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 .
The Applicant is reminded that structure defines how an apparatus differs from prior art apparatuses and when no difference in structure is defined, the assumption is made that the prior art structure meets the limitations. The Examiner will not give patentable weight to descriptive material absent a new and unobvious functional relationship between the descriptive material and the substrate. See In re Lowry, 32 F.3d 1579, 1582-1583 (Fed. Cir. 1994); In re Ngai, 367 F.3d 1336,1339 (Fed. Cir. 2004) (nonfunctional descriptive material cannot render nonobvious an invention that would have otherwise been obvious). See also Ex parte Mathias, 84 USPQ2d 1276 (BPAI 2005) (nonprecedential), aff’d, 191 Fed. Appx. 959 (Fed. Cir. 2006).
For purposes of Examination, the interpretation of “or” is “at least one of”. Based on the broadest reasonable interpretation this implicitly allows for the possibility that both elements could occur, but as noted above in order to meet the claim limitation “at least one of” must be met.
Double Patenting
Claims 21-40 are rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1,3,10-11,14,16-18 and 20 of U.S. Patent No.11,705,219. Although the claims at issue are not identical, they are not patentably distinct from each other because
it is clear that all the elements of the application claim 21-40 are to be found in patent claims 1,3,10-11,14,16-18 and 20 (as the application claim 21-40 fully encompasses patent claim claims 1,3,10-11,14,16-18 and 20). The difference between the application claims 21-40 and the patent claims 1,3,10-11,14,16-18 and 20 lies in the fact that the patent claim includes many more elements and is thus much more specific. Thus the invention of claims 1,3,10-11,14,16-18 and 20 of the patent is in effect a “species” of the “generic” invention of the application claim 21-40. It has been held that the generic invention is “anticipated” by the “species”. See In re Goodman, 29 USPQ2d 2010 (Fed. Cir. 1993). Since application claim 21-40 is anticipated by claims 1,3,10-11,14,16-18 and 20 of the patent, it is not patentably distinct from claims 1,3,10-11,14,16-18 and 20 of the patent.
Claim Rejections - 35 USC § 102
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (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 the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
Claim(s) 21-40 is/are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Brendan et al CA 2894317.
Consider Claim 21, 31 and 36, Brendan teaches a system comprising: at least one processor; a non-transitory computer readable medium storing instructions that, when executed by the at least one processor, cause the system to: access a data file comprising sequencing data output by a sequencer instrument based on processing a genomic sample -[0058] The system may further comprise a computing device 110 communicatively linked to the CVP 101 for controlling operations carried out in the CVP. The computing device may comprise further input and output devices, such as input peripherals (such as a computer mouse or keyboard), and/or a display. The computing device 110 may further be linked to a remote device 112 over a wired or wireless network 108 for transmitting and receiving data. In embodiments, genetic information is received over the network 108 from the remote device 112 for storage in memory 106. Cell variable predictions and lists of variants priorities may be displayed to a user via the display]; [0040] As used herein, the term "reference sequence" means: in the context of evaluating a variant (as described below), whereupon the systems described herein compare the variant to a 'reference sequence', the reference sequence is a DNA or RNA sequence obtained using genome sequencing, exome sequencing or gene sequencing of an unrelated individual or a closely related individual (e.g., parent, sibling, child). Alternatively, the reference sequence may be derived from the reference human genome, or it may be an artificially designed sequence.) identify, within the data file, a group of reads that span a candidate variant at a target base position; process data associated with the group of reads within the data file through a neural network for evaluation of the candidate variant at the target base position; and generate, based on the neural network processing the data associated with the group of reads, at least one classification score indicating a likelihood that the candidate variant at the target base position is a variant(e.g., see at least [0060] In one embodiment, the variant and a reference sequence are fed into the input layer of the CVP 101 and the amount of change in the cell variable is quantified and used to score, categorize and prioritize the variant by the secondary analysis unit 114. – See also Claim 24 - 24. The method of claim 23 wherein the error is one or more of: a. a sum of negative log likelihoods of observing the measured condition-specific cell variables as determined using a corresponding set of probabilities computed by the deep neural network; b. a sum of squared differences between the measured condition-specific cell variables and the corresponding condition-specific cell variables computed by the deep neural network; C. a sum of absolute differences between the measured condition-specific cell variables and the corresponding condition-specific cell variables computed by the neural network; d. a sum of Kullback-Leibler divergences between measured condition-specific cell variable probabilities and corresponding condition-specific cell variable probabilities computed by the neural network.).
Consider Claim 22, Brendan teaches the claimed invention further comprising instructions that, when executed by the at least one processor, cause the system to process data associated with the group of reads within the data file through a neural network by providing, to the neural network, an array of input features generated from the data associated with the group of reads (e.g., see at least claim 1 - a. computing a set of variant features from a DNA or RNA variant sequence; b. applying a deep neural network of at least two layers of processing units to the variant features to compute one or more condition-specific variant cell variables).
Consider Claim 23, Brendan teaches wherein the array of input features comprises one or more bases at the target base position, one or more bases flanking the target base position, or one or more corresponding base features for bases within the group of reads(e.g., see at least [0043] As used herein, the term "variant analysis" means: the procedure (computational or otherwise) of processing a variant, possibly in addition to surrounding DNA or RNA sequence that establishes context, for the purpose of variant scoring, categorization, prioritization, and interpretation- see also 0135).
Consider Claim 24, Brendan teaches wherein the one or more corresponding base features for bases within the group of reads comprise one or more of: a corresponding reference base in a reference read corresponding to a reference genome(e.g., this is met based on at least 0040); a base call accuracy score of calling a base in a read of the group of reads(e.g., this is met based on at least 0040 ); a strandedness of calling the base; an insertion count of changes adjoining a position of the base(e.g., this is met based on the discussion in 0135-DNA sequences by definitions are encoded in either of two antiparallel strands of the double stranded DNA molecule ); a deletion flag at the position of the base; or a mapping quality score of aligning a corresponding read that contains the base to the reference read.
Consider Claim 25, Brendan teaches wherein the neural network comprises a deep neural network(e.g., see at least claim 1 - a. computing a set of variant features from a DNA or RNA variant sequence; b. applying a deep neural network of at least two layers of processing units to the variant features to compute one or more condition-specific variant cell variables).
Consider Claim 26 ,32 and 37, Brendan teaches the claimed invention further comprising instructions that, when executed by the at least one processor, cause the system to process the data associated with the group of reads through a one or more of an input layer, a convolution layer, a batch normalization layer, a max pooling layer, or a flattening layer (see clam 15 - The system of claim 12, wherein at least part of the deep neural network is configured to form one or more of: a. a convolutional neural network; b. a recurrent neural network; C. a long-term short-term memory recurrent neural network.).
Consider Claims 27, 33 and 38, Brendan teaches further comprising instructions that, when executed by the at least one processor, cause the system to: process the data associated with the group of reads through one or more layers of the neural network to generate output features; concatenate the output features with per-variant characterization data corresponding to the candidate variant at the target base position to generate concatenated features; and generate the at least one classification score based on an analysis of the concatenated features by one or more layers of the neural network( see context of score noted in 0060, see also at least claim 6. - The method of claim 1, wherein the variant-induced changes in the one or more condition-specific cell variables are combined to output a single numerical variant score for each of the one or more variants, the variant score computed by one of: a. outputting the score for a fixed condition; b. summing the variant-induced changes across conditions; C. computing the maximum of the absolute variant-induced changes across conditions.).
Consider Claim 28, Brendan teaches wherein the data file comprises a text file or a Binary Alignment Map (BAM) file (i.e., data -[0058] The system may further comprise a computing device 110 communicatively linked to the CVP 101 for controlling operations carried out in the CVP. The computing device may comprise further input and output devices, such as input peripherals (such as a computer mouse or keyboard), and/or a display. The computing device 110 may further be linked to a remote device 112 over a wired or wireless network 108 for transmitting and receiving data. In embodiments, genetic information is received over the network 108 from the remote device 112 for storage in memory 106. Cell variable predictions and lists of variants priorities may be displayed to a user via the display]).
Consider Claims 29, 34 and 39, Brendan teaches further comprising instructions that, when executed by the at least one processor, cause the system to generate the at least one classification score by generating a first probability that the candidate variant is a true variant and a second probability that the candidate variant is a false variant( see context of score noted in 0060, see also at least claim 6. - The method of claim 1, wherein the variant-induced changes in the one or more condition-specific cell variables are combined to output a single numerical variant score for each of the one or more variants, the variant score computed by one of: a. outputting the score for a fixed condition; b. summing the variant-induced changes across conditions; C. computing the maximum of the absolute variant-induced changes across conditions.).
Consider Claims 30 and 40, Brendan teaches further comprising instructions that, when executed by the at least one processor, cause the system to generate the at least one classification score by generating one or more of a first score that the candidate variant is a homozygous variant, a second score that the candidate variant is a heterozygous variant, a third score that the candidate variant is a non-variant, or a fourth score that the candidate variant is a complex- variant (e.g., see variant and scoring in at least 0041-0043).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
US 20190228836 A1 – teaches machine learning algorithms are often configured to identify patterns in training data sets so that the algorithms “learn” or become “trained” how to predict possible outcomes when presented with new input data. Notably, there are numerous types of machine learning algorithms. Receiving in a compendium of markers received from a subject's sample, wherein the markers comprise missense mutations in an read; assigning a classification score to each missense mutation marker based on the number and/or types of missense features associated therewith; (3) assigning a variant score.
CA 3065784 A1 teaches a variant classifier that uses trained deep neural networks to predict whether a given variant is somatic or germline. Our model has two deep neural networks: a convolutional neural network (CNN) and a fully-connected neural network (FCNN), and two inputs: a DNA sequence with a variant and a set of metadata features correlated with the variant. The metadata features represent the variant's mutation characteristics, read mapping statistics, and occurrence frequency. The CNN processes the DNA sequence and produces an intermediate convolved feature. A feature sequence is derived by concatenating the metadata features with the intermediate convolved feature. The FCNN processes the feature sequence and produces probabilities for the variant being somatic, germline, or noise. A transfer learning strategy is used to train the model on two mutation datasets. Results establish advantages and superiority of our model over traditional classifiers.
US 20260074018 A1 teaches a deep learning-based framework, which identifies sequence patterns that cause sequence-specific errors (SSEs). Systems and methods train a variant filter on large-scale variant data to learn causal dependencies between sequence patterns and false variant calls. The variant filter has a hierarchical structure built on deep neural networks such as convolutional neural networks and fully-connected neural networks. Systems and methods implement a simulation that uses the variant filter to test known sequence patterns for their effect on variant filtering. The premise of the simulation is as follows: when a pair of a repeat pattern under test and a called variant is fed to the variant filter as part of a simulated input sequence and the variant filter classifies the called variant as a false variant call, then the repeat pattern is considered to have caused the false variant call and identified as SSE-causing.
US 20260074018 A1 teaches methods, non-transitory computer readable media, and systems that can train a genome-location-classification model to classify or score genomic coordinates or regions by the degree to which nucleobases can be accurately identified at such genomic coordinates or regions. For instance, the disclosed systems can determine sequencing metrics for sample nucleic-acid sequences or contextual nucleic-acid subsequences surrounding particular nucleobase calls. By leveraging ground-truth classifications for genomic coordinates, the disclosed systems can train a genome-location-classification model to relate data from one or both of the sequencing metrics and contextual nucleic-acid subsequences to confidence classifications for such genomic coordinates or regions. After training, the disclosed systems can also apply the genome-location-classification model to sequencing metrics or contextual nucleic-acid subsequences to determine individual confidence classifications for individual genomic coordinates or regions and then generate at least one digital file comprising such confidence classifications for display on a computing device.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to CHARLES TERRELL SHEDRICK whose telephone number is (571)272-8621. The examiner can normally be reached 8A-5P.
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, Matthew D Anderson can be reached at 571 272 4177. 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.
/CHARLES T SHEDRICK/Primary Examiner, Art Unit 2646
Prosecution Insights