DETAILED ACTION
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Herein “the previous Office action” refers to the Non-Final Rejection filed 12/19/2024.
Amendments Received
Amendments to the claims were received on 4/18/2025.
Priority
As detailed on the Filing Receipt filed 6/11/2024, the instant application claims priority to as early as 1/19/2018. At this point in prosecution, the claims are accorded the earliest claimed priority date.
Information Disclosure Statement
The Information Disclosure Statement filed on 2/12/2025 is in compliance with the provisions of 37 CFR 1.97 and has been considered in full. A signed copy of the IDS is included with this Office Action.
Claim Status
Claims 1-9, 13, 17 and 24-46 are canceled.
Claims 10-12, 14-16 and 18-23 are pending, and under examination.
Withdrawn Objections/Rejections
The rejection of claims 1-3, 10-12, 14-16, 18-23, 26, 31, 35 and 40-42 under 35 USC § 112(b), as being indefinite, is hereby withdrawn in view of Applicant’s amendment of the claims to remove indefinite language and cancelation of claims 1-3, 26, 31, 35 and 40-42.
The rejection of claims 1-3, 10-12, 14-16, 18-23, 26, 31, 35 and 40-42 under 35 USC § 112(a), as failing to comply with the written description requirement, is hereby withdrawn in view of Applicant’s amendment of the claims to remove unsupported language and cancelation of claims 1-3, 26, 31, 35 and 40-42.
The rejection of claims 1-3, 26, 35 and 40-42 under 35 USC § 103, as being unpatentable over Martin, in view of Al-Murrani, Struck and Lopez-Giacoman, is hereby withdrawn in view of Applicant’s cancelation of the claims.
The rejection of claim 31 under 35 USC § 103, as being unpatentable over Martin, in view of Al-Murrani, Struck, Lopez-Giacoman and Lipton, is hereby withdrawn in view of Applicant’s cancelation of the claim.
Claim Interpretation
The claims in this application are given their broadest reasonable interpretation using
the plain meaning of the claim language in light of the specification as it would be understood
by one of ordinary skill in the art. This section documents the Examiner’s interpretation of
certain claim elements as contingent limitations under this standard.
“The broadest reasonable interpretation of a method (or process) claim having
contingent limitations requires only those steps that must be performed and does not include
steps that are not required to be performed because the condition(s) precedent are not met”
(MPEP 2111.04 § II).
Claim 10 recites the limitations of “determining… whether the feline is at risk of contracting developing CKD in the plurality of future” (line 9-11) and “administering a treatment diet responsive to determining [that] the feline is at risk of developing CKD” (line 30-31).
The latter limitation requires performance of an active step when a condition precedent
is met (i.e., when it is determined that the feline is at risk), and is therefore considered as a contingent limitation. For purposes of prosecution, contingent limitations are not included in the broadest reasonable interpretation of the claims (MPEP 2111.04 § II).
Claim Objections
Claim 10 is objected to because of the following informalities:
With respect to claim 10, the recited “at least one of the one or more biomarkers comprise” (lines 5-6) should read, e.g., “at least one of the one or more biomarkers comprises”.
Additionally, the recited “a likelihood of the feline will develop CKD in future” (lines 12-14) should read, e.g., “a likelihood that the feline will develop CKD in the future”.
Additionally, the recited “developing CKD in future” (lines 21 and 31-32) should read, e.g., “developing CKD in the future”.
Additionally, the carriage return between “determined” (line 23) and “by applying” (line 26) should be removed.
Additionally, the recited “determining the feline is at risk” (line 31) should read, e.g., “determining that the feline is at risk”.
Additionally, the recited “a polyunsaturated fatty acid supplement diet having a PUFA level” (line 37) should read, e.g., “a polyunsaturated fatty acid (PUFA) supplement diet having a PUFA level”.
Appropriate correction is required.
Claim Rejections - 35 USC § 101
35 USC § 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 10-12, 14-16 and 18-23 are rejected under 35 USC § 101 because the claimed invention is directed to an abstract idea and a natural phenomenon without significantly more (i.e., non-statutory subject matter). This rejection is maintained from the previous Office action, and has been revised to address the amended claims (filed 4/18/2025).
"Claims directed to nothing more than judicial exceptions, natural phenomena, and laws of nature are not eligible for patent protection" (MPEP 2106.04 § I).
Abstract ideas include mathematical concepts (including formulas, equations and calculations), and procedures for evaluating, analyzing or organizing information, which are a type of mental process (MPEP 2106.04(a)(2)). Natural phenomena include principles, relations, and products that are naturally occurring or do not have markedly different characteristics compared to what occurs in nature (MPEP 2106.04(b)).
The claims as a whole, considering all claim elements both individually and in combination, do not amount to significantly more than an abstract idea and a natural phenomenon.
Step 1: The Four Categories of Statutory Subject Matter (MPEP 2106.03)
The claims are directed to methods (claims 10-12) and computer systems (claims 14-16 and 18-23), which fall under categories of statutory subject matter.
Step 2A, Prong One: Whether the Claims Set Forth or Describe a Judicial Exception (MPEP 2106.04 § II.A.1)
‘Mathematical concepts’ are relationships between variables and numbers, numerical formulas or equations, or acts of calculation, which need not be expressed in mathematical symbols (MPEP 2106.04(a)(2) § I). The claims recite elements that encompass mathematical concepts at least under their broadest reasonable interpretation, including:
“executing a machine learning algorithm to transform… input level[s] of… biomarker[s] and… age… to determine a probability score” (claim 10), i.e., evaluating said algorithm for said input to calculate said score;
“a training dataset” (claim 10), i.e., a set of values for optimizing an algorithm;
“[a] threshold value is determined by applying a linear discriminant analysis to… measurements of… biomarkers” (claim 10), e.g., calculating said value from said measurements using mathematical functions; and
“comparing the probability score with a threshold value” (claim 10), e.g., evaluating a mathematical relationship (whether A ≥ B).
Further claim elements delimit claimed embodiments of the above mathematical concepts, including:
“the training dataset comprising… information relating to… biomarkers and ages from a first set of sample felines that have been diagnosed with CKD and… biomarkers and ages from a second set of sample felines that have not been diagnosed with CKD” (claim 10);
“the machine learning algorithm is developed using a supervised training algorithm “ (claim 14);
“the machine learning algorithm is developed using an unsupervised training algorithm “ (claim 15);
The… input level[s] of the… biomarkers comprise[] sequential measurements… measured at different time points” (claim 16);
“the training dataset is stratified into two or more folds for cross-validation” (claim 18);
“the training dataset is filtered by… inclusion or exclusion criteria” (claim 19);
“the machine learning algorithm is based on… logistic regression, artificial neural network… recurrent neural network (RNN), K-nearest neighbor (KNN), Naïve Bayes, support vector machine… random forest, or AdaBoost” (claim 20);
“the machine learning algorithm is based on KNN with dynamic time-warping” (claim 21);
“the machine learning algorithm is based on RNN with long short-term memory” (claim 22); and
“the machine learning algorithm is based on a regularization algorithm comprising 5% or more dropout” (claim 22).
Thus, the claims recite elements encompassing acts of calculation, mathematical functions, and a mathematical relationship (i.e., mathematical concepts).
‘Mental processes’ are processes that can be performed in the human mind at least with use of a physical aid, e.g., a slide rule or pen and paper (MPEP 2106.04(a)(2) § III). The claims recite steps of evaluating information that are practicably performable in the human mind, including the following:
“determining, based on the probability score, whether the feline is at risk of developing CKD in [the] future” (claim 10), i.e., making a corresponding conclusion.
Thus, the claims recite elements encompassing mental processes.
Mathematical concepts and mental processes constitute two enumerated groupings of abstract ideas (MPEP 2106.04(a)(2) §§ I and III). Hence, the claims recite elements that, individually and in combination, constitute an abstract idea.
Additionally, the claims require analysis of “level[s] of… biomarker[s] from [a] feline and an age of the feline” (claim 10), i.e., naturally occurring properties, to “determine a probability score indicating a likelihood [that] the feline will develop CKD in [the] future” (claim 10) and thereby “determine… whether the feline is at risk of developing CKD in [the] future” (claim 10). This determination step relies upon natural correlations, resulting from feline biological processes, between expression of particular biomarker values and progression/development of chronic kidney disease in felines.
Further claim elements delimit the particular biomarkers and natural correlations relied upon, including:
“at least one of a urine specific gravity level, a creatinine level, a urine protein level, a blood urea nitrogen (BUN) or urea level, a white blood cell count (WBC), or urine pH” (claim 10).
Hence, the claims recite elements that constitute a natural phenomenon.
The claims must therefore be examined further to determine whether they integrate these judicial exceptions into a practical application (MPEP 2106.04(d)).
Step 2A, Prong Two: Whether the Claims Contain Additional Elements that Integrate the Judicial Exception(s) into a Practical Application (MPEP 2106.04 § II.A.2)
The claims recite additional elements that require performance of method steps using computer hardware, including:
“determining at least one input level of one or more biomarkers from the feline and an age of the feline” (claim 10) by “receiving the at least one input level… from a remote second system via a communication device” (claim 12), i.e., receiving electronic data over a network, wherein:
“the… input level[s]… comprise[] sequential measurements of the… biomarkers… at different time points” (claim 16);
“the machine learning algorithm comprises code” (claim 10), i.e., computer software instructions;
“displaying information… on a graphical user interface” (claim 11) , i.e., displaying electronic data using computer software; and
“transmitting information… to the remote second system via the communication device” (claim 12), e.g., transmitting electronic data over a network.
The specification states that “Various general purpose systems may be used with the application” (pg. 45), providing evidence that all claimed components of said computer systems may be general purpose computer hardware. The claims do not describe any specific computational steps by which computer hardware performs or carries out the judicial exceptions, nor do they provide any details of how specific structures of computer hardware are used to implement these functions. Thus, the elements directed to computer hardware amount to instructions to apply the judicial exceptions using generic computer hardware.
Processes of receiving, transmitting and displaying electronic data are functions considered to be coextensive with a general-purpose computer (see MPEP 2106.05(d) and EON Corp. IP Holdings LLC v. AT&T Mobility LLC, 785 F.3d 616, 114 USPQ2d 1711 (Fed. Cir. 2015)). Thus, the steps directed to these functions similarly amount to instructions to apply the judicial exceptions using generic computer hardware.
The claims state nothing more than that computer hardware performs the functions
that constitute the judicial exceptions. Mere instructions to apply judicial exceptions using generic computer hardware are insufficient to integrate the judicial exceptions into a practical application (MPEP 2106.04(d) § I and 2106.05(f)), therefore the claims do not integrate the judicial exceptions into a practical application.
Additionally, the recited step of “determining” gathers data required for performance of claimed functions. Necessary data gathering is considered an insignificant extra-solutional activity, and as such insufficient to integrate judicial exceptions into a practical application (MPEP 2106.05(g)).
No further additional elements are recited.
When the claims are considered as a whole: they do not improve the functioning of a computer, other technology, or technical field (MPEP 2106.04(d)(1) and 2106.05(a)); they do not apply the judicial exceptions to effect a particular treatment or prophylaxis for a disease or medical condition (MPEP 2106.04(d)(2)); they do not implement the judicial exceptions with, or in conjunction with, a particular machine (MPEP 2106.05(b)); they do not effect a transformation or reduction of a particular article to a different state or thing (MPEP 2106.05(c)); and they do not apply or use the judicial exceptions in some other meaningful way beyond linking the use of the judicial exceptions to a particular field of use (i.e., clinical management of feline chronic kidney disease; MPEP 2106.05(h)).
Hence, the claims do not integrate the judicial exceptions into a practical application. See MPEP 2106.04(d) § I.
Because the claims recite an abstract idea and a natural phenomenon, and do not integrate those judicial exceptions into a practical application, the claims are directed to those judicial exceptions. Claims that are directed to judicial exceptions must be examined further to determine whether the additional elements besides the judicial exceptions render the claims significantly more than the judicial exceptions. Additional elements besides the judicial exceptions may constitute inventive concepts that are sufficient to render the claims significantly more (MPEP 2106.05).
Step 2B: Whether the Claims Contain Additional Elements that Amount to an Inventive Concept (MPEP 2106.05)
As noted above, certain recited additional elements amount to insignificant extra-solution activity. Mere addition of insignificant extra-solution activity does not amount to an inventive concept that would render the claims significantly more than the recited judicial exceptions, particularly when the activities are well-understood or conventional (MPEP 2106.05(g)). The conventionality of recited additional elements that amount to insignificant extra-solution activity must be further considered.
Recited additional elements amounting to insignificant extra-solution activity encompass computer-implemented functions of receiving, transmitting and displaying electronic data, which the courts have recognized as coextensive with a general-purpose computer. See MPEP 2106.05(d) and the court decision in EON Corp. IP Holdings LLC v. AT&T Mobility LLC, 785 F.3d 616, 622, 114 USPQ2d 1711, 1714 (Fed. Cir. 2015). Well-understood, routine and conventional activity is insufficient to constitute an inventive concept that would render the claims significantly more than the abstract idea (MPEP 2106.05(d)).
Mere instructions to implement the judicial exceptions using computer hardware are likewise insufficient to constitute an inventive concept that would render the claims significantly more than the judicial exceptions (MPEP 2106.05(f)). Similarly, insignificant extra-solution activities are insufficient to constitute inventive concepts that would render the claims significantly more than the judicial exceptions (MPEP 2106.05(g)).
When the claims are considered as a whole: they do not improve the functioning of a computer, other technology, or technical field (MPEP 2106.04(d)(1) and 2106.05(a)); they do not implement the judicial exceptions with, or in conjunction with, a particular machine (MPEP 2106.05(b)); they do not effect a transformation or reduction of a particular article to a different state or thing (MPEP 2106.05(c)); they do not add specific limitations or steps, other than what is well-understood, routine and conventional activity in the field, that confine the claims to a particular useful application (MPEP 2106.05(d)); and they do not provide meaningful limitations beyond linking the use of the judicial exceptions to a particular field of use (i.e., clinical management of feline chronic kidney disease; MPEP 2106.05(h)).
Hence, the claims do not include additional elements that are sufficient to amount to significantly more than the judicial exceptions themselves. See MPEP 2106.05.
Conclusion: Claims are Directed to Non-statutory Subject Matter
For these reasons, the claims, when the limitations are considered individually and as a whole, are directed to judicial exceptions and lack an inventive concept. Hence, the claimed invention does not constitute significantly more than the judicial exceptions, so the claims are rejected under 35 USC § 101 as being directed to non-statutory subject matter.
Response to Arguments - Claim Rejections Under 35 USC § 101
In the Remarks, Applicant traverses the rejection under 35 USC § 101 and presents supporting arguments.
Applicant alleges that the claims are not directed to an abstract idea but are rather directed to a treatment method (pg. 7, para. 5). Applicant also cites Example 49 of the July 2024 Guidance Update on Patent Subject Matter Eligibility, Including on Artificial Intelligence (hereafter “July 2024 Guidance”) and submits that, similar to a claim exemplified therein, the instant claims recite administration of a particular treatment (pg. 8, paras. 3-4).
The recited administration step is considered contingent as written, and therefore not included in the broadest reasonable interpretation of the claims (see ‘Claim Interpretation’ section). Thus, the arguments are found unpersuasive and the rejection is maintained. Please note that, were the administration step rendered non-contingent by appropriate amendment, the presented arguments regarding provision of a particular treatment would likely be found persuasive.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 USC §§ 102 and 103 is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 USC § 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 USC § 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 USC § 102(b)(2)(C) for any potential 35 USC § 102(a)(2) prior art against the later invention.
Claims 10-12, 14-16 and 18-20 are rejected under 35 USC § 103 as being unpatentable over Martin et al (US 2015/0178639; published 7/25/2015), in view of Al-Murrani (US 2013/0344196; published 12/26/2013) and Struck et al (US 2012/0003752; published 1/5/2012). This rejection is maintained from the previous Office action, and has been revised to address the amended claims (filed 4/18/2025).
Claim 10 recites “[a] method for treating chronic kidney disease (CKD) in a feline in need thereof… comprising”:
a) “determining at least one input level of one or more biomarkers from the feline and an age of the feline, wherein”:
1. “at least one of the… biomarkers comprise[s] information relating to… a urine specific gravity level, a creatinine level, a urine protein level, a blood urea nitrogen (BUN) or urea level, a white blood cell count (WBC), or urine pH”;
b) “executing a machine learning algorithm to transform the at least one input level… to determine a probability score indicating a likelihood [that] the feline will develop CKD in [the] future, wherein”:
1. “the machine learning algorithm comprises code from a training dataset… comprising medical information relating to both a first plurality of biomarkers and ages from a first set of sample felines that have been diagnosed with CKD and a second plurality… from a second set of sample felines that have not been diagnosed with CKD”;
c) “determining, based on the probability score, whether the feline is at risk of developing CKD in [the] future by comparing the probability score with a threshold value, wherein”:
1. “the threshold value is determined by applying a linear discriminant analysis to… medical records of… felines… compris[ing] measurements of the… biomarkers”; and
d) “administering a treatment diet responsive to determining [that] the feline is at risk of developing CKD in [the] future, the treatment diet comprising at least one of” diets including levels within specific recited ranges of phosphorous, protein, sodium, potassium or polyunsaturated fatty acids, an anti-oxidant supplement diet, or a vitamin B supplement diet.
With respect to claim 10, Martin discloses “methods for correcting a data set and classifying the data set” (Abstract) comprising:
a) “receiv[ing] training data and test data” (pg. 3, para. 0025) which “may represent any experimental or otherwise obtained data from which a classification may be drawn, such as… phenotypic characteristics such as levels of any biologically significant analyte” (pg. 4, para. 0026) and “can be derived from a living organism” (pg. 4, para. 0030), wherein:
1. “data received… may represent… expression values… [such as] the amount of a molecule encoded by [a] gene, e.g., a[]… polypeptide” (pg. 4, para. 0026), i.e., protein, and “each data set comprises… samples… Sample types include… urine” (pg. 4, para. 0029), i.e., input data may comprise a urine protein level,
b) “an iterative approach to… class prediction”, wherein “the processor: transforms the test data set… and classif[ies] the elements in the transformed data set” (pg. 1, para. 0008), comprising:
1. “generat[ing] [the] classifier… by applying a machine learning technique” (pg. 1, para. 0008) and “generat[ing] a… classifier for [a] training dataset”, wherein “each data set may include data samples that each correspond to one of a plurality of biological sample classes” (pg. 4, para. 0028), e.g., a first set of samples and a second set of samples,
c) “output… a set of predicted classifications” (pg. 6, para. 0042) wherein “classes… can include… risk for a disease; [and] likelihood of recurrence of disease” (pg. 4 para. 0028), i.e., a probability score that indicates a likelihood that the subject will develop a disease in the future, and:
1. “The classification engine may use… linear discriminant analysis techniques” (pg. 5, para. 0032);
Martin does not disclose determining input level(s) of biomarkers from, and an age of, a feline; determining risk that a feline will develop CKD; determining a threshold value; comparing the probability scores with the threshold value; or administering a treatment diet comprising at least one of [the recited diets].
Al-Murrani discloses “a method for… prognosis of kidney disorder in a feline” (pg. 16, para. 0204), and teaches “utilizing at least one relevant biomarker isolated and measured from a biological test sample taken from such feline” (pg. 2, para. 0021), wherein “the level of expression of the one or more biomarkers… is normalized” (pg. 6, para. 0063). Al-Murrani exemplifies “acute or chronic abnormal loss of kidney function, such as renal failure” (pg. 10, para. 0157), i.e., CKD, and “measurements… including… urinary protein levels” (pg. 3, para. 0022).
Al-Murrani further teaches application comprising “predict[ing], detect[ing], and diagnos[ing] in such feline a change from a normal state to an abnormal state leading to a kidney disorder” (pg. 13, para. 0189), and teaches that their method is “predictive of a decline in renal function, as might otherwise be diagnosed at a later time” (pg. 3, para. 0023).
Additionally, Al-Murrani teaches “providing a kidney protective diet… to a feline having a kidney disorder as diagnosed or diagnosable by” i.e., responsive to, “the method[s]” (pg. 6, paras. 0079-0080), wherein “the kidney protective diet comprises one or more of the following modifications relative to a standard feline diet: Reduced phosphorus, Reduced levels of protein, Reduced sodium, Increased levels of omega-3 fatty acids”, i.e., polyunsaturated fatty acids, “Increased levels of B-complex vitamins, [and] Increased antioxidants” (pg. 6, paras. 0088-0094).
In particular, Al-Murrani exemplifies a treatment diet providing “from about 3.6 to about 7.9 g/100 kcal ME protein” (pg. 6, para. 0096), i.e., a protein level between about 36 to about 79g/1000 kcal. This disclosed range overlaps the claimed specific range of a protein level less than 70g/1000 kcal. Where prior art discloses a range which overlaps or approaches a claimed range, the prior art disclosure renders the claimed range as obvious (MPEP 2144.05 § I).
Additionally, Al-Murrani exemplifies a diet providing “from about 0.04 to about 0.17 g/100 kcal ME phosphorus” (pg. 6, para. 0096), i.e., a phosphorus level between about 0.4 to about 1.7 g/1000 kcal. This disclosed range falls within (i.e., overlaps) the claimed specific range of a phosphorus level between 0.1 g/1000 kcal and 20 g/1000 kcal.
Additionally, Al-Murrani exemplifies a diet providing “from about 0.008 to about 0.07 g/100 kcal ME sodium” (pg. 6, para. 0096), i.e., a sodium level between about 8 mg to about 70 mg/1000 kcal. This disclosed range falls within (i.e., overlaps) the claimed specific range of a sodium level between 0.1 mg/1000 kcal and 50 mg/1000 kcal.
Al-Murrani does not teach utilizing an age of the feline; determining a threshold value; or comparing with the threshold value.
Struck discusses “assays and in vitro methods for prediction of the progression of primary chronic kidney disease (CKD)” (Abstract) and teaches methods involving “comparing the level of [each] marker for [an] individual with a predetermined… optimal cut-off value” (pg. 4, para. 0065), i.e., threshold value. In particular, Struck teaches “the prediction of the progression… may be improved by… determining and using the level of… laboratory parameter[s]… selected from… creatinine… [or] proteinuria… [and] at least one clinical parameter… selected from… age” (pg. 3, para. 0039).
Struck exemplifies determination of cut-off values through “Kaplan-Meier curve analyses of… patients with CKD… stratified into two groups according to the median of [biomarker levels] at baseline” (pg. 8, para. 0111). Struck depicts their results in the form of “Kaplan-Meier plots (FIG. 11-14), where[in] the occurrence of events i.e. in the present case: kidney disease progression, over time is depicted for… the investigated patient population… separated in two subgroups each” (pg. 4, para. 0069). Struck specifies that “the term ‘patient’ as used herein refers to a living human or non-human organism… Thus, the methods and assays described herein are also applicable to both human and veterinary disease” (pg. 6, para. 0089).
The depicted Figs. 11-14 comprise average disease progression curves for patients having biomarker levels above, and below, determined threshold levels at baseline (Time 0). From baseline, each stepped progression curve displays likelihoods of disease progression in the future for patients having biomarker levels either above or below the threshold value.
In this way, Struck teaches utilizing an input level of age; determining a threshold value; and comparing with the threshold value.
With respect to claim 11, Martin discloses that “the processor… interface[s] with computer peripheral devices (e.g., a video display, a keyboard, computer mouse, etc)” (pg. 7, para. 0055) and “output[s] the predicted classifications to a display device” (pg. 6, para. 0042), e.g., via a graphical user interface.
With respect to claim 12, Martin discloses that “instructions may initially be borne on… a remote computer… The remote computer can… send the instructions… using a modem. A communications device local to a computing device… can receive the data… and place the data on a system bus for the processor” (pg. 7, para. 0057), and “output[ting] the predicted classifications to… another device in communication… across a network” (pg. 6, para. 0042), i.e., remote.
With respect to claim 14, Martin discloses “receiv[ing] a training data set and training class set, the training class set identifying a class associated with each of the elements in the training data set… [and] generat[ing] a first classifier… by applying a machine learning technique to the training data set and the training class set” (pg. 1, para. 0008). In other words, supervised learning.
With respect to claim 15, Martin discloses that “any machine-learning technique may be applied to generate the classifier” (pg. 5, para. 0041), wherein “In some implementations, no known classes for the data samples… are available, and thus… [a] class set is not provided to the classification engine” (pg. 4, para. 0025). In other words, unsupervised learning.
With respect to claim 16, Martins exemplifies “data sets… generated by collection of samples from the same population of patients at different times” (pg. 4, para. 0029).
With respect to claim 18, Martins discloses “divid[ing] a data set into a discovery/training set and a test/validation set” (pg. 1, para. 0004), and a “data pre-processing engine, which receives bulk data and splits the bulk data… into… data set[s]… [which] may be of the same… size[]” (pg. 4, para. 0031), i.e., folds. Martin further discloses performance of a “cross validation process in training” (pg. 8, para. 0070).
With respect to claim 19, Martin discloses “pool[ing] data from multiple sources to form a combined data set” (pg. 1, para. 0004), wherein “Data sets… can be collected from multiple sources… using different exclusion or inclusion criteria” (pg. 4, para. 0029).
With respect to claim 20, Martin discloses that “The classification engine may use any one or more known machine-learning algorithms… including… support vector machine techniques… Random Forest techniques, k-nearest neighbors techniques… neural network-based techniques” (pg. 5, para. 0032) and “linear classifiers such as… [a] naive Bayes classifier” (pg. 2, para. 0012).
An invention would have been obvious to one of ordinary skill in the art if some teaching in the prior art would have led that person to combine prior art reference teachings to arrive at the claimed invention. Before the effective filing date of the claimed invention, said practitioner would have utilized the biomarker analysis method of Martin to predict risk of contracting CKD in felines, as taught by Al-Murrani, because Al-Murrani teaches that analyzing biomarkers is an enabled method of predicting this risk (pg. 2, para. 0021; pg. 13, para. 0189).
Said practitioner would have achieved this combination of teachings by training a machine learning algorithm on biomarker data from felines diagnosed with CKD and not diagnosed with CKD, because Martin teaches that machine learning classifiers can be trained on associated biomarker data from grouped presence/absence samples to predict disease risk (pg. 4, paras. 0026 and 0028-29; pg. 5, para. 0031) and additionally one of ordinary skill in the art would have understood that a machine learning classifier must be trained on data representing the classes which it is intended to distinguish.
Additionally, said practitioner would have combined the disclosed dietary recommendations with the method of Martin because Al-Murrani teaches that these dietary recommendations have utility for treating felines at risk of kidney disease (pg. 6, paras. 0079-0080). Said practitioner would have had a reasonable expectation of success because Martin and Al-Murrani both discuss analyzing biomarker levels (e.g., urine protein levels) to prognose disease risk.
An invention would have been obvious to one of ordinary skill in the art if some teaching in the prior art would have led that person to combine prior art reference teachings to arrive at the claimed invention. Before the effective filing date of the claimed invention, said practitioner would have implemented the features of analyzing age; determining risk in a plurality of future time points; determining a threshold value; and comparing with the threshold value, as taught by Struck, with the biomarker analysis method taught by Martin, in view of Al-Murrani, because Struck teaches that prediction can be improved by additionally analyzing a clinical parameter such as age (pg. 3, para. 0039), and further teaches enabled techniques for analyzing biomarker levels and age to calculate disease progression curves and thresholds (pg. 8, para. 011; Figs. 11-14), which provide additional information relative to a single score. Said practitioner would have had a reasonable expectation of success because Martin and Struck both discuss analyzing biomarker levels (e.g., urine protein levels) to prognose disease risk.
In this way the disclosure of Martin, in view of Al-Murrani and Struck, makes obvious the limitations of claims 1-3, 10-12, 14-16 and 18-20. Thus, the invention is prima facie obvious.
Claim 21 is rejected under 35 USC § 103 as being unpatentable over Martin, in view of Al-Murrani and Struck, as applied to claim 1 above, and further in view of Oehmcke et al (Proceedings of the 2016 International Joint Conference on Neural Networks, pp. 2774-2781, IEEE Xplore; published 11/3/2016). This rejection is maintained from the previous Office action, and has been revised to address the amended claims (filed 4/18/2025).
With respect to claim 21, Martin discloses that “The classification engine may use any one or more known machine-learning algorithms… including… k-nearest neighbors techniques” (pg. 5, para. 0032) and “data sets… generated by collection of samples from the same population of patients at different times” (pg. 4, para. 0029), e.g., time series data. Martin does not disclose dynamic time warping.
Al-Murrani teaches “select[ing] among a number of algorithms for analyzing… data” (pg. 19, para. 0234). Al-Murrani does not teach dynamic time warping.
Struck teaches performance of various forms of “Statistical analysis” (pg. 9, para. 0126). Struck does not teach dynamic time warping.
Oehmcke discusses “analysis… [of] time series data”, and teaches that “consecutively missing values… would result in serious information loss if simply dropped from the dataset. To address this problem, we adapt the k-Nearest Neighbors algorithm… for multivariate time series imputation. The algorithm employs Dynamic Time Warping as [the] distance metric instead of point-wise distance measurements… Experimental results show accurate imputations… [and] our algorithm shows better results with consecutively missing values than state-of-the-art algorithms” (pg. 2774, Abstract).
An invention would have been obvious to one of ordinary skill in the art if some teaching in the prior art would have led that person to combine prior art reference teachings to arrive at the claimed invention. Before the effective filing date of the claimed invention, said practitioner would have implemented use of dynamic time warping, as taught by Oehmcke, within the biomarker analysis method taught by Martin, in view of Al-Murrani and Struck, because Oehmcke teaches that dynamic time warping allows use of datasets with missing values and is more effective than other methods of achieving the same (pg. 2774, Abstract). Said practitioner would have had a reasonable expectation of success because Martin and Oehmcke both discuss analysis of time series data using a k-nearest neighbors algorithm.
In this way the disclosure of Martin, in view of Al-Murrani, Struck and Oehmcke, makes obvious the limitations of claim 21. Thus, the invention is prima facie obvious.
Claims 22-23 are rejected under 35 USC § 103 as being unpatentable over Martin, in view of Al-Murrani and Struck, as applied to claims 1 and 26 above, and further in view of Lipton et al (arXiv, article no. 1511.03677v7 [cs:LG], pp. 1-18; published 3/21/2017, previously cited). This rejection is maintained from the previous Office action, and has been revised to address the amended claims (filed 4/18/2025).
With respect to claim 22, Martin discloses that “The classification engine may use any one or more known machine-learning algorithms… including… neural-network based techniques” (pg. 2, para. 0012) and “data sets… generated by collection of samples from the same population of patients at different times” (pg. 4, para. 0029), e.g., time series data. Martins does not disclose recurrent neural networks with long short-term memory.
Al-Murrani teaches “select[ing] among a number of algorithms for analyzing… data” (pg. 19, para. 0234). Al-Murrani does not teach recurrent neural networks with long-short term memory.
Struck teaches performance of various forms of “Statistical analysis” (pg. 9, para. 0126). Struck does not teach recurrent neural networks with long-short term memory.
Lipton discusses “Clinical medical data… [and] Recurrent Neural Networks (RNNs), particularly those using Long Short-Term Memory (LSTM)… powerful and increasingly effective models”, and “establish[es] the effectiveness of a simple LSTM network for modelling clinical data” (pg. 1, Abstract). Lipton further teaches that “The data is difficult to mine effectively, owing to varying length, irregular sampling and missing data… Trained once on raw time series, our models outperform several strong baselines” (pg. 1, Abstract). Additionally, Lipton teaches that “While we could not locate any published papers using LSTMs for multilabel classification in the medical domain, several papers use feedforward nets for this task. One of the earliest papers to investigate multi-task neural networks modeled risk in pneumonia patients” (pg. 3, section 2.3). In this way, Lipton suggests applicability of their disclosed methods to modeling of clinical risk.
With respect to claim 23, Lipton teaches that “regularization, including judicious use of dropout, is crucial to our performance” (pg. 3, section 2.6) and exemplifies “dropout probability 0.5” (pg. 15, section B), i.e., 50%.
An invention would have been obvious to one of ordinary skill in the art if some teaching in the prior art would have led that person to combine prior art reference teachings to arrive at the claimed invention. Before the effective filing date of the claimed invention, said practitioner would have implemented use of a recurrent neural network featuring long short-term memory, as taught by Lipton, within the biomarker analysis method taught by Martin, in view of Al-Murrani and Struck, because Lipton teaches that use of recurrent neural networks featuring long short-term memory allows for use of datasets with varying lengths, sampling protocols, and/or missing values and is more effective than other methods of achieving the same (pg. 1, Abstract). Said practitioner would have had a reasonable expectation of success because Martin and Lipton both discuss analysis of time series data using a neural network algorithm.
In this way the disclosure of Martin, in view of Al-Murrani, Struck and Lipton makes obvious the limitations of claims 22-23. Thus, the invention is prima facie obvious.
Response to Arguments - Claim Rejections Under 35 USC § 103
In the Remarks, Applicant traverses the rejections under 35 USC § 103 and presents supporting arguments.
Applicant alleges that Al-Murrani fails to disclose, teach or suggest specific ranges of nutrients as recited in claim 10 (pg. 10, para. 4 - pg. 11, para. 1). As described in the rejections, Al-Murrani discloses specific nutrient ranges that overlap with the claimed ranges. Thus, the argument of deficiency in Al-Murrani with respect to the claimed nutrient ranges is found unpersuasive. See MPEP 2144.05 § I.
Applicant notes that Al-Murrani fails to disclose, teach or suggest administering a treatment diet when a feline is at risk of developing CKD in the future (pg. 11, para. 1).
Al-Murrani teaches that their method is “predictive of a decline in renal function, as might otherwise be diagnosed at a later time” (pg. 3, para. 0023). Al-Murrani also discusses application of their methods to selecting appropriate agents such as drugs, nutritional compositions, or supplements for therapeutic, or prophylactic, use in the prevention or treatment of renal disease in felines (pg. 2, paras. 0017-18). Prophylactic treatment is administered to subjects at risk of developing a given medical condition in the future. Thus, the argument is found unpersuasive.
Applicant notes that Al-Murrani discloses preparation of diets for animals having kidney disorders and alleges, as best understood, that the intended purpose of said diets (i.e., administration as treatment to animals having kidney problems) renders said diets as unsatisfactory for the claimed administration as treatment to a feline at risk of developing CKD in the future (pg. 11, para. 1).
As discussed above, Al-Murrani discusses prophylactic treatment of renal disease in felines (pg. 2, paras. 0017-18) and discloses specific dietary nutrient ranges that overlap, and in some cases wholly fall within, claimed dietary nutrient ranges. The disclosed diets of Al-Murrani, comprising specific nutrient ranges that anticipate or make obvious those claimed, are considered satisfactory for the purpose, discussed by Al-Murrani, of prophylactic administration to felines who have not yet developed renal disease. Thus, the argument is found unpersuasive. Applicant alleges that Struck and Lopez-Giacoman do not remedy the alleged deficiencies of Martin and Al-Murrani (pg. 11, para. 2). As the alleged deficiencies are not recognized as deficiencies, the argument is found unpersuasive.
Applicant alleges that each of dependent claims 21-23 is allowable at least for their dependence from an allowable base claim and the aforementioned reasons (pg. 11, para. 5). No further points of alleged distinction are presented. Thus, the argument of dependence from an allowable claim is found unpersuasive.
For the above reasons, the arguments are found unpersuasive. In light of Applicant’s amendment to remove limitations pertaining to risk prediction more than one year in advance of diagnosis, the Lopez-Giacoman reference is considered unnecessary. The rejections have been revised to address the amended claims, including removal of the Lopez-Giacoman reference. Please note that relying on fewer references in support of a rejection under 35 USC § 103 does not constitute a new ground of rejection (MPEP 1207.03(a) § II).
Double Patenting
The nonstatutory double patenting rejection is based on a judicially created doctrine grounded in public policy (a policy reflected in the statute) so as to prevent the unjustified or improper timewise extension of the “right to exclude” granted by a patent and to prevent possible harassment by multiple assignees. A nonstatutory double patenting rejection is appropriate where the conflicting claims are not identical, but at least one examined application claim is not patentably distinct from the reference claim(s) because the examined application claim is either anticipated by, or would have been obvious over, the reference claim(s). See, e.g., In re Berg, 140 F.3d 1428, 46 USPQ2d 1226 (Fed. Cir. 1998); In re Goodman, 11 F.3d 1046, 29 USPQ2d 2010 (Fed. Cir. 1993); In re Longi, 759 F.2d 887, 225 USPQ 645 (Fed. Cir. 1985); In re Van Ornum, 686 F.2d 937, 214 USPQ 761 (CCPA 1982); In re Vogel, 422 F.2d 438, 164 USPQ 619 (CCPA 1970); In re Thorington, 418 F.2d 528, 163 USPQ 644 (CCPA 1969).
A timely filed terminal disclaimer in compliance with 37 CFR 1.321(c) or 1.321(d) may be used to overcome an actual or provisional rejection based on nonstatutory double patenting provided the reference application or patent either is shown to be commonly owned with the examined application, or claims an invention made as a result of activities undertaken within the scope of a joint research agreement. See MPEP § 717.02 for applications subject to examination under the first inventor to file provisions of the AIA as explained in MPEP § 2159. See MPEP § 2146 et seq. for applications not subject to examination under the first inventor to file provisions of the AIA . A terminal disclaimer must be signed in compliance with 37 CFR 1.321(b).
The filing of a terminal disclaimer by itself is not a complete reply to a nonstatutory double patenting (NSDP) rejection. A complete reply requires that the terminal disclaimer be accompanied by a reply requesting reconsideration of the prior Office action. Even where the NSDP rejection is provisional the reply must be complete. See MPEP § 804, subsection I.B.1. For a reply to a non-final Office action, see 37 CFR 1.111(a). For a reply to final Office action, see 37 CFR 1.113(c). A request for reconsideration while not provided for in 37 CFR 1.113(c) may be filed after final for consideration. See MPEP §§ 706.07(e) and 714.13.
The USPTO Internet website contains terminal disclaimer forms which may be used. Please visit www.uspto.gov/patent/patents-forms. The actual filing date of the application in which the form is filed determines what form (e.g., PTO/SB/25, PTO/SB/26, PTO/AIA /25, or PTO/AIA /26) should be used. A web-based eTerminal Disclaimer may be filled out completely online using web-screens. An eTerminal Disclaimer that meets all requirements is auto-processed and approved immediately upon submission. For more information about eTerminal Disclaimers, refer to www.uspto.gov/patents/apply/applying-online/eterminal-disclaimer.
Instant claims 10-12, 14-16 and 18-20 are rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1, 3-5, 7 and 15 of U.S. Patent No. 11,771,113 (hereafter, “‘113”), in view of Martin and Struck. ‘113 shares a joint inventor (Vincent Biourge) and a common assignee (Mars, Incorporated) with the instant application. This rejection is maintained from the previous Office action, and has been revised to address the amended claims (filed 4/18/2025).
Instant claim 10 recites “[a] method for treating chronic kidney disease (CKD) in a feline in need thereof… comprising”:
a) “determining at least one input level of one or more biomarkers from the feline and an age of the feline, wherein”:
1. “at least one of the… biomarkers comprise[s] information relating to… a urine specific gravity level, a creatinine level, a urine protein level, a blood urea nitrogen (BUN) or urea level, a white blood cell count (WBC), or urine pH”;
b) “executing a machine learning algorithm to transform the at least one input level… to determine a probability score indicating a likelihood [that] the feline will develop CKD in [the] future, wherein”:
1. “the machine learning algorithm comprises code from a training dataset… comprising medical information relating to both a first plurality of biomarkers and ages from a first set of sample felines that have been diagnosed with CKD and a second plurality… from a second set of sample felines that have not been diagnosed with CKD”;
c) “determining, based on the probability score, whether the feline is at risk of developing CKD in [the] future by comparing the probability score with a threshold value, wherein”:
1. “the threshold value is determined by applying a linear discriminant analysis to… medical records of… felines… compris[ing] measurements of the… biomarkers”; and
d) “administering a treatment diet responsive to determining [that] the feline is at risk of developing CKD in [the] future, the treatment diet comprising at least one of” diets including levels within specific recited ranges of phosphorous, protein, sodium, potassium or polyunsaturated fatty acids, an anti-oxidant supplement diet, or a vitamin B supplement diet.
With respect to instant claim 10, ‘113 discloses “a method of diagnosing and treating an animal at risk for chronic kidney disease (CKD) … compris[ing]” (claim 1):
a) “obtaining a blood sample from the animal… determining an amount of magnesium in the blood sample” (claim 7); i.e., receiving a biomarker level from the animal, and
1. “determining an amount of at least one further biomarker selected from… creatinine [and] blood urine nitrogen (BUN)” (claim 5);
b/c) “comparing the amount… to predetermined reference values” (claim 5) and “diagnosing the animal as being at risk of contracting CKD if the amount… is below a first predetermined value or a second predetermined value” (claim 1), i.e., based on the analysis, wherein
1. “the predetermined reference values are based on… a control population” (claim 1);
e) “providing the animal with a treatment regimen if the amount… is below a first predetermined value or a second predetermined value” (claim 1), i.e., based on the analysis, wherein “the treatment regimen comprises… a dietary therapy” (claim 5).
‘113 exemplifies application “wherein the animal is a… cat” (claim 15). ‘113 does not disclose utilizing an age; transforming the input level(s) to determine a probability score; a machine learning algorithm; a training dataset comprising a first and second set; or linear discriminant analysis.
Martin teaches “methods for correcting a data set and classifying the data set” (Abstract) including “an iterative approach to class prediction… [wherein] at least one processor receives a training data and… a test data set… generates a first classifier… by applying a machine learning technique to the training data set… transforms the test data set… [and] classif[es] the elements in the transformed test data set” (pg. 1, para. 0008), wherein “each data set may include data samples that each correspond to one of a plurality of biological sample classes” (pg. 4, para. 0028), e.g., a first set of samples and a second set of samples.
Martin exemplifies use of data including “levels of any biologically significant analyte” (pg. 4, para. 0026) which “can be derived from a living organism” (pg. 4, para. 0030), and further teaches that “The classification engine may use any one or more of known machine-learning algorithms, including… linear discriminant analysis techniques” (pg. 5, para. 0032) and “classes… can include… risk for a disease; [and] likelihood of recurrence of disease” (pg. 4 para. 0028), i.e., a probability score.
Additionally, Martin teaches “collection of samples from the same population of patients at different times” (pg. 4, para. 0029), and that “early solutions faced several disadvantages… [they] generated many false positive signatures and were not robust… Accordingly, there is a need for improved techniques” (pg. 1, paras. 0006-0007), i.e., the methods therein disclosed.
Martin does not teach utilizing an age.
Struck discusses “assays and in vitro methods for prediction of the progression of primary chronic kidney disease (CKD)” (Abstract) and teaches methods involving “comparing the level of [each] marker for [an] individual with a predetermined… optimal cut-off value” (pg. 4, para. 0065). In particular, Struck teaches “the prediction of the progression… may be improved by… determining and using the level of… laboratory parameter[s]… selected from… creatinine… [or] proteinuria… [and] at least one clinical parameter… selected from… age” (pg. 3, para. 0039).
Struck exemplifies determination of cut-off values through “Kaplan-Meier curve analyses of… patients with CKD… stratified into two groups according to the median of [biomarker levels] at baseline” (pg. 8, para. 0111). Struck depicts their results in the form of “Kaplan-Meier plots (FIG. 11-14), where[in] the occurrence of events i.e. in the present case: kidney disease progression, over time is depicted for… the investigated patient population… separated in two subgroups each” (pg. 4, para. 0069). Struck specifies that “the term ‘patient’ as used herein refers to a living human or non-human organism… Thus, the methods and assays described herein are also applicable to both human and veterinary disease” (pg. 6, para. 0089).
The depicted Figs. 11-14 comprise average disease progression curves for patients having biomarker levels above, and below, determined threshold levels at baseline (Time 0). From baseline, each stepped progression curve displays likelihoods of disease progression at a continuous range of future time points for patients having biomarker levels either above or below the threshold value.
In this way, Struck teaches utilizing an age.
With respect to instant claim 11, Martin discloses that “the processor… interface[s] with computer peripheral devices (e.g., a video display, a keyboard, computer mouse, etc)” (pg. 7, para. 0055) and “output[s] the predicted classifications to a display device” (pg. 6, para. 0042), e.g., via a graphical user interface.
With respect to instant claim 12, Martin discloses that “instructions may initially be borne on… a remote computer… The remote computer can… send the instructions… using a modem. A communications device local to a computing device… can receive the data… and place the data on a system bus for the processor” (pg. 7, para. 0057), and “output[ting] the predicted classifications to… another device in communication… across a network” (pg. 6, para. 0042), i.e., remote.
With respect to instant claim 14, Martin discloses “receiv[ing] a training data set and training class set, the training class set identifying a class associated with each of the elements in the training data set… [and] generat[ing] a first classifier… by applying a machine learning technique to the training data set and the training class set” (pg. 1, para. 0008). In other words, supervised learning.
With respect to instant claim 15, Martin discloses that “any machine-learning technique may be applied to generate the classifier” (pg. 5, para. 0041), wherein “In some implementations, no known classes for the data samples… are available, and thus… [a] class set is not provided to the classification engine” (pg. 4, para. 0025). In other words, unsupervised learning.
With respect to instant claim 16, Martins exemplifies “data sets… generated by collection of samples from the same population of patients at different times” (pg. 4, para. 0029).
With respect to instant claim 18, Martins discloses “divid[ing] a data set into a discovery/training set and a test/validation set” (pg. 1, para. 0004), and a “data pre-processing engine, which receives bulk data and splits the bulk data… into… data set[s]… [which] may be of the same… size[]” (pg. 4, para. 0031), i.e., folds. Martin further discloses performance of a “cross validation process in training” (pg. 8, para. 0070).
With respect to instant claim 19, Martins discloses “pool[ing] data from multiple sources to form a combined data set” (pg. 1, para. 0004), wherein “Data sets… can be collected from multiple sources… using different exclusion or inclusion criteria” (pg. 4, para. 0029).
With respect to instant claim 20, Martin discloses that “The classification engine may use any one or more known machine-learning algorithms… including… support vector machine techniques… Random Forest techniques, k-nearest neighbors techniques… neural network-based techniques” (pg. 5, para. 0032) and “linear classifiers such as… [a] naive Bayes classifier” (pg. 2, para. 0012).
An invention would have been obvious to one of ordinary skill in the art if some teaching in the prior art would have led that person to combine prior art reference teachings to arrive at the claimed invention. Before the effective filing date of the claimed invention, said practitioner would have predicted risk for a feline of diagnosis with CKD, as disclosed by ‘113, by training a machine learning algorithm, as taught by Martin, on biomarker data from felines diagnosed with CKD and not diagnosed with CKD because Martin teaches that machine learning classifiers can be trained on associated biomarker data from grouped presence/absence samples to predict disease risk (pg. 4, paras. 0026 and 0028-29; pg. 5, para. 0031) and additionally one of ordinary skill in the art would have understood that a machine learning classifier must be trained on data representing the classes which it is intended to distinguish.
An invention would have been obvious to one of ordinary skill in the art if some teaching in the prior art would have led that person to combine prior art reference teachings to arrive at the claimed invention. Before the effective filing date of the claimed invention, said practitioner would have implemented analysis of an age, as taught by Struck, with the biomarker analysis method taught by ‘113, in view of Martin, because Struck teaches that prediction can be improved by additionally analyzing a clinical parameter such as age (pg. 3, para. 0039), and further teaches enabled techniques for analyzing biomarker levels and age to calculate disease progression curves and thresholds (pg. 8, para. 011; Figs. 11-14). Said practitioner would have had a reasonable expectation of success because ‘113 and Struck both discuss analyzing biomarker levels (e.g., urine protein levels) to prognose disease risk.
In this way the disclosure of ‘113, in view of Martin and Struck, makes obvious the limitations of instant claims 10-12, 14-16 and 18-20. Thus, the invention is prima facie obvious.
Response to Arguments - Double Patenting
In the Remarks, Applicant contemplates future filing of a terminal disclaimer to obviate the double patenting rejection contingent upon indication that pending claims are otherwise allowable (pg. 11, para. 5). This amounts to a request that the nonstatutory double patenting rejection be held in abeyance.
“A complete response to a nonstatutory double patenting (NSDP) rejection is either a reply by applicant showing that the claims subject to the rejection are patentably distinct from the reference claims, or the filing of a terminal disclaimer… with a reply to the Office action… Such a response is required… [and] should not be held in abeyance. Only compliance with objections or requirements as to form not necessary for further consideration of the claims may be held in abeyance until allowable subject matter is indicated” (MPEP 804 § I.B.1).
Applicant has neither filed an appropriate terminal disclaimer nor filed showings that the claims subject to each rejection are patentably distinct from those of the reference patent (US 11,771,113), in view of the cited art (Martin and Struck). The omission of such filing renders Applicant’s reply as not fully responsive to the previous Office action. However, failure to treat all rejections is viewed as a minor deficiency and the overall response is thus considered a substantial, bona fide attempt to advance the application. Accordingly, completion of the response has not been required. However, the rejection is maintained.
Conclusion
At this point in prosecution, no claim is allowed.
THIS ACTION IS MADE FINAL. Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
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/T.C.S./Examiner, Art Unit 1685
/JESSE P FRUMKIN/Primary Examiner, Art Unit 1685 June 13, 2025