DETAILED ACTION
Applicant' s response, filed 22 June 2026, has been fully considered. The following rejections and/or objections are either reiterated or newly applied. They constitute the complete set presently being applied to the instant application.
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Claim Status
Claims 1-20 are pending and examined herein.
Claims 1-20 are rejected.
Priority
Claims 1-20 are granted the claim to the benefit of priority to PCT/US2024/058756 filed 05 December 2024 which claims benefit of U.S. Provisional applications 63/606500, 63/625763, 63/550,993, and 63/675678 filed 05 December 2023, 26 January 2024, 07 February 2024, and 25 July 2024 respectively. Thus, the effective filling date of claims 1-20 is 05 December 2023.
Drawings
The drawings received 22 June 2026. The objection to the drawings in Office action mailed 20 March 2026 is withdrawn in view of the replacement drawings and amendments to the specification received 22 June 2026.
Nucleotide and/or Amino Acid Sequence Disclosures
The sequence listing is in compliance with the requirements set out in 37 CFR 1.821 – 1.825.
Claim Rejections - 35 USC § 112
The rejection on the ground of 112/b of claims 2, 3, 8, 9, 14 and 15 for claims 2 and 14 recite “a tumor fraction that is lower than or on the order of 0.01%” and claims 3 and 15 recite “a tumor fraction that is lower than or on the order of 0.001%” in Office action mailed 20 March 2026 is withdrawn in view of the amendment of “wherein the tumor fraction is 0.01% or less” (for claims 2 and 14) and “wherein the tumor fraction is 0.001% or less” (for claims 3 and 15) received 22 June 2026.
The rejection on the ground of 112/b of claims 11-19 for claim 11 reciting "the level of disease" in Office action mailed 20 March 2026 is withdrawn in view of the amendment of “determining that the SNV quality metric for the SNV is above a predetermined threshold, thereby determining a level of disease” received 22 June 2026.
112/a New Matter
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.
The rejection below is newly recited necessitated by amendment.
Claims 11-19 are rejected under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA ), first paragraph, as failing to comply with the written description requirement. The claim(s) contains subject matter which was not described in the specification in such a way as to reasonably convey to one skilled in the relevant art that the inventor or a joint inventor, or for applications subject to pre-AIA 35 U.S.C. 112, the inventor(s), at the time the application was filed, had possession of the claimed invention.
Claim 11 recites “determining that the SNV quality metric for the SNV is above a predetermined threshold, thereby determining a level of disease”. There is not an adequate written description for determining that the SNV quality metric for the SNV is above a predetermined threshold is the means for determining a level of disease. The instant disclosure provides that circulating tumor fraction (which is a measure of a level of disease) is calculated by totaling the number of detected tumor profile SNVs passing a quality which is then normalized for the assessed coverage at each of the profile positions (see instant disclosure [0559]). It is noted that the previous claim language recited a step of determining the level of disease when the SNV quality metric for the SNV is above a threshold (i.e., a separate step performed when a condition was met). In the provided amendment of “determining that the SNV quality metric for the SNV is above a predetermined threshold, thereby determining a level of disease” is interpreted as reciting a causal relationship between the SNV quality metric for the SNV being above a predetermined threshold and the determination of a level of disease (i.e., the quality metric of the SNV being above the predetermined threshold results in determining a level of disease). There is not an adequate written description for determining that the SNV quality metric for the SNV is above a predetermined threshold results in the determination of a level of a disease. Thus, this limitation constitutes as new matter. Dependent claims 12-19 are rejected by virtue of their dependency on a rejected claim without alleviating the issue.
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.
The rejection below has been modified necessitated by amendment.
Claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more.
(Step 1)
Claims 1-20 fall under the statutory category of a process.
(Step 2A Prong 1)
Under the BRI, the instant claims recite judicial exceptions that are an abstract idea of the type that is in the grouping of a “mental process”, such as procedures for evaluating, analyzing or organizing information, and forming judgement or an opinion. The instant claims further recite judicial exceptions that are an abstract idea of the type that is in the grouping of a “mathematical concept”, such as mathematical relationships and mathematical equations.
Claim 1 recites mental processes of “generating a sequencing read of the template nucleic acid molecule using the sequencing data set, wherein the sequencing read comprises a single nucleotide variant (SNV)” and “determining a tumor fraction based at least on the SNV quality metric”.
Claim 1 recites mathematical concepts of “outputting a SNV quality metric for the SNV, wherein the SNV quality metric is specific to a base type that is different from a base type of the SNV in the sequencing read”, “determining a tumor fraction based at least on the SNV quality metric”, and “wherein outputting the SNV quality metric comprises processing the sequencing data set using one or more algorithms that comprise a trained machine learning model”.
Claim 11 recites mental processes of “generating a sequencing read of the template nucleic acid molecule using the sequencing data set, wherein the sequencing read comprises a single nucleotide variant (SNV)”, and “determining that the SNV quality metric for the SNV is above a predetermined threshold, thereby determining a level of disease”.
Claim 11 recites mathematical concepts of “outputting a SNV quality metric for the SNV, wherein the SNV quality metric is specific to a base type that is different from a base type of the SNV in the sequencing read” and “wherein outputting the SNV quality metric comprises processing the sequencing data set using one or more algorithms that comprise a trained machine learning model”.
Claim 20 recites a mental process of “generating the sequencing read of the nucleic acid using the plurality of sequencing signals” and “based at least in part on the determined quality metrics and a reference sequence, modifying the first base type to a different base type, thereby increasing an overall quality metric of the sequencing read”.
Claim 20 recites mathematical concepts of “for a locus in the sequencing read, wherein the locus comprises a first base type, determining a respective quality metric specific to each base type different from the first base type” and “wherein determining the respective quality metric specific to each base type different from the first base type comprises processing the plurality of sequencing signals using one or more algorithms that comprise a trained machine learning model”.
Dependent claim 7 recites a mental process of “calling one or more genetic variants using the sequencing read, wherein an error in the sequencing read is excluded from variant calling, wherein the error is determined based on information from both strands of the template nucleic acid molecule”. Dependent claim 10 recites a mental process of “determining a base quality metric for the SNV, wherein the base quality metric corresponds to an aggregate of a respective SNV quality metric for each alternative base for the SNV”. Dependent claim 19 recites a mental process of determining a base quality metric for the SNV, wherein the base quality metric corresponds to an aggregate of a respective SNV metric for the substituted base and each alternative base for the SNV”.
The claims recite mental processes of analyzing sequencing data produced by sequencing a nucleic acid sequence to generate a sequence read that contains a SNV, determining a SNV quality metric is above a predetermined threshold, modifying a base call based on sequencing metrics. The human mind is capable of analyzing nucleic acid data produced by a sequencing process, making a determination that a value representing a SNV quality metric is above a predetermined threshold, and modifying a base in a dataset. The claims recite a mathematical concept of determining a respective quality metric with machine learning models (which encompasses any machine learning model such as a linear regression model or logistic regression model which produces a score of quality and is interpreted as being a mathematical calculation) estimating a tumor fraction and determining a level of disease which encompasses calculating a ratio of tumor nucleic molecules and all nucleic acid molecules in a sample. Dependent claims 2, 3, 5, 8, 9, 12-15, and 17 further limit the mental process/mathematical concept recited in the independent claim but do not change their nature as a mental process/mathematical concept.
(Step 2A Prong 2)
Claims found to recite a judicial exception under Step 2A, Prong 1 are then further analyzed to determine if the claims as a whole integrate the recited judicial exception into a practical application or not (Step 2A, Prong 2). Integration into a practical application is evaluated by identifying whether there are any additional elements recited in the claim and evaluating those additional elements to determine whether they integrate the exception into a practical application.
The additional element in claims 1 and 11 of sequencing a template nucleic acid molecule to generate a sequencing data set, the additional element in claims 4 and 16 of wherein the sequencing comprises flow-based sequencing, wherein the flow-based sequencing comprises flowing a series of non-terminated, single-base nucleotide mixtures in a predetermined flow order to contact the template nucleic acid molecule, or derivatives thereof, wherein a sequencing step in the flow-based sequencing comprises detecting a signal or lack thereof after flowing in a non-terminated, single-base nucleotide mixture, of the series of non-terminated, single-base nucleotide mixtures, to contact the template nucleic acid molecule, or derivatives thereof, the additional element in claims 6 and 18 of wherein the template nucleic acid molecule is double- stranded, wherein sequencing the template nucleic acid molecule comprises collecting sequencing signals from an amplification cluster derived from the template nucleic acid molecule, wherein the sequencing read is determined to encode information from both strands of the template nucleic acid molecule, and the additional element in claim 20 of sequencing the nucleic acid to generate a plurality of sequencing signals of the nucleic acid do not integrate the judicial exceptions into a practical application because these limitations are insignificant extra solution activity of data gathering. These additional elements constitute as data gathering because these additional elements only interact with the judicial exceptions in manner by providing data to the judicial exceptions to process.
Thus, the additional elements do not integrate the judicial exceptions into a practical application and claims 1-20 are directed to the abstract idea.
(Step 2B)
Claims found to be directed to a judicial exception are then further evaluated to determine if the claims recite an inventive concept that provides significantly more than the judicial exception itself (Step 2B). The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception because:
The additional element in claims 1 and 11 of sequencing a template nucleic acid molecule to generate a sequencing data set, the additional element in claims 4 and 16 of wherein the sequencing comprises flow-based sequencing, wherein the flow-based sequencing comprises flowing a series of non-terminated, single-base nucleotide mixtures in a predetermined flow order to contact the template nucleic acid molecule, or derivatives thereof, wherein a sequencing step in the flow-based sequencing comprises detecting a signal or lack thereof after flowing in a non-terminated, single-base nucleotide mixture, of the series of non-terminated, single-base nucleotide mixtures, to contact the template nucleic acid molecule, or derivatives thereof, the additional element in claims 6 and 18 of wherein the template nucleic acid molecule is double- stranded, wherein sequencing the template nucleic acid molecule comprises collecting sequencing signals from an amplification cluster derived from the template nucleic acid molecule, wherein the sequencing read is determined to encode information from both strands of the template nucleic acid molecule, and the additional element in claim 20 of sequencing the nucleic acid to generate a plurality of sequencing signals of the nucleic acid are conventional as shown by Almogy et al. (US 20200392584 A1; previously cited), Etzioni et al. (US 20230343416 A1), Lakdawalla et al. (US 20210230669 A1; previously cited), and Simmons et al. (Nature Biotechnology 41.2 (2023): 204-211; previously cited) which show flow-based sequencing by synthesis methods for determining sequence reads of template nucleic acid molecules.
Thus, the additional elements are not sufficient to amount to significantly more than the judicial exception because they are conventional.
Response to Arguments
Applicant's arguments filed 22 June 2026 have been fully considered but they are not persuasive.
Argument 1
Applicant argues that the claims now recite the outputting and determining of quality metrics utilize machine learning models to perform and no longer recite a mental process and thus do not fall under the judicial exception (Reply p. 19-20). Applicant further argues that while the use of “SNV quality metric” to determine a “tumor fraction” or a “level of disease” does involve mathematical operations the claims do not limit the use of any specific mathematical equation, relationship or calculation and do not recite a mathematical concept (Reply p. 20).
This argument has been fully considered but found to be not persuasive due to these limitations now reciting mathematical concepts of mathematical calculations. The MPEP states “There is no particular word or set of words that indicates a claim recites a mathematical calculation. That is, a claim does not have to recite the word "calculating" in order to be considered a mathematical calculation. For example, a step of "determining" a variable or number using mathematical methods or "performing" a mathematical operation may also be considered mathematical calculations when the broadest reasonable interpretation of the claim in light of the specification encompasses a mathematical calculation” (see MPEP 2106.04(a)(2)(I)(C)). The BRI of the recite machine learning models for producing quality metrics encompasses utilizing a trained linear regression or logistic regression model to produce numerical scores form input data which is interpreted as being a mathematical calculation. Thus, these limitations fall under the judicial exception of a mathematical concept because these steps encompass being a mathematical calculation. Further, determining a tumor fraction recites a mathematical calculation because it encompasses calculating a tumor fraction in accordance with the equation in [0559] of the instant disclosure. Further, determining a level of disease is no longer interpreted as reciting a mathematical concept because the claim recites the determination of a level of disease is performed by comparing a quality metric to a predetermined threshold (which falls under the judicial exception of a mental process).
Argument 2
Applicant argues that the claimed invention provides improvements in the technical fields of disease detection and nucleic acid sequencing, especially with respect to increasing the confidence in a called base (Reply p. 21). Applicant argues that the claimed metrics provide decomposed alternate base-specific error probabilities which improves sequencing accuracy through enabling platforms to achieve background error rates lower than conventional quality metrics, the claimed quality metrics enables detection of ctDNA at low tumor fractions, the quality metric-based method enables minimal residual disease detection using relatively low-depth whole genome sequencing (Reply p. 21). Applicant further argues that the claimed quality metrics produce a defined technological output that can then be applied to determine tumor fraction, determine level of disease, or refine the base calls in a sequencing read to increase overall sequencing qualities (Reply p. 22). Applicant argues that these downstream applications demonstrate that the independent claims as amended are not merely “outputting” data in the abstract, but rather generating a specific technical output integral to improving the accuracy and clinical utility of nucleic acid sequencing (Reply p. 22).
This argument has been fully considered but found to be not persuasive. The MPEP states at 2106.05(a) “It is important to note, the judicial exception alone cannot provide the improvement. The improvement can be provided by one or more additional elements… In addition, the improvement can be provided by the additional element(s) in combination with the recited judicial exception”. In the instant case, the additional elements are interpreted as being insignificant extra solution activity of data gathering because they only interact with the judicial exceptions by providing data to the judicial exceptions to process. Thus, the additional elements in combination with the recited judicial exceptions do not provide an improvement due to their interaction interpreted as insignificant extra solution activity of data gathering. The arguments provided rely on the quality metric as providing the improvement in technology. These quality metrics are fall under the judicial exception which cannot provide the improvement to technology. Thus, the improvement is provided by judicial exception alone and does not constitute as an improvement to technology. Further, the downstream analysis provided which are improved with the implementation of the improved quality metrics also fall under the judicial exception as described above (e.g., determining a tumor fraction, determining a level of disease, and recalibrating base calls). Although the abstract analysis is improved, it is interpreted that this analysis falls under the judicial exception which cannot provide the improvement to technology.
Claim Rejections - 35 USC § 102
The rejection on the ground of 102 of claims 11-17 as being anticipated by Almogy et al. (US 20200392584 A1; previously cited) in Office action mailed 20 March 2026 is withdrawn in view of the amendments of “determining that the SNV quality metric for the SNV is above a predetermined threshold, thereby determining a level of disease” received 22 June 2026.
The rejection on the ground of 102 of claim 20 as being anticipated by Parnaby et al. (US 20230021577 A1; previously cited) in Office action mailed 20 March 2026 is withdrawn in view of the amendments of “wherein determining the respective quality metric specific to each base type different form the first base type comprises processing the plurality of sequencing signals using one or more algorithms that comprise a trained machine learning model” received 22 June 2026.
Claim Rejections - 35 USC § 103
The rejection on the ground of 103 of claims 1-5, 8-10, and 19 as being unpatentable over Almogy et al. (US 20200392584 A1; previously cited) in view of Landau et al. (US 20210002728 A1; previously cited) in Office action mailed 20 March 2026 is withdrawn in view of the amendments of “outputting a SNV quality metric for the SNV, wherein the SNV quality metric is specific to a base type that is different from a base type of the SNV in the sequencing read… wherein outputting the SNV quality metric comprises processing the sequencing data set using one or more algorithms that comprise a trained machine learning model” received 22 June 2026.
The rejection on the ground of 103 of claim 6 and 7 as being unpatentable over as being unpatentable over Almogy et al. (US 20200392584 A1; previously cited) in view of Landau et al. (US 20210002728 A1; previously cited) as applied to claims 1 above, and further in view of Lakdawalla et al. (US 20210230669 A1; previously cited). in Office action mailed 20 March 2026 is withdrawn in view of the amendment to claim 1 of “outputting a SNV quality metric for the SNV, wherein the SNV quality metric is specific to a base type that is different from a base type of the SNV in the sequencing read… wherein outputting the SNV quality metric comprises processing the sequencing data set using one or more algorithms that comprise a trained machine learning model” received 22 June 2026.
The rejection on the ground of 103 of claim 18 as being unpatentable over Almogy et al. (US 20200392584 A1; previously cited) as applied to claim 11 under 35 U.S.C. 102 above, and further in view of Lakdawalla et al. (US 20210230669 A1; previously cited) in Office action mailed 20 March 2026 is withdrawn in view of the amendment to claim 11 of “determining that the SNV quality metric for the SNV is above a predetermined threshold, thereby determining a level of disease” received 22 June 2026.
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.
The rejection below is newly recited and necessitated by claim amendment.
Claims 1-5 and 8-10 are rejected under 35 U.S.C. 103 as being unpatentable over Almogy et al. (US 20200392584 A1; previously cited) in view of Jaganathan et al. (US 20200327377 A1; newly cited) in view of Landau et al. (US 20210002728 A1; previously cited).
Claim 1 is directed to method for precise tumor fraction determination, the method comprising: sequencing a template nucleic acid molecule to generate a sequencing data set
Almogy et al. shows flow-based sequencing of a template nucleic acid molecule to generate flow signals representing a base count indicative of the number of bases in the sequenced nucleic acid molecule that are incorporated at each flow position (Almogy et al. [0087]-[0089], [0091], [0097], and [0099]).
generating a sequencing read of the template nucleic acid molecule using the sequencing data set, wherein the sequencing read comprises a single nucleotide variant (SNV)
Almogy et al. shows generating a sequencing read of the template nucleic acid molecule where the sequencing read comprises a SNV (Almogy et al. [0102] - [0108]).
Almogy et al. does not show outputting a SNV quality metric for the SNV, wherein the SNV quality metric is specific to a base type that is different from a base type of the SNV in the sequencing read, wherein outputting the SNV quality metric comprises processing the sequencing data set using one or more algorithms that comprise a trained machine learning model
Like Almogy et al., Jaganathan et al. shows analyzing nucleotide sequencing signals which include fluorescence signals from base incorporation events. Jaganathan et al. show processing sequence imaging data which includes intensity data of a detected incorporation event with a neural network which uses a softmax function to generate the base call confidence probabilities as softmax scores for the base being A, C, T, and G at a specified location (Jaganathan et al. [0541] and [0542]). It is interpreted that these confidence probabilities which includes the called base (e.g., at a location called as a SNV would be the SNV base type) and also includes a metric of confidence in a different base type where each confidence probability for each of the different base types are a separate metric (each of these separate metrics are interpreted as the SNV quality metric). Jaganathan et al. shows that there is a correspondence between the softmax scores and binned quality scores (e.g., a 0.80 softmax score corresponds to binned quality score of Q06) (Jaganathan et al. [0546]). Further, Jaganathan et al. shows the base quality metric corresponds to an aggregate of a respective SNV quality metric for the substituted base and for each alternative base for the SNV by showing the correspondence of softmax scores corresponding to binned quality scores for the selected base (i.e., the base with the highest softmax score) such that a softmax score of 0.80 which is the aggregate score of all the scores produced by the neural network produced by the softmax layer (e.g., if A has a softmax score of 0.80 the softmax scores of G, C, and T are equal to 0.20 because the softmax layer is normalized to be out of 1) (Jaganathan et al. [0548]-[0552])
Almogy et al. in view of Jaganathan et al. does not show determining a tumor fraction based at least on the SNV quality metric.
Like Almogy et al. in view of Jaganathan et al., Landau et al. shows detecting residual disease using single nucleotide variants in sample derived from an individual. Landau et al. further shows computing an estimated tumor fraction for SNV markers utilizing a mathematical equation (Landau et al. [0032]).
Claim 2 are directed to wherein the tumor fraction is 0.01% or less. Claim 3 are directed to wherein the tumor fraction is 0.001% or less.
Almogy et al. in view of Jaganathan et al. in view of Landau et al. shows that the tumor fraction is between about 0.001% to about 1% (Landau et al. [0098]). Landau et al. shows the word “about” means a range of plus or minus 10% of that value (Landau et al. [0055]). This tumor fractions which are below 0.001%.
Claim 4 is directed to wherein the sequencing comprises flow-based sequencing, wherein the flow-based sequencing comprises flowing a series of non-terminated, single-base nucleotide mixtures in a predetermined flow order to contact the template nucleic acid molecule, or derivatives thereof, wherein a sequencing step in the flow-based sequencing comprises detecting a signal or lack thereof after flowing in a non-terminated, single-base nucleotide mixture, of the series of non-terminated, single-base nucleotide mixtures, to contact the template nucleic acid molecule, or derivatives thereof.
Almogy et al. in view of Jaganathan et al. in view of Landau et al. shows utilizing flow-based sequencing which flows a series of non-terminated single base nucleotide mixtures in a flow order which is predetermined to contact the template nucleic acid molecule and detecting a signal or lack thereof after flowing these non-terminated single base nucleotide mixtures to contact the template nucleic acid molecule (Algomy et al. [0088] and [0089]).
Claims 5 is directed to wherein a base position of the SNV in the sequencing read corresponds to a flow order cycle-skipping sequencing step of the flow-based sequencing, wherein the base position corresponding to the flow order cycle-skipping sequencing step comprises a position that differs from a reference sequence data set at four or more consecutive flow positions.
Almogy et al. in view of Jaganathan et al. in view of Landau et al. shows a SNV induces a cycle shift when sequencing data associated with a nucleic acid molecule having the SNV shifts relative to reference sequencing data associated with a reference sequence by one or more flow cycles (Almogy et al. [0086] and [0103]-[0108]). Almogy et al. further shows a particular base of a SNV containing nucleic acid molecule is off by four flow positions from the reference sequencing data (Almogy et al. [0105] and Fig. 8C).
Claim 8 is directed to wherein the SNV quality metric of the base type that is different from the base type of the SNV is at least 50, indicative of a probability of an incorrect base call of the base type that is different from the base type of the SNV at less than 1 in 105. Claim 9 is directed to wherein the SNV quality metric of the base type that is different from the base type of the SNV is at least 60, indicative of a probability of an incorrect base call of the base type that is different from the base type of the SNV at less than 1 in 106.
Almogy et al. in view of Jaganathan et al. in view of Landau et al. shows, Jaganathan et al. shows that there is a correspondence between the softmax scores and binned quality scores, which are interpreted as Phred quality Q-scores, such that a 0.80 softmax score corresponds to binned quality score of Q06 and a softmax score of 0.99999, and 0.999999 corresponded with quality scores Q50 and Q60 (Jaganathan et al. [0545] and [0546]). Further, Landau et al. shows if Phred assigns a quality score of 30 to a base, the chances that this base is called incorrectly are 1 in 1000 and typically, the BQ of a sequencing read is between 10 and 50 (Landau et al. [0204]). It would have been obvious to one of ordinary skill to have SNVs with higher base quality scores such as 50 or 60 Phred scores because this indicates the probability that this base is called incorrectly are in 1 in one hundred thousand (Phred score of 50) and 1 in one million (Phred score of 60).
Claim 10 is directed to further comprising determining a base quality metric for the SNV, wherein the base quality metric corresponds to an aggregate of a respective SNV quality metric for each alternative base for the SNV.
Almogy et al. in view of Jaganathan et al. in view of Landau et al. shows that the base quality metric corresponds to an aggregate of a respective SNV quality metric for the substituted base and for each alternative base for the SNV by showing the correspondence of softmax scores corresponding to binned quality scores for the selected base (i.e., the base with the highest softmax score) such that a softmax score of 0.80 which is the aggregate score of all the scores produced by the neural network produced by the softmax layer (e.g., if A has a softmax score of 0.80 the softmax scores of G, C, and T are equal to 0.20 because the softmax layer is normalized to be out of 1) (Jaganathan et al. [0548]-[0552]).
An invention would have been obvious to one or ordinary skill in the art if some motivation in the prior art would have led that person to combine reference teachings to arrive at the claimed invention. It would have been obvious to one of ordinary skill in the art before the effective filling date of the invention to have modified the flow-based sequencing and flow-based sequencing analysis to identifying potential SNVs based on cycle shifts of Almogy et al. with the process of utilizing a neural network model to process signals obtained from base incorporation events to produce confidence probabilities of alternative bases from the called base of Jaganathan et al. because this would allow for a process which can identify sequencing metric which can be used to produce an overall quality of the SNV base call along with information of confidence probabilities of non-called bases (Jaganathan et al. [0541]-[0542] and [0544]-[0546]).
It would have been further obvious to one of ordinary skill in the art before the effective filling date to have combined the process determining quality metrics for SNVs of Almogy et al. in view of Jaganathan et al. with the estimating a tumor fraction which is based on base quality scores and utilizing series of filters to filter potential SNVs based on quality metrics of Landau et al. because this would allow for a process which uses the base quality scores of Almogy et al. in view of Jaganathan et al. which is based on the SNV quality scores produced by the neural network to collect data and provide candidate SNVs to be further filtered based on base quality metrics to filter out SNVs attributed to noise (Landau et al. [0032]) for estimating a tumor fraction using high confident SNVs. One would have a reasonable expectation of success because Almogy et al. and Jaganathan et al. both show processing sequencing signals to identify base types at a location and Almogy et al. in view of Jaganathan et al. and Landau et al. apply these processes for detecting residual disease in a patient using SNVs.
The rejection below is newly recited and necessitated by claim amendment.
Claims 6 and 7 are rejected under 35 U.S.C. 103 as being unpatentable over Almogy et al. (US 20200392584 A1; previously cited) in view of Jaganathan et al. (US 20200327377 A1; newly cited) in view of Landau et al. (US 20210002728 A1; previously cited) as applied to claims 1 above, and further in view of Lakdawalla et al. (US 20210230669 A1; previously cited).
Claim 6 is directed to wherein the template nucleic acid molecule is double-stranded, wherein sequencing the template nucleic acid molecule comprises collecting sequencing signals from an amplification cluster derived from the template nucleic acid molecule, wherein the sequencing read is determined to encode information from both strands of the template nucleic acid molecule. Claim 7 is directed to further comprising calling one or more genetic variants using the sequencing read, wherein an error in the sequencing read is excluded from variant calling, wherein the error is determined based on information from both strands of the template nucleic acid molecule.
Almogy et al. in view of Jaganathan et al. in view of Landau et al. shows sequencing reads may be compared, for example, to a reference genome, and the variants selected based on the differences between the sequencing reads and the reference genome (Almogy et al. [0062]). Almogy et al. shows performing this step before applying filters to variants which implicitly shows that an error in the sequencing read is excluded from calling these variants (Almogy et al. [0062]).
Almogy et al. in view of Jaganathan et al. in view of Landau et al. as applied to claim 1 does not explicitly show wherein the template nucleic acid molecule is double-stranded, collecting sequencing signals from an amplification cluster derived from the template nucleic acid molecule, wherein the sequencing read is determined to encode information from both strands of the template nucleic acid molecule.
Like Almogy et al. in view of Jaganathan et al. in view of Landau et al., Lakdawalla et al. shows sequencing by synthesis. Lakdawalla et al. the starting template is a double-stranded nucleic acid molecule and performing amplification on the template double strand molecule to produce a population (cluster) of molecules related to the template nucleic acid (Lakdawalla et al. [0022], [0042], [0133], [0346]). Lakdawalla et al. shows performing sequencing on these cluster which encode information from both strands of the template double-stranded nucleic acid molecule (Lakdawalla et al. [0006]).
An invention would have been obvious to one or ordinary skill in the art if some motivation in the prior art would have led that person to modify reference teachings to arrive at the claimed invention. It would have been obvious to one of ordinary skill in the art before the effective filling date of the invention to have modified the template strand and sample processing before flow-based sequencing of Almogy et al. in view of Jaganathan et al. in view of Landau et al. with the double-stranded template DNA and clonal amplification to produce many copies of the double-stranded template DNA that are then subjected to sequencing of Lakdawalla et al. because this would allow for a process which produces many copies of a template double-stranded nucleic acid molecule which contains information of a sequence and its complementary sequence. One would be motivated to make this modification because Lakdawalla et al. shows this sample preparation as being useful for sequencing to identify genetic aberrations for cancer detection (Lakdawalla et al. [0008]). One would have a reasonable expectation of success because Almogy et al. in view of Jaganathan et al. in view of Landau et al. using flow-based sequencing (which is a sequencing by synthesis process) for determining SNVs and estimating tumor fraction while Lakdawalla et al. shows a sample preparation method utilizing double-stranded template DNA and amplification methods prior to sequencing by synthesis which is useful for identifying generic aberrations for cancer detection.
The rejection below is newly recited and necessitated by claim amendment.
Claim 20 are rejected under 35 U.S.C. 103 as being unpatentable over Parnaby et al. (US 20230021577 A1; previously cited) in view of Jaganathan et al. (US 20200327377 A1; newly cited).
Claim 20 is directed to a method for refining a sequence read of a nucleic acid, comprising, (a) sequencing the nucleic acid to generate a plurality of sequencing signals of the nucleic acid (b) generating the sequencing read of the nucleic acid using the plurality of sequencing signals
Parnaby et al. shows a base calling model in a base-caller-recalibration system that generates or determines a set of nucleotide base calls from nucleotide reads captured via fluorescent imaging of a sample nucleotide sequence (at a particular genomic coordinate) (Parnaby et al. [0020]).
and (d) based at least in part on the determined quality metrics and a reference sequence, modifying the first base type to a different base type, thereby increasing an overall quality metric of the sequencing read.
Parnaby et al. shows the base-caller-recalibration system utilizes a call-recalibration-machine-learning model to generate, from the sequencing metrics a set of variant call classifications that impact or reflect the accuracy of identifying a variant at a particular genomic coordinate (that corresponds to a nucleotide- base call of a nucleotide read from a sample nucleotide sequence) (Parnaby et al. [0022]). Parnaby et al. shows the base-caller-recalibration system can utilize the variant-call classifications to update the nucleotide – base call for improved accuracy (Parnaby et al. [0024]).
Parnaby et al. does not show (c) for a locus in the sequencing read, wherein the locus comprises a first base type, determining a respective quality metric specific to each base type different from the first base type; wherein determining the respective quality metric specific to each base type different form the first base type comprises processing the plurality of sequencing signals using one or more algorithms that comprise a trained machine learning model.
Like Parnaby et al, Jaganathan et al. show processing sequence imaging data which includes intensity data of a detected incorporation event with a neural network which uses a softmax function to generate the base call confidence probabilities as softmax scores for the base being A, C, T, and G at a specified location (Jagananthan et al. [0541] and [0542]). It is interpreted that these confidence probabilities which includes the called base (e.g., at a location called as a SNV would be the SNV base type) and also includes a metric of confidence in a different base type where each confidence probability for each of the different base types are a separate metric (each of these separate metrics are interpreted as the SNV quality metric).
An invention would have been obvious to one or ordinary skill in the art if some motivation in the prior art would have led that person to modify reference teachings to arrive at the claimed invention. It would have been obvious to one of ordinary skill in the art before the effective filling date to have modified the initial base caller model in the base-caller-recalibration system Parnaby et al. to be the base caller model of Jagananthan et al. because this would allow for a process of producing more sequencing metrics from the base caller model in the form of confidence probabilities in the form of softmax values of differing base types at a location which correspond to phred quality scores to be utilized in the base recalibration system that modifies base type based on these metrics (Jaganathan et al. [0541]-[0542] and [0544]-[0546]). One would have a reasonable expectation of success because both Parnaby et al. and Jagananthan et al. both show a base caller model which processes signals from sequencing images of a nucleotide sequencer.
Response to Arguments
Applicant provides arguments with respect to claims 1-10 and 20 that have been considered but are moot because the new ground of rejection does not rely on previously cited art to show the newly amended limitations. Arguments that still apply to the new ground of rejection is addressed below.
Applicant argues that Landau et al. does not teach the limitation of “determining a tumor fraction based at least one the SNV quality metric” because Landau et al. shows using the base quality score rather than the SNV quality metric (Reply p. 16-17).
This argument has been fully considered but found to be not persuasive. In response to applicant's arguments against the references individually, one cannot show nonobviousness by attacking references individually where the rejections are based on combinations of references. See In re Keller, 642 F.2d 413, 208 USPQ 871 (CCPA 1981); In re Merck & Co., 800 F.2d 1091, 231 USPQ 375 (Fed. Cir. 1986). As set out above, Jagananthan et al. shows determining SNV quality metrics utilizing a neural network and further shows that these SNV quality metrics can be further processed to generate a base quality score and Landau et al. which shows determining a tumor fraction using base quality scores. The cited references in combination shows determining a tumor fraction based on SNV quality metrics (which are processed into base quality scores before determining the tumor fraction) Thus, the claims are rendered obvious by Almogy et al. (US 20200392584 A1; previously cited) in view of Jaganathan et al. (US 20200327377 A1; newly cited) in view of Landau et al. (US 20210002728 A1; previously cited)
Conclusion
No claims are allowed.
Claims 11-19 are free of the prior art of record. The closest prior art of record is Jaganathan et al. (US 20200327377 A1; newly cited)which shows processing sequence signal data using a machine learning model to generate confidence probability scores for each base type which is interpreted as being a SNV quality score (Jaganathan et al. [0541]-[0542] and [0544]-[0546]). However, the prior art of record does not show the limitation of “determining that the SNV quality metric for the SNV is above a predetermined threshold, thereby determining a level of disease”.
Applicant's amendment necessitated the new ground(s) 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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/J.E.H./Examiner, Art Unit 1685
/KAITLYN L MINCHELLA/Primary Examiner, Art Unit 1685
