DETAILED ACTION
Applicant’s response filed 02/20/2026 has been fully considered. The following rejections and/or objections are either reiterated or newly applied.
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 02/20/2026 has been entered.
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Claim Status
Claims 2, 8-9, 13, 16-19 and 26-27 are cancelled by Applicant.
Claims 1, 3-7, 10-12, 14-15 and 20-25 are currently pending and are herein under examination.
Claims 1, 3-7, 10-12, 14-15 and 20-25 are rejected.
Claim 25 is objected.
Priority
Applicant’s claim under 35 USC § 120 for the benefit of prior-filed Application No. 14/761,918 is acknowledged. Applicant’s claim under 35 USC § 119(e) for the benefit of prior-filed Provisional Application No. 61/753,842 is acknowledged. Claims 1, 3-7, 10-12, 14-15 and 20-25 are examined as though they had an effective filing date of 17 Jan 2013.
Withdrawn Rejections
35 USC 112(a)
The rejection of claims 24-25 under 35 USC 112(a) is withdrawn in view of claim amendments.
35 USC 103
The rejection of claims 24-25 under 35 U.S.C. 103 as being unpatentable over Carter 1986 et al. in view of MathWorks and SAS Institute and in further in view of Fang et al. is withdrawn in view of claim amendment.
Claim Objections
The objection to claims 21-22, 25 and 27 are withdrawn in view of claim amendments.
Claim 25 is objected to because of the following informality: “of drugs for drug development” should be “of drugs for use in clinical trials” to correspond to the preamble of claim 20.
Appropriate correction is required.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1, 3-7, 10-12, 14-15 and 20-25 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Any newly recited portions herein are necessitated by claim amendment.
Step 1:
Step 1 asks whether the claims recite statutory subject matter. In the instant application, claims 1, 3-7, 10-12, 14-15 and 21-24 recite a method and claims 20 and 25 recite a method. As such, these claims recite statutory subject matter (Step 1: YES).
Step 2A, Prong 1:
Claims that recite statutory subject matter are analyzed under Step 2A, Prong 1 to determine if they recite any concepts that equate to an abstract idea, law of nature or natural phenomena. The instant claims recite the following limitations that equate to one or more categories of judicial exception:
Claim 1 recites “for a pool of drugs including N total drugs with N being at least 9, representing a multi- dimensional response surface of drug efficacy as a quadratic function of drug dosages with m coefficients, and m = 1 + 2N+ (N(N- 1))/2 or one drug dosage from the pool of drugs is kept constant, and m = 1 + 2(N- 1) + ((N- 1)(N- 2))/2; based on m, designing n experimental tests of drugs on biological samples, wherein a search technique is used to identify optimized combinations of the N drugs, along with optimized ratios, and dosages of the optimized combinations of the N drugs; fitting results of the n tests into the response surface of drug efficacy for treating a medical condition, wherein the experimental tests are designed such that a tested dosage of at least one of the N drugs has discrete levels that lie on either side of a peak or maximum in the response surface, and such that n has a number greater than m and is selected in accordance with a number of the discrete levels of the tested dosage; and identifying optimized sub-combinations of drugs from the pool of drugs based on the fitted response surface for use in therapeutic intervention or clinical evaluation; and preparing the identified optimized sub-combinations of drugs for administration to a patient with the medical condition.”
Claim 3 recites “wherein N> 10.”
Claim 4 recites “wherein fitting results is performed using multi-dimensional fitting.”
Claim 5 recites “wherein fitting the results of the tests includes deriving values of the m coefficients.”
Claim 6 recites “wherein identifying the optimized sub-combinations of drugs includes identifying at least one extremum in the response surface.”
Claim 7 recites “wherein a space sampling technique for identifying salient features of the multi-dimensional response surface is used in the designing of experimental tests to determine the n tests.”
Claim 20 recites “for a pool of drugs including N total drugs with N being 3 or greater, representing a multi- dimensional response surface of drug efficacy as a quadratic function of drug dosages with m coefficients, and m = 1 + 2N+ (N(N- 1))/2 or one drug dosage from the pool of drugs is kept constant, and m = 1 + 2(N- 1) + ((N- 1)(N- 2))/2;based on m, designing n experimental tests of drugs on biological samples, wherein a search technique is used to identify optimized combinations of the N drugs, along with optimized ratios, and dosages of the optimized combinations of the N drugs; fitting results of the n tests into the response surface of drug efficacy for treating a medical condition, wherein the experimental tests are designed such that a tested dosage of at least one of the N drugs has discrete levels that lie on either side of a peak or maximum in the response surface, and such that n has a number greater than m and is selected in accordance with a number of the discrete levels of the tested dosage; using the fitted response surface of drug efficacy, identifying optimized sub-combinations of drugs from the pool of drugs for use in the clinical trials; selecting the optimized sub-combination of drugs to yield a desired drug efficacy from the optimized sub-combinations of drugs, the selected sub-combination of drugs having a number of drugs that is less than N; evaluating the selected sub-combination of drugs to identify an optimized combination of dosages of the selected sub-combination of drugs; preparing the selected sub-combination of drugs with the optimized combination of dosages for administration to a patient with the medical condition.”
Claim 21 recites “selecting the optimized sub-combination of drugs to yield a desired drug efficacy from the optimized sub-combinations of drugs, the selected sub-combination of drugs having a number of drugs that is less than N.”
Claim 22 recites “evaluating the selected sub-combination of drugs to identify an optimized combination of dosages of the selected sub-combination of drugs.”
Claim 23 recites “wherein the designing of the n experimental tests is configured to guide the selection of drug dosages for respective tests so that drug dosages can be narrowed down into two or more discrete levels and at least one tested dosage lies on either side of a peak or maximum in the response surface so as to model the response surface as a quadratic function.”
Claim 24 recites “wherein the number of the discrete levels of the tested dosage of at least one drug is three.”
Claim 25 recites “wherein the number of the discrete levels of the tested dosage of at least one drug is three.”
Limitations reciting a mental process.
Claims 1, 3-7, and 21-22 contain limitations that are recited at such a high level of generality that they equate to a mental process because they are similar to the concepts of collecting information, analyzing it, and displaying certain results of the collection and analysis in Electric Power Group, LLC, v. Alstom (830 F.3d 1350, 119 USPQ2d 1739 (Fed. Cir. 2016)), which the courts have identified as concepts that can be practically performed in the human mind.
These claims recite the following limitations that equate to a mental process: “designing n experimental tests”, “a search technique is used to identify optimized combinations”, “fitting results”, “using the response surface of drug efficacy, identifying optimized sub-combinations”, “using multi-dimensional fitting”, “deriving values of the m coefficients”, “identifying at least one extremum”, “a space sampling technique for identifying salient features”, “selecting the optimized sub-combinations of drugs”, and “evaluating the selected sub-combinations of drugs to identify. The broadest reasonable interpretation (BRI) of these limitations includes a human using observation, evaluation, judgment, and opinion, which are mental processes (MPEP 2106.04(a)(2)). A human could practically use pen and paper or their mind to design experiments, perform fitting, make selections, and derive coefficients.
Regarding claims 1 and 20, the BRI of preparing identified optimized sub-combinations and preparing selected sub-combination for drugs with the optimized combinations of dosages includes a mental process because the word “preparing” is so generically recited that it could mean performing further mental evaluations. This interpretation is reinforced by there being no affirmative administration step. The limitation “for administration to a patient” is an intended use. If Applicant intends to actually synthesize/administer the drugs, amend the claims such that there is an affirmative step of manufacturing and/or administering the drugs.
Limitations reciting a mathematical concept.
Claims 1, 4-5, 7, 20 and 23 contain limitations that equate to a mathematical concept because they are similar to the concepts of organizing and manipulating information through mathematical correlations in Digitech Image Techs., LLC v Electronics for Imaging, Inc. (758 F.3d 1344, 111 U.S.P.Q.2d 1717 (Fed. Cir. 2014)), which the courts have identified as mathematical concepts. The limitations in these claims that recite mathematical concepts are “representing a multi-dimensional response surface of drug efficacy as a quadratic function”, “a search technique”, “fitting the results”, “fitting results using multi-dimensional fitting”, “fitting results of the tests includes deriving values of the m coefficients”, “a space sampling technique”, and “so as to model the response surface as a quadratic function”. The BRI of a search technique includes a binary algorithm that performs calculations. The BRI of space sampling technique includes a Markov Chain Monte Carlo method which is a mathematical function that performs calculations.
Limitations included in the recited judicial exception.
Claims 1, 20 and 23-24 recite limitations that further limit the recited judicial exception but do not change the fact that the limitations being further limited still recite a judicial exception. These claims recite the following limitations that are included in the recited judicial exception: “wherein the experimental tests are designed such that”, “wherein N ≥ 10”, “wherein the designing of the n experiment tests is configured to guide”, and “wherein the number of the discrete levels of the tested dosage is no more than three”.
As such, claims 1, 3-7, 10-12, 14-15 and 20-25 recite an abstract idea (Step 2A, Prong 1: YES).
Additional Elements:
Once limitations have been identified that recite a judicial exception, the claims are evaluated for additional elements. The additional elements are then analyzed under Step 2A, Prong 2 then Step 2B. The instant claims recite the following additional elements:
Claim 1 recites “conducting the n tests by applying the identified optimized combinations of the N drugs at the optimized ratios and dosages on the biological samples;”
Claim 10 recites “the biological samples of the experimental drug tests are cell lines.”
Claim 11 recites “the biological samples of the experimental drug tests are animal models.”
Claim 12 recites “the biological samples of the experimental drug tests are in vitro cell cultures.”
Claim 14 recites “the biological samples of the experimental drug tests are animal models.”
Claim 15 recites “the biological samples of the experimental drug tests are in vitro cell cultures.”
Claim 20 recites “conducting the designed n experimental tests by applying the identified optimized combinations of drugs and respective optimized ratios and dosages on the biological samples, wherein the experimental tests are performed to obtain the optimized sub-combination of drugs for use in clinical trials;”
These above recited additional elements are analyzed below under both Step 2A, Prong 2 and Step 2B:
Step 2A, Prong 2:
Claims found to recite a judicial exception under Step 2A, Prong 1 are then further analyzed to determine if the claims as a whole integrate the recited judicial exception into a practical application or not (Step 2A, Prong 2). The judicial exception is not integrated into a practical application because the claims do not recite additional elements that reflect an improvement to a computer, technology, or technical field (MPEP § 2106.04(d)(1) and 2106.5(a)), require a particular treatment or prophylaxis for a disease or medical condition (MPEP § 2106.04(d)(2)), implement the recited judicial exception with a particular machine that is integral to the claim (MPEP § 2106.05(b)), effect a transformation or reduction of a particular article to a different state or thing (MPEP § 2106.05(c)), nor provide some other meaningful limitation (MPEP § 2106.05(e)). Rather, the claims include limitations that equate to an equivalent of the words “apply it” and/or to instructions to implement an abstract idea on a computer (MPEP § 2106.05(f)), insignificant extra-solution activity (MPEP § 2106.05(g)), and field of use limitations (MPEP § 2106.05(h)). The paragraphs below discuss the additional elements recited above in the instant claims.
Limitations reciting insignificant, extra-solution activity.
Claims 1 and 20 recite, respectively, the following limitations that equate to insignificant, extra-solution activity: “conducting the n tests by applying the identified optimized combinations of the N drugs at the optimized ratios and dosages” and “conducting the designed n experimental tests by applying the identified optimized combinations of drugs and respective optimized ratios and dosages, wherein the experimental tests are performed to obtain the optimized sub-combination of drugs for use in clinical trials”. These limitations equate to pre-solution activity of mere data gathering because they obtain results that are then used to perform the judicial exception of “fitting results of the n tests into the response surface of drug efficacy”. Limitations that recite insignificant, extra-solution activity do not integrate a judicial exception into a practical application (MPEP 2106.04(d).I).
Furthermore, the above cited limitations in claim 10-12 and 14-15 also equate to insignificant, extra-solution activity because they limit the biological samples which are used in the experimental tests, wherein the experimental tests have also been identified as reciting mere data gathering activity.
As such, claims 1, 3-7, 10-12, 14-15 and 20-25 are directed to an abstract idea (Step 2A, Prong 2: NO).
Step 2B:
Claims found to be directed to a judicial exception are then further evaluated to determine if the claims recite an inventive concept that provides significantly more than the judicial exception itself (Step 2B). These claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception because these claims recite additional elements that equate to instructions to apply the recited exception in a generic way and/or in a generic computing environment (MPEP § 2106.05(f)) and to well-understood, routine and conventional (WURC) limitations (MPEP § 2106.05(d)). The paragraphs below discuss the additional elements recited above in the instant claims.
The above cited limitations in claims 1, 10-12, 14-15 and 20 have been identified as reciting insignificant, extra-solution activity, as discussed in the section above regarding Step 2, Prong 2. MPEP 2106.05(g) recites that the addition of insignificant, extra-solution activity does not amount to an inventive concept. See Parker v. Flook, 437 U.S. 584, 588-89, 198 USPQ 193, 196 (1978).
Furthermore, when the additional elements of claims 1, 10-12, 14-15 and 20 are viewed in combination they equate to WURC limitations of a response surface methodology for identifying optimized drug combinations as disclosed by Fang et al. (“Fang”; Statistics in medicine 27, no. 16 (2008): 3071-3083; previously cited on PTO892 mailed 08/21/2025), Carter et al. (“Carter”; ref. A24 on IDS filed 07/30/2021; previously cited), and Chang et al. (“Chang”; Journal of food and drug analysis 14, no. 3 (2006): 5; previously cited on PTO892 mailed 08/21/2025). The following paragraphs discuss the WURC nature of these limitations:
Carter discloses selecting a combination of two, three, or more chemotherapy drugs (pg. 135) (pg. 136) (Table 1) (sec. “Experimental Design”) and representing the efficacy of these drugs using a response surface model (pg. 133) (sec. “Definition of RSM”). Carter teaches designing and performing in vivo drug activity tests using animals (pg. 135) (sec. “Experimental design”).
Fang discloses “statistical methods for experimental design and data analysis of combination studies of drugs that have log-linear dose–response curves” (abstract), and “we develop a maximum power experimental design based on uniform measures for combination studies of drugs with log-linear dose–response curves by approximating the additive model adequately” (pg. 3073, para. 2). Fang states that their method can be used with in vivo and in vitro experiments and provides a proof of concept on the HL-60 cell line (abstract). Table 1 shows the combinations of Etoposide and Vorinostat for combination experiment, and Figure 1 shows the response surface of the combinations against the cell lines.
Chang discloses in vitro evaluation of meloxicam permeation using response surface methodology (abstract). Table 1 shows the level, composition, and responses of meloxicam model formulations. Table 2 shows the independent and depend variables of the uniform design. Figure 1 shows the in vitro penetration-time profile of meloxicam model formulations through in vitro rat skin.
When these additional elements are considered individually and in combination, they do not provide an inventive concept because they all equate to WURC components of a standard drug optimization procedure using a response surface methodology, as described above by Carter, Chang, and Fang. Therefore, these additional elements do not transform the claimed judicial exception into a patent-eligible application of the judicial exception and do not amount to significantly more than the judicial exception itself (STEP 2B: NO).
As such, claims 1, 3-7, 10-12, 14-15 and 20-25 are not patent eligible.
Response to Arguments under 35 USC 101
Applicant's arguments filed 02/20/2026 have been fully considered but they are not persuasive.
Applicant argues that claims 1 and 20 recite the following additional elements that confer a practical application: “conducting the n tests by applying the identified optimized combinations of the N drugs at the optimized ratios and dosages on the biological samples; fitting results of the n tests into the response surface of drug efficacy for treating a medical condition; identifying optimized sub-combinations of drugs from the pool of N drugs based on the fitted response surface for use in therapeutic intervention or clinical evaluation; and preparing the identified optimized sub-combinations of drugs for administration to a patient with the medical condition”. Applicant also appears to argue a particular treatment (pg. 8, para. 1 – pg. 9, para. 3 of Applicant’s remarks). Applicant’s arguments are not persuasive for the following reasons:
The only additional element recited above is “conducting the n tests” while the remaining limitations recite a judicial exception or an intended use. Conducting the n tests equates to insignificant, extra-solution activity of necessary data gathering because it gathers data used to perform the judicial exception of “fitting the results of the n tests”. Necessary data gathering does not provide a practical application (MPEP 2106.05(g)(3)).
It is noted that “for treating a medical condition”, “for use in therapeutic intervention or clinical evaluation”, and “for administration to a patient with the medical condition” all equate to an intended use. None of these limitations recite an affirmative administration step, which is required for a particular treatment (MPEP 2106.04(d)(2)).
Applicant argues that “conducting the n tests” is not insignificant, extra-solution activity because it is integral to performing “identifying optimized sub-combinations” and “preparing the identified optimized sub-combinations” (pg. 9, last 2 para. of Applicant’s remarks). Applicant’s argument is not persuasive for the following reasons:
MPEP 2106.05(g)(3) recites “Whether the limitation amounts to necessary data gathering and outputting, (i.e., all uses of the recited judicial exception require such data gathering or data output).” The limitation in claim 1 of “conducting the n tests” equates to necessary data gathering because it gathers data necessary to perform the judicial exception in claim 1 of “fitting results of the n tests”, “identifying optimized sub-combinations” and “preparing the identified optimized sub-combinations”. MPEP 2106.05(g) discusses that data gathering is an insignificant, extra-solution activity that does not integrate a judicial exception into a practical application.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The text of those sections of Title 35, U.S. Code not included in this action can be found in a prior Office action.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claims 1, 3-7, 11, 14 and 20-23 are rejected under 35 U.S.C. 103 as being unpatentable over Carter et al. (“Carter 1986”; Cancer Treatment Reports 1986; ref. A24 on IDS of 30 Jul 2021; previously cited) in view of MathWorks, Inc. (“MathWorks”; MATLAB Statistics Toolbox 2011; ref. A48 on IDS of 30 Jul 2021; previously cited) and SAS Institute, Inc. (“SAS”; JMP® 8 Design of Experiments Guide 2008; ref. A45 on IDS of 30 Jul 2021; previously cited).
Any newly recited portions herein are necessitated by claim amendment.
Claims 1 and 20-22 are directed to a method comprising:
(a) "representing a multi-dimensional response surface of drug efficacy as a quadratic function of drug dosages …"
(b) "designing 𝑛 experimental tests of drugs on biological samples"
(c) "conducting the 𝑛 tests …" (clm 1) or "conducting the designed 𝑛 experimental tests" (clm 20)
(d) "fitting results of the 𝑛 tests into the response surface of drug efficacy"
(e)
(f) " identifying optimized sub-combinations of drugs from the pool of drugs based on the fitted response surface"
(g) "selecting an optimized sub-combination of drugs …" (clm 20 and 21)
(h) "evaluating the selected sub-combination of drugs to identify an optimized combination of dosages of the selected sub-combination of drugs" (clm 20 and 22)
(i) “preparing the identified sub-combinations of drugs” (clm 1) or “preparing the selected sub-combinations of drugs with the optimized combination of dosages” (clm 20)
Claim 1 further recites that the identification of step (f) is "for use in therapeutic intervention or clinical evaluation". Claim 20 further recites that the obtained optimized sub-combination of drugs is “for use in clinical trials” and that the identified optimized sub-combinations are “for use in the clinical trials”. Claim 23 recites "wherein the designing of the 𝑛 experimental tests is configured to guide the selection of drug dosages …". These further limitations are non-limiting intended uses of the method. They do not recite any active method steps, and their descriptions do not impose any limitations on other active steps of the claims.
With respect to claims 1 and 20–22, Carter 1986 teaches:
(a) selecting a combination of two, three, or more chemotherapy drugs (p. 135 § "Experimental Design"; p. 136, Table 1) and representing the efficacy of these drugs using a response surface model (p. 133 § "Definition of RSM"); "the simplest [response surface] model with [desirable] characteristics is the quadratic, which is typically used" (p. 137 § "Model Selection and Parameter Estimation")
(b) designing in vivo drug activity tests (p. 135 § "Experimental Design"); these activity tests constitute "a search technique" and they are "used to identify optimized combinations of the 𝑁 drugs, along with optimized ratios, and dosages of the optimized combinations of the 𝑁 drugs" (p. 133 § "Introduction")
(c) performing in vivo drug activity tests (p. 135 § "Experimental Design")
(d) estimating the RSM parameters using the activity data (p. 137 § "Model Selection and Parameter Estimation")
(e) — Figure 1 shows that drug dosages are selected from either side of the anticipated optimal dose.
(f) —
(g) —
(h) identifying the maximum of the RSM as the optimal dosage of the drug combination (p. 134 § "Optimum Dose"; p. 137 § "Exploration of the Fitted Surface")
(i) —
Carter 1986 teaches that three or more variables (i.e. drugs) can be used in RSM (pg. 133, col 2, para. 1) (pg. 134, col. 2, para. 1) (pg. 137, col. 1, para. 1). The broad range of Carter 1986 renders prima facie obvious the narrower ranges in the instant claims of three or more (instant claim 20), at least 9 (instant claim 1), and greater than or equal to 10 (instant claim 3). MPEP 2144.05(I) recites “[A] prior art reference that discloses a range encompassing a somewhat narrower claimed range is sufficient to establish a prima facie case of obviousness" In re Peterson, 315 F.3d 1325, 1330, 65 USPQ2d 1379, 1382-83 (Fed. Cir. 2003).
Carter 1986 teaches performing a plurality of trials for optimizing the dosage for a combination of drugs, but does not teach "wherein one drug dosage from the pool of drugs is kept constant".
SAS teaches software for designing experiments and analyzing the results of those experiments. SAS teaches specifying experimental factors, and that one or more of the factors can be held constant (p. 58). SAS also teaches that "response surface experiments traditionally involve a small number (generally 2 to 8) of continuous factors" (p. 29 § "Creating Response Surface Experiments"). SAS teaches that factors may be held constant "because certain factors may be difficult and expensive to change from one run to the next" (p. 58). SAS further teaches creating a RSM to analyze the experimental results (p. 119). SAS teaches that this software can be used for any kind of experiments, including chemical mixtures (p. 151 § "A Chemical Mixture Example").
SAS teaches that for some experiments, it may be beneficial or necessary to hold one of the factors constant. In Carter 1986, the factors are drugs and their associated dosages. So the combination of Carter 1986 and SAS teaches that for some drug optimization experiments, it may be beneficial or necessary to hold the dosage of one of the drugs constant throughout the experiment.
Carter 1986 also does not teach "identifying optimized sub-combinations of drugs from the pool of drugs".
SAS teaches that "screening designs are some of the most popular designs for industrial experimentation. They examine many factors to see which have the greatest effect on the results of a process" (p. 85). SAS teaches that for many experiments, not all of the factors will contribute significantly to the outcome. In Carter 1986, the factors are drugs and their associated dosages. So the combination of Carter 1986 and SAS teaches that for some drug optimization experiments, not all of the drugs will contribute significantly to the response; i.e. that from the pool of all drugs, only an optimized sub-combination of drugs is necessary to achieve the desired response.
Neither Carter 1986 nor SAS teach that a quadratic response surface model is "a quadratic function … with m coefficients, and m = 1+2(N−1)+((N−1)(N−2))/2".
MathWorks teaches (p. 9-59) that a quadratic response surface model has the form:
PNG
media_image1.png
101
459
media_image1.png
Greyscale
where N is the number of factors in the model. SAS teaches that constant factors are not included in the RSM (p. 119). So when the experiment involves N drugs, but the dosage of one drug is held constant, the RSM will be based on only N−1 drug dosage factors. A quadratic response surface model with N−1 drug dosage factors will inherently have 1+2(N−1)+((N−1)(N−2))/2 coefficients: one 𝑎0 coefficient for the intercept, N−1 𝑎𝑖 coefficients for the effect of each drug, ((N−1)(N−2))/2 𝑎𝑖𝑗 coefficients for the two-way interaction terms for each pair of drugs, and N−1 𝑎𝑖𝑖 coefficients for the quadratic self-interaction terms of each drug.
Consequently, when the pool has 12 drugs, there will be at most 1+24+(12×11)/2=91 coefficients. When the pool has 15 drugs, there will be at most 1+30+(15×14)/2=136 coefficients. The number of experiments must be greater than or equal to the number of coefficients (MathWorks, p. 9-5), so designing experiments for 12–15 drugs "results in a number of [experiments] having an upper limit between 90–200".
Carter 1986 also teaches that any number of drug dosage trials can be performed, but does not specifically teach performing a number of drug dosage trials that is equal to or greater than the number of coefficients in the RSM (i.e. "such that n has a number greater than m").
MathWorks teaches that when solving systems of linear equations — such as RSMs (p. 9-59) — the number of response measurements 𝑛 can be equal to or greater than the number of model coefficients 𝑝 (p. 9-5).
Carter 1986 discloses identifying the maximum of the RSM as the optimal dosage of the drug combination (p. 134 § "Optimum Dose"; p. 137 § "Exploration of the Fitted Surface"). Carter 1986 and SAS together teach that some drug optimizations, not all drugs will contribute significantly to the response, i.e. that from the pool of all drugs, only an optimized sub-combination of drugs is necessary to achieve the desired response. However, neither SAS, Carter 1986, nor MathWorks discloses “preparing the identified optimized sub-combinations” nor “preparing the selected sub-combination of drugs with optimized combination of dosages”. These limitations were obvious over the teachings of Carter 1986, which is directed toward discovering optimized drug combinations for cancer patients. Upon discovery of such combinations, it would be prima facie obvious to have manufactured the optimized drug sub-combinations with optimized dosages in order to treat patients. One of ordinary skill in the art would have had a reasonable expectation of success for manufacturing the drug combinations because Carter 1986 already discloses using different drug combinations in animal studies.
With respect to claim 3, Carter 1986 teaches that the number of drugs in combination is preferably two or more (pg. 133, col. 2, para. 1) (pg. 134, col. 2, para. 1), which satisfies the claim limitations of ten or more. SAS also teaches that "response surface experiments traditionally involve a small number (generally 2 to 8) of continuous factors" (p. 29 § "Creating Response Surface Experiments").
With respect to claims 4 and 5, Carter 1986 teaches estimating the values of the model parameters (p. 137 § "Model Selection and Parameter Estimation"), which constitutes "multi-dimensional fitting" (because the model is multidimensional) and "deriving values of the m coefficients".
With respect to claim 6, Carter 1986 teaches finding the maximum activity of the combination of drugs (p. 134 § "Optimum Dose"; p. 137 § "Exploration of the Fitted Surface").
With respect to claim 7, Carter 1986 teaches that the experiments are samples of an experimental design space, and that "another major aspect of efficient design in RSM is to place the treatment points in the region of the treatment space where optimum treatments would be expected to be found" (p. 135 § "Experimental Design"); i.e., "identifying salient features of the multi-dimensional response surface".
With respect to claims 11 and 14, Carter 1986 teaches animal experiments (p. 135 § "Experimental Design").
Claim 23 recites a non-limiting intended use of the invention. The fact that the method "is configured to guide the selection of drug dosages …" does not recite any additional steps, nor does it further limit the steps already recited in claim 1. In other words, the positively-recited steps of claim 1 are already "configured to guide the selection of drug dosages …" as recited in claim 23.
An invention would have been prima facie obvious to one of ordinary skill in the art if some teaching in the prior art would have led that person to modify prior art reference teachings to arrive at the claimed invention. Prior to the time of invention, said practitioner would have followed the teachings of SAS, that some factors in an experimental design can be held constant if they are difficult to change, and modified the method of Carter 1986 to hold the dosage of a drug constant in situations where the dosage of that drug is difficult to change. Given that SAS teaches that any factor in an experimental model can be held constant when appropriate, said practitioner would have readily predicted that the modification would successfully result in a method of optimizing dosages of a combination of drugs, where the dosage of one of the drugs is held constant throughout the trial.
Said practitioner also would have followed the teachings of MathWorks — that both consistent and overdetermined systems of linear equations can be solved — and modified the method of Carter 1986 and SAS to fit a RSM using a number of test results that is equal to or greater than the number of coefficients in the RSM. Given that Carter and SAS both teach that any number of experiments can be performed, and that MathWorks teaches than any RSM can be fit to a set of results having a cardinality equal to or greater than the number of RSM coefficients, said practitioner would have readily predicted that the modification would successfully result in a method of optimizing a drug dosage using a RSM, the RSM having been fit using data from a number of drug dosage trials that is equal to or greater than the number of coefficients in the RSM.
Claims 10, 12 and 15 are rejected under 35 U.S.C. 103 as being unpatentable over Carter et al. (“Carter 1986”; Cancer Treatment Reports 1986; ref. A24 on IDS of 30 Jul 2021; previously cited) in view of MathWorks, Inc. (“MathWorks”; MATLAB Statistics Toolbox 2011; ref. A48 on IDS of 30 Jul 2021; previously cited) and SAS Institute, Inc. (“SAS”; JMP® 8 Design of Experiments Guide 2008; ref. A45 on IDS of 30 Jul 2021; previously cited), as applied to claims 1 and 7 above, and in further in view of Carter et al. (“Carter 2004”; US 2004/0138826; ref. A7 on IDS of 30 Jul 2021; previously cited).
This rejection is maintained from the previous office action mailed 08/21/20205.
The limitations of claims 1 and 7 have been taught in the rejection above by Carter 1986, SAS, and MathWorks.
Regarding claims 10, 12 and 15, the combination of Carter 1986, SAS and MathWorks teaches assaying chemotherapeutic drug response using animal models, but does not teach assays using cell lines or cell cultures.
Carter 2004 teaches "methods … for the statistical analysis of interactions among agents in a combination of agents (i.e. departures from additivity), with respect to a quantifiable result of exposure to the combination of agents" (Abstract). Carter 2004 teaches that the agent interactions can be assayed using animal models and cell cultures [92] as well as cell lines [67] [551].
An invention would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the instant invention if some teaching in the prior art would have led that person to modify prior art reference teachings to arrive at the claimed invention. Prior to the time of invention, said practitioner would have followed the teachings of Carter 2004, and modified the method of Carter 1986, SAS and MathWorks to assay the response to combinations of drugs using animal models, cell lines and cell cultures. Given that Carter 2004 teaches that these assays are generally applicable to assaying any kind of drug combination, said practitioner would have readily predicted that the modification would successfully result in a method of assaying chemotherapeutic drug response using animal models, cell lines and cell cultures. The invention is therefore prima facie obvious.
Claims 24-25 are rejected under 35 U.S.C. 103 as being unpatentable over Carter et al. (“Carter 1986”; Cancer Treatment Reports 1986; ref. A24 on IDS of 30 Jul 2021; previously cited) in view of MathWorks, Inc. (“MathWorks”; MATLAB Statistics Toolbox 2011; ref. A48 on IDS of 30 Jul 2021; previously cited) and SAS Institute, Inc. (“SAS”; JMP® 8 Design of Experiments Guide 2008; ref. A45 on IDS of 30 Jul 2021; previously cited), as applied to claims 1 and 20 above, and in further in view of Jaynes et al. (“Jaynes”; Statistics in medicine 32, no. 2 (2013): 307-318; newly cited).
This rejection is newly recited in view of claim amendment.
The limitations of claims 1 and 20 have been taught in the rejection above by Carter 1986, SAS, and MathWorks.
Regarding claims 24-25, Carter 1986 discloses selecting dosages of drugs for use in experimentation based on the response surface methodology and discusses treatment levels such as using a 4 and 5 treatment level in factorial and central composite design (Table 1) (Figure 2).
However, Carter 1986, SAS, and MathWorks do not use 3 treatment levels. Jaynes teaches three-level fractional factorial designs to screen for drug combinations to treat HSV-1 (abstract). Table V shows a three-level design for antiviral drugs.
It would have been prima facie obvious to one of ordinary skill in the art to have modified the method of Carter 1986, SAS, and MathWorks for using response surface methodology to describe the dose-response relationship for a combination of agents by using a three-level design which uses three doses per drug as taught by Jaynes. The motivation for doing so is provided by Jaynes who recites “Three-level designs are widely used in practice to study the nonlinear relationship for quantitative factors” (sec. 3.1) and because it can be used to screen important drugs and drug interactions (abstract). One of ordinary skill in the art would have had a reasonable expectation of success for the combination because Carter 1986, SAS, and MathWorks would use a three-level drug design, wherein Carter 1986 shows that RSM can use different levels for factors (Table 1) (Figure 2).
Response to Arguments under 35 USC 103
Applicant's arguments filed 02/20/2026 have been fully considered but they are not persuasive.
Applicant argues that Carter 1986 uses per-selected drugs whereas the instant claims screen a large pool to discover new combinations (pg. 10, para. 2 of Applicant’s remarks). Applicant’s argument is not persuasive because the instant claims do not require that the pool of drugs not be “pre-selected”.
Applicant argues that the maximum number of drugs disclosed in Carter 1986 is 6, as shown in Table 1 (pg. 11, para. 2 of Applicant’s remarks). Applicant’s argument is not persuasive for the following reasons:
In Table 1, Carter 1986 shows an illustrative range of 2 to 6 drugs. However, Carter 1986 does not explicitly say that 6 is the maximum that can be used in response surface methodology (RSM). Rather, Carter 1986 recites that RSM can use three or more variables (pg. 133, col. 2, para. 1) (pg. 134, col. 2, para. 1). The BRI of three or more variables includes the claimed range of at least 9 drugs in instant claim 1 and of 3 or greater in instant claim 20.
Moreover, Carter 1986 teaches that RSM is a collection of mathematical and statistical methods which include experimental designs (pg. 133, col. 1, last para. 1). One of the experimental designs used in RSM is central composite design (pg. 135, col. 2, last para.). Thus, central composite design can manage three or more variables as part of a RSM.
Applicant argues that Carter 1986 states that a larger number of drugs is infeasible (pg. 11, para. 2 of Applicant’s remarks). Applicant’s argument is not persuasive for the following reasons:
Applicant appears to reference the following portion of Carter 1986: “Animal experiments using drug combinations have usually employed factorial designs. Virtually all published studies use only two drugs, since factorial designs rapidly became prohibitive due to the large number of groups required by such designs (table 1)” (pg. 135, col. 2, para. 4). This quote refers to factorial design rather than central composite design. Carter 1986 then further recites “The central composite design was developed as an alternative to the factorial design for use in fitting response surface models. The savings in the number of treatment groups relative to that required by the factorial design increases with the number of variables (table 1)” (pg. 135, col. 2, last para.) Thus, central composite design is feasible with a larger number of variables (i.e. drugs).
Applicant’s remarks regarding SAS and MathWorks are not persuasive because it’s not clear whether the instant claims require “mapping the response surface for all possible combinations” and because Carter 1986 is relied upon for “N being at least 9” (pg. 11, para. 3 of Applicant’s remarks).
Applicant argues that the claimed range is critical and achieves unexpected results relative to the prior art range (pg. 11, para. 4 – pg. 13 of Applicant’s remarks). Applicant’s arguments are not persuasive for the following reasons:
There is no mention of computational infeasibility mentioned in the instant disclosure. Rather, the “infeasibility” is associated with a large number of treatment groups used to experimentally test a large number of drug combinations. However, Carter 1986 teaches that savings in a number of treatment groups increases as a number of drugs increases when using central composite design compared to factorial design (Table 1) (pg. 135, col. 2, last para.). Carter 1986 uses a quadratic model to estimate model parameters (pg. 137, col. 1, para. 2). Because Carter 1986 discloses that RSM can use three or more variables (pg. 133, col. 2, para. 1), which includes the claimed range of at least 9, using 9 drugs in a central composite would continue producing savings in the number of experimental groups, particularly when compared to factorial design (Table 1). This would not constitute a new or unexpected result. If anything, it would constitute a matter of degree (i.e., less treatment groups) rather than a matter of kind. MPEP 2144.05.III.A recites “A difference of degree is not as persuasive as a difference in kind – i.e., if the range produces ‘a new property dissimilar to the known property,’ rather than producing a predictable result but to an unexpected extent.”
It is noted that the prior art does teach using at least 9 factors in a central composite design. Sanchez et al. (CM Transactions on Modeling and Computer Simulation (TOMACS) 15, no. 4 (2005): 362-377; newly cited) tabulates two-level V fractional factorial designs as well as central composite designs allowing estimation of full second-order models, for experiments involving up to 120 factors (abstract) (pg. 363, last para.). Li et al. (Quality Technology & Quantitative Management 6, no. 4 (2009): 433-449; newly cited) compares three central composite second-order response surface designs for a number of factors that range from
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6 ≤ k
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≤ 10 (abstract).
Conclusion
No claims are allowed.
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/N.A.A./Examiner, Art Unit 1687
/KAITLYN L MINCHELLA/Primary Examiner, Art Unit 1685