DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Status of the Claims
Claims 1-8, 10-11, 15, 19, and 21-28 are pending and under consideration in this action. Claims 9 and 13-14 were canceled in the amendment filed 3/2/2026. Claims 12, 16-18, and 20 were previously canceled.
Priority
The instant application claims domestic benefit to U.S. Provisional Application No. 63/108,716, filed 11/02/2020. The claim for domestic benefit for claims 1-8, 10-11, 15, 19, and 21-28 is acknowledged. As such, the effective filing date of claims 1-8, 10-11, 15, 19, and 21-28 is 11/02/2020.
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 3/17/2026 is in compliance with the provisions of 37 CFR 1.97. Accordingly, the IDS has been considered by the examiner.
Drawings
The objections to the drawings are withdrawn in view of Applicant’s amendments to the Specification filed 3/2/2026 (Applicant’s Remarks, Pg. 11).
Specification
The objection to the Specification is withdrawn in view of Applicant’s amendments to the Specification filed 3/2/2026 (Applicant’s Remarks, Pg. 11).
Claim Rejections - 35 USC § 112(b)
The rejection of claims 5, 11, and 21 under 35 U.S.C. 112(b) as being indefinite is withdrawn in view of Applicant’s amendments to the claims filed 3/2/2026 (Applicant’s Remarks, Pg. 11-12).
Response to Declaration
The Subject Matter Eligibility Declaration under 37 CFR 1.132 filed 3/2/2026 is insufficient to overcome the rejection of claims 1-8, 10-11, 15, 19, and 21-28 based upon being directed to an abstract idea without significantly more under 35 U.S.C. 101 as set forth in the last Office action because: The presented statements are not commensurate in scope with the claims, as the presented statements recite aspects and improvements from the Specification, which are not incorporated in the claim limitations. Additional details are set forth in the Response to Arguments section under 35 U.S.C. 101 below.
Claim Rejections - 35 USC § 101
Maintained Rejections
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 1-8, 10-11, 15, 19, and 21-28 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. The claims recite both (1) mathematical concepts (mathematical relationships, formulas or equations, or mathematical calculations) and (2) mental processes, i.e., concepts performed in the human mind (including observations, evaluations, judgements or opinions) (see MPEP § 2106.04(a)).
Any newly recited portion is necessitated by claim amendment.
Step 1:
In the instant application, claims 1-8, 10-11, 15, 19, and 21-28 are directed towards a method, which falls into one of the categories of statutory subject matter (Step 1: YES).
Step 2A, Prong One:
In accordance with MPEP § 2106, claims found to recite statutory subject matter (Step 1: YES) are then analyzed to determine if the claims recite any concepts that equate to an abstract idea, law of nature or natural phenomenon (Step 2A, Prong One). The following instant claims recite limitations that equate to one or more categories of judicial exceptions:
Claim 1 recites a mathematical concept in “calculating, based on a solvent accessible surface (SAS) of the one or more residues in the one or more regions of the one or more mAbs, a weighting factor”; a mental process (i.e., an evaluation of charge values for adjustment) in “adjusting, based on the weighting factor, the one or more charge values associated with the one or more residues”; a mental process (i.e., an evaluation of charges to determine a regional charge value) in “determining, based on the adjusted one or more charge values associated with the one or more residues, a charge value associated with each region of the one or more regions”; and a mathematical concept (i.e., generating a model) in “generating, based on the experimental data and the computationally-derived data, a predictive model, wherein the computationally-derived data comprises the charge values associated with each region of the one or more regions”.
Claim 2 recites a mental process (i.e., an evaluation of the mAbs) in “wherein the one or more mAbs comprise one or more of an IgG1 antibody or an IgG4 antibody”.
Claims 3 and 24 recite a mental process (i.e., an evaluation of the experimental data) in “wherein the experimental data comprises experimental viscosity data and wherein the experimental viscosity data comprises one or more of dynamic viscosity values or kinematic viscosity values”.
Claim 4 recites a mental process (i.e., an evaluation of the regions of the mAbs) in “wherein the one or more regions are associated with a sequence of the one or more mAbs”.
Claims 6 and 27 recite mental processes (i.e., an evaluation of the homology model to determine charge values or SAS) in “determining, based on a homology model of the one or more mAbs, the one or more charge values associated with the one or more residues in the one or more regions of the one or more mAbs” and “determining, based on the homology model of the one or more mAbs, the solvent accessible surface (SAS) of the one or more residues in the one or more regions”.
Claim 7 recites mathematical concepts in "identifying one or more experimental parameters of the experimental data as dependent variables", "identifying one or more computational parameters of the computationally-derived data as independent variables", and "generating, based on a stepwise regression algorithm, based on the dependent variables, and based on the independent variables, the predictive model".
Claim 8 recites a mathematical concept in “determining, for the predictive model, an Akaike Information Criterion (AIC) score”.
Claim 10 recites a mathematical concept (i.e., providing data to the model) in "providing, to the predictive model, the computationally-derived data" and a mathematical concept (i.e., using the model to determine a viscosity score) in "determining, based on the predictive model, a viscosity score associated with the query mAb".
Claim 19 recites a mathematical concept in “calculating, based on a solvent accessible surface (SAS) of the one or more residues in the one or more regions of the query mAb, a weighting factor”; a mental process (i.e., an evaluation of charge values for adjustment) in “adjusting, based on the weighting factor, the one or more charge values associated with the one or more residues”; a mental process (i.e., determining a regional charge value) in “determining, based on the adjusted one or more charge values associated with the one or more residues, a charge value associated with each region of the one or more regions”; a mathematical concept (i.e., providing data a model) in “providing, to a predictive model, the query computationally-derived data, wherein the query computationally-derived data comprises the charge values associated with each region of the one or more regions”; and a mental process (i.e., evaluating the output of the model) in “determining, based on the predictive model, a viscosity score associated with the query mAb”.
Claim 22 recites a mental process (i.e., an evaluation of sequence data) in “determining, based on the sequence data, the query computationally-derived data”.
Claim 23 recites a mathematical concept in “training, based on the experimental data and the computationally-derived data for the one or more mAbs, the predictive model”.
Claim 25 recites a mental process (i.e., an evaluation of the computational data) in “wherein the computationally-derived data comprises charge data associated with one or more regions associated with a sequence of the one or more mAbs, modified charge data associated with the one or more regions based on a solvent accessible surface of a residue in a homology model of the one or more mAbs, a hydrophobicity index (HI), a dipole moment, or an isoelectric point (pl)”.
Claims 26 and 28 recite a mental process (i.e., an evaluation of the training data) in “extracting, based on the training data set, a plurality of parameters”; and a mathematical concept in “training, based on the training data set and the plurality of parameters, a machine learning-based classification model configured to predict a viscosity of an antibody”.
These recitations are similar to the concepts of collecting information, and displaying certain results of the collection and analysis is Electric Power Group, LLC, v. Alstom (830 F.3d 1350, 119 USPQ2d 1739 (Fed. Cir. 2016)), comparing information regarding a sample or test to a control or target data in Univ. of Utah Research Found. v. Ambry Genetics Corp. (774 F.3d 755, 113 U.S.P.Q.2d 1241 (Fed. Cir. 2014)) and Association for Molecular Pathology v. USPTO (689 F.3d 1303, 103 U.S.P.Q.2d 1681 (Fed. Cir. 2012)), and 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)) that the courts have identified as concepts that can be practically performed in the human mind or mathematical relationships.
The abstract ideas recited in the claims are evaluated under the broadest reasonable interpretation (BRI) of the claim limitations when read in light of and consistent with the specification, and are determined to be directed to mental processes that in the simplest embodiments are not too complex to practically perform in the human mind. Additionally, the recited limitations that are identified as judicial exceptions from the mathematical concepts grouping of abstract ideas are abstract ideas irrespective of whether or not the limitations are practical to perform in the human mind.
Specifically, claim 1 involves nothing more than calculating a weighting factor, adjusting charge values, determining a charge value for a region, and generating a predictive model. Claim 19 involves nothing more than calculating a weighting factor, adjusting charge values, determining a charge value for a region, providing data to a model, and analyzing the output viscosity score of the predictive model. The steps reciting calculating a weighting factor, generating a predictive model, and providing data to the model are, under the BRI, performed using mathematical operations. The instant Specification (see Para. [0070]) discloses that the charge on each residue may be multiplied by a weighting factor calculated using the SAS of the residue relative to the total SAS of either full antibody or Fab depending on which model was being used. The instant Specification (see Para. [0079]) also discloses that the predictive model could be adaptive context tree weighting, neural network, CART (classification and regression tree), projection pursuit regression, stepwise regression, linear regression, elastic net, multivalent models, MARS (multivariate adaptive regression splines), power law, primal graphical LASSO, ridge regression and generalized additive model (GAM). Additionally, since there are no specifics in the methodology, the steps reciting adjusting the charge values, determining a charge for a region, and analyzing the output viscosity score of the predictive model, are something that under BRI, one could perform mentally. Therefore, the claimed steps are not further defined beyond something that reads on performing a calculation using a computer as a tool, and merely looking at data and making a determination. As such, said steps are directed to judicial exceptions. The instant claims must therefore be examined further to determine whether they integrate the abstract idea into a practical application (Step 2A, Prong One: YES).
Step 2A, Prong Two:
In determining whether a claim is directed to a judicial exception, further examination is performed that analyzes if the claim recites additional elements that when examined as a whole integrates the judicial exception(s) into a practical application (MPEP § 2106.04(d)). A claim that integrates a judicial exception into a practical application will apply, rely on, or use the judicial exception in a manner that imposes a meaningful limit on the judicial exception. The claimed additional elements are analyzed to determine if the abstract idea is integrated into a practical application (MPEP § 2106.04(d)(I)). If the claim contains no additional elements beyond the abstract idea, the claim fails to integrate the abstract idea into a practical application (MPEP § 2106.04(d)(III)). The following independent claims recite limitations that equate to additional elements:
Claim 1 recites “determining experimental data associated with one or more monoclonal antibodies (mAbs)” and “determining computationally-derived data associated with the one or more mAbs, wherein the computationally-derived data comprises one or more computational parameters, wherein the one or more computational parameters comprises one or more charge values associated with one or more residues in one or more regions of the one or more mAbs”.
Claim 19 recites “receiving query computationally-derived data associated with a query monoclonal antibody (mAb), wherein the computationally-derived data comprises one or more computational parameters, wherein the one or more computational parameters comprises one or more charge values associated with one or more residues in one or more regions of the query mAb”.
Regarding the above cited limitations in claims 1 and 19 of (i) determining experimental data associated with one or more monoclonal antibodies (mAbs); (ii) determining computationally-derived data associated with the one or more mAbs, wherein the computationally-derived data comprises one or more computational parameters, wherein the one or more computational parameters comprises one or more charge values associated with one or more residues in one or more regions of the one or more mAbs; and (iii) receiving query computationally-derived data associated with a query monoclonal antibody (mAb), wherein the computationally-derived data comprises one or more computational parameters, wherein the one or more computational parameters comprises one or more charge values associated with one or more residues in one or more regions of the query mAb. These limitations equate to insignificant, extra-solution activity of mere data gathering because these limitations gather data before the recited judicial exceptions of calculating weighting factors, adjusting charge values, determining a regional charge value, and generating a predictive model (see MPEP § 2106.04(d)).
Additionally, none of the recited dependent claims recite additional elements which would integrate the judicial exception into a practical application. Specifically, claims 3 and 24 further limit the experimental data; claims 5 and 15 further limit the computationally derived data; claims 10 and 22-23 recites data gather analogous to claims 1 and 19 above; and claims 11, 21, 26 and 28 provide extra-solution steps. As such, claims 1-8, 10-11, 15, 19, and 21-28 are directed to an abstract idea (Step 2A, Prong Two: 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). The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. The instant independent claims recite the same additional elements described in Step 2A, Prong Two above.
Regarding the above cited limitations in claims 1 and 19 of (i) determining experimental data associated with one or more monoclonal antibodies (mAbs); (ii) determining computationally-derived data associated with the one or more mAbs, wherein the computationally-derived data comprises one or more computational parameters, wherein the one or more computational parameters comprises one or more charge values associated with one or more residues in one or more regions of the one or more mAbs; and (iii) receiving query computationally-derived data associated with a query monoclonal antibody (mAb), wherein the computationally-derived data comprises one or more computational parameters, wherein the one or more computational parameters comprises one or more charge values associated with one or more residues in one or more regions of the query mAb. These limitations when viewed individually and in combination, are WURC limitations as taught by Sharma et al. (U.S. Patent Application Publication US 2017/0091377 A1; previously cited). Sharma et al. discloses a method for determining whether an antibody has one or more physiochemical characteristics, including predicted viscosity, that satisfy one or more design criteria. The design criteria may be for a pharmaceutical formulation comprising the antibody (Para. [0009] and [0030]-[0031]). Sharma et al. further discloses the measurement of viscosity using a rheometer (limitation (i)) (Para. [0140]). Sharma et al. further discloses that the structures of the Fabs can be obtained from the 3D crystal structure or by generating a homology model, which is subsequently used in molecular dynamics simulations. Molecular dynamics simulations can be used to determine structural parameters such as RMSF, or time-averaged solvent accessible surface area of specific amino acid residues (limitation (ii)) (Para. [0056]-[0058] and [0265]). Sharma et al. further teaches that sets of 10 and 14 mAbs were used for model evaluation (limitation (iii)) (Para. [0279]-[0281]).
These additional elements do not comprise an inventive concept when considered individually or as an ordered combination that transforms the claimed judicial exception into a patent-eligible application of the judicial exception. Therefore, the instant claims do not amount to significantly more than the judicial exception itself (Step 2B: NO). As such, claims 1-8, 10-11, 15, 19, and 21-28 are not patent eligible.
Response to Arguments under 35 U.S.C. 101
Applicant’s arguments and Subject Matter Eligibility Declaration filed 3/2/2026 have been fully considered but they are not persuasive.
1. Applicant argues that the claims are not directed to a judicial exception because the claimed steps do not recite math. Specifically, among other limitations, “determining, based on the experimental data and the computationally-derived data, a plurality of candidate predictive models” is directed to mathematical concepts. Applicant notes that this limitation is no longer recited in the present claims. However, Applicant respectfully submits that no mathematical relationships or formulas are recited. (Applicant’s Remarks, Pg. 12-13).
It is respectfully submitted that this is not persuasive for the following reasons:
Claim 1 recites the following limitations that have been identified as reciting mathematical concepts in Step 2A, Prong One above: “calculating, based on a solvent accessible surface (SAS) of the one or more residues in the one or more regions of the one or more mAbs, a weighting factor” and “generating, based on the experimental data and the computationally-derived data, a predictive model, wherein the computationally-derived data comprises the charge values associated with each region of the one or more regions”.
MPEP § 2106.04(a)(2)(I)(C) recites:
“A claim that recites a mathematical calculation, when the claim is given its broadest reasonable interpretation in light of the specification, will be considered as falling within the "mathematical concepts" grouping. A mathematical calculation is a mathematical operation (such as multiplication) or an act of calculating using mathematical methods to determine a variable or number, e.g., performing an arithmetic operation such as exponentiation. There is no particular word or set of words that indicates a claim recites a mathematical calculation. That is, a claim does not have to recite the word "calculating" in order to be considered a mathematical calculation. For example, a step of "determining" a variable or number using mathematical methods or "performing" a mathematical operation may also be considered mathematical calculations when the broadest reasonable interpretation of the claim in light of the specification encompasses a mathematical calculation.”
The instant Specification (see Para. [0079]) discloses that the predictive model could be adaptive context tree weighting, neural network, CART (classification and regression tree), projection pursuit regression, stepwise regression, linear regression, elastic net, multivalent models, MARS (multivariate adaptive regression splines), power law, primal graphical LASSO, ridge regression and generalized additive model (GAM). Therefore, when given its broadest reasonable interpretation in light of the Specification, “generating, based on the experimental data and the computationally-derived data, a predictive model, wherein the computationally-derived data comprises the charge values associated with each region of the one or more regions” equates to a mathematical calculation to generate a model using any of the above mathematical techniques.
Additionally, the instant Specification (see Para. [0070]) discloses that the charge on each residue may be multiplied by a weighting factor calculated using the SAS of the residue relative to the total SAS of either full antibody or Fab depending on which model was being used. Therefore, when given its broadest reasonable interpretation in light of the Specification, he limitation of “calculating, based on a solvent accessible surface (SAS) of the one or more residues in the one or more regions of the one or more mAbs, a weighting factor” equates to the calculation of a weighting factor using the SAS.
Therefore, claim 1 recites mathematical concepts, and this argument is not persuasive.
2. Applicant argues that the claims are not directed to a mental process because at least the limitations of generating a predictive model for antibody modeling cannot be practically be performed in the human mind. Here, the method involves a "several-step manipulation of data" by "determining experimental data," "determining computationally-derived data," "calculating ... a weighting factor," and "determining ... a charge value," to then generate a predictive model "for predicting physical properties of one or more monoclonal antibodies." Thus, like the example listed in the MPEP, this method does not recite a mental process (Applicant’s Remarks, Pg. 13-14).
It is respectfully submitted that this is not persuasive for the following reasons:
Claim 1 recites the following limitations that equate to mental processes: “adjusting, based on the weighting factor, the one or more charge values associated with the one or more residues” and “determining, based on the adjusted one or more charge values associated with the one or more residues, a charge value associated with each region of the one or more regions”. These limitations do not recite any steps for how to adjust the charge values using the weighting factor, or for how to determine a charge value for a region based on the adjusted charge values. Additionally, the claim limitations are for one or more charge values of all the charges/residues/regions, etc. of the antibody. Under the BRI, these limitations equate to an evaluation of charge(s) for adjustment and an evaluation of charge(s) to determine a region charge value. Given that the claim recites one or more charge values, and the lack of steps to perform these limitations, these limitations can be practically be performed in the human mind. Therefore, claim 1 recites mental processes and this argument is not persuasive.
3. Applicant also argues that the present claims cannot practically be performed in the human mind. Applicant has submitted a Subject Matter Eligibility Declaration (SMED) containing evidence explaining that the claim limitations cannot practically be performed in the human mind. The SMED provides expert testimony on how one of ordinary skill in the art would interpret the claim limitations, in view of Applicant's Specification, as being unable to practically be performed in the human mind and the underlying factual basis for that conclusion. See Subject Matter Eligibility Declaration (SMED), pp. 2-4. In particular, the SMED includes testimony on multi-step computational pipeline, including constructing full-antibody or Fab homology models, calculating solvent accessible surface values for every residue, computing SAS-based weighting factors, adjusting charge values across all residues and regions, and generating a machine learning-based predictive model cannot practically be performed in the human mind, and provides objective evidence supporting that conclusion (Applicant’s Remarks, Pg. 13).
It is respectfully submitted that this is not persuasive for the following reasons:
Applicant’s SMED has been fully considered, but it is not persuasive. The limitation, as recited in claim 1 of “generating, based on the experimental data and the computationally-derived data, a predictive model, wherein the computationally-derived data comprises the charge values associated with each region of the one or more regions of the monoclonal antibodies" recites a mathematical concept, when given the broadest reasonable interpretation read in light of the Specification, as discussed in the arguments directly above, and as described in the SMED, pg. 3-4. The algorithms recited in the Specification provide examples for the predictive model, but claim 1 merely recites “a predictive model”, not any of the algorithms recited in the Specification. Therefore, when given the broadest reasonable interpretation, the predictive model could be generated using any mathematical model.
Regarding the determination of computationally-derived data, this limitation was identified as an additional element in Step 2A, Prong Two above. While the SMED (see Pg. 2-3) highlights techniques that can be used to generate the computational data. While these techniques are described in the SMED and the Specification, and computational techniques not reflected in the limitations in claim 1. Therefore, when given the broadest reasonable interpretation, the computational data recited in “determining computationally-derived data with one or more mAbs…” can be determined using any computational technique. Additionally, Examiner also notes that since this limitation recites an additional element, it does not need to be practically performed in the human mind. This argument is thus not persuasive.
4. Applicant argues that at least the step of “generating…a predictive model” is akin to Example 39 of the Subject Matter Eligibility Examples. As described in Applicant's Specification, a predictive model can be generated using various machine learning techniques. Specifically, the method may "use machine learning ('ML') techniques to train, based on an analysis of one or more training data sets 210 by a training module 220, at least one ML module 230 that is configured to predict a protein viscosity score and/or a protein aggregation score for a given antibody." Thus, "generating ... a predictive model" may be accomplished by training a ML module on various training data sets to predict protein properties. In Example 39, a claim directed to training a neural network was found to not recite a mental process "because the steps are not practically performed in the human mind." Thus, the USPTO has recognized that training machine learning modules on various data sets cannot practically be performed in the human mind. Thus, for at least the same reasons articulated by the USPTO with respect to its published Example 39, the claims here do not fall within the judicial exception, i.e., "the claim does not recite a mental process because the steps are not practically performed in the human mind." (Applicant’s Remarks, Pg. 14-15).
It is respectfully submitted that this is not persuasive for the following reasons:
As discussed in the arguments directly above, the limitation of “generating, based on the experimental data and the computationally-derived data, a predictive model, wherein the computationally-derived data comprises the charge values associated with each region of the one or more regions”, does not recite the use of a machine learning model. While Applicant’s Specification discloses that the predictive model can be a machine learning model, it is not reflected in the claims. The predictive model can be any mathematical model, and is not limited to a machine learning model. Therefore, the claim 1 is different from published Example 39, and recites judicial exceptions. This argument is this not persuasive.
5. Applicant also argues that since claim 19 recites similar subject matter, Applicant respectfully submits that claim 19 is directed to patent-eligible subject matter for at least similar reasons (Applicant’s Remarks, Pg. 15).
It is respectfully submitted that this is not persuasive for the following reasons:
Claim 19 recites the following judicial exceptions in Step 2A, Prong One above: “calculating, based on a solvent accessible surface (SAS) of the one or more residues in the one or more regions of the query mAb, a weighting factor”; “adjusting, based on the weighting factor, the one or more charge values associated with the one or more residues”; “determining, based on the adjusted one or more charge values associated with the one or more residues, a charge value associated with each region of the one or more regions”; “providing, to a predictive model, the query computationally-derived data, wherein the query computationally-derived data comprises the charge values associated with each region of the one or more regions”; and “determining, based on the predictive model, a viscosity score associated with the query mAb”. For at least the same reasons as described for claim 1 above, these judicial exceptions are not integrated into a practical application. This argument is thus not persuasive.
6. Applicant argues that the present claims are directed to a solution to a technical problem; specifically, the problem of the development of protein formulations for therapeutic monoclonal antibodies (mAbs). Applicant has submitted a SMED to provide expert testimony to establish the state of the art at the time of filing and how one of ordinary skill in the art would interpret the disclosed invention as improving the predictive modeling of physical properties of monoclonal antibodies for the purpose of developing therapeutic solutions, and the underlying factual basis for that conclusion. Specifically, the SMED provides expert testimony regarding the specification's discussion of the prior art and how the invention improves the way predictive models are generated. SMED, pp. 4-6. The SMED also explains where the improvement is reflected in the claim (Applicant’s Remarks, Pg. 16).
It is respectfully submitted that this is not persuasive for the following reasons:
MPEP 2106.04(d)(II) recites:
The analysis under Step 2A Prong Two is the same for all claims reciting a judicial exception, whether the exception is an abstract idea, a law of nature, or a natural phenomenon (including products of nature). Examiners evaluate integration into a practical application by: (1) identifying whether there are any additional elements recited in the claim beyond the judicial exception(s); and (2) evaluating those additional elements individually and in combination to determine whether they integrate the exception into a practical application, using one or more of the considerations introduced in subsection I supra, and discussed in more detail in MPEP §§ 2106.04(d)(1), 2106.04(d)(2), 2106.05(a) through (c) and 2106.05(e) through (h).
The limitation of “generating, based on the experimental data and the computationally-derived data, a predictive model, wherein the computationally-derived data comprises the charge values associated with each region of the one or more regions” has been identified as a judicial exception in Step 2A, Prong One above. The integration of a judicial exception into a practical application can only be achieved by additional elements, not by a limitation that recites a judicial exception. Thus, the recited limitation is not considered as an improvement in an improvement in development of protein formulations for mAbs.
Additionally, with regards to the SMED (see Pg. 4-7), improved models are shown for predicting viscosity over previous models. However, this improvement is also not reflected in claim 1, as the claim is drawn to a method of generating a predictive model for physical properties of mAbs, and not to a specific property (i.e., viscosity), as recited in the SMED and Specification. Therefore, even if the generation of the predictive model was an additional element, an improved predictive model for viscosity is not reflected in the claim limitations. This argument is thus not persuasive.
7. Applicant argues that the Office Action's allegations that the additional elements "equate to insignificant, extra-solution activity of mere data gathering" is improper because it evaluates the claims at an unacceptably high-level of generality. As noted by the Director in Ex parte Desjardins, "Examiners and panels should not evaluate claims at such a high level of generality," and"§§ 102, 103 and 112 are the traditional and appropriate tools to limit patent protection to its proper scope. By condensing additional elements of the claims – which provide a technological improvement to a technological filed to “mere data gathering”, the Office Action has gone against the position of the Director (Applicant’s Remarks, Pg. 16-17)
It is respectfully submitted that this is not persuasive for the following reasons:
With regards to the data gathering steps recited in claim 1, these include “determining experimental data associated with one or more monoclonal antibodies (mAbs)” and “determining computationally-derived data associated with the one or more mAbs, wherein the computationally-derived data comprises one or more computational parameters, wherein the one or more computational parameters comprises one or more charge values associated with one or more residues in one or more regions of the one or more mAbs”. When viewed under the BRI, they encompass any experimental data and any computational data that includes charge values for mAbs. These are used to build the model, and are therefore determined to be data gathering steps under Step 2A, Prong Two above. Further analysis under Step 2B shows that these steps are well-understood, routine, and conventional (WURC) limitations as taught by Sharma et al. Since the steps are broadly recited, Sharma teaches these claim limitations. Therefore, these limitations do not provide a technological improvement and this argument is not persuasive.
Claim Rejections - 35 USC § 102
The rejection of claims 1-2, 4-5, 13, and 15 under 35 U.S.C. 102(a)(2) as being anticipated by Sharma et al. is withdrawn in view of Applicant’s amendments to the claims and Applicant’s remarks were found persuasive (Applicant’s Remarks, Pg. 17-19). Specifically, Sharma et al. discloses the calculation of residue specific SASA, but does not disclose the use of SASA to calculate weighting factors or for subsequent adjustment of the charge values based on the weighting factors.
Claim Rejections - 35 USC § 103
The rejection of claims 3, 9-11, and 14 under 35 U.S.C. 103 as being unpatentable over Sharma et al. in view of Cloutier et al. is withdrawn in view of Applicant’s amendments to the claims and Applicant’s remarks were found persuasive (Applicant’s Remarks, Pg. 19-25). Specifically, Cloutier et al. discloses the calculation of feature weights for SASA, but also does not disclose the adjustment of charge values based on the weighting factors.
The rejection of claims 6, 19, and 22 under 35 U.S.C. 103 as being unpatentable over Sharma et al. in view of Trout et al. is withdrawn in view of Applicant’s amendments to the claims and Applicant’s remarks were found persuasive (Applicant’s Remarks, Pg. 19-25). Specifically, Trout et al. discloses a model to determine viscosity based on an atom-based calculation of a spatial charge map for regions of negative charge on the solvent accessible surface of proteins, but also does not disclose the adjustment of charge values based on the weighting factors.
The rejection of claims 7-8 under 35 U.S.C. 103 as being unpatentable over Sharma et al. in view of Hebditch et al. is withdrawn in view of Applicant’s amendments to the claims and Applicant’s remarks were found persuasive (Applicant’s Remarks, Pg. 19-25). Specifically, Hebditch et al. discloses a set of machine learning models to predict biophysical properties based on sequence data, but also does not disclose the calculation of weighting factors based on SAS, or the subsequent adjustment of the charge values based on the weighting factors.
The rejection of claims 21 and 23-25 under 35 U.S.C. 103 as being unpatentable over Sharma et al. in view of Trout et al. and Cloutier et al. is withdrawn in view of Applicant’s amendments to the claims and Applicant’s remarks were found persuasive (Applicant’s Remarks, Pg. 19-25). Specifically, neither Trout et al. nor Cloutier et al. disclose the adjustment of the charge values based on the weighting factors as described above.
Conclusion
No claims allowed.
Claims 1-8, 10-11, 15, 19, and 21-28 appear to be free from the prior art because the prior art does not fairly suggest or teach the adjustment of charge values for residues based on a weighting factor calculated from a solvent accessible surface of the residues. The closest prior art is Cloutier et al. (Machine Learning Models of Antibody-Excipient Preferential Interactions for Use in Computational Formulation Design. Mol Pharm. 17(9): 3589-3599 (2020); previously cited). Cloutier et al. discloses a feature set describing an antibody’s surface which is used to train machine learning models to predict viscosity. Cloutier further discloses that the feature set includes the calculation of solvent accessible surface area, as well as the determination of optimal weights for the model. However, Cloutier et al. does not teach the subsequent use of the weights to adjust residue charge values (i.e., “adjusting, based on the weighting factor, the one or more charge values associated with the one or more residues”), as disclosed in instant claims 1 and 19. Claims 2-8, 10-11, 15, and 21-28 appear to be free from the prior art due to their dependency on claims 1 and 19.
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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/D.P.S./Examiner, Art Unit 1687
/Lori A. Clow/Primary Examiner, Art Unit 1687
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