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 .
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 30 April 2026 has been entered.
Status of the Claims
The amended claim set received 30 April 2026 has been entered into the application.
Claims 3-4, 7, 9-10, 12, 20, 22-25, 27-29, 31-35, 41-45, 48-56, 58-62, and 64-70 are cancelled.
Claims 71-78 are new.
Claims 37 are amended.
Claim(s) 37, 39-40, 46-47, 57, 63, and 71-78 are pending.
Election/Restrictions
Applicant’s elects without traverse Group II including claims 37, 39-40, 46-47, 57, and 63, drawn to a method for determining a copy number of an interrogated segment with a region of interest in the reply, filed on 08 April 2024 is acknowledged.
Claims 1-2, 5-6, 8, 11, 13-19, 21, 26, 30, 36, 38 are withdrawn from further consideration pursuant to 37 CFR 1.142(b) as being drawn to a nonelected invention, there being no allowable generic or linking claim. Election was made without traverse in the reply filed on 4/8/2024.
Priority
This application is a continuation (CON) of PCT/US2019/034998 filed 31 May 2019 which claims benefit to Provisional Application 62/733,842 filed 20 September 2018 and Provisional Application 62/681,517 filed 06 June 2018.
Claim Rejections - 35 USC § 112
35 USC § 112(b)
The instant rejection is maintained for reason for record in the Office Action mailed 29 January 2026 and modified in view of the amendments filed 30 April 2026. It is noted the amendments received 30 April 2026 are necessitated by new ground(s) of rejection.
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 37, 39-40, 46-47, 57, and 63 rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Claim 37 step (b) was amended to recite “thereby providing a copy number…”. The claimed step renders the claim indefinite because using the transition word “thereby” functions as an adverb that connects two ideas by showing that one action is the direct result of another. Here, previous step (a) and step (b) does not contain elements/limitations/steps such that a copy of the interrogated segment has been provided. Thus, it is not clear how a copy number is determined or “thereby provided” because steps (a) and (b) do not contain steps or limitations for providing or determining a copy number. It is recommended to amend the transition word “thereby” from the claimed step because step (a-b) do not contain steps for providing copy number the interrogated segment. Additionally, it is noted claim 71 step (a) recites “sequencing a test sequencing library enriched from the genomic DNA sample with one or more direct targeted sequencing capture probes that hybridize to a target sequence within the region of interest, thereby obtaining a plurality of about 100 to about 10,000 sequencing reads…”. Here, claim 71 provides previous sequencing steps such that 100 to about 10,000 sequencing reads can thereby be obtained.
Claims 37, 39-40, 46-47, 57, and 63 are rejected because they fail to provide limitations to overcome the deficiencies of the base claim(s).
Claim Rejections - 35 USC § 101
The instant rejection is maintained for reason for record in the Office Action mailed 29 January 2026 and modified in view of the amendments filed 30 April 2026. It is noted the amendments received 30 April 2026 are necessitated by new ground(s) of rejection.
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 37, 39-40, 46-47, 57, and 63 are rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter.
Step I - Process, Machine, Manufacture or Composition
Claims 37, 39-40, 46-47, 57, and 63 are directed to a method for determining a copy number of an interrogated segment, so a process.
Claims 71-78 are directed to a method, so a process.
2A Prong I - Identification of an Abstract Idea
Claim 37 recite(s):
Step (b) – wherein the sequencing reads are mapped to an interrogated segment in the region of interest.
This step encompasses mapping genetic sequence mapping which encompasses performing mathematical concepts such as classical genetic mapping (calculating gene location via recombination frequencies) and/or modern sequence alignment (using algorithms like graph theory to assemble a genome), for example, which reads on abstract ideas.
Step (c) –thereby providing the copy number of the interrogated segment, wherein the copy number is the most probable copy number of the interrogated segment as determined by a parameterized hidden Markov model
This step can be performed in the human mind by following instructions to determine a copy number of the interrogated segment and is therefore an abstract idea. Furthermore, this step encompasses performing mathematical/statistical modeling computations (i.e., determining a copy number, parameterizing HMM) for quantifying data and determining a parameterized hidden Markov model to determine the copy number of the interrogated segment and therefore reads on abstract ideas/mathematical concepts. This step also describes the copy number of the interrogated segment as the most probable copy number of the interrogated segment as determined by a parameterized hidden Markov mode.
Step (c) subset (i) - one or more hidden states comprising a copy number corresponding to the interrogated segment or a plurality of sub-segments within the interrogated segment.
This step encompasses using one or more hidden states of a hidden Markov model which encompasses using mathematical and statistical operations/numerical variables and therefore reads on abstract ideas/mathematical concepts. This step encompasses hidden states which encompass quantitative data/numerical variables (i.e., copy number corresponding to the interrogated segment or a plurality of sub-segments within the interrogated segment) and therefore further reads on abstract ideas/mathematical concepts.
Step (c) sub step (ii) - an observation state comprising a number of sequencing reads aligned to the interrogated segment.
This step encompasses an observed state which encompasses observable data/measurements/numerical variables (i.e., a number of sequencing reads aligned to the interrogated segment) that are used within a statistical model (i.e., hidden Markov Model) and therefore read on abstract ideas/mathematical concepts.
Step (c) subset (iii) a copy number likelihood model using one or more likelihood distributions for an expected number of sequencing reads aligned to the interrogated segment
This limitation reads on a hidden Markov model which includes a copy number likelihood model with a likelihood distribution. This limitation is drawn to math and is therefore an abstract idea. Here, hidden Markov model, a copy number likelihood model with a likelihood distribution are mathematical concepts.
wherein the parameterized hidden Markov model is parameterized by adjusting the copy number likelihood model to fit the determined number of sequencing reads aligned to the interrogated segment, using an analytic first derivative gradient and second derivative Hessian of one or more parameters in the copy number likelihood model
This step can be performed in the human mind by organizing data (i.e., adjusting likelihood model data using analytic first derivative gradient and second derivative Hessian of the likelihood model) to fit a number of sequences aligned to an interrogated segment and is therefore an abstract idea. This step encompasses using mathematical and statistical variables (i.e., an analytic first derivative gradient and second derivative Hessian of one or more parameters) and statistical distributions of aligned sequence reads for parameterizing the HMM and using a first derivative gradient and second derivative Hessian as mathematical/numerical variables (i.e., parameters) and therefore reads on abstract ideas/mathematical concepts.
wherein the one or more parameters comprise a dispersion of aligned sequence reads (c) and an average of aligned sequence reads
This step encompasses quantitative parameters that comprise a dispersion of aligned sequence reads (d) and an average of aligned sequence reads (µ) which are mathematical/statistical variables and therefore read on abstract ideas/mathematical concepts.
Claims 39-40, 46-47, 57, and 63 are further drawn to limitations that describe the abstracts ideas in claim 37 and are therefore also abstract ideas.
Claim 71 recites:
wherein the sequencing reads are mapped to an interrogated segment in the region of interest
This step describes the sequencing reads as mapped to an interrogated segment of interest.
(b) parametrizing, by a computer, a hidden Markov model based on:(i) one or more hidden states comprising a copy number corresponding to the interrogated segment or a plurality of sub-segments within the interrogated segment, (ii) an observation state comprising a number of sequencing reads mapped to the interrogated segment and (iii) a copy number likelihood model using one or more likelihood distributions for an expected number of sequencing reads mapped to the interrogated segment.
This step can be performed in the human mind by organizing information from hidden states, observation states, and a copy number likelihood models for parameterizing a Hidden Markov Model and is therefore an abstract idea. This step encompasses performing mathematical computations (i.e., parametrizing a hidden Markov model) which on abstract ideas.
This step ((b) subset (i)) encompasses using one or more hidden states of a hidden Markov model which encompasses using mathematical and statistical operations/numerical variables and therefore reads on abstract ideas/mathematical concepts. This step encompasses hidden states which encompass quantitative data/numerical variables (i.e., copy number corresponding to the interrogated segment or a plurality of sub-segments within the interrogated segment) and therefore further reads on abstract ideas/mathematical concepts.
This step ((b) sub step (ii)) further encompasses an observed state which encompasses observable data/measurements/numerical variables (i.e., a number of sequencing reads aligned to the interrogated segment) that are used within a statistical model (i.e., hidden Markov Model) and therefore read on abstract ideas/mathematical concepts.
This step (b) subset (iii)) encompasses a Hidden Markov model that is parametrized using a copy number likelihood model which is mathematical concepts for parametrizing probabilities of the observe and hidden states which reads on abstract ideas [Spec, page 16 para 0050]. This limitation reads on a hidden Markov model that includes a copy number likelihood model with a likelihood distribution. This limitation is drawn to math and is therefore an abstract idea. Here, hidden Markov model and a copy number likelihood model with a likelihood distribution are mathematical concepts.
by adjusting the copy number likelihood model to fit a determined number of sequencing reads mapped to the interrogated segment, using an analytic first derivative gradient and second derivative Hessian of one or more parameters in the copy number likelihood model
This step can be performed in the human mind by organizing data (i.e., adjusting likelihood model data using analytic first derivative gradient and second derivative Hessian of the likelihood model) to fit a number of sequences aligned to an interrogated segment and is therefore an abstract idea. This step encompasses using mathematical and statistical variables (i.e., an analytic first derivative gradient and second derivative Hessian of one or more parameters) and statistical distributions of aligned sequence reads for parameterizing the HMM and using a first derivative gradient and second derivative Hessian as mathematical/numerical variables (i.e., parameters) which read on abstract ideas/mathematical concepts.
(c) detecting an effect of noise in the number of mapped sequencing reads;
This step can be performed in the human mind by observing and evaluating mapped sequence reads to detect an effect of noise and is therefore an abstract idea.
(d) adjusting, by a computer, the copy number likelihood model by weighing a level of noise in the number of mapped sequencing reads from the test sequencing library
This step can be performing in the human mind by organizing information (i.e., number of mapped sequence reads) for weighting the a level or noise for adjusting the copy number likelihood model and is therefore an abstract idea. This step encompasses performing weighting of noise levels to adjust copy number variation which encompass performing weighting computations which reads on abstract ideas.
wherein a most probable copy number of the interrogated segment is not called if the noise in the number of mapped sequencing reads is above a predetermined threshold
This step can be performed in the human mind by observing and evaluating data (i.e., mapped sequence read noise) is above threshold to not call the most probable copy number of a sequence read segment and is therefore an abstract. This step encompasses using equalities and inequalities to mathematically determine whether is greater than a threshold which reads on abstract ideas.
wherein sequencing reads from overlapping direct targeted sequencing capture probes are merged
This step can be performed in the human mind by organizing/combining information (i.e., merging data) of sequence read overlap and is therefore an abstract idea. Here, it is noted the limitation “sequencing reads from overlapping direct targeted sequencing capture probes” under broadest reasonable interpretation (BRI) is being interpreted as “data” not merging physical sequences.
(e) detecting the copy number of the interrogated segment
This step can be performed in the human mind by observing and evaluating information (i.e., interrogated segment) to detect copy number of the interrogated segment and is therefore an abstract idea.
wherein the copy number is the most probable copy number of the interrogated segment as determined by the parameterized hidden Markov model.
This step encompasses genetic sequence mapping which encompasses performing mathematical concepts such as classical genetic mapping (calculating gene location via recombination frequencies) and/or modern sequence alignment (using algorithms like graph theory to assemble a genome), for example, which reads on abstract ideas.
Claims 72-78 are further drawn to limitations that describe the abstracts ideas in claim 37 and are therefore also abstract ideas.
2A Prong II – Consideration of Practical Application
Claims 37 and 71 do not recite an additional element that integrate the abstract idea into a practical application. Here, practicing the claims only results in data analysis for determining/detecting the copy number of the interrogated segment. Such a result only produces new information and does not provide for a practical application in the physical-realm of physical things and acts i.e., the claims do not use or involve the data generated by the judicial exception to affect any type of change in the physical-realm. Thus, the claims do not utilize the enriched, sequenced, and mapped nucleic acids, the determined copy number, and the abstract ideas to construct a practical application such as treating a subject, transformation of matter, or improving upon an existing technology. See MPEP 2106.04(d)(I).
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 affect 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.
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 element of enriching nucleic acids of claims 37 and 71 step (a) do not add more than the recited judicial exception because enriching nucleic acid libraries using direct targeted sequencing capture probes is deemed a well-known and conventional. See MPEP 2106.05(d)(II)(ii-iii, v, and vii).
The recited additional element of sequencing data of claim 37 step (b) and claim 71 step (a) do not add more than the recited judicial exception because sequencing nucleic acids to obtain an amount of sequence reads (i.e., 100 to 10,000 sequencing reads) that are subsequently analyzed by the abstract ideas is deemed a well-known and conventional extra-solution activity. See MPEP 2106.05(d)(II)(vii) and 2106.05(g).
The recited additional element of enriching nucleic acids of claims 37 step (a) and claim 71 step (a) do not integrate the recited judicial exception into a practical application because enriching nucleic acid libraries using direct targeted sequencing capture probes is deemed an extra-solution activity. See MPEP 2106.05(d)(II) (ii-iii, v, and vii).
The recited additional element of using computer processes, components, and equipment of claim 71 does not integrate the recited judicial exception into a practical application because using a computer for subsequently processing sequence read data in a computer environment is merely tangential to the claimed method. See MPEP 2106.05(b) and 2106.05(g).
In conclusion and when 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.
Response to Argument
Applicant's arguments filed 30 April 2026 have been fully considered but the rejection is maintained.
The Applicant states the claims are not drawn to a judicial exception, even if a judicial exception is involved. The Applicant states the cancellation of the determining step of claim 37 renders the abstract of the claimed step moot [remarks, page 14 A].
In response, the amended claimed step remains rejected under Step 2A Prong I of the 101 analyses because even though the claim was amended to recites “thereby providing…”, the claimed element still reads on abstract ideas/mathematical concepts as “thereby providing” reads on an alternative term for calculating. See MPEP 2106.04(a)(2)(I)(C).
The Applicant points to USPTO Example 39 for guidance with respect to the remaining elements of claim 37. The Applicant states, as in Example 39, the claims do not recite judicial exception. The Applicant states the word “encompassing” an alleged abstract idea or mental process is similar to the usage “involves”. [remarks, pages 14-15]. The Applicant states the claims are therefore patent eligible because the claims do not recite steps for performing specific mathematical formulas and instead, the limitation "wherein the copy number is most probable copy number of the interrogated segment as determined by a parameterized hidden Markov model" and further elements defining the model are merely descriptions or characteristics of "the copy number." NOT a recitation of a specific calculation [remarks, pages 14-15].
In response, the argument is not persuasive because claimed analysis step of claim 37 encompasses the abstract ideas. Here, even though the claim was amended to recite “thereby providing a copy number…”, the claimed element still reads on abstract ideas/mathematical concepts as “thereby providing” reads on an alternative term for calcualting. See MPEP 2106.04(a)(2)(I)(C). With respect to Example 39, the claim was found not encompass abstract ideas because the steps of applying transformations (mirroring, rotating) and training a neural network on large, data-heavy datasets cannot be practically performed in the human mind. Whereas, in the instant case, the claims are drawn to determining copy number of nucleic acid sequences which can be performed in the human mind.
The Applicant points to the PTAB decision Ex parte Hannun for guidance. The Applicant points to Examiner rationale for guidance. The Applicant states the PTAB explained, however, that the mathematical formula was not recited in the claims of Hannun, and therefore the claims were determined not to recite a mathematical concept, even if one was potentially involved. The Applicant further states the PTAB also noted in Hannun that, although transcription generally can be performed in the human mind, the claims at hand were directed to a specific implementation that could not practically be performed mentally. The Applicant states similar to Ex parte Hannun, the claims (i) do not recite a judicial exception, but rather may "involve" mathematical concepts; and (ii) the steps that are recited cannot be practically performed in the human mind. [remarks, pages 16-17].
In response, and with respect to the PTAB decision of Hannun, Examiners are to examine using the MPEP, specifically, the MPEP 2106.04(a)(2), for determining whether claims encompass abstract idea groupings. Here, the claims of Hannun are directed to a specific implementation including the steps of normalizing an input file, generating a jitter set of audio files, generating a set of spectrogram frames, obtaining predicted character probabilities from a trained neural network and decoding a transcription of the input audio using the predicted character probability outputs which are not steps that practically can be performed mentally. In contrast, the instant claims are drawn to nucleic acid statistical analysis resulting in determining the copy number of an interrogated nucleic acid fragment. Therefore, the claims are not patent eligible.
The Applicant states the Examiner position in the Advisory Action was improper [remarks, pages 17 B]. The Applicant points to the Examiners Advisory Action filed on April 6, 2026. The Applicant the rationale applying the mathematical concepts of USPTO Example 47 claim 3 is incorrect. The Applicant states "This example illustrates the application of the eligibility analysis to claims that recite limitations specific to artificial intelligence, particularly the use of an artificial neural network to identify or detect anomalies." The present claims do not involve or recite any elements relating to artificial intelligence. Because Example 47 explicitly notes it is "specific to artificial intelligence," the Examiner's reliance on the analysis of this Example is inapt here.” [remarks, page 17].
In response, the utilization of claim 3 of Example 47 was to exemplify that mapping, hidden Markov model, and observed states are drawn to mathematical concepts such as backpropagation and descent without expressive recites a formula (i.e., he Arrhenius equation). See MPEP 2106.04(a)(2)(I)(B)(ii). Furthermore, and similar to the instant claims, the Subject Matter Eligibility Update (AI) [Example 47 Claim 2] provides that the use of back propagation and gradient descent were considered mathematical concepts because back propagation and gradient descent encompass/contain mathematical calculations even though no mathematical formulas are recited in the claims as back propagation and gradient descent are rooted in mathematical operations/formulas similar to mapping nucleic acids, using hidden Markov model, and using observed states of the instant claims. Furthermore, the MPEP 2106.04(a)(2)(I)(A) states “A mathematical relationship is a relationship between variables or numbers. A mathematical relationship may be expressed in words or using mathematical symbols. For example, pressure (p) can be described as the ratio between the magnitude of the normal force (F) and area of the surface on contact (A), or it can be set forth in the form of an equation such as p = F/A” while MPEP 2106.04(a)(2)(I)(C) states “A mathematical calculation is a mathematical operation (such as multiplication) or an act of calculating using mathematical methods to determine a variable or number, e.g., performing an arithmetic operation such as exponentiation. There is no particular word or set of words that indicates a claim recites a mathematical calculation”. Therefore, mapping, hidden Markov model, and observed states are drawn to mathematical concepts under Step 2A Prong I of the 101 analyses.
The Applicant states that if the Examiner believes Example 47 is applicable, then new claim 71 should allowable. The Applicant points to a table of claim 3 of Example 47 and new claim 71 for guidance [remarks, pages 17-19]. The Applicant states claim 71 improves the functioning of a computer or improves another technology or technical field and improves accuracy of copy number calling. The Applicant points to the specification [0062, 0066-0067] for guidance. The Applicant states claim 71 reflects an improvement in the technical field of copy number detection [remarks, pages 17-20].
In response, it is noted claim 71 is a new claim pending examination. The Applicant is directed to the 35 U.S.C § 101 above, with respect to the rejection of claim 71.
Claim Rejections - 35 USC § 103
It is noted the amendments received 30 April 2026 necessitate new ground(s) of rejection.
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
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) 37 and 39-40 are rejected under 35 U.S.C. 103 as being unpatentable over Hyland et al. (US Patent Pub US 2012/0046877) in view Talasaz (US Patent Pub: US 2016/0333417, Patent Pub Date: 17 November 2016) in view of Zhang et al. (Patent Pub US 2014/0336950), in view of Reid et al (US Patent Pub US 2016/0342733), and in view of Dymarski et al. (Cited in the Office Action mailed 10 July 2024) in view of McGuigan (NextGENe CNV Detection-Dispersion and HMM https://softgenetics.com/PDF/NextGENe_CNVdetection_AppNote.pdf (2018)).
Claims 37 recites:
Claim 37 step (a) recites preparing a test sequencing library by enriching genomic DNA using capture probes hybridized to the target sequences. Claim 37 step (b) recites sequencing the test sequencing library to obtain 100-10,000 sequence reads. Claim 37 step (b) recites wherein the sequencing reads are mapped to an interrogated segment in the region of interest.
Claim 37 step (b) recites thereby providing a copy number of the interrogated segment using the determined copy number of the interrogated segment as the most probable copy number which is determined by a parameterized hidden Markov Model.
Claim 37 step (b) recites wherein the copy number is the most probable copy number of the interrogated segment as determined by a parameterized hidden Markov model comprising: (i) one or more hidden states comprising a copy number corresponding to the interrogated segment or a plurality of sub-segments within the interrogated segment.
Claim 37 step (b) (ii) recites an observation state comprising a number of sequencing reads mapped to the interrogated segment.
Claim 37 step (b) (iii) recites a copy number likelihood model using one or more likelihood distributions for an expected number of sequencing reads mapped to the interrogated segment.
Claim 37 step (b) (iii) recites wherein the parameterized hidden Markov model is parameterized by adjusting the copy number likelihood model to fit a determined number of sequencing reads mapped to the interrogated segment, using an analytic first derivative gradient and second derivative Hessian of one or more parameters in the copy number likelihood model, and wherein the one or more parameters comprise a dispersion of mapped sequence reads (d) and an average of mapped sequence reads(μ).
Hyland discloses nucleic acid sequence read data can be generated using hybridization-based system [Hyland, page 1 para [0009]], as in instant claim 37 step (a) preparing a test sequencing library by enriching genomic DNA using capture probes hybridized to the target sequences.
Hyland discloses mapping engine that aligns nucleic acids to a reference sequence [Hyland, claims 1]. Hyland discloses a method using mapping for determining copy number variation [Hyland, claim 20], as in instant claim 37 step (b) wherein the sequencing reads are mapped to an interrogated segment in the region of interest.
Hyland discloses using Hidden Markov Model (HMM) for identifying copy number variation [Hyland, claim 14]. Hyland discloses output copy number variations in the reference mapped sequence reads [Hyland, claim 10]. Hyland discloses copy number variation analysis wherein the stochastic modeling algorithm is a Hidden Markov Model algorithm [Hyland, claim 6], as in instant claim 37 step (b) thereby providing a copy number of the interrogated segment using the determined copy number of the interrogated segment as the most probable copy number which is determined by a parameterized hidden Markov Model.
Hyland discloses an algorithm, HMM algorithm, is used to convert the normalized read coverage for each window region (i.e., aligned and mapped reads) to discrete copy number states (i.e., hidden states comprising a copy number) [Hyland page 7 left col para 0084]. Hyland discloses the copy number variation identification engine is configured to designate window regions with copy number states of greater than two as copy number amplifications [Hyland, claim 8], as in claim 37 step (b) (i) one or more hidden states comprising a copy number corresponding to the interrogated segment or a plurality of sub-segments within the interrogated segment.
Hyland does not disclose
Hyland does not discloses claim 37 step (b) sequencing the test sequencing library, thereby obtaining a plurality of about 100 to about 10,000 sequencing reads.
Hyland does not disclose claim 37 step (b) recites wherein the copy number is the most probable copy number of the interrogated segment as determined by a parameterized hidden Markov model
Hyland does not disclose claim 37 step (b) recites (ii) an observation state comprising a number of sequencing reads mapped to the interrogated segment.
Hyland does not disclose claim 37 step (b) recites (iii) a copy number likelihood model using one or more likelihood distributions for an expected number of sequencing reads mapped to the interrogated segment.
Hyland does not disclose claim 37 step (b) recites (iii) wherein the parameterized hidden Markov model is parameterized by adjusting the copy number likelihood model to fit a determined number of sequencing reads mapped to the interrogated segment, using an analytic first derivative gradient and second derivative Hessian of one or more parameters in the copy number likelihood model, and wherein the one or more parameters comprise a dispersion of mapped sequence reads (d) and an average of mapped sequence reads(μ).
Hyland does not disclose claims 39-40.
Talasaz et al. (Talasaz) discloses sampling a number of amplified progeny polynucleotides from amplified progeny polynucleotides that are equal to the number of unique tagged parent polynucleotides in the set of tagged parent polynucleotides (particularly when the number is at least 10,000) [Talasaz, page 6 left col para 0091]. Talasaz discloses cell free polynucleotides may be sequenced with at least 1,000-10,000 times [Talasaz, page 9 right col para 0122], as in instant claim 37 step (b) sequencing the test sequencing library, thereby obtaining a plurality of about 100 to about 10,000 sequencing reads.
Zhang et al. (Zhang) recites the hidden state at segment t is denoted as x(t) which is the true copy number of segment t and is unknown and the observed mean copy number for segment t and sample j is denoted as y(t) where i=1,..., n as the transitional probability from state t-1 to t is denoted as P(t) and the emission probability from state t to observation y(t) is denoted as P(t) [disclosure [0029 left col]]. Zhang recites the copy-number model defines a hidden Markov model with hidden states corresponding to copy numbers at the markers and observations corresponding to components of the copy-number vectors at the markers [Zhang, claim 4], as in instant claim 37 step (b) recites (ii) an observation state comprising a number of sequencing reads mapped to the interrogated segment.
Dymarski et al. (Dymarski) teach that the Newton-Raphson method uses not only the first derivative but also the second derivative, i.e., the Hessian matrix, may also be used [page 139 Newton-Raphson method]. Dymarski teaches a Hessian matrix is not always a positive-definite matrix, the following second derivative, approximated using only diagonal elements [page 139], as in claim 37 step (b) wherein the parameterized hidden Markov model is parameterized step “…using an analytic first derivative gradient and second derivative Hessian of one or more parameters…”.
Reid et al. (Reid) discloses the system is configured to identify CNV states as regions where the maximum likelihood sequence of states [Reid page 7 left col para 0074]. Reid discloses fitting the plurality of mixture models to the normalized sample coverage data using an expectation-maximization algorithm to determine a likelihood for each copy number at each of one or more calling windows [Reid, claim 13]. Reid discloses mixture model can comprise a probability distribution that represents can expected normalized coverage conditional on a particular copy number [Reid, page 9 left col para 0093], as in claim 37 step (b) wherein the copy number is the most probable copy number of the interrogated segment as determined by a parameterized hidden Markov model and step (b) (iii) a copy number likelihood model using one or more likelihood distributions for an expected number of sequencing reads mapped to the interrogated segment.
Reid discloses correcting measurement data for the sequence coverages bias comprising performing adjustments based on additional weighting factor associated with individual mappings to compensate for bias [Reid, page 3 left col para 0033]. Reid discloses methods and systems can normalize coverage data to correct for systematic biases and characterize the expected coverage profile given diploid copy number so that true CNVs can be distinguished from noise [Reid, page 3 left col para 0034]. Reid discloses that mappability scores for each genomic regions [Reid, page 5 right col para 0057], as in instant claim 37 step (b) wherein the parameterized hidden Markov model is parameterized by adjusting the copy number likelihood model to fit a determined number of sequencing reads mapped to the interrogated segment.
McGuigan et al. (McGuigan) recites using the software NextGENe for CNV detection-Dispersion and HMM [title]. McGuigan recites an interface for dispersion and HMM and inputting data into the software [page 1 fig 2]. McGuigan recites dispersion HMM setting using log10(dispersion) [page 2 fig 3]. McGuigan recites software first splits the data up into groups based on the total coverage, generating a summary “fitting point” for each group based on measured dispersion and the median coverage as a line is fit to these “fitting points” and the equation for this line is used to calculate dispersion for every individual region [page 3 discussion]. McGuigan teaches the coverage ratio (sample divided by sample plus control) is used as the basis for CNV detection and this coverage can be measured as a standard RPKM value (Reads Per Kilobase per Million mapped reads) or as a more intuitive normalized read count value [page 1 introduction], as in instant claim 37 step (b) wherein the parameterized hidden Markov model is parameterized step wherein the one or more parameters comprise a dispersion of mapped sequence reads (d) and an average of mapped sequence reads(μ). Here, although McGuigan does not teach the mathematical variables (d) and (µ), using data or symbols to represent quantitative data (i.e., (d) = a dispersion of mapped sequence reads, (µ) = an average of mapped sequence reads) for equations or algorithms is obvious because McGuigan teaches using dispersion and averaged mapped sequence reads (i.e., RPKM).
Obvious claims(s): 39-40
McGuigan et al. (McGuigan) teaches using the software NextGENe for CNV detection-Dispersion and HMM [title]. McGuigan teaches an interface for dispersion and HMM and inputting data into the software [page 1 fig 2]. McGuigan teaches dispersion HMM setting using log10(dispersion) [page 2 fig 3]. McGuigan recites software first splits the data up into groups based on the total coverage, generating a summary “fitting point” for each group based on measured dispersion and the median coverage as a line is fit to these “fitting points” and the equation for this line is used to calculate dispersion for every individual region [page 3 discussion], as in instant claim 39. Here, although McGuigan does not teach the mathematical variables (dj) and (µj), for example, using symbols (i.e., µ) to represent quantitative data (i.e., (d) = a dispersion of mapped sequence reads, (µ) = an average of mapped sequence reads) for subsequent input into equations, formulas, or algorithms is obvious.
Hyland teaches copy number variation identification engine is configured to designate window regions with copy number states of greater than two as copy number amplifications. Reid teaches a probability distribution that represents an expected normalized coverage conditional on a particular copy number. Halpern teaches Emission probabilities may be modeled as Poisson distributions, negative binomial (i.e., negative binomial distribution is not a Poisson distribution), mixtures of Poisson distributions, piecewise models fit to the data [disclosure page 10 right col [0135]]. Reid teaches identifying one or more copy number variants (CNVs) according to a Hidden Markov Model (HMM) based on the normalized sample coverage data and the fitted mixture model [Reid, claim 1]. Reid teaches determining a median coverage for the sample coverage data across the one or more calling windows conditional on a GC-fraction [Reid, claim 6]. Halpern teaches hybridizing one or more sequencing probes to said random array to form perfectly matched duplexes between said one or more sequencing probes and fragments of target nucleic acid [Halpern, claim 11]. Halpern teaches correcting the measurement data for the sequence coverage bias comprises performing adjustments to account for GC bias in the library construction and sequencing process [claim 7]. Hyland does not teach “an average number of mapped sequencing reads at a corresponding segment across a plurality of sequencing libraries and an average number of mapped sequencing reads across a plurality of segments of interest within the test sequencing library, wherein the average number of mapped sequencing reads at a corresponding segment a cross a plurality of sequencing libraries or the average number of mapped sequencing reads across a plurality of segments of interest within the test sequencing library is a normalized average”. McGuigan teaches the coverage ratio (sample divided by sample plus control) is used as the basis for CNV detection and this coverage can be measured as a standard RPKM value (Reads Per Kilobase per Million mapped reads) or as a more intuitive normalized read count value [page 1 introduction], as in instant claim 40.
It would have been obvious to one of ordinary skill in the art by the effective filing date of the claimed invention to modify Hyland in view of Talasaz because Talasaz discloses method for detecting copy number variation [Talasaz, title]. One of ordinary skill in the art would recognize that, similar to Hyland, Talasaz discloses method for sequencing nucleic acids from a sample, mapping sequence reads, and analyzing the sequence read data using Hidden Markov Model (HMM) [Talasaz, page 11 left col para 0139]. However, Talasaz discloses cell-free polynucleotides can be sequenced 1,000-10,000 [Talasaz, page 9 right col para 0122]. Here, one of ordinary skill in the art would be motivated to combine the methods of Hyland in view of obtained sequence read counts of Talasaz such that the copy number for 10,000 sequence reads can be determined/provided. As such, one of ordinary skill in the art would have a reasonable expectation of success combining the method of Hyland with the sequence read counts of Talasaz because although Hyland does not discloses using 1,000-10,000, the method of Talasaz illustrates determining and processing copy numbers using HMM [Talasaz page 15 right col para 0194]. Therefore, combining the methods of Hyland and Talasaz would yield a predictable method for determining copy numbers using between 1,000 and 10,000 sequences reads.
It would have been obvious to one of ordinary skill in the art by the effective filing date of the claimed invention to modify Hyland in view of Talasaz in view of Zhang because Zhang teaches clustering copy-number values for segments of genomic data [title]. One of ordinary skill in the art would recognize that Hyland, Talasaz, and Zhang are in the similar field of endeavor such nucleic acid data for determining copy number variation. One of ordinary skill in the art would expect reasonable success in combining Hyland in view of Talasaz in view of Zhang because the method of Zhang includes a Hidden Markov Model (HMM) based clustering algorithm that has particular applicability for identifying comparative genomic hybridization (aCGH) DNA copy number data [abstract], and Zhang discloses using observation states which provides true copy number of the segments [disclosure page 3 [0029]]. Therefore, combining the methods of Hyland in view of Talasaz in view of Zhang would yield a predictable claimed step that can process the data of 1,000-10,000 sequence reads and convert the data into observation states of an HMM for determining copy number of polynucleotide.
It would have been obvious to one of ordinary skill in the art by the effective filing date of the claimed invention to modify Hyland in view of Talasaz in view of Zhang in view of Reid because Reid also teaches methods for copy number detection [title]. One of ordinary skill in the art would recognize that Hyland, Talasaz, Zhang, and Reid are in the similar fields of endeavor such as analyzing nucleic acids to determine copy number/copy number variation. One ordinary skill in the art would be motivated to combine Hyland in view of Talasaz in view of Zhang in view of Reid because Reid teaches methods for detecting copy number variants according to a Hidden Markov model based on normalized sample coverage and outputting the copy number variants [abstract] which uses maximum likelihood sequence of states (i.e., copy number likelihood). One of ordinary skill in the art would expect a reasonable success in combining Hyland in view of Talasaz in view of Zhang in view of Reid because Reid discloses methods for fitting models and provides methods for identifying one or more copy number variants according to a Hidden Markov Model (HMM) based on the normalized sample coverage data and the fitted mixture model, and receiving an indication of the one or more copy number variants [claim 17]. Therefore, combining Hyland in view of Talasaz in view of Zhang in view of Reid would a construct a method that can implement an HMM model for determining a copy number of a segment of chromosomal DNA, for example.
It would have been obvious to one of ordinary skill in the art by the effective filing date of the claimed invention to modify Hyland in view of Talasaz in view of Zhang in view of Reid in view of Dymarski because Dymarski teach method for applying HMM to gene expression data to predict DNA copy number [Dymarski, page 261 Chapter 12 section 1 introduction] to develop a DNA copy number alteration (CNA) prediction tool based on HMMs to infer genetic abnormalities in human tumor cells from microarray gene expression data. One of ordinary skill in the art would be motivated to combine Hyland in view of Talasaz in view of Zhang in view of Reid in view of Dymarski because Dymarski teaches methods for maximizing the likelihood of HMMs by means of a direct gradient-Hessian approach, without resorting to the EM algorithm. One of ordinary skill in the art would recognize that Dymarski teaches the mathematical and statistical theory to which a skilled artisan could successfully develop and construct am HMM to analyze sequencing data to determine copy number variation of an interrogated segment. Therefore, combining the methods of Hyland in view of Talasaz in view of Zhang in view of Reid in view of the mathematical theory of Dymarksi would yield a claimed step that can parameterized an HMM by adjusting the copy number likelihood model to fit a determined number of sequencing reads mapped to the interrogated segment(s) using an analytic first derivative gradient and second derivative Hessian of one or more parameters in the copy number likelihood model.
It would have been obvious to one of ordinary skill in the by the effective filing date of the claimed invention to modify Hyland in view of Talasaz in view of Zhang in view of Reid in view of Dymarski in view of McGuigan because McGuigan teach methods for CNV detection-dispersion and HMM [title]. One of ordinary skill in the art would recognize that Hyland in view of Talasaz in view of Zhang in view of Reid in view of Dymarski in view of McGuigan because McGuigan are within the similar field of endeavor of analyzing nucleic acid data for detecting copy numbers. One of ordinary skill in the art would be motivated to combine Hyland in view of Talasaz in view of Zhang in view of Reid in view of Dymarski in view of McGuigan because McGuigan teaches a general procedure for inputting sequencing data and adjusting data parameters for generating “fitting points” for describing the dispersion at a given level of coverage [page 2 step 4]. One of ordinary skill in the art would expect a reasonable success in combining Hyland in view of Talasaz in view of Zhang in view of Reid in view of Dymarski in view of McGuigan because McGuigan teaches dispersion values calculated for each region are used to generate normalized beta-binomial distributions and teaches total read, dispersion [pages 3-4 fig 6]. Here, one of ordinary skill in the art would have a reasonable expectation of success combining Hyland in view of Talasaz in view of Zhang in view of Reid in view of Dymarski in view of the dispersion methods McGuigan because McGuigan teaches a NextGENe CNV detection methods that processes sequencing data for modeling the amount of dispersion (noise) of sequencing data. Therefore, combining the methods of Hyland in view of Talasaz in view of Zhang in view of Reid in view of Dymarski in view of McGuigan would yield a predictable method step that can determine the amount of dispersion for 1,000-10,000 polynucleotide segments such that a copy number of said segments can be determined.
Claim(s) 46-47 and 63 are rejected under 35 U.S.C. 103 as being unpatentable over Hyland in view of Talasaz in view of Zhang in view of Reid in view of Dymarski in view of McGuigan, as applied to claim 37 and 39-40 above, and in further view of Halpern (US Patent Pub US2012/0095697).
Hyland in view of Talasaz in view of Zhang in view of Reid in view of Dymarski in view of McGuigan teach claims 37 and 39-40.
Hyland in view of Talasaz in view of Zhang in view of Reid in view of Dymarski in view of McGuigan teach a method HMM method for determining a copy of an interrogated segment(s).
Hyland in view of Talasaz in view of Zhang in view of Reid in view of Dymarski in view of McGuigan do not teach claims 46-47 and 63.
With respect to claim 46, the claim is rendered obvious because Reid teaches determining one or more emission probabilities of the HMM based on the mixture model and identifying a calling window of the one or more calling windows as a CNV if a maximum likelihood sequence of states of the calling window is non-diploid [Reid, claim 14]. Zhang recites the copy-number model defines a hidden Markov model with hidden states corresponding to copy numbers at the markers and observations corresponding to components of the copy-number vectors at the markers [Zhang, claim 4]. Zhang recites estimating values for transitions between copy numbers at adjacent markers for a sample in a genomic data set to determine the transitional probabilities [Zhang, claim 5]. Neither Reid nor Zhang teaches the interrogated segment are determined based on using observations of called copy number variants in a human population. Halpern teaches the method further comprises performing population-based no-calling and identification of low-confidence regions [Zhang, claim 3].
With respect to claim 47, the claim is rendered obvious because Zhang recites the copy-number model defines a hidden Markov model with hidden states corresponding to copy numbers at the markers and observations corresponding to components of the copy-number vectors at the markers [Zhang, claim 4]. Zhang teaches estimating values for transitions between copy numbers at adjacent markers for a sample in a genomic data set to determine the transitional probabilities [Zhang, claim 5]. Zhang does not teach interrogated segment are determined based on using observations of called copy number variants in a human population. Halpern teaches that the method further comprises performing population-based no-calling and identification of low-confidence regions [Halpern, claim 3].
Halpern discloses the method further comprises performing population-based no-calling and identification of low-confidence regions [Halpern, claim 3]. Halpern discloses estimating a total copy number value and region-specific copy number value for each of a plurality of genomic regions [Halpern, claim 1], as in instant claim 63.
It would have been obvious to one of ordinary skill in the art by the effective filing date of the claimed invention to modify the methods of Hyland in view of Talasaz in view of Zhang in view of Reid in view of Dymarski in view of McGuigan, and in further view of Halpern because Halpern discloses methods for estimating genome-wide copy number variations [title]. One of ordinary skill in the art would recognize that Hyland, Talasaz, Zhang, Ried, Dymarski, and McGuigan, Halpern are in the similar fields of endeavor such as analyzing nucleic acid copy numbers using statistical methods. One of ordinary skill in the art would be motivated to combine Hyland in view of Talasaz in view of Zhang in view of Reid in view of Dymarski in view of McGuigan, and in further view of Halpern because Halpern expands on using data gathering elements of using hybridization methods for capturing probes, and Halpern discloses using Poisson and non-Poisson distributions for determining copy number. One of ordinary skill in the art would expect a reasonable success combining Hyland in view of Talasaz in view of Zhang in view of Reid in view of Dymarski in view of McGuigan, and in further view of Halpern because Halpern discloses using Hidden Markov Model (HMM) segmentation, scoring, and output are performed, and population-based no calling and identification of low-confidence regions may also be performed as a total copy number value and region-specific copy number value for a plurality of regions are then estimated. One of ordinary skill in the art would have a reasonable expectation combining Hyland in view of Talasaz in view of Zhang in view of Reid in view of Dymarski in view of McGuigan, and in further view of Halpern because Halpern discloses a method for adjusting the baseline samples themselves according to estimates of copy number to preserve a linear relationship between copy number and relative coverage, thus, allowing a more accurately inference absolute copy number [Halpern page 13 left col para 0174]. Therefore, combining Hyland in view of Talasaz in view of Zhang in view of Reid in view of Dymarski in view of McGuigan, and in further view of Halpern
Double Patenting
It is noted the amendments received 30 April 2026 are necessitated by new ground(s) of rejection.
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.
Claims 37, 39-40, 46-47, 57, and 63 are rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1-3, 5-10 and 20-27 of U.S. Patent No. 11,232,850 (Hass et al.) in view of Hyland et al. (US Patent Pub US 2012/0046877), and in view of Dymarski et al. (2011) (Hidden Markov models: theory and applications).
Claims 37 recite:
Claim 37 step (a) recites preparing a test sequencing library by enriching genomic DNA using capture probes hybridized to the target sequences. Claim 37 step (b) recites sequencing the test sequencing library to obtain 100-10,000 sequence reads. Claim 37 step (b) recites wherein the sequencing reads are mapped to an interrogated segment in the region of interest.
Claim 37 step (b) recites thereby providing a copy number of the interrogated segment using the determined copy number of the interrogated segment as the most probable copy number which is determined by a parameterized hidden Markov Model.
Claim 37 step (b) recites wherein the copy number is the most probable copy number of the interrogated segment as determined by a parameterized hidden Markov model comprising: (i) one or more hidden states comprising a copy number corresponding to the interrogated segment or a plurality of sub-segments within the interrogated segment.
Claim 37 step (b) recites (ii) an observation state comprising a number of sequencing reads mapped to the interrogated segment.
Claim 37 step (b) recites (iii) a copy number likelihood model using one or more likelihood distributions for an expected number of sequencing reads mapped to the interrogated segment.
Claim 37 step (b) recites (iii) wherein the parameterized hidden Markov model is parameterized by adjusting the copy number likelihood model to fit a determined number of sequencing reads mapped to the interrogated segment, using an analytic first derivative gradient and second derivative Hessian of one or more parameters in the copy number likelihood model, and wherein the one or more parameters comprise a dispersion of mapped sequence reads (d) and an average of mapped sequence reads(μ).
Hass discloses parameterizing the hidden Markov model [Hass, claims 1-2 steps (e)]. Hass discloses determining a most probable copy number of a section within the region of interest, wherein the section comprises a plurality of spatially adjacent segments comprising the interrogated segment [Hass, claim 3], as in claim 37 step (b) thereby providing the copy number of the interrogated segment; wherein the copy number is the most probable copy number of the interrogated segment as determined by a parameterized hidden Markov model.
Hass discloses a plurality of hidden states comprising a copy number for each of the spatially adjacent segments or a plurality of sub - segments within each of the spatially adjacent segments [Hass, claim 1 step (d)(ii)], as in claim 37 step (b)(i).
Hass discloses a plurality of observation states comprising the number of sequencing reads mapped to each spatially adjacent segment [Hass, claim 1 step (d)(ii)], as in claim 37 step (b)(ii).
Hass discloses the copy number likelihood model [Hass claim 2 step (e)]. Hass discloses determining a most probable copy number of a section within the region of interest, wherein the section comprises a plurality of spatially adjacent segments comprising the interrogated segment [Hass, claim 3].
Briefly, and regarding the limitations of instant claim 40, Hass et al. teach these limitations at claims 2 step d step (i), 3, and 5-8.
Briefly, and regarding the limitations of instant claim 46, Hass et al. teach these limitations at claims 9 and 12-13.
Briefly, and regarding the limitations of instant claim 47, Hass et al. teach these limitations at claims 10 and 12-13.
Briefly, and regarding the limitations of instant claim 57, Hass et al. teach these limitations at claims 20-26.
Briefly, and regarding the limitations of instant claim 63, Hass et al. teach these limitations at claim 27.
Regarding claim 39 it would be obvious to provide limitations to described the mathematical variables associated with the parameters of the copy number likelihood model.
Hass does not teach claim 37 step (a). Hass does not teach using an analytic first derivative gradient and second derivative Hessian of one or more parameters in the copy number likelihood model.
With respect to claim 37 step (a), Hyland teaches some examples of next generation sequencing techniques include, but are not limited to, sequencing by synthesis, sequencing by ligation, and sequencing by hybridization. More specifically, the SOLiD Sequencing System of Life Technologies Corp. provides massively parallel sequencing with enhanced accuracy. The SOLiD System and associated workflows, protocols, chemistries, etc. are described in more detail in PCT Publication No. WO 2006/084132, entitled "Reagents, Methods, and Libraries for Bead-Based Sequencing," international filing date Feb. 1, 2006, U.S. patent application Ser. No. 12/873,190, entitled "Low-Volume Sequencing System and Method of Use," filed on Aug. 31, 2010, and U.S. patent application Ser. No. 12/873, 13 2, entitled "Fast-Indexing Filter Wheel and Method of Use," filed on Aug. 31, 2010 [Specification para [0036]]. Hass discloses the historical population comprises about 1000 or more sequencing libraries (such as about 5000 or more, about 10,000 or more, about 25,000 or more, about 50,000 or more, about 100,000 or more, about 250,000 or more, or about 500,000 or more sequencing libraries) [Hass col 22lines 38-48].
With respect to claim 37 step (d) sub step (iii) “…first derivative but also the second derivative Hessian…”, Dymarski et al. teach that the Newton-Raphson method uses not only the first derivative but also the second derivative, i.e., the Hessian matrix, may also be used [page 139 Newton-Raphson method]. Dymarski teaches a Hessian matrix is not always a positive-definite matrix, the following second derivative, approximated using only diagonal elements [page 139].
It would be obvious to one of ordinary skill in the art by the effect filing date of the claimed invention to modify Hass in view of Hyland because Hyland teaches enriching sequencing libraires of genomic DNA. It would be obvious to one of ordinary skill in the art to combine Hass with the enriching methods by hybridization capture of Hyland because using enriching sequencing by hybridization methods are well-known in the art. One of ordinary skill in the art would recognize that combining claim 1 of Hass with what is well known in the art, such as the teaching of Hyland using sequencing and enriching methods, renders claim 37 obvious. Furthermore, it would be further obvious to one of ordinary skill to use a Hessian derivative in the HMM model, as evidenced by Dymarski because Dymarski teaches using HMM, theory, and application of the HMM with respect genetic sequencing [page 22 fig 8].
Claim 71 recites:
Claim 71 step (a) recites preparing a test sequencing library by enriching genomic DNA using capture probes hybridized to the target sequences. Claim 71 step (a) recites sequencing the test sequencing library to obtain 100-10,000 sequence reads. Claim 71 step (a) wherein the sequencing reads are mapped to an interrogated segment in the region of interest.
Claim 71 step recites parameterizing, by a computer, a hidden Markov Model based on (i) one or more hidden states comprising a copy number corresponding to the interrogated segment or a plurality of sub-segments within the interrogated segment, (ii) an observation state comprising a number of sequencing reads mapped to the interrogated segment, and (iii) a copy number likelihood model using one or more likelihood distributions for an expected number of sequencing reads mapped to the interrogated segment to fit a determined number of sequencing reads mapped to the interrogated segment, using an analytic first derivative gradient and second derivative Hessian of one or more parameters in the copy number likelihood model.
Claim 71 step (c) recites detecting an effect of noise in the number of mapped sequencing reads.
Claim 71 step (d) recites adjusting, by a computer, the copy number likelihood model by weighing a level of noise in the number of mapped sequencings reads from the test sequencing library. Claim 71 step (d) recites wherein a most probable copy number of the interrogated segment is not called if the noise in the number of mapped sequencing reads is above a predetermined threshold. Claim 71 step (d) recites wherein sequencing reads from overlapping direct targeted sequencing capture probes are merged.
Claim 71 step (e) recites detecting the copy number of the interrogated segment, wherein the copy number is the most probable copy number of the interrogated segment as determined by the parameterized hidden Markov model.
Hass discloses determine the copy number of the interrogated segment, wherein the copy number is the most probable copy number of the interrogated segment as determined by an optimized parameterized hidden Markov model comprising: (i) one or more hidden states comprising a copy number corresponding to the interrogated segment or a plurality of sub-segments within the interrogated segment; (ii) an observation state comprising a number of sequencing reads mapped to the interrogated segment; and (iii) a copy number likelihood model using one or more likelihood distributions for an expected number of sequencing reads mapped to the interrogated segment, wherein the hidden Markov model is parameterized by adjusting the copy number likelihood model to fit the determined number of sequencing reads mapped to the interrogated segment [Hass claim 1], as in instant claim 37 step (b) (i-ii) and (iii) for an expected number of sequencing reads mapped to the interrogated segment and by adjusting the copy number likelihood model to fit a determined number of sequencing reads mapped to the interrogated segment.
Hass discloses accounting for noise in the number of mapped sequencing reads [Hass, claim 10], as in claim 71 step (c).
Hass discloses adjusting the copy number likelihood model to fit determined number of sequencing reads mapped to the interrogated segment by allowing portions of the likelihood distributions to float. Hass discloses weighing a level of noise in the number of mapped sequencing reads from the test sequencing library. Hass discloses wherein the most probable copy number of the interrogated segment is not called if the noise in the number of mapped sequencing reads is above a predetermined threshold [Hass, claim 10], as in instant claim 71 step (d)].
Hass discloses determine the copy number of the interrogated segment, wherein the copy number is the most probable copy number of the interrogated segment as determined by an optimized parameterized hidden Markov model [Hass, claim 1 step (c)], as in claim 71 step (e).
Claims 72-78
Briefly, and regarding the limitations of instant claim 72, Hass discloses wherein the average number of mapped sequencing reads at a corresponding segment across a plurality of sequencing libraries or the average number of sequencing reads across a plurality of segments of interest within the test sequencing library is a normalized average [Hass, claim 5]. Hass discloses wherein accounting for noise in the number of mapped sequencing reads comprises adjusting the copy number likelihood model by adjusting a dispersion of a copy number likelihood distribution in the copy number likelihood model, wherein adjusting the copy number likelihood model to account for the noise comprises adjusting the dispersion of the copy number likelihood distribution in the copy number likelihood model via an expectation-maximization step [Hass, claim 10].
Briefly, and regarding the limitations of instant claim 73, Hass discloses a dispersion of a copy number likelihood distribution in the copy number likelihood model, wherein adjusting the copy number likelihood model to account for the noise comprises adjusting the dispersion of the copy number likelihood distribution in the copy number likelihood model via an expectation-maximization step [Hass claim 10]. Hass discloses the copy number likelihood model can be further characterized by a mean (μ) and a dispersion (d). Hass discloses the mean and the dispersion of the likelihood distribution are optimized by using a determined expected number of sequencing [Hass col 24 lines 16-20]. Hass discloses an average number of mapped sequencing reads at a corresponding segment across a plurality of sequencing libraries and an average number of mapped sequencing reads across a plurality of segments of interest within the test sequencing library, wherein the average number of mapped sequencing reads at a corresponding segment across a plurality of sequencing libraries or the average number of sequencing reads across a plurality of segments of interest within the test sequencing library is a normalized average [Hass claim 5], as in instant claim 73.
Briefly, and regarding the limitations of instant claim 74, Hass et al. teach these limitations at claims 2 step d step (i), 3, 5-8, and 10.
Briefly, and regarding the limitations of instant claim 75, Hass et al. teach these limitations at claim 8.
Briefly, and regarding the limitations of instant claim 76, Hass et al. teach these limitations at claims 7 and 9-10.
Briefly, and regarding the limitations of instant claim 77, Hass et al. teach these limitations at claims 10 and 12-13.
Briefly, and regarding the limitations of instant claim 78, Hass et al. teach these limitations at claims 12-13.
Hass does not disclose claim 71 step (a). Hass does discloses using an analytic first derivative gradient and second derivative Hessian of one or more parameters in the copy number likelihood model of claim 71 step (b)(iii).
With respect to claim 71 step (a), Hyland teaches some examples of next generation sequencing techniques include, but are not limited to, sequencing by synthesis, sequencing by ligation, and sequencing by hybridization. More specifically, the SOLiD Sequencing System of Life Technologies Corp. provides massively parallel sequencing with enhanced accuracy. The SOLiD System and associated workflows, protocols, chemistries, etc. are described in more detail in PCT Publication No. WO 2006/084132, entitled "Reagents, Methods, and Libraries for Bead-Based Sequencing," international filing date Feb. 1, 2006, U.S. patent application Ser. No. 12/873,190, entitled "Low-Volume Sequencing System and Method of Use," filed on Aug. 31, 2010, and U.S. patent application Ser. No. 12/873, 13 2, entitled "Fast-Indexing Filter Wheel and Method of Use," filed on Aug. 31, 2010 [Specification para [0036]]. Hass discloses the historical population comprises about 1000 or more sequencing libraries (such as about 5000 or more, about 10,000 or more, about 25,000 or more, about 50,000 or more, about 100,000 or more, about 250,000 or more, or about 500,000 or more sequencing libraries) [Hass col 22lines 38-48].
With respect to claim 71 step (b) (iii) “…first derivative but also the second derivative Hessian…”, Dymarski et al. teaches that the Newton-Raphson method uses not only the first derivative but also the second derivative, i.e., the Hessian matrix, may also be used [page 139 Newton-Raphson method]. Dymarski teacheses a Hessian matrix is not always a positive-definite matrix, the following second derivative, approximated using only diagonal elements [page 139].
It would be obvious to one of ordinary skill in the art by the effect filing date of the claimed invention to modify Hass in view of Hyland because Hyland teaches enriching sequencing libraires of genomic DNA. It would be obvious to one of ordinary skill in the art to combine Hass with the enriching methods by hybridization capture of Hyland because using enriching sequencing by hybridization methods are well-known in the art. One of ordinary skill in the art would recognize that combining claim 1 of Hass with what is well known in the art, such as the teaching of Hyland using sequencing and enriching methods, renders claim 37 obvious. Furthermore, it would be further obvious to one of ordinary skill to use a Hessian derivative in the HMM model, as evidenced by Dymarski because Dymarski teaches using HMM, theory, and application of the HMM with respect genetic sequencing [page 22 fig 8].
Although the claims at issue are not identical, they are not patentably distinct from each other because both applications arrive at the same results of determining copy number using HMM models and the instant claims use hybridizing capture probes and using Hessian derivative. Although Hass does not teach using hybridizing capture probes and using Hessian derivative, it would be obvious to use Hyland and Dymarski, as evidence above, to provide the limitations not encompasses by Hass.
It would be obvious to one of ordinary skill in the art by the effect filing date of the claimed invention to modify Hass in view of Hyland because Hyland teaches enriching sequencing libraires of genomic DNA. It would be obvious to one of ordinary skill in the art to combine Hass with the enriching methods by hybridization capture of Hyland because using enriching sequencing by hybridization methods are well-known in the art. One of ordinary skill in the art would recognize that combining claim 1 of Hass with what is well known in the art, such as the teaching of Hyland using sequencing and enriching methods, renders claim 37 obvious. Furthermore, it would be further obvious to one of ordinary skill to use a Hessian derivative in the HMM model, as evidenced by Dymarski because Dymarski teaches using HMM, theory, and application of the HMM with respect genetic sequencing [page 22 fig 8].
Although the claims at issue are not identical, they are not patentably distinct from each other because both applications arrive at the same results of determining copy number using HMM models and the instant application uses hybridizing capture probes and using Hessian derivative. Although Hass does not teach using hybridizing capture probes and using Hessian derivative, it would be obvious to use Hyland and Dymarski, as evidenced above, to provide the limitations not encompasses by Hass.
Provisional Double Patenting
Claim 37, 39-40, 46-47, 57, 63, and 71-78 are provisionally rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1, 2, 3, 5-10, and 12-13 of copending U.S Patent Application No. 17/554,721 (Hass et al.) in view of Hyland et al. (US Patent Pub US 2012/0046877), and in view of Dymarski et al. (2011) (Hidden Markov models: theory and applications).
Claim 37
Claim 37 step (a) recites preparing a test sequencing library by enriching genomic DNA using capture probes hybridized to the target sequences. Claim 37 step (b) recites sequencing the test sequencing library to obtain 100-10,000 sequence reads. Claim 37 step (b) recites wherein the sequencing reads are mapped to an interrogated segment in the region of interest.
Claim 37 step (b) recites thereby providing a copy number of the interrogated segment using the determined copy number of the interrogated segment as the most probable copy number which is determined by a parameterized hidden Markov Model.
Claim 37 step (b) recites wherein the copy number is the most probable copy number of the interrogated segment as determined by a parameterized hidden Markov model comprising: (i) one or more hidden states comprising a copy number corresponding to the interrogated segment or a plurality of sub-segments within the interrogated segment.
Claim 37 step (b) recites (ii) an observation state comprising a number of sequencing reads mapped to the interrogated segment.
Claim 37 step (b) recites (iii) a copy number likelihood model using one or more likelihood distributions for an expected number of sequencing reads mapped to the interrogated segment.
Claim 37 step (b) recites (iii) wherein the parameterized hidden Markov model is parameterized by adjusting the copy number likelihood model to fit a determined number of sequencing reads mapped to the interrogated segment, using an analytic first derivative gradient and second derivative Hessian of one or more parameters in the copy number likelihood model, and wherein the one or more parameters comprise a dispersion of mapped sequence reads (d) and an average of mapped sequence reads(μ).
Hass discloses determine the copy number of the interrogated segment, wherein the copy number is the most probable copy number of the interrogated segment as determined by an optimized parameterized hidden Markov model comprising: (i) one or more hidden states comprising a copy number corresponding to the interrogated segment or a plurality of sub-segments within the interrogated segment; (ii) an observation state comprising a number of sequencing reads mapped to the interrogated segment; and (iii) a copy number likelihood model using one or more likelihood distributions for an expected number of sequencing reads mapped to the interrogated segment, wherein the hidden Markov model is parameterized by adjusting the copy number likelihood model to fit the determined number of sequencing reads mapped to the interrogated segment [Hass claim 1], as in instant claim 37 step (b) (i-iii).
Briefly, and regarding the limitations of instant claim 39, Hass discloses a dispersion of a copy number likelihood distribution in the copy number likelihood model, wherein adjusting the copy number likelihood model to account for the noise comprises adjusting the dispersion of the copy number likelihood distribution in the copy number likelihood model via an expectation-maximization step [Hass claim 10]. Hass discloses the copy number likelihood model can be further characterized by a mean (μ) and a dispersion (d). Hass discloses the mean and the dispersion of the likelihood distribution are optimized by using a determined expected number of sequencing [Hass col 24 lines 16-20]. Hass discloses an average number of mapped sequencing reads at a corresponding segment across a plurality of sequencing libraries and an average number of mapped sequencing reads across a plurality of segments of interest within the test sequencing library, wherein the average number of mapped sequencing reads at a corresponding segment across a plurality of sequencing libraries or the average number of sequencing reads across a plurality of segments of interest within the test sequencing library is a normalized average [Hass claim 5], as in instant claim 39.
Briefly, and regarding the limitations of instant claim 40, Hass et al. teach these limitations at claims 2 step d step (i), 3, and 5-8.
Briefly, and regarding the limitations of instant claim 46, Hass et al. teach these limitations at claims 9 and 12-13.
Briefly, and regarding the limitations of instant claim 47, Hass et al. teach these limitations at claims 9-10 and 12-13.
Briefly, and regarding the limitations of instant claim 57, Hass et al. teach these limitations at claim 10.
Briefly, and regarding the limitations of instant claim 63, Hass et al. teach these limitations at claims 12-13.
Hass does not teach claim 37 step (a). Hass does not teach using an analytic first derivative gradient and second derivative Hessian of one or more parameters in the copy number likelihood model.
With respect to claim 37 step (a), Hyland teaches some examples of next generation sequencing techniques include, but are not limited to, sequencing by synthesis, sequencing by ligation, and sequencing by hybridization. More specifically, the SOLiD Sequencing System of Life Technologies Corp. provides massively parallel sequencing with enhanced accuracy. The SOLiD System and associated workflows, protocols, chemistries, etc. are described in more detail in PCT Publication No. WO 2006/084132, entitled "Reagents, Methods, and Libraries for Bead-Based Sequencing," international filing date Feb. 1, 2006, U.S. patent application Ser. No. 12/873,190, entitled "Low-Volume Sequencing System and Method of Use," filed on Aug. 31, 2010, and U.S. patent application Ser. No. 12/873, 13 2, entitled "Fast-Indexing Filter Wheel and Method of Use," filed on Aug. 31, 2010 [Specification para [0036]].
With respect to claim 37 step (d) sub step (iii) “…first derivative but also the second derivative Hessian…”, Dymarski et al. teach that the Newton-Raphson method uses not only the first derivative but also the second derivative, i.e., the Hessian matrix, may also be used [page 139 Newton-Raphson method]. Dymarski teaches a Hessian matrix is not always a positive-definite matrix, the following second derivative, approximated using only diagonal elements [page 139].
Claim 71 recites:
Claim 71 step (a) recites preparing a test sequencing library by enriching genomic DNA using capture probes hybridized to the target sequences. Claim 71 step (a) recites sequencing the test sequencing library to obtain 100-10,000 sequence reads. Claim 71 step (a) wherein the sequencing reads are mapped to an interrogated segment in the region of interest.
Claim 71 step recites parameterizing, by a computer, a hidden Markov Model based on (i) one or more hidden states comprising a copy number corresponding to the interrogated segment or a plurality of sub-segments within the interrogated segment, (ii) an observation state comprising a number of sequencing reads mapped to the interrogated segment, and (iii) a copy number likelihood model using one or more likelihood distributions for an expected number of sequencing reads mapped to the interrogated segment to fit a determined number of sequencing reads mapped to the interrogated segment, using an analytic first derivative gradient and second derivative Hessian of one or more parameters in the copy number likelihood model.
Claim 71 step (c) recites detecting an effect of noise in the number of mapped sequencing reads.
Claim 71 step (d) recites adjusting, by a computer, the copy number likelihood model by weighing a level of noise in the number of mapped sequencings reads from the test sequencing library. Claim 71 step (d) recites wherein a most probable copy number of the interrogated segment is not called if the noise in the number of mapped sequencing reads is above a predetermined threshold. Claim 71 step (d) recites wherein sequencing reads from overlapping direct targeted sequencing capture probes are merged.
Claim 71 step (e) recites detecting the copy number of the interrogated segment, wherein the copy number is the most probable copy number of the interrogated segment as determined by the parameterized hidden Markov model.
Hass discloses determine the copy number of the interrogated segment, wherein the copy number is the most probable copy number of the interrogated segment as determined by an optimized parameterized hidden Markov model comprising: (i) one or more hidden states comprising a copy number corresponding to the interrogated segment or a plurality of sub-segments within the interrogated segment; (ii) an observation state comprising a number of sequencing reads mapped to the interrogated segment; and (iii) a copy number likelihood model using one or more likelihood distributions for an expected number of sequencing reads mapped to the interrogated segment, wherein the hidden Markov model is parameterized by adjusting the copy number likelihood model to fit the determined number of sequencing reads mapped to the interrogated segment [Hass claim 1], as in instant claim 37 step (b) (i-ii) and (iii) for an expected number of sequencing reads mapped to the interrogated segment and by adjusting the copy number likelihood model to fit a determined number of sequencing reads mapped to the interrogated segment.
Hass discloses accounting for noise in the number of mapped sequencing reads [Hass, claim 10], as in claim 71 step (c).
Hass discloses adjusting the copy number likelihood model to fit determined number of sequencing reads mapped to the interrogated segment by allowing portions of the likelihood distributions to float. Hass discloses weighing a level of noise in the number of mapped sequencing reads from the test sequencing library. Hass discloses wherein the most probable copy number of the interrogated segment is not called if the noise in the number of mapped sequencing reads is above a predetermined threshold [Hass, claim 10], as in instant claim 71 step (d)].
Hass discloses determine the copy number of the interrogated segment, wherein the copy number is the most probable copy number of the interrogated segment as determined by an optimized parameterized hidden Markov model [Hass, claim 1 step (c)], as in claim 71 step (e).
Claims 72-78
Briefly, and regarding the limitations of instant claim 72, Hass discloses wherein the average number of mapped sequencing reads at a corresponding segment across a plurality of sequencing libraries or the average number of sequencing reads across a plurality of segments of interest within the test sequencing library is a normalized average [Hass, claim 5]. Hass discloses wherein accounting for noise in the number of mapped sequencing reads comprises adjusting the copy number likelihood model by adjusting a dispersion of a copy number likelihood distribution in the copy number likelihood model, wherein adjusting the copy number likelihood model to account for the noise comprises adjusting the dispersion of the copy number likelihood distribution in the copy number likelihood model via an expectation-maximization step [Hass, claim 10].
Briefly, and regarding the limitations of instant claim 73, Hass discloses a dispersion of a copy number likelihood distribution in the copy number likelihood model, wherein adjusting the copy number likelihood model to account for the noise comprises adjusting the dispersion of the copy number likelihood distribution in the copy number likelihood model via an expectation-maximization step [Hass claim 10]. Hass discloses the copy number likelihood model can be further characterized by a mean (μ) and a dispersion (d). Hass discloses the mean and the dispersion of the likelihood distribution are optimized by using a determined expected number of sequencing [Hass col 24 lines 16-20]. Hass discloses an average number of mapped sequencing reads at a corresponding segment across a plurality of sequencing libraries and an average number of mapped sequencing reads across a plurality of segments of interest within the test sequencing library, wherein the average number of mapped sequencing reads at a corresponding segment across a plurality of sequencing libraries or the average number of sequencing reads across a plurality of segments of interest within the test sequencing library is a normalized average [Hass claim 5], as in instant claim 73.
Briefly, and regarding the limitations of instant claim 74, Hass et al. teach these limitations at claims 2 step d step (i), 3, 5-8, and 10.
Briefly, and regarding the limitations of instant claim 75, Hass et al. teach these limitations at claim 8.
Briefly, and regarding the limitations of instant claim 76, Hass et al. teach these limitations at claims 7 and 9-10.
Briefly, and regarding the limitations of instant claim 77, Hass et al. teach these limitations at claims 10 and 12-13.
Briefly, and regarding the limitations of instant claim 78, Hass et al. teach these limitations at claims 12-13.
Hass does not disclose claim 71 step (a). Hass does discloses using an analytic first derivative gradient and second derivative Hessian of one or more parameters in the copy number likelihood model of claim 71 step (b)(iii).
With respect to claim 71 step (a), Hyland teaches some examples of next generation sequencing techniques include, but are not limited to, sequencing by synthesis, sequencing by ligation, and sequencing by hybridization. More specifically, the SOLiD Sequencing System of Life Technologies Corp. provides massively parallel sequencing with enhanced accuracy. The SOLiD System and associated workflows, protocols, chemistries, etc. are described in more detail in PCT Publication No. WO 2006/084132, entitled "Reagents, Methods, and Libraries for Bead-Based Sequencing," international filing date Feb. 1, 2006, U.S. patent application Ser. No. 12/873,190, entitled "Low-Volume Sequencing System and Method of Use," filed on Aug. 31, 2010, and U.S. patent application Ser. No. 12/873, 13 2, entitled "Fast-Indexing Filter Wheel and Method of Use," filed on Aug. 31, 2010 [Specification para [0036]]. Hass discloses the historical population comprises about 1000 or more sequencing libraries (such as about 5000 or more, about 10,000 or more, about 25,000 or more, about 50,000 or more, about 100,000 or more, about 250,000 or more, or about 500,000 or more sequencing libraries) [Hass col 22lines 38-48].
With respect to claim 71 step (b) (iii) “…first derivative but also the second derivative Hessian…”, Dymarski et al. teaches that the Newton-Raphson method uses not only the first derivative but also the second derivative, i.e., the Hessian matrix, may also be used [page 139 Newton-Raphson method]. Dymarski teacheses a Hessian matrix is not always a positive-definite matrix, the following second derivative, approximated using only diagonal elements [page 139].
It would be obvious to one of ordinary skill in the art by the effect filing date of the claimed invention to modify Hass in view of Hyland because Hyland teaches enriching sequencing libraires of genomic DNA. It would be obvious to one of ordinary skill in the art to combine Hass with the enriching methods by hybridization capture of Hyland because using enriching sequencing by hybridization methods are well-known in the art. One of ordinary skill in the art would recognize that combining claim 1 of Hass with what is well known in the art, such as the teaching of Hyland using sequencing and enriching methods, renders claim 37 obvious. Furthermore, it would be further obvious to one of ordinary skill to use a Hessian derivative in the HMM model, as evidenced by Dymarski because Dymarski teaches using HMM, theory, and application of the HMM with respect genetic sequencing [page 22 fig 8].
Although the claims at issue are not identical, they are not patentably distinct from each other because both applications arrive at the same results of determining copy number using HMM models and the instant claims use hybridizing capture probes and using Hessian derivative. Although Hass does not teach using hybridizing capture probes and using Hessian derivative, it would be obvious to use Hyland and Dymarski, as evidence above, to provide the limitations not encompasses by Hass.
This is a provisional nonstatutory double patenting rejection because the patentably indistinct claims have not in fact been patented.
Conclusion
Claims 37, 39-40, 46-47, 57, 63, and 71-78 are rejected.
No claims are allowed.
Finality
This Office action is a Non-Final action. A shortened statutory period for reply to this action is set to expire THREE MONTHS from the mailing date of this action.
Inquiries
Any inquiry concerning this communication or earlier communications from the examiner should be directed to JOSEPH C PULLIAM whose telephone number is (571)272-8696. The examiner can normally be reached 0730-1700 M-F.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Karlheinz Skowronek can be reached at (571) 272-9047. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/J.C.P./ Examiner, Art Unit 1687
/Anna Skibinsky/
Primary Examiner, AU 1635
Prosecution Insights