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 1-27 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. Domestic benefit is acknowledged. At this point in examination, the effective filing date of claims 1-27 is 28 July 2021.
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 15 August 2022 and 16 November 2022 comply with 37 CFR 1.98. Accordingly, all references listed have been considered by the examiner.
Claim Interpretation
The following is a quotation of 35 U.S.C. 112(f):
(f) Element in Claim for a Combination. – An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The following is a quotation of pre-AIA 35 U.S.C. 112, sixth paragraph:
An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
This application includes one or more claim limitations that do not use the word “means,” but are nonetheless being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, because the claim limitation uses a generic placeholder that is coupled with functional language without reciting sufficient structure to perform the recited function and the generic placeholder is not preceded by a structural modifier. Such claim limitation is: a normalization module configured to receive the original intensity emissions and remap the original intensity emissions, such that a remapped intensity emission has a different intensity value relative to the original intensity emission in claim 27. The generic placeholder “module” is coupled with functional language of receiving and remapping data without sufficient structure to perform receiving and remapping.
Because this claim limitation is being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, it is being interpreted to cover the corresponding structure described in the specification as performing the claimed function, and equivalents thereof. The specification does not disclose any specific structure for the normalization module. The specification discloses at para. [243] that the normalization module is configured to receive data, normalize the data, and provide the normalized data to a base caller. The specification does not provide any additional details regarding how the acts of receiving, normalizing, or providing are actually performed.
If applicant does not intend to have this limitation interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, applicant may: (1) amend the claim limitation to avoid it being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph (e.g., by reciting sufficient structure to perform the claimed function); or (2) present a sufficient showing that the claim limitation recites sufficient structure to perform the claimed function so as to avoid it being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph.
Claim Rejections - 35 USC § 112
The following is a quotation of the first paragraph of 35 U.S.C. 112(a):
(a) IN GENERAL.—The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor or joint inventor of carrying out the invention.
The following is a quotation of the first paragraph of pre-AIA 35 U.S.C. 112:
The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor of carrying out his invention.
Claim 27 is rejected under 35 U.S.C. 112(a) as failing to comply with the written description requirement. A claim limitation expressed in means- (or step-) plus-function language shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof. If the specification fails to disclose sufficient corresponding structure, materials, or acts that perform the entire claimed function, then the claim limitation is indefinite because the applicant has in effect failed to particularly point out and distinctly claim the invention as required by 35 U.S.C. 112(b). In re Donaldson Co., 16 F.3d 1189, 1195, 29 USPQ2d 1845, 1850 (Fed. Cir. 1994) (en banc). Such a limitation also lacks an adequate written description as required by 35 U.S.C. 112(a) because an indefinite, unbounded functional limitation would cover all ways of performing a function and indicate that the inventor has not provided sufficient disclosure to show possession of the invention. For computer-implemented means-plus-function limitations, the necessary structure includes hardware and the algorithm that the hardware uses to perform the function. See MPEP § 2181(II)(B).
Claim 27 contains the limitation of “a normalization module configured to receive the original intensity emissions and remap the original intensity emissions.” This limitation invokes 35 U.S.C. 112(f) but fails to recite a combination of elements as required by that statutory provision and thus cannot rely on the specification to provide the structure, material or acts to support the claimed function. The claimed functions of the normalization module are receiving and remapping data. The specification does not disclose any structure or hardware for the normalization module, nor does it disclose an algorithm for receiving or remapping data. The claim limitation is indefinite because the specification does not particularly point out and distinctly claim the recited normalization module. Therefore, claim 27 is rejected under U.S.C. 112(a) as failing to comply with the written description requirement because the limitation is indefinite.
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claim 27 is rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Claim limitation “a normalization module configured to receive the original intensity emissions and remap the original intensity emissions” invokes 35 U.S.C. 112(f). However, the written description fails to disclose the corresponding structure, material, or acts for performing the entire claimed function and to clearly link the structure, material, or acts to the function. For computer-implemented means-plus-function limitations, the necessary structure includes hardware and the algorithm that the hardware uses to perform the function. See MPEP § 2181(II)(B).
The claimed functions of the normalization module are receiving and remapping data. The specification does not disclose any structure or hardware for the normalization module. The specification also does not disclose an algorithm for receiving or remapping data. Therefore, claim 27 is indefinite and is rejected under 35 U.S.C. 112(b) or pre-AIA 35 U.S.C. 112, second paragraph.
Applicant may:
(a) Amend the claim so that the claim limitation will no longer be interpreted as a limitation under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph;
(b) Amend the written description of the specification such that it expressly recites what structure, material, or acts perform the entire claimed function, without introducing any new matter (35 U.S.C. 132(a)); or
(c) Amend the written description of the specification such that it clearly links the structure, material, or acts disclosed therein to the function recited in the claim, without introducing any new matter (35 U.S.C. 132(a)).
If applicant is of the opinion that the written description of the specification already implicitly or inherently discloses the corresponding structure, material, or acts and clearly links them to the function so that one of ordinary skill in the art would recognize what structure, material, or acts perform the claimed function, applicant should clarify the record by either:
(a) Amending the written description of the specification such that it expressly recites the corresponding structure, material, or acts for performing the claimed function and clearly links or associates the structure, material, or acts to the claimed function, without introducing any new matter (35 U.S.C. 132(a)); or
(b) Stating on the record what the corresponding structure, material, or acts, which are implicitly or inherently set forth in the written description of the specification, perform the claimed function. For more information, see 37 CFR 1.75(d) and MPEP §§ 608.01(o) and 2181.
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 17-26 and 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 receiving a range of data from a flow cell, identifying a second range containing a threshold percentage of the data, mapping a subset of the second range to a third range to normalize the data, and processing the normalized data in a base caller to call a corresponding base. 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.
Claim 17 recites software stored on a tangible medium that, when executed, receives data depicting a target cluster, identifies a second range containing a threshold percentage of the data, maps that threshold percentage to a distinct third range, and processes the threshold percentage to generate the probabilities of an unknown analyte being an A, C, T, or G. The core limitations involve mathematical operations – identifying a range via threshold percentage (statistical quantile determination), mapping/rescaling 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, and probability assessment 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 storing images depicting original intensity emissions of a set of analytes, remapping the original intensity emissions in a normalization module to generate distinct remapped intensity values, and processing the remapped intensity emissions to generate base calls for the set of analytes in a base caller. The core functionality of this claim involves mathematical transformations – remapping intensities (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 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. This limitation inherits the abstract ideas from 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 13 further defines the data being received in claim 1 by specifying that the data comprises a section of an image from a flow cell with corresponding intensities. This claim inherits the abstract ideas of claim 1, but does not recite any additional judicial exceptions.
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 collecting/assigning those four scores as a plurality and remapping at least some of them to a new remapped quality score. This claim inherits the abstract ideas of claim 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.
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. Claim 1 recites the additional elements of a computer-implemented method comprising receiving data from a flow cell. Claim 17 recites the additional elements of software stored on a tangible medium that, when executed, receives data depicting a target cluster from a flow cell. The elements of receiving 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. Additionally, the recitation of using a computer or a computer readable medium as a tool to perform the abstract ideas do not integrate the abstract ideas into a practical application.
Claim 27 recites the additional elements of memory storing images depicting intensity emissions of a set of analytes, a normalization module configured to receive the intensity emissions, and a base caller configured to process the remapped intensity emissions. Similar to the pre-solution data gathering discussed above, stored images depicting intensity emissions of a set of analytes is a nominal addition of pre-solution activity for use in a process, which constitutes an insignificant extra-solution activity that does not integrate the abstract ideas into a practical application. Additionally, the memory, normalization model, and base caller 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.
Moreover, 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)].
Regarding claim 1, the recitation of receiving data from a flow cell is routine pre-solution data gathering constituting insignificant extra-solution activity (see Step 2A, Prong Two above). Identifying ranges via thresholds, mapping, and processing in a base caller are standard mathematical pre-processing and classification steps long used in sequencing signal/image processing. The generic computer implementation does not meaningfully limit claim 1 because mere instructions to implement an abstract idea on a generic computer does not add significantly more in Step 2B [MPEP § 2106.05(f)]. Additionally, the sequence (gather data [Wingdings font/0xE0] map [Wingdings font/0xE0] process) is conventional pipeline ordering in the application of neural networks, meaning there is no unconventional configuration.
Regarding claim 17, the recitation of non-transitory medium storing instructions for the abstract method of claim 1 is a conventional Beauregard claim format. Generic processor execution of normalization and probability generation is routine software implementation [MPEP § 2106.05(f)]. Additionally, claim 17 merely encodes the abstract ideas in software, constituting no unconventional technical solution beyond applying known algorithms on standard computers.
Regarding claim 27, the recitation of memory storing images/intensities is generic storage. The normalization module and base caller are functional black boxes, meaning they broadly cover any software/hardware performing conventional scaling and classification. Additionally, the ordered combination of memory, pre-processing module, and analysis module is a standard computing architecture for data pipelines, meaning no non-conventional arrangement or specific technical details.
Claims 2-12, 14-16, and 18-26 do not include any additional elements beyond further limiting the standard mathematical pre-processing and classification steps of the claims upon which they depend. Claim 13 further describes the data received in claim 1, but further specification as to the type of data gathered during an insignificant extra-solution activity does not meaningfully limit the claim.
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].
Claim Rejections - 35 USC § 102
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
Claims 1-8, 10-14, 17-22, and 24-27 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Fernandez-Gomez (US 2018/0173844 A1, published 21 June 2018), 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, para. [0248]; fig. 19, methods of preprocessing sequencing data to determine bases, 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, para. [0148]; fig.10 (1010); see also Mager, paras. [151] and [228], fig.18 (1810), from a nanopore-based sequencing sensor chip (flow cell), para. [0003] fig. 17 (1710), where the dynamic range of the sequencing output can be optimized to provide a specific range, para. [0123] (receiving a plurality of sensor data from a flow cell, 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, para. [155], by identifying low and high cutoff values such that some specified fraction of the signal values falls between the two cutoffs, 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. 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. 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). 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. Para. [0106].
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. 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. 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. 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. 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). 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 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, para. [0157], and normalizing to provide a range between 0 and 1, 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, para. [0035], of a corresponding pixel in an image, 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. 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. 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 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]. 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, wherein an individual intensity value depicts a target cluster or an immediate vicinity of the target cluster of the flow cell, the target cluster 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).
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 value).
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 higher 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] (a system for base calling). 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] (memory storing images that depict original intensity emissions of a set of analytes, the original intensity emissions generated by analytes in the set of analytes during sequencing cycles of a sequencing run). To normalize the received signal values, 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] (remap the original intensity emissions to generate remapped intensity emissions, such that a remapped intensity emission has a different intensity value relative to the original intensity emission). While Fernandez-Gomez does not explicitly teach a normalization module configured to receive the signal values, Fernandez-Gomez notes that any steps can be performed with modules or other means for performing the steps. Para. [0254]. A normalization module to carry out the method of Fernandez-Gomez would necessarily be configured to receive the signal values when normalization cannot occur before the data to be normalized is received. Fernandez-Gomez discloses determining the base for the analyte of interest by processing the normalized signal values in a hidden Markov model. Paras. [0183-84] (a base caller configured to process 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.
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 9 and 23 are rejected under 35 U.S.C. 103 as being unpatentable over Fernandez-Gomez in view of Koren (Sergey Koren et al., Genome Res., Vol. 27 (May 2017)).
Regarding claims 9 and 23, Fernandez-Gomez discloses the method of claim 4 and 20 (see 102 rejections above), and 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 fails 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 15 is rejected under 35 U.S.C. 103 as being unpatentable over Fernandez-Gomez in view of Chung (Jade Chung, bioRxiv (27 April 2017)), as evidenced by Griffiths (Sarah Griffiths, EPI2ME (1 July 2021)).
Regarding claim 15, Fernandez-Gomez teaches the method of claim 14 (see 102 rejection above) and further 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. Para. [0184] (the method of claim 14, further comprising: assigning a plurality of quality scores that includes the first quality score, the second quality score, the third quality score, and the fourth quality score). Fernandez-Gomez fails to teach remapping each of at least a subset of the plurality of quality scores to a corresponding remapped quality score.
However, Chung discloses a software tool that receives a set of quality scores assigned to each base to be called, and recalibrates the scores to yield recalibrated quality scores. At ll. 67-79. Chung notes that recalibration of base quality scores improves downstream base calling, at ll. 40-41, by effectively isolating and correcting the errors introduced during the sequencing process, at ll. 237-38.
Fernandez-Gomez discloses a base method of preprocessing and normalizing output signal values to determine bases of a nucleic acid sequence. Fernandez-Gomez discloses assigning probabilities indicating the chance of the called base being an A, T, C, and G. Chung discloses a technique of recalibrating quality scores, which improves downstream base calling. Quality scores can be determined from the probabilities by taking the negative log of the sum of the probabilities of bases not called (error probability), as evidenced by Griffiths, at paras. 4-6. One of ordinary skill in the art would recognize that applying the quality score recalibration technique of Chung to the method of Fernandez-Gomez would predictably yield more accurate quality scores by isolating and correcting errors introduced in the sequencing process. This, in turn, would result in an improved method of base calling because recalibrating quality scores improves downstream base calling. 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 quality score recalibration technique of Chung. 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 Chung as applied to claim 15 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 Chung disclose the method of claim 15 (see 103 and 102 rejections 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 Chung discloses a technique of recalibrating 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 Chung 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 Chung, 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).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Botond Sipos (@bsipos), GitHub (nanoporetech), ONT assembly and Illumina polishing pipeline. (8 December 2020), https://github.com/nanoporetech/ont-assembly-polish. Discloses an assembly and polishing pipeline consisting of assembly using Canu and mapping using Burrows-Wheeler Aligner.
Sergey Nurk et al., HiCanu: accurate assembly of segmental duplications, satellites, and allelic variants from high-fidelity long reads, Genome Res. Vol. 30 (September 2020), at 1291-305. Discloses HiCanu, a modification of the Canu assembler designed to leverage the full potential of HiFi reads via homopolymer compression, overlap-based error correction, and aggressive false overlap filtering.
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/E.A.D./Examiner, Art Unit 1686
/OLIVIA M. WISE/Supervisory Patent Examiner, Art Unit 1685