DETAILED ACTION
Applicant's response, filed 9 February 2026, has been fully considered. Rejections and/or objections not reiterated from previous Office Actions are hereby withdrawn. The following rejections and/or objections are either reiterated or newly applied. They 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 .
Claim Status
Claims 1-3, 6-8, 10, and 13-25 are currently pending and under exam herein.
Claims 21-25 are newly added.
Claims 4-5, 9, and 11-12 have been cancelled.
The instant application is examined under Track I status.
Information Disclosure Statement
The Information Disclosure Statements filed 4 February 2026 is in compliance with the provisions of 37 CFR 1.97 and has therefore been considered. A signed copy of the IDS document is included with this Office Action.
Outstanding Claim Objections
The outstanding claim objections over claims 1-20 have been withdrawn in view of the submitted claim amendments.
Claim Interpretation
The instant claims 1-3, 6-8, 10, and 13-19 are directed to “an AI-based platform” that includes data and “an AI-based agent” wherein the “platform” and “agent” as recited are interpreted under the Broadest Reasonable Interpretation (BRI) to read on “software”. The Specification does not limit the instant recitations to a particular definition. For example, the Specification discloses that the “agent” may be a software model [0731]. The “platform” is disclosed as including the technological environment for the infrastructure of AI, that includes language models etc. (e.g., Figure 1). However, there are no structural components of said “platform” claimed and therefore the claims read on “software” only, as the “platform” only contains software (AI-agent) and data sets.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-3, 6-8, 10, and 13-25 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
The instant rejection reflects the framework as outlined in the MPEP at 2106.04:
Framework with which to Evaluate Subject Matter Eligibility:
(1) Are the claims directed to a process, machine, manufacture or composition of matter;
(2A) Prong One: Do the claims recite a judicially recognized exception, i.e. a law of nature, a natural phenomenon, or an abstract idea;
Prong Two: If the claims recite a judicial exception under Prong One, then is the judicial exception integrated into a practical application (Prong Two); and
(2B) If the claims do not integrate the judicial exception, do the claims provide an inventive concept.
Framework Analysis as Pertains to the Instant Claims:
Step 1 Analysis: Are claims directed to process, machine, manufacture/composition of matter
With respect to step (1): yes, the claims are directed to a method (claims 20-25).
(No) Claims 1-3, 6-8, 10, and 13-19 are rejected as non-statutory as they recite “a platform” with an “AI-agent”. Therefore the claims read on carrier waves (transitory propagating signals) which are not proper patentable subject matter because they do not fit within any of the four statutory categories of invention (In re Nuijten, Federal. Circuit, 2006).
For purposes of compact prosecution, however, the “platform” will also be examined herein (claims 1-3, 6-8, 10, and 13-19), even though said platform reads on “software”. Recitation of “non-transitory computer readable medium” would resolve this particular issues but claims are subject to the framework as cited above otherwise, or recitation of specific hardware, AI structure (see claim interpretation above).
Step 2A, Prong 1 Analysis: Do claims recite abstract idea
With respect to step (2A)(1), the claims recite abstract ideas. The MPEP at 2106.04(a)(2) further explains that abstract ideas are defined as:
mathematical concepts, (mathematical formulas or equations, mathematical relationships and mathematical calculations);
certain methods of organizing human activity (fundamental economic practices or principles, managing personal behavior or relationships or interactions between people); and/or
mental processes (procedures for observing, evaluating, analyzing/ judging and organizing information).
With respect to the instant claims, under the (2A)(1) evaluation, the claims are found herein to recite abstract ideas that fall into the grouping of mental processes (in particular procedures for observing, analyzing and organizing information) and/or mathematical processes (training).
The claim steps to abstract ideas are as follows:
Claim 1:
generate, by the reinforcement learning neural network model, a model output comprising control parameters that specify an experiment definition for the synthetic biology experiment based on the model of the biologic process; cause the synthetic biology experiment to be performed based on the experiment definition; comprising determining a reward characterizing the at least one outcome of the synthetic biology experiment, wherein the reward characterizes at least a yield or a rate of the biologic process during the synthetic biology experiment; train the reinforcement learning neural network model on the reward by a reinforcement learning training technique, which are directed to operations, save for the “AI-agent” and “train” elements, that may be performed by mental steps wherein one could generate experimental definitions on paper, configured biological interactions based on the definitions established, perform evaluations in various experimental scenarios and update any given model with the findings. There are no specifics as to the operation of the AI-agent, other than it is used for said operations. As such, the AI-agent is a tool to perform said abstract process only. Further to “training”, the steps involve nothing more than providing data to further operate “reinforcement leaning” techniques which are mathematical operations as per the Specification at least at [0064]; [1649]; [1658]; [1711]; [1712]; [2256]; and the like.
Steps of dependent claims further include those that provide operations that are mental in nature, such as “generating a hypothesis” or “generating an experimental definition” and as such further limit the judicial exceptions herein.
Claim 20:
generate, by the reinforcement learning neural network model, a model output comprising control parameters that specify an experiment definition for the synthetic biology experiment based on the model of the biologic process; cause the synthetic biology experiment to be performed based on the experiment definition; comprising determining a reward characterizing the at least one outcome of the synthetic biology experiment, wherein the reward characterizes at least a yield or a rate of the biologic process during the synthetic biology experiment; train the reinforcement learning neural network model on the reward by a reinforcement learning training technique, which are directed to operations, save for the “AI-agent” and “train” elements, that may be performed by mental steps wherein one could generate experimental definitions on paper, configured biological interactions based on the definitions established, perform evaluations in various experimental scenarios and update any given model with the findings. There are no specifics as to the operation of the AI-agent, other than it is used for said operations. As such, the AI-agent is a tool to perform said abstract process only. Further to “training”, the steps involve nothing more than providing data to further operate “reinforcement leaning” techniques which are mathematical operations as per the Specification at least at [0064]; [1649]; [1658]; [1711]; [1712]; [2256]; and the like.
Steps of dependent claims further include those that provide operations that are mental in nature, such as “generating a hypothesis” or “generating an experimental definition” and as such further limit the judicial exceptions herein.
Hence, the claims explicitly recite numerous elements that, individually and in combination, constitute abstract ideas.
The abstract ideas recited in the claims are evaluated under the Broadest Reasonable Interpretation (BRI) and determined herein to each cover performance either in the mind (calculations by hand or pen and paper) or by mathematical process. There are no specifics as to the methodology involved in “generating” or in “performing” or “training” and experiment beyond operations that define what the generations are; or what the evaluations and training comprise. Such is a description of characteristics only and not actual operations by way of specific algorithms, for example to do so. Thus, under the BRI, one could simply, for example, perform said operation with pen and paper, or, alternatively with the aid of a generic computer as a tool to perform said calculations. These recitations are similar to the concepts of collecting information, analyzing it and providing certain results from the collection and analysis (Electric Power Group, LLC, v. Alstom (830 F.3d 1350, 119 USPQ2d 1739 (Fed. Cir. 2016)), organizing and manipulating information through mathematical correlations (Digitech Image Techs., LLC v Electronics for Imaging, Inc. (758 F.3d 1344, 111 U.S.P.Q.2d 1717 (Fed. Cir. 2014)) and 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)) that the courts have identified as concepts that can be practically performed in the human mind with pen and paper, and can include mathematical concepts.
Further, see MPEP § 2106.04(a)(2), subsection III. The courts do not distinguish between mental processes that are performed entirely in the human mind and mental processes that require a human to use a physical aid (e.g., pen and paper or a slide rule) to perform the claim limitation (see, e.g., Benson, 409 U.S. at 67, 65, 175 USPQ at 674-75, 674: noting that the claimed "conversion of [binary-coded decimal] numerals to pure binary numerals can be done mentally," i.e., "as a person would do it by head and hand."); Synopsys, Inc. v. Mentor Graphics Corp., 839 F.3d 1138, 1139, 120 USPQ2d 1473, 1474 (Fed. Cir. 2016): holding that claims to a mental process of "translating a functional description of a logic circuit into a hardware component description of the logic circuit" are directed to an abstract idea, because the claims "read on an individual performing the claimed steps mentally or with pencil and paper"). Nor do the courts distinguish between claims that recite mental processes performed by humans and claims that recite mental processes performed on a computer. As the Federal Circuit has explained, "[c]ourts have examined claims that required the use of a computer and still found that the underlying, patent-ineligible invention could be performed via pen and paper or in a person’s mind" (see Versata Dev. Group v. SAP Am., Inc., 793 F.3d 1306, 1335, 115 USPQ2d 1681, 1702 (Fed. Cir. 2015); Mortgage Grader, Inc. v. First Choice Loan Servs. Inc., 811 F.3d 1314, 1324, 117 USPQ2d 1693, 1699 (Fed. Cir. 2016): holding that computer-implemented method for "anonymous loan shopping" was an abstract idea because it could be "performed by humans without a computer"). In this instance, the recitation of the “AI-agent” is merely a tool as claimed herein, as it is generically recited in the context of a “platform” that has no structure. This holds true also to any “Training” as claimed. The court in Desjardins, for example, included that, “the determination requires us to "evaluate the significance of the additional elements relative to the invention," while being mindful that "the ultimate question" is "whether the exception is integrated into a practical application." MPEP § 2106.04(d)(II). On the one hand, claims "[g]enerally linking the use of a judicial exception to a particular technological
environment or field of use" are not patent eligible. See MPEP § 2106.05(h), ( citing Affinity Labs ofTex. v. DirecTV, LLC, 838 F .3d 1253 (Fed. Cir.2016) and Elec. Power Grp., LLC v. Alstom SA., 830 F.3d 1350, 1354 (Fed.Cir. 2016)). On the other, claims directed to an improvement in the functioning of a computer, or an improvement to other technology or technical field are patent eligible. See MPEP §§ 2106.04(d)(l) and 2106.05(a) (citing Enfish, LLC v. Microsoft Corp., 822 F.3d 1327, 1339 (Fed. Cir. 2016) and McRO, Inc. v. Bandai Namco Games Am. Inc., 837 F.3d 1299, 1315 (Fed. Cir. 2016)). Here it is not apparent that such exists for the instant set of claims, given the complex nature of making said determinations for all data sets in synthetic biology, and given the lack of “reward” characteristics that would so define such to the end goal of training an network.
Step 2A, Prong 2 Analysis: Integration to a Practical Application
Because the claims do recite judicial exceptions, direction under (2A)(2) provides that the claims must be examined further to determine whether they integrate the abstract ideas into a practical application (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. This is performed by analyzing the additional elements of the claim 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 is said to fail to integrate the abstract idea into a practical application (MPEP 2106.04(d).III).
With respect to the instant recitations, the claims recite the following additional elements:
Claim 1:
AI-based platform; an experiment data set defining a synthetic biology experiment based on a model of a biologic process; an AI-based agent wherein said platform and agent are generically recited. Said dataset, is the data for which is provided to perform said abstract ideas and constitute extra-solution activity herein as it is not integrated into any meaningful or practical application beyond use in the abstract idea.
Claim 20
AI-agent of an AI platform which is generically recited and reads on any computing environment.
Further with respect to the additional elements in the instant claims, those steps directed to “data” serve as gathering functions of collecting the data needed to carry out the abstract idea. Data gathering does not impose any meaningful limitation on the abstract idea, or on how the abstract idea is performed. Data gathering steps are not sufficient to integrate an abstract idea into a practical application. (MPEP 2106.05(g).
Further steps herein directed to additional non-abstract elements of “platform” and “agent” do not describe any specific computational steps by which the “computer parts” perform or carry out the abstract idea, nor do they provide any details of how specific structures of the computer, such as the computer-readable recording media, are used to implement these functions. The claims state nothing more than a generic computer which performs the functions that constitute the abstract idea. Hence, these are mere instructions to apply the abstract idea using a computer, and therefore the claim does not integrate that abstract idea into a practical application. The courts have weighed in and consistently maintained that when, for example, a memory, display, processor, machine, etc… are recited so generically (i.e., no details are provided) that they represent no more than mere instructions to apply the judicial exception on a computer, and these limitations may be viewed as nothing more than generally linking the use of the judicial exception to the technological environment of a computer. (see MPEP 2106.05(f)).
Step 2B Analysis: Do Claims Provide an Inventive Concept
The claims are lastly evaluated using the (2B) analysis, wherein it is determined that because the claims recite abstract ideas, and do not integrate that abstract ideas into a practical application, the claims also lack a specific inventive concept. Applicant is reminded that the judicial exception alone cannot provide the inventive concept or the practical application and that the identification of whether the additional elements amount to such an inventive concept requires considering the additional elements individually and in combination to determine if they provide significantly more than the judicial exception. (MPEP 2106.05.A i-vi).
With respect to the instant claims, the additional elements of data gathering described above do not rise to the level of significantly more than the judicial exception. As directed in the Berkheimer memorandum of 19 April 2018 and set forth in the MPEP, determinations of whether or not additional elements (or a combination of additional elements) may provide significantly more and/or an inventive concept rests in whether or not the additional elements (or combination of elements) represents well-understood, routine, conventional activity. Said assessment is made by a factual determination stemming from a conclusion that an element (or combination of elements) is widely prevalent or in common use in the relevant industry, which is determined by either a citation to an express statement in the specification or to a statement made by an applicant during prosecution that demonstrates a well-understood, routine or conventional nature of the additional element(s); a citation to one or more of the court decisions as discussed in MPEP 2106(d)(II) as noting the well-understood, routine, conventional nature of the additional element(s); a citation to a publication that demonstrates the well-understood, routine, conventional nature of the additional element(s); and/or a statement that the examiner is taking official notice with respect to the well-understood, routine, conventional nature of the additional element(s).
With respect to the instant claims, the instant Specification also discloses the agent as software model (see, for example, [0731]). As such, the computer-related elements or the general purpose computer do not rise to the level of significantly more than the judicial exception and constitute no more than a general link to a technological environment, which is insufficient to constitute an inventive concept that would render the claims significantly more than an abstract idea (see MPEP 2106.05(b)I-III).
Dependent claims 2-19 have been analyzed with respect to step 2B and none of these claims provide a specific inventive concept, as they all fail to rise to the level of significantly more than the identified judicial exception.
For these reasons, the claims, when the limitations are considered individually and as a whole, are rejected under 35 USC § 101 as being directed to non-statutory subject matter.
It is suggested that particular structures of the specialized platform be claimed or particular data structures of the AI-agent, which may be helpful to overcome the above rejection. As it stands, the claims merely use AI in a manner in which is generically recited for automation of a known task, i.e. design an experiment, without detail on the technical innovations of the AI itself.
Response to Applicant’s Arguments
1. Applicant states that, “this rejection should be withdrawn because, under step 2A, prong 2 of the eligibility analysis, the claims are integrated into a practical application.
Applicant submits that, even if the claims recite an abstract idea (which is not conceded), the claims are not directed to an abstract idea because any abstract ideas recited the claims are integrated into a practical application” and further includes that, “a claim can integrate an abstract idea into a practical application "when the claimed invention improves the functioning of a computer or improves another technology or technical field." 2106.04(d)(1)”. Applicant provides that the technical improvements are to computer functionality and that, in particular, “The claimed invention provides an improvement to computer functionality by improving computational efficiency (thus reducing latency and power consumption) in converging on control parameters to optimize the biologic process. In particular, the claimed invention leverages reinforcement learning to cause the neural network model to "develop a policy based on both continued exploration and refinement of previously evaluated actions [control parameters] that are likely to advance the objective function [process yield and/or rate] and novel exploration of previously unevaluated actions that might yield even better options for advancing the objective function" (Specification at [2258]). That is, training the neural network model by a reinforcement learning training technique causes the neural network model to efficiently search a high-dimensional space of possible control parameters by balancing "exploration" of new control strategies with "exploitation" of information about performance of previously evaluated control strategies.
It is respectfully submitted that this is not persuasive. It is not apparent in the instant claim set that the neural network model “develop[s] a policy based on both continued exploration and refinement of previously evaluated actions…that are likely to advance the objective function” as per the claimed subject matter. The claims are not directed to reduction in high-dimensional space of any possible control parameters by exploration balancing or exploitation of previous control strategies in any specific manner that provide for how this is achieved in the claim. The claims are defined in functional terms, i.e., what the objectives are but not how to specifically achieve them in the environment of the complex data requirements of all of synthetic biology approaches and experimental data known. Further, the features as described as those of continued exploration and refinement are what neural nets are designed by nature to achieve and without any specifics of how this achieved, the instant invention employs generic operations only.
2. Applicant further remarks that, “the claimed invention provides an improvement to computer functionality by enabling the a computer to perform a complex task that was previously technologically infeasible. More specifically, approaches like supervised learning may fail when applied to optimizing biologic processes because "the vast number of combination of states... prevents training with even a minimally comprehensive training data set." (Specification at [2256]). To address this issue, the claimed invention leverages reinforcement learning techniques which are robust to technical challenges like a lack of labeled training data and "enable the development of an artificial neural network 4002 for more complex scenarios to which backpropagation 4116 cannot be effectively applied." (Specification at [2257])”.
However, it is maintained, as in the above, that the claims are defined in functional terms, i.e., what the objectives are but not how to specifically achieve them in the environment of the complex data requirements of all of synthetic biology approaches and experimental data known. Further, the features as described as those of continued exploration and refinement are what neural nets are designed by nature to achieve and without any specifics of how this achieved, the instant invention employs generic operations only.
As such, the claims are not eligible under 35 USC 101 herein.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
1. Claims 1 and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Bhardwaj et al. (Life (2022) Vol. 12:20 pages), in view of Treloar et al. (PLoS Comput Biol 18(11): e1010695:24 pages).
This is a new grounds of rejection and is necessitated by claim amendment. The prior art to Bhardwaj et al. is taught with respect to the italicized portions below. The prior art to Treloar et al. is further set forth as underlined and addressed thereafter.
With respect to claims 1 and 20, the AI platform and agent are not structurally defined in the claims, and as such said claims are interpreted as obvious over using known AI platforms that would allow for input of data and design of an experimental definition. For example, with respect to Claims 1 and 20,
Claim 1 is directed to:
An AI-based platform, comprising:
an experiment data set defining a synthetic biology experiment based on a model of a biologic process; (Bhardwaj et al. disclose capabilities of AI systems and algorithms that store and process large amounts of data from multiple resources, such as sequencing data, molecular data, protein data, multi-omics data and beyond-pages 4-5)
an AI-based agent configured to perform operations for training a reinforcement learning neural network model to optimize the biologic process, the operations comprising, at each of a plurality of training cycles,
generate by the reinforcement learning neural network model, a model output comprising control parameters that specify an experiment definition for the synthetic biology experiment based on the model of the biologic process (Bhardwaj et al. disclose capabilities of AI systems that use biological information for experimental design-page 4)
cause the synthetic biology experiment to be performed based on the experiment definition (Bhardwaj et al. disclose capabilities of AI systems that use biological information for experimental design-page 5-6)
perform an evaluation of at least one outcome of the synthetic biology experiment, comprising determining a reward characterizing the at least one outcome of the synthetic biology experiment, wherein the reward characterizes at least a yield or a rate of the biologic process during the synthetic biology experiment (Bhardwaj et al. disclose capabilities of AI systems that use biological information for experimental design-for evaluation of outcomes-page 6 including effects of proteins on tissues for example), and
train the reinforcement learning neural network model on the reward by a reinforcement learning training technique. (Bhardwaj et al. disclose capabilities of AI systems that use biological information for experimental design-for evaluation of outcomes-page 6 including effects of proteins on tissues for example, and learning based on outcomes for improvement purposes (updates—page 6).
Claim 20 is directed to:
A method performed by an AI-based agent of an AI-based platform, comprising:
accessing an experiment data set defining a synthetic biology experiment based on a model of a biologic process; (Bhardwaj et al. disclose capabilities of AI systems and algorithms that store and process large amounts of data from multiple resources, such as sequencing data, molecular data, protein data, multi-omics data and beyond-pages 4-5)
performing operations for training a reinforcement learning neural network model to optimize the biologic process, the operations comprising, at each of a plurality of training cycle:
generating by the reinforcement learning neural network model, a model output comprising control parameters that specify an experiment definition for the synthetic biology experiment based on the model of the biologic process (Bhardwaj et al. disclose capabilities of AI systems that use biological information for experimental design-page 4)
causing the synthetic biology experiment to be performed based on the experiment definition (Bhardwaj et al. disclose capabilities of AI systems that use biological information for experimental design-page 5-6)
performing an evaluation of at least one outcome of the synthetic biology experiment, comprising determining a reward characterizing the at least one outcome of the synthetic biology experiment, wherein the reward characterizes at least a yield or a rate of the biologic process during the synthetic biology experiment (Bhardwaj et al. disclose capabilities of AI systems that use biological information for experimental design-for evaluation of outcomes-page 6 including effects of proteins on tissues for example), and
training the reinforcement learning neural network model on the reward by a reinforcement learning training technique. (Bhardwaj et al. disclose capabilities of AI systems that use biological information for experimental design-for evaluation of outcomes-page 6 including effects of proteins on tissues for example, and learning based on outcomes for improvement purposes (updates—page 6).
The prior art to Bhardwaj et al. does not specifically disclose an AI-agent configured to or performing operations for training that include the specific training of reinforcement learning, and that further include the specifics associated with reinforcement learning which are reward determination that characterize outcomes and training based on the reward, as now amended into claims 1 and 20.
However, the prior art to Treloar et al. disclose that the field of optimal experiment design uses mathematical techniques to make determinations for experiments that are maximally informative from given experimental setup. Treloar et al. specifically disclose that the technique of reinforcement learning performs favorably in comparison to other types of algorithmic approaches for the inference of bacterial growth patterns [abstract]. Further Treloar et al. disclose the specifics of said RL that includes, “reinforcement learning is a branch of machine learning concerned with optimizing an agent’s behavior within an environment. The agent responds to observations of its environment by selecting from a set of actions that, in turn, impact the environment. From a reward structure imposed on this interaction, the agent learns an optimal behavior policy [p. 3; Figure 1B]” Further Treloar et al. disclose that, “once training is complete, the trained agent can act as a feedback con troller to provide real-time inputs to the experimental system [p.5]” and that they are specifically concerned with a bacterial growth model [p.5].
As such, 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 incorporated into the AI agent and method teachings of Bhardwaj et al. with the specific RL systems as disclosed by Treloar et al. because Bhardwaj et al. specifically disclose that the “use of AI through machine learning (ML) and deep-learning-based smart programs, [enable] one [to] modify the metabolic pathways of living systems to obtain the best possible outputs with the minimal inputs” [abstract]. Further Bhardwaj et al. motivate the use of a myriad of types of machine learning/neural networks disclosing that, “various machine learning algorithms, including deep learning, have facilitated in optimizing the bioprocess parameters and exploring a larger metabolic space that is linked to the biosynthesis of a target bioproduct [133]. This trend is also influencing biotechnology businesses to adopt ML techniques more frequently in the creation of their production systems and platform technologies [134]” [p.12]. As such, one would have had a reasonable expectation of success in using the specific machine learning that is RL, as taught by Treloar et al., as both references are in the same field of endeavor and would have expected to work cooperatively therein, using a substitute for one type of machine learning technique or techniques, as in Bhardwaj et al. with the RL technique of Treloar et al.
2. Claims 2-3, 6-8, 10, 13-19, 21-25 are rejected under 35 U.S.C. 103 as being unpatentable over Bhardwaj et al. (Life (2022) Vol. 12:20 pages), in view of Treloar et al. (PLoS Comput Biol 18(11): e1010695:24 pages), as applied to claim 1 above and in view of National Academies of Sciences, Engineering, and Medicine. 2002. Scientific Research in Education. Washington, DC: The National Academies Press. https://doi.org/10.17226/10236:Chapter 3; 44 pages (hereinafter Natl. Acad. Press).
Claim 1 is directed to the limitations as disclosed above. Claim 1 is taught by the prior art to Bhardwaj et al. and Treloar et al. above. With respect to claims 2-3, 6-8, 10, 13-19, 21-25 the following is set forth.
With the understanding that AI engines assume input from users so as to provide evaluations based on user design, implementation of experimental design using set hypothesis and updates to modeling is obvious in view of disclosures in Bhardwaj et al. and Treloar et al. in combination with the scientific approach, which includes operations such as experimental definitions, hypotheses and evaluation in a laboratory. Such details are explained in, for example, Scientific Research in Education as published by the Natl. Acad. Press wherein said disclosure includes that fundamentals of inquiry include “seeking conceptual (theoretical) understanding, posing empirically testable and refutable hypotheses, designing studies that test and can rule out competing counterhypotheses, using observational methods linked to theory that enable other scientists to verify their accuracy, and recognizing the importance of both independent replication and generalization” (page 51) and “what unites scientific inquiry is the primacy of empirical test of conjectures and formal hypotheses using well-codified observation methods and rigorous designs, and subjecting findings to peer review.
Guiding principles further are established that include posing significant questions that can be empirically investigated; linking research to relevant theory; using methods that permit direct investigation of the questions; providing coherent and explicit chains of reasoning; replication and generalization using multiple studies; and disclosure of research (page 52). Further details as to each principals are disclosed at pages 54-73. As such, the implementation of said principals, when combined with data and teachings disclosed by Bhardwaj et al. and Treloar et al., provide that the instant claims steps directed to experimental definitions and hypotheses are obvious over said references.
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 used the basis of scientific principal and research to outline questions to facilitate AI experimental design of biological experiments. The instant system is not defined beyond use of the AI and therefore the data, or any biological data for that matter, as disclosed in Bhardwaj et al. and Treloar et al. would have been obvious to use as the experimental data in question to pose to systems, like those disclosed in the same and for prompting the AI system to investigate using the scientific principals as disclose by Natl. Acad. Press. One would have had a reasonable expectation of success in so doing as In re Venner (In re Venner, 262 F.2d 91, 95, 120 USPQ 193, 194 (CCPA 1958)) provides that providing an automatic or mechanical means to replace a manual activity which accomplished the same result is not sufficient to distinguish over the prior art and therefore without any structural components of the claimed platform, the methods are reasonably interpreted to run on any platform using routine scientific prompts (scientific methods) to construct experimental designs. The claims are therefore prima facie obvious herein.
Conclusion
No claims are allowed.
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). 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.
E-mail Communications Authorization
Per updated USPTO Internet usage policies, Applicant and/or applicant’s representative is encouraged to authorize the USPTO examiner to discuss any subject matter concerning the above application via Internet e-mail communications. See MPEP 502.03. To approve such communications, Applicant must provide written authorization for e-mail communication by submitting following form via EFS-Web or Central Fax (571-273-8300): PTO/SB/439. Applicant is encouraged to do so as early in prosecution as possible, so as to facilitate communication during examination.
Written authorizations submitted to the Examiner via e-mail are NOT proper. Written authorizations must be submitted via EFS-Web or Central Fax (571-273-8300). A paper copy of e-mail correspondence will be placed in the patent application when appropriate. E-mails from the USPTO are for the sole use of the intended recipient, and may contain information subject to the confidentiality requirement set forth in 35 USC § 122. See also MPEP 502.03.
Inquiries
Papers related to this application may be submitted to Technical Center 1600 by facsimile transmission. Papers should be faxed to Technical Center 1600 via the PTO Fax Center. The faxing of such papers must conform to the notices published in the Official Gazette, 1096 OG 30 (November 15, 1988), 1156 OG 61 (November 16, 1993), and 1157 OG 94 (December 28, 1993) (See 37 CFR § 1.6(d)). The Central Fax Center Number is (571) 273-8300.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Lori A. Clow, whose telephone number is (571) 272-0715. The examiner can normally be reached on Monday-Thursday from 12:00PM to 10:00PM ET.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Karlheinz Skowronek can be reached on (571) 272-9047.
Any inquiry of a general nature or relating to the status of this application or proceeding should be directed to (571) 272-0547.
Patent applicants with problems or questions regarding electronic images that can be viewed in the Patent Application Information Retrieval system (PAIR) can now contact the USPTO’s Patent Electronic Business Center (Patent EBC) for assistance. Representatives are available to answer your questions daily from 6 am to midnight (EST). The toll free number is (866) 217-9197. When calling please have your application serial or patent number, the type of document you are having an image problem with, the number of pages and the specific nature of the problem. The Patent Electronic Business Center will notify applicants of the resolution of the problem within 5-7 business days. Applicants can also check PAIR to confirm that the problem has been corrected. The USPTO’s Patent Electronic Business Center is a complete service center supporting all patent business on the Internet. The USPTO’s PAIR system provides Internet-based access to patent application status and history information. It also enables applicants to view the scanned images of their own application file folder(s) as well as general patent information available to the public.
/Lori A. Clow/ Primary Examiner, Art Unit 1687