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 .
Claim Status
Claims 1-20 are pending and under examination.
Claims 1-20 are rejected.
Claim 7 is objected to.
Claims 1, 19, and 20 are independent.
No claims are allowed, amended, canceled, new, or withdrawn.
Office Action Outline
Rejections applied
Abbreviations
x
112/b Indefiniteness
PHOSITA
"a Person Having Ordinary Skill In The Art before the effective filing date of the claimed invention"
112/b "Means for"
BRI
Broadest Reasonable Interpretation
112/a Enablement,
Written description
CRM
"Computer-Readable Media" and equivalent language
112 Other
IDS
Information Disclosure Statement
x
102, 103
JE
Judicial Exception
x
101 JE(s)
112/a
35 USC 112(a) and similarly for 112/b, etc.
101 Other
N:N
page:line
x
Double Patenting
MM/DD/YYYY
date format
Priority
As detailed in the 03/25/2025 filing receipt, this application is a continuation of PCT/GB2022/050332, filed 02/08/2022. This application also claims priority to foreign application GB 2101703.3, filed 02/08/2021. At this point in examination, all claims have been interpreted as being accorded the priority date of 02/08/2021.
Acknowledgment is made of applicant's claim for foreign priority based on an application filed in the United Kingdom on 02/08/2021. It is noted, however, that applicant has not filed a certified copy of the GB 2101703.3 application as required by 37 CFR 1.55.
Claim Objections
Claim 7 is objected to because of the following informalities: Claim 7 is missing a space in the recitation between "to" and "a" in the recitation ..."each of the first probability distributions corresponding toa"... which should be corrected to: ..."each of the first probability distributions corresponding [[toa]]to a"... Appropriate correction is required.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 17 and 18 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
In claim 15, 17, and 18, it is unclear if "the synthesized compounds" (recited once each in claims 15, 17, and 18) are meant to refer to the same synthesized compounds or not. If it is Applicant's intention to that these instances of "the synthesized compounds" are distinct, then it might help overcome the rejection by possibly amending, e.g., claim 17 to recite "the synthesized compounds of the determined new dataset" and by possibly amending claim 18 to recite "synthesizing the selected compounds of the iteratively determined new subset..... adding the synthesized compounds of the iteratively determined new subset to the training set"...
Note, there is an additional 112b rejection below involving the same limitation; both rejections should be considered together when amending. The above suggestion would likely not overcome the below 112b rejection.
In claims 15, 17, and 18, the connection is unclear between the synthesized compounds and their addition to the training set. Claims 15, 17, and 18 each recite similar limitations for adding the synthesized compounds to the training set, but it is unclear in what way a physical compound would be added to a training set. For examination purposes, the limitations will be interpreted as adding properties of compounds (as claim 1 recites "defining...a training set of compounds for which a plurality of properties is known) are added to the training set.
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 one or more judicial exceptions without significantly more.
MPEP 2106 details the following framework to analyze Subject Matter Eligibility:
• Step 1: Are the claims directed to a category of statutory subject matter (a process, machine, manufacture, or composition of matter)? (see MPEP § 2106.03)
• Step 2A, Prong One: Do the claims recite a judicially recognized exception, i.e. an abstract idea, a law of nature, or a natural phenomenon? (see MPEP § 2106.04(a), 2106.04(a)(2) & 2106.04(b)).
• Step 2A, Prong Two: If the claims recite a judicial exception under Prong One, then is the judicial exception integrated into a practical application? (see MPEP § 2106.04(d))
• Step 2B: If the claims do not integrate the judicial exception, do the claims provide an inventive concept? (see MPEP § 2106.05)
Step 1:
Claims 1-18 are directed to a 101 process, here a method. Claim 19 is directed to a 101 machine or manufacture, here a non-transitory, computer-readable storage medium. Claim 20 is directed to a 101 machine or manufacture, here a computing device. As such, claims 1-20 are directed to a related method, CRM, and system, which fall under categories of statutory subject matter. (See MPEP § 2106.03). (Step 1: Yes.)
Step 2A, Prong One:
The claims are found to recite a judicial exception (JE) of abstract ideas in the form of mental processes and mathematical concepts, as follows:
The independent claims recite the following mental processes (and mathematical concepts as indicated by **):
• defining a population of compounds (claim 1)
• defining a training set of compounds for which properties are known (claim 1)
• defining objectives which each define a desired property (claim 1)
• training a Bayesian Statistical model to output a probability distribution approximating compound properties as a function of structural features ** (claims 1, 19, and 20; also, dependent claim 18)
• determining a subset of compounds not in the training set according to optimization of acquisition function based on the probability distribution and the defined objectives ** (claims 1, 19, and 20; also, dependent claim 18)
• selecting some compounds in the determined subset for synthesis (claim 1)
The dependent claims recite the following mental processes (and mathematical concepts as indicated by **):
• mapping a preference associated with a property of an objective by applying a utility function to the probability distribution to obtain a preference-modified probability distribution ** (claim 2)
• the preference is indicative of a priority of the respective objective relative to the other objectives (claim 3)
• a first probability distribution associated with greater preference when an associated uncertainty value decreases ** (claim 4)
• evaluating the acquisition function to determine a respective acquisition function value ** (claim 5)
• the subset of the plurality of compounds is determined based on a plurality of acquisition function values ** (claim 5)
• optimization of the acquisition function provides a Pareto-optimal set of compounds ** (claim 6)
• the determined subset of compounds includes compound(s) selected from the Pareto-optimal set. (claim 6)
• the probability distribution from the Bayesian statistical model includes first probability distributions ** (claim 7)
• mapping the first probability distributions from the Bayesian statistical model to a one-dimensional aggregated probability distribution by applying an aggregation function to the first probability distributions ** (claim 8)
• the acquisition function has multiple dimensions, each corresponding to an objective (claim 9)
• tuning a plurality of hyperparameters of the Bayesian statistical model including applying a combination of a maximum likelihood estimation technique and a cross validation technique ** (claim 10)
• identifying one compound from the population that is not in the training set by
optimizing the acquisition function ** (claim 11)
• repeating steps of retraining the Bayes statistical model and of identifying a compound not in the training set by optimizing acquisition function, until the compounds have been identified ** (claim 11)
• setting fake property values for the identified compounds in the Bayesian statistical model according to a kriging believer approach or a constant liar approach ** (claim 12)
• the Bayesian statistical model is a Gaussian process model ** (claim 13)
• weighting parameters are modified with a desired strategy including optimization of exploitation strategies dependent on a weighting parameter of the acquisition function associated with the posterior mean and with the posterior variance ** (claim 14)
• updating the training set by adding the synthesized compounds (claims 15, 17, 18)
• training a Bayesian Statistical model to output a probability distribution using the updated training set ** (claim 16 and 18)
• determining a new subset of compounds from the population which are
not in the updated training set (claim 16 and 18)
• selecting some compounds in the determined new subset for synthesis (claim 16 and 18)
Step 2A Prong One Summary: The claims recite mental processes and mathematical concepts. When considering the broadest reasonable interpretation (BRI) of the claims, the mental processes recited in independent claim 1 (e.g., defining a population of compounds; defining a training set of compounds; defining objectives which each define a desired property; etc.) are directed to processes that may be performed in the human mind, or with pen and paper, as there are no particular limitations recited in claim 1 which would prevent the mental processes from being performed in the human mind or with pen and paper. The claims recite inherent mathematical concepts in, e.g., training a Bayesian Statistical model to output probability distributions; optimization of acquisition function based on the probability distribution; applying an aggregation function to the probability distribution; etc. These mathematical concepts are discussed throughout the Specification, e.g., at [0105-0147]. Although the method is computationally based, and claims include a computing device, processors, memory and computer readable medium with instructions, a claim that requires a computer may still recite a mental process [see MPEP 2106.04(a)(2)(III)(C)]. While these mathematical concepts performed mentally, or with paper and pencil, may take considerable time and effort, and although a general-purpose computer can perform these calculations at a rate and accuracy that can far exceed the mental performance of a skilled artisan, the nature of the activity is essentially the same, and therefore constitutes an abstract idea. Therefore, the claims recite elements that constitute a judicial exception in the form of an abstract ideas, (Step 2A, Prong One: Yes.)
Step 2A, Prong Two:
In Step 2A, Prong One above, claim steps and/or elements were identified as part of one or more judicial exceptions (JEs). Here at Step 2A, Prong Two, any remaining steps and/or elements not identified as JEs are therefore in addition to the identified JE(s) and are considered additional elements. Because the claims have been interpreted as being directed to judicial exceptions (abstract ideas in this instance) then Step 2A, Prong Two provides that the claims be examined further to determine whether the judicial exception is integrated into a practical application [see MPEP § 2106.04(d)]. A claim can be said to integrate a judicial exception into a practical application when it applies, relies on, or uses the judicial exception in a manner that imposes a meaningful limit on the judicial exception.
MPEP § 2106.04(d)(I) lists the following five example considerations for evaluating whether a judicial exception is integrated into a practical application:
(1) An improvement in the functioning of a computer or an improvement to other technology or another technical field, as discussed in MPEP §§ 2106.04(d)(1) and 2106.05(a).
(2) Applying or using a judicial exception to effect a particular treatment or prophylaxis for a disease or medical condition, as discussed in MPEP § 2106.04(d)(2).
(3) Implementing a judicial exception with, or using a judicial exception in conjunction with, a particular machine or manufacture that is integral to the claim, as discussed in MPEP § 2106.05(b).
(4) Effecting a transformation or reduction of a particular article to a different state or thing, as discussed in MPEP § 2106.05(c).
(5) Applying or using the judicial exception in some other meaningful way beyond generally linking the use of the judicial exception to a particular technological environment, such that the claim as a whole is more than a drafting effort designed to monopolize the exception, as discussed in MPEP § 2106.05(e).
The claims recite additional elements as follows:
Additional elements of data gathering, inputting, and outputting steps: Receiving data in claims 19 and 20. Data gathering steps are additional elements which perform functions of inputting, collecting, and outputting the data needed to carry out the abstract idea. These steps are considered insignificant extra-solution activity and are not sufficient to integrate an abstract idea into a practical application as they do not impose any meaningful limitation on the abstract idea or how it is performed, nor do they provide an improvement to technology (see MPEP § 2106.04(d)(I) and 2106.05(g)).
Additional elements of synthesizing compounds in claims 15, 17, and 18. This step is considered insignificant extra-solution activity and is not sufficient to integrate an abstract idea into a practical application as it does not impose any meaningful limitation on the abstract idea or how it is performed, nor does it provide an improvement to technology (see MPEP § 2106.04(d)(I) and 2106.05(g)).
Additional elements of computer components: a non-transitory, computer-readable storage medium, a computer system, processors, memory, and or computer device in claims 19 and 20. The claims require only generic computer components, which do not improve computer technology, and do not integrate the recited judicial exception into a practical application (see MPEP § 2106.04(d)(1) and MPEP § 2106.05(f)).
Step 2A Prong Two summary: The claims have been further analyzed with respect to Step 2A, Prong Two, and no additional elements have been found, alone or in combination, that would integrate the judicial exception into a practical application. At this point in examination, it is not yet the case that any of the Step 2A Prong Two considerations enumerated above clearly demonstrates integration of the identified JE(s) into a practical application. Referring to the considerations above, none of: (1) an improvement, (2) a treatment, (3) a particular machine, or (4) a transformation is clear in the record. For example, regarding the first consideration for improvement at MPEP 2106.04(d)(1), the record, including the Specification, does not yet clearly disclose an explanation of improvement over the previous state of the technology field, and the claims do not yet clearly result in such an improvement. (Step 2A, Prong Two: No).
Step 2B analysis:
Because the additional claim elements do not integrate the abstract idea into a practical application, the claims are further examined under Step 2B, which evaluates whether the additional elements, individually and in combination, amount to significantly more than the judicial exception itself by providing an inventive concept. An inventive concept is furnished by an element or combination of elements that is recited in the claim in addition to the judicial exception, and is sufficient to ensure that the claim, as a whole, amounts to significantly more than the judicial exception itself (see MPEP § 2106.05).
The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the claims recite additional elements that are well-understood, routine, and conventional. Those additional elements are as follows:
Additional elements of data gathering, inputting, and outputting steps: The additional element of receiving data in claims 19 and 20 do not cause the claims to rise to the level of significantly more than the judicial exception. The courts have recognized receiving or transmitting data over a network and storing and retrieving information in memory [see MPEP§2106.05(d)(II)], as well-understood, routine, conventional activity when they are claimed in a merely generic manner (e.g., at a high level of generality) or as extra-solution activity.
Additional elements of synthesizing compounds in claims 15, 17, and 18. This step does not impose meaningful limits on the claims and is not sufficient to cause the claims to rise to the level of significantly more than the judicial exception. Additionally, the element of synthesizing compounds is conventional, as shown by the following reference:
Mouchlis (International journal of molecular sciences, vol. 22(4):1676, pages 1-22 (07 Feb 2021); cited on the attached form PTO-892), presents a review on drug design and shows synthesizing compounds (under section "7.3 Synthetic Feasibility" bridging p.12-13).
Additional elements of computer components: The additional elements of a non-transitory, computer-readable storage medium, a computer system, processors, memory, and or computer device in claims 19 and 20 do not cause the claims to rise to the level of significantly more than the judicial exception, and as such do not provide an inventive concept; these are conventional computer components.
All limitations of claims 1-20 have been analyzed with respect to Step 2B, and none provides a specific inventive concept, as they all fail to rise to the level of significantly more than the identified judicial exception, and thus do not transform the judicial exception into a patent eligible application of the exceptions. Step2B: NO.
Therefore, the claims, when the limitations are considered individually and as a whole, are rejected under 35 U.S.C. § 101 as being directed to non patent-eligible subject matter.
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.
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 Plumbley (US-2021/0012862-A1, filed 01-14-2021; cited on the attached form PTO-892); in view of Ogura, (2019. Scientific reports, vol. 9(1):12220, pages 1-12; cited on the attached form PTO-892); in view of Ramachandran (2019. Harnessing Auxiliary Knowledge Towards Efficient Bayesian Optimisation (Doctoral dissertation, Deakin University) ; cited on the attached form PTO-892).
In independent claims 1, 19, and 20, the recited training a ...statistical model to output a probability distribution approximating compound properties as a function of structural features reads on " training a machine learning technique based on a labelled training dataset corresponding to multiple compounds and their association with a particular property . " (Plumbley, [0045], and "receive a prediction result list output from a property model for predicting whether a plurality of compounds are associated with a particular property and an property model score" (Plumbley, [0033]).
In independent claims 1, 19, and 20, the recited determining a subset of compounds not in the training set according to optimization of acquisition function based on the probability distribution and the defined objectives reads on "the selection model may be used to select a shortlist of compounds each time a property model is presented with a plurality of compounds most of which the property model might not have seen before ( i.e. not part of the labelled training dataset used to initially train the property model) (Plumbley, [0067]).
"In independent claim 1, the recited selecting some compounds in the determined subset for synthesis reads on "Validation device 106b may validate one or more of the selected shortlist of compounds in relation to the particular property using either the labelled training dataset, computer analysis / simulation , and / or laboratory experimentation ( or experiments ) to establish the association each compound may have in relation to the particular property (Plumbley, [0100]). Here, laboratory experimentation is interpreted to include synthesis of the selected compound(s).
In independent claims 19 and 20, the recited non-transitory, computer-readable storage medium storing instructions, computer, and/or memory reads on limitations of claims 30 and 32 of Plumbley.
In claim 2, the recited mapping a preference associated with a property of an objective by applying a utility function to the probability distribution to obtain a preference-modified probability distribution reads on " the prediction score comprises or represents data representative of a value representative or indicative of the ML Model predicting whether a compound has or has not a particular property" (Plumbley [0122]). "when the validation method to perform computer analysis is selected and it is determined that computer analysis will yield an improvement in an property model score for the property model based on previous property model scores calculated from corresponding prediction result lists generated after each shortlist of compounds has been validated , the method further comprising : rewarding the selection model during retraining ; and selecting the validation method to perform computer analysis" Plumbley [0022].
In claim 7, the recited the probability distribution from the statistical model includes first probability distributions, each corresponding to a property associated with an objective reads on "the prediction result list comprises a prediction property score indicating the association said each compound has with the particular property" (claim 14 of Plumbley)
In claim 11, the recited identifying one compound from the population that is not in the training set by optimizing the acquisition function and repeating steps of retraining the statistical model and of identifying a compound not in the training set by optimizing acquisition function, until the compounds have been identified reads on "selecting a shortlist of compounds using the retrained selection model from the plurality of compounds; sending the selected shortlist of compounds for validation with the particular property, wherein the property model is updated based on the validation" (claim 1 of Plumbley).
In claim 15, 17, and 18 the recited synthesizing some selected compounds of the determined subset and adding them to the training set to update the training set (and until a stop condition is finished set in claim 18) reads on "the laboratory experimentation outputs laboratory experimentation validation results for estimating the association each compound on the selected shortlist of compounds has with the particular property, wherein the laboratory experimental validation results are used for updating the property model" (claim 4 of Plumbley), and "repeating the process 130 for the next iteration. Iterating...may be performed until it is determined the selection model has been validly trained or when a stopping criterion has been reached or met" (Plumbley, [0120]).
In claim 16 and 18, the recited training a ...Statistical model to output a probability distribution using the updated training set reads on "updated property model over a previous property model when the property model score is indicative of meeting or exceeding the property
model performance threshold compared with the corresponding previous property model score ; and retraining the selection model"... (claim 17 of Plumbley).
In claim 16 and 18, the recited determining a new subset of compounds not in the updated training set according to optimization of acquisition function based on the probability distribution and the defined objectives reads on "the selection model may be used to select a shortlist of compounds each time a property model is presented with a plurality of compounds most of which the property model might not have seen before ( i.e. not part of the labelled training dataset used to initially train the property model) (Plumbley, [0067]), and "retrain the selection model based on the property model score; select a shortlist of compounds using the retrained selection model from the plurality of compounds (claim 32 of Plumbley).
In claim 16 and 18, the recited selecting some compounds reads on "Validation device 106b may validate one or more of the selected shortlist of compounds in relation to the particular property using either the labelled training dataset, computer analysis/simulation, and/or laboratory experimentation (or experiments) to establish the association each compound may have in relation to the particular property (Plumbley, [0100]). Here, laboratory experimentation is interpreted to include synthesis of the selected compound(s).
Plumbley does not show:
Plumbley does not explicitly show defining a population of compounds having structural features and defining a training set of compounds for which properties are known of claim 1 (shown by Ogura).
Plumbley does not show defining objectives which each define a desired property of claim 1 (shown by Ogura).
Plumbley does not show Bayesian statistical model of claims 1-20 (shown by Ramachandran).
Plumbley does not show the probability distribution of claims 1, 19, and 20 (shown by Ramachandran).
Plumbley does not show preference is indicative of a priority of the respective objective relative to the other objective of claim 3 (shown by Ogura).
Plumbley does not show first probability distribution is associated with greater preference when an associated uncertainty value decreases of claim 4 (shown by Ramachandran).
Plumbley does not show evaluating the acquisition function to determine a respective acquisition function value of claim 5 (shown by Ramachandran).
Plumbley does not show subset of compounds is determined based on acquisition function values of claim 5 (shown by Ogura).
Plumbley does not show optimization of the acquisition function provides a Pareto-optimal set of compounds, and the determined subset of compounds includes compound(s) selected from the Pareto-optimal set, of claim 6 (shown by Ogura).
Plumbley does not show mapping the first probability distributions from the Bayesian statistical model to a one-dimensional aggregated probability distribution by applying an aggregation function of claim 8 (shown by Ramachandran).
Plumbley does not show acquisition function has multiple dimensions, each corresponding to an objective of claim 9 (shown by Ramachandran).
Plumbley does not show tuning hyperparameters of the Bayesian statistical model by applying a combination of a maximum likelihood estimation technique and cross validation of claim 10 (shown by Ramachandran).
Plumbley does not show setting fake property values for the identified compounds in the Bayesian statistical model according to a kriging believer approach or a constant liar approach of claim 12 (shown by Ramachandran).
Plumbley does not show Bayesian statistical model is a Gaussian process model of claim 13 (shown by Ramachandran).
Plumbley does not show weighting parameters are modified with optimization of: exploitation strategies dependent on a weighting parameter of the acquisition function associated with the posterior mean and with the posterior variance of claim 14 (shown by Ramachandran).
Plumbley does not show receiving (i) data indicative of compounds having structural features, (ii) data indicative of a training set of compounds for which biological properties are known, and (iii data indicative of objectives defining a desired biological property of claim 19 and 20 (shown by Ogura).
Ogura:
In independent claim 1, the recited defining a population of compounds having structural features, and defining a training set of compounds for which properties are known, reads on "previous research integrated the hERG-associated information from ChEMBL, GOSTAR, NIH Chemical Genomics Center dataset registered in PubChem, and hERGCentral into a dataset consisting of more than 291,000 structurally diverse compounds" (Ogura, p.2, ¶ 3; and p.3, table 1) and "The dataset was randomly split into the training set ...and the test set." (Ogura, p.3, ¶ 3).
In independent claim 1, the recited defining objectives which each define a desired property reads on "NSGA-II is an optimization algorithm to find the Pareto optimal for multiple objective functions" (Ogura, p.4, ¶ 5), and "The average kappa statistics of the 5-fold cross validation (kappa CV) and the number of used descriptors were defined as the objective functions for NSGA-II." (Ogura, p.4, ¶ 5).
In claim 3, the recited preference is indicative of a priority of the respective objective relative to the other objectives reads on " NSGA-II is an optimization algorithm to find the Pareto optimal for multiple objective functions...The individuals in each population are sorted according to their dominance levels" (Ogura, p.4, ¶4).
In claim 5, the recited subset of compounds is determined based on acquisition function values reads on " the predicted pIC50 value of 5.0 was defined as the criterion to classify positive and negative compounds" (Ogura, p. 5, ¶ 2). Here, the criterion is interpreted as the acquisition function.
In claim 6, the recited optimization of the acquisition function provides a Pareto-optimal set of compounds, and the recited determined subset of compounds includes compound(s) selected from the Pareto-optimal set, reads on "To avoid overfitting of the prediction model, the descriptor set was optimized to show high accuracy with a small number of descriptors by a genetic algorithm, to find the Pareto optimal for multiple objective functions. Subsequently, the importance of each of the selected descriptors for hERG prediction was assessed, according to the results of the descriptor selection (Ogura, p. 3, ¶ 1).
In independent claim 19 and 20, the recited receiving (i) data indicative of compounds having structural features, (ii) data indicative of a training set of compounds for which biological properties are known, and (iii data indicative of objectives defining a desired biological property reads on "previous research integrated the hERG-associated information from ChEMBL, GOSTAR, NIH Chemical Genomics Center dataset registered in PubChem, and hERGCentral into a dataset consisting of more than 291,000 structurally diverse compounds" (Ogura, p.2, ¶ 3; and p.3, table 1) and "The dataset was randomly split into the training set ...and the test set." (Ogura, p.3, ¶ 3).
Ramachandran:
In claims 1-20, the recited training a Bayesian statistical model reads on a "promising area of research under Bayesian optimisation is multi-objective optimisation, also called Pareto optimization" (Ramachandran, p.38, ¶ 4) and "Bayesian optimization redefines the training set based on the pairwise ranking and standard Bayesian optimisation (see Algorithm 2.1) is iteratively performed using this redefined training set" (Ramachandran, p.43, ¶ 2).
In claims 1-20, the recited probability distribution reads on "probability distribution(s)" of Ramachandran (p. 25, ¶ 5; p. 71, ¶ 4-5; p. 79, ¶1).
In claim 4, the recited first probability distribution is associated with greater preference when an associated uncertainty value decreases reads on "as alternate approach to address the constraint optimisation problem based on a stepwise uncertainty reduction" (Ramachandran, p. 38, ¶ 2).
In claim 5, the recited evaluating the acquisition function to determine a respective acquisition function value reads on "improved optimisation of the acquisition function" (Ramachandran, p.35, ¶ 1).
In claim 8, the recited mapping the first probability distributions from the Bayesian statistical model to a one-dimensional aggregated probability distribution by applying an aggregation function reads on "Bayesian optimisation of a one-dimensional function over four iterations" (Ramachandran, bridging p.13-14).
In claim 9, the recited acquisition function has multiple dimensions, each corresponding to an objective reads on "In high-dimensional space, Gaussian process requires exponentially many observations implying more function evaluations" (Ramachandran, p.33, ¶ 4).
In claim 10, the recited tuning hyperparameters of the Bayesian statistical model by applying a combination of a maximum likelihood estimation technique and cross validation reads on section "2.3.4 Hyperparameter Tuning" and "Tuning sometimes requires...cross validation" (Ramachandran, p.32, ¶ 2, Section 2.3.4) and "maximum likelihood estimate is used for estimating Gaussian process kernel length-scale" (Ramachandran, p.100, ¶ 2).
In claim 12, the recited setting fake property values for the identified compounds in the Bayesian statistical model according to a kriging believer approach or a constant liar approach reads on "A Kriging (probabilistic metamodel) based approach for batch optimisation is presented... Specifically, expected improvement selects a point and the function value at this point is set to a value (called “lie”) set artificially by the user... Most of the previous methods use the true maxima of acquisition function as the initial point and then use different methods such as constant liar heuristic or use ‘fake’ observations of the model)" (Ramachandran, p.36, ¶ 1).
In claim 13, the recited Bayesian statistical model is a Gaussian process model reads on a "multi-objective model-based optimisation methods...a Gaussian process model based algorithm called ParEGO - an extension of efficient global optimisation (EGO) algorithm" (Ramachandran, p.39, ¶ 2).
In claim 14, the recited weighting parameters are modified with optimization of: exploitation strategies dependent on a weighting parameter of the acquisition function associated with the posterior mean and with the posterior variance reads on "observations from this source function are augmented with the observations of the function being optimised, to update the posterior mean and variance of the Gaussian process" (Ramachandran, p.4, ¶ 2).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the optimization method for selecting a subset and retraining an active learning method of Plumbley, with the elements of Bayes statistical optimization models of Ramachandran, with the Pareto optimization and property (descriptor) selection methods of Ogura to come to a method for computational drug design, because Plumbley provides motivation to modify by mentioning Bayes as a further example of a machine learning method which may be included (Plumbley [0075-76]); while Ogura discloses increases in public databases have accelerated the improvement of statistical models using machine learning techniques, finally, Ramachandran shows methods for improving the optimisation efficiency through Bayesian optimisation. One of ordinary skill would have had a reasonable expectation of success, as Plumbley, Ogura, and Ramachandran are drawn to methods which can be employed in drug design, and as such the combination would have been obvious.
Note about relevant prior art not relied upon
The following art made of record and not relied upon in this Office Action is considered relevant to Applicant’s disclosure: Reker (2016. Multi-objective active machine learning rapidly improves structure–activity models and reveals new protein–protein interaction inhibitors. Chemical science, vol. 7(6), pp.3919-3927; cited on the 12/30/2024 IDS).
Double Patenting
The nonstatutory double patenting rejection is based on a judicially created doctrine grounded in public policy (a policy reflected in the statute) so as to prevent the unjustified or improper timewise extension of the “right to exclude” granted by a patent and to prevent possible harassment by multiple assignees. A nonstatutory double patenting rejection is appropriate where the conflicting claims are not identical, but at least one examined application claim is not patentably distinct from the reference claim(s) because the examined application claim is either anticipated by, or would have been obvious over, the reference claim(s). See, e.g., In re Berg, 140 F.3d 1428, 46 USPQ2d 1226 (Fed. Cir. 1998); In re Goodman, 11 F.3d 1046, 29 USPQ2d 2010 (Fed. Cir. 1993); In re Longi, 759 F.2d 887, 225 USPQ 645 (Fed. Cir. 1985); In re Van Ornum, 686 F.2d 937, 214 USPQ 761 (CCPA 1982); In re Vogel, 422 F.2d 438, 164 USPQ 619 (CCPA 1970); In re Thorington, 418 F.2d 528, 163 USPQ 644 (CCPA 1969).
A timely filed terminal disclaimer in compliance with 37 CFR 1.321(c) or 1.321(d) may be used to overcome an actual or provisional rejection based on nonstatutory double patenting provided the reference application or patent either is shown to be commonly owned with the examined application, or claims an invention made as a result of activities undertaken within the scope of a joint research agreement. See MPEP § 717.02 for applications subject to examination under the first inventor to file provisions of the AIA as explained in MPEP § 2159. See MPEP § 2146 et seq. for applications not subject to examination under the first inventor to file provisions of the AIA . A terminal disclaimer must be signed in compliance with 37 CFR 1.321(b).
The filing of a terminal disclaimer by itself is not a complete reply to a nonstatutory double patenting (NSDP) rejection. A complete reply requires that the terminal disclaimer be accompanied by a reply requesting reconsideration of the prior Office action. Even where the NSDP rejection is provisional the reply must be complete. See MPEP § 804, subsection I.B.1. For a reply to a non-final Office action, see 37 CFR 1.111(a). For a reply to final Office action, see 37 CFR 1.113(c). A request for reconsideration while not provided for in 37 CFR 1.113(c) may be filed after final for consideration. See MPEP §§ 706.07(e) and 714.13.
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NSDP rejection:
Claims 1-20 are rejected on the ground of nonstatutory double patenting as being unpatentable over one or more claims of U.S. Patent No. 12,525,324 (the '324 patent, from Application No. 18/138,021) in view of Plumbley (US-2021/0012862-A1, filed 01-14-2021; cited on the attached form PTO-892); in view of Ogura, (2019. Scientific reports, vol. 9(1):12220, pages 1-12; cited on the attached form PTO-892); in view of Ramachandran (2019. Harnessing Auxiliary Knowledge Towards Efficient Bayesian Optimisation (Doctoral dissertation, Deakin University) ; cited on the attached form PTO-892).
Although the claims at issue are not identical, they are not patentably distinct from each other because the '324 patent and the instant application both recite methods and devices for computational drug design which involve defining a population of compounds, defining a training set, selecting a subset of compounds, etc.
While the instant claims recite limitations for training a Bayes model, defining objectives which each define a desired property, optimization of an acquisition function, and selecting compounds for synthesis, these elements are shown by Ramachandran (training a Bayes model, p.43, ¶ 2); Plumbley (selecting some compounds for synthesis shown by laboratory experimentation, [0100]; Ogura (the Pareto optimal for multiple objective functions; p.4, ¶ 5), such that this narrowing versus the '324 patent is now interpreted as obvious, and as such the instant invention would have been prima facie obvious in view of the cited art.
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 action.
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/M.A.V./Examiner, Art Unit 1687
/G. STEVEN VANNI/Primary patents examiner, Art Unit 1686
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