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 .
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.
Election/Restrictions
Applicant's election with traverse of Group II, drawn to claims 7-15 and 33-50 in the reply filed on May 28 2026 is acknowledged. The traversal is on the ground(s) that the common concept identified in the lack of unity links the claims to generate animal-related predictive models, classifications, and outputs, such that the groups are not merely unrelated inventions but reflect related aspects of a common computer-implemented microbiota analysis platform. Applicant further submits that the additional features in the groups are technically related rather than independent, and Applicant disagrees that the claims lack the same or corresponding special technical features or fail to form a single general inventive concept. This is not found persuasive because the single general concept linking the claims together is a method, system, or non- transitory computer-readable storage medium that uses machine-learning algorithms on gut microbiota data does not make a contribution over the art as described in the restriction requirement mailed Apr 9 2026. As set forth in PCT Rule 13, “Where a group of inventions is claimed in one and the same international application, the requirement of unity of invention referred to in Rule 13.1 shall be fulfilled only when there is a technical relationship among those inventions involving one or more of the same or corresponding special technical features. The expression "special technical features" shall mean those technical features that define a contribution which each of the claimed inventions, considered as a whole, makes over the prior art.” (see MPEP 1850).
The requirement is still deemed proper and is therefore made FINAL.
Claims 1-6, 16-32, and 51-58 are withdrawn from further consideration pursuant to 37 CFR 1.142(b), as being drawn to a nonelected inventions, there being no allowable generic or linking claim. Applicant timely traversed the restriction (election) requirement in the reply filed on May 28 2026.
Claim Status
Claims 1-58 are pending.
Claims 1-6, 16-32, and 51-58 are withdrawn from further consideration pursuant to 37 CFR 1.142(b) as being drawn to a non-elected invention, as described above.
Claims 10,12-15,36,38-41,45 and 47-50 are objected to.
Claims 7-15 and 33-50 are rejected.
Priority
The instant Application claims domestic benefit to US provisional application 63/032,376, filed May 29 2020.
Applicant's claim for the benefit of a prior-filed application, PCT/US2021/034499, filed May 27 2021, is acknowledged.
Accordingly, each of claims 7-15 and 33-50 are afforded the effective filing date of May 29 2020.
Information Disclosure Statement
The information disclosure statements (IDS) filed on Nov 29 2022, Mar 27 2024, Oct 28 2025, and May 28 2026 are in compliance with the provisions of 37 CFR 1.97 and have therefore been considered. Signed copies of the IDS documents are included with this Office Action.
Drawings
The Drawings submitted Nov 29 2022 are accepted.
Specification
The amendments to the specification submitted Nov 29 2022 are accepted.
Claim Objections
The claims are objected to for the following informalities:
Claims 10, 36, and 45 recite “the nutrient content”, but previously recite “a nutritional content”. The claims should be amended to recite “the nutritional content” to maintain consistent claim language.
Claims 12-15 recites “in gastrointestinal tract of the animal”, which should be amended to recite “in the gastrointestinal tract” because claim 7 already recites “a gastrointestinal tract of the animal”. Claims 38-41 and 47-50 are similarly objected to.
Claims 13, 39, and 48 recite “an specified body mass”, which should be amended to recite “a[[n]] specified body mass”.
Claims 14, 40, and 49 recite “that the animal is food safety risk”, which should be amended to recite “that the animal is a food safety risk”.
Claims 41 and 50 include an “and” at the end of the second limitation which should be removed.
Claim Rejections - 35 USC § 112
35 U.S.C. 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 7-15 and 33-50 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.
Claim 7, first limitation, recites “the lifecycle”. There is insufficient antecedent basis for this limitation in the claim as there is no previous recitation of a lifecycle. The rejection may be overcome by clarifying the antecedent basis of the limitation. Claims 33 and 42 are similarly rejected. Claims 8-15, 34-41, and 43-50 are rejected based on their dependency from claims 7, 33, and 42.
Claim 7, final limitation, recites “the classification”. There is insufficient antecedent basis for this limitation in the claim as there is no previous recitation of a classification in the claim. For compact examination, it is assumed that the claim intends to recite “providing the model” that is determined in the second limitation. Claims 8-15 are rejected based on their dependency from claim 7.
Claim 9 recites “the second microbiota model engine being trained”. Claims 35 and 44 recite “wherein the second microbiota model engine is trained”. It is unclear whether the wherein clause is intended to require training the second microbiota model engine within the metes and bounds of the claimed invention, or if it is only further limiting the second microbiota model engine such that the training is not required within the metes and bounds of the invention. As set forth in MPEP 2111.04.I, “wherein” clauses raise the question as to the limiting effect of the language in a claim. As the claims do not recite an active performance of the training the second microbiota model engine, the metes and bounds of the claims are unclear. For compact examination, it is assumed that the training is not required to be performed. The rejection may be overcome by clarifying what steps are required to be performed.
Claim 10 recites “the presence or deficiency of one or more nutrients”. There is insufficient antecedent basis for this limitation in the claim as there is no previous recitation of a presence or deficiency of one or more nutrients. The rejection may be overcome by clarifying the antecedent basis of the limitation. Claims 36 and 45 are similarly rejected.
Claim 13 recites “the likelihood that an offspring…”. There is insufficient antecedent basis for this limitation in the claim as there is no previous recitation of a likelihood. The rejection may be overcome by clarifying the antecedent basis of the limitation. Claims 39 and 48 are similarly rejected.
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 7-15 and 33-50 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 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 non-transitory computer readable storage 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).
The claims also recite a law of nature or a natural phenomenon. The MPEP at 2106.04(b) further explains that laws of nature and natural phenomena include naturally occurring principles/relations and nature-based products that are naturally occurring or that do not have markedly different characteristics compared to what occurs in nature.
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) as well as a law of nature or a natural phenomenon are as follows:
Independent claims 7, 33, and 42: determining, based on the first data and using a first microbiota model…, a model for the animal, the first microbiota model… trained using supervised learning and data obtained from gastrointestinal tracts of two or more animals.
Dependent claims 9, 35, and 44: selecting the predetermined subset of the total microbiota using a second microbiota model…, wherein the second microbiota model… is trained using the total microbiota obtained from gastrointestinal tracts of a second set of two or more animals.
Dependent claims 10, 36, and 45: generating a prediction of a nutritional content of the animal, the nutrient content being indicative of the presence or deficiency of one or more nutrients.
Dependent claims 11, 37, and 46: generating a prediction of a body mass of the animal.
Dependent claims 12, 38, and 47: generating a prediction of a concentration of second microbiota in gastrointestinal tract of the animal; and
determining, based on the prediction, an adjustment to a feed product or other nutrient to provide to the animal to improve at least one of a body mass of the animal or a food safety risk of the animal.
Dependent claims 13, 39, and 48: generating a prediction of a concentration of second microbiota in gastrointestinal tract of the animal; and
determining, based on the prediction, an adjustment to a feed product provided to the animal to adjust the microbiota of the animal, wherein the adjustment is selected to improve the likelihood that an offspring of the animal will have a specified body mass or microbiota concentration.
Dependent claims 14, 40 and 49: generating a prediction of a concentration of second microbiota in gastrointestinal tract of the animal; and
determine, based on the prediction, a likelihood that the animal is food safety risk.
Dependent claims 15, 41, and 50: generating a prediction of a concentration of second microbiota in gastrointestinal tract of the animal;
identifying a feed product that is associated with the second microbiota; and
determining, based on the model and the identifying, an adjustment to an additive or nutrient of the feed product to increase or decrease a concentration of the second microbiota in the animal.
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 determine a model for an animal based on its microbiota. Without further detail as to the methodology involved in “determining”, “selecting”, “generating”, and “identifying”, under the BRI, one may simply, for example, use pen and paper to select a subset of the total microbiota of an animal using a second microbiota model and determine a model for an animal using a first microbiota model and the subset of microbiota, where determining the model comprises: generating a prediction of the nutritional content or body mass of the animal; generating a prediction of the concentration of second microbiota and determining an adjustment to the animal’s food or whether the animal is a safety risk. These actions encompass observations, evaluations, judgments, which are concepts able to be performed in the human mind. The steps directed to using a first and second microbiota model require mathematical techniques as the only supported embodiments, as is disclosed in the specification at [0024; 0044-0047], which discusses the use of machine learning models used in the tasks of the invention, where examples of suitable supervised machine learning algorithms can include Bayesian networks, decision trees, K-nearest neighbors, linear classifiers, linear regression, logistic regression, naive Bayesian algorithms, quadratic classifiers, random forests, support vector machines, and other suitable algorithms, and examples of suitable unsupervised machine learning algorithms include expectation-maximization algorithms, vector quantization, and information bottleneck methods, each of which are considered to recite mathematical models.
The claims also recite a natural relationship between an animal’s gut microbiome and its nutrition content, body mass, and its food safety risk. Therefore, the claims recite a law of nature or a natural phenomenon.
Therefore, claim 7, 33, and 42 and those claims dependent therefrom recite an abstract idea and a law of nature/natural phenomenon [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 7, 33, and 42: obtaining first data that is indicative of genetic material of first microbiota obtained from a gastrointestinal tract of an animal at specified intervals in the lifecycle of the animal; and
providing the classification in a computer readable data structure for display on a graphical user interface.
Dependent claims 8, 34, and 43: processing gastrointestinal samples obtained from the animal using an intestinal flora chip, the intestinal flora chip being configured to generate genetic information that is indicative of a predetermined subset of the total microbiota obtained from the gastrointestinal tract of the animal.
The claims also include non-abstract computing elements. For example, independent claims 7, 33, and 42 include an engine, which is interpreted as software; independent claim 33 includes a system comprising: hardware processing circuitry; a hardware memory, comprising instructions that when executed configure the hardware processing circuitry to perform operations; and independent claim 42 includes a non-transitory computer readable storage medium comprising instructions that when executed configure hardware processing circuitry to perform operations.
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 “processing” samples and “obtaining” data, and to data outputting, such as “providing” a classification or model, perform functions of collecting 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)).
Further steps directed to additional non-abstract 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 engines (i.e., software) that are used for determining a model and selecting a subset of the microbiota which does not integrate the judicial exceptions because the models are generically recited and are nothing but mathematical operations. Thus, the limitations only generically link the use of the judicial exceptions to the technological environment of a computer.
The specification as published discloses that farm animal performance and pathogen risks and can be improved through the identification of relationships between populations of GIT microbiota and by techniques that link animal nutrition to GIT microbiota based on these identified relationships at [0023], 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 noted that if the claims were amended to recite the features disclosed at [0037] in the specification as published (“a specially fabricated DNA or RNA microarray chip (hereinafter, “array chip”) that is configured to selectively process a biological sample based on a set of predetermined biomarkers”), based on the predetermined subset of the total microbiota selected as recited in claims 9, 35, and 44 [0046; 0054; 0058], the claims may then recite an additional element which provides a practical application to the claims by incorporating the subset of selected microbiota into “an intestinal flora chip” and using that chip to generate the subsequent data which is processed by the first microbiota model.
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 prior art to Kim et al. (Asian-Australasian Journal of Animal Sciences, 2017, 30(11), p.1515-1528; newly cited) discloses that phylogenetic microarrays, which are 16S rRNA gene chips composed of a large number of oligonucleotide probes to detect microbes, have been widely used to investigate the human and ruminal GI microbiome, demonstrating that “processing gastrointestinal samples obtained from the animal using an intestinal flora chip, the intestinal flora chip being configured to generate genetic information that is indicative of a predetermined subset of the total microbiota obtained from the gastrointestinal tract of the animal” is a data gathering element that is routine, well-understood and conventional in the art. Said portions of the prior art are, for example, p. 1520, PHYLOGENETIC MICROARRAY. Further, 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 claims 7, 33, and 42 and those claims dependent therefrom, the computer-related elements or the general purpose computer do not rise to the level of significantly more than the judicial exception. 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 (Alice Corp., 573 U.S. at 225-26, 110 USPQ2d at 1984; see MPEP 2106.05(A)). The specification also notes that computer processors and systems, as example, are commercially available or widely used at [0089; 0097]. 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.
Claim Rejections - 35 USC § 102
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claims 7-15 and 33-50 are rejected under 35 U.S.C. 102(a)(1) and (a)(2) as being anticipated by McNulty et al. (US 20170220731; newly cited; corresponds to WO 2016/007544, cited on the Nov 20 2022 IDS).
Claim 7 discloses a method. Claim 33 discloses a system comprising: hardware processing circuitry; a hardware memory, comprising instructions that when executed configure the hardware processing circuitry to perform operations. Claim 42 discloses a non-transitory computer readable storage medium comprising instructions that when executed configure hardware processing circuitry to perform operations.
McNulty discloses the use of a gut microbiome as a predictor of animal growth or health (title). McNulty teaches a system comprising at least one processor (i.e., hardware processing circuitry), memory that includes non-volatile computer system readable media, and computer system executable instructions, where program modules carry out the methodologies of the disclosed technology [0081-0089].
The method of claim 7 and the operations of claims 33 and 42 comprise:
obtaining first data that is indicative of genetic material of first microbiota obtained from a gastrointestinal tract of an animal at specified intervals in the lifecycle of the animal;
McNulty teaches determining microbial nucleic acid features (i.e., first data) in a subject [0095], where the nucleic acid sample of the subject refers to a plurality of heterogeneous nucleic acids produced by a subject's gut microbiota [0117]. McNulty teaches examining specific patterns of microbial abundance in the early life of animals [0037], where subjects are examined over their life cycle [0067; 0076]. McNulty teaches determining the development of microbiota over time ([0028; 0058; 0063-0064; 0066-0070]; FIG. 2).
determining, based on the first data and using a first microbiota model engine, a model for the animal, the first microbiota model engine trained using supervised learning and data obtained from gastrointestinal tracts of two or more animals; and
McNulty teaches applying a predictive model (i.e., a first microbiota model engine) to the nucleic acid features of the particular subject (i.e., first data) to predict at least one particular characteristic of that particular subject (i.e., a model) [0015]. McNulty teaches defining a predictive model at the at least one processor, the predictive model defining a relationship between the discriminatory microbial nucleic acid features of gut microbiota of a plurality of subjects and the characteristic [0015; 0095-0107], where the model/classifier is trained on a training set [0090; 0097]. McNulty teaches that the learning application is a supervised learning algorithm used to build predictive models [0091]. See also FIG. 1A, 1B, and 1C.
providing the model in a computer readable data structure for display on a graphical user interface.
McNulty teaches generating a predictive result, at the at least one processor, for display, the predictive result comprising at least one particular characteristic for the particular subject [0015]. McNulty also teaches that the computer system includes devices that enable a user to interact with the computer system [0089]. It is noted that “for display on a graphical user interface” is interpreted as an intended use of the provided model and is not required by the metes and bounds of the claims.
Regarding claims 8, 34, and 43, McNulty teaches claims 7, 33, and 42 as described above. Claims 8, 34, and 43 further add processing gastrointestinal samples obtained from the animal using an intestinal flora chip, the intestinal flora chip being configured to generate genetic information that is indicative of a predetermined subset of the total microbiota obtained from the gastrointestinal tract of the animal.
McNulty teaches scanners capable of generating array data that captures the intensity of each position on the array for use in producing a microbial nucleic acid feature, where the scanner may be a GeneChip or PhyloChip Scanner [0054-0055]. McNulty teaches that the array-based data acquisition component also comprises an array (i.e., a chip), the array comprising multiple oligonucleotide probes arrayed onto a solid surface, where the array may be commercially available or custom [0055]. McNulty teaches using phylogenetic arrays with probes designed to be sensitive to only a specified branch of a taxonomic tree (i.e., an intestinal flora chip being configured to generate genetic information that is indicative of a predetermined subset of the total microbiota obtained from the gastrointestinal tract of the animal) [0056].
Regarding claims 9, 35, and 44, McNulty teaches claims 7-8, 33-34, and 42-43 as described above. Claims 9, 35, and 44 further add that the predetermined subset of the total microbiota is selected using a second microbiota model engine, the second microbiota model engine being trained using the total microbiota obtained from gastrointestinal tracts of a second set of two or more animals.
McNulty teaches identifying a discriminatory microbial nucleic acid feature (i.e., selecting the predetermined subset) with a database comprising: (i) a first data set, the first data set comprising a plurality of microbial nucleic acid features for each of a plurality of subjects, wherein (1) each of the plurality of subjects are of the same species, and (2) there is inter-subject variability in the nucleic acid features; (ii) a second data set, the second data set comprising at least one measurement of at least one characteristic for each subject from step (i) and a defining relationship between each characteristic measurement and the subject, wherein there is inter-subject variability in the measured characteristic of each subject; (b) at least one processor; and (c) a learning application (i.e., second microbiota model engine) executed by the at least one processor to: (i) process the first data set and the second data set to identify inter-subject variation in the nucleic acid features of the first data set that relate to inter-subject variation in the characteristic measurements in the second data set; and (ii) identify microbial nucleic acid features that positively or negatively discriminate a characteristic [0003; 0042-0053; 0058-0080]. McNulty teaches performing bacterial 16S rRNA and fungal ITS region amplification and sequencing as well as viral and protist sequencing to determine taxonomy of a sample (i.e., total microbiota) [0044]. McNulty teaches a validation data (i.e., a second set of two or more animals) [0097].
Regarding claims 10, 36, and 45, McNulty teaches claims 7, 33, and 42 as described above. Claims 10, 36, and 45 further add generating a prediction of a nutritional content of the animal, the nutrient content being indicative of the presence or deficiency of one or more nutrients.
McNulty teaches generating a predictive result from the predictive model, the predictive result comprising at least one particular characteristic for the particular subject [0015]. McNulty teaches that a characteristic refers to any measurable aspect of a subject's performance and health at a point in time after the collection of the fecal sample from which the first data set was produced, where the characteristics may be nutritional and include nutrient utilization from feed, blood glucose concentration, blood triglyceride concentration, and serum IGF-1 concentration [0075], each of which read on a nutritional content of an animal as instantly claimed. Therefore, it is considered that McNulty fairly teaches a model for predicting nutritional content of the animal. The limitation “the nutrient content being indicative of the presence or deficiency of one or more nutrients” is interpreted as an intended interpretation of the amount of the nutrient content being predicted by the end user, and not a notification or result that is required to be produced. It is noted that McNulty teaches predicting the feed efficiency of the subject to make interventions for animals with low feed efficiencies, so as to increase feed efficiency by including different feed rations or administration of different supplements [0102-0103], which reads on addressing a deficiency of a nutrient.
Regarding claims 11, 37, and 46, McNulty teaches claims 7, 33, and 42 as described above. Claims 11, 37, and 46 further add generating a prediction of a body mass of the animal.
McNulty teaches generating a predictive result from the predictive model, the predictive result comprising at least one particular characteristic for the particular subject [0015]. McNulty teaches that a characteristic refers to any measurable aspect of a subject's performance and health at a point in time after the collection of the fecal sample from which the first data set was produced, where the characteristics may be growth, body composition, weight, and body mass [0075; 0100-0101].
Regarding claims 12, 38, and 47, McNulty teaches claims 7, 33, and 42 as described above. Claims 12, 38, and 47 further add generating a prediction of a concentration of second microbiota in gastrointestinal tract of the animal; and determining, based on the prediction, an adjustment to a feed product or other nutrient to provide to the animal to improve at least one of a body mass of the animal or a food safety risk of the animal.
McNulty teaches generating a predictive result from the predictive model, the predictive result comprising at least one particular characteristic for the particular subject [0015]. McNulty teaches that a characteristic refers to any measurable aspect of a subject's performance and health at a point in time after the collection of the fecal sample from which the first data set was produced, where the characteristics may be a characteristic measurement is a measurement of one or more of disease duration, disease severity, disease frequency, morbidity, mortality, resistance to disease (e.g. to enteropathogen infection), susceptibility to disease including but not limited to infectious diseases, pathogen carriage, pathogen shedding of the offspring, pathogen specific antibody count, and gut inflammation [0075]. McNulty teaches a predictive model may be used to identify subjects at risk for a particular disease or disorder, including pathogen carriage, pathogen shedding, and pathogen specific antibody count [0104], each of which rad on prediction of a concentration of a second microbiota as instantly claimed. McNulty teaches that interventions may be ordered for susceptible animals to reduce incidence of disease or disorders, including different feed rations, different housing conditions, administration of different supplements and/or medications, administration of one or more vaccines, or a combination thereof [0104], which would either improve at least one of a body mass or a food safety risk of the animal, as instantly claimed, by reducing the presence of the pathogen in the animal.
Regarding claims 13, 39, and 48, McNulty teaches claims 7, 33, and 42 as described above. Claims 13, 39, and 48 further add generating a prediction of a concentration of second microbiota in gastrointestinal tract of the animal; and determining, based on the prediction, an adjustment to a feed product provided to the animal to adjust the microbiota of the animal, wherein the adjustment is selected to improve the likelihood that an offspring of the animal will have an specified body mass or microbiota concentration.
McNulty teaches generating a predictive result from the predictive model, the predictive result comprising at least one particular characteristic for the particular subject [0015]. McNulty teaches that a characteristic refers to any measurable aspect of a subject's performance and health at a point in time after the collection of the fecal sample from which the first data set was produced, where the characteristics may be a characteristic measurement is a measurement of one or more of disease duration, disease severity, disease frequency, morbidity, mortality, resistance to disease (e.g. to enteropathogen infection), susceptibility to disease including but not limited to infectious diseases, pathogen carriage, pathogen shedding of the offspring, pathogen specific antibody count, and gut inflammation [0075]. McNulty teaches a predictive model may be used to identify subjects at risk for a particular disease or disorder, including pathogen carriage, pathogen shedding, and pathogen specific antibody count [0104], each of which rad on prediction of a concentration of a second microbiota as instantly claimed. McNulty also teaches a predictive model that relates to performance of the offspring of the subjects for whom nucleic acid features are determined, where offspring performance relates to growth and body composition characteristics of the offspring and susceptibility to one or more diseases and the manifestations of a disease in the offspring [0105]. McNulty teaches that interventions may be ordered for susceptible animals to reduce incidence of disease or disorders, including different feed rations, different housing conditions, administration of different supplements and/or medications, administration of one or more vaccines, or a combination thereof [0104], which would result in an improvement of the likelihood that an offspring of the animal will have an specified body mass or microbiota concentration as instantly claimed because the interventions would be targeting the pathogenic bacteria that put the offspring at risk.
Regarding claims 14, 40, and 49, McNulty teaches claims 7, 33, and 42 as described above. Claims 14, 40, and 49 further add generating a prediction of a concentration of second microbiota in gastrointestinal tract of the animal; and determine, based on the prediction, a likelihood that the animal is food safety risk.
McNulty teaches generating a predictive result from the predictive model, the predictive result comprising at least one particular characteristic for the particular subject [0015]. McNulty teaches that a characteristic refers to any measurable aspect of a subject's performance and health at a point in time after the collection of the fecal sample from which the first data set was produced, where the characteristics may be a characteristic measurement is a measurement of one or more of disease duration, disease severity, disease frequency, morbidity, mortality, resistance to disease (e.g. to enteropathogen infection), susceptibility to disease including but not limited to infectious diseases, pathogen carriage, pathogen shedding of the offspring, pathogen specific antibody count, and gut inflammation [0075]. McNulty teaches a predictive model may be used to identify subjects at risk for a particular disease (i.e., a likelihood that the animal is a food safety risk) or disorder, including pathogen carriage, pathogen shedding, and pathogen specific antibody count [0104], each of which rad on prediction of a concentration of a second microbiota as instantly claimed.
Regarding claims 15, 41, and 50, McNulty teaches claims 7, 33, and 42 as described above. Claims 15, 41, and 50 further add generating a prediction of a concentration of second microbiota in gastrointestinal tract of the animal; and identifying a feed product that is associated with the second microbiota; and determining, based on the model and the identifying, an adjustment to an additive or nutrient of the feed product to increase or decrease a concentration of the second microbiota in the animal.
McNulty teaches generating a predictive result from the predictive model, the predictive result comprising at least one particular characteristic for the particular subject [0015]. McNulty teaches that a characteristic refers to any measurable aspect of a subject's performance and health at a point in time after the collection of the fecal sample from which the first data set was produced, where the characteristics may be a characteristic measurement is a measurement of one or more of disease duration, disease severity, disease frequency, morbidity, mortality, resistance to disease (e.g. to enteropathogen infection), susceptibility to disease including but not limited to infectious diseases, pathogen carriage, pathogen shedding of the offspring, pathogen specific antibody count, and gut inflammation [0075]. McNulty teaches a predictive model may be used to identify subjects at risk for a particular disease or disorder, including pathogen carriage, pathogen shedding, and pathogen specific antibody count [0104], each of which rad on prediction of a concentration of a second microbiota as instantly claimed. McNulty teaches that interventions may be ordered for susceptible animals to reduce incidence of disease or disorders, including different feed rations, different housing conditions, administration of different supplements and/or medications, administration of one or more vaccines, or a combination thereof [0104]. It is considered at least that medications and vaccines encompass treatments that would reduce the concentration of the pathogen, or second microbiota, as instantly claimed.
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.
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Claims 7-15 and 33-50 are provisionally rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1, 6, and 8-10 of copending Application No. 19/165,231 in view of McNulty et al. (US 20170220731; newly cited; corresponds to WO 2016/007544, cited on the Nov 20 2022 IDS).
This is a provisional nonstatutory double patenting rejection.
Reference claims 1, 6 (animal life stage), and 8 (trained models) disclose the limitations of instant claims 7, 33, and 42 except for “providing the classification in a computer readable data structure for display on a graphical user interface” and the system components of claim 33 and the non-transitory computer readable storage medium of claim 42.
However, McNulty discloses the use of a gut microbiome as a predictor of animal growth or health (title). McNulty teaches a system comprising at least one processor (i.e., hardware processing circuitry), memory that includes non-volatile computer system readable media, and computer system executable instructions, where program modules carry out the methodologies of the disclosed technology [0081-0089]. McNulty teaches generating a predictive result, at the at least one processor, for display, the predictive result comprising at least one particular characteristic for the particular subject [0015]. McNulty also teaches that the computer system includes devices that enable a user to interact with the computer system [0089]. It is noted that “for display on a graphical user interface” is interpreted as an intended use of the provided model and is not required by the metes and bounds of the claims.
The reference application does not disclose instant claims 8, 34, and 43.
However, McNulty teaches scanners capable of generating array data that captures the intensity of each position on the array for use in producing a microbial nucleic acid feature, where the scanner may be a GeneChip or PhyloChip Scanner [0054-0055]. McNulty teaches that the array-based data acquisition component also comprises an array (i.e., a chip), the array comprising multiple oligonucleotide probes arrayed onto a solid surface, where the array may be commercially available or custom [0055]. McNulty teaches using phylogenetic arrays with probes designed to be sensitive to only a specified branch of a taxonomic tree (i.e., an intestinal flora chip being configured to generate genetic information that is indicative of a predetermined subset of the total microbiota obtained from the gastrointestinal tract of the animal) [0056].
The reference application does not disclose instant claims 9, 35, and 44.
However, McNulty teaches identifying a discriminatory microbial nucleic acid feature (i.e., selecting the predetermined subset) with a database comprising: (i) a first data set, the first data set comprising a plurality of microbial nucleic acid features for each of a plurality of subjects, wherein (1) each of the plurality of subjects are of the same species, and (2) there is inter-subject variability in the nucleic acid features; (ii) a second data set, the second data set comprising at least one measurement of at least one characteristic for each subject from step (i) and a defining relationship between each characteristic measurement and the subject, wherein there is inter-subject variability in the measured characteristic of each subject; (b) at least one processor; and (c) a learning application (i.e., second microbiota model engine) executed by the at least one processor to: (i) process the first data set and the second data set to identify inter-subject variation in the nucleic acid features of the first data set that relate to inter-subject variation in the characteristic measurements in the second data set; and (ii) identify microbial nucleic acid features that positively or negatively discriminate a characteristic [0003; 0042-0053; 0058-0080]. McNulty teaches performing bacterial 16S rRNA and fungal ITS region amplification and sequencing as well as viral and protist sequencing to determine taxonomy of a sample (i.e., total microbiota) [0044].
Reference claims 9-10 disclose the limitations of instant 10-11, 36-37, and 45-46.
The reference application does not disclose the limitations of claims 12-15, 38-41, and 47-50.
However, regarding claims 12, 38, and 47, McNulty teaches generating a predictive result from the predictive model, the predictive result comprising at least one particular characteristic for the particular subject [0015]. McNulty teaches that a characteristic refers to any measurable aspect of a subject's performance and health at a point in time after the collection of the fecal sample from which the first data set was produced, where the characteristics may be a characteristic measurement is a measurement of one or more of disease duration, disease severity, disease frequency, morbidity, mortality, resistance to disease (e.g. to enteropathogen infection), susceptibility to disease including but not limited to infectious diseases, pathogen carriage, pathogen shedding of the offspring, pathogen specific antibody count, and gut inflammation [0075]. McNulty teaches a predictive model may be used to identify subjects at risk for a particular disease or disorder, including pathogen carriage, pathogen shedding, and pathogen specific antibody count [0104], each of which rad on prediction of a concentration of a second microbiota as instantly claimed. McNulty teaches that interventions may be ordered for susceptible animals to reduce incidence of disease or disorders, including different feed rations, different housing conditions, administration of different supplements and/or medications, administration of one or more vaccines, or a combination thereof [0104], which would either improve at least one of a body mass or a food safety risk of the animal, as instantly claimed, by reducing the presence of the pathogen in the animal.
However, regarding claims 13, 39, and 48, McNulty teaches generating a predictive result from the predictive model, the predictive result comprising at least one particular characteristic for the particular subject [0015]. McNulty teaches that a characteristic refers to any measurable aspect of a subject's performance and health at a point in time after the collection of the fecal sample from which the first data set was produced, where the characteristics may be a characteristic measurement is a measurement of one or more of disease duration, disease severity, disease frequency, morbidity, mortality, resistance to disease (e.g. to enteropathogen infection), susceptibility to disease including but not limited to infectious diseases, pathogen carriage, pathogen shedding of the offspring, pathogen specific antibody count, and gut inflammation [0075]. McNulty teaches a predictive model may be used to identify subjects at risk for a particular disease or disorder, including pathogen carriage, pathogen shedding, and pathogen specific antibody count [0104], each of which rad on prediction of a concentration of a second microbiota as instantly claimed. McNulty also teaches a predictive model that relates to performance of the offspring of the subjects for whom nucleic acid features are determined, where offspring performance relates to growth and body composition characteristics of the offspring and susceptibility to one or more diseases and the manifestations of a disease in the offspring [0105]. McNulty teaches that interventions may be ordered for susceptible animals to reduce incidence of disease or disorders, including different feed rations, different housing conditions, administration of different supplements and/or medications, administration of one or more vaccines, or a combination thereof [0104], which would result in an improvement of the likelihood that an offspring of the animal will have an specified body mass or microbiota concentration as instantly claimed because the interventions would be targeting the pathogenic bacteria that put the offspring at risk.
However, regarding claims 14, 40, and 49, McNulty teaches generating a predictive result from the predictive model, the predictive result comprising at least one particular characteristic for the particular subject [0015]. McNulty teaches that a characteristic refers to any measurable aspect of a subject's performance and health at a point in time after the collection of the fecal sample from which the first data set was produced, where the characteristics may be a characteristic measurement is a measurement of one or more of disease duration, disease severity, disease frequency, morbidity, mortality, resistance to disease (e.g. to enteropathogen infection), susceptibility to disease including but not limited to infectious diseases, pathogen carriage, pathogen shedding of the offspring, pathogen specific antibody count, and gut inflammation [0075]. McNulty teaches a predictive model may be used to identify subjects at risk for a particular disease (i.e., a likelihood that the animal is a food safety risk) or disorder, including pathogen carriage, pathogen shedding, and pathogen specific antibody count [0104], each of which rad on prediction of a concentration of a second microbiota as instantly claimed.
However, regarding claims 15, 41, and 50, McNulty teaches generating a predictive result from the predictive model, the predictive result comprising at least one particular characteristic for the particular subject [0015]. McNulty teaches that a characteristic refers to any measurable aspect of a subject's performance and health at a point in time after the collection of the fecal sample from which the first data set was produced, where the characteristics may be a characteristic measurement is a measurement of one or more of disease duration, disease severity, disease frequency, morbidity, mortality, resistance to disease (e.g. to enteropathogen infection), susceptibility to disease including but not limited to infectious diseases, pathogen carriage, pathogen shedding of the offspring, pathogen specific antibody count, and gut inflammation [0075]. McNulty teaches a predictive model may be used to identify subjects at risk for a particular disease or disorder, including pathogen carriage, pathogen shedding, and pathogen specific antibody count [0104], each of which rad on prediction of a concentration of a second microbiota as instantly claimed. McNulty teaches that interventions may be ordered for susceptible animals to reduce incidence of disease or disorders, including different feed rations, different housing conditions, administration of different supplements and/or medications, administration of one or more vaccines, or a combination thereof [0104]. It is considered at least that medications and vaccines encompass treatments that would reduce the concentration of the pathogen, or second microbiota, as instantly claimed.
Regarding claims 7-15 and 33-50, 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 method of the reference application and McNulty because both references disclose methods for predicting the future performance of an animal based on the animal’s microbiome. The motivation for including the features of McNulty in the claimed invention of the reference application would have been to make predictions of future characteristics (e.g., probability of specific parameters of health and disease in the future) based on the structure or function of gut microbiota, as taught by McNulty [0002].
Conclusion
No claims are allowed.
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/JANNA NICOLE SCHULTZHAUS/Examiner, Art Unit 1685
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