DETAILED ACTION
The Applicant’s filing, received 08 July 2022 has been fully considered. The following rejections and/or objections constitute the complete set presently being applied to the instant application.
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Status of the Claims
Claims 1-20 are pending.
Claims 1-20 are rejected.
Priority
Claims 1-20 are given the benefit of priority to U.S. Provisional Application No. 63/219,578, filed 08 July 2021.
Therefore, the effective filing date of the claimed invention is 08 July 2021.
Information Disclosure Statement
The information disclosure statements (IDS) received on 26 June 2023, 15 February 2024, and 01 May 2024 are in compliance with the provisions of 37 CFR 1.97. Accordingly, these information disclosure statements have been considered by the examiner.
Drawings
The drawings were received 08 July 2022. These drawings are acceptable.
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 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. The claims recite: (a) mathematical concepts, (e.g., mathematical relationships, formulas or equations, mathematical calculations); and (b) mental processes, i.e., concepts performed in the human mind, (e.g., observation, evaluation, judgement, opinion).
Subject matter eligibility evaluation in accordance with MPEP 2106.
Eligibility Step 1: Step 1 of the eligibility analysis asks: Is the claim to a process, machine, manufacture or composition of matter?
Claims 1-11 recite a method for deriving hidden variables based on antibody competition data to discover binding patterns (i.e., a process); claims 12-19 recite a system comprising a processor and a memory (i.e., a machine or manufacture); and claim 20 recites a non-transitory computer readable medium having instructions stored thereon (i.e., a machine or manufacture).
Therefore, these claims are encompassed by the categories of statutory subject matter, and thus, satisfy the subject matter eligibility requirements under step 1.
[Step 1: YES]
Eligibility Step 2A: First it is determined in Prong One whether a claim recites a judicial exception, and if so, then it is determined in Prong Two whether the recited judicial exception is integrated into a practical application of that exception.
Eligibility Step 2A Prong One: In determining whether a claim is directed to a judicial exception, examination is performed that analyzes whether the claim recites a judicial exception, i.e., whether a law of nature, natural phenomenon, or abstract idea is set forth or described in the claim.
Independent claim 1 recites the following steps which fall within the mental processes and/or mathematical concepts groupings of abstract ideas:
processing the antibody competition data to generate training data (i.e., mental processes and mathematical concepts); and
deriving, using the training data and an optimization engine, a plurality of hidden variables and affinity scores for the hidden variables (i.e., mental processes and mathematical concepts),
wherein affinity scores for the hidden variables are derived for each antibody and the hidden variables represent competition factors for the antigen that cause competition among the antibodies (i.e., mental processes and mathematical concepts).
Independent claim 12 recites a system comprising a processor and a memory for executing the abstract ideas recited in independent claim 1, as noted above.
Independent claim 20 recites a non-transitory computer readable medium having instructions stored thereon that, when executed by a processor, cause the processor to execute the abstract ideas recited in independent claim 1, as noted above.
Dependent claims 2-11 and 13-19 further recite the following steps which fall within the mental processes and/or mathematical concepts groupings of abstract ideas, as noted below.
Dependent claims 2 and 13 further recite:
a first hidden variable represents a first competition factor for the antigen (i.e., mental processes), and
a derived affinity score for the first hidden variable associated with a given antibody indicates the given antibody's degree of competition over the first competition factor (i.e., mental processes and mathematical concepts).
Dependent claims 3 and 14 further recite:
the first competition factor corresponds to an epitope of the antigen that causes competition among the antibodies (i.e., mental processes).
Dependent claims 4 and 15 further recite:
the received antibody competition data comprises data from multiple experimental runs (i.e., mental processes),
each experimental run generates data values indicative of pairwise competition among a set of antibodies (i.e., mental processes), and
the multiple experimental runs generate antibody competition data for different sets of antibodies (i.e., mental processes).
Dependent claims 5 and 16 further recite:
processing the antibody competition data comprises combining the antibody competition data from the multiple experimental runs (i.e., mental processes).
Dependent claims 6 and 17 further recite:
deriving the plurality of hidden variables and the affinity scores for the hidden variables comprises deriving affinity scores for the antibodies from the different sets of antibodies (i.e., mental processes and mathematical concepts).
Dependent claims 7 and 18 further recite:
the hidden variables are derived by optimizing hidden logit values for the antibodies using pairwise competition data values from the training data, the hidden logit values representing the antibodies' affinity scores for the hidden variables (i.e., mental processes and mathematical concepts).
Dependent claims 8 and 19 further recite:
the antibodies' hidden logit values are optimized using a loss function, the pairwise competition data values from the training data, and a gradient technique that adjusts the hidden logit values to optimize the loss function (i.e., mental processes and mathematical concepts).
Dependent claim 9 further recites:
the hidden variables and the affinity scores for the hidden variables are derived by:
initially optimizing the antibodies' hidden logit values for a first hidden variable (i.e., mental processes and mathematical concepts); and
sequentially adding additional hidden variables after the initial optimization of the first hidden variable and jointly optimizing antibodies' hidden logit values for the first hidden variable and each sequentially added additional hidden variable (i.e., mental processes and mathematical concepts).
Dependent claim 10 further recites:
generating a pairwise competition score prediction for two antibodies using the hidden logit values optimized for the two antibodies (i.e., mental processes and mathematical concepts).
Dependent claim 11 further recites:
the received antibody competition data does not include pairwise competition data for the two antibodies (i.e., mental processes).
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. As noted in the foregoing section, the claims are determined to contain limitations that can practically be performed in the human mind with the aid of a pen and paper (e.g., processing data to generate training data), and therefore recite judicial exceptions from the mental process grouping of abstract ideas. Additionally, the recited limitations that are identified as judicial exceptions from the mathematical concepts grouping of abstract ideas (e.g., deriving hidden variables and affinity scores) are abstract ideas irrespective of whether or not the limitations are practical to perform in the human mind.
Therefore, claims 1-20 recite an abstract idea.
[Step 2A Prong One: YES]
Eligibility 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); MPEP 2106.05(a-h)). 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 judicial exceptions identified in Eligibility Step 2A Prong One are not integrated into a practical application because of the reasons noted below.
Dependent claims 2-11 do not recite any elements in addition to the judicial exception, and thus are part of the judicial exception.
The additional elements in independent claim 1 include:
receiving antibody competition data for a plurality of antibodies and an antigen, the antibody competition data comprising data values indicative of pairwise competition between antibodies.
The additional elements in independent claim 12 include:
a system comprising a processor and a memory storing instructions; and
receive antibody competition data for a plurality of antibodies and an antigen, the antibody competition data comprising data values indicative of pairwise competition between antibodies.
The additional elements in independent claim 20 include:
a non-transitory computer readable medium having instructions stored thereon;
a processor; and
receive antibody competition data for a plurality of antibodies and an antigen, the antibody competition data comprising data values indicative of pairwise competition between antibodies.
The additional elements in dependent claims 13-19 include:
a system comprising a processor and memory storing instructions (claims 13-19).
The additional elements of a system comprising a processor and a memory storing instructions (claims 12-19); and a non-transitory computer readable medium having instructions stored thereon, and a processor (claim 20); invoke a computer and/or computer-related components merely as tools for use in the claimed process, and therefore are not an improvement to computer functionality itself, or an improvement to any other technology or technical field, and thus, do not integrate the judicial exceptions into a practical application (MPEP 2106.04(d)(1)).
The additional element of receiving antibody competition data for a plurality of antibodies and an antigen, the antibody competition data comprising data values indicative of pairwise competition between antibodies (i.e., receiving data) (claims 1, 12, and 20) is merely a pre-solution activity of gathering data for use in the claimed process – a nominal addition to the claims that does not meaningfully limit the claims, and therefore does not add more than insignificant extra-solution activity to the judicial exceptions (MPEP 2106.05(g)).
Thus, the additionally recited elements merely invoke a computer and/or computer related components as tools; and/or amount to insignificant extra-solution activity; and as such, when all limitations in claims 1-20 have been considered as a whole, the claims are deemed to not recite any additional elements that would integrate a judicial exception into a practical application, and therefore claims 1-20 are directed to an abstract idea (MPEP 2106.04(d)).
[Step 2A Prong Two: NO]
Eligibility Step 2B: Because the claims recite an abstract idea, and do not integrate that abstract idea into a practical application, the claims are probed for a specific inventive concept. The judicial exception alone cannot provide that inventive concept or practical application (MPEP 2106.05). Identifying whether the additional elements beyond the abstract idea amount to such an inventive concept requires considering the additional elements individually and in combination to determine if they amount to significantly more than the judicial exception (MPEP 2106.05A i-vi).
The claims do not include any additional elements that are sufficient to amount to significantly more than the judicial exception(s) because of the reasons noted below.
Dependent claims 2-11 do not recite any elements in addition to the judicial exception(s).
The additional elements recited in independent claims 1, 12, and 20 and dependent claims 13-19 are identified above, and carried over from Step 2A Prong Two along with their conclusions for analysis at Step 2B. Any additional element or combination of elements that was considered to be insignificant extra-solution activity at Step 2A Prong Two was re-evaluated at Step 2B, because if such re-evaluation finds that the element is unconventional or otherwise more than what is well-understood, routine, conventional activity in the field, this finding may indicate that the additional element is no longer considered to be insignificant; and all additional elements and combination of elements were evaluated to determine whether any additional elements or combination of elements are other than what is well-understood, routine, conventional activity in the field, or simply append well-understood, routine, conventional activities previously known to the industry, specified at a high level of generality, to the judicial exception, per MPEP 2106.05(d).
The additional elements of a system comprising a processor and a memory storing instructions (claims 12-19); a non-transitory computer readable medium having instructions stored thereon, and a processor (claim 20); and receiving data (claims 1, 12, and 20); are conventional computer components and/or functions (see MPEP at 2106.05(b) and 2106.05(d)(II) regarding conventionality of computer components and computer processes).
Therefore, when taken alone, all additional elements in claims 1-20 do not amount to significantly more than the above-identified judicial exception(s). Even when evaluated as a combination, the additional elements fail to transform the exception(s) into a patent-eligible application of that exception. Thus, claims 1-20 are deemed to not contribute an inventive concept, i.e., amount to significantly more than the judicial exception(s) (MPEP 2106.05(II)).
[Step 2B: NO]
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.
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 1-20 are rejected under 35 U.S.C. 103 as being unpatentable over Jojic et al. (“Shift-Invariant Predictions.” US 2007/0192039, as cited in the Information Disclosure Statement (IDS) received 26 June 2023) in view of Brooks et al. (“Characterizing Epitope Binding Regions of Entire Antibody Panels by Combining Experimental and Computational Analysis of Antibody: Antigen Binding Competition.” Molecules, 2020, Vol. 25, No. 3659, pp. 1-19).
Jojic et al. shows methods that facilitate predicting information about binding information (e.g., binding energy, binary binding event, binding probability, etc.) wherein, for example, the relative position of a peptide in the an MHC (Major Histocompatibility Complex) groove can be treated as a hidden variable and the ensemble of different binding configurations can then be modeled, with the model trained utilizing a training procedure that iterates the predictions with re-estimation of the parameters of a binding groove model, such that the model generalizes to new MHC class II alleles that were not part of the training set, and thus, the predictions can be used as an alternative to laboratory experiments (para. [0007]).
Brooks et al. shows that antibody-antigen binding can be characterized early in a process (e.g., vaccine and immunotherapies development) for whole panels of antibodies by combining experimental and computational analyses of competition between monoclonal antibodies for binding to an antigen, and experimental “epitope binning” of monoclonal antibodies uses high-throughput surface plasmon resonance to reveal which antibodies compete, while a new complementary computational analysis evaluates antibody-antigen docking models to identify why and where they might compete, in terms of possible binding sites on the antigen (Abstract).
Independent claims 1, 12, and 20 are directed to deriving hidden variables based on antibody competition data to discover binding patterns.
Dependent claims 2-11 and 13-19 further define the model and the data used by the model.
Regarding independent claims 1, 12, and 20, Jojic et al. shows shift-invariant prediction models (e.g., modeling techniques that identify patterns regardless of their precise position in the data) for predicting binding information relating to a binding of a protein and a ligand, including a trained binding model and a prediction component, wherein the trained binding model can include a hidden variable representing an unknown alignment of the ligand at a binding site of the protein (Abstract); the binding energy between and MHC molecule and a peptide can be characterized by a binding free energy, where the lower the binding free energy, the greater the affinity between the two proteins, with the binding free energy being the difference between the free energy of the bound and unbound states, and that the binding energy for an MHC-peptide complex can be directly measured by competition experiments with a standard peptide (para. [0015]); the assumption that the binding energy is dominated by the potentials of pairwise amino acid interactions that occur when the amino acids are in close proximity, and a further assumption that the pairwise potentials depend only on the amino acids themselves and not on their context in the molecule, and thus the energy becomes a sum of pairwise potentials taken from a symmetric 20x20 matrix of pairwise potentials between amino acids, with these parameters computed based on the amino acid binding physics and there are several published sets derived in different ways (para. [0004]); training a model and deriving parameters that minimize the error of approximation (para. [0030]); a hidden random integer variable can be used to represent the starting index of the segment of the peptide bound to the MHC groove, and other hidden variables that describe the binding configuration can be used in the model, such as the particular geometric configuration of the amino acids in the groove of the MHC molecule obtained from an available crystal structure (para. [0020]).
Regarding independent claims 1, 12, and 20, Jojic et al. does not show receiving antibody competition data for a plurality of antibodies and an antigen; or deriving affinity scores for the hidden variables.
Regarding independent claims 1, 12, and 20, Brooks et al. shows an integrated experimental-computational approach to characterize epitopes for an entire panel of antibodies against an antigen by combining experimental binning with dock binning, a computational counterpart based on analysis of docking models for all of the antibodies, where the experimental data is collected to probe the hypothesized epitope regions, and further used to focus docking, redefining bins, better characterizing competition, and better localizing epitope binding regions (page 3, paras. 1-2; and Figure 1); and further shows that the experimental data allowed re-docking to focus the antibody-antigen models toward (with “affinity”) residues confirmed to be important for an antibody’s binding and away from (with “repulsion”) those determined not to be (page 8, para. 1); and further shows generating competition scores (page 14, para. 1) and competition profiles that provide insight into where the antigen and antibodies might be interacting (page 5, Figure 3); and competition between a pair of docking models was assessed with three different scores based on residue-level distances and biophysical interactions across the antibody-antigen interface (page 13, Section 4.5.1.).
Regarding dependent claims 2, 7, 8, 9, 13, 18, and 19, Jojic et al. further shows developing the trained model by incorporating a hidden variable (para. [0062]) (claims 2 & 13); optimizing a model by training and testing parameters and further using gradient descent optimization (paras. [0026] & [0030]) (claims 7 & 18, and 8 & 19; i.e., logits are used instead of probabilities during training because they are unbounded, allowing gradients to propagate more effectively and avoiding the vanishing gradient problem that occurs when sigmoid or softmax functions saturate); and an iterative learning algorithm (para. [0032]) (claim 9).
Regarding dependent claims 3, 4, 5, 6, 10, 11, 14, 15, 16, 17, Brooks et al. further shows an experimental-computational approach to characterize epitopes for an entire panel of antibodies against an antigen (page 3, para. 2) (claims 3 & 14); experimental epitope binning for identifying cross-competition between antibodies for binding an antigen (Figure 2; and page 13, Section 4.3.) (claims 4 & 15, and 5 & 16); competition scores (page 14; para. 1) (claims 6 & 17); and epitope binning is a competitive immunoassay where antibodies are tested in a pairwise manner for their simultaneous binding to their specific antigen, thereby generating a blocking profile for each antibody showing how it blocks or does not block the other in the panel (page 2, Section 1.2., para. 1) (claims 10 and 11; the pairwise competition data could be excluded from the received dataset).
Therefore, it would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the method shown by Jojic et al. by incorporating methods for gathering and analyzing antibody: antigen competitive binding data, as shown by Brooks et al., and discussed above. One of ordinary skill in the art would have been motivated to combine the methods of Jojic et al. with the methods of Brooks et al., because Jojic et al. shows methods for training and optimizing a model for predicting protein binding interactions using hidden variables, and Brooks et al. shows characterizing epitope binding regions of entire antibody panels by combining experimental and computational analysis of antibody: antigen binding competition data. This modification would have had a reasonable expectation of success given that Jojic et al. shows training and using machine learning algorithms for such an analysis and Brooks et al. discloses that machine learning methods could be incorporated in order to train models to integrate the upstream predictions and experimental data, e.g., in a consensus or weighted fashion, and that new data-driven models that directly seek to predict competition/bins (instead of general-purpose epitope prediction) could be developed (page 12, #3).
Conclusion
No claims are allowed.
This Office action is a Non-Final action. A shortened statutory period for reply to this action is set to expire THREE MONTHS from the mailing date of this application.
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/S.W.B./Examiner, Art Unit 1687
/Joseph Woitach/Primary Examiner, Art Unit 1687