawn Rejections/Objections
The rejection of claim 27 under 35 U.S.C. §112(a) and 35 U.S.C. §112(b) in the Office action mailed 12 February 2026 is withdrawn in view of the amendments filed 12 May 2026.
The rejection of claims 1-8, 10-14, 17-22, and 24-27 under 35 U.S.C. §102 over Fernandez-Gomez in the Office action mailed 12 February 2026 is withdrawn in view of the amendments filed 12 May 2026.
The rejection of claims 9 and 23 under 35 U.S.C. §103 over Fernandez-Gomez in view of Koren in the Office action mailed 12 February 2026 is withdrawn in view of the amendments filed 12 May 2026.
The rejection of claim 15 under 35 U.S.C. §103 over Fernandez-Gomez in view of Chung in the Office action mailed 12 February 2026 is withdrawn in view of the amendments filed 12 May 2026.
The rejection of claim 16 under 35 U.S.C. §103 over Fernandez-Gomez in view of Chung and in further view of Greenfield and Yu in the Office action mailed 12 February 2026 is withdrawn in view of the amendments filed 12 May 2026.
Rejections and/or objections not reiterated from previous office actions are hereby withdrawn. The following rejections and/or objections are either reiterated or newly applied. They constitute the complete set presently being applied to the instant application.
Claim Status
Claims 1, 15, 17, 19-20, and 27 were amended by Amendment filed 12 May 2026.
Claims 1-27 are currently pending and under exam herein.
Claims 1-27 are rejected.
Priority
The instant application claims benefit to provisional application No. 63/226,707 filed on 28 July 2021. At this point in examination, the effective filing date of claims 1-27 is 28 July 2021.
Response to Arguments - 35 USC § 112
Applicant’s arguments, see p. 15 ll. 14-20, filed 12 May 2026, with respect to claim 27 have been fully considered and are persuasive. The 112(a) and 112(b) rejections of claim 27 have been withdrawn.
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 1-27 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (abstract ideas) without significantly more. Under MPEP § 2106, subject matter is patent eligible when the claimed invention is to one of the four statutory categories of invention [Step 1], and the claim is not directed to a judicial exception [Step 2A] unless the claim as a whole includes additional limitations amounting to significantly more than the exception [Step 2B].
Step 1
Claims 1-27 describe inventions that are to one of the statutory categories. In Step 1, a claim must fall within one of the four enumerated categories of statutory subject matter (process, machine, manufacture, or composition of matter); a claim falling outside these categories is ineligible without further analysis [MPEP § 2106.03]. Claims 1-16 are properly to one of the four statutory categories because the claimed invention is a method, which falls into the process category [Step 1: Yes]. Claims 17-27 are properly to one of the four statutory categories because the claimed invention is a system or a non-transitory computer readable storage medium impressed with computer program instructions, which fall into the manufacture category [Step 1: Yes].
Step 2A
Under Step 2A, a claim is directed to a judicial exception if, under the broadest reasonable interpretation, it recites an abstract idea, law of nature, or natural phenomena [Prong One] without the claim as a whole integrating the exception into a practical application [Prong Two]. Abstract ideas include mathematical concepts, mental processes, and certain methods of organizing human activity. Mathematical concepts encompass mathematical relationships, formulas, equations, and mathematical calculations [MPEP § 2106.04(a)(2)(I)]. Mental processes involve concepts that can be performed in the human mind or by a human with the aid of pen and paper, such as observations, evaluations, judgments, or opinions [MPEP § 2106.04(a)(2)(III)]. Certain methods of organizing human activity include fundamental economic principles, commercial or legal interactions, and managing personal behavior or relationships [MPEP § 2106.04(a)(2)(II)]. Laws of nature and natural phenomena, include naturally occurring principles/relations and nature-based products that are naturally occurring or that do not have markedly different characteristics compared to what occurs in nature [MPEP § 2106.04(b)-(c)].
Prong One
A claim recites a judicial exception when it sets forth or describes a law of nature, natural phenomenon, or abstract idea. Claims 1-27 recite abstract ideas that fall into the groupings of mathematical concepts and mental processes.
Independent Claims
Claim 1 recites the following limitations, which describe abstract ideas within the mathematical concepts and/or mental processes groupings:
identifying a second range, such that at least a threshold percentage of the plurality of sensor data are within the second range;
mapping at least a subset of the plurality of sensor data, that are within the second range, to a third range, thereby generating a plurality of normalized sensor data;
processing the plurality of normalized sensor data in a base caller, to call, for the plurality of normalized sensor data, one or more corresponding bases with a plurality of quality scores; and
remapping each of at least a subset of the plurality of quality scores to generate at least a remapped quality score for the one or more corresponding bases.
The limitation of identifying a second range based on a threshold percentage entails statistical operations, which are mathematical concepts, and could be performed mentally or with pen and paper for small datasets, which constitutes a mental process. The limitation of mapping a subset of the second range to normalize the data uses mathematical transformations, which is a mathematical concept, and could be performed mentally or with a pen and paper for small datasets, which constitutes a mental process. The limitation of processing the normalized data in a base caller describes applying mathematical formulas and calculations, which fall under the mathematical concepts grouping of abstract ideas. The limitation of remapping a subset of the quality scores uses mathematical transformations, which is a mathematical concept, and could be performed mentally or with a pen and paper for small datasets, which constitutes a mental process.
Claim 17 recites the following limitations, which describe abstract ideas within the mathematical concepts and/or mental processes groupings:
identifying a second range that includes at least a threshold percentage of the plurality of intensity values;
mapping the threshold percentage of the plurality of intensity values to a third range that is different from the second range;
subsequent to the mapping, processing the threshold percentage of the plurality of intensity values, to generate likelihoods of the unknown analyte being an A, C, T, or G; and
remapping each of at least a subset of the likelihoods of the unknown analyte to generate at least a remapped likelihood of the unknown analyte.
These limitations involve mathematical operations – identifying a range via threshold percentage (statistical quantile determination), mapping/remapping values (affine transformation or formula), and processing to generate likelihoods (probability calculations) – which fall under the mathematical concepts grouping of abstract ideas. For small datasets, range selection, mapping, probability assessment, and remapping could be performed by human judgement, or by a human using pen and paper, which falls under the mental processes grouping of abstract ideas.
Claim 27 recites the following limitations, which describe abstract ideas within the mathematical concepts and/or mental processes groupings:
remap the original intensity emissions to generate remapped intensity emissions, such that a remapped intensity emission has a different intensity value relative to an original intensity emission;
generate base calls and a plurality of quality scores for the set of analytes by processing the remapped intensity emissions, to generate base calls for the set of analytes; and
remap each of at least a subset of the plurality of quality scores to generate remapped quality scores for the base calls.
These limitations involve mathematical transformations – remapping intensities/scores (scaling, affine transformation or formula, or clipping via formulas) and processing to generate base calls (convolutions, activations, probability calculations) – which are mathematical calculations/relationships that fall under the mathematical concepts grouping of abstract ideas. For small datasets, remapping values and inferring bases could be performed by human judgement or evaluation, or by a human using pen and paper, which falls under the mental processes grouping of abstract ideas.
Dependent Claims
Claims 2 and 18 recite the limitation wherein the second range is fully encompassed within the first range, and claim 3 and 19 recite the limitation wherein one or more outlier sensor data within the first range are absent from the second range of sensor data. These limitations inherit the abstract ideas from the claims on which they depend, and express a mathematical relationship between the first range and the second range, which is a mathematical concept. Additionally, a human could conceptually or visually determine that one range is within another, or that certain outliers should not be included in a selected sub-range when evaluating data distributions, constituting mental processes.
Claims 4 and 20 further limit the step of identifying the second range by determining low and high boundary values based on lower and upper threshold values, where the second range is then bounded by those low and high values. This limitation inherits the abstract ideas from claim 1 and further recites mathematical concepts (determining percentiles via threshold percentages to set boundaries) and mental processes (judging boundaries where specifies percentages fall below/above values).
Claims 5-8 and 21-22 narrow the percentile-based range of the claim upon which they depend by specifying numerical thresholds for the lower and/or upper percentages used to determine the low/high boundary values. The claims inherit the abstract ideas from the claims upon which they depend, and express explicit mathematical constraints or relationships by specifying numeric values for the thresholds. For small datasets, a human could use evaluations and judgements to determine boundaries at specific percentages, constituting mental processes.
Claims 9-10 and 23-24 add specific outlier-handling techniques to the percentile-based range identification of claim on which they depend by identifying outlier values lower and higher than the low and high boundary values, and either assigning the boundary values to the outliers before mapping or excluding the outliers during mapping. These claims inherit the abstract ideas from claims on which they depend, and include additional mathematical operations because conditional comparisons and value replacement/omission are implementable via simple equations, which fall into the mathematical concepts grouping of abstract ideas. Additionally, for small datasets, a human could identify outliers and assign/omit the outliers using observations and judgments, which falls into the mental processes grouping of abstract ideas.
Claims 11 and 25 narrow the mapping limitation of the claim upon which they depend by describing mapping in terms of applying a transformation to at least two distinct data points within the second range, and converting their original values to new values inside a third range. These claims inherit the abstract ideas from the claims upon which they depend, and explicitly describes a mathematical transformation, which is a mathematical relationship or calculation that falls into the mathematical concepts grouping of abstract ideas. For small datasets, a human could mentally, or with a pen and paper, map values from one range to another using observations and evaluations, which falls into the mental processes grouping of abstract ideas.
Claims 12 and 26 recite the limitation wherein at least a part of the second range is non-overlapping with the third range. Similar to claims 2-3 and 18-19 above, these claims inherit the abstract ideas of claims upon which they depend, and further express a mathematical relationship between the second range and the third range, which falls within the mathematical concepts grouping of abstract ideas. Additionally, a human could conceptually or visually determine that one range is within another when evaluating data distributions, which falls within the mental processes grouping of abstract ideas.
Claim 14 further limits the processing limitation of claim 1 by specifying that the method assigns four quality score indicating the probability of each called base being an A, C, T and G. This claim inherits the abstract ideas of claim 1, and recites computing and assigning probabilities for each possible base – core mathematical calculations, which fall into the mathematical concepts grouping of abstract ideas. For small datasets, a human could evaluate the signal data and judge the likelihoods of each base, which falls within the mental processes grouping of abstract ideas.
Claim 15 adds post-processing steps to the probabilistic quality scores from claim 14 by assigning those four scores as the plurality of quality scores of claim 1 and remapping at least some of them to a new remapped quality score based on sequence-specific context associated with corresponding bases. This claim inherits the abstract ideas of claims 1 and 14, and directly recites a mathematical transformation of probability values to a new score, which are mathematical calculations/relationships falling under the mathematical concepts grouping of abstract ideas. Additionally, evaluating probabilities and mentally adjusting them to a different scale could be done using human judgement, which falls under the mental processes grouping of abstract ideas.
Claim 16 adds a final post-processing step to the remapped quality scores from claim 15 by quantizing or reducing each plurality of remapped quality scores to a corresponding plurality of quantized remapped quality score. This claim inherits the abstract ideas of claims 1, 14, and 15, and directly recites the mathematical operation/transformation of quantizing, which falls within the mathematical concepts grouping of abstract ideas. Additionally, quantizing scores could be performed mentally by a human, or by a human using pen and paper, which falls into the mental processes grouping of abstract ideas.
Finally, claim 13 inherits the abstract ideas of claim 1, but does not recite any additional judicial exceptions. Therefore, claims 1-27 recite abstract ideas – namely mathematical concepts and mental processes [Step 2A, Prong One: Yes].
Prong Two
Claims 1-27 as a whole do not integrate the recited judicial exception into a practical application. A claim that recites a judicial exception [Prong One] is deemed to be directed to a judicial exception [Step 2A] unless the claim as a whole contains additional elements that integrate the exception into a practical application [Prong Two]. A claim that integrates a judicial exception into a practical application will apply, rely on, or use the judicial exception in a manner that imposes a meaningful limit on the judicial exception, 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 [MPEP § 2106.04(d) and MPEP § 2106.05(e)]. A claim does not integrate a judicial exception into a practical application by reciting insignificant extra-solution activity, generally linking the exception to a particular technological environment or field of use, merely reciting to apply the exception, merely including instructions to implement an abstract idea on a computer, or merely using a computer as a tool to perform an abstract idea [MPEP § 2106.04(d)(I)]. Insignificant extra-solution activities are nominal or tangential additions to a claim that are incidental to the primary process or product, including both pre-solution and post-solution activity (e.g. pre-solution data gathering for use in a process). If integrated into a practical application, the claim is eligible; otherwise, it is directed to the judicial exception, necessitating further analysis at Step 2B.
The additional elements in claims 1, 17, and 27 do not integrate the recited abstract ideas into a practical application. The claims recite the following limitations, which are additional elements:
Claim 1 recites a computer-implemented method of generating base calls by a base caller, comprising: receiving a plurality of sensor data from a flow cell by accessing a plurality of sections of an image depicting intensity emission values of one or more clusters of nucleic acid molecules output by a sequencing instrument during a sequencing run, wherein the plurality of sensor data is within a first range.
Claim 17 recites a non-transitory computer readable storage medium impressed with computer program instructions that, when executed on a processor, implement a method comprising: receiving a plurality of intensity values from a flow cell within a first range by accessing a plurality of sections of an image depicting intensity emission values of one or more clusters of nucleic acid molecules output by a sequencing instrument during a sequencing run, wherein an individual intensity value depicts a target cluster of nucleic acid molecules or an immediate vicinity of the target cluster of nucleic acid molecules of the flow cell, the target cluster of nucleic acid molecules populated with an unknown analyte.
Claim 27 recites a system for base calling, comprising: at least one processor; and a non-transitory computer readable medium comprising instructions that, when executed by the at least one processor, cause the system to: store images that depict original intensity emissions of a set of analytes output by a sequencing instrument during a sequencing run, the original intensity emissions generated by analytes in the set of analytes during sequencing cycles of the sequencing run; and receive the original intensity emissions.
The limitations of receiving/storing data in these claims are nominal additions of pre-solution data gathering for use in a process, which constitutes an insignificant extra-solution activity that does not integrate the abstract ideas into a practical application. See MPEP § 2106.05(g). Additionally, these limitations are data gathering steps that are limited to a particular type of data or data source, which merely indicates a field of use or technological environment in which to apply a judicial exception and cannot integrate a judicial exception into a practical application. See MPEP § 2106.05(h). The recitation of using a computer or a computer readable medium and processor as a tool to perform the abstract ideas are described at a high level of generality, amounting to “apply it” instructions for abstract data manipulation, which does not integrate the abstract ideas into a practical application. See MPEP § 2106.05(f).
Claim 13 adds an additional element by specifying that the data received in claim 1 comprises a section of an image from a flow cell with corresponding intensities. The element of receiving data in claim 1 is a nominal addition of pre-solution data gathering for use in a process, and further specification as to the type of data gathered does not integrate the abstract ideas into a practical application. Finally, claims 2-12, 14-16, and 18-26 do not include any additional elements.
While the specification discusses handling intensity variations in sequencing, the claims themselves do not recite a particular manner of remapping or base calling beyond functional results. The claims as a whole merely recite insignificant extra-solution activities and abstract ideas implemented on generic computer components without meaningful limitations that tie it to a specific technological improvement. Therefore, claims 1-27 do not contain additional elements that integrate the recited abstract ideas into a practical application [Step 2A, Prong Two: No].
Step 2B
Claims 1-27 do not include additional elements, whether considered individually or in combination, that are sufficient to amount to significantly more than the judicial exception itself. Under Step 2B, the claim is analyzed to determine whether there are any additional elements that, individually or in combination, constitute an “inventive concept" sufficient to ensure that the claim, as a whole, amounts to significantly more than the judicial exception itself [MPEP § 2106.05; Alice Corp. Pty. Ltd. v. CLS Bank Int'l, 573 U.S. 208, 217-18, 110 USPQ2d 1976, 1981 (2014)].
Claims 1, 13, 17, and 27 recite the following limitations, which are additional elements:
Claim 1 recites a computer-implemented method of generating base calls by a base caller, comprising: receiving a plurality of sensor data from a flow cell by accessing a plurality of sections of an image depicting intensity emission values of one or more clusters of nucleic acid molecules output by a sequencing instrument during a sequencing run, wherein the plurality of sensor data is within a first range.
Claim 13 recites wherein individual sensor data of the plurality of sensor data comprises corresponding intensity of a corresponding section of an image generated from the flow cell.
Claim 17 recites a non-transitory computer readable storage medium impressed with computer program instructions that, when executed on a processor, implement a method comprising: receiving a plurality of intensity values from a flow cell within a first range by accessing a plurality of sections of an image depicting intensity emission values of one or more clusters of nucleic acid molecules output by a sequencing instrument during a sequencing run, wherein an individual intensity value depicts a target cluster of nucleic acid molecules or an immediate vicinity of the target cluster of nucleic acid molecules of the flow cell, the target cluster of nucleic acid molecules populated with an unknown analyte.
Claim 27 recites a system for base calling, comprising: at least one processor; and a non-transitory computer readable medium comprising instructions that, when executed by the at least one processor, cause the system to: store images that depict original intensity emissions of a set of analytes output by a sequencing instrument during a sequencing run, the original intensity emissions generated by analytes in the set of analytes during sequencing cycles of the sequencing run; and receive the original intensity emissions.
The limitations of receiving/storing data in these claims are conventional insignificant extra-solution activity that merely indicate a field of use in which to apply the judicial exceptions and does not amount to significantly more than the exceptions themselves. See OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015); Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1334, 115 USPQ2d 1681, 1701 (Fed. Cir. 2015); University of Utah Research Foundation v. Ambry Genetics, 774 F.3d 755, 764, 113 USPQ2d 1241, 1247 (Fed. Cir. 2014); Genetic Techs. Ltd. v. Merial LLC, 818 F.3d 1369, 1376, 118 USPQ2d 1541, 1546 (Fed. Cir. 2016); and MPEP §§ 2106.05(d) & (g)-(h). The recitation of using a computer or a computer readable medium and processor as a tool to perform the abstract ideas are conventional and are described at a high level of generality, amounting to “apply it” instructions for abstract data manipulation, which does not amount to significantly more than the judicial exceptions themselves. See MPEP § 2106.05(f); and C3.ai., Infrastructure: Machine Learning Hardware Requirements, §§ Processors: CPUs, GPUs, TPUs, and FPGAs – Memory and Storage (15 May 2021). Claims 2-12, 14-16, and 18-26 do not include any additional elements.
Overall, claims 1-27 amount to no more than insignificant extra-solution activities and implementing the abstract ideas on conventional computers in a routine way. The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception itself because the claims recite additional elements that equate to insignificant extra-solution activity and mere instructions to apply the recited abstract ideas in a generic way or in a generic computing environment. Therefore, claims 1-27 are rejected for failing to set forth patent eligible subject matter under 35 U.S.C. 101 because the claimed invention recites abstract ideas [Step 2A, Prong One: Yes] and the additional elements do not integrate the judicial exception into a practical application [Step 2A, Prong Two: No] and do not amount to claiming significantly more than the recited exception [Step 2B: No].
Response to Arguments
Applicant's arguments filed 12 May 2026 have been fully considered but they are not persuasive. Applicant argues that amended independent claims 1, 17, and 27 recite a practical application because they reflect an improvement to the technical field of image processing of a sequencing instrument. See Amendment p. 13 ll. 20-22. Applicant argues that, similar to the method at issue in Research Corp. Techs. v. Microsoft Corp., 627 F.3d 859, 97 USPQ2d 1274 (Fed. Cir. 2010), the technology of claims 1, 17, and 27 improves image processing on a sequencing instrument by manipulating the sensor data depicted in the images to reduce variability between images. Id. p. 14 ll. 12-15. Applicant concludes that amended independent claims 1, 17, and 27 recite a practical application because the claimed invention transforms the sensor data through normalization which further improves the processing of sequencing images and detection of signals of clusters of nucleic acid molecules.
The claims at issue in Research Corp. improved how computers render images. The court found that the patents at issue claim patent-eligible subject matter because the invention is not abstract when it presents functional and palpable applications in the field of computer technology, addresses a specific need in the art for precise halftone rendering using digital processors, and involves concrete steps (pixel-by-pixel comparison with a blue noise mask) tied to real-word image processing for output devices. Conversely, in SAP, the court invalidated claims to statistical analyses of investment data because they were directed to abstract ideas of selecting certain information, analyzing it using mathematical techniques, and reporting or displaying the results. The court found that “the focus of the claims is not a physical-realm improvement but an improvement in wholly abstract ideas – the selection and mathematical analysis of information.” The court makes clear that even when a process of collecting and analyzing information is limited to particular data type or a particular source, that limitation does not make the collection and analysis anything other than abstract. The court notes that the claimed invention may be assumed to be groundbreaking, innovative, or even brilliant, but no amount of novelty or utility in the field could render the subject matter patent-eligible.
Here, the instant claims are more analogous to that of SAP because the instant claims are directed to improving statistical modeling and data preprocessing for probabilities, rather than an improvement in image processing. The instant claims focus on selecting/normalizing sensor data ranges (statistical thresholding/percentiles), mathematical mapping to a new distribution, feeding into models for probability outputs (base calls/quality scores), and calibration (remapping, quantization, loss penalization). The specification emphasizes expected calibration error, over/under confidence adjustments, and binning – core mathematical/statistical techniques applied to sequencing data. See paras. [0259]-[0263]; and Figs. 15A-16. Therefore, Applicant’s arguments are not persuasive, and the rejection under 35 U.S.C. 101 is maintained.
Response to Arguments - 35 USC § 102
Applicant’s arguments, see p. 16, filed 12 May 2026, with respect to claims 1-8, 10-14, 17-22, and 24-27 have been fully considered and are persuasive. The 102 rejection of claims 1-8, 10-14, 17-22, and 24-27 has been withdrawn.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 1-8, 10-15, 17-22, and 24-27 are rejected under 35 U.S.C. 103 as being unpatentable over Fernandez-Gomez (US 2018/0173844 A1, published 21 June 2018) in view of Illumina (Understanding Illumina Quality Scores, Pub. No. 770-2012-058 (23 April 2014)), as evidenced by Mager (US 2017/0370903 A1, published 28 December 2017) and Boufounos (Journal of the Franklin Institute, 2004). The italicized text within parenthesis corresponds to the instant claim limitations.
Regarding claim 1, Fernandez-Gomez discloses computer-implemented methods of preprocessing sequencing data to determine bases. At para. [0248]; fig. 19; para. [0040]; fig. 8 (870) (a computer-implemented method of generating base calls by a base caller). Fernandez-Gomez discloses receiving a plurality of signal values from a nanopore-based sequencing sensor chip (flow cell) by processing a plurality of raw image data frames depicting intensities of cells, which are determined to be clusters of nucleic acid molecules after normalization. At para. [0148]; fig.10 (1010); para. [0003] fig. 17 (1710); para. [0183]; para. [0189]; para. [0191] (receiving a plurality of sensor data from a flow cell by accessing a plurality of sections of an image depicting intensity emission values of one or more clusters of nucleic acid molecules output by a sequencing instrument during a sequencing run). Fernandez-Gomez teaches that the dynamic range of the sequencing output can be optimized to provide a specific range. At para. [0123] (wherein the plurality of sensor data is within a first range). Fernandez-Gomez identifies a range of open channel voltages within the received signal values by identifying low and high cutoff values such that some specified fraction of the signal values falls between the two cutoffs. At para. [155]; paras. [0156-57] (identifying a second range such that at least a threshold percentage of the plurality of sensor data are within the second range). Fernandez-Gomez discloses normalizing the signal values from the cells identified within the thresholded range by dividing each measured point by the open channel voltage of that cell, which rescales the raw signal value to a normalized range. At para. [0171] (mapping at least a subset of the plurality of sensor data, that are within the second range, to a third range, thereby generating a plurality of normalized sensor data). Fernandez-Gomez teaches further processing the normalized signal values for base calling to determine the nucleotide corresponding to each cluster, with a probability function assigning probabilities of the nucleotide being in each cell state. At paras. [0183-84] (processing the plurality of normalized sensor data in a base caller, to call, for the plurality of normalized sensor data, one or more corresponding bases with a plurality of quality scores). Although the calibration steps disclosed occur before the disclosed sequencing operation steps, Fernandez-Gomez notes that calibration and normalization may be performed as part of the sequencing operation. At para. [0106].
Fernandez-Gomez fails to disclose remapping each of at least a subset of the plurality of quality scores to generate at least a remapped quality score for the one or more corresponding bases. However, Illumina discloses remapping quality predictor values using a quality table to generate useable quality scores. At 1 col.1 para.3 – col.2 para.2. Illumina teaches that data storage and transfer costs are a significant part of the total cost of sequencing, and scores can be compressed into fewer quality bins without affecting data quality or downstream analysis. At 1 col.2 para.5. A person having ordinary skill in the art would be motivated to combine the method of Fernandez-Gomez with the teachings of Illumina because remapping the probabilities of Fernandez-Gomez would reduce data storage and transfer costs associated with sequencing. One of ordinary skill in the art would reasonably expect success in this combination because compressing the scores into fewer bins does not affecting data quality or downstream analysis. Therefore, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to take the method of Fernandez-Gomez and apply the remapping quality score technique of Illumina. Some teaching, suggestion, or motivation in the prior art that would have led one of ordinary skill to modify the prior art reference or to combine prior art reference teachings to arrive at the claimed invention is likely to be obvious. See KSR International Co. v. Teleflex Inc., 550 U.S. 398, 415-421, USPQ2d 1385, 1395 – 97 (2007); and MPEP § 2143, G).
Regarding claim 2, the method of Fernandez-Gomez receives a range of signal values and identifies a second range within the first range based on percentiles. Fig. 12 exemplifies a first range of 0 to 250 and an identified second range of 29 to 115, which is within the first range (the method of claim 1, wherein the second range is fully encompassed within the first range).
Regarding claim 3, Fernandez-Gomez discloses removing or ignoring signal values outside of the cutoffs, which results in outliers within the first range being absent from the second range. At para. [0157] (the method of claim 1, wherein one or more outlier sensor data within the first range are absent from the second range of sensor data).
Regarding claim 4, Fernandez-Gomez discloses identifying lower and upper cutoff signal values such that a specified fraction of the of the signal values falls between the two cutoffs. At para. [156]. This results in the lower percentage of signal values having a value lower than the low cutoff, and an upper percentage of signal values having a value higher than the high cutoff. Fig. 12 (identifying, within the first range, a low value, such that a lower threshold percentage of the plurality of sensor data have a value that is lower than the low value; and identifying, within the first range, a high value, such that an upper threshold percentage of the plurality of sensor data have a value that is higher than the high value, wherein the second range is defined by the low value and the high value).
Regarding claims 5-8, Mager, which Fernandez-Gomez incorporates by reference at para. [0181], discloses thresholding the signal values to a 99.9% level. At para. [159]. In order to maintain 99.9% of the signal values within the thresholded range, the sum of the lower threshold and the upper threshold must be less than 0.1%. Therefore, each of the lower threshold and the upper threshold must be 0.1% or less (5. the method of claim 4, wherein at least one of the lower threshold percentage or the upper threshold percentage is 0.5% or less; 6. the method of claim 4, wherein at least one of the lower threshold percentage or the upper threshold percentage is 1.0% or less; 7. the method of claim 4, wherein each of the lower threshold percentage and the upper threshold percentage is 0.5% or less; 8. the method of claim 4, wherein each of the lower threshold percentage and the upper threshold percentage is 1% or less).
Regarding claim 10, Fernandez-Gomez discloses removing or ignoring signal values lower than the low cutoff and signal values higher than the high cutoff. At para. [0157] (the method of claim 4, further comprising: identifying (i) a first outlier sensor data of the plurality of sensor data that is lower than the low value and (ii) a second outlier sensor data of the plurality of sensor data that is higher than the high value; and excluding the first outlier sensor data and the second outlier sensor data from the subset of the plurality of sensor data during the mapping, for being outside the second range, such that the first outlier sensor data and the second outlier sensor data are not mapped to the third range).
Regarding claim 11, Fernandez-Gomez discloses normalizing the signal values from the cells identified within the thresholded range by dividing each signal value (first data within subset from a first value in second range) by the open channel voltage of that cell, which rescales (maps) the raw signal value to a normalized range (to a second value within third range). At para. [0141]. Fernandez-Gomez discloses that normalization of signal values occurs point-by-point, meaning a second value within the thresholded range will be rescaled to a new value within the normalized range. At para. [0150] (the method of claim 1, wherein mapping at least a subset of the plurality of sensor data comprises: mapping a first sensor data within the subset from a first value that is within the second range to a second value that is within the third range; and mapping a second sensor data within the subset from a third value that is within the second range to a fourth value that is within the third range).
Regarding claim 12, Fernandez-Gomez discloses thresholding the received signal values to a range of 29 to 115 and normalizing to provide a range between 0 and 1, At para. [0157]; para. [0141] (the method of claim 1, wherein at least a part of the second range is non-overlapping with the third range).
Regarding claim 13, Fernandez-Gomez discloses that signal values can correspond to a light intensity of a corresponding pixel in an image. At para. [0035]; para. [0189] (the method of claim 1, wherein individual sensor data of the plurality of sensor data comprises corresponding intensity of a corresponding section of an image generated from the flow cell).
Regarding claim 14, Fernandez-Gomez discloses that after normalization, bases can be determined based on probability functions and the normalized signal values. At para. [0184]. Fernandez-Gomez teaches assigning four probabilities for each signal value using four probability functions, where each probability function can assign the probability of the base being an A, T, C, and G. At paras. [0184-85] (the method of claim 1, further comprising: processing the plurality of normalized sensor data in a base caller, to assign, for each base call, a first quality score indicating a probability of the called base being an A, a second quality score indicating a probability of the called base being a C, a third quality score indicating a probability of the called base being a T, and a fourth quality score indicating a probability of the called base being a G).
Regarding claim 15, Fernandez-Gomez assigns four quality scores for each cell state, where each probability indicates the chance of the called base being an A, T, C, and G. At para. [0184] (the method of claim 14, further comprising: assigning the plurality of quality scores that includes the first quality score, the second quality score, the third quality score, and the fourth quality score). Illumina discloses remapping quality predictor values using a quality table to generate useable quality scores. At 1 col.1 para.3 – col.2 para.2 (remapping each of at least a subset of the plurality of quality scores to a corresponding remapped quality score). Illumina discloses that the quality tables are specific to the sequencing context involved in the base calling process. At 2 col.1 paras.1-2 (based on sequence-specific context associated with the one or more called corresponding bases).
Regarding claim 17, Fernandez-Gomez discloses a non-transitory computer readable storage medium with instructions to be executed by a processor, para. [0252], to carry out methods of preprocessing and normalizing output signal values to determine bases, paras. [0004-5] (a non-transitory computer readable storage medium impressed with computer program instructions that, when executed on a processor, implement a method comprising). Fernandez-Gomez discloses receiving a plurality of signal values, para. [0148]; fig.10 (1010); see also Mager, para. [228], fig.18 (1810), from a nanopore-based sequencing sensor chip, para. [0043], by processing a plurality of raw image data frames depicting intensities of cells, which are determined to be clusters of nucleic acid molecules after normalization, para. [0183]; para. [0189]; para. [0191]. Fernandez-Gomez defines signal value to include values corresponding to the intensity of each nanopore cell in the sequencing chip, para. [0035], where each cell sequences a nucleic acid molecule containing an analyte of interest, para. [0046] (receiving a plurality of intensity values from a flow cell within a first range by accessing a plurality of sections of an image depicting intensity emission values of one or more clusters of nucleic acid molecules output by a sequencing instrument during a sequencing run, wherein an individual intensity value depicts a target cluster of nucleic acid molecules or an immediate vicinity of the target cluster of nucleic acid molecules of the flow cell, the target cluster of nucleic acid molecules populated with an unknown analyte). Fernandez-Gomez identifies a range of open channel voltages within the received signal values by identifying low and high cutoff values such that some specified fraction of the signal values falls between the two cutoffs. Paras. [0155-56] (identifying a second range that includes at least a threshold percentage of the plurality of intensity values). Fernandez-Gomez discloses normalizing the signal values from the cells identified within the thresholded range by dividing each measured point by the open channel voltage of that cell, which rescales the raw signal value to a normalized range. Para. [0141] (mapping the threshold percentage of the plurality of intensity values to a third range that is different from the second range). Following normalization, Fernandez-Gomez teaches further processing the normalized signal values, para. [0083], and assigning probabilities of the analyte of interest being an A, T, C, and G, paras. [0184-85] (subsequent to the mapping, processing the threshold percentage of the plurality of intensity values, to generate likelihoods of the unknown analyte being an A, C, T, or G). Illumina discloses remapping quality predictor values using a quality table to generate useable quality scores. At 1 col.1 para.3 – col.2 para.2 (remapping each of at least a subset of the likelihoods of the unknown analyte to generate at least a remapped likelihood of the unknown analyte).
Regarding claim 18, the method of Fernandez-Gomez receives a range of signal values and identifies a second range within the first range based on percentiles. Fig. 12 exemplifies a first range of 0 to 250 and an identified second range of 29 to 115, which is within the first range (the non-transitory computer readable storage medium of claim 17, wherein the second range is fully encompassed within the first range).
Regarding claim 19, Fernandez-Gomez discloses removing or ignoring signal values outside of the cutoffs, which results in outliers within the first range being absent from the second range. Para. [0157] (the non-transitory computer readable storage medium of claim 17, wherein one or more outlier intensity values within the first range are absent from the threshold percentage of the plurality of intensity values).
Regarding claim 20, Fernandez-Gomez discloses identifying lower and upper cutoff signal values such that a specified fraction of the of the signal values falls between the two cutoffs. Para. [156]. This results in the lower percentage of signal values having a value lower than the low cutoff, and an upper percentage of signal values having a value higher than the high cutoff. Fig. 12 (the non-transitory computer readable storage medium of claim 17, wherein identifying the second range comprises: identifying, within the first range, a low value, such that a lower threshold percentage of the plurality of intensity values have a value that is lower than the low value; and identifying, within the first range, a high value, such that an upper threshold percentage of the plurality of intensity values have a value that is higher than the high value, wherein the threshold percentage is a sum of the lower threshold percentage and the upper threshold percentage, wherein the second range is defined by the low value and the high value).
Regarding claims 21-22, Mager discloses thresholding the signal values to a 99.9% level. Para. [159]. In order to maintain 99.9% of the signal values within the thresholded range, the sum of the lower threshold and the upper threshold must be less than 0.1%. Therefore, each of the lower threshold and the upper threshold must be 0.1% or less (21. the non-transitory computer readable storage medium of claim 20, wherein at least one of the lower threshold percentage or the upper threshold percentage is 0.5% or less; 22. the non-transitory computer readable storage medium of claim 20, wherein each of the lower threshold percentage and the upper threshold percentage is 1.0% or less).
Regarding claim 24, Fernandez-Gomez discloses removing or ignoring signal values lower than the low cutoff and signal values higher than the high cutoff before normalization. Para. [0157] (the non-transitory computer readable storage medium of claim 20, further comprising: identifying (i) a first outlier intensity value of the plurality of intensity values that is lower than the low value and (ii) a second outlier intensity value of the plurality of intensity values that is higher than the high value; and excluding the first outlier intensity value and the second outlier intensity value from the subset of the plurality of intensity values during the mapping, for being outside the second range, such that the first outlier intensity value and the second outlier intensity value are not mapped to the third range).
Regarding claim 25, Fernandez-Gomez discloses normalizing the signal values from the cells identified within the thresholded range by dividing each signal value (first data within subset from a first value in second range) by the open channel voltage of that cell, which rescales (maps) the raw signal value to a normalized range (to a second value within third range). Para. [0141]. Fernandez-Gomez discloses that normalization of signal values occurs point-by-point, meaning a second value within the thresholded range will be rescaled to a new value within the normalized range. Para. [0150] (the non-transitory computer readable storage medium of claim 17, wherein the mapping comprises: mapping a first intensity value from a first value that is within the second range to a second value that is within the third range; and mapping a second intensity value from a third value that is within the second range to a fourth value that is within the third range).
Regarding claim 26, Fernandez-Gomez discloses thresholding the received signal values to a range of 29 to 115, para. [0157], and normalizing to provide a range between 0 and 1, para. [0141] (the non-transitory computer readable storage medium of claim 17, wherein at least a part of the second range is non-overlapping with the third range).
Regarding claim 27, Fernandez-Gomez discloses a system, para. [0008], for determining bases, para. [0005], comprising a processor and a non-transitory computer readable medium having software instructions thereon, para. [0252]; fig. 2 (224) & (226) (a system for base calling comprising: at least one processor; and a non-transitory computer readable medium comprising instructions). Fernandez-Gomez discloses a local memory that may store raw data frames, paras. [0190-91]; fig. 13 (1325), including an intensity corresponding to each nanopore cell in the sequencing chip, para. [0035-36], where each cell sequences a nucleic acid molecule containing an analyte of interest, para. [0046] (store images that depict original intensity emissions of a set of analytes output by a sequencing instrument during a sequencing run, the original intensity emissions generated by analytes in the set of analytes during sequencing cycles of the sequencing run). Fernandez-Gomez discloses receiving the intensity values by processing a plurality of raw data frames, para. [0189]; para. [0191], and thresholding the received signal values to a range of 29 to 115, para. [0157], and normalizing to provide a range between 0 and 1, para. [0141] (receive the original intensity emissions and remap the original intensity emissions to generate remapped intensity emissions, such that a remapped intensity emission has a different intensity value relative to an original intensity emission). Fernandez-Gomez discloses determining the base for the analyte of interest by processing the normalized signal values in a hidden Markov model, and assigning probabilities of the analyte of interest being an A, T, C, and G. At paras. [0183-85]. (generate base calls and a plurality of quality scores for the set of analytes by processing the remapped intensity emissions, to generate base calls for the set of analytes). A hidden Markov model is a statistical framework used for base calling, as evidenced by Boufounos, at 24, paras. 2-3. Illumina discloses remapping quality predictor values using a quality table to generate useable quality scores. At 1 col.1 para.3 – col.2 para.2 (remap each of at least a subset of the plurality of quality scores to generate remapped quality scores for the base calls).
Claims 9 and 23 are rejected under 35 U.S.C. 103 as being unpatentable over Fernandez-Gomez and Illumina as applied to claims 1-8, 10-15, 17-22, and 24-27 above, and further in view of Koren (Sergey Koren et al., Genome Res., Vol. 27 (May 2017)).
Regarding claims 9 and 23, Fernandez-Gomez and Illumina disclose the method of claims 4 and 20 (see 103 rejections above). Fernandez-Gomez further discloses removing or ignoring signal values identified lower than the low cutoff and signal values identified higher than the high cutoff. Para. [0157] (the method of claim 4/20, further comprising: identifying (i) a first outlier sensor data of the plurality of sensor data that is lower than the low value and (ii) a second outlier sensor data of the plurality of sensor data that is higher than the high value).
Fernandez-Gomez and Illumina fail to teach assigning the low value to the first outlier sensor data/intensity value, and assigning the high value to the second outlier sensor data/intensity value, such that the first outlier sensor data/intensity value and the second outlier sensor data/intensity value are within the second range subsequent to the assignment. However, Koren teaches a statistical overlap filter (Canu) that generates a histogram to correct sequencing reads by selecting a low overlap cutoff and a maximum overlap cutoff. At 724, fig. 1; 734, col. 1 para. 2. Unsupported regions in the input are identified and trimmed or split to their longest supported range. Fig. 1 caption. Koren discloses assigning values not within the maximum and minimum cutoffs to the maximum and minimum values. At 732, col. 1 paras. 3-4. Koren notes that Canu improves runtime for mammalian genomes and outperforms other methods of genome assembly by reducing misassembles. At 723, col. 1 para. 1; 725, col.1 para.5-col.2 para.1.
Fernandez-Gomez discloses a base method of preprocessing and normalizing output signal values to determine bases of a nucleic acid sequence. Koren discloses a statistical overlap filter that results in better runtime and performance when compared with other genome assembly methods. The method of Fernandez-Gomez mirrors that of Koren regarding the steps of identifying low and high cutoffs and identifying values outside the cutoffs. However, Fernandez-Gomez only discloses removing or ignoring outliers, while Koren discloses trimming and splitting outliers. One of ordinary skill in the art would recognize that applying the trimming and splitting technique of Koren to the method of Fernandez-Gomez would predictably yield a quicker and more accurate genome assembly by reducing misassembles. This, in turn, would result in an improved method of preprocessing and normalizing data to determine bases because a quicker and more accurate genome assembly method will result in a quicker and more accurate base call. Therefore, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to take the method of Fernandez-Gomez and apply the trimming and splitting technique of Koren. Applying a known technique to a known device (method or product) ready for improvement to yield predictable results is likely to be obvious. See KSR International Co. v. Teleflex Inc., 550 U.S. 398, 415-421, USPQ2d 1385, 1395 – 97 (2007) (see MPEP § 2143, D).
Claim 16 is rejected under 35 U.S.C. 103 as being unpatentable over Fernandez-Gomez and Illumina as applied to claims 1-8, 10-15, 17-22, and 24-27 above, and further in view of Greenfield (Daniel L. Greenfield et al., Bioinformatics, Vol. 32 (15 October 2016)) and Yu (Y William Yu, Nat Biotechnol. (March 2015)).
Regarding claim 16, Fernandez-Gomez and Illumina disclose the method of claim 15 (see 103 rejection above), but fail to teach quantizing each of a plurality of remapped quality scores to a corresponding one of a plurality of quantized remapped quality score. However, Greenfield discloses a technique of quantizing quality scores to produce quantized quality scores, § 2.4.3, and notes that the goal of quantizing quality scores is to improve compressibility while preserving genotyping accuracy, at 3125 col.1 para.2. Additionally, Yu teaches that quality score compression improves the accuracy of a base call by reducing the noise in the raw quality scores. At 3 para. 2.
Fernandez-Gomez discloses a base method of preprocessing and normalizing output signal values to determine bases of a nucleic acid sequence, and Illumina discloses a technique of remapping quality scores. Greenfield discloses a technique of quantizing quality scores to improve compressibility, which improves the accuracy of a base call by reducing the noise in the quality scores. One of ordinary skill in the art would recognize that applying the quantizing quality scores technique of Greenfield to the method of Fernandez-Gomez and Illumina would predictably yield compressed recalibrated quality scores that preserve genotyping accuracy. This, in turn, would result in an improved method of base calling because compressing quality score data improves the accuracy of a base call by reducing the noise in the raw quality scores. Therefore, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to take the method of Fernandez-Gomez and Illumina, and apply the quantizing quality score technique of Greenfield. Applying a known technique to a known device (method or product) ready for improvement to yield predictable results is likely to be obvious. See KSR International Co. v. Teleflex Inc., 550 U.S. 398, 415-421, USPQ2d 1385, 1395 – 97 (2007) (see MPEP § 2143, D).
Response to Arguments
Applicant’s arguments, see pp. 17-19, filed 12 May 2026, with respect to the rejections of claims 9, 15-16, and 23 under 35 U.S.C. 103 have been fully considered and are persuasive. Therefore, the rejection has been withdrawn. However, upon further consideration, a new grounds of rejection is made in view of Illumina.
Applicant first argues that Fernandez-Gomez, whether considered singly or in combination with the other cited references, fails to teach each limitation described by amended independent claims 1 and 17. Applicant is correct in this assertion. However, amended independent claims 1 and 17 are taught by Fernandez-Gomez in combination with Illumina (see 103 rejection above).
Applicant also argues that Koren fails to remedy the deficiencies of Fernandez-Gomez because Koren fails to disclose generating and remapping quality scores. Applicant is correct in this assertion. However, Fernandez-Gomez teaches generating quality scores and Illumina teaches remapping quality scores.
Applicant finally argues that Chung describes generating recalibrated quality scores using aggregate statistical inference based on histograms of grouped sequencing bases, singular value decomposition, and Bayesian calculations, which do not constitute the claimed remapped quality scores. Applicant asserts that Chung does not teach the claimed invention because Chung recalculates quality scores globally from sequencing statistics rather than selectively remapping already-assigned quality scores within a base-calling pipeline. Applicant’s interpretation of Chung’s teachings is correct. However, Illumina discloses utilizing a quality table to remap existing quality values, and does not recompute quality values as taught by Chung. While Illumina does not indicate whether the existing quality scores are globally or selectively remapped, “at least a subset of the plurality of quality scores” could include some or all of the existing quality scores. Therefore, the claimed invention is taught by Fernandez-Gomez in combination with Illumina (see 103 rejections above).
Conclusion
No claims are allowed.
Applicant's amendment necessitated the new grounds of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
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/E.A.D./Examiner, Art Unit 1686
/OLIVIA M. WISE/Supervisory Patent Examiner, Art Unit 1685
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