DETAILED ACTION
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 19 May 2026 has been entered.
Applicant's response 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 Rejections and/or objections not reiterated from previous Office Actions are hereby withdrawn.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 4-5, 9, and 11-12 have been cancelled.
Specification
All reference to the Specification herein refers to the published Specification: US20260030416A1.
Claim Rejections - 35 USC § 112(a)
The following is a quotation of the first paragraph of 35 U.S.C. 112(a):
(a) IN GENERAL.—The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor or joint inventor of carrying out the invention.
Claims 1-3, 6-8, 10, and 13-25 are rejected under 35 U.S.C. 112(a) because the specification, while being enabling for embodiments directed to fermentation and automated sampling and quenching integration with mass spectrometry, does not reasonably provide enablement for operations for any and all laboratory equipment and handling of samples for any context, as currently claimed. The specification does not enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to use or make the vast array of laboratory operations commensurate in scope with these claims.
Claims 1 and 20 and those dependent therefrom have been amended herein to recite:
automatically outputting robotic handling instructions to an automated laboratory system to initiate, for each of the plurality of model outputs generated by the reinforcement learning neural network model, a synthetic biology experiment that implements the control parameters specified in the model output on a parallelized hardware device comprising a plurality of sensors and an automated sampling mechanism configured to capture real-time measurements of the synthetic biology experiment,
wherein the outputting comprises translating the control parameters into an executable protocol that directs the automated laboratory system to: (i) perform automated strain construction to implement the genetic modification parameters, and (ii) operate the automated sampling mechanism to perform a plurality of sampling operations by controlling a motorized base to position sample wells beneath a valve to quench cellular metabolism with quenching solution;
performing an evaluation of at least one outcome of each of the synthetic biology experiments specified by the plurality of model outputs of the reinforcement learning neural network, comprising, for each synthetic biology experiment, 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, and wherein the reward is determined based on experimental measurements generated by the automated sampling mechanism; and
training the reinforcement learning neural network model on the reward by a reinforcement learning training technique.
With respect to the instantly amended claims, the Specification does not include written enablement for the amendments that include a computer system or systems that perform the steps that include outputting robotic handling instructions to an automated laboratory system for model outputs that include genetic modification parameters and environmental parameters to initiate a synthetic biology experiments in which those parameters operate on hardware device with sensors and sampling mechanism, wherein the output parameters are translated into an executable protocol to direct a laboratory system to perform automated strain construction for genetic modification and automated sampling operation that controls a motorized base as claimed.
The sampling system, in the instant Specification is disclosed as a platform that comprises an automated sampling mechanism to collect samples and provide near instantaneous quenching of cellular metabolism as integrated with mass spectrometry analysis [0045]. Further this relates specifically to fermentation systems as in, e.g., [0127]; [0131]; [0139]; [0541]; [0550]. In the context of parameters that include genetic modifications and environmental nutrients, temperature profiles and pH setpoints, the parameters are disclosed only with respect to control of fermentation in the context of a reinforcement neural network model (RL) that specify experimental definition for a synthetic biology experiment as claimed [0551]-[0586]; [0591]-[0592]. The AI-guided synthetic biology platform disclosed includes operations only with respect to detailed operation using RL in a general discussion without specific training of the RL as pertains to any and all parameters for any and all laboratory systems. In fact, the neural network models as disclosed are generalized models that include no specific training as relate to the parameters as disclosed herein, and no particular algorithms that include the parameters as disclosed. Rather, any RL neural network strategy may be used for the laboratory automation. As such, enablement herein rests on the disclosure of a specific system for fermentation parameters, the fermentation operations as disclosed. The fermentation operation on an AI-guided platform is disclosed in detail only at [1165]-[1692] that includes how said operations would include parameters that control adjustment by using measured variable of the system. As such, the instant claims are enabled only insofar as with respect to a specific fermentation operation and system.
Claim Rejections - 35 USC § 112(b)
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.
Claims 1-3, 6-8, 10, and 13-25 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.
Claims 1 and 20 and those dependent therefrom recite, “generating, generate, by the reinforcement learning neural network model, a plurality of model outputs each comprising control parameters that specify an experiment definition for the synthetic biology experiment based on the model of the biologic process…”, wherein there is insufficient antecedent basis in the claim for the recitation of “the synthetic biology experiment” as no “synthetic biology experiment” is previously recited in the claim. The instant rejection may be overcome by amended to recite, “ a synthetic biology experiment”. Clarification is requested.
Claims 1 and 20 and those dependent therefrom recite, “automatically outputting robotic handling instructions to an automated laboratory system to initiate, for each of the plurality of model outputs generated by the reinforcement learning neural network model, a synthetic biology experiment that implements the control parameters specified in the model output on a parallelized hardware device comprising a plurality of sensors and an automated sampling mechanism configured to capture real-time measurements of the synthetic biology experiment”, wherein it is unclear as to the robotic handling instructions that would initiate a synthetic biology experiment whereby a control parameter (genetic modification and/or environmental parameter) would be implemented into an executable protocol that directs the laboratory system. There are no steps by which said control parameters operate in this fashion and is unclear as to said operation for the automated system. As such, the claims are interpreted as data into a laboratory system only. Clarification is requested.
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. Any newly recited portions herein are necessitated by claim amendment.
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 system and a method.
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:
generating, by the reinforcement learning neural network model, a plurality of model outputs each comprising control parameters that specify an experiment definition for the synthetic biology experiment based on the model of the biologic process, wherein the control parameters include: (i) genetic modification parameters, and (ii) environmental parameters comprising at least one of a nutrient feed rate, a temperature profile, or a pH setpoint;… wherein the outputting comprises translating the control parameters into an executable protocol that directs the automated laboratory system to: (i) perform automated strain construction to implement the genetic modification parameters, and (ii) operate the automated sampling mechanism to perform a plurality of sampling operations by controlling a motorized base to position sample wells beneath a valve to quench cellular metabolism with quenching solution…; performing an evaluation of at least one outcome of each of the synthetic biology experiments specified by the plurality of model outputs of the reinforcement learning neural network, comprising, for each 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 and wherein the reward is determined based on experimental measurements generated by the automated sampling mechanism; train the reinforcement learning neural network model on the reward by a reinforcement learning training technique, which are directed to operations, save for the “computing system” 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 for operation of laboratory equipment. There are no specifics as to the operation of the training a reinforcement learning neural network model, other than it is used for said operations. As such, the neural network 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 plurality of model outputs each comprising control parameters that specify an experiment definition for the synthetic biology experiment based on the model of the biologic process, wherein the control parameters include: (i) genetic modification parameters, and (ii) environmental parameters comprising at least one of a nutrient feed rate, a temperature profile, or a pH setpoint; automatically outputting robotic handling instructions to an automated laboratory system to initiate, for each of the plurality of model outputs generated by the reinforcement learning neural network model, a synthetic biology experiment that implements the control parameters specified in the model output on a parallelized hardware device comprising a plurality of sensors and an automated sampling mechanism configured to capture real-time measurements of the synthetic biology experiment, wherein the outputting comprises translating the control parameters into an executable protocol that directs the automated laboratory system to: (i) perform automated strain construction to implement the genetic modification parameters, and (ii) operate the automated sampling mechanism to perform a plurality of sampling operations by controlling a motorized base to position sample wells beneath a valve to quench cellular metabolism with quenching solution; performing an evaluation of at least one outcome of each of the synthetic biology experiments specified by the plurality of model outputs of the reinforcement learning neural network, comprising, for each synthetic biology experiment, 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 and wherein the reward is determined based on experimental measurements generated by the automated sampling mechanism; train the reinforcement learning neural network model on the reward by a reinforcement learning training technique, which are directed to operations, save for the “computer” 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 for operation of laboratory equipment. 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 to the training and operation of a neural network, the instant claims include no algorithms beyond those of generalize reinforcement learning neural networks that provide the automation scenario as claimed. The automation of tasks that can be performed mentally or with the aid of computing are still abstract operations. The claim fail to include the details of instruction of the system herein and thus the system is defined only by function herein. It is important to note that a general purpose computer that applies a judicial exception, such as an abstract idea, by use of conventional computer functions does not qualify as a particular machine. Ultramercial, Inc. v. Hulu, LLC, 772 F.3d 709, 716-17, 112 USPQ2d 1750, 1755-56 (Fed. Cir. 2014). See also TLI Communications LLC v. AV Automotive LLC, 823 F.3d 607, 613, 118 USPQ2d 1744, 1748 (Fed. Cir. 2016) (mere recitation of concrete or tangible components is not an inventive concept); Eon Corp. IP Holdings LLC v. AT&T Mobility LLC, 785 F.3d 616, 623, 114 USPQ2d 1711, 1715 (Fed. Cir. 2015) (noting that Alappat’s rationale that an otherwise ineligible algorithm or software could be made patent-eligible by merely adding a generic computer to the claim was superseded by the Supreme Court’s Bilski and Alice Corp. decisions)
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:
system; one or more storage devices communicatively coupled to one or more computers…automatically outputting wherein said computer systems is generically recited. Said dataset, is the data for which is provided to perform said abstract ideas and constitute extra-solution activity (output) herein as it is not integrated into any meaningful or practical application beyond use in the abstract idea.
Claim 20
Method performed on a computer 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 robotic handling instructions and automated laboratory system 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 generic computing systems and or laboratory equipment operation. 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).
Further, the prior art discloses numerous operations of laboratory equipment with machine learning, such as in the art to Hall et al. (WO2023122224) disclosing iterative cell culture procedures wherein an automated agent may be implemented that uses deep learning such as RL to improve culture conditions (abstract). Further, the art to Kok et al. (US20220328128) disclose automated machine learning systems for optimization of bioprocesses including plate reading optimization in strain analysis (abstract; [0011]). As such, using RL for laboratory instruction operation is well-known and conventional in the art.
Dependent 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.
Response to Applicant’s Arguments
1. Applicant states that, “the claimed invention provides a specific technical solution to the fundamental engineering challenge of optimizing complex biological systems by integrating a reinforcement learning (RL) neural network with a specialized physical apparatus. By coupling the RL model's iterative decision-making directly with automated strain construction and high precision metabolic quenching, the claimed invention enables the generation of high-fidelity, large-scale "reward" data (such as accurate yield and rate measurements) that is challenging to achieve through conventional, non-integrated laboratory methods.
One aspect of the technological improvement lies in the synergistic coupling of the RL training process with a physical apparatus specifically configured to provide the robust feedback signals necessary for RL model convergence. The effectiveness of RL in biological contexts is historically limited by the "noisy" and "sparse" nature of experimental data. The claimed invention overcomes this by using an automated sampling mechanism featuring a motorized base and specialized valves to achieve rapid quenching of cellular metabolism. Without this mechanical intervention, enzymes continue to metabolize substrates after a sample is drawn, altering metabolite concentrations and generating inaccurate performance metrics. By halting enzymatic activity using a quenching solution, the claimed invention preserves a high-fidelity "snapshot" of the metabolic state, allowing for the precise determination of the yield and productivity rates used as RL rewards. This automated, hardware-driven precision enables the rapid collection of large volumes of accurate reward data across multiple experiments, providing the high-density training signal required for the reinforcement learning model to effectively optimize complex, non-linear biological processes.
It is respectfully submitted that this is not persuasive. The integration of reinforcement learning (RL) neural networks with a specialized apparatus is not claimed herein. The claim include the generation of any RL neural network model that includes the data for genetic modification and environmental parameters. However, the particular steps that would indicate a specific RL network are not included in the instant claim nor are there steps by which the “automated laboratory system” is a specialized system herein. The claims include recitation of function without the specific structure integration to achieve said function or the “how” of achieving that function other than by generating a generalized model via RL that includes data and somehow implementing a biology experiment that would control a device. As such, the claims recite merely routine laboratory equipment that is “automated”.
With respect to “using an automated sampling mechanism featuring a motorized base and specialized valves to achieve rapid quenching of cellular metabolism. Without this mechanical intervention, enzymes continue to metabolize substrates after a sample is drawn, altering metabolite concentrations and generating inaccurate performance metrics. By halting enzymatic activity using a quenching solution, the claimed invention preserves a high-fidelity "snapshot" of the metabolic state, allowing for the precise determination of the yield and productivity rates used as RL rewards”, the instant claims fail to provide any specific mechanisms by which the control parameters claimed direct a laboratory equipment to perform strain construction or operate any sampling mechanism, as described by Applicant above. As such, the claims are merely operational on a laboratory system that is routine and whereby the abstract ideas are not integrated into the system in a meaningful way nor are they operational to provide more that what is well-known and conventional in the art.
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), in further view of WO2023122224A2 to Hall et al.
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 neural network model is not structurally defined in the claims to interact with the specific operation of an automated laboratory system, and as such said claims are interpreted as obvious over using known AI models 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:
A system, comprising:
one or more computers; and one or more storage devices communicatively coupled to the one or more computers, wherein the one or more storage devices store instructions that, when executed by the one or more computers, cause the one or more computers to perform operations comprising:
training a reinforcement learning neural network model to optimize a biologic process, comprising, at each of a plurality of training cycles,
generating by the reinforcement learning neural network model, a plurality of model outputs each comprising control parameters that specify an experiment definition for the synthetic biology experiment based on the model of the biologic process, wherein the control parameters include: (i) genetic modification parameters, and (ii) environmental parameters comprising at least one of a nutrient feed rate, a temperature profile, or a pH setpoint; (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; Bhardwaj et al. further disclose applications of optimization of cellulose production in solid-state fermentation as achieved by optimization with AI modeling-page 12; Bhardwaj et al. further disclose applications for scale up optimization in bioprocesses for enzyme production, for example-page 12);
automatically outputting robotic handling instructions to an automated laboratory system to initiate, for each of the plurality of model outputs generated by the reinforcement learning neural network model, a synthetic biology experiment that implements the control parameters specified in the model output on a parallelized hardware device comprising a plurality of sensors and an automated sampling mechanism configured to capture real-time measurements of the synthetic biology experiment, wherein the outputting comprises translating the control parameters into an executable protocol that directs the automated laboratory system to: (i) perform automated strain construction to implement the genetic modification parameters, and (ii) operate the automated sampling mechanism to perform a plurality of sampling operations by controlling a motorized base to position sample wells beneath a valve to quench cellular metabolism with quenching solution;
performing an evaluation of at least one outcome of each of the synthetic biology experiment, specified by the plurality of model outputs of the reinforcement learning neural network, comprising, for each synthetic biology experiment, 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,
wherein the reward is determined based on experimental measurements generated by the automated sampling mechanism; (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).
Claim 20 is directed to:
A method performed by one or more computers the method comprising:
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 plurality of model outputs each comprising control parameters that specify an experiment definition for the synthetic biology experiment based on the model of the biologic process, wherein the control parameters include: (i) genetic modification parameters, and (ii) environmental parameters comprising at least one of a nutrient feed rate, a temperature profile, or a pH setpoint; (Bhardwaj et al. disclose capabilities of AI systems that use biological information for experimental design-page 4; (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; Bhardwaj et al. further disclose applications of optimization of cellulose production in solid-state fermentation as achieved by optimization with AI modeling-page 12; Bhardwaj et al. further disclose applications for scale up optimization in bioprocesses for enzyme production, for example-page 12 )
automatically outputting robotic handling instructions to an automated laboratory system to initiate, for each of the plurality of model outputs generated by the reinforcement learning neural network model, a synthetic biology experiment that implements the control parameters specified in the model output on a parallelized hardware device comprising a plurality of sensors and an automated sampling mechanism configured to capture real-time measurements of the synthetic biology experiment, wherein the outputting comprises translating the control parameters into an executable protocol that directs the automated laboratory system to: (i) perform automated strain construction to implement the genetic modification parameters, and (ii) operate the automated sampling mechanism to perform a plurality of sampling operations by controlling a motorized base to position sample wells beneath a valve to quench cellular metabolism with quenching solution;
performing an evaluation of at least one outcome of each of the synthetic biology experiments, specified by the plurality of model outputs of the reinforcement learning neural network, comprising, for each synthetic biology experiment, 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, and wherein the reward is determined based on experimental measurements generated by the automated sampling mechanism; (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 artificial intelligence system 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 neural network techniques 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.
Neither Bhardwaj et al. nor Treloar et al. specifically disclose the specific environmental and genetic modification parameter control output as now claimed or the control of the automated laboratory system including strain construction and operation of sampling as now claimed.
However, the prior art to Hall et al. disclose iterative culture condition for culture output processes wherein the iterative method implements reinforcement learning procedures (abstract) and wherein the “high throughput fermentation platform leverages an autonomous agent such as machine learning (ML) empowered real-time optimization based on specific resource consumption rates (mass of resource consumed per hour per cell), specific production rates (mass of product produced per hour per cell), and production economics. This real-time automatic optimization uses a robust continuous fermentation platform” [0007]. Further, Hall et al. disclose that “The high-throughput fermentation data generation can be automated by a computational algorithm such as a machine learning based algorithm” [0011] and further optimization for input parameters that include pH, temperature, feed rate and other chemical compositions [0012]. Further, Hall et al. disclose that “the culture product can be a molecular entity that is the product of fermentation or gene expression and which, typically, is a product to be harvested from the culture and commercialized. Culture products contemplated herein include polypeptides, e.g., proteins, enzymes, antibodies. Culture products also include organic molecules that are the product of synthetic pathways in the cell, e.g., mediated by enzymes” [0037] and that measurement values can be obtained [0042]. In addition, Hall et al. disclose that “optimization of culture conditions for a culture output can involve three general phases. In a first phase 100, genetic stability of the cells in culture are established. In a second phase 200, culture conditions are optimized for cell growth 210 and/or for culture output or a proxy therefore 220. In a third phase 300, culture conditions are validated at scale. Each of these three phases can be performed independently, or together” [0044]. Hall et al. include that an intelligent agent may select culture condition [0058] and operate via a computer system to communicate with other systems [0068]-[0077] and “perform certain actions in observing the rewards/results which it gets from those actions. A schematic for reinforcement learning is depicted in Figure 3. An agent takes an action (at) on its environment. This produces information about the environment state (St) and a reward (Rt) indicating whether the result is better than the previous result. The agent works on the hypothesis of reward maximization” [0083], making it obvious to operate sampling mechanisms as informed by RL.
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 operated instructions for handling of a laboratory system using artificial intelligence models, including RL, as disclosed in both Hall et al. and Treloar et al. as combined with the general principles as disclosed in Bhardwaj et al. and had a reasonable expectation of success in so doing because each of said references is in the same area of endeavor and Bhardwaj et al. further 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].
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), and Hall et al.as applied to claims 1 and 20 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. and Hall 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., Treloar et al., and Hall 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.
Response to Applicant’s Arguments
Applicants arguments have been considered but are moot in view of the new grounds of rejection, as necessitated by claim amendment, above.
Conclusion
No claims are allowed.
Inquiries
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/Lori A. Clow/ Primary Examiner, Art Unit 1687
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