Patent Application 16865743 - PREDICTING ANTIBIOTIC RESISTANCE AND

Title: PREDICTING ANTIBIOTIC RESISTANCE AND COMPLEMENTARY ANTIBIOTIC COMBINATIONS

Application Information

Invention Title: PREDICTING ANTIBIOTIC RESISTANCE AND COMPLEMENTARY ANTIBIOTIC COMBINATIONS
Application Number: 16865743
Submission Date: 2025-05-14T00:00:00.000Z
Effective Filing Date: 2020-05-04T00:00:00.000Z
Filing Date: 2020-05-04T00:00:00.000Z
National Class: 702
National Sub-Class: 019000
Examiner Employee Number: 96576
Art Unit: 1685
Tech Center: 1600

Rejection Summary

102 Rejections: 0
103 Rejections: 2

Cited Patents

The following patents were cited in the rejection:

US 0253917🔗

Office Action Text

DETAILED ACTION
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on Oct 7 2024 has been entered.
Applicant’s response, filed Oct 7 2024, has been fully considered. Rejections and/or objections not reiterated from previous Office Actions are hereby withdrawn. The following rejections and/or objections are either reiterated or newly applied. They constitute the complete set presently being applied to the instant application.
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 .
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 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.
Claim Status
Claims 1-5, 7-13, 15-17, and 26-35 are pending.
Claims 6, 14, and 18-25 are canceled.
Claim 26 is objected to.
Claims 1-5, 7-13, 15-17, and 26-35 are rejected.
Priority
The instant Application was filed May 4 2020 and does not claim the benefit of an earlier filed application.
Drawings
The replacement drawing sheets submitted Oct 7 2024 are accepted and the outstanding objections from the previous Office Action are withdrawn.
Specification
The amendments to the abstract submitted Oct 7 2024 are accepted and the outstanding objections from the previous Office Action are withdrawn.
Claim Objections
The outstanding objections to the claims from the previous Office Action are withdrawn in view of the amendments submitted herein.
The claims are objected to because of the following informalities. The instant objection is newly stated and is based upon further consideration of the claims.
Claim 26 recites “A computer program product for representing identifying antibiotic compounds”, where either “representing” or “identifying” should be deleted.
Claim Rejections- 35 USC § 112
Unless reiterated below, the outstanding 35 USC 112(a) written description and enablement rejections and the 35 USC 112(b) rejections to the claims from the previous Office Action are withdrawn in view of the amendments submitted herein.
35 USC § 112(b)
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.
Claims 1-5, 7-13, 15-17, and 26-35 are rejected under 35 U.S.C. 112(b) as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor, regards as the invention. The instant rejection is maintained from the previous Office Action and updated based upon further consideration of the claims.
Claim 1, 4th limitation, recites “combining… the functional capacity vectors for the codes”. However, the 2nd limitation recites “identifying… one or more proteins of a genome of the identified organism that have at least one functional domain associated with at least one code selected from a coding system” and the 3rd limitation recites “for respective codes of the coding system, modelling the one or more proteins as a functional capacity vector for the respective code”. First, there is insufficient antecedent basis for functional capacity vectors, because the claim recites previously only one functional capacity vector. It is therefore further not clear whether the claim actually requires at least two codes selected from a coding system for one or more proteins in the 2nd limitation in order to model at least two functional capacity vectors in the 3rd limitation, which are then combined in the 4th limitation, because there is insufficient antecedent basis for multiple codes and functional capacity vectors. Further, if only one code is actually required or if only one functional capacity vector is modelled, it is further not clear if the combining step is conditional upon at least two codes being identified and two functional vectors being modelled. That the coding system comprises a plurality of codes does not clarify the combining step because the claims encompass an embodiment where one protein with one code is modelled as a functional capacity vector, and it is not clear that one functional capacity vector would be combined with itself. The specification as published discusses creating a composite functional capacity vector for the genome using each selected code [0031; 0057; 0068]. For compact examination, any art that reads on creating at least one functional capacity vector to represent a genome or subsets of a genome will be considered relevant. The rejection may be overcome by clarifying the metes and bounds of the claim. Claims 10 and 26 are similarly rejected. Claims 2-9, 11-17, and 27-35 are rejected based on their dependency from claims 10 and 26.
Response to Applicant Arguments
At p. 12, section III., Applicant submits that the claims have been amended to address the 35 USC 112(b) rejection.
It is respectfully submitted that this is not persuasive. The claims encompass an embodiment where one protein with one code is modelled as a functional capacity vector, and it is not clear that one functional capacity vector would be combined with itself. Therefore the metes and bounds of claims 1, 10, and 26 remain unclear.
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 1-5, 7-13, 15-17, and 26-35 are rejected under 35 U.S.C. 101 because the claimed invention is directed to one or more judicial exceptions without significantly more. Any newly recited portions are necessitated by claim amendment.
MPEP 2106 organizes judicial exception analysis into Steps 1, 2A (Prongs One and Two) and 2B as follows below. MPEP 2106 and the following USPTO website provide further explanation and case law citations: uspto.gov/patent/laws-and-regulations/examination-policy/examination-guidance-and-training-materials.
Framework with which to Evaluate Subject Matter Eligibility:
Step 1: Are the claims directed to a process, machine, manufacture, or composition of matter;
Step 2A, Prong One: Do the claims recite a judicially recognized exception, i.e. a law of nature, a natural phenomenon, or an abstract idea;
Step 2A, Prong Two: If the claims recite a judicial exception under Prong One, then is the judicial exception integrated into a practical application (Prong Two); and
Step 2B: If the claims do not integrate the judicial exception, do the claims provide an inventive concept.
Framework Analysis as Pertains to the Instant Claims:
Step 1
With respect to Step 1: yes, the claims are directed to a method, a system, and a computer program product comprising a non-transitory computer readable medium, i.e., a process, machine, or manufacture within the above 101 categories [Step 1: YES; See MPEP § 2106.03].
Step 2A, Prong One
With respect to Step 2A, Prong One, the claims recite judicial exceptions in the form of abstract ideas. The MPEP at 2106.04(a)(2) further explains that abstract ideas are defined as:
mathematical concepts (mathematical formulas or equations, mathematical relationships and mathematical calculations);
certain methods of organizing human activity (fundamental economic practices or principles, managing personal behavior or relationships or interactions between people); and/or
mental processes (procedures for observing, evaluating, analyzing/ judging and organizing information).
With respect to the instant claims, under the Step 2A, Prong One evaluation, the claims are found to recite abstract ideas that fall into the grouping of mental processes (in particular procedures for observing, analyzing and organizing information) and mathematical concepts (in particular mathematical relationships and formulas) are as follows:
Independent claims 1, 10, and 26: identifying/identifies/identify… one or more proteins of a genome of the identified organism that have at least one functional domain associated with at least one code selected from a coding system for a phenotypic space, wherein the coding system comprises a plurality of codes;
for respective codes of the coding system, modelling/models/model… the one or more proteins as a functional capacity vector, wherein the functional capacity vector represents a number of times a functional domain associated with the code appears in the genome;
combining/combines/combine… the functional capacity vectors for the codes into a combined functional capacity vector for the genome; and
predicting/predicts/predict… using a machine learning model, based on a comparison of the combined functional capacity vector for the genome of the identified organism against combined functional capacity vectors of other genomes in a dimensional reduced dataset for a set of genomes of organisms having known antimicrobial resistance statuses against one or more antibiotic compounds of a set of antibiotic compounds in the phenotypic space, an antibiotic compound of the set of antibiotic compounds to which the identified organism is susceptible, wherein the genome of the identified organism doesn't have a known antimicrobial resistance status against the antibiotic compound to which the identified organism is predicted to be susceptible.
Dependent claims 2 and 11: selecting/selects the coding system based on a phenotype of interest.
Dependent claims 3, 32, and 34: applying one or more restrictions for the coding system in association with the selecting, wherein the one or more restrictions specifies a defined type of structure for a taxonomy of the coding system.
Dependent claims 33 and 35 are further limit the defined type of structure of the taxonomy of claims 32 and 34 to an acyclic graph.
Dependent claims 4, 12, and 27: selecting/selects/select the at least one code based on a phenotype of interest.
Dependent claims 5, 7-9, 13, 15-17, and 28-31: predicting/predicts/predict… using the machine learning model, one or more antibiotic compounds of the set of antibiotic compounds to which the identified organism is resistant (claims 5, 13, and 28); predicting/predicts/predict… using the machine learning model, one or more combinations of antibiotic compounds of the set of antibiotic compounds to which the identified organism is susceptible (claims 7, 15, and 29); predicting/predicts/predict… using the machine learning model, one or more minimum inhibitory concentrations for one or more antibiotic compounds of the set of antibiotic compounds against the identified organism (claims 8, 16, and 30); and predicting/predicts/predict… using the machine learning model, one or more minimum inhibitory concentrations for one or more combinations of antibiotic compounds of the set of antibiotic compounds against the identified organism (claims 9, 17, and 31).
The abstract ideas recited in the claims are evaluated under the Broadest Reasonable Interpretation (BRI) and determined to each cover performance either in the mind and/or by mathematical operation because the method only requires a user to manually model proteins as function capacity vectors and employ those vectors to identify antibiotics or predict doses. Without further detail as to the methodology involved in “identifying”, “modelling”, “combining”, “predicting”, “selecting”, and “applying”, under the BRI, one may simply, for example, use pen and paper to identify proteins with certain functional domains associated with a code, model those proteins as vectors, combine the vectors into a combine vector for the genome, compare the combined vector against another combined vector of other genomes of organisms with known antimicrobial resistance statuses, and predict an antibiotic compound to which the identified organism is susceptible. Some of these steps, such as those directed to using a machine learning model, encompass purely mathematical techniques, as is disclosed in the specification as published at least at: [0096], which discloses that suitable machine learning algorithms/models that can be used by the susceptibility forecasting component to evaluate the degrees of similarity between the target FCV and reference FCVs for the known genomes in the reference functional omics data can include but are not limited to: a nearest neighbor algorithm, a naïve Bayes algorithm, a decision tree algorithm, a boosting algorithm, a gradient boosting algorithm, a linear regression algorithm, a neural network algorithm, a clustering algorithm, a k-means clustering algorithm, an association rules algorithm, a q-learning algorithm, a temporal difference algorithm, and a deep adversarial network algorithm.
Therefore, claims 1, 10, and 26, and those claims dependent therefrom recite an abstract idea [Step 2A, Prong 1: YES; See MPEP § 2106.04].
Step 2A, Prong Two
Because the claims do recite judicial exceptions, direction under Step 2A, Prong Two, provides that the claims must be examined further to determine whether they integrate the judicial exceptions into a practical application (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. This is performed by analyzing the additional elements of the claim to determine if the judicial exceptions are integrated into a practical application (MPEP 2106.04(d).I.; MPEP 2106.05(a-h)). If the claim contains no additional elements beyond the judicial exceptions, the claim is said to fail to integrate the judicial exceptions into a practical application (MPEP 2106.04(d).III).
Additional elements, Step 2A, Prong Two
With respect to the instant recitations, the claims recite the following additional elements:
Independent claims 1, 10, and 26: receiving/receives/receive… a query related to an identified organism that has infected a patient; and
providing/provides/provide… a response to the query indicating the antibiotic compound to which the identified organism is predicted to be susceptible for treatment of the infection of the patient.
The claims also include non-abstract computing elements. For example, independent claim 1 includes a system operatively coupled to at least one processor; claim 10 includes a system, comprising: a memory that stores at least one computer executable component; a processor that executes the at least one computer executable component stored in the memory , wherein the computer executable components comprise: a protein identification component… and a vectorization component; and claim 26 includes a computer program product comprising a non-transitory computer readable medium having program instructions embodied therewith, the program instructions executable by a processor.
Considerations under Step 2A, Prong Two
With respect to Step 2A, Prong Two, the additional elements of the claims do not integrate the judicial exceptions into a practical application for the following reasons. Those steps directed to data gathering, such as “receiving” a request, and to data outputting, such as “providing” a response, perform functions of collecting and outputting the data needed to carry out the judicial exceptions. Data gathering and outputting do not impose any meaningful limitation on the judicial exceptions, or on how the judicial exceptions are performed. Data gathering and outputting steps are not sufficient to integrate judicial exceptions into a practical application (MPEP 2106.05(g)).
Those steps directed to additional non-abstract elements of the above recited computing elements do not describe any specific computational steps by which the “computer parts” perform or carry out the judicial exceptions, nor do they provide any details of how specific structures of the computer, such as the computer-readable recording media, are used to implement these functions. The claims state nothing more than a generic computer which performs the functions that constitute the judicial exceptions. Hence, these are mere instructions to apply the judicial exceptions using a computer, and therefore the claim does not integrate that judicial exceptions into a practical application. The courts have weighed in and consistently maintained that when, for example, a memory, display, processor, machine, etc.… are recited so generically (i.e., no details are provided) that they represent no more than mere instructions to apply the judicial exception on a computer, and these limitations may be viewed as nothing more than generally linking the use of the judicial exception to the technological environment of a computer (MPEP 2106.05(f)). Further, the computer system contains the recited components (i.e., software) that are used for identifying proteins that have one or more functional domains, modelling the proteins as a functional capacity vector (claim 11), and employing the functional capacity vector or distances between the functional capacity vectors (claims 13-17), which is nothing but instructions to perform the recited judicial exceptions on a computer. Thus, the limitations only generically link the use of the judicial exceptions to the technological environment of a computer.
The specification discloses the method 100 of FIG.1 can be applied to provide substantial clinical improvements in optimizing clinical treatment and minimizing the development of multidrug-resistant (MDR) bacteria which has become a serious global threat at [0038], but does not provide a clear explanation for how the additional elements provide these improvements. Therefore, the additional elements do not clearly improve the functioning of a computer, or comprise an improvement to any other technical field. Further, the additional elements do not clearly affect a particular treatment; they do not clearly require or set forth a particular machine; they do not clearly effect a transformation of matter; nor do they clearly provide a nonconventional or unconventional step (MPEP2106.04(d)). It is further asserted that the method of claim 1 is not fully born out in the instant claims.
Thus, none of the claims recite additional elements which would integrate a judicial exception into a practical application, and the claims are directed to one or more judicial exceptions [Step 2A, Prong 2: NO; See MPEP § 2106.04(d)].
Step 2B (MPEP 2106.05.A i-vi)
According to analysis so far, the additional elements described above do not provide significantly more than the judicial exception. A determination of whether additional elements provide significantly more also rests on whether the additional elements or a combination of elements represents other than what is well-understood, routine, and conventional. Conventionality is a question of fact and may be evidenced as: a citation to an express statement in the specification or to a statement made by an applicant during prosecution that demonstrates a well-understood, routine or conventional nature of the additional element(s); a citation to one or more of the court decisions as discussed in MPEP 2106(d)(II) as noting the well-understood, routine, conventional nature of the additional element(s); a citation to a publication that demonstrates the well-understood, routine, conventional nature of the additional element(s); and/or a statement that the examiner is taking official notice with respect to the well-understood, routine, conventional nature of the additional element(s).
With respect to the instant claims, the courts have found that receiving and outputting data are well-understood, routine, and conventional functions of a computer when claimed in a merely generic manner or as insignificant extra-solution activity (see Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information), buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355, 112 USPQ2d 1093, 1096 (Fed. Cir. 2014) (computer receives and sends information over a network), Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1334, 115 USPQ2d 1681, 1701 (Fed. Cir. 2015), and OIP Techs., 788 F.3d at 1363, 115 USPQ2d at 1092-93, as discussed in MPEP 2106.05(d)(II)(i)). As such, the claims simply append well-understood, routine, conventional activities previously known to the industry, specified at a high level of generality, to the judicial exception (MPEP2106.05(d)). The data gathering steps as recited in the instant claims constitute a general link to a technological environment which is insufficient to constitute an inventive concept which would render the claims significantly more than the judicial exception (MPEP2106.05(g)&(h)).
With respect to the computer elements of the claims, the computer-related elements or the general purpose computer do not rise to the level of significantly more than the judicial exception. The specification also notes that computer processors and systems, as example, are commercially available or widely used at [0043]. The claims state nothing more than a generic computer which performs the functions that constitute the judicial exceptions. Hence, these are mere instructions to apply the judicial exceptions using a computer, which the courts have found to not provide significantly more when recited in a claim with a judicial exception (see Voter Verified, Inc. v. Election Systems & Software, LLC, 887 F.3d 1376, 1385, 126 USPQ2d 1498, 1504 (Fed. Cir. 2018), MPEP 2106.06(A)). The additional elements are set forth at such a high level of generality that they can be met by a general purpose computer. Therefore, the computer components constitute no more than a general link to a technological environment, which is insufficient to constitute an inventive concept that would render the claims significantly more than the judicial exceptions (see MPEP 2106.05(b)I-III).
Taken alone, the additional elements do not amount to significantly more than the above-identified judicial exception(s). Even when viewed as a combination, the additional elements fail to transform the exception into a patent-eligible application of that exception. Thus, the claims as a whole do not amount to significantly more than the exception itself [Step 2B: NO; See MPEP § 2106.05].
Therefore, the instant claims are not drawn to eligible subject matter as they are directed to one or more judicial exceptions without significantly more. For additional guidance, applicant is directed generally to the MPEP § 2106.
Response to Applicant Arguments
At p. 13-33, Applicant submits that the claims must be examined as a whole to determine whether they are directed to a judicial exception, pointing to CONTOUR IP HOLDING LLC v. GOPRO, INC., No. 22-1654 (Fed. Cir. 2024). Applicant points to the specification to describe the problems in conventional methods that are overcome by the invention, where the use of functional domains of proteins to predict antibiotic response in microbial infections results in an improvement to predicting a treatment. Applicant submits that combining the genomic data and protein functional domain data of an organism with unknown susceptibility to existing antibiotics into a dimensionally reduced functional capacity vector associated with a phenotypic space provide a more accurate predictor of biological reactions related to antibiotic resistance and susceptibility. Applicant submits that the recommended treatment is more effective and reduces development of multidrug resistance in the new organism.
It is respectfully submitted that this is not persuasive. The instant claims are not analogous to those at issue in CONTOUR. The court found that those claims were directed to a specific means that improved a camera, whereas the instant claims are directed to providing a predicted treatment, as submitted by Applicant at p. 33, par. 2. A camera is a physical item that may be improved, whereas predicting a treatment is a judicial exception which cannot be improved, and providing such a prediction is merely insignificant, extra-solution activity as described in the above rejection. The instant claims do not provide an improvement in the additional element of “providing” information, but rather an improvement in predicting a treatment. The act of predicting a treatment (including those limitations involved in the prediction and pointed to by Applicant as the limitation which provide the improvement, such as those directed to “identifying” proteins, “modelling” the proteins as functional capacity vectors, “combining” the functional capacity vectors, and “predicting” an antibiotic compound) is a judicial exception and cannot provide a practical application of itself or other judicial exceptions. The analysis outlined in the above rejection follows the Alice steps, as discussed by Applicant, but finds that at Step 2A, Prong 1, the claims recite a judicial exception, and at Step 2A, Prong 2, are directed to a judicial exception because the additional elements in the claims do not integrate the judicial exception into a practical application. The courts have made clear that a judicial exception is not eligible subject matter (Bilski, 561 U.S. at 601, 95 USPQ2d at 1005-06 (quoting Chakrabarty, 447 U.S. at 309, 206 USPQ at 197 (1980)) if there are no additional claim elements besides the judicial exception, or if the additional claim elements merely recite another judicial exception that is insufficient to integrate the judicial exception into a practical application. See, e.g., RecogniCorp, LLC v. Nintendo Co., 855 F.3d 1322, 1327, 122 USPQ2d 1377 (Fed. Cir. 2017) ("Adding one abstract idea (math) to another abstract idea (encoding and decoding) does not render the claim non-abstract"); Genetic Techs. v. Merial LLC, 818 F.3d 1369, 1376, 118 USPQ2d 1541, 1546 (Fed. Cir. 2016) (eligibility "cannot be furnished by the unpatentable law of nature (or natural phenomenon or abstract idea) itself."). For a claim reciting a judicial exception to be eligible, it is the additional elements (if any) in the claim that must "transform the nature of the claim" into a patent-eligible application of the judicial exception, Alice Corp., 573 U.S. at 217, 110 USPQ2d at 1981, either at Prong Two or in Step 2B. If there are no additional elements in the claim, then it cannot be eligible. It is submitted here that the instant claims do not include any additional elements that provide for a practical application. When examined as a whole, the additional elements of the claim recite merely data gathering and outputting functions which are necessary for the judicial exceptions.
It is also noted that while preemption is the concern underlying the judicial exceptions, it is not a standalone test for determining eligibility (Rapid Litig. Mgmt. v. CellzDirect, Inc., 827 F.3d 1042, 1052, 119 USPQ2d 1370, 1376 (Fed. Cir. 2016)). It is necessary to evaluate eligibility using the Alice/Mayo test, because while a preemptive claim may be ineligible, the absence of complete preemption does not demonstrate that a claim is eligible (Diamond v. Diehr, 450 U.S. 175, 191-92 n.14, 209 USPQ 1, 10-11 n.14 (1981); “We rejected in Flook the argument that because all possible uses of the mathematical formula were not pre-empted, the claim should be eligible for patent protection”; see MPEP 2106.04).
It is further noted that the claims do not require a treatment of the individual with the predicted antibiotic compound. Therefore, the improvements of effective treatment and reduced development of multidrug resistance in the new organism as submitted by Applicant are not commensurate with the scope of the claims.

At p. 34, Applicant submits that a human mind is not capable of processing the massive amount of genomic and protein functional domain data of the known and new organisms required by the claims in a practicably reasonable timeframe to treat a patient currently infected with the new organism.
It is respectfully submitted that this is not persuasive. Applicant’s arguments are not commensurate with the scope of the claims for the following reasons. First, the claim requires only identifying one or more proteins associated with at least one code. It is submitted that the human mind is capable of identifying one protein which is associated with one code, even without the use of pen and paper or a computer as a tool to perform the action. The claim does not require that all the proteins in a genome be analyzed. Second, the claim does not require that the patient be treated with the predicted antibiotic. Therefore, there is no timeline required by the claim to perform the judicial exceptions.

Claim Rejections - 35 USC § 103
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.

The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
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.
A. Claims 1-5, 7-13, 15-17, 26-32, and 34 are rejected under 35 U.S.C. 103 as being unpatentable over Kaufman et al. (IEEE 34th International Conference on Data Engineering Workshops, 2018, p. 17-20; previously cited) in view of Walker et al. (US 2017/0253917; previously cited). The instant rejection is newly stated and is necessitated by claim amendment.
Claim 1 discloses a method where the steps are performed by a system operatively coupled to at least one processor. Claim 10 discloses a system, comprising a memory that stores computer executable components, a processor that executes the computer executable components stored in the memory. Claim 26 discloses a computer program product for representing identifying antibiotic compounds for organisms, the computer program product comprising a non-transitory computer readable medium having program instructions embodied therewith.
Kaufman discloses the use of cellular processes of interest for providing the context for classification of thousands of organisms based on their functional potential (abstract). Kaufman is considered as teaching a dimensionally reduced coding vector that represents one or more target functions as described below. As Kaufman teaches performing their method using various algorithms and generating heat maps, it is considered that Kaufman teaches a computer-implemented method.
The steps of claims 1, 10, and 26 comprise:
receiving, by a system operatively coupled to at least one processor, a query related to an identified organism that has infected a patient;
Kaufman does not teach this limitation. See below for teachings by Walker regarding this limitation.
identifying, by the system, one or more proteins of a genome of the identified organism that have at least one functional domain associated with at least one code selected from a coding system for a phenotypic space, wherein the coding system comprises a plurality of codes;
Kaufman teaches that sets of domains with assigned functional gene-ontology GO codes map to sets of function (i.e., functional domains) (p. 18, col. 2, par. 2). Kaufman teaches identifying and annotating every gene of the genome using the GO codes (i.e., codes selected from a coding system for a phenotypic space) associated with the proteins encoded by the genes (p. 19, col. 1, par. 3-4) as well as all the domains from 2128 genomes of the genus Burkholderia (p. 19, col. 1, par. 5 through col. 2, par. 1). Interpretation of Gene Ontology codes as reading on the instantly claimed codes selected from a coding system for a set of phenotypes is supported in the specification at least at [0029].
for respective codes of the coding system, modelling, by the system, the one or more proteins as a functional capacity vector for the respective code, wherein the functional capacity vector represents a number of times a functional domain associated with the code appears in the genome;
combining, by the system, the functional capacity vectors for the codes into a combined functional capacity vector for the genome;
Regarding the above two limitations, Kaufman teaches creating a vector of gene ontology codes for each genome (p. 19, col. 1, par. 5 through col. 2, par. 3). As Kaufman teaches that domains are biological objects with functional capacity (p. 18, col. 1, par. 2), and because the vectors are created from these functional domains, it is considered that Kaufman fairly teaches functional capacity vectors. As it is not clear whether “combining the functional capacity vectors for the codes into a combined functional capacity vector for the genome” is required to be performed, as discussed in the above 35 USC 112(b) rejection, and as Kaufman teaches making a vector representing all the codes in a genome, it is considered that Kaufman fairly teaches “a combined functional capacity vector for the genome”.
predicting, by the system, using a machine learning model, based on a comparison of the combined functional capacity vector for the genome of the identified organism against combined functional capacity vectors of other genomes in a dimensional reduced dataset for a set of genomes of organisms having known antimicrobial resistance statuses against one or more antibiotic compounds of a set of antibiotic compounds in the phenotypic space, an antibiotic compound of the set of antibiotic compounds to which the identified organism is susceptible, wherein the genome of the identified organism doesn't have a known antimicrobial resistance status against the antibiotic compound to which the identified organism is predicted to be susceptible; and
Kaufman teaches creating a vector for each genome of over 2000 genomes (i.e., combined functional capacity vectors of other genomes) and comparing the vectors or functional code using a similarity map (p. 19, col. 1, par. 5 through col. 2, par. 1). Kaufman teaches calculating the distance between all vectors to make a matrix that shows function-function similarity (p. 19, col. 2, par. 3; Fig. 4). Kaufman does not teach predicting, using a machine learning model, an antibiotic compound to which the identified organism is susceptible.
See below for teachings by Walker regarding this limitation.
providing, by the system, a response to the query indicating the antibiotic compound to which the identified organism is predicted to be susceptible for treatment of the infection of the patient.
Kaufman does not teach this limitation. See below for teachings by Walker regarding this limitation.
Kaufman does not teach the system or computer program product of the instant claims, or the limitations directed to “receiving” a query, “predicting”, using a machine learning model, an antibiotic compound to which the identified organism is susceptible, or “providing” a response, as indicated above.
However, the prior art to Walker discloses methods, systems, and machine-readable medium for determining an appropriate therapeutic regimen for treating an infection caused by antibiotic resistant bacteria (abstract). Walker teaches a system for implementing software code stored in memory on any suitable processor/computer [0110-0117]. Walker teaches receiving input from a user [0112].
Walker teaches identifying, in a biological sample, a genetic profile of an infection source [0038] in order to predict phenotypic antibiotic resistance (claims 5 and 13) of a pathogenic bacteria [0010; 0017; 0019] and to determine whether an infection source will be susceptible (claims 6 and 14) to an antibiotic [0014; 0016] by detecting the presence or absence of at least one antibiotic resistance gene in comparison to a control profile including information associating antibiotic resistance genes with susceptibility or resistance to specific antibiotics ([0010; 0014; 0016-0017; 0019; 0038; 0121-0127]; Tables 3-5 and 7-8). Walker teaches using a predictive algorithm for determining susceptibility to an antibiotic, where the algorithm can be a decision tree analysis [0122-0126], general linear models [0133-0135], or any number of algorithms including Kth-Nearest Neighbor, Bayesian Networks, Boosting, regression, neural networks, and principal components analysis algorithms [0072]. As the instant specification as published discloses that some suitable machine learning models for evaluating functional capacity vectors include a nearest neighbor algorithm, a naïve Bayes algorithm, a decision tree algorithm, a boosting algorithm, a gradient boosting algorithm, a linear regression algorithm, a neural network algorithm, a clustering algorithm, a k-means clustering algorithm, an association rules algorithm, a q-learning algorithm, a temporal difference algorithm, a deep adversarial network algorithm, or a combination thereof [0096], it is considered that Walker fairly teaches using a machine learning model to perform the prediction. Walker teaches methods of treating the patient with the infection by making a recommendation to a subject for a desired course of action or treatment regimen, e.g., a prescription, including a recommended dosage to inhibit growth of a microorganism using a recommended dosage of an antimicrobial agent to which the microorganism is susceptible [0048; 0062-0064].
Regarding claims 1, 10, and 26, it would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine, in the course of routine experimentation and with a reasonable expectation of success, the methods of Kaufman and the computing elements of Walker because both references disclose methods for analyzing microbial genomic data. As the courts have held that broadly providing an automatic or mechanical means to replace a manual activity which accomplished the same result is not sufficient to distinguish over the prior art (In re Venner, 262 F.2d 91, 95, 120 USPQ 193, 194 (CCPA 1958)), it would have been obvious to perform the computer implemented method taught by Kaufman on the computing system taught by Walker to provide an automatic means for performing the method and obtaining the same result as if the method were performed manually. It would have been obvious to substitute the vectors taught by Kaufman for the gene data in the machine learning predictive algorithm of Walker because each element (the vectors and the gene data) are merely representations of similar types of data, and represent the simple substitution of one known element for another.

Regarding claim 2-4, 11-12, 27, 32, and 34, Kaufman in view of Walker teaches the method, system, and computer program product of claims 1, 10, and 26 as described above. Claims 2 and 11 further add selecting the coding system based on a phenotype of interest. Claims 3, 32, and 34 further add applying, by the system, one or more restrictions for the coding system restrictions in association with the selecting, wherein the one or more restrictions specifies a defined type of structure for a taxonomy of the coding system. Claims 4, 12, and 27 further add selecting the at least one code based on a phenotype of interest. Kaufman teaches classifying organisms by functions or functions of interest based on the core functions of the taxonomic groups (i.e., phenotypes of interest) (p. 19, col. 1, par. 2), based on the GO codes (i.e., selected coding system; claims 2 and 11) associated with proteins from the organism (p. 19, col. 2, par. 3). As Kaufman contemplates using their method to classify organisms based on a function of interest, and teaches representing a genome by its function, with rectangles attributed to single functional GO codes (Fig. 3), it is considered that Kaufman fairly teaches selecting codes based on a phenotype of interest (claims 4, 12, and 27) where the coding system is restricted to GO codes, a defined type of structure for a taxonomy of the coding system (claims 3, 32, and 34).

Regarding claims 5, 7-9, 13, 15-17, and 28-31, Kaufman in view of Walker teaches the method, system, and computer program product of claims 1, 10, and 26 as described above. Claims 5, 13, and 28 further add predicting, by the system, using the machine learning model, based on the comparison, one or more antibiotic compounds of the set of antibiotic compounds to which the identified organism is resistant. Claims 7, 15, and 29 further add predicting, by the system, using the machine learning model, based on the comparison, one or more combinations of antibiotic compounds of the set of antibiotic compounds to which the identified organism is susceptible. Claims 8, 16, and 30 further add predicting, by the system, using the machine learning model, based on the comparison, one or more minimum inhibitory concentrations for one or more antibiotic compounds of the set of antibiotic compounds against the identified organism. Claims 9, 17, and 31 further add predicting, by the system, using the machine learning model, based on the comparison, one or more minimum inhibitory concentrations for one or more combinations of antibiotic compounds of the set of antibiotic compounds against the identified organism. Kaufman teaches calculating the distance between all vectors to make a matrix that shows function-function similarity (i.e., predicting functions) (p. 19, col. 2, par. 3; Fig. 4). However, Kaufman does not teach predicting the antibiotic responses as claimed.
However, Walker teaches predicting phenotypic antibiotic resistance (claims 5, 13, and 28) of a pathogenic bacteria (i.e., organism) [0010; 0017; 0019] and for determining whether an infection source will be susceptible to an antibiotic [0014; 0016] by detecting the presence or absence of at least one antibiotic resistance gene ([0010; 0014; 0016-0017; 0019; 0121-0127]; Tables 3-5 and 7-8). Walker teaches predicting susceptible and resistant phenotypes for in E. coli isolates to multiple antibiotics, including combinations of trimethoprim/sulfamethoxazole, amoxicillin/K clavulanate, ampicillin/sulbactam, ticarcillin/k clavulanate, and piperacillin/tazobactam (i.e., antibiotic compound combinations; claims 7, 15, and 29) ([0128-0138]; Tables 9-10). Walker teaches predicting a minimal inhibitory concentration (claims 8, 16, and 30) of an antibiotic for bacterial isolates by determining the presence or absence of one or more antibiotic resistance genes ([0011; 0013; 0015; 0121-0127]; Tables 2 and 6). Walker does not specifically teach predicting minimum inhibitory concentrations for antibiotic compound combinations (claims 9, 17, and 31), however see below.
Regarding claims 9, 17, and 31, it would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify, in the course of routine experimentation and with a reasonable expectation of success, the combined methods of Kaufman and Walker as applied to claims 1, 10, and 26 as described above because both references disclose methods for analyzing microbial genomic data. Although Walker does not teach predicting minimum inhibitory concentrations for antibiotic compound combinations, Walker does teach predicting minimum inhibitory concentrations for multiple antibiotic compounds, separately, and predicting susceptibility and resistance to antibiotic compound combinations, as described above (see at least Tables 2-10). It therefore would have been obvious to modify the teachings of Walker to predict minimum inhibitory concentrations for antibiotic compound combinations because Walker teaches each of the elements separately. One could have therefore combined the elements as claimed by the known methods of Kaufman and Walker, and that in combination, each element merely would have performed the same function as it did separately for the predictable result of predicting bacterial phenotypic responses to antibiotic compounds based on vector representations of specific functional domains.
B. Claims 33 and 35 are rejected under 35 U.S.C. 103 as being unpatentable over Kaufman in view of Walker, as applied to claims 10-11, 26-27, 32, and 34 as described above, as evidenced by Pletcher et al. (Current Biology, 2002, 12(9):712-723; newly cited). The instant rejection is newly stated and is necessitated by claim amendment.
Regarding claims 33 and 35, Kaufman in view of Walker teaches the system and computer program product of claims 10-11, 26-27, 32, and 34 as described above. Claims 33 and 35 further add that the defined type of structure for the taxonomy of the coding system is an acyclic graph. Kaufman teaches selecting codes based on a phenotype of interest (claims 4, 12, and 27) where the coding system is restricted to GO codes, a defined type of structure for a taxonomy of the coding system (p. 19, col. 1, par. 2 through col. 2, par. 3). As Pletcher discloses that GO annotations are technically a directed acyclic graph (p. 722, col. 1, par. 5), it is considered that Kaufman as evidenced by Pletcher teaches using a defined type of structure for the taxonomy of the coding system that is an acyclic graph.
Conclusion
No claims are allowed.
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/J.N.S./Examiner, Art Unit 1685

/OLIVIA M. WISE/Supervisory Patent Examiner, Art Unit 1685