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 .
DETAILED ACTION
Claim Status
Currently, claims 1-2, 4-6, 9-10, 31-50, 54-55 are pending in the instant application. Claims 3, 7-8, 11-30, 51-53 have been canceled and claims 54-55 have been added. This action is written in response to applicant’s correspondence submitted 11/3/2025. All the amendments and arguments have been thoroughly reviewed but were found insufficient to place the instantly examined claims in condition for allowance. The following rejections are either newly presented, as necessitated by amendment, or are reiterated from the previous office action. Any rejections not reiterated in this action have been withdrawn as necessitated by applicant’s amendments to the claims. The text of those sections of Title 35, U.S. Code not included in this action can be found in a prior Office action. This action is Final.
Withdrawn Rejection
The objection to claims 35 and 42 is withdrawn in view of the amendment to the claims.
The rejection of claim 10, 39, 44, under 35 USC 112(a), new matter rejection is withdrawn in view of the amendment to the claims.
The rejection of claims 1-2, 4-6, 9-10, 31-38, 40-45, 49-53 under 35 U.S.C. 103 as being unpatentable over Braman (US20220292674A1) in view of Poore (WO 2020093040A1) and Cameron (PLOS ONE, 2017, 12(5): e0177062, pp. 1-17) is withdrawn in view of the amendment to the claims.
Maintained Rejections
Claim Rejections - 35 USC § 112- New Matter
The following is a quotation of the first paragraph of 35 U.S.C. 112(a):
(a) IN GENERAL.—The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor or joint inventor of carrying out the invention.
The following is a quotation of the first paragraph of pre-AIA 35 U.S.C. 112:
The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor of carrying out his invention.
Claims 6, 31, 46, are rejected under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA ), first paragraph, as failing to comply with the written description requirement. The claim(s) contains subject matter which was not described in the specification in such a way as to reasonably convey to one skilled in the relevant art that the inventor or a joint inventor, or for applications subject to pre-AIA 35 U.S.C. 112, the inventor(s), at the time the application was filed, had possession of the claimed invention. This is a new matter rejection. This rejection was previously presented and has been rewritten to address the amendment to the claims.
Claim 31 recites separating the human nucleic acid sequencing reads from non-human nucleic acid sequencing reads by aligning metagenomic sequencing to one or more human reference genomes to identify a plurality of non-human nucleic acid sequencing reads. Claim 6 requires one or more human reference genomes. The specification teaches aligning the de novo metagenomic assembly to produce an aligned bind abundance for the plurality of non-human nucleic acid sequencing reads. The non-human nucleic acid molecule sequencing reads maybe aligned to a database of microbial genomes to identify a plurality of microbial sequencing reads (see para 54). Example 1 describes reads aligning to the human genome were separated from non-human reads by alignment to the complete human reference genome T2T-CHM13 using Bowtie2. Non-human reads were aligned to the RefSeq database release 206 (see para 152). The specification further exemplifies human genome were separate from non-ham reads by alignment to the human reference genome GRCh38 and non-human reads were aligned to the RefSEq database (see para 171). The specification does not teach using more than one human reference genomes to separate human nucleic acid sequences from non-human nucleic acid sequences. The specification discloses uses more than one specific human reference genome separately when separating human nucleic acid sequencing. In other words the disclosure has support for separating human nucleic acid sequencing reads from non-human nucleic acid sequencing by aligning sequences reads from either alignment to reference genome GRCH38 or T2T-CHM13, but not both simultaneously which is what is encompassed by claim 31 and claim 10.
Claim 46 recites each predictive module with the first ensemble of predictive models is trained with the following data: unsupervised batch corrected and center log ration transformed tumor associated microbial bin abundances, plasma concentration of CEA and OPN, mayo cancer probability score, brock probability score, subject smoking status of never smoker, former smoker or current smoker, lung nodule size in mm, lung nodule solidity obtained via imagining, presence of lung nodule in upper lung, presence of nodule spiculation, presence of emphysema or combination thereof. The specification does not teach uses the combination recited within the claim for data in the first ensemble of predictive models trained. The specification does not disclose using the Mayo and Brock probability score in the predictive model. The specification teaches these feature may be combined as part of the clinical data features but does not disclose using both probability score with CEA, OPM plasma concentration with predictive models for first ensemble. The specification teaches stacked ensemble training models using clinical data and sequencing data (see para 168) but does not teach using the combined data in a training model as recited within the claim.
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 33, 46, and 54-55 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention. This rejection was previously presented and has been rewritten to address the amendment to the claims.
Claim 33 recites the limitation "the benign pulmonary tumors" in line 1 of the claim. There is insufficient antecedent basis for this limitation in the claim. Claim 33 depends from claim 31 and neither of the claims require a benign pulmonary tumor.
Claim 54 is indefinite over the recitation of “less than about 3 centimeters or less than about 8 millimeters”. The recitation of less than about renders the claim indefinite. It is unclear if the claim requires the tumor size to be less than or about 3 centimeters or less than or about 8 millimeters. A broad range or limitation together with a narrow range or limitation that falls within the broad range or limitation (in the same claim) may be considered indefinite if the resulting claim does not clearly set forth the metes and bounds of the patent protection desired. See MPEP § 2173.05(c). In the present instance, claim 54 recites the broad recitation diameter less than about 3 centimeters, and the claim also recites less than about 8 millimeters which is the narrower statement of the range/limitation. The claim(s) are considered indefinite because there is a question or doubt as to whether the feature introduced by such narrower language is (a) merely exemplary of the remainder of the claim, and therefore not required, or (b) a required feature of the claims. Claim 55 depends from claim 54 and is indefinite for the reasons applied.
Claim 46 recites the first ensemble of predictive models is trained with the following data…to produce the cancer risk score. Claim 46 only requires that the first ensemble of predictive models produces the cancer risk score. Claim 46 depends from claim 31 and claim 31 requires a second predictive meta-learner model processes cancer probability scores of the first ensemble of predictive models to produce the subjects cancer risk score. Claim 31 requires both eh first and second models produce a cancer risk score. It is unclear the metes and bound of claim 46. Does claim 46 require only the first ensemble to produce a cancer risk score or is claim 46 attempting to limit what is encompassed by the first ensemble? If it is the later the claim should be amended to clearly recite that the first ensemble is limited to the recited data and remove the recitation of “to produce the cancer risk score”.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(d):
(d) REFERENCE IN DEPENDENT FORMS.—Subject to subsection (e), a claim in dependent form shall contain a reference to a claim previously set forth and then specify a further limitation of the subject matter claimed. A claim in dependent form shall be construed to incorporate by reference all the limitations of the claim to which it refers.
The following is a quotation of pre-AIA 35 U.S.C. 112, fourth paragraph:
Subject to the following paragraph [i.e., the fifth paragraph of pre-AIA 35 U.S.C. 112], a claim in dependent form shall contain a reference to a claim previously set forth and then specify a further limitation of the subject matter claimed. A claim in dependent form shall be construed to incorporate by reference all the limitations of the claim to which it refers.
Claim 46 is rejected under 35 U.S.C. 112(d) or pre-AIA 35 U.S.C. 112, 4th paragraph, as being of improper dependent form for failing to further limit the subject matter of the claim upon which it depends, or for failing to include all the limitations of the claim upon which it depends. Claim 46 depends from claim 31. Claim 46 requires that the first ensemble of predictive models produces the cancer risk score. Claim 31 requires a first and second model to produce the cancer risk score. Claim 46 does not further limit claim 31 as it requires only a first model to produce a cancer risk score whereas independent claim 31 requires both a first and second model to produce a cancer risk score. Applicant may cancel the claim(s), amend the claim(s) to place the claim(s) in proper dependent form, rewrite the claim(s) in independent form, or present a sufficient showing that the dependent claim(s) complies with the statutory requirements.
Maintained Rejections
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-2, 4-6, 9-10, 31-50, 54-55 are rejected under 35 U.S.C. 101 because the claimed invention is directed to abstract idea and a law of nature without significantly more. The claims recite abstract ideas that include mathematical concept that include encompasses a mathematical equation to produce a risk score. This judicial exception is not integrated into a practical application because the additional elements recited in the claims do not integrate the abstract idea. This rejection was previously presented and has been rewritten to address the amendment to the claims.
The following three inquiries are used to determine whether a claim is drawn to patent-eligible subject matter:
Step 1. Is the claim directed to a process, machine, manufacture, or composition of matter? Yes all of the claims are directed to methods.
Step 2A. Is the claim directed to a law of nature, a natural phenomenon or an abstract idea (judicially recognized exception) and does the claim recite additional elements that integrate the judicial exception into a practical application?
Yes the claims are directed to abstract ideas but the additional elements do not integrate the judicial exception into a practical application.
With regard to claim 1 and 31, the preamble recites a method of producing a cancer risk score is directed to a mathematical process including data processing and evaluation of data. The broadest reasonable interpretation covers processing and evaluating data to determine disease risk. For example each of the steps within claim 1 and claim 31 includes embodiments in which a practioner implements a computer to perform an abstract idea, specifically it recites processing data with a set of trained predictive model and producing a cancer risk score. The step of producing a cancer risk score is a mathematical process in which one aligns sequence (data processing) processes bin abundance (data analysis), and produces a risk score by analysis of data with a predictive model. The recitation of processing data with a set of trained predictive model to produce a subjects cancer risk score is a recitation of a using a generic computer to evaluate data, to produce a risk score that is mathematical concept determined by data analysis using a generic computer and the use of the computer merely implements the process of data and computer output. The additional elements recited in the claim do not integrate the judicial exception. The recitation of using a trained predictive model is mere instruction to implement an abstract idea and using a predictive model or computer as a tool to perform the abstract idea of processing data and determining disease. Implementing an abstract idea on a computer or using known generic devices does not integrate a judicial exception into a practical application (see MPEP 2106.05(f)).
Moreover, the additional elements of the claims do not recite an improvement in the functioning of a computer or other technology or technical field, the claimed steps are not performed using a particular machine, the claimed steps do not effect a transformation, and the claims do not apply the judicial exception in any meaningful way beyond generically linking the use of the judicial exception to a particular technological environment (see MPEP 2106.04(d)).
With regard to claims 2, 4-6, 9-10, 32-53, the claims do not add additional elements that integrate the judicial exceptions. The dependent claims are computer-implemented methods and computing instructions. With regard to claim 2, claim 2, 44-53 limits the trained predictive model, while claims 4-6 and 9-10, 41-43 limit the sequencing. Claims 32-33 limit the tumor. Claim 34 limit the sample type, claim 35 limit the medical record information, claim 36 limit the clinical risk score, claim 38-40 limit the protein and radiological data. None of these claims recite additional elements that integrate the abstract idea.
Step 2B - Does the claim recite additional elements that amount to significantly more than the judicial exception? No.
With regard to claim 1, additional steps of receiving a biological sample, electronic medical record information and radiological data is well-understood, routine, and conventional activities in the art. The receiving a biological sample and sequencing a plurality of non-human nucleic acid molecules of said biological sample merely instructs a scientist to use well established, routine and conventional nucleic acid techniques to gather samples for diagnostic analysis. Additionally the step of sequencing microbial sequences is well-established, routine and conventional in the art. There is no combination of elements in this step that distinguishes it from well-understood, routine and conventional data gathering activity engaged in by scientists prior to applicant’s invention and at the time the application was filed. The specification teaches that many different training models that are well known in the art (see para 100-107). Further, Grady (US20230268041 A1) teaches detecting data comprising microbiome sequencing data and displaying generated report using a first and second training classifier and Lefkofsky (US2021/0118559 A1) teaches training classifiers, electronic medical reports, radiological imaging and microbiome sequencing to detect health status of individuals. Thus the prior art and specification demonstrates it was routine, well-known and conventional in the art to determine microbial sequence reads and determine disease state by using training classifier models. The claim limitations are general data gathering and analysis methods that were well-known, routine and conventional in the art, but not practical method steps that serve to create a method that is significantly different than the judicial exception which the claims recite and the claim as a whole does not amount to significantly more than the exception itself. Consideration of the additional elements as a combination also adds no other meaningful limitations to the exceptions not already present when the elements are considered separately because the steps of the claim merely direct one to gather data necessary to implement the abstract steps step using widely used conventional techniques.
Accordingly, it is determined that the instant claims are not directed to patent eligible subject matter.
Response to Arguments
The response traverses the rejection on pages 10-11 of the remarks mailed 11/3/2025. The response asserts the claims have been amended to not require differentiating between disease states or between malignant and benign tumor states and therefore the rejection based on differentiating is moot. It is noted that the rejection has been rewritten to address the amendment to the claims.
The response addresses MPEP 2106.04(a)(2)(III)(A) and asserts that the claims cannot be performed in the human mind and thus are not directed to judicial exception of a mental process. The response asserts that the sequencing of samples is prohibitively large number to process in the human mind as well as compare to the RefSeq database. This response has been thoroughly considered but not found persuasive. The recitation of processing data with a set of trained predictive model and differentiating malignant pulmonary tumors from benign pulmonary tumors is a recitation of a using a generic computer to evaluate data, the evaluation is a mental process and the use of the computer merely implements the mental process and the determining a disease state merely encompasses a mental analysis of data and computer output. As previously addressed, MPEP 2106.04(a)(2)(III)(C) addresses that a claim may still recite a mental process when performing a mental process on a generic computer, in a computer environment, and as a tool to perform a mental process. Here the data analysis is performed on a generic computer in a computer environment. However the claims have been amended to recite producing a cancer risk score which encompasses a mathematical concept that is performed on a generic computer using data analysis. Each of the steps recited within the claim encompass data analysis, including sequencing reads. The use of algorithms and generic computers to determine cancer risk score is an example of a mathematical calculation, similar to MPEP 2106.04(a)(2)(C) example v.
The response further asserts the alleged judicial exception is integrated into a practical exception. The response asserts the method using producing sequencing data and analyzing it as an active step and the stacked machine learning approach integrates multiple sources of data to produce the results of the method. The response asserts that claims 1 and claim 31 require the outputs of the first ensemble of predictive modules are inputted into the second predictive meta learner model to produce a pulmonary malignant score that is an indication of the probability that the pulmonary nodule is malignant compared to benign. This response has been fully considered but not found persuasive. As addressed in the previous office action, the step of sequencing data and analyzing sequencing data is generically recited and does not apply the judicial exception. The step of sequencing data is a data gathering step and does not integrate the judicial exception. With regard to the step of stacked machine learning approach, the claims do not require a specific stacked machine learning approach and there is no implementation of the judicial exception with a particular machine that is integral to the claim. There are no additional limitations provided that provide details as to how the computer performs the stacked machine learning approach as asserted by applicant. The claims are directed to collecting, displaying and manipulating using trained data set but there is nothing in the claims indicating what specific steps are undertaken to result in a stacked machine learning approach, as asserted by applicant. The claims as a whole do not integrate the judicial exception because the claims include data gathering steps that are generically recited and the data analysis step and the additional limitations provide only a result oriented solution and lack details as to how the computer performed the modification which is equivalent to the words “apply it”. The required output that is indicative of the probability of cancer risk score. Comparing data analysis to produce a cancer risk score encompasses mathematical procedure and uses a generic processor to implement an abstract idea. Each of the additional elements recited within the claim amounts to merely using different devices or tools to perform claimed abstract idea. For these reasons and reasons of record this rejection is maintained.
New Grounds of Rejection- Necessitated by Amendment to the Claims
Claim Rejections - 35 USC § 103
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.
Claims 1-2, 4-6, 9-10, 31-38, 40-45, 49-53, are rejected under 35 U.S.C. 103 as being unpatentable over Poore (WO2020093040A1) in view of Braman (US20220292674) and Fang (US 2020/0160936A1)
Poore teaches presence of microbial signatures are associated with types of cancers. Poore teaches the presence of microbial signatures can diagnose the presence, kind, and/or subtype of cancer in a human (see para 8). Poore teaches utilizing a diagnostic model based on machine learning architecture (see para 24). Poore teaches identifying and removing contaminants termed noise and selectively retaining other non-mammalian features comprising microbial (see para 27). Poore teaches combining additional information about the host to create a diagnostic model that has greater predictive performance than only having microbial presence of abundance information alone. Poore teaches detecting microbial nucleic acid detection by ecological shotgun sequencing and host whole genome sequencing (See para 31) (claim 4). Poore teaches detecting simultaneously with nucleic acid from host, host nucleic acids are depleted and microbial nucleic are retained prior to sequencing of a combined nucleic acid pool (see para 34, claim 6). Poore teaches the tissue is blood (claim 2) (para35). Poore exemplifies sequencing reads identified as microbial by bioinformatic microbial detection pipeline and genus taxonomy (see para 37, fig 1) (claims 7). Poore teaches taking a blood sample from a patient, extracting nucleic acids and amplifying sequences for specific microbial genes, obtaining a digital read-out of the presence of microbial sequences, normalizing the presence of the data and feeding it into a previously trained machine learning model, reading a prediction and degree of confidence for likelihood of cancer, type of cancer and location (see para 72). Poore teaches accuracy of at least 80% for lung cancer diagnosis (see fig 16a-f). While Poore teaches combining additional information about the host to create a diagnostic model that has greater predictive performance than only having microbial presence of abundance information alone, Poore does not teach the additional information that is combined to produce a cancer risk score. Poore does not teach a database comprising do novo metagenomic bins comprising one or more genomic contigs to obtain aligned metagenomic bin abundances.
Braman teaches a method for identifying a multimodal prediction for a tumor sample based on radiology, pathology and molecular data (see fig 1 and para 33). Braman teaches obtaining datasets collected from a tumor sample comprising image based modalities, radiomic image modalities, clinical modalities including clinical data, and molecular modalities (see para 37). Braman teaches combining radiology, pathology and genomic data with a deep learning framework for outcome prediction (see para 39). Braman teaches radiomic image modality refers to data from radiographic images from computed tomography or positron emission tomography images (see para 40) (claim 40). Braman teaches pathology image modality dataset comprises data from pathology images from tissue samples including immune biomarker status (see para 41). Braman teaches molecular modality dataset comprises sequences reads from a sample (see para 42). Braman teaches the molecular dataset may contain raw sequencing results. Braman teaches clinical modality dataset comprises data associated with patient including patients demographics, age, sex, smoking status, family medical history, and personal history (see para 43, claim 35). Braman teaches the clinical modality dataset includes data pertaining to microbiome features and proteomic features (see para 43) and can comprise metagenomics (see para 44) (metagenomic sample). Braman teaches a machine learning model set may include genomic results of sequencing, tumor origin, age at diagnosis, gender, race and symptoms (see para 45). Braman teaches the multimodal biomarker prediction system received datasets from sequencing system and integrated with other systems (see para 55-56). Braman teaches the multimodal biomarker prediction system includes a deep learning framework trained to analyze different modality data sets and generate a prognostic predication of pathology state or condition (see para 58). Braman teaches the deep learning framework is configured to implement various machine learning algorithms to analyze each or some subset of different modality datasets (see para 58). Braman teaches deep learning frameworks comprise predictive models and teaches the neural network comprises a specific top layer of a logistic regression (second meta-learner predictive model) (para 86). Braman teaches the deep learning framework that encompass an ensemble of trained predictive models includes machine learning models and neural networks (claim 2, 45 and 50 ) (para 86). Braman teaches an output comprising determining cancer state (see para 57) and teaches predictions to determine state or condition of patient (see para 58). Braman teaches multimodal embedding predictions (see para 64). Braman teaches a prediction score (see para 21).
It was known in the art to use different methods for generating sequencing information and output of sequencing information of microbial nucleic acids. Fang teaches whole metagenome shotgun sequencing allows for a comprehensive approach for characterizing complex microbial communities. Fang teaches the use of single molecule long read sequencing technologies enable comprehensive detection of DNA in bacteria (see para 9-10) Fan teaches sequencing microbial nucleic acids using single molecule long read sequencing technology by sequencing single molecules reads and assembling contigs from single molecule nucleic acid, assigning scores, filtering to identify sequence motifs, determining profiles of assembled contigs, separating assembled contigs and single molecule reads into bins corresponding to specific prokaryotic organisms, assembling the bins and obtaining assembled genomes of distinct organisms in the sample and deconvoluting genomes of organisms in a microbiome sample (see para 18-27). Fang teaches metagenomic de novo assembly (see para 87).
Therefore it would have been prima facie obvious to one of ordinary skill in the art at the time the invention was made to improve the method of diagnosing cancer by sequencing microbial nucleic acids in a sample and include additional patient data, including electronic medical record data and radiological data in the training model and include a second training model for output including multimodal predictions to allow for a more robust and personalized diagnostic result as taught by Braman. The ordinary artisan would have used well-known assembly of metagenomic sequences including de novo metagenomic assembly databases, and training predictive model to align bins of microbial sequence reads as taught by Fang and contig alignment of the metagenomic sequencings of Poore using the methods of Fan. The ordinary artisan would have been motivated to improve the method of diagnosing cancer by sequencing microbial nucleic acids by Poore because Poore teaches combining additional information about the subject to create a diagnostic model that has greater predictive performance and use the model of Braman along with using known metagenomic assemblies as taught by Fang. The ordinary artisan would have had a reasonable expectation of success that the use of electronic medical data and imaging data as taught by Braman could be used in diagnostic method of Poole and include output of the data using multimodal analysis that includes cancer risk score because Braman teaches analysis of electronic medical data, radiological imaging data and sequencing of metagenomic nucleic acids using multimodal models allow for personalized results and Poore teaches using additional data from subjects in the diagnosis of cancer for a more accurate analysis and metagenomic analysis as taught by Fang because Fang teaches a single molecule long sequence read method to allow for comprehensive detection of DNA in bacteria and low abundance of bacteria in a sample. Because both Poole, Braman, and Fang teach diagnosis of disease using training model of sequence information, including metagenomics, it would have been obvious to one skilled in the art to include additional data in, including electronic medical and radiological data as taught Braman and analysis of metagenomic sequence reads as taught by Fang in the method of Poole in order to achieve the predictable result of generating cancer risk score using metagenomic sequencing of nucleic acids, radiological and electronic medical data in a training model to allow for a personalized and robust diagnostic model.
Claims 54-55 are rejected under 35 U.S.C. 103 as being unpatentable over Poore (WO2020/093040A1) in view of Braman (US20220292674) and Fang (US 2020/0160936A1) and further in view of Ren (Trend Lung Cancer Res, 2019, 8(3):235-246).
Poore in view of Lefkosky is set forth above. Poore in view of Lefkosky does not teach lung cancer with a tumor mass of less than 3 cm.
Ren teaches metagenomics of lung cancer samples. Ren teaches tumor size of lung cancer was .5 to 1.6 cm (see table 1). Ren teaches core microbiota in lung cancer.
Therefore it would have been prima facie obvious to one of ordinary skill in the art at the time the invention was made to improve the method of Poole in view of Braman and Fang of diagnosing cancer by sequencing microbial nucleic acids, electronic medical record data and radiological data using a first predictive model and generating an output using a second predictive model to include analysis of lung cancer including stage of lung cancer and lung cancer with a tumor mass of less than 3 cm to allow for a robust analysis of different lung cancer tumor sizes. The ordinary artisan would have been motivated to improve the method of Poole in view of Braman and Fang to include analysis of lung cancer samples less than 3 cm because Poole in view of Braman teach diagnosis of lung cancer including different stages and Renn teach analysis of lung cancer and exemplify lung cancer tumor sizes. The ordinary artisan would have had a reasonable expectation of success that the use of lung cancer tumor size of less than 3 cm could be used in the method of Poole in view of Braman and Fang because Renn teaches metagenomic analysis of lung cancer in samples with less than 3 cm in size of lung cancer tumors. Because Poole in view of Braman and Fang teach sequencing and aligning of sequence reads in metagenomics and teach diagnosis of lung cancer using training model of sequence information, including metagenomics, it would have been obvious to one skilled in the art to include a predictive model that uses lung cancer samples of varying sizes including samples from less than 3 cm in size as taught by Renn in the method of Poole in view of Braman in order to achieve the predictable result of diagnosing lung cancer using metagenomic sequencing of nucleic acids, radiological and electronic medical data in a training model to allow for a personalized and robust diagnostic model.
Double Patenting
The nonstatutory double patenting rejection is based on a judicially created doctrine grounded in public policy (a policy reflected in the statute) so as to prevent the unjustified or improper timewise extension of the “right to exclude” granted by a patent and to prevent possible harassment by multiple assignees. A nonstatutory double patenting rejection is appropriate where the conflicting claims are not identical, but at least one examined application claim is not patentably distinct from the reference claim(s) because the examined application claim is either anticipated by, or would have been obvious over, the reference claim(s). See, e.g., In re Berg, 140 F.3d 1428, 46 USPQ2d 1226 (Fed. Cir. 1998); In re Goodman, 11 F.3d 1046, 29 USPQ2d 2010 (Fed. Cir. 1993); In re Longi, 759 F.2d 887, 225 USPQ 645 (Fed. Cir. 1985); In re Van Ornum, 686 F.2d 937, 214 USPQ 761 (CCPA 1982); In re Vogel, 422 F.2d 438, 164 USPQ 619 (CCPA 1970); In re Thorington, 418 F.2d 528, 163 USPQ 644 (CCPA 1969).
A timely filed terminal disclaimer in compliance with 37 CFR 1.321(c) or 1.321(d) may be used to overcome an actual or provisional rejection based on nonstatutory double patenting provided the reference application or patent either is shown to be commonly owned with the examined application, or claims an invention made as a result of activities undertaken within the scope of a joint research agreement. See MPEP § 717.02 for applications subject to examination under the first inventor to file provisions of the AIA as explained in MPEP § 2159. See MPEP § 2146 et seq. for applications not subject to examination under the first inventor to file provisions of the AIA . A terminal disclaimer must be signed in compliance with 37 CFR 1.321(b).
The filing of a terminal disclaimer by itself is not a complete reply to a nonstatutory double patenting (NSDP) rejection. A complete reply requires that the terminal disclaimer be accompanied by a reply requesting reconsideration of the prior Office action. Even where the NSDP rejection is provisional the reply must be complete. See MPEP § 804, subsection I.B.1. For a reply to a non-final Office action, see 37 CFR 1.111(a). For a reply to final Office action, see 37 CFR 1.113(c). A request for reconsideration while not provided for in 37 CFR 1.113(c) may be filed after final for consideration. See MPEP §§ 706.07(e) and 714.13.
The USPTO Internet website contains terminal disclaimer forms which may be used. Please visit www.uspto.gov/patent/patents-forms. The actual filing date of the application in which the form is filed determines what form (e.g., PTO/SB/25, PTO/SB/26, PTO/AIA /25, or PTO/AIA /26) should be used. A web-based eTerminal Disclaimer may be filled out completely online using web-screens. An eTerminal Disclaimer that meets all requirements is auto-processed and approved immediately upon submission. For more information about eTerminal Disclaimers, refer to www.uspto.gov/patents/apply/applying-online/eterminal-disclaimer.
Claims 1-2, 4, 31-35, 40-42 are provisionally rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1-15 of copending Application No. 18044541 in view of Lefkofsky (US2021/0118559 A1).
‘541 claims a method of determining a presence or absence of metastatic cancer by detecting microbial presence in a biological sample, removing contaminated microbial features, comparing microbial presence to one or more biological sample from one or more subject with cancer and determining the presence of metastatic cancer. Dependent claims require shotgun sequencing for microbial presence (claim 6) and lung cancer (claim 9), liquid biopsy (claim 15) teaches presence of microbial signatures are associated with types of cancers. Instant claims 1-15 recite a method for determining presence of a disease by determining sequencing microbial nucleic acids and using a training model that comprises electronic medical records, sequencing information, and radiological data to determine disease state. Dependent claims recite lung cancer (claim 13-15), liquid biopsy (claim 2), and shotgun sequencing (claim 4). ‘541 does not claim a combination of electronic medical record data, radiological data, and sequencing data for diagnosis of disease.
However, it was well known in the art to combine electronic medical records and radiological data with metagenomic sequencing in a training model to provide disease output. Lefkosky teaches the method allows for personalization and precision medicine appropriate to allow for a more personalized and contextualized results (see para 22). Lefkosky teaches that in additional to personalized results, analytics associated with cohorts of similar subjects provide for subject informing the subject’s physician of therapy insights which may not have otherwise been apparent (see para 23). Lefkosky teaches a method of obtaining a biological sample, electronic medical records and radiological imaging data, performing metagenomic sequencing, and processing sequencing reads, medical record and radiological data to a training model and providing disease output. Lefkofsky teaches AI component of each analysis include random forests, regression model (first predictive model) (claim 2). Neural network may be trained from a training data set and include imagining, pathology, clinical, and molecular reports of a subject including EHR and genetic sequencing reports (first and second reports, neural networks and logistic regression model, claim 2) . The training data may be based upon features of the subject’s clinic profile. Lefkosky teaches lab tests include A1C, TSH and liver panel (plasma protein) (see para 11) (claim 5) Lefkofsky teaches features from imaging data (radiological data) include reports associated with stained slide, size of tumor, tumor size differentials over time and other features using machine learning appropriates (see para 190). Lefkofsky teaches features include TMB, large nuclei, cell state alterations, ploidy, purity, nuclear-cytoplasmic, size, tumor budding chromatin morphology (see para 196) (claim 12).
Therefore it would have been prima facie obvious to one of ordinary skill in the art at the time the invention was made to improve the method of diagnosing cancer by sequencing microbial nucleic acids in a sample and include additional patient data, including electronic medical record data and radiological data in the training model and include a second training model for output to allow for a more robust and personalized diagnostic result as taught by Lefkosky.
This is a provisional nonstatutory double patenting rejection.
Claims 1-2, 4, 31-35, 40-42 are provisionally rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1-88 of copending Application No. 18579487 in view of Lefkofsky (US2021/0118559 A1).
‘487 claims a method of predicting cancer by detecting fungal presence in a biological sample, removing contaminated microbial features, comparing microbial presence to one or more biological sample from one or more subject with cancer and determining the presence of metastatic cancer. Dependent claims require shotgun sequencing for microbial presence (claim 6) and lung cancer (claim 9), liquid biopsy (claim 15) teaches presence of microbial signatures are associated with types of cancers. Dependent claims recite lung cancer (claim 13-15), liquid biopsy (claim 2), and shotgun sequencing (claim 4). Claims 30-88 recite methods of training a predictive model by receiving a fungal presence and health state from a database, removing fungal features and training a predictive model. Claims 30-88 encompass all that is recited in claims 1-15 of the instant application. However claims 1-29 of ‘487 does not claim ‘a combination of electronic medical record data, radiological data, and sequencing data for diagnosis of disease.
However, it was well known in the art to combine electronic medical records and radiological data with metagenomic sequencing in a training model to provide disease output. Lefkosky teaches the method allows for personalization and precision medicine appropriate to allow for a more personalized and contextualized results (see para 22). Lefkosky teaches that in additional to personalized results, analytics associated with cohorts of similar subjects provide for subject informing the subject’s physician of therapy insights which may not have otherwise been apparent (see para 23). Lefkosky teaches a method of obtaining a biological sample, electronic medical records and radiological imaging data, performing metagenomic sequencing, and processing sequencing reads, medical record and radiological data to a training model and providing disease output. Lefkofsky teaches AI component of each analysis include random forests, regression model (first predictive model) (claim 2). Neural network may be trained from a training data set and include imagining, pathology, clinical, and molecular reports of a subject including EHR and genetic sequencing reports (first and second reports, neural networks and logistic regression model, claim 2) . The training data may be based upon features of the subject’s clinomic profile. Lefkofsky teaches lab tests include A1C, TSH and liver panel (plasma protein) (see para 11) (claim 5) Lefkofsky teaches features from imaging data (radiological data) include reports associated with stained slide, size of tumor, tumor size differentials over time and other features using machine learning appropriates (see para 190). Lefkofsky teaches features include TMB, large nuclei, cell state alterations, ploidy, purity, nuclear-cytoplasmic, size, tumor budding chromatin morphology (see para 196) (claim 12).
Therefore it would have been prima facie obvious to one of ordinary skill in the art at the time the invention was made to improve the method of diagnosing cancer by sequencing microbial nucleic acids in a sample and include additional patient data, including electronic medical record data and radiological data in the training model and include a second training model for output to allow for a more robust and personalized diagnostic result as taught by Lefkosky.
This is a provisional nonstatutory double patenting rejection.
Response to Arguments
The response traverses the rejections on pages 16-20 of the remarks mailed 11/03/2025. The response addressed the independent claims of ‘541. It is noted that the double patenting rejection is over independent claim 1 not claim 17 or 33. The response further asserts that the claims required a stacked predictive model and assert a person of ordinary skill in the art would not have looked to the teaching of Lefkofsky to provide the missing requirements because the methods disclosed in Lefkofsky are significantly from those claimed in the instant application This response has been thoroughly reviewed but not found persuasive. However Leftosky teaches a trained predictive model that includes neural networks, which comprises a stacked predictive model and including medical information and radiological data along with the use of trained predictive models to allow for a robust and personalized diagnostic result. Therefore while the claims do not suggest this analysis, the ordinary artisan would have modified the claims of ‘541 to include medical information and radiological data along with trained predictive models to allow for a personalized robust diagnostic result.
The response addresses ‘487 and assert that all of the rejected claims include the element of differentiating between malignant and benign nodule or using metagenomic, radiological, and clinical data to train a two stage ensemble predictive model. The response asserts the claims of ‘487 would not have provided a reasons for these elements required by ‘487 and do not consider additional electronic medical information or radiological data. This response has been reviewed but not found persuasive. Leftosky was cited to address the additional elements that are not recited in ‘487. Additionally the claims do not require differentiating between malignant and benign nodule. As addressed previously, Leftosky teaches using radiological and clinical data with metagenomic data to train a predictive model. For these reasons and reasons of record this rejection is maintained.
Conclusion
No claims are allowable.
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
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/SARAE L BAUSCH/Primary Examiner, Art Unit 1699