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 .
Remarks
In response to communications sent June 3, 2024 claim(s) 2-21 are pending in this application; of these claims 2, 9, and 16 are in independent form. Claim 1 is cancelled.
Response to Amendment
The preliminary amendments filed June 3, 2024 are acknowledged and have been entered into the record.
Drawings
The drawing(s) filed on March 14, 2024 are accepted by the Examiner.
Information Disclosure Statement
The Information Disclosure Statement(s) is/are acknowledged and the references contained therein have been considered by the Examiner. This includes the Information Disclosure Statements(s) filed on: June 21, 2024.
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 2-21 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. The claim(s) recite(s) mathematics. This judicial exception is not integrated into a practical application because inputting and outputting information is necessary extra-solution activity for the abstract idea. In addition, claims 9-21 are applied on a general purpose computer The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception because inputting, outputting, and general purpose computers are well-understood, routine, and conventional. Claims 8, 15, and 21 include additional elements involve laboratory techniques, but these techniques are necessary pre-solution activities and are well-understood, routine, and conventional, according to MPEP § 2106.05(d) regarding “Analyzing DNA to provide sequence information or detect allelic variants, Genetic Techs. Ltd., 818 F.3d at 1377; 118 USPQ2d at 1546”.
2. A method comprising:
accessing sequence reads of a cell free nucleic acid sample of a subject obtained from a first source of the subject, the sequence reads of the cell free nucleic acid sample comprising first depths and first alternate depths for a plurality of positions on a reference allele (the Examiner interprets this as accessing information about the reads, rather than performing laboratory activities outside of a computer; as such, these elements are necessary pre-solution activity of inputting information for the judicial exception);
accessing sequence reads of a genomic nucleic acid sample of a subject obtained from a second source of the subject different than the first source of the subject, the sequence reads of the genomic nucleic acid sample comprising second depths and second alternate depths for the plurality of positions on a reference allele (the Examiner interprets this as accessing information about the reads, rather than performing laboratory activities outside of a computer; as such, these elements are part of the abstract idea);
determining a first likelihood of true alternate frequency of the cell free nucleic acid sample by applying a first function to model the first alternate depths for the cell free nucleic acid sample and adding a first noise level to an output for the first function, wherein the first noise level describes expected noise rates of mutations in cell free nucleic acid samples per position of the plurality of positions on the reference allele (mathematical modeling of frequencies and probabilities given noise rates);
determining a second likelihood of true alternate frequency of the genomic nucleic acid sample by applying a second function to model the second alternate depths of the genomic nucleic acid sample and adding a second noise level to an output for the second function, wherein the second noise level describes expected noise rates of mutations in genomic nucleic acid samples per position of the plurality of positions on the reference allele (mathematical modeling of frequencies and probabilities given noise rates); and
outputting one or more candidate variants of the cell free nucleic acid sample based on the first likelihood of the true alternate frequency of the cell free nucleic acid sample and the second likelihood of the true alternate frequency of the genomic nucleic acid sample (necessary post-solution activity for the judicial exception).
3. The method of claim 2, wherein the first function is a first Poisson distribution function parameterized by a first product of the first depths and a true alternate frequency of the cell free nucleic acid sample, and wherein the second function is a second Poisson distribution function parameterized by a second product of the second depths and a true alternate frequency of the genomic nucleic acid sample (mathematics).
4. The method of claim 2, wherein outputting the one or more candidate variants of the cell free nucleic acid sample further comprises:
determining a probability that the true alternate frequency of the cell free nucleic acid sample is greater than a function of the true alternate frequency of the genomic nucleic acid sample, wherein the probability represents a confidence level that mutations from the first sequence reads from the cell free nucleic acid sample are not found in the second sequence reads from the genomic nucleic acid sample (mathematics); and
filtering the plurality of candidate variants based on the determined probability (mathematical filtering); and
outputting the filtered candidate variants (necessary post-solution activity).
5. The method of claim 4, wherein determining the probability comprises:
determining a joint likelihood of the first likelihood and the second likelihood (mathematics) by:
determining a cumulative sum of one of the first and second likelihoods (mathematics); and
determining an integral of the other of the first and second likelihoods (mathematics).
6. The method of claim 2, further comprising:
determining the first noise level of mutations with respect to the cell free nucleic acid samples using a third function parameterized by first parameters (mathematics); and
determining the second noise level of mutations with respect to the genomic nucleic acid samples using a fourth function parameterized by second parameters (mathematics).
7. The method of claim 6, wherein the first and second parameters represent parameters of distributions that encode noise levels of mutations with respect to a given position of a sequence read (mathematics).
8. The method of claim 2, further comprising:
collecting the cell free nucleic acid sample from a blood sample of the subject (necessary pre-solution activity that is well-understood, routine, and conventional); and
performing enrichment on the cell free nucleic acid sample to generate the first sequence reads (necessary pre-solution activity that is well-understood, routine, and conventional).
9. A non-transitory computer readable storage medium storing computer program instructions that when executed by a processor cause the processor to:
access sequence reads of a cell free nucleic acid sample of a subject obtained from a first source of the subject, the sequence reads of the cell free nucleic acid sample comprising first depths and first alternate depths for a plurality of positions on a reference allele (the Examiner interprets this as accessing information about the reads, rather than performing laboratory activities outside of a computer; as such, these elements are necessary pre-solution activity of inputting information for the judicial exception);
access sequence reads of a genomic nucleic acid sample of a subject obtained from a second source of the subject different than the first source of the subject, the sequence reads of the genomic nucleic acid sample comprising second depths and second alternate depths for the plurality of positions on a reference allele (the Examiner interprets this as accessing information about the reads, rather than performing laboratory activities outside of a computer; as such, these elements are part of the abstract idea);
determine a first likelihood of true alternate frequency of the cell free nucleic acid sample by applying a first function to model the first alternate depths for the cell free nucleic acid sample and adding a first noise level to an output for the first function, wherein the first noise level describes expected noise rates of mutations in cell free nucleic acid samples per position of the plurality of positions on the reference allele (mathematical modeling of frequencies and probabilities given noise rates);
determine a second likelihood of true alternate frequency of the genomic nucleic acid sample by applying a second function to model the second alternate depths of the genomic nucleic acid sample and adding a second noise level to an output for the second function, wherein the second noise level describes expected noise rates of mutations in genomic nucleic acid samples per position of the plurality of positions on the reference allele (mathematical modeling of frequencies and probabilities given noise rates); and
output one or more candidate variants of the cell free nucleic acid sample based on the first likelihood of the true alternate frequency of the cell free nucleic acid sample and the second likelihood of the true alternate frequency of the genomic nucleic acid sample (necessary post-solution activity for the judicial exception).
10. The non-transitory computer readable storage medium of claim 9, wherein the first function is a first Poisson distribution function parameterized by a first product of the first depths and a true alternate frequency of the cell free nucleic acid sample, and wherein the second function is a second Poisson distribution function parameterized by a second product of the second depths and a true alternate frequency of the genomic nucleic acid sample (mathematics).
11. The non-transitory computer readable storage medium of claim 9, wherein instructions for outputting the one or more candidate variants of the cell free nucleic acid sample further cause the processor to:
determine a probability that the true alternate frequency of the cell free nucleic acid sample is greater than a function of the true alternate frequency of the genomic nucleic acid sample, wherein the probability represents a confidence level that mutations from the first sequence reads from the cell free nucleic acid sample are not found in the second sequence reads from the genomic nucleic acid sample (mathematics); and
filter the plurality of candidate variants based on the determined probability (mathematical filtering); and
output the filtered candidate variants (necessary post-solution activity).
12. The non-transitory computer readable storage medium of claim 11, wherein instructions for determining the probability further cause the processor to:
determine a joint likelihood of the first likelihood and the second likelihood (mathematics) by:
determining a cumulative sum of one of the first and second likelihoods (mathematics); and
determining an integral of the other of the first and second likelihoods (mathematics).
13. The non-transitory computer readable storage medium of claim 9, further comprising instructions that cause the processor to:
determine the first noise level of mutations with respect to the cell free nucleic acid samples using a third function parameterized by first parameters (mathematics); and
determine the second noise level of mutations with respect to the genomic nucleic acid samples using a fourth function parameterized by second parameters (mathematics).
14. The non-transitory computer readable storage medium of claim 13, wherein the first and second parameters represent parameters of distributions that encode noise levels of mutations with respect to a given position of a sequence read (mathematics).
15. The non-transitory computer readable storage medium of claim 9, further comprising instructions that cause the processor to:
collect the cell free nucleic acid sample from a blood sample of the subject (necessary pre-solution activity that is well-understood, routine, and conventional); and
perform enrichment on the cell free nucleic acid sample to generate the first sequence reads (necessary pre-solution activity that is well-understood, routine, and conventional).
16. A system comprising a processor and a computer memory, the computer memory storing computer program instructions that when executed by a processor cause the processor to:
access sequence reads of a cell free nucleic acid sample of a subject obtained from a first source of the subject, the sequence reads of the cell free nucleic acid sample comprising first depths and first alternate depths for a plurality of positions on a reference allele (the Examiner interprets this as accessing information about the reads, rather than performing laboratory activities outside of a computer; as such, these elements are necessary pre-solution activity of inputting information for the judicial exception);
access sequence reads of a genomic nucleic acid sample of a subject obtained from a second source of the subject different than the first source of the subject, the sequence reads of the genomic nucleic acid sample comprising second depths and second alternate depths for the plurality of positions on a reference allele (the Examiner interprets this as accessing information about the reads, rather than performing laboratory activities outside of a computer; as such, these elements are necessary pre-solution activity of inputting information for the judicial exception);
determine a first likelihood of true alternate frequency of the cell free nucleic acid sample by applying a first function to model the first alternate depths for the cell free nucleic acid sample and adding a first noise level to an output for the first function, wherein the first noise level describes expected noise rates of mutations in cell free nucleic acid samples per position of the plurality of positions on the reference allele (the Examiner interprets this as accessing information about the reads, rather than performing laboratory activities outside of a computer; as such, these elements are necessary pre-solution activity of inputting information for the judicial exception);
determine a second likelihood of true alternate frequency of the genomic nucleic acid sample by applying a second function to model the second alternate depths of the genomic nucleic acid sample and adding a second noise level to an output for the second function, wherein the second noise level describes expected noise rates of mutations in genomic nucleic acid samples per position of the plurality of positions on the reference allele (the Examiner interprets this as accessing information about the reads, rather than performing laboratory activities outside of a computer; as such, these elements are necessary pre-solution activity of inputting information for the judicial exception); and
output one or more candidate variants of the cell free nucleic acid sample based on the first likelihood of the true alternate frequency of the cell free nucleic acid sample and the second likelihood of the true alternate frequency of the genomic nucleic acid sample (necessary post-solution activity for the judicial exception).
17. The system of claim 16, wherein the first function is a first Poisson distribution function parameterized by a first product of the first depths and a true alternate frequency of the cell free nucleic acid sample, and wherein the second function is a second Poisson distribution function parameterized by a second product of the second depths and a true alternate frequency of the genomic nucleic acid sample (mathematics).
18. The system of claim 16, wherein instructions for outputting the one or more candidate variants of the cell free nucleic acid sample further cause the processor to:
determine a probability that the true alternate frequency of the cell free nucleic acid sample is greater than a function of the true alternate frequency of the genomic nucleic acid sample, wherein the probability represents a confidence level that mutations from the first sequence reads from the cell free nucleic acid sample are not found in the second sequence reads from the genomic nucleic acid sample (mathematics); and
filter the plurality of candidate variants based on the determined probability (mathematical filtering); and
output the filtered candidate variants (necessary post-solution activity).
19. The system of claim 18, wherein instructions for determining the probability further cause the processor to:
determine a joint likelihood of the first likelihood and the second likelihood by (mathematics):
determining a cumulative sum of one of the first and second likelihoods (mathematics); and
determining an integral of the other of the first and second likelihoods (mathematics).
20. The system of claim 16, further comprising instructions that cause the processor to:
determine the first noise level of mutations with respect to the cell free nucleic acid samples using a third function parameterized by first parameters (mathematics); and
determine the second noise level of mutations with respect to the genomic nucleic acid samples using a fourth function parameterized by second parameters (mathematics).
21. The system of claim 16, further comprising instructions that cause the processor to:
collect the cell free nucleic acid sample from a blood sample of the subject (necessary pre-solution activity that is well-understood, routine, and conventional); and
perform enrichment on the cell free nucleic acid sample to generate the first sequence reads (necessary pre-solution activity that is well-understood, routine, and conventional).
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claim(s) 2-4, 6-11, 13-18, and 21 is/are rejected under 35 U.S.C. 103 as being unpatentable over “Kockan” in view of “Halperin”, wherein these references are:
Kockan: Kockan, Can, et al. "SiNVICT: ultra-sensitive detection of single nucleotide variants and indels in circulating tumour DNA." Bioinformatics 33.1 (2017): 26-34.
Halperin: US-20190362808-A1.
Note that Halperin is supported by provisional patent application 62/453,492.
As to claim 2, Kockan teaches a method comprising:
accessing sequence reads of a cell free nucleic acid sample of a subject obtained from a first source of the subject, the sequence reads of the cell free nucleic acid sample comprising first depths and first alternate depths for a plurality of positions on a reference allele (Kockan page 28 paragraphs 1-2 of section 2.2: accessing read count data to obtain a first set of calls);
determining a first likelihood of true alternate frequency of the cell free nucleic acid sample by applying a first function to model the first alternate depths for the cell free nucleic acid sample and adding a first noise level to an output for the first function, wherein the first noise level describes expected noise rates of mutations in cell free nucleic acid samples per position of the plurality of positions on the reference allele (Kockan page 28 paragraphs 3-4 of section 2.2: determining P-value of a mutation at a particular position by modeling an error rate using a Poisson cumulative distribution function);
determining a second likelihood of true alternate frequency of the genomic nucleic acid sample by applying a second function to model the second alternate depths of the genomic nucleic acid sample and adding a second noise level to an output for the second function, wherein the second noise level describes expected noise rates of mutations in genomic nucleic acid samples per position of the plurality of positions on the reference allele (Kockan page 28-29 paragraphs 5-6 of section 2.2: determining the likelihood of the mutation being somatic by applying a second Poisson model for a germline variant at a particular location); and
outputting one or more candidate variants of the cell free nucleic acid sample based on the first likelihood of the true alternate frequency of the cell free nucleic acid sample and the second likelihood of the true alternate frequency of the genomic nucleic acid sample (Kockan page 28 paragraph 1 of section 2.2: outputting, for each variant/position, the P-value and confidence score for the potential mutation as well as for the mutation being a somatic mutation).
However, Kockan does not teach:
accessing sequence reads of a genomic nucleic acid sample of a subject obtained from a second source of the subject different than the first source of the subject, the sequence reads of the genomic nucleic acid sample comprising second depths and second alternate depths for the plurality of positions on a reference allele.
Instead, Kockan assumes a model of heterozygosity of putative genomic variants.
Nevertheless, Halperin teaches:
accessing sequence reads of a genomic nucleic acid sample of a subject obtained from a second source of the subject different than the first source of the subject, the sequence reads of the genomic nucleic acid sample comprising second depths and second alternate depths for the plurality of positions on a reference allele (Halerpin Para [0011], [0013] of Halperin’s provisional and Para [0105]-[0106] of Halperin’s non-provisional : accessing reads from a blood sample to determine allele fractions of a variant and calling them using Haplotype Caller in Para [0019] of Halperin’s provisional or Para [0117] of Halperin’s non-provisional; these samples were for use comparing germline to somatic distributions, as noted in the abstract of Halperin’s provisional and non-provisional applications).
Kockan and Halperin are in the same field of bioinformatics. Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the teachings of Kockan to include the teachings of Halperin because Halperin’s model is a more precise refinement compared to Kockan’s assumption of heterozygosity (see Halperin’s abstract). There would be a reasonable expectation of success because Kockan’s invention is validated (see Section 3 “Results” in Kockan) and Halperin’s refinement should improve the accuracy by better modeling allelic copy number, as noted in Halperin’s abstract.
As to claim 3, Kockan in view of Halperin teaches the method of claim 2, wherein the first function is a first Poisson distribution function parameterized by a first product of the first depths and a true alternate frequency of the cell free nucleic acid sample (Kockan page 28 Para 4 of section 2.2: the Poisson parameter is the product of N and r, where N is the depth at a position and the r is the error rate), and wherein the second function is a second Poisson distribution function parameterized by a second product of the second depths and a true alternate frequency of the genomic nucleic acid sample (Kockan page 28 Para 5 of section 2.2: the second Poisson distribution is parameterized by a product of the depth and a factor of ½ to reflect heterozygosity assumption).
As to claim 4, Kockan in view of Halperin teaches the method of claim 2, wherein outputting the one or more candidate variants of the cell free nucleic acid sample further comprises:
determining a probability that the true alternate frequency of the cell free nucleic acid sample is greater than a function of the true alternate frequency of the genomic nucleic acid sample, wherein the probability represents a confidence level that mutations from the first sequence reads from the cell free nucleic acid sample are not found in the second sequence reads from the genomic nucleic acid sample (Halperin Para [0007]: “determining whether each candidate variant is a somatic variant or a germline variant by comparing the observed allelic fraction to the expected allelic fraction”); and
filtering the plurality of candidate variants based on the determined probability (Halperin: filtered output as per Figure 1A); and
outputting the filtered candidate variants (Halperin: filtered output as per Figure 1A).
As to claim 6, Kockan in view of Halperin teaches the method of claim 2, further comprising:
determining the first noise level of mutations with respect to the cell free nucleic acid samples using a third function parameterized by first parameters (Kockan page 28 paragraphs 3-4 of section 2.2: determining an error rate using a Poisson cumulative distribution function which inputs the parameter lambda1); and
determining the second noise level of mutations with respect to the genomic nucleic acid samples using a fourth function parameterized by second parameters (Kockan page 28-29 paragraphs 5-6 of section 2.2: determining the likelihood of the mutation being somatic by applying a second Poisson model for a germline variant at a particular location using a parameter lambda2).
As to claim 7, Kockan in view of Halperin teaches the method of claim 6, wherein the first and second parameters represent parameters of distributions that encode noise levels of mutations with respect to a given position of a sequence read (Kockan page 28-29: the parameterized Poisson distributions encode noise levels for each position).
As to claim 8, Kockan in view of Halperin teaches the method of claim 2, further comprising:
collecting the cell free nucleic acid sample from a blood sample of the subject (Halerpin Para [0011], [0013] of Halperin’s provisional and Para [0105]-[0106] of Halperin’s non-provisional : accessing reads from a blood sample to determine allele fractions of a variant and calling them using Haplotype Caller in Para [0019] of Halperin’s provisional or Para [0117] of Halperin’s non-provisional; these samples were for use comparing germline to somatic distributions, as noted in the abstract of Halperin’s provisional and non-provisional applications); and
performing enrichment on the cell free nucleic acid sample to generate the first sequence reads (Halerpin Para [0011], [0013] of Halperin’s provisional and Para [0105]-[0106] of Halperin’s non-provisional : using Qiagen kits for enrichment).
As to claim 9, Kockan teaches a non-transitory computer readable storage medium storing computer program instructions that when executed by a processor cause the processor to:
access sequence reads of a cell free nucleic acid sample of a subject obtained from a first source of the subject, the sequence reads of the cell free nucleic acid sample comprising first depths and first alternate depths for a plurality of positions on a reference allele (Kockan page 28 paragraphs 1-2 of section 2.2: accessing read count data to obtain a first set of calls);
determine a first likelihood of true alternate frequency of the cell free nucleic acid sample by applying a first function to model the first alternate depths for the cell free nucleic acid sample and adding a first noise level to an output for the first function, wherein the first noise level describes expected noise rates of mutations in cell free nucleic acid samples per position of the plurality of positions on the reference allele (Kockan page 28 paragraphs 3-4 of section 2.2: determining P-value of a mutation at a particular position by modeling an error rate using a Poisson cumulative distribution function);
determine a second likelihood of true alternate frequency of the genomic nucleic acid sample by applying a second function to model the second alternate depths of the genomic nucleic acid sample and adding a second noise level to an output for the second function, wherein the second noise level describes expected noise rates of mutations in genomic nucleic acid samples per position of the plurality of positions on the reference allele (Kockan page 28-29 paragraphs 5-6 of section 2.2: determining the likelihood of the mutation being somatic by applying a second Poisson model for a germline variant at a particular location); and
output one or more candidate variants of the cell free nucleic acid sample based on the first likelihood of the true alternate frequency of the cell free nucleic acid sample and the second likelihood of the true alternate frequency of the genomic nucleic acid sample (Kockan page 28 paragraph 1 of section 2.2: outputting, for each variant/position, the P-value and confidence score for the potential mutation as well as for the mutation being a somatic mutation).
However, Kockan does not teach:
access sequence reads of a genomic nucleic acid sample of a subject obtained from a second source of the subject different than the first source of the subject, the sequence reads of the genomic nucleic acid sample comprising second depths and second alternate depths for the plurality of positions on a reference allele;
Instead, Kockan assumes a model of heterozygosity of putative genomic variants.
Nevertheless, Halperin teaches:
access sequence reads of a genomic nucleic acid sample of a subject obtained from a second source of the subject different than the first source of the subject, the sequence reads of the genomic nucleic acid sample comprising second depths and second alternate depths for the plurality of positions on a reference allele (Halerpin Para [0011], [0013] of Halperin’s provisional and Para [0105]-[0106] of Halperin’s non-provisional : accessing reads from a blood sample to determine allele fractions of a variant and calling them using Haplotype Caller in Para [0019] of Halperin’s provisional or Para [0117] of Halperin’s non-provisional; these samples were for use comparing germline to somatic distributions, as noted in the abstract of Halperin’s provisional and non-provisional applications).
Kockan and Halperin are in the same field of bioinformatics. Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the teachings of Kockan to include the teachings of Halperin because Halperin’s model is a more precise refinement compared to Kockan’s assumption of heterozygosity (see Halperin’s abstract). There would be a reasonable expectation of success because Kockan’s invention is validated (see Section 3 “Results” in Kockan) and Halperin’s refinement should improve the accuracy by better modeling allelic copy number, as noted in Halperin’s abstract.
As to claim 10, Kockan in view of Halperin teaches the non-transitory computer readable storage medium of claim 9, wherein the first function is a first Poisson distribution function parameterized by a first product of the first depths and a true alternate frequency of the cell free nucleic acid sample (Kockan page 28 Para 4 of section 2.2: the Poisson parameter is the product of N and r, where N is the depth at a position and the r is the error rate), and wherein the second function is a second Poisson distribution function parameterized by a second product of the second depths and a true alternate frequency of the genomic nucleic acid sample (Kockan page 28 Para 5 of section 2.2: the second Poisson distribution is parameterized by a product of the depth and a factor of ½ to reflect heterozygosity assumption).
As to claim 11, Kockan in view of Halperin teaches the non-transitory computer readable storage medium of claim 9, wherein instructions for outputting the one or more candidate variants of the cell free nucleic acid sample further cause the processor to:
determine a probability that the true alternate frequency of the cell free nucleic acid sample is greater than a function of the true alternate frequency of the genomic nucleic acid sample, wherein the probability represents a confidence level that mutations from the first sequence reads from the cell free nucleic acid sample are not found in the second sequence reads from the genomic nucleic acid sample (Halperin Para [0007]: “determining whether each candidate variant is a somatic variant or a germline variant by comparing the observed allelic fraction to the expected allelic fraction”); and
filter the plurality of candidate variants based on the determined probability (Halperin: filtered output as per Figure 1A); and
output the filtered candidate variants (Halperin: filtered output as per Figure 1A).
As to claim 13, Kockan in view of Halperin teaches the non-transitory computer readable storage medium of claim 9, further comprising instructions that cause the processor to:
determine the first noise level of mutations with respect to the cell free nucleic acid samples using a third function parameterized by first parameters (Kockan page 28 paragraphs 3-4 of section 2.2: determining an error rate using a Poisson cumulative distribution function which inputs the parameter lambda1); and
determine the second noise level of mutations with respect to the genomic nucleic acid samples using a fourth function parameterized by second parameters (Kockan page 28-29 paragraphs 5-6 of section 2.2: determining the likelihood of the mutation being somatic by applying a second Poisson model for a germline variant at a particular location using a parameter lambda2).
As to claim 14, Kockan in view of Halperin teaches the non-transitory computer readable storage medium of claim 13, wherein the first and second parameters represent parameters of distributions that encode noise levels of mutations with respect to a given position of a sequence read (Kockan page 28-29: the parameterized Poisson distributions encode noise levels for each position).
As to claim 15, Kockan in view of Halperin teaches the non-transitory computer readable storage medium of claim 9, further comprising instructions that cause the processor to:
collect the cell free nucleic acid sample from a blood sample of the subject (Halerpin Para [0011], [0013] of Halperin’s provisional and Para [0105]-[0106] of Halperin’s non-provisional : accessing reads from a blood sample to determine allele fractions of a variant and calling them using Haplotype Caller in Para [0019] of Halperin’s provisional or Para [0117] of Halperin’s non-provisional; these samples were for use comparing germline to somatic distributions, as noted in the abstract of Halperin’s provisional and non-provisional applications); and
perform enrichment on the cell free nucleic acid sample to generate the first sequence reads (Halerpin Para [0011], [0013] of Halperin’s provisional and Para [0105]-[0106] of Halperin’s non-provisional : using Qiagen kits for enrichment).
As to claim 16, Kockan teaches a system comprising a processor and a computer memory, the computer memory storing computer program instructions that when executed by a processor cause the processor to:
access sequence reads of a cell free nucleic acid sample of a subject obtained from a first source of the subject, the sequence reads of the cell free nucleic acid sample comprising first depths and first alternate depths for a plurality of positions on a reference allele (Kockan page 28 paragraphs 1-2 of section 2.2: accessing read count data to obtain a first set of calls);
determine a first likelihood of true alternate frequency of the cell free nucleic acid sample by applying a first function to model the first alternate depths for the cell free nucleic acid sample and adding a first noise level to an output for the first function, wherein the first noise level describes expected noise rates of mutations in cell free nucleic acid samples per position of the plurality of positions on the reference allele (Kockan page 28 paragraphs 3-4 of section 2.2: determining P-value of a mutation at a particular position by modeling an error rate using a Poisson cumulative distribution function);
determine a second likelihood of true alternate frequency of the genomic nucleic acid sample by applying a second function to model the second alternate depths of the genomic nucleic acid sample and adding a second noise level to an output for the second function, wherein the second noise level describes expected noise rates of mutations in genomic nucleic acid samples per position of the plurality of positions on the reference allele (Kockan page 28-29 paragraphs 5-6 of section 2.2: determining the likelihood of the mutation being somatic by applying a second Poisson model for a germline variant at a particular location); and
output one or more candidate variants of the cell free nucleic acid sample based on the first likelihood of the true alternate frequency of the cell free nucleic acid sample and the second likelihood of the true alternate frequency of the genomic nucleic acid sample (Kockan page 28 paragraph 1 of section 2.2: outputting, for each variant/position, the P-value and confidence score for the potential mutation as well as for the mutation being a somatic mutation).
However, Kockan does not teach:
access sequence reads of a genomic nucleic acid sample of a subject obtained from a second source of the subject different than the first source of the subject, the sequence reads of the genomic nucleic acid sample comprising second depths and second alternate depths for the plurality of positions on a reference allele;
Instead, Kockan assumes a model of heterozygosity of putative genomic variants.
Nevertheless, Halperin teaches:
access sequence reads of a genomic nucleic acid sample of a subject obtained from a second source of the subject different than the first source of the subject, the sequence reads of the genomic nucleic acid sample comprising second depths and second alternate depths for the plurality of positions on a reference allele (Halerpin Para [0011], [0013] of Halperin’s provisional and Para [0105]-[0106] of Halperin’s non-provisional : accessing reads from a blood sample to determine allele fractions of a variant and calling them using Haplotype Caller in Para [0019] of Halperin’s provisional or Para [0117] of Halperin’s non-provisional; these samples were for use comparing germline to somatic distributions, as noted in the abstract of Halperin’s provisional and non-provisional applications).
Kockan and Halperin are in the same field of bioinformatics. Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the teachings of Kockan to include the teachings of Halperin because Halperin’s model is a more precise refinement compared to Kockan’s assumption of heterozygosity (see Halperin’s abstract). There would be a reasonable expectation of success because Kockan’s invention is validated (see Section 3 “Results” in Kockan) and Halperin’s refinement should improve the accuracy by better modeling allelic copy number, as noted in Halperin’s abstract.
As to claim 17, Kockan in view of Halperin teaches the system of claim 16, wherein the first function is a first Poisson distribution function parameterized by a first product of the first depths and a true alternate frequency of the cell free nucleic acid sample (Kockan page 28 Para 4 of section 2.2: the Poisson parameter is the product of N and r, where N is the depth at a position and the r is the error rate), and wherein the second function is a second Poisson distribution function parameterized by a second product of the second depths and a true alternate frequency of the genomic nucleic acid sample (Kockan page 28 Para 4 of section 2.2: the Poisson parameter is the product of N and r, where N is the depth at a position and the r is the error rate).
As to claim 18, Kockan in view of Halperin teaches the system of claim 16, wherein instructions for outputting the one or more candidate variants of the cell free nucleic acid sample further cause the processor to:
determine a probability that the true alternate frequency of the cell free nucleic acid sample is greater than a function of the true alternate frequency of the genomic nucleic acid sample, wherein the probability represents a confidence level that mutations from the first sequence reads from the cell free nucleic acid sample are not found in the second sequence reads from the genomic nucleic acid sample (Halperin Para [0007]: “determining whether each candidate variant is a somatic variant or a germline variant by comparing the observed allelic fraction to the expected allelic fraction”); and
filter the plurality of candidate variants based on the determined probability (Halperin: filtered output as per Figure 1A); and
output the filtered candidate variants (Halperin: filtered output as per Figure 1A).
As to claim 20, Kockan in view of Halperin teaches the system of claim 16, further comprising instructions that cause the processor to:
determine the first noise level of mutations with respect to the cell free nucleic acid samples using a third function parameterized by first parameters (Kockan page 28 paragraphs 3-4 of section 2.2: determining an error rate using a Poisson cumulative distribution function which inputs the parameter lambda1); and
determine the second noise level of mutations with respect to the genomic nucleic acid samples using a fourth function parameterized by second parameters (Kockan page 28-29 paragraphs 5-6 of section 2.2: determining the likelihood of the mutation being somatic by applying a second Poisson model for a germline variant at a particular location using a parameter lambda2).
As to claim 21, Kockan in view of Halperin teaches the system of claim 16, further comprising instructions that cause the processor to:
collect the cell free nucleic acid sample from a blood sample of the subject (Halerpin Para [0011], [0013] of Halperin’s provisional and Para [0105]-[0106] of Halperin’s non-provisional : accessing reads from a blood sample to determine allele fractions of a variant and calling them using Haplotype Caller in Para [0019] of Halperin’s provisional or Para [0117] of Halperin’s non-provisional; these samples were for use comparing germline to somatic distributions, as noted in the abstract of Halperin’s provisional and non-provisional applications); and
perform enrichment on the cell free nucleic acid sample to generate the first sequence reads (Halerpin Para [0011], [0013] of Halperin’s provisional and Para [0105]-[0106] of Halperin’s non-provisional : using Qiagen kits for enrichment).
Double Patenting
The nonstatutory double patenting rejection is based on a judicially created doctrine grounded in public policy (a policy reflected in the statute) so as to prevent the unjustified or improper timewise extension of the “right to exclude” granted by a patent and to prevent possible harassment by multiple assignees. A nonstatutory double patenting rejection is appropriate where the conflicting claims are not identical, but at least one examined application claim is not patentably distinct from the reference claim(s) because the examined application claim is either anticipated by, or would have been obvious over, the reference claim(s). See, e.g., In re Berg, 140 F.3d 1428, 46 USPQ2d 1226 (Fed. Cir. 1998); In re Goodman, 11 F.3d 1046, 29 USPQ2d 2010 (Fed. Cir. 1993); In re Longi, 759 F.2d 887, 225 USPQ 645 (Fed. Cir. 1985); In re Van Ornum, 686 F.2d 937, 214 USPQ 761 (CCPA 1982); In re Vogel, 422 F.2d 438, 164 USPQ 619 (CCPA 1970); In re Thorington, 418 F.2d 528, 163 USPQ 644 (CCPA 1969).
A timely filed terminal disclaimer in compliance with 37 CFR 1.321(c) or 1.321(d) may be used to overcome an actual or provisional rejection based on nonstatutory double patenting provided the reference application or patent either is shown to be commonly owned with the examined application, or claims an invention made as a result of activities undertaken within the scope of a joint research agreement. See MPEP § 717.02 for applications subject to examination under the first inventor to file provisions of the AIA as explained in MPEP § 2159. See MPEP § 2146 et seq. for applications not subject to examination under the first inventor to file provisions of the AIA . A terminal disclaimer must be signed in compliance with 37 CFR 1.321(b).
The filing of a terminal disclaimer by itself is not a complete reply to a nonstatutory double patenting (NSDP) rejection. A complete reply requires that the terminal disclaimer be accompanied by a reply requesting reconsideration of the prior Office action. Even where the NSDP rejection is provisional the reply must be complete. See MPEP § 804, subsection I.B.1. For a reply to a non-final Office action, see 37 CFR 1.111(a). For a reply to final Office action, see 37 CFR 1.113(c). A request for reconsideration while not provided for in 37 CFR 1.113(c) may be filed after final for consideration. See MPEP §§ 706.07(e) and 714.13.
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Claims 2, 3, 5, 9, 10, 16, 17, are rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1, 2, 7, 27, and 28 of U.S. Patent No. 11,961,589. Although the claims at issue are not identical, they are not patentably distinct from each other because the claims of the instant application are a genus of the species of the patented claims with minor variations that are at once envisaged.
Instant Application 18/605,798
U.S. Patent 11,961,589
2. A method comprising:
accessing sequence reads of a cell free nucleic acid sample of a subject obtained from a first source of the subject, the sequence reads of the cell free nucleic acid sample comprising first depths and first alternate depths for a plurality of positions on a reference allele;
accessing sequence reads of a genomic nucleic acid sample of a subject obtained from a second source of the subject different than the first source of the subject, the sequence reads of the genomic nucleic acid sample comprising second depths and second alternate depths for the plurality of positions on a reference allele;
determining a first likelihood of true alternate frequency of the cell free nucleic acid sample by applying a first function to model the first alternate depths for the cell free nucleic acid sample and adding a first noise level to an output for the first function, wherein the first noise level describes expected noise rates of mutations in cell free nucleic acid samples per position of the plurality of positions on the reference allele;
determining a second likelihood of true alternate frequency of the genomic nucleic acid sample by applying a second function to model the second alternate depths of the genomic nucleic acid sample and adding a second noise level to an output for the second function, wherein the second noise level describes expected noise rates of mutations in genomic nucleic acid samples per position of the plurality of positions on the reference allele; and
outputting one or more candidate variants of the cell free nucleic acid sample based on the first likelihood of the true alternate frequency of the cell free nucleic acid sample and the second likelihood of the true alternate frequency of the genomic nucleic acid sample.
1. A method comprising: generating a plurality of candidate variants of a cell free nucleic acid sample of a subject; for each position of a plurality of positions of a reference allele: determining a first depth and a first alternate depth of first sequence reads from the cell free nucleic acid sample, wherein the first sequence reads are obtained from a sample of blood, whole blood, plasma, serum, urine, cerebrospinal fluid, feces, saliva, tears, a tissue biopsy, pleural fluid, pericardial fluid, or peritoneal fluid of the subject, and wherein the first depth represents a total number of the first sequence reads at the position, and wherein the first alternate depth represents a number of the first sequence reads having a mutation at the position based on the reference allele; and determining a second depth and a second alternate depth of second sequence reads from a genomic nucleic acid sample of the subject, wherein the second sequence reads are obtained from a sample of white blood cells or tumor cells of a tumor biopsy of the subject, and wherein the second depth represents a total number of the second sequence reads at the position, and wherein the second alternate depth represents a number of the second sequence reads having a mutation at the position based on the reference allele; determining a first likelihood of true alternate frequency of the cell free nucleic acid sample by applying a Bayesian hierarchical model to model the first alternate depths using aa first function parameterized by the first depths and the true alternate frequency of the cell free nucleic acid sample and (ii) a first noise level of mutations with respect to healthy cell free nucleic acid samples, wherein the first noise level describes expected noise rates per position of the first sequence reads; determining a second likelihood of true alternate frequency of the genomic nucleic acid sample by applying the Bayesian hierarchical model to model the second alternate depths using aa second function parameterized by the second depths and the true alternate frequency of the genomic nucleic acid sample and (ii) a second noise level of mutations with respect to healthy genomic nucleic acid samples, wherein the second noise level describes expected noise rates per position of the second sequence reads; filtering the plurality of candidate variants at least by a machine learning model using the first likelihood and the second likelihood to determine a probability that the true alternate frequency of the cell free nucleic acid sample is greater than a function of the true alternate frequency of the genomic nucleic acid sample; and outputting the filtered candidate variants.
3. The method of claim 2, wherein the first function is a first Poisson distribution function parameterized by a first product of the first depths and a true alternate frequency of the cell free nucleic acid sample, and wherein the second function is a second Poisson distribution function parameterized by a second product of the second depths and a true alternate frequency of the genomic nucleic acid sample.
2. The method of claim 1, wherein the first function is a Poisson distribution function parameterized by a product of one of the first depths and the true alternate frequency of the cell free nucleic acid sample, and wherein the second function is another Poisson distribution function parameterized by another product of one of the second depths and the true alternate frequency of the genomic nucleic acid sample.
5. The method of claim 4, wherein determining the probability comprises:
determining a joint likelihood of the first likelihood and the second likelihood by:
determining a cumulative sum of one of the first and second likelihoods; and
determining an integral of the other of the first and second likelihoods.
7. The method of claim 1, wherein determining the probability comprises numerically approximating a joint likelihood of the first likelihood and the second likelihood by: determining a cumulative sum of one of the first and second likelihoods; and determining an integral of the other of the first and second likelihoods.
9. A non-transitory computer readable storage medium storing computer program instructions that when executed by a processor cause the processor to:
access sequence reads of a cell free nucleic acid sample of a subject obtained from a first source of the subject, the sequence reads of the cell free nucleic acid sample comprising first depths and first alternate depths for a plurality of positions on a reference allele;
access sequence reads of a genomic nucleic acid sample of a subject obtained from a second source of the subject different than the first source of the subject, the sequence reads of the genomic nucleic acid sample comprising second depths and second alternate depths for the plurality of positions on a reference allele;
determine a first likelihood of true alternate frequency of the cell free nucleic acid sample by applying a first function to model the first alternate depths for the cell free nucleic acid sample and adding a first noise level to an output for the first function, wherein the first noise level describes expected noise rates of mutations in cell free nucleic acid samples per position of the plurality of positions on the reference allele;
determine a second likelihood of true alternate frequency of the genomic nucleic acid sample by applying a second function to model the second alternate depths of the genomic nucleic acid sample and adding a second noise level to an output for the second function, wherein the second noise level describes expected noise rates of mutations in genomic nucleic acid samples per position of the plurality of positions on the reference allele; and
output one or more candidate variants of the cell free nucleic acid sample based on the first likelihood of the true alternate frequency of the cell free nucleic acid sample and the second likelihood of the true alternate frequency of the genomic nucleic acid sample.
27. A computer-product comprising a non-transitory computer readable medium storing a plurality of instructions for controlling a computer system to: generate a plurality of candidate variants of a cell free nucleic acid sample of a subject; for each position of a plurality of positions of a reference allele: determine a first depth and a first alternate depth of first sequence reads from the cell free nucleic acid sample, wherein the first sequence reads are obtained from a sample of blood, whole blood, plasma, serum, urine, cerebrospinal fluid, feces, saliva, tears, a tissue biopsy, pleural fluid, pericardial fluid, or peritoneal fluid of the subject, and wherein the first depth represents a total number of the first sequence reads at the position, and wherein the first alternate depth represents a number of the first sequence reads having a mutation at the position based on the reference allele; and determine a second depth and a second alternate depth of second sequence reads from a genomic nucleic acid sample of the subject, wherein the second sequence reads are obtained from a sample of white blood cells or tumor cells of a tumor biopsy of the subject, and wherein the second depth represents a total number of the second sequence reads at the position, and wherein the second alternate depth represents a number of the second sequence reads having a mutation at the position based on the reference allele; determine a first likelihood of true alternate frequency of the cell free nucleic acid sample by applying a Bayesian hierarchical model to model the first alternate depths using aa first function parameterized by the first depths and the true alternate frequency of the cell free nucleic acid sample and (ii) a first noise level of mutations with respect to healthy cell free nucleic acid samples wherein the first noise level describes expected noise rates per position of the first sequence reads; determine a second likelihood of true alternate frequency of the genomic nucleic acid sample by applying the Bayesian hierarchical model to model the second alternate depths using aa second function parameterized by the second depths and the true alternate frequency of the genomic nucleic acid sample and (ii) a second noise level of mutations with respect to healthy genomic nucleic acid samples, wherein the second noise level describes expected noise rates per position of the second sequence reads; filter the plurality of candidate variants at least by a machine learning model using the first likelihood and the second likelihood to determine a probability that the true alternate frequency of the cell free nucleic acid sample is greater than a function of the true alternate frequency of the genomic nucleic acid sample; and output the filtered candidate variants.
10. The non-transitory computer readable storage medium of claim 9, wherein the first function is a first Poisson distribution function parameterized by a first product of the first depths and a true alternate frequency of the cell free nucleic acid sample, and wherein the second function is a second Poisson distribution function parameterized by a second product of the second depths and a true alternate frequency of the genomic nucleic acid sample.
2. The method of claim 1, wherein the first function is a Poisson distribution function parameterized by a product of one of the first depths and the true alternate frequency of the cell free nucleic acid sample, and wherein the second function is another Poisson distribution function parameterized by another product of one of the second depths and the true alternate frequency of the genomic nucleic acid sample.
Although claim 2 is a method, the non-transitory medium that gives rise to the method is an obvious variation with reasonable expectation of success. One would be motivated to carry out the method given the medium in order to benefit from the execution.
16. A system comprising a processor and a computer memory, the computer memory storing computer program instructions that when executed by a processor cause the processor to:
access sequence reads of a cell free nucleic acid sample of a subject obtained from a first source of the subject, the sequence reads of the cell free nucleic acid sample comprising first depths and first alternate depths for a plurality of positions on a reference allele;
access sequence reads of a genomic nucleic acid sample of a subject obtained from a second source of the subject different than the first source of the subject, the sequence reads of the genomic nucleic acid sample comprising second depths and second alternate depths for the plurality of positions on a reference allele;
determine a first likelihood of true alternate frequency of the cell free nucleic acid sample by applying a first function to model the first alternate depths for the cell free nucleic acid sample and adding a first noise level to an output for the first function, wherein the first noise level describes expected noise rates of mutations in cell free nucleic acid samples per position of the plurality of positions on the reference allele;
determine a second likelihood of true alternate frequency of the genomic nucleic acid sample by applying a second function to model the second alternate depths of the genomic nucleic acid sample and adding a second noise level to an output for the second function, wherein the second noise level describes expected noise rates of mutations in genomic nucleic acid samples per position of the plurality of positions on the reference allele; and
output one or more candidate variants of the cell free nucleic acid sample based on the first likelihood of the true alternate frequency of the cell free nucleic acid sample and the second likelihood of the true alternate frequency of the genomic nucleic acid sample.
28. A system comprising a computer processor and a memory, the memory storing computer program instructions that when executed by the computer processor cause the processor to perform steps comprising: generating a plurality of candidate variants of a cell free nucleic acid sample of a subject; for each position of a plurality of positions of a reference allele: determining a first depth and a first alternate depth of first sequence reads from the cell free nucleic acid sample, wherein the first sequence reads are obtained from a sample of blood, whole blood, plasma, serum, urine, cerebrospinal fluid, feces, saliva, tears, a tissue biopsy, pleural fluid, pericardial fluid, or peritoneal fluid of the subject, and wherein the first depth represents a total number of the first sequence reads at the position, and wherein the first alternate depth represents a number of the first sequence reads having a mutation at the position based on the reference allele; and determining a second depth and a second alternate depth of second sequence reads from a genomic nucleic acid sample of the subject, wherein the second sequence reads are obtained from a sample of white blood cells or tumor cells of a tumor biopsy of the subject, and wherein the second depth represents a total number of the second sequence reads at the position, and wherein the second alternate depth represents a number of the second sequence reads having a mutation at the position based on the reference allele; determining a first likelihood of true alternate frequency of the cell free nucleic acid sample by applying a Bayesian hierarchical model to model the first alternate depths using (i) a first function parameterized by the first depths and the true alternate frequency of the cell free nucleic acid sample and (ii) a first noise level of mutations with respect to healthy cell free nucleic acid samples, wherein the first noise level describes expected noise rates per position of the first sequence reads; determining a second likelihood of true alternate frequency of the genomic nucleic acid sample by applying the Bayesian hierarchical model to model the second alternate depths using aa second function parameterized by the second depths and the true alternate frequency of the genomic nucleic acid sample and (ii) a second noise level of mutations with respect to healthy genomic nucleic acid samples, wherein the second noise level describes expected noise rates per position of the second sequence reads; filtering the plurality of candidate variants at least by a machine learning model using the first likelihood and the second likelihood to determine a probability that the true alternate frequency of the cell free nucleic acid sample is greater than a function of the true alternate frequency of the genomic nucleic acid sample; and outputting the filtered candidate variants.
17. The system of claim 16, wherein the first function is a first Poisson distribution function parameterized by a first product of the first depths and a true alternate frequency of the cell free nucleic acid sample, and wherein the second function is a second Poisson distribution function parameterized by a second product of the second depths and a true alternate frequency of the genomic nucleic acid sample.
2. The method of claim 1, wherein the first function is a Poisson distribution function parameterized by a product of one of the first depths and the true alternate frequency of the cell free nucleic acid sample, and wherein the second function is another Poisson distribution function parameterized by another product of one of the second depths and the true alternate frequency of the genomic nucleic acid sample.
Although claim 2 is a method, the system that gives rise to the method is an obvious variation with reasonable expectation of success. One would be motivated to carry out the method given the system in order to benefit from the execution.
Conclusion
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/JESSE P FRUMKIN/Primary Examiner, Art Unit 1685 March 7, 2026