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 .
Claim Status
Claims 1-15 are pending and under examination
Claims 1-15 are rejected.
Claims 8 and 14 are objected to.
Claims 1, 8, and 14 are independent.
No claims are allowed, amended, canceled, new, or withdrawn.
.
Office Action Outline
Rejections applied
Abbreviations
x
112/b Indefiniteness
PHOSITA
"a Person Having Ordinary Skill In The Art before the effective filing date of the claimed invention"
112/b "Means for"
BRI
Broadest Reasonable Interpretation
112/a Enablement,
Written description
CRM
"Computer-Readable Media" and equivalent language
112 Other
IDS
Information Disclosure Statement
x
102, 103
JE
Judicial Exception
x
101 JE(s)
112/a
35 USC 112(a) and similarly for 112/b, etc.
101 Other
N:N
page:line
Double Patenting
MM/DD/YYYY
date format
Priority
As detailed in the 08/29/2023 filing receipt, this application is a 371 of PCT/IN2021/051128, filed 11/30/2021. This application also claims priority to foreign application IN 202041055641, filed 12/21/2020. At this point in examination, all claims have been interpreted as being accorded the priority date of 12/21/2020.
Receipt is acknowledged of certified copies of papers required by 37 CFR 1.55. See paper entered 04/21/2023.
Claim Objections
Claims 8 and 14 are objected to because of the following informalities:
Claims 8 and 14 each recite "a nucleotide sequence data," in which the "a" should be deleted to recite "[[a]] nucleotide sequence data."
Appropriate correction is required.
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 7, 8-11, and 15 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.
The preamble of claim 7 recites "The system as claimed in claim 1." It is not clear if "the system" of claim 7 is referring to "the processor based system" of claim 1, or if "the system" of claim 7 should be amended to recite "the method" instead of the system to be consistent with the other dependent claims of claim 1. It is suggested to amend claim 7 to recite "The method as claimed in claim 1..." The claim will be interpreted as suggested to amend.
Claims 8-10 each recite the phrase "is to," which appears to be missing a word, making the claims unclear; for this, claims 8-10 are rejected similarly as follows:
Claim 8 recites "the detection engine is to: obtain." It is suggested to amend claim 8 to recite "the detection engine is configured to: obtain"
Claim 9 recites "the detection engine...is to cause." It is suggested to amend claim 9 to recite "the detection engine...is configured to cause."
Claim 10 recites "the detection engine is to locate." It is suggested to amend claim 10 to recite "the detection engine is configured to locate."
Claims 8-10 will be interpreted as suggested to amend.
Claim 11 recites "wherein the nucleotide sequence data of the target strain is obtained from a test sample," which does not further limit independent claim 8, which recites "obtain a nucleotide sequence data of the target strain of the pathogen, wherein the target strain is obtained from a test sample." 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.
In claim 14, the relationship is unclear between mutations recited in: "to locate a mutation" (line 18); "analyzing the mutation" (line 21); and "a mutation" (line 23). To overcome this rejection, it is suggested to amend "analyzing the mutation" (line 21) to recite "analyzing the located mutation." The claim will be interpreted as suggested to amend.
Claim 15 recites the elements: "the non-transitory computer-readable medium as claimed in claim 13," "the instructions," "the located mutation," "the mutation of the target strain," and "the flagged mutation," each of which requires but lacks clear antecedent. It is suggested to amend claim 15 to depend from claim 14, which would provide antecedent basis for listed elements. For compact examination purposes, claim 15 will be examined as if it depends from claim 14.
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 1-15 are rejected under 35 U.S.C. 101 because the claimed invention is directed to one or more judicial exceptions without significantly more.
MPEP 2106 details the following framework 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)
• Step 2A, Prong One: Do the claims recite a judicially recognized exception, i.e. an abstract idea, a law of nature, or a natural phenomenon? (see MPEP §§ 2106.04(a); 2106.04(a)(2) & 2106.04(b)).
• 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))
• Step 2B: If the claims do not integrate the judicial exception, do the claims provide an inventive concept? (see MPEP § 2106.05)
Step 1:
Claims 1-7 are directed to a 101 process, here a method. Claims 8-13 are directed to a 101 machine or manufacture, here a system. Claims 14-15 are directed to a 101 machine or manufacture, here a non-transitory computer-readable medium (CRM). As such, claims 1-15 are directed to a related method, system, and CRM, which fall under categories of statutory subject matter. (See MPEP § 2106.03). (Step 1: Yes.)
Step 2A, Prong One:
The claims are found to recite abstract ideas in the form of mental processes and mathematical concepts, as follows:
The claims recite mental processes (and mathematical concepts where indicated) as follows:
Independent claim 1 recites the mental process and mathematical concept of training to determine an association of drug resistance of the target strain of the pathogen with respect to a target drug based on presence of a genetic marker.
Claim 2 recites the mental process of comparing and determining variation between nucleotide sequences; and the mental processes and mathematical concepts of correlating an indication of variation between sequences; and training based on the indication.
Claim 3 further limits the variation recited in claim 1.
Claim 4 recites the mental process for the indication being prescribed in text based nucleic acid or amino acid sequence format.
Claim 5 further limits the training of claim 1.
Claim 6 recites the mental process and mathematical concept of validating the trained system.
Claim 7 further limits the target genetic marker of claim 1.
Claim 8 recites the mental processes of to analyze the nucleotide sequence data to locate variation; analyze the genetic variation to identify association of target strain variation with drug resistance based on a susceptibility detection model; and the mental process and mathematical concept of the model is trained.
Claim 9 recites the mental processes of determining drug susceptibility; and generation of a report.
Claim 10 recites the mental processes of to locate the genetic variation based on comparison of sequence data.
Claims 11 and 12 respectively further limit the nucleotide sequence data and the pathogen of claim 8.
Claim 13 further limits the target drug of claim 12.
Claim 14 recites the mental processes for analyzing the nucleotide sequence data to locate a mutation; determining drug resistance by analyzing the mutation; and training the susceptibility detection model.
Claim 14 recites the mental processes for determining whether the located mutation is recorded; flagging the (located) mutation in response to determining it to be unrecorded; and training the susceptibility-detection model based on the flagged mutation upon identifying the target strain as a training base strain.
Step 2A Prong One Summary: The claims recite mental processes and mathematical concepts. When considering the broadest reasonable interpretation (BRI) of the claims, the mental processes recited in the claims (e.g., "training the system to determine an association of drug resistance"; "comparing and determining variation between nucleotide sequences"; "correlating an indication of variation", etc.) are directed to processes that may be performed in the human mind, or with pen and paper, as there are no particular limitations recited in the claims which would prevent the mental processes from being performed in the human mind or with pen and paper. The claims are considered to recite inherent mathematical processes (in e.g., training the system, correlating an indication of variation, validating the trained, etc.), although details are not shown the specification. Although the method is processor based, and the computer readable medium instructions are executed by a processor of a computing device, a claim that requires a computer may still recite a mental process [see MPEP 2106.04(a)(2)(III)(C)]. Further, although a general-purpose computer can perform the analysis at a rate and accuracy that can far exceed the mental performance of a skilled artisan, the nature of the activity is essentially the same, and therefore constitutes an abstract idea. Therefore, the claims recite elements that constitute a judicial exception in the form of abstract ideas (Step 2A, Prong One: Yes.)
Step 2A, Prong Two:
In Step 2A, Prong One above, claim steps and/or elements were identified as part of one or more judicial exceptions (JEs). Here at Step 2A, Prong Two, any remaining steps and/or elements not identified as JEs are therefore in addition to the identified JE(s), and are considered additional elements. Because the claims have been interpreted as being directed to judicial exceptions (abstract ideas in this instance) then Step 2A, Prong Two provides that the claims be examined further to determine whether the judicial exception is integrated into a practical application [see MPEP § 2106.04(d)]. A claim can be said to integrate a judicial exception into a practical application when it applies, relies on, or uses the judicial exception in a manner that imposes a meaningful limit on the judicial exception.
MPEP § 2106.04(d)(I) lists the following five example considerations for evaluating whether a judicial exception is integrated into a practical application:
(1) An improvement in the functioning of a computer or an improvement to other technology or another technical field, as discussed in MPEP §§ 2106.04(d)(1) and 2106.05(a).
(2) Applying or using a judicial exception to effect a particular treatment or prophylaxis for a disease or medical condition, as discussed in MPEP § 2106.04(d)(2).
(3) Implementing a judicial exception with, or using a judicial exception in conjunction with, a particular machine or manufacture that is integral to the claim, as discussed in MPEP § 2106.05(b).
(4) Effecting a transformation or reduction of a particular article to a different state or thing, as discussed in MPEP § 2106.05(c).
(5) Applying or using 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, as discussed in MPEP § 2106.05(e).
The claims recite additional elements as follows:
Additional elements of data gathering, inputting, and outputting steps: Obtaining data (claims 1, 2, 8, 11, and 14); and obtaining sequence data from a test sample (claims 8 and 11). Data gathering steps are additional elements which perform functions of inputting, collecting, and outputting the data needed to carry out the abstract idea. These steps are considered insignificant extra-solution activity, and are not sufficient to integrate an abstract idea into a practical application as they do not impose any meaningful limitation on the abstract idea or how it is performed, nor do they provide an improvement to technology (see MPEP § 2106.04(d)(I)).
Additional elements of computer components: A processor based system (claim 1); a system and a detection engine (claim 8); and a non-transitory computer readable medium (CRM) comprising computer readable instructions, a processor, and a computing device (claim 14). The claims require only generic computer components, which do not improve computer technology, and do not integrate the recited judicial exception into a practical application (see MPEP § 2106.04(d)(1) and MPEP § 2106.05(f)).
Step 2A Prong Two summary: The claims have been further analyzed with respect to Step 2A, Prong Two, and no additional elements have been found, alone or in combination, that would integrate the judicial exception into a practical application. At this point in examination, it is not yet the case that any of the Step 2A Prong Two considerations (see MPEP 2106.04 (d).I) enumerated above clearly demonstrates integration of the identified JE(s) into a practical application. Referring to the considerations above, none of: (1) an improvement, (2) a treatment, (3) a particular machine, or (4) a transformation is clear in the record. For example, regarding the first consideration for improvement to technology or a technical field at MPEP 2106.04(d)(1), the record, including the Specification, does not yet clearly disclose an explanation of improvement over the previous state of the technology field, and the claims do not yet clearly result in such an improvement. Further, regarding the second consideration for a particular therapy (see MPEP 2106.04(d)(2)), there is no nexus in the claims in which the judicial exception (JE) informs administration of a treatment, and therefore no particular therapy, although there is a hint of a possibility of a therapy in claim 9. It might be helpful to include explanations of both an improvement to technology as well as of a particular therapy in an attempt to show integration of the JE into a practical application at Step 2A Prong 2; Specification paragraph [0038], and perhaps [0036] and [0040], might possibly provide a reasonable basis for future remarks.
(Step 2A, Prong Two: No).
If needed, Applicant is encouraged to request an interview by Automated Interview Request (see link in conclusion section of this Office action).
Step 2B analysis:
Because the additional claim elements do not integrate the abstract idea 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. An inventive concept is furnished by an element or combination of elements that is recited in the claim in addition to the judicial exception, and is sufficient to ensure that the claim, as a whole, amounts to significantly more than the judicial exception itself (see MPEP § 2106.05).
The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the claims recite additional elements that are well-understood, routine, and conventional. Those additional elements are as follows:
Additional elements of data gathering, inputting, and outputting steps: The additional elements of obtaining data (claims 1, 2, 8, 11, and 14), and obtaining sequence data from a test sample (claims 8 and 11), do not cause the claims to rise to the level of significantly more than the judicial exception. The courts have recognized receiving or transmitting data over a network; storing and retrieving information in memory; using polymerase chain reaction to amplify and detect DNA; detecting DNA or enzymes in a sample; analyzing DNA to provide sequence information or detect allelic variants; and amplifying and sequencing nucleic acid sequences, [see MPEP§2106.05(d)(II)], as well-understood, routine, conventional activity when they are claimed in a merely generic manner (e.g., at a high level of generality) or as extra-solution activity.
Additional elements of computer components: The additional elements of a processor based system (claim 1); a system and a detection engine (claim 8); and a non-transitory computer readable medium (CRM) comprising computer readable instructions, a processor, and a computing device (claim 14), do not cause the claims to rise to the level of significantly more than the judicial exception, and as such do not provide an inventive concept; these are conventional computer components.
All limitations of claims 1-15 have been analyzed with respect to Step 2B, and none provides a specific inventive concept, as they all fail to rise to the level of significantly more than the identified judicial exception, and thus do not transform the judicial exception into a patent eligible application of the exceptions. Step2B: NO.
Therefore, the claims, when the limitations are considered individually and as a whole, are rejected under 35 U.S.C. § 101 as being directed to non patent-eligible subject matter.
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 1-15 are rejected under 35 U.S.C. 103 as being unpatentable over Jamal (Scientific reports, vol.10(1):5487, pages 1-16 (March 2020); cited on the attached form PTO-892) in view of Feuerriegel (BMC microbiology, vol. 12(1):90, pages 1-10 (2012); cited on the attached form PTO-892).
Regarding claim 1, the recited obtaining association mappings, which associate a genetic marker of a training pathogen to drug resistance of a reference drug, reads on Table 1, which shows "Total number of variations obtained from TBDReaMDB and GMTV database for each TB drug and the number of mutations obtained." (Jamal, p.2, Table 1.)
Regarding claim 1, the recited training the system to determine association of the target drug resistance of the target strain of the pathogen based on presence of the genetic marker reads on "Artificial Intelligence (AI) and ML algorithms were used to classify single nucleotide variations (SNVs) as being resistant or susceptible in TB and predict novel resistance conferring mutations" (Jamal, p.2, ¶ 2); and "Number of genes/mutations included in the final training dataset and testing dataset, and the actual number of resistant and susceptible mutations included in both training and test dataset" (Jamal, p.2, Table 2).
Regarding claim 2, the recited obtaining a nucleotide sequence of the training base strain of the pathogen reads on "Single nucleotide variations were obtained for rpoB, inhA, katG, pncA, gyrA, and gyrB" (Jamal, p.2, ¶ 3). Also see: "Number of genes/mutations included in the final training dataset" (Jamal, p.2, Table 2).
Regarding claim 2, the recited training base strain, and training the system based on the indication, reads on Table 2, which shows the "Number of genes/mutations included in the final training dataset and testing dataset, and the actual number of resistant and susceptible mutations included in both training and test dataset" (listed by drug) (Jamal, p.2, Table 2).
Regarding claim 3, the training base strain reads on "Single nucleotide variations were obtained for rpoB, inhA, katG, pncA, gyrA, and gyrB" (Jamal, p.2, ¶ 3). Also see: "Number of genes/mutations included in the final training dataset" (Jamal, p.2, Table 2).
Regarding claim 4, the recited the indication in text based format used for representing amino acid sequences reads on the amino acid sequences (protein mutation nomenclature) of the wild type and mutants listed in Tables 5 and 6 (Jamal, respectively p.5 and p.8).
Regarding claim 5, the recited training is further based on a set of clinical parameters reads on "The GMTV database...contains data...which lists the genetic markers associated with TB drug resistance profiles as well as clinical outcomes" (with clinical outcomes being the clinical parameter) (Jamal, p.8, ¶ 1).
Regarding claim 6, the recited validating the trained processor-based system based on a predefined repository of association mappings which correlate genetic markers of corresponding training base strains with corresponding drug resistance and corresponding drug susceptibility, with respect to reference drugs reads on "External dataset validation...the models were evaluated on a blind testing dataset. This testing dataset contained mutations obtained from the MUBII-TB-DB33 database, which includes a set of M.tb (Mycobacterium tuberculosis) mutations associated with rpoB, pncA, inhA, katG, gyrA, gyrB, and rrs...Prior to testing, the dataset was made non-redundant by removing the mutations that were part of the training or testing dataset used for model generation and validation, respectively." (Jamal, p.6, ¶ 2.)
Regarding claim 8, the recited detection engine reads on "The present study describes an integrative computational approach to generate AI and ML based models using the various sequence and structural features of SNVs in M.tb (Mycobacterium tuberculosis) genes for the prediction of resistance conferring mutations.(Jamal, p.2, ¶ 2.)
Regarding claim 8, the recited analyzing the genetic variation to identify association of the genetic variation of the strain with drug resistance with respect to a target drug based on a susceptibility-detection model, which is trained based on association mappings, each mapping associating a genetic variation of a training base strain with a drug resistance to one or more drugs reads on "Artificial Intelligence (AI) and ML algorithms were used to classify single nucleotide variations (SNVs) as being resistant or susceptible in TB and predict novel resistance conferring mutations" (Jamal, p.2, ¶ 2); and Table 2, which shows the "Number of genes/mutations included in the final training dataset and testing dataset, and the actual number of resistant and susceptible mutations included in both training and test dataset" (Jamal, p.2, Table 2).
Regarding claim 12, the recited pathogen is Mycobacterium tuberculosis reads on "prediction of resistant and susceptible mutations in Mycobacterium tuberculosis," (Jamal, p.1, title).
Regarding claim 13, the recited target drug is one of lsoniazid, Rifampicin, ... Pyrazinamide,... Ciprofloxacin, Moxifloxacin...and Levofloxacin reads on "Four ML algorithms were used to generate learned model systems for genes associated with the first-line TB drugs rifampicin (rpoB), isoniazid (katG and inhA), pyrazinamide (pncA) and fluoroquinolones (gyrA and gyrB) (Jamal, p.7, ¶ 1).
Regarding claim 14, the recited CRM, instructions, processor and computing device reads on "The present work is a computational framework that uses artificial intelligence (AI) based machine learning (ML) approaches for predicting resistance in genes" (Jamal, p.1, abstract).
Regarding claim 14, the recited determining drug resistance of the strain with respect to a target drug by analyzing the mutation based on a susceptibility-detection model, wherein the susceptibility-detection model is trained based on association mappings, each mapping associating a mutation of a training base strain with drug resistance to one or more drugs reads on "Artificial Intelligence (AI) and ML algorithms were used to classify single nucleotide variations (SNVs) as being resistant or susceptible in TB and predict novel resistance conferring mutations" (Jamal, p.2, ¶ 2); and Table 2, which shows the "Number of genes/mutations included in the final training dataset and testing dataset, and the actual number of resistant and susceptible mutations included in both training and test dataset" (Jamal, p.2, Table 2).
Regarding claim 15, the recited determining whether the located mutation is recorded in a predefined repository; and in response to determining that the located mutation is unrecorded, flagging the mutation of the strain reads on "Artificial Intelligence (AI) and ML algorithms were used to classify single nucleotide variations (SNVs) as being resistant or susceptible in TB and predict novel resistance conferring mutations" (Jamal, p.2, ¶ 2).
Regarding claim 15, the recited causing training of the susceptibility-detection model based on the flagged mutation, upon identifying the strain as one of a training base strain, reads on "In the non-redundant testing data, we were able to categorize mutations as susceptible or resistant with an accuracy ranging between 66.66–100%. (Table 4)" (Jamal, p. 2, ¶ 4); and " The non-redundant blind dataset consisted of the mutations not present in the 80% training dataset. This was the 20% of data not used to train the models and kept separate to evaluate the performance of the predictive models" (Jamal, p. 3, ¶ 2).
Jamal does not specifically show drug resistant associated genetic markers of a target strain of claim 1 (Shown by Feuerriegel).
While Jamal shows training base strain and "training in genes/mutations included in the final training dataset" (Jamal, p.2, Table 2); "Single nucleotide variations were obtained for rpoB, inhA, katG, pncA, gyrA, and gyrB" (Jamal, p.2, ¶ 3), and binding free energy of amino acid mutations compared to wildtype MTB (p.4, ¶ 7; and Tables 5-7), Jamal does not explicitly show comparing nucleotide sequences to a reference strain of the pathogen; Jamal does not specifically show determining a variation between the nucleotide sequences of the training base strain and of the reference strain; and Jamal does not specifically show correlating an indication of the variation between the nucleotide sequences of the training base strain and of the reference strain of claim 2 (Shown by Feuerriegel).
Jamal does not specifically show variation between the nucleotide sequence of the base strain and the nucleotide sequence of the reference strain is due to a mutation of claim 3 (shown by Feuerriegel).
While Jamal shows an indication in text format representing amino acid sequences, Jamal does not show text format representing nucleotide sequences of claim 4 (shown by Feuerriegel).
Jamal does not specifically show the recited the target genetic marker is indicative of a mutation on the target strain of claim 7 (shown by Feuerriegel ).
Jamal does not specifically show obtaining nucleotide sequence data of a target strain of a pathogen obtained from a test sample of claims 8 and 11 (shown by Feuerriegel).
Jamal does not specifically show analyzing the nucleotide sequence data to locate a genetic variation in a nucleotide sequence of the target strain of claim 8 (shown by Feuerriegel).
Jamal does not show generation of a report indicating a prospective treatment based on the target drug of claim 9 (shown by Feuerriegel).
Jamal does not specifically show comparison of the nucleotide sequence data of the target strain with nucleotide sequence data of a reference strain of claim 10 (shown by Feuerriegel).
Jamal does not specifically show obtaining nucleotide sequence data of a target strain of a pathogen obtained from a test sample of claim 14 (shown by Feuerriegel).
Jamal does not specifically show analyzing the nucleotide sequence data to locate a mutation in a nucleotide sequence of the target strain of claim 14 (shown by Feuerriegel).
Regarding claim 1, the associated genetic markers of a target strain reads on "A total of 97 MTBC strains isolated from previously treated patients were included in this study" (Feuerriegel, p.2, col.2, ¶ 3); and the Mycobacterium tuberculosis strains, mutations listed by rifampin (RIF), streptomycin (SM), and pyrazinamide (PZA) resistance as shown in Table 2 (Feuerriegel, p. 7, Table 2).
Regarding claim 2, the recited comparing nucleotide sequences of the base strain and of a reference strain of the pathogen reads on "sequence data was analyzed..., with M. tuberculosis H37Rv DNA as reference sequence." (Feuerriegel, p. 3, ¶ col.1.)
Regarding claim 2, the recited determining a variation between the nucleotide sequences of the base strain and of the reference strain reads on the Mycobacterium tuberculosis strains and their mutations listed by rifampin (RIF), streptomycin (SM), and pyrazinamide (PZA) resistance as shown in Table 2 (with reference strain H37Rv listed as control) (Feuerriegel, p. 7, Table 2).
Regarding claim 2, the recited correlating an indication of the variation between the nucleotide sequences of the base strain and of the reference strain with the reference drug reads on the Mycobacterium tuberculosis strains and their mutations listed by rifampin (RIF), streptomycin (SM), and pyrazinamide (PZA) resistance as shown in Table 2 (with reference strain H37Rv listed as control) (Feuerriegel, p. 7, Table 2)
Regarding claim 3, the recited variation between the nucleotide sequence of the base strain and the nucleotide sequence of the reference strain is due to a mutation in the training base strain reads on the Mycobacterium tuberculosis strains and their mutations listed by rifampin (RIF), streptomycin (SM), and pyrazinamide (PZA) resistance as shown in Table 2 (with reference strain H37Rv listed as control) (Feuerriegel, p. 7, Table 2).
Regarding claim 4, the recited the indication in text based format used for representing nucleotide sequences and amino acid sequences reads on the nucleotide and amino acid sequences of the mutation listed in Tables 1 and 2 (Feuerriegel, respectively p.4-5 and p.7).
Regarding claim 7, the recited the target genetic marker is indicative of a mutation on the target strain reads on the nucleotide and amino acid sequences of the mutation listed in Table 1 (Feuerriegel, p.4-5).
Regarding claims 8 and 11, the recited obtaining nucleotide sequence data of a target strain of a pathogen obtained from a test sample reads on "A total of 97 MTBC strains isolated from previously treated patients" (Feuerriegel, p.2, col.2, ¶ 3), and "DNA was isolated... and amplified...The PCR products were sequenced" (Feuerriegel, p.3, col.1, ¶ 3).
Regarding claim 8, the recited analyzing the nucleotide sequence data to locate a genetic variation in a nucleotide sequence of the target strain reads on "sequence data was analyzed ... All strains were sequenced in predominant resistance determining regions (RDR)" (Feuerriegel, p.3, col.1, ¶ 3-4).
Regarding claim 9, the recited detection engine, on determining drug susceptibility of the target strain with respect to the target drug, is (configured) to cause generation of a report indicating a prospective treatment based on the target drug reads on "Table 2 Determination of minimal inhibitory concentrations (MICs) of potential low-level resistant strains (to RIF, SM, PZA)" (Feuerriegel, p.7, Table 2).
Regarding claim 10, the recited the detection engine is to locate the genetic variation based on comparison of the nucleotide sequence data of the target strain with nucleotide sequence data of a reference strain reads on "sequence data was analyzed..., with M. tuberculosis H37Rv DNA as reference sequence." (Feuerriegel, p. 3, ¶ col.1.)
Regarding claim 14, the recited obtaining nucleotide sequence data of a target strain of a pathogen obtained from a test sample reads on "A total of 97 MTBC strains isolated from previously treated patients" (Feuerriegel, p.2, col.2, ¶ 3), and "DNA was isolated... and amplified...The PCR products were sequenced" (Feuerriegel, p.3, col.1, ¶ 3).
Regarding claim 14, the recited analyzing the nucleotide sequence data to locate a mutation in a target strain nucleotide sequence reads on "sequence data was analyzed ... All strains were sequenced in predominant resistance determining regions (RDR)" (Feuerriegel, p.3, col.1, ¶ 3-4), and Table 1, which shows "mutations detected in all strains analyzed" (Feuerriegel, p.4, Table 1).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the method for training and testing a computational machine learning model for predicting resistance and susceptibility associations in the Mycobacterium tuberculosis (MTB) genes of Jamal to include determining drug resistance and susceptibility in nucleotide sequence data target (patient sample) MTB strains of Feuerriegel. This is because Feuerriegel carries out an in depth investigation of molecular resistance mechanisms by correlating particular genomic variants with phenotypic resistance in clinical isolates from a high-incidence setting in West Africa. One of ordinary skill in the art would have understood how to and been motivated to modify Jamal with Feuerriegel to result in a method and system for training, testing, and using a model for determining drug resistance in target pathogen strains which could improve patient care. One would have had a reasonable expectation of success in doing so because Jamal and Feuerriegel are generally drawn to related teaching of analyzing mutations in MTB strains to determine genetically associated drug resistance and sensitivity, and as such, the combination would have been obvious.
Conclusion
No claims are allowed.
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.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Meredith A Vassell whose telephone number is (571)272-1771. The examiner can normally be reached 8:30 - 4:30.
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/M.A.V./Examiner, Art Unit 1687
/Karlheinz R. Skowronek/Supervisory Patent Examiner, Art Unit 1687
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