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 .
Information Disclosure Statement
The IDS filed 5/15/2024 has been considered by the Examiner.
Priority
Applicant’s claim for the benefit of a prior-filed application under 35 U.S.C. 119(e) or under 35 U.S.C. 120, 121, or 365(c) is acknowledged. Priority of US application 62/593,095 filed 11/30/2017 is acknowledged.
Claim Status
Claims 1-14 are cancelled.
Claims 15-22 are pending and are examined on the merits.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 15-18 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
The term “minority” in claim 15 (3rd and 5th steps, 2nd line) is a relative term which renders the claim indefinite. The term “minority” is not defined by the claim, the specification does not provide a standard for ascertaining the requisite degree, and one of ordinary skill in the art would not be reasonably apprised of the scope of the invention. The claim hence is indefinite regarding when to eliminate sequence difference during collapsing sequence reads. It is unclear how one of ordinary skill would determine what the criteria for a “minority” is so that the differences are eliminating in only the minority and not throughout the entire subset. Minority is a term of degree and therefore indefinite.
To advance compact prosecution, the “minority” here is interpreted as if per column based base count (in a situation of multiple sequence alignment), which is the opposite of “majority” in base count.
The term ”baseline” in the first lien of the last step of claim 15 is not defined. After a sequencing operation only reads are generated and there is no designation of something like a “baseline sequence”. “Baseline” is hence indefinite.
To advance compact prosecution, “baseline” is interpreted here as a virtual concept that when a reference sequence is not available for identifying variants, the “correct” base which people believe to have no error, no variation is the “baseline base” and similarly, this concept extends to “baseline sequence”.
Applicant can amend claim 15 accordingly.
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 15-22 are rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter.
Step 1: Process, Machine, Manufacture or Composition
Claims 15-18 are to a method, so a process.
Claims 19-22 are to a sequencing device, so a machine or manufacturer.
Step 2A Prong One: Identification of Abstract Ideas
Identifying first sequence differences within a first subset of the plurality of sequence reads associated with a first unique molecular identifier (claim 15);
This step recites identifying the sequence difference within a subset. Under a broadest reasonable interpretation (BRI), this step can be achieved in human mind with the help of a pen and paper. Hence, this step equates to an abstract idea of mental processes.
Collapsing the first subset to yield a collapsed first subset sequence read, wherein the collapsing comprises eliminating sequence differences present in a minority of the sequencing reads of the first subset (claim 15);
This step recites data manipulation operations of collapsing and eliminating sequences. Under a BRI, this step can be achieved in human mind with the help of a pen and paper. Hence, this step equates to an abstract idea of mental processes.
Identifying second sequence differences within a second subset of the plurality of sequence reads associated with a second unique molecular identifier, the second unique molecular identifier being complementary at least in part to the first unique molecular identifier (claim 15);
This step recites identifying the sequence difference within a subset. Under a BRI, this step can be achieved in human mind with the help of a pen and paper. Hence, this step equates to an abstract idea of mental processes.
Collapsing the second subset to yield a collapsed second subset sequence read, wherein the collapsing comprises eliminating sequence differences present in a minority of the sequencing reads of the second subset (claim 15);
This step recites data manipulation operations of collapsing and eliminating sequences. Under a BRI, this step can be achieved in human mind with the help of a pen and paper. Hence, this step equates to an abstract idea of mental processes.
Determining that a sequence variant relative to a baseline in the collapsed first subset, the collapsed second subset, or a duplex of the collapsed first subset and the collapsed second subset is valid based on a function of an error rate of the genomic sequence data, wherein the error rate is determined based in part on the identified first sequence differences and the identified second sequence differences (claim 15);
This step recites “a function of an error rate of the genomic sequence data, wherein the error rate is determined based in part on the identified first sequence differences and the identified second sequence differences”, which is a mathematical operation with defined input data. “Determining that a sequence variant … is valid” reads on a decision-making process (based on the result of the mathematical function). Therefore, this step equates to abstract ideas of mental processes and Mathematical concepts.
Identify a plurality of errors in the genomic sequence data based on sequence disagreement between sequence reads associated with each unique molecular identifier of the plurality of unique molecular identifiers to generate an error rate of the genomic sequence data (claim 19);
This step recites identifying the sequence errors within a subset of same unique molecular identifier, and to generate an error rate. Under a BRI, this step can be achieved in human mind with the help of a pen and paper. Hence, this step equates to an abstract idea of mental processes.
Identify a plurality of potential sequence variants in the genomic sequence data relative to a reference sequence (claim 19); and
This step recites identifying a plurality of potential sequence variants. Under a BRI, this step can be achieved in human mind with the help of a pen and paper. Hence, this step equates to an abstract idea of mental processes.
Determine a validity of the plurality of potential sequence variants based at least in part on the error rate (claim 19).
This step recites determining a validity of the plurality of potential sequence variants. Under a BRI, this step can be achieved in human mind with the help of a pen and paper. Hence, this step equates to an abstract idea of mental processes.
Step 2A Prong Two: Consideration of Practical Application
The claims result in a process of determining a validity of the plurality of potential sequence variants based at least in part on the error rate, which is to an abstract idea. The claims do not recite any additional elements that integrate the abstract idea/judicial exception into a practical application.
This judicial exception is not integrated into a practical application because the claims do not meet any of the following criteria:
An additional element reflects an improvement in the functioning of a computer, or an improvement to other technology or technical field;
an additional element that applies or uses a judicial exception to effect a particular treatment or prophylaxis for a disease or medical condition;
an additional element implements a judicial exception with, or uses a judicial exception in conjunction with, a particular machine or manufacture that is integral to the claim;
an additional element effects a transformation or reduction of a particular article to a different state or thing; and
an additional element applies or uses the judicial exception in some other meaningful way beyond generally linking the use of the judicial exception to a particular technological environment, such that the claim as a whole is more than
a drafting effort designed to monopolize the exception.
Step 2B: Consideration of Additional Elements and Significantly More
The claimed method also recites "additional elements" that are not limitations drawn to an abstract idea. The recited additional elements are drawn to:
receiving genomic sequence data of a first biological sample, wherein the genomic sequence data comprises a plurality of sequence reads, each sequence read being associated with a unique molecular identifier of a plurality of unique molecular identifiers (claim 15);
A sequencing device configured to identify sequence variants in genomic sequence data of a biological sample (claim 19),
a memory device comprising executable application instructions stored therein (claim 19);
a processor configured to execute the application instructions stored in the memory device (claim 19);
receive genomic sequence data of a biological sample, wherein the genomic sequence data comprises a plurality of sequence reads, each sequence read being associated with a unique molecular identifier of a plurality of unique molecular identifiers (claim 19).
The claims do not include additional elements that are sufficient to amount of significantly more than the judicial exception because it is routine and conventional to perform the acts of receiving genomic sequence data of a biological sample (MPEP §2106.05(d).II(i and iv)). The second element of the method include a generic sequencing device, which is available in commercial service, hence are well-understood, routine, and conventional. Other elements of the method include a memory device and a processor which are recitation of generic computer structures that serve to perform generic computer functions, hence are well-understood, routine, and conventional activities previously known to the pertinent industry. Viewed as a whole, these additional claim element(s) do not provide meaningful limitation(s) to transform the abstract idea recited in the instantly presented claims into a patent eligible application of the abstract idea such that the claim(s) amounts to significantly more than the abstract idea itself.
Therefore, the claim(s) are rejected under 35 U.S.C. 101 as being directed to non-statutory subject matter.
Claim Rejections - 35 USC § 102
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
Claims 15-22 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Gnerre (“Error Suppression In Sequenced DNA Fragments Using Redundant Reads With Unique Molecular Indices (UMIS) “, US 20160319345 A1, Date Published 2016-11-03. Cited on the 5/15/2024 IDS).
Claim 15 is interpreted as a computer-implemented method for detecting sequence variants. Regarding claim 15, Gnerre provides (par. [025]) “the computer system to implement a method for determining sequence information of a sequence of interest in a sample using unique molecular indices (UMIs). The program code includes: (a) code for obtaining reads of a plurality of amplified polynucleotides, wherein the plurality of amplified polynucleotides are obtained by amplifying double-stranded DNA fragments in the sample including the sequence of interest and attaching adapters to the double-stranded DNA fragments:, which teaches a computer-implemented method for receiving genomic sequence data of a sample, wherein the genomic sequence data comprises a plurality of sequence reads, each sequence read being associated with a unique molecular identifier of a plurality of unique molecular identifiers.
Gnerre provides (par. [025]) “(b) code for identifying a plurality of physical UMIs in the reads of the plurality of amplified polynucleotides, wherein each physical UMI is found in an adapter attached to one of the double-stranded DNA fragment”, which teaches identifying first sequence differences within a first subset of the plurality of sequence reads associated with a first unique molecular identifier.
Gnerre provides (par. [0026]) “in some implementations, the code for determining sequences of the double-stranded DNA fragments includes: (i) code for collapsing reads having a same first physical UMI into a first group to obtain a first consensus nucleotide sequence”, which teaches collapsing the first subset to yield a collapsed first subset sequence read, wherein “to obtain a first consensus nucleotide sequence” suggests to eliminate sequence differences present in a minority of the sequencing reads of the first subset, because this is the necessary step in building a consensus sequence from a cluster of multiple alignment.
Gnerre provides (par. [025]) “(c) code for identifying a plurality of virtual UMIs in the received reads of the plurality of amplified polynucleotides, wherein each virtual UMI is found in an individual molecule of one of the double-stranded DNA fragments;” and (par. [0011]) “In some implementations, the adapters each include a physical UMI on each strand of the adapters in a double-stranded region of the adapters, wherein the physical UMI on one strand is complementary to the physical UMI on the other strand. In some implementations, operation (f) includes: (i) combining reads having a first physical UMI, at least one virtual UMI, and a second physical UMI in the 5′ to 3′ direction and reads having the second physical UMI, the at least one virtual UMI, and the first physical UMI in the 5′ to 3′ direction to determine a consensus nucleotide sequence”, which teaches identifying second sequence differences within a second subset of the plurality of sequence reads associated with a second unique molecular identifier, the second unique molecular identifier being complementary at least in part to the first unique molecular identifier.
Gnerre provides (par. [0026]) “(ii) code for collapsing reads having a same second physical UMI into a second group to obtain a second consensus nucleotide sequence”, which teaches collapsing the second subset to yield a collapsed second subset sequence read, wherein “to obtain a second consensus nucleotide sequence” means to eliminate sequence differences present in a minority of the sequencing reads of the first subset. Because to obtaining a consensus the minority sequence have to be discarded.
Gnerre provides (par. [0168]) “using UMI and collapsing scheme described herein, various embodiments can suppress different sources of error affecting the determined sequence of a fragment even if the fragment includes alleles with very low allele frequencies. Reads sharing the same UMIs (physical and/or virtual) are grouped together. By collapsing the grouped reads, variants (SNV and small indels) due to PCR, library preparation, clustering, and sequencing errors can be eliminated”, which teaches Determining that a sequence variant relative to a baseline in the collapsed first subset, the collapsed second subset, or a duplex of the collapsed first subset and the collapsed second subset is valid.
Gnerre provides (par. [0268]) “FIG. 7A shows profile of error rate (allele frequency of second highest base) of high quality bases (>Q30) using standard method (the mean error rate is 0.04%). FIG. 7B shows profile of error rate of collapsing/UMI pipeline (the mean error rate is 0.007%). Note that these results are based on prototype code, and further reduction of error rate may be achieved with refined methods” and (par.[0271[) “FIG. 10 shows different errors occur in three samples processed with random UMIs in tabular form. The first three rows of data indicate the percentages of different types of errors 43 samples. The last row shows error rates averaged across the samples. As shown in the table, 97.58% of the UMIs contain no errors, and 1.07% of the UMIs contain one recoverable era. Over 98.65% of all the UMIs are usable for indexing individual DNA fragments. Many of the rest may still be usable when combined with contextual information”, which suggests a function of an error rate of the genomic sequence data, wherein the error rate is determined based in part on the identified sequence differences.
Regarding claim 16, Gnerre provides (par. [0171]) “FIG. 4A illustrates how a first-level collapsing may suppress sequencing errors. Sequencing errors occur on the sequencing platform after sample and library preparation (e.g., PCR amplification). Sequencing errors may introduce different erroneous bases into different reads. True positive bases are illustrated by solid letters, while false positive bases are illustrated by hatched letters. False positive nucleotides on different reads in the α-ρ-φ family have been excluded from the a consensus sequence. The true positive nucleotide “A” illustrated on the left ends of the α-ρ-φ family reads is retained for the a consensus sequence. Similarly, false positive nucleotides on different reads in the β-φ-ρ family have been excluded from the β consensus sequence, retaining the true positive nucleotide “A”. As illustrated here, the first level collapsing can effectively remove sequencing errors. FIG. 4A also shows an optional second-level collapsing relying on the virtual UMIs ρ and φ. This second-level collapsing may further suppress errors as explained above, but such errors are not illustrated in FIG. 4A”, which teaches determining a variant in a third subset with a third UMI is valid based on the function of the error rate, because the second-level collapsing relying on the virtual UMIs ρ and φ is the third subset (par. [0170]).
Regarding claim 17, Gnerre provides (par. [0173]) “in some sequencing platforms, homopolymer errors occur to introduce small indel errors into homopolymers of repeating single nucleotides. FIGS. 4C and 4E illustrate homopolymer error correction using the methods described herein. In the α-ρ-φ (FIG. 4C) or α-ρ-φ-β (FIG. 4E) family reads, two “T” nucleotides have been deleted from the second read from the top, and one “T” nucleotide has been deleted from the third read from the top. In the β-φ-ρ (FIG. 4C) or β-φ-ρ-α (FIG. 4E) family reads, one “A” nucleotides has been inserted into the first read from the top. Similar to sequencing error illustrated in FIG. 4A, homopolymer errors occur after PCR amplification, therefore different reads have different homopolymer errors. As a result, the first level collapsing can effectively remove indel errors”, which teaches determining that an additional sequence variant in a third subset associated with a third unique molecular identifier is a false positive based on the function of the error rate.
Regarding claim 18, Gnerre provides (par. [0173]) “in some sequencing platforms, homopolymer errors occur to introduce small indel errors into homopolymers of repeating single nucleotides. FIGS. 4C and 4E illustrate homopolymer error correction using the methods described herein. In the α-ρ-φ (FIG. 4C) or α-ρ-φ-β (FIG. 4E) family reads, two “T” nucleotides have been deleted from the second read from the top, and one “T” nucleotide has been deleted from the third read from the top. In the β-φ-ρ (FIG. 4C) or β-φ-ρ-α (FIG. 4E) family reads, one “A” nucleotides has been inserted into the first read from the top. Similar to sequencing error illustrated in FIG. 4A, homopolymer errors occur after PCR amplification, therefore different reads have different homopolymer errors. As a result, the first level collapsing can effectively remove indel errors”, which teaches eliminating the additional sequence variant from an indication of sequence variants in the genomic sequence data.
Regarding claim 19, Gnerre provides (par. [0234]) “the computer product may contain instructions for performing any one or more of the above-described methods for determining a sequence of interest. As explained, the computer product may include a non-transitory and/or tangible computer readable medium having a computer executable or compliable logic (e.g., instructions) recorded thereon for enabling a processor to determine a sequence of interest. In one example, the computer product includes a computer readable medium having a computer executable or compliable logic (e.g., instructions) recorded thereon for enabling a processor to diagnose a condition or determine a nucleic acid sequence of interest”, which teaches a memory device comprising executable application instructions, and a processor configured to execute the application instructions.
Gnerre provides (par. [025]) “the computer system to implement a method for determining sequence information of a sequence of interest in a sample using unique molecular indices (UMIs). The program code includes: (a) code for obtaining reads of a plurality of amplified polynucleotides, wherein the plurality of amplified polynucleotides are obtained by amplifying double-stranded DNA fragments in the sample including the sequence of interest and attaching adapters to the double-stranded DNA fragments:, which teaches a computer-implemented method for receiving genomic sequence data of a sample, wherein the genomic sequence data comprises a plurality of sequence reads, each sequence read being associated with a unique molecular identifier of a plurality of unique molecular identifiers.
Gnerre provides (par. [0049]) “FIG. 2E schematically illustrates a nonrandom UMI design that provides a mechanism for detecting errors that occur in the UMI sequence during a sequencing process”, and (par. [0268]) “FIG. 7A shows profile of error rate (allele frequency of second highest base) of high quality bases (>Q30) using standard method (the mean error rate is 0.04%). FIG. 7B shows profile of error rate of collapsing/UMI pipeline (the mean error rate is 0.007%)”, which teaches identifying errors in the genomic sequence data, and generate error rate in sequence data based on UMI.
Gnerre provides (par. [0213]) “for each sample, reads of similar stretches of base calls are locally clustered. Forward and reversed reads are paired creating contiguous sequences. These contiguous sequences are aligned to the reference genome for variant identification”, which teaches identifying variants in sequence data relative to a sequence reference.
Gnerre provides (par. [0168]) “using UMI and collapsing scheme described herein, various embodiments can suppress different sources of error affecting the determined sequence of a fragment even if the fragment includes alleles with very low allele frequencies. Reads sharing the same UMIs (physical and/or virtual) are grouped together. By collapsing the grouped reads, variants (SNV and small indels) due to PCR, library preparation, clustering, and sequencing errors can be eliminated”, which teaches determining a validity of sequence variants based at least in part on the error rate.
Regarding claim 20, Gnerre provides (par. [0268]) “FIG. 7A and FIG. 7B show experimental data demonstrating the effectiveness of error suppression using the methods disclosed herein. Experimenters used sheared gDNA of NA12878. They used TruSeq library preparation and enrichment with custom panel (˜130 Kb). Sequencing was performed at 2×150 bp using HiSeq2500 rapid mode, and mean target coverage was ˜10,000×. FIG. 7A shows profile of error rate (allele frequency of second highest base) of high quality bases (>Q30) using standard method (the mean error rate is 0.04%). FIG. 7B shows profile of error rate of collapsing/UMI pipeline (the mean error rate is 0.007%)”, which suggests the validity (of variant identification) is based on a function of the error rate and a sequence coverage of an individual potential sequence variant.
Regarding claim 21, Gnerre provides (par. [0243]) “in one example, a user provides a sample into a sequencing apparatus. Data is collected and/or analyzed by the sequencing apparatus which is connected to a computer. Software on the computer allows for data collection and/or analysis. Data can be stored, displayed (via a monitor or other similar device), and/or sent to another location”, and (par. [0244]) “in some embodiments, the methods also include collecting data regarding a plurality of polynucleotide sequences (e.g., reads, tags and/or reference chromosome sequences) and sending the data to a computer or other computational system. For example, the computer can be connected to laboratory equipment, e.g., a sample collection apparatus, a nucleotide amplification apparatus, a nucleotide sequencing apparatus, or a hybridization apparatus. The computer can then collect applicable data gathered by the laboratory device”, which teaches an interface for data input and the input can be sample type.
Regarding claim 22, Gnerre provides (par. [0003]) “next generation sequencing technology is providing increasingly high speed of sequencing, allowing larger sequencing depth. However, because sequencing accuracy and sensitivity are affected by errors and noise from various sources, e.g., sample defects, PCR during library preparation, enrichment, clustering, and sequencing, increasing depth of sequencing alone cannot ensure detection of sequences of very low allele frequency, such as in fetal cell-free DNA (cfDNA) in maternal plasma, circulating tumor DNA (ctDNA), sub-clonal mutations in pathogens”, which suggests the error rate is affected by (hence weighted based on) the sample type.
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.
The USPTO Internet website contains terminal disclaimer forms which may be used. Please visit www.uspto.gov/patent/patents-forms. The actual filing date of the application in which the form is filed determines what form (e.g., PTO/SB/25, PTO/SB/26, PTO/AIA /25, or PTO/AIA /26) should be used. A web-based eTerminal Disclaimer may be filled out completely online using web-screens. An eTerminal Disclaimer that meets all requirements is auto-processed and approved immediately upon submission. For more information about eTerminal Disclaimers, refer to www.uspto.gov/patents/apply/applying-online/eterminal-disclaimer.
Claim 15 is rejected on the ground of nonstatutory double patenting as being unpatentable over claim 1 of U.S. Patent No. US 12040047B2. Although the claims at issue are not identical, they are not patentably distinct from each other because every step in instant claim 15 is anticipated by the reference claim 1.
Conclusion
No claims are allowed.
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/GL/
Patent Examiner
Art Unit 1686
/Anna Skibinsky/
Primary Examiner, AU 1635
Prosecution Insights