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and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114.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 4/2/2026 has been entered.
Withdrawn Rejections/Objections
The objection to claim 14 in the Office action mailed 10/2/2025 is withdrawn in view of the amendments filed 4/2/2026.
Claims Status
Claims 1-18 and 46-47 are pending.
Claims 19-45 are cancelled.
Claim 47 is new.
Claims 1-18 and 46-47 are rejected.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claim 1 is rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Specifically claim 1 is directed to a computer system for determining copy number variants using a trained model (probabilistic machine learning model), however, it is unclear if those limitations are intended to require that the training of the model is performed within the metes and bounds of the claimed invention or if they are merely limiting the process by which the model was previously trained. Claims 2-4, and 46-47 depend from claim 1 and do not resolve the dependency issue and as such are also rejected.
The term “polymorphic variability” in claim 1 is a relative term which renders the claim indefinite. The term “polymorphic variability” is not defined by the claim, the specification does not provide a standard for ascertaining the requisite degree, and one of ordinary skill in the art would not be reasonably apprised of the scope of the invention. The use of the term renders indefinite and unclear the bounds which encompass “unavailable for analysis”.
The term “low sequence complexity” in claim 1 is a relative term which renders the claim indefinite. The term “low sequence complexity” is not defined by the claim, the specification does not provide a standard for ascertaining the requisite degree, and one of ordinary skill in the art would not be reasonably apprised of the scope of the invention. The use of the term renders indefinite and unclear the bounds which encompass “unavailable for analysis”.
The term “accurate alignment” in claim 1 is a relative term which renders the claim indefinite. The term “accurate alignment” is not defined by the claim, the specification does not provide a standard for ascertaining the requisite degree, and one of ordinary skill in the art would not be reasonably apprised of the scope of the invention. The use of the term renders indefinite and unclear the bounds which encompass “unavailable for analysis”.
Claim Rejections - 35 USC § 101
Response to Amendment
In view of applicant’s amendments to the claims, the prior analysis of the claims under 35 U.S.C. 101 has been updated and is provided below.
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.
Claim 1-18 and 46-47 are rejected under 35 U.S.C. 101 because the claimed invention is directed to abstract ideas without significantly more. The claims recite a method of diagnosis based on the detection of a genetic state of a locus of interest in genetic material using probabilistic and machine learning models. This judicial exception is not integrated into a practical application because while claims 1-18 and 46-47 attempt to integrate the exception into a practical application, said practical application is a generically recited computer element that does not add a meaningful limitation to the abstract idea as it is simply implementing the abstract idea on a computer. The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the computer elements only store and retrieve information in memory as well as perform basic calculations that are known to be well-understood, routine and conventional computer functions as recognized by the decisions listed in MPEP § 2106.05(d).
Framework with which to Analyze Subject Matter Eligibility:
Step 1: Are the claims directed to a category of statutory subject matter (a process, machine,
manufacture, or composition of matter)? [see MPEP § 2106.03]
Claims are directed to statutory subject matter, specifically computer systems (claims 1-18, and 46-57).
Step 2A Prong One: Do the claims recite a judicially recognized exception, i.e., an abstract idea, law of nature, or natural phenomenon? [see MPEP § 2106.04(a)]
The claims herein recite abstract ideas.
With respect to the step 2A Prong One evaluation, the instant claims are found herein to recite abstract ideas that fall into the grouping of mathematical concepts.
The following claims recite abstract ideas (mathematical concepts):
Claim 1: Mapping the sequence reads to the reference sample, generating a copy number score, filtering regions, partitioning the sequence into a plurality of segments, extracting a copy number segment score, generating a segment weight, generating a weighted score, aggregating the weighted scores, and generating a diagnostic and therapeutic report based on the CNV prediction, are all processes of collecting, comparing/contrasting, calculating, and summarizing data that can be done via pen and paper or within the human mind and are therefore abstract ideas, specifically mental processes. The locus being unavailable due to polymorphic variability or low sequence complexity, the historical performance data comprising co-occurrence data, the sequence reads being derived from cell-free DNA, the copy number score representing sequence coverage, the segmented copy number score representing a deviation from the baseline, model trained to generate a probability that the locus includes a CNV based on weighted scores, and the weight indicating a level of correlation are directed to merely further limiting the data itself, which is an abstract idea, specifically a mental process.
Claim 2: Training the machine learning classifier via maximum likelihood, is a verbal articulation of a mathematical process, specifically a mathematical concept.
Claim 3: Training the machine learning classifier via a Hidden Markov Model, is a verbal articulation of a mathematical process, specifically a mathematical concept.
Claim 4: Training the machine learning classifier via Bayesian inference, is a verbal articulation of a mathematical process, specifically a mathematical concept.
Claim 5: Retrieving correlation parameters, applying a machine learning classifier, generating a prediction of the locus, and predicting a disease condition are processes of collecting, comparing/contrasting, calculating, and summarizing data that can be done via pen and paper or within the human mind and are therefore abstract ideas, specifically mental processes. The locus being unavailable due to polymorphic variability or low sequence complexity, the historical performance data comprising co-occurrence data, the sequence reads being derived from cell-free DNA, the copy number score representing sequence coverage, the segmented copy number score representing a deviation from the baseline, and the weight indicating a level of correlation are directed to merely further limiting the data itself, which is an abstract idea, specifically a mental process.
Claim 6: The genetic material comprising cell free DNA is directed to the data itself, which is an abstract idea, rendering this an abstract idea, specifically a mental process.
Claim 7: Determining a level of deletion of the chromosome is a process of assessing and comparing information which can be done via pen and paper or within the human mind and is therefore an abstract idea, specifically a mental process.
Claim 8: Determining the physical distance between the segment and locus of interest is a process of assessing and comparing information which can be done via pen and paper or within the human mind and is therefore an abstract idea, specifically a mental process.
Claim 9: Analyzing sequence coverage information are processes of collecting, comparing/contrasting, and calculating data that can be done via pen and paper or within the human mind and are therefore abstract ideas, specifically mental processes.
Claim 10: Generating a prediction that the locus of interest is in the second genetic state is a process of comparing/contrasting, and calculating data that can be done via pen and paper or within the human mind and are therefore abstract ideas, specifically mental processes.
Claim 11: Generating a prediction that the locus of interest is in the second genetic state is a process of comparing/contrasting, and calculating data that can be done via pen and paper or within the human mind and are therefore abstract ideas, specifically mental processes.
Claim 12: The locus of interest being associated with the disease condition is directed to the data itself, which is an abstract idea, rendering this an abstract idea, specifically a mental process.
Claim 13: The locus of interest comprising an HLA locus is directed to the data itself, which is an abstract idea, rendering this an abstract idea, specifically a mental process.
Claim 14: One or more target genes and one or more reference genes not being in the same regulation pathway is directed to the data itself, which is an abstract idea, rendering this an abstract idea, specifically a mental process.
Claim 15: The target genes being in genetic linkage with referene genes is directed to the data itself, which is an abstract idea, rendering this an abstract idea, specifically a mental process. Identifying one or more segments to analyze is a process of comparing/contrasting and picking data that can be done via pen and paper or within the human mind and are therefore abstract ideas, specifically mental processes.
Claim 16: The first genetic state comprising at least of the specified states listed in the group provided is directed to the data itself, which is an abstract idea, rendering this an abstract idea, specifically a mental process.
Claim 17: Determining a treatment based on the predicted disease condition is a process of comparing/contrasting, and calculating data that can be done via pen and paper or within the human mind and are therefore abstract ideas, specifically mental processes.
Claim 18: Determining a segment includes a copy number loss, and determining that the locus of interest also includes a copy number loss are processes of comparing/contrasting, calculating, and summarizing data that can be done via pen and paper or within the human mind and are therefore abstract ideas, specifically mental processes.
Claim 46: Partitioning the sequence into a plurality of segments is a process of selecting and dividing information that can be done via pen and paper or within the human mind and is therefore an abstract idea, specifically a mental process.
Claim 47: The treatment options comprising at least one of those specified is directed to merely further limiting the data itself, which is an abstract idea, specifically a mental process.
Step 2A Prong Two: If the claims recite a judicial exception under prong one, then is the judicial exception integrated into a practical application? [see MPEP § 2106.04(d)]
Because the claims do recite judicial exceptions, direction under Step 2A Prong Two provides that the claims must be examined further to determine whether they integrate the abstract ideas into a practical application.
The following claims recite the following additional elements in the form of non-abstract elements:
Claim 1: A computer system, memory, and a processor are generic and nonspecific elements of a computer that do not improve the functioning of any computer or technology described herein [See MPEP § 2106.04(d)(1) and MPEP § 2106.05(d)]. Accessing a plurality of sequence reads is an insignificant extra solution activity, specifically mere data gathering [See MPEP § 2106.04(g)]. A reference sequence comprising a sequence of interest and locus of interest, and a segment-performance data comprising data associated with historical performance of a given segment are further limiting the data stored on the memory but there is no indication that the data type is changing it from a generic computer data storage (Storing and retrieving information in memory, Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1334, 115 USPQ2d 1681, 1701 (Fed. Cir. 2015)) [See MPEP § 2106.04(d)(II)].
Claim 5: A computer system, non-transitory memory, and a processor are generic and nonspecific elements of a computer that do not improve the functioning of any computer or technology described herein [See MPEP § 2106.04(d)(1) and MPEP § 2106.05(d)]. Accessing a plurality of sequence reads is an insignificant extra solution activity, specifically mere data gathering [See MPEP § 2106.04(g)]. A reference sequence comprising a sequence of interest and locus of interest, and a segment-performance data comprising data associated with historical performance of a given segment are further limiting the data stored on the memory but there is no indication that the data type is changing it from a generic computer data storage (Storing and retrieving information in memory, Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1334, 115 USPQ2d 1681, 1701 (Fed. Cir. 2015)) [See MPEP § 2106.04(d)(II)].
Claim 9: Accessing a plurality of sequence reads is an insignificant extra solution activity, specifically mere data gathering [See MPEP § 2106.04(g)].
Step 2B: If the claims do not integrate the judicial exception, do the claims provide an inventive concept? [see MPEP § 2106.05]
Because the additional claim elements do not integrate the abstract idea or law of nature into a practical application, the claims are further examined under Step 2B, which evaluates whether the additional elements, individually and in combination, amount to significantly more than the judicial exception itself by providing an inventive concept.
The claims do not recite additional elements that are sufficient to amount to significantly more than the judicial exceptions because the claims recite additional elements that are generic, conventional, or nonspecific. These additional elements include:
The additional elements of a computer system, memory, non-transitory memory, and a processor are generic and nonspecific elements of a computer that are well-understood, routine and conventional within the art and therefore do not improve the functioning of any computer or technology described therein (See Receiving or transmitting data over a network, e.g., using the Internet to gather data, Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information), Performing repetitive calculations, Flook, 437 U.S. at 594, 198 USPQ2d at 199 (recomputing or readjusting alarm limit values), and Storing and retrieving information in memory, Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1334, 115 USPQ2d 1681, 1701 (Fed. Cir. 2015)) [See MPEP § 2106.05(d)(II)]. Therefore, taken both individually and as a whole, the additional elements do not amount to significantly more than the judicial exception by providing an inventive concept.
The additional elements of a reference sequence comprising a sequence of interest and locus of interest, and a segment-performance data comprising data associated with historical performance of a given segment are further limiting the data stored on the memory but there is no indication that the data type is changing it from a generic computer data storage which is just conventional data storage (Storing and retrieving information in memory, Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1334, 115 USPQ2d 1681, 1701 (Fed. Cir. 2015)) [See MPEP § 2106.04(d)(II)].
The additional element of accessing a plurality of sequence reads (Conventional: Specification Paragraph [0081]) is an insignificant extra solution activity, specifically mere data gathering (Storing and retrieving information in memory, Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1334, 115 USPQ2d 1681, 1701 (Fed. Cir. 2015)) [See MPEP § 2106.04(g)].
Therefore, claims 1-18, and 46, when the limitations are considered individually and as a whole, are rejected under 35 U.S.C. § 101 as being directed to non-statutory subject matter.
Response to Arguments
Applicant's arguments filed 4/2/2026 have been fully considered but they are not persuasive.
Applicant asserts on page 8 of the Remarks filed 4/2/2026 that the claims are directed to an improvement to technology and specifically that the improvement is in the “computational structures (segment- performance data store with co-occurrence data), specific algorithmic approaches (partitioning based on genetic linkage, weighting based on learned correlations), and the use of ML techniques (e.g., as described in the specification: HMM, Bayesian inference, MLE)”. Applicant further asserts an improvement to technology citing both McRO, and Enfish, which are directed to cases in which the invention improves upon the functioning of the computer itself, now being able to perform a task which it was previously incapable of performing. However, "a segment-performance data store" is merely memory within a computer with a further limitation as to what data is being stored. This inherently cannot be the basis for an improvement as the limitation of what data is being stored is an abstract idea, specifically a mental process, as described in the rejection above., the algorithmic approaches are abstract ideas inherently as they are merely performing a set of tasks based upon specific limitations that are inherently abstract ideas – merely limiting the data itself.
Finally, while the use of a machine learning model can be considered an additional element, this is only if said machine learning tool is specific or performing tasks that are not capable in the human mind. Here the stored data is merely genetic loci with correlations that are processed by a machine learning algorithm using linkage disequilibrium values to obtain a CNV prediction. This is merely limiting the data itself, and plugging it into a generic model which can be done via pen and paper or within the human mind. MPEP 2106.05(a) states It is important to note, the judicial exception alone cannot provide the improvement. The improvement can be provided by one or more additional elements. The improvements of algorithms and data structures based upon merely limiting the data that is being processed or aggregated are not improvements to the algorithms or data structures themselves but rather limiting information which is an abstract idea. While the trained model can be an additional element, the use of a generic model to generate a probability based upon a previous selection of data invokes the use of it merely as a tool for calculation which is a mathematical concept.
Subject Matter Potentially Free Over Prior Art
In view of the current amendments specifically for claims 1 and 5 “a segment-performance data store comprising data associated with historical performance of a given segment at predicting a CNV at the locus of interest across a plurality of samples of different subjects”, in addition to “partition, based on the segment-performance data store, the sequence of interest into a plurality of segments” and “aggregate the weighted scores into machine learning (ML) input data and input the ML input data to a trained ML copy-number classifier whose parameters were iteratively trained, using the segment-performance data store”, are not found within a search of the prior art. Specifically while databases exist for the express purpose of developing and benchmarking CNV prediction models such as DGV, dbVAR, ClineGen, TCGA, GDC, etc., there is no collection of CNV segment level performance data. The cited such as ClinGen provide a region name, the location on the chromosome of the region, whether they believe there is sufficient evidence to link to a clinical outcome, and a list of appearances in previous research. Comparatively however, the claimed invention uses segments of regions and stores historical performance to predict a CNV at a locus of interest specifically storing multiple tests across multiple samples across multiple individuals, which are then used to generate predictions. The most similar prior art found was a paper by Zhang et al. (PLoS computational biology (2015) 1-27), which uses segmentation but also read depth to classify CNV regions as SCNAs or not. While this method is similar in use of its segmentation and classification of CNV regions it does not use historical performance of the region segmentations nor does it solely do so, but rather relies on read depth. As such no adequate teachings were found to read fully on claims 1 and 5 of the current application and as such, all dependent claims.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to KEENAN NEIL ANDERSON-FEARS whose telephone number is (571)272-0108. The examiner can normally be reached M-Th, alternate F, 8-5.
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/K.N.A./ Examiner, Art Unit 1687
/LARRY D RIGGS II/Supervisory Patent Examiner, Art Unit 1686
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