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 .
This action is in response to the amendment and remarks filed August 27th, 2026. In the amendment, claims 1, 6, 10, 15, and 19 were amended and no claims were cancelled or added. As such, claims 1-23 are pending.
Continued Examination Under 37 CFR 1.114
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finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114.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 August 27th, 2026, has been entered.
Response to Arguments
Applicant’s arguments with respect to the rejections of claims 1-23 under 35 U.S.C § 103 are
not persuasive for the following reasons:
35 U.S.C 103:
Applicant’s arguments with respect to claim(s) 1-23 have been considered but are moot because the new ground of rejection does not rely on any reference applied in the prior rejection of record for any teaching or matter specifically challenged in the argument.
Claim Objections
Claims 19-23 are objected to because of the following informalities:
Claim 19: "program instructions on or more computer readable storage media" should read "program instructions on one or more computer readable storage media".
Appropriate correction is required.
Specification
The disclosure is objected to because of the following informalities:
Paragraph 2: "The key to discovery a successful polymer" should read "The key to discovering a successful polymer".
Paragraph 8: "program instructions on or more computer readable storage media" should read "program instructions on one or more computer readable storage media".
Paragraph 21: "the terms "logical neural networks" and "LNN" refer an AI protocol" should read "…refer to an AI protocol".
Appropriate correction is required.
The use of the terms Pytorch, OpenAI, TensorFlow, Keras, DeepMind, and scikit-learn, which are trade names or marks used in commerce, have been noted in this application. The terms should be accompanied by the generic terminology; furthermore the terms should be capitalized wherever it appears or, where appropriate, include a proper symbol indicating use in commerce such as ™, SM , or ® following the term.
Although the use of trade names and marks used in commerce (i.e., trademarks, service marks, certification marks, and collective marks) is permissible in patent applications, the proprietary nature of the marks should be respected and every effort made to prevent their use in any manner which might adversely affect their validity as commercial marks.
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.
Claims 1-23 are rejected under 35 U.S.C. 103 as being unpatentable over Kimura ("Neuro-Symbolic Reinforcement Learning with First-Order Logic", 2021) in view of Ristoski (US 20210303762 A1), further in view of Triplet (US 20240007504 A1), and further in view of Knox ("Autonomous polymer synthesis delivered by multi-objective closed-loop optimisation", 2022).
Regarding claim 1,
Kimura teaches programming a reinforcement learning (RL) computing environment comprising an RL algorithm acting as a reward-based RL agent programmed to make decisions in furtherance of a goal… (Pg. 2 Paragraph 1 of Section 3.1, “As text-based games are sequential decision-making problems, they can naturally be applied to RL. These games are partially observable… where the observation text does not include the entire information of the environment… The objective for the agent is to maximize the expected discounted reward E”);
and integrating a logical optimal action (LOA) computing framework (neuro-symbolic framework) with the RL computing environment, wherein the LOA computing framework comprises (Page 1 Under the Figure Caption of the Introduction, “In order to train logical rules, a recent neuro-symbolic framework called the Logical Neural Network (LNN) (Riegel et al., 2020) has been proposed to simultaneously provide key properties of both the neural network (learning) and the symbolic logic (reasoning). The LNN can train the symbolic rules with logical functions in the neural networks by having an end-to-end differentiable network minimizes a contradiction loss.”, Page 2 Paragraph 2 of Introduction, “In this paper, we propose an action knowledge acquisition method featuring a neuro-symbolic LNN framework for the RL algorithm.”
The specification of the instant case states that the logical optimal action refers to neuro-symbolic LNN that is capable of optimizing the LNN. In this case, the reference teaches using a neuro-symbolic framework using Logical Neural Network integrated with the reinforced learning environment.)
(ii) …constraining rules comprising predicates and logical operations, that limit the scope of the information in the at least one dataset (Page 5 Paragraph 1 Under Discussion about ethics, “The data set used in our experiment does not contain any sensitive information.”, Pg. 1 Under Figure 1 Caption in Introduction Section, “…the Logical Neural Network (LNN)… has been proposed to simultaneously provide key properties of both the neural network (learning) and the symbolic logic (reasoning). The LNN can train the symbolic rules with logical functions in the neural networks by having an end-to-end differentiable network minimizes a contradiction loss. Every neuron in the LNN has a component for a formula of weighted real-valued logics from a unique logical conjunction, disjunction, or negation nodes… the trained LNN can extract obtained logical rules by selecting high weighted connections that represent the important rules for an action policy”, Page 3 of Section 3.2.1, “The FOL converter converts a given natural observation text ot and observation history (ot−1, ot−2, ...) into first-order logic facts…", Page 2 Introduction, “The method then converts first-order logical (predicates) facts from the propositional logics and categories of each word, such as ∃x ∈ {south, north},⟨find x⟩ = True and ∃x ∈ {east, west},⟨find x⟩ = False.”, Page 4 Section 4, “We attached the extracted rules from the medium level games here: ∃x ∈ W_direction (⟨find x⟩∧¬⟨visited x⟩ ∧¬⟨initial x⟩)∨ (⟨find x⟩∧⟨all are visited⟩ ∧ ⟨initial x⟩) → ⟪go x⟫,”
The "constraining rules comprising predicates and logical operations that limit the scope of the information in the at least one dataset" correspond to the symbolic logical rules used in the Logical Neural network (LNN). The FOL facts are explicitly predicates (e.g., find x, visited x, initial x) combined via logical operations (AND, OR, NOT) within the LNN’s neuron structure. The rules are constrained to logical predicate rules, thus limiting the scope of the information in the dataset based on the results of the FOL converter and the predicate logic of the LNN.),
and (2) a logical neural network (LNN) that is trained on the at least one dataset and the constraining rules and establishes LNN policy rules based on the training (Page 1 of Introduction Under Figure 1 Caption, “The LNN can train the symbolic rules with logical functions in the neural networks by having an end-to-end differentiable network minimizes a contradiction loss… At the same time, the trained LNN can extract obtained logical rules by selecting high weighted connections that represent the important rules for an action policy.”, See Figure 1,
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Here, the dataset corresponds to the observation in the Figure, the constraining rules correspond to the symbolic logical rules generated from the FOL converter, and the LNN policy rules corresponds to the output of the logical neural network which provides the rules used for the agent to make decisions. The observation and the rules are fed into the LNN to generate the policy rules for the agent to perform the actions.),
wherein the at least one dataset… and the LNN policy rules are input into the RL computing environment where the RL agent makes decisions on the information in the at least one dataset that comply with the LNN policy rules and the RL agent is rewarded for decisions that advance the goal (Page 5 Paragraph 1 Under Discussion about ethics, “The data set used in our experiment does not contain any sensitive information.”, Page 3 of Section 3.2.1, “The FOL converter converts a given natural observation text ot and observation history (ot−1, ot−2, ...) into first-order logic facts… the agent understands an opened direction from the current room. The agent then retrieves the class type c of the word meaning in propositional logic l_i,t by using Concept Net… or the network of another word’s definition.”, Page 3 Paragraph 1 of Section 3.2.2, “The LNN training component is for obtaining an action policy from the given FOL logics.”, See Figure 1,
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The reference teaches that observation data from the environment, in the form of natural language text, constitutes a dataset that is processed and converted into first-order logical facts and input into a Logical Neural Network (LNN), as shown in Figure 1 and described in Section 3.2. The LNN learns logical policy rules that are input into the reinforcement learning computing environment and directly governs the RL agent’s action selection. The RL agent makes decisions that comply with the LNN policy rules and is rewarded based on the outcome of those decisions.).
wherein the decisions that advance the goal are used to refine the LNN policy rules (Page 3 Section 3.2.2, “The LNN trains by this fact inputs and reward; that means it forwards from input facts through LNN, calculates a loss value from the reward value, and optimizes weights in LNN.”, See Algorithm 1 on Page 3,
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Kimura teaches that goal-advancing decisions are stored in the replay buffer are used to update the LNN weights via gradient updates (line 14 of Algorithm 1). The reward signal, which corresponds to decisions that advance the goal of maximizing expected discounted reward, drives the optimization of the LNN weights, which in turn refines the policy rules extracted from the LNN. Specifically, Kimura shows that the reward from goal advancing decisions is used to update the LNN weights at line 14 of Algorithm 1. The LNN weights encode the logical connections that define the LNN policy rules. Thus, updating the weights based on goal advancing decisions directly refines those policy rules.).
Kimura does not teach a system processor set one or more computer-readable storage media: and program instructions stored on the one or more computer-readable storage media to cause the processor set to perform operations comprising: programming, an interface for entry of (i) at least one dataset comprising information relating to existing polymers, and a dataset relating to the discovery of new polymer materials.
Ristoski, in the same field of endeavor, teaches [a] system comprising a processor set one or more computer-readable storage media: and program instructions stored on the one or more computer-readable storage media to cause the processor set to perform operations comprising: (Paragraph 9 of Ristoski, “A system, in one aspect, can include a hardware processor and a memory device coupled with the hardware processor. The hardware processor can be configured to generate a set of material candidates expected to yield materials with target properties. The hardware processor can also be configured to receive subject matter expert's decision indicating accepted and rejected material candidates from the set of material candidates. The hardware processor can also be configured to, based on the subject matter expert's input, train a machine learning model to replicate the subject matter expert's decision.”, Paragraph 12, “A computer readable storage medium storing a program of instructions executable by a machine to perform one or more methods described herein also may be provided.”),
an interface for entry of (i) at least one dataset comprising information relating to existing polymers (Paragraph 91, “For instance, at least one hardware processor 1102 may receive training data set which can include candidate material for polymerization, identify one or more desired features in the candidate material, and train a machine learning model to generate a new material having one or more of the desired features.”, Paragraph 0088, “In an embodiment, e.g., optionally, the identified desired features can be presented or caused to be presented on a user interface, e.g., for user interaction or view. In an embodiment, a user (e.g., an SME) may select from the desired features, a desired feature to include in the new material being generated.”
The reference teaches an interface or GUI that allows for the user to select desired features, which is considered an entry due to the fact that the user is entering constraints into the model.)
relating to the discovery of new polymer materials (Paragraph 80, “FIG. 8 is a diagram illustrating a method of polymer discovery in one embodiment… At 802, the method can include generating a set of material candidates expected to yield materials with target properties. Examples of material candidates include, but not limited to, molecules, monomers, and/or polymer repeat units.”)
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date to combine Kimura’s teaching of integrating a neuro-symbolic Logical Neural Network (LNN) framework with a reinforcement learning environment for decision making with Ristoski’s teaching of providing a user interface for entering polymer related constraints in order to apply the reinforcement learning and LNN to the discovery of new polymer materials, thereby improving the efficiency and guidance of polymer discovery (Paragraph 2 of Ristoski).
Kimura in view of Ristoski does not teach subject-matter-expert-defined constraining rules.
Triplet, in the same field of endeavor, teaches subject-matter-expert-defined constraining rules (Paragraph 97 of Triplet, "rules manually defined by SME to map each policy to a remediation template", Paragraph 41, "AI may be used to discover these rules and policies... Configuring policies and rules may be part of an ML training process."). Triplet teaches rules defined by a subject matter expert that constrain machine-learning decision-making, corresponding to the claimed subject-matter-expert-defined constraining rules. Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date to combine Kimura in view of Ristoski’s teaching with Triplet's teaching of SME-defined constraining rules in order to allow an SME to establish policy constraints that guardrail machine learning decisions toward goal advancing outcomes (Paragraph 2 of Triplet).
Kimura in view of Ristoski in further view of Triplet does not teach the information including polymer-synthesis experiments and corresponding outcomes; the rules specifying lower and upper limits for polymer-synthesis reaction parameters; and the at least one dataset including polymer-synthesis experiments satisfying the lower and upper limits.
Knox, in the same field of endeavor, teaches the information including polymer-synthesis experiments and corresponding outcomes (Page 3 Results and discussion Section of Knox, “The rich dataset (Fig. 2a), generated with no human inter action comprises conversion and data over several reaction times, across a broad temperature range.”, Page 4 Fig. 2 Caption, "Conditions and subsequent monomer conversion and molar mass dispersity, for the automated polymerisation", Page 8 Conclusions, "The experiments produced rich datasets regarding the reaction.", Page 1 Introduction, “Some initial multi-objective work in the field of polymer synthesis includes the in silico and sub sequent manual optimization…”, See Figure 1 Knox's platform autonomously runs RAFT polymerizations and records the result of each run. Each polymerization is a polymer-synthesis experiment, and the monomer conversion and molar mass dispersity measured on-line for that run are its corresponding outcomes. The rich dataset is composed of experiment and outcome records, corresponding to the information that includes polymer-synthesis experiments and corresponding outcomes.)
the rules specifying lower and upper limits for polymer-synthesis reaction parameters (Page 2 Results and discussion Section, “Programmed RAFT polymerisation… was performed… at pre-defined reaction temperatures between 80 °C and 116 °C and reaction times between 4 and 20 minutes.”, Page 2 Introduction, “…we present an automated polymer synthesis plat form, combining orthogonal online NMR spectroscopy and GPC which enables closed-loop multi-objective optimisation of RAFT polymerisations.”
Before Knox's platform runs, the user fixes bounded ranges for the polymerization variables where these condition limits govern which reactions the platform is permitted to perform. Temperature and time are reaction parameters of the RAFT polymerization, and Knox assigns each an explicit lower and upper bound (e.g., 80–116 °C and 4–20 min).)
the at least one dataset including polymer-synthesis experiments satisfying the lower and upper limits (Page 4 Results and discussion, “The system used Latin hypercube (LHC) sampling to generate ten experimental conditions distributed throughout the reaction parameter space. The reactor executed these experiments autonomously…”, Page 5 Results and discussion, “These results do not discount the possibility to obtain greater conversions using TTC-3 as the RAFT agent, but instead explore that which is achievable within the conditions explored (4–20 minutes residence time, 80–120 °C)”, Page 8 Conclusion, “The experiments produced rich datasets regarding the reaction.” Knox generates the experimental conditions by sampling within the user-defined condition limits and then physically executes those reactions on the flow reactor. Since every executed reaction is drawn from and confined to the defined limits, each experiment in the resulting dataset falls within the specified bounds.) Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date to combine Kimura in view of Ristoski in further view of Triplet's teaching with Knox's teaching of a dataset of polymer-synthesis experiments and outcomes bounded by user-set lower and upper reaction parameter limits in order to constrain the polymer dataset to synthesis experiments within defined reaction-parameter limits and thereby enable efficient, targeted exploration of the polymer reaction parameter space with a reduced experimental workload (Introduction of Knox).
Regarding claim 2,
Kimura does not teach the constraining rules limit the scope of the information in the at least one dataset to available materials and laboratory equipment.
Ristoski, in the same field of endeavor, teaches the constraining rules limit the scope of the information in the at least one dataset to available materials and laboratory equipment (Paragraph 0042, “The general model tries to learn general rules for synthesizing new materials that are shared across all labs and SMEs, e.g., each monomer suitable for polymerization must contains a polymerizable group and should not contain pendant groups that will be active under polymerization conditions.”, Paragraph 0050, “By way of example only, a training data set to train or build the classification model 106 can include a random sample from a combinatorial library of polymerizable components suitable for the preparation of polyimides, including dianhydrides, dicarboxylic acids, and diamines.”).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date to modify Kimura’s teaching of integrating a neuro-symbolic Logical Neural Network (LNN) framework with a reinforcement learning environment based on game rules by incorporating Ristoski’s teaching of constraining rules derived from polymer materials and laboratory equipment in order to improve practical applicability and reliability of the learned policies (Paragraph 0002 of Ristoski).
Regarding claim 3,
Kimura teaches the LOA (neuro-symbolic framework) further comprises (3) an internal regressor (FOL converter) that parses the at least one dataset into experiments and outcomes (Page 1 Under Figure 1 Caption, “In order to train logical rules, a recent neuro-symbolic framework called the Logical Neural Network (LNN)… has been proposed to simultaneously provide key properties of both the neural network (learning) and the symbolic logic (reasoning).”, Page 1 Figure 1 Caption, “…and the first-order logical facts are extracted from an FOL converter that uses a semantic parser, Concept Net, and history.” Page 3 Section 3.2.1, “The FOL converter converts a given natural observation text ot and observation history (ot−1, ot−2, ...) into first-order logic facts. The method first converts text into propositional logics li, t by a semantic parser from ot, such as, the agent understands an opened direction from the current room.”
The experiments correspond to the RL agent’s action executions in the environment based on the observation, and the outcomes correspond to the resulting state information and rewards from the logical facts generated by the FOL converter and LNN.),
wherein the LNN converts the experiments and outcomes into symbolic language understood by the RL agent (Figure 1 Caption, “The agent takes a text observation from the environment, and the first-order logical facts are extracted from an FOL converter that uses a semantic parser, Concept Net, and history… when the agent finds a direction x and the direction x has not been visited, the agent takes a “Go x” action. Dashed lines show the initial connections before training.”, See Figure 1,
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).
Regarding claim 4,
Kimura teaches the LNN policy rules are updated to reflect decisions made by the RL agent that successfully advance the goal and the updated LNN policy rules direct future decisions by the RL agent to achieve the goal (Page 2 Section 3.1, “The objective for the agent is to maximize the expected discounted reward E…”, Page 3 Under Section 3.2.2, “The LNN trains by this fact inputs and reward; that means it forwards from input facts through LNN, calculates a loss values from the reward value, and optimizes weights in LNN.”, See Algorithm 1 RL by FOL-LNN,
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The LNN uses the extracted logic from the FOL, and is used to train the Logic Neural Network in order to provide an output for the agent to take an action and get an action. The reward of the action that the agent takes is used to update the rules from the LNN in order to improve and optimize the agent’s future moves.)
Regarding claim 5,
Kimura does not teach a subject matter expert (SME) establishes the constraining rules and reviews the decisions by the RL agent to eliminate decisions that do not advance the goal.
Ristoski, in the same field of endeavor, teaches a subject matter expert (SME)… reviews the decisions by the RL agent to eliminate decisions that do not advance the goal (Paragraph 2 of Ristoski, “Approaches have emerged, such as computational screening, inverse design, generative modeling, reinforcement learning as ways to accelerate the design of polymer materials. The drawback of these approaches is that they generate a large number of candidates for new molecules, which then need to be manually reviewed by subject matter experts who select only a dozen for further investigation.”, Paragraph 63, “SeqGAN is a Sequence Generative Adversarial Network using reinforcement learning. The system includes generator and discriminator, where the generator is treated as reinforcement learning agent… the generated tokens represent the state and the action is the next token to be generated.”, Paragraph 4, “Subject matter experts (SMEs) such as polymer chemists and synthetic organic chemists review many pages of candidates and select the ones that appear viable for further testing. With the very large number of candidates, and the fact that only a few will be selected for experiment manually”
The reinforcement learning agent generates material candidates and the outcome of the agent is used to have a SME review the decisions of the agent, and selects certain candidates for further testing).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date to combine Kimura’s reinforcement learning system employing neuro-symbolic policy reasoning with Ristoski’s teaching of subject matter expert review of generated material candidates from the reinforcement learning agent in order to apply Ristoski’s SME to evaluate and eliminate agent decisions that do not advance the goal, thus improving the quality and relevance of selected outcomes (Paragraph 0032 of Ristoski).
Kimura in view Ristoski do not teach a subject matter expert (SME) establishes the constraining rules.
Triplet, in the same field of endeavor, teaches a subject matter expert (SME) establishes the constraining rules (Paragraph 97 of Triplet, “The methods may detect compliance violations in real-time and predict remediation actions to non-compliant events, such as by using a) rules manually defined by SME to map each policy to a remediation template, b) collaborative filtering algorithms to predict which template can best remediate a given violation based on historical data, c) supervised ML to predict which template can best remediate a given violation, d) reinforcement learning algorithms for more complex scenarios, to predict a sequence of remediations actions on one or several devices, among others.”).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date to combine Kimura in view of Ristoski’s teaching with Triplet’s teaching of SME defined constraining rules in order to allow an SME to establish policy constraints that guardrail machine learning decisions toward goal-advancing outcomes (Paragraph 2 of Triplet).
Regarding claim 6,
Kimura teaches programming a reinforcement learning (RL) computing environment comprising an RL algorithm acting as a reward-based RL agent programmed to make decisions in furtherance of a goal… (Pg. 2 Paragraph 1 of Section 3.1, “As text-based games are sequential decision-making problems, they can naturally be applied to RL. These games are partially observable… where the observation text does not include the entire information of the environment… The objective for the agent is to maximize the expected discounted reward E”);
and integrating a logical optimal action (LOA) computing framework (neuro-symbolic framework) with the RL computing environment, wherein the LOA computing framework comprises (Page 1 Under the Figure Caption of the Introduction, “In order to train logical rules, a recent neuro-symbolic framework called the Logical Neural Network (LNN) (Riegel et al., 2020) has been proposed to simultaneously provide key properties of both the neural network (learning) and the symbolic logic (reasoning). The LNN can train the symbolic rules with logical functions in the neural networks by having an end-to-end differentiable network minimizes a contradiction loss.”, Page 2 Paragraph 2 of Introduction, “In this paper, we propose an action knowledge acquisition method featuring a neuro-symbolic LNN framework for the RL algorithm.”
The specification of the instant case states that the logical optimal action refers to neuro-symbolic LNN that is capable of optimizing the LNN. In this case, the reference teaches using a neuro-symbolic framework using Logical Neural Network integrated with the reinforced learning environment.)
and (2) a logical neural network (LNN) that is trained on the at least one dataset… and establishes LNN policy rules based on the training (Page 1 of Introduction Under Figure 1 Caption, “The LNN can train the symbolic rules with logical functions in the neural networks by having an end-to-end differentiable network minimizes a contradiction loss… At the same time, the trained LNN can extract obtained logical rules by selecting high weighted connections that represent the important rules for an action policy.”, See Figure 1,
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Here, the dataset corresponds to the observation in the Figure, the constraining rules correspond to the symbolic logical rules generated from the FOL converter, and the LNN policy rules corresponds to the output of the logical neural network which provides the rules used for the agent to make decisions. The observation and the rules are fed into the LNN to generate the policy rules for the agent to perform the actions.),
(ii) …rules comprising predicates and logical operations… that constrain the scope of the at least one dataset… (Pg. 1 Under Figure 1 Caption in Introduction Section, “…the Logical Neural Network (LNN)… Every neuron in the LNN has a component for a formula of weighted real-valued logics from a unique logical conjunction, disjunction, or negation nodes”, Page 3 of Section 3.2.1, “The FOL converter converts a given natural observation text ot and observation history (ot−1, ot−2, ...) into first-order logic facts…", Page 2 Introduction, “The method then converts first-order logical (predicates) facts from the propositional logics and categories of each word, such as ∃x ∈ {south, north},⟨find x⟩ = True and ∃x ∈ {east, west},⟨find x⟩ = False.”, Page 4 Section 4, “We attached the extracted rules from the medium level games here: ∃x ∈ W_direction (⟨find x⟩∧¬⟨visited x⟩ ∧¬⟨initial x⟩)∨ (⟨find x⟩∧⟨all are visited⟩ ∧ ⟨initial x⟩) → ⟪go x⟫,”
The rules comprising predicates and logical operations that limit the scope of the at least one dataset correspond to the symbolic logical rules used in the Logical Neural network (LNN). The FOL facts are explicitly predicates (e.g., find x, visited x, initial x) combined via logical operations (AND, OR, NOT) within the LNN’s neuron structure. The rules are constrained to logical predicate rules, thus limiting the scope of the information in the dataset based on the results of the FOL converter and the predicate logic of the LNN.)
wherein the at least one dataset and the policy rules are input into the RL computing environment where the RL agent makes decisions on the information in the at least one dataset that comply with the LNN policy rules and the RL agent is rewarded for decisions that advance the goal (Page 5 Paragraph 1 Under Discussion about ethics, “The data set used in our experiment does not contain any sensitive information.”, Page 3 of Section 3.2.1, “The FOL converter converts a given natural observation text ot and observation history (ot−1, ot−2, ...) into first-order logic facts… the agent understands an opened direction from the current room. The agent then retrieves the class type c of the word meaning in propositional logic l_i, t by using Concept Net… or the network of another word’s definition.”, Page 3 Paragraph 1 of Section 3.2.2, “The LNN training component is for obtaining an action policy from the given FOL logics.”, See Figure 1,
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The reference teaches that observation data from the environment, in the form of natural language text, constitutes a dataset that is processed and converted into first-order logical facts and input into a Logical Neural Network (LNN), as shown in Figure 1 and described in Section 3.2. The LNN learns logical policy rules that are input into the reinforcement learning computing environment and directly governs the RL agent’s action selection. The RL agent makes decisions that comply with the LNN policy rules and is rewarded based on the outcome of those decisions.).
Kimura does not teach a system comprising: a processor set: one or more computer-readable storage media; and program instructions stored on the one or more computer-readable storage media to cause the processor set to perform operations, an interface for entry of (i) at least one dataset comprising information relating to existing polymers, (ii) rules… that constrain the scope of the at least one dataset to available materials and laboratory equipment, a reward-based RL agent programmed to make decisions in furtherance of a goal relating to the discovery of new polymer materials, rules defined by a subject matter expert (SME), and train[ing] on… the SME-defined constraining rules.
Ristoski, in the same field of endeavor, teaches [a] system comprising: a processor set: one or more computer-readable storage media; and program instructions stored on the one or more computer-readable storage media to cause the processor set to perform operations comprising: (Paragraph 9 of Ristoski, “A system, in one aspect, can include a hardware processor and a memory device coupled with the hardware processor. The hardware processor can be configured to generate a set of material candidates expected to yield materials with target properties. The hardware processor can also be configured to receive subject matter expert's decision indicating accepted and rejected material candidates from the set of material candidates. The hardware processor can also be configured to, based on the subject matter expert's input, train a machine learning model to replicate the subject matter expert's decision.”, Paragraph 12, “A computer readable storage medium storing a program of instructions executable by a machine to perform one or more methods described herein also may be provided.”)
(1) an interface for entry of (i) at least one dataset comprising information relating to existing polymers (Paragraph 0091, “For instance, at least one hardware processor 1102 may receive training data set which can include candidate material for polymerization, identify one or more desired features in the candidate material, and train a machine learning model to generate a new material having one or more of the desired features.”, Paragraph 0088, “In an embodiment, e.g., optionally, the identified desired features can be presented or caused to be presented on a user interface, e.g., for user interaction or view. In an embodiment, a user (e.g., an SME) may select from the desired features, a desired feature to include in the new material being generated.”
The reference teaches an interface or GUI that allows for the user to select desired features, which is considered an entry due to the fact that the user is entering constraints into the model.)
(ii) rules… that constrain the scope of the at least one dataset to available materials and laboratory equipment (Paragraph 0042, “The general model tries to learn general rules for synthesizing new materials that are shared across all labs and SMEs, e.g., each monomer suitable for polymerization must contains a polymerizable group and should not contain pendant groups that will be active under polymerization conditions.”, Paragraph 0050, “By way of example only, a training data set to train or build the classification model 106 can include a random sample from a combinatorial library of polymerizable components suitable for the preparation of polyimides, including dianhydrides, dicarboxylic acids, and diamines.”),
a reward-based RL agent programmed to make decisions in furtherance of a goal relating to the discovery of new polymer materials (Paragraph 0063 of Ristoski, “SeqGAN is a Sequence Generative Adversarial Network using reinforcement learning. The system includes generator and discriminator, where the generator is treated as reinforcement learning agent. In such a scenario, the generated tokens represent the state and the action is the next token to be generated… The reward can be calculated by the discriminator on a complete sequence via Monte Carlo search.”, Paragraph 0002, “Approaches have emerged, such as computational screening, inverse design, generative modeling, reinforcement learning as ways to accelerate the design of polymer materials.”)
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date to combine Kimura’s teaching of integrating a neuro-symbolic Logical Neural Network (LNN) framework with a reinforcement learning environment for decision making with Ristoski’s teaching of providing a user interface for entering polymer related constraints in a system in order to apply the reinforcement learning and LNN to the discovery of new polymer materials, thereby improving the efficiency and guidance of polymer discovery (Paragraph 0002 of Ristoski).
Kimura in view of Ristoski do not teach rules defined by a subject matter expert (SME) and train[ing] on… the SME-defined constraining rules.
Triplet, in the same field of endeavor, teaches rules… defined by a subject matter expert (SME) (Paragraph 97 of Triplet, “The methods may detect compliance violations in real-time and predict remediation actions to non-compliant events, such as by using a) rules manually defined by SME to map each policy to a remediation template, b) collaborative filtering algorithms to predict which template can best remediate a given violation based on historical data, c) supervised ML to predict which template can best remediate a given violation, d) reinforcement learning algorithms for more complex scenarios, to predict a sequence of remediations actions on one or several devices, among others.”)
train[ing] on… the SME-defined constraining rules (Paragraph 0097, “The methods may detect compliance violations in real-time and predict remediation actions to non-compliant events, such as by using a) rules manually defined by SME to map each policy to a remediation template… c) supervised ML to predict which template can best remediate a given violation, d) reinforcement learning algorithms for more complex scenarios, to predict a sequence of remediations actions on one or several devices, among others.”, Paragraph 41, “In some embodiments, AI may be used to discover these rules and policies and/or generate the rules to improve some area of rules compliance. Configuring policies and rules may be part of an ML training process.”)
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date to combine Kimura in view of Ristoski’s teaching with Triplet’s teaching of SME defined constraining rules in order to allow an SME to establish policy constraints that guardrail machine learning decisions toward goal-advancing outcomes (Paragraph 2 of Triplet).
Kimura in view of Ristoski in further view of Triplet does not teach the information including polymer-synthesis experiments and corresponding outcomes; the rules specifying lower and upper limits for polymer-synthesis reaction parameters; and the at least one dataset including polymer-synthesis experiments satisfying the lower and upper limits.
Knox, in the same field of endeavor, teaches the information including polymer-synthesis experiments and corresponding outcomes (Page 3 Results and discussion Section of Knox, “The rich dataset (Fig. 2a), generated with no human inter action comprises conversion and data over several reaction times, across a broad temperature range.”, Page 4 Fig. 2 Caption, "Conditions and subsequent monomer conversion and molar mass dispersity, for the automated polymerisation", Page 8 Conclusions, "The experiments produced rich datasets regarding the reaction.", Page 1 Introduction, “Some initial multi-objective work in the field of polymer synthesis includes the in silico and sub sequent manual optimization…”, See Figure 1 Knox's platform autonomously runs RAFT polymerizations and records the result of each run. Each polymerization is a polymer-synthesis experiment, and the monomer conversion and molar mass dispersity measured on-line for that run are its corresponding outcomes. The rich dataset is composed of experiment and outcome records, corresponding to the information that includes polymer-synthesis experiments and corresponding outcomes.)
and the rules specifying lower and upper limits for polymer-synthesis reaction parameters (Page 2 Results and discussion Section, “Programmed RAFT polymerisation… was performed… at pre-defined reaction temperatures between 80 °C and 116 °C and reaction times between 4 and 20 minutes.”, Page 2 Introduction, “…we present an automated polymer synthesis plat form, combining orthogonal online NMR spectroscopy and GPC which enables closed-loop multi-objective optimisation of RAFT polymerisations.”
Before Knox's platform runs, the user fixes bounded ranges for the polymerization variables where these condition limits govern which reactions the platform is permitted to perform. Temperature and time are reaction parameters of the RAFT polymerization, and Knox assigns each an explicit lower and upper bound (e.g., 80–116 °C and 4–20 min).)
the at least one dataset including polymer-synthesis experiments satisfying the lower and upper limits (Page 4 Results and discussion, “The system used Latin hypercube (LHC) sampling to generate ten experimental conditions distributed throughout the reaction parameter space. The reactor executed these experiments autonomously…”, Page 5 Results and discussion, “These results do not discount the possibility to obtain greater conversions using TTC-3 as the RAFT agent, but instead explore that which is achievable within the conditions explored (4–20 minutes residence time, 80–120 °C)” Knox generates the experimental conditions by sampling within the user-defined condition limits and then physically executes those reactions on the flow reactor. Since every executed reaction is drawn from and confined to the defined limits, each experiment in the resulting dataset falls within the specified bounds.) Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date to combine Kimura in view of Ristoski in view of Triplet's teaching with Knox's teaching of a dataset of polymer-synthesis experiments and outcomes bounded by user-set lower and upper reaction parameter limits in order to constrain the polymer dataset to synthesis experiments within defined reaction-parameter limits and thereby enable efficient, targeted exploration of the polymer reaction parameter space with a reduced experimental workload (Introduction of Knox).
Claim 7 is an article of manufacture claim that recites identical limitations to method claim 3. Therefore, claim 7 is rejected using the same rationale as claim 3.
Claim 8 is an article of manufacture claim that recites identical limitations to method claim 4. Therefore, claim 8 is rejected using the same rationale as claim 4.
Regarding claim 9,
Kimura does not teach the SME reviews the decisions by the RL agent to eliminate decisions that do not advance the goal.
Ristoski, in the same field of endeavor, teaches the SME reviews the decisions by the RL agent to eliminate decisions that do not advance the goal (Paragraph 0002 of Ristoski, “Approaches have emerged, such as computational screening, inverse design, generative modeling, reinforcement learning as ways to accelerate the design of polymer materials. The drawback of these approaches is that they generate a large number of candidates for new molecules, which then need to be manually reviewed by subject matter experts who select only a dozen for further investigation.”, Paragraph 0063, “SeqGAN is a Sequence Generative Adversarial Network using reinforcement learning. The system includes generator and discriminator, where the generator is treated as reinforcement learning agent… the generated tokens represent the state and the action is the next token to be generated.”, Paragraph 0004, “Subject matter experts (SMEs) such as polymer chemists and synthetic organic chemists review many pages of candidates and select the ones that appear viable for further testing. With the very large number of candidates, and the fact that only a few will be selected for experiment manually”
The reinforcement learning agent generates material candidates and the outcome of the agent is used to have a SME review the decisions of the agent, and selects certain candidates for further testing).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date to combine Kimura’s reinforcement learning system employing neuro-symbolic policy reasoning with Ristoski’s teaching of subject matter expert review of generated material candidates from the reinforcement learning agent in order to apply Ristoski’s SME to evaluate and eliminate agent decisions that do not advance the goal, thus improving the quality and relevance of selected outcomes (Paragraph 32 of Ristoski).
Regarding claim 10,
Kimura teaches [a] computer-implemented method comprising (Page 2 Paragraph 2 in Introduction, “In this paper, we propose an action knowledge acquisition method featuring a neuro-symbolic LNN framework for the RL algorithm.”),
programming a reinforcement learning (RL) algorithm to act as a reward-based RL agent within an RL computing environment (See Algorithm 1 RL by FOL-LNN,
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),
wherein the RL agent makes decisions in furtherance of a goal… (Pg. 2 Paragraph 1 of Section 3.1, “As text-based games are sequential decision-making problems, they can naturally be applied to RL. These games are partially observable… where the observation text does not include the entire information of the environment… The objective for the agent is to maximize the expected discounted reward E”);
and integrating a logical optimal action (LOA) computing framework with the RL computing environment, wherein the LOA computing framework comprises (Page 1 Under the Figure Caption of the Introduction, “In order to train logical rules, a recent neuro-symbolic framework called the Logical Neural Network (LNN) (Riegel et al., 2020) has been proposed to simultaneously provide key properties of both the neural network (learning) and the symbolic logic (reasoning). The LNN can train the symbolic rules with logical functions in the neural networks by having an end-to-end differentiable network minimizes a contradiction loss.”, Page 2 Paragraph 2 of Introduction, “In this paper, we propose an action knowledge acquisition method featuring a neuro-symbolic LNN framework for the RL algorithm.”
The specification of the instant case states that the logical optimal action refers to neuro-symbolic LNN that is capable of optimizing the LNN. In this case, the reference teaches using a neuro-symbolic framework using Logical Neural Network integrated with the reinforced learning environment.)
(ii) …constraining rules comprising predicates and logical operations that limit the scope of the information in the at least one dataset (Page 5 Paragraph 1 Under Discussion about ethics, “The data set used in our experiment does not contain any sensitive information.”, Pg. 1 Under Figure 1 Caption In Introduction Section, “…the Logical Neural Network (LNN)… has been proposed to simultaneously provide key properties of both the neural network (learning) and the symbolic logic (reasoning). The LNN can train the symbolic rules with logical functions in the neural networks by having an end-to-end differentiable network minimizes a contradiction loss. Every neuron in the LNN has a component for a formula of weighted real-valued logics from a unique logical conjunction, disjunction, or negation nodes… the trained LNN can extract obtained logical rules by selecting high weighted connections that represent the important rules for an action policy”, Page 3 of Section 3.2.1, “The FOL converter converts a given natural observation text ot and observation history (ot−1, ot−2, ...) into first-order logic facts…", Page 4 Section 4, “We attached the extracted rules from the medium level games here: ∃x ∈ W_direction (⟨find x⟩∧¬⟨visited x⟩ ∧¬⟨initial x⟩)∨ (⟨find x⟩∧⟨all are visited⟩ ∧ ⟨initial x⟩) → ⟪go x⟫,”
The "constraining rules that limit the scope of the information in the at least one dataset" correspond to the symbolic logical rules used in the Logical Neural network (LNN). The symbolic logical rules are the parsed observation from the FOL converter that is used in the LNN. The rules are constrained to logical rules only, thus limiting the scope of the information in the dataset based on the effect of the FOL converter.),
and (2) a logical neural network (LNN) that is trained on the at least one dataset and the constraining rules and establishes LNN policy rules based on the training (Page 1 of Introduction Under Figure 1 Caption, “The LNN can train the symbolic rules with logical functions in the neural networks by having an end-to-end differentiable network minimizes a contradiction loss… At the same time, the trained LNN can extract obtained logical rules by selecting high weighted connections that represent the important rules for an action policy.”),
wherein the at least one dataset and the LNN policy rules are input into the RL computing environment where the RL agent makes decisions on the information in the at least one dataset that comply with the LNN policy rules and the RL agent is rewarded for decisions that advance the goal (Page 5 Paragraph 1 Under Discussion about ethics, “The data set used in our experiment does not contain any sensitive information.”, Page 3 of Section 3.2.1, “The FOL converter converts a given natural observation text ot and observation history (ot−1, ot−2, ...) into first-order logic facts… the agent understands an opened direction from the current room. The agent then retrieves the class type c of the word meaning in propositional logic l_i,t by using Concept Net… or the network of another word’s definition.”, Page 3 Paragraph 1 of Section 3.2.2, “The LNN training component is for obtaining an action policy from the given FOL logics.”, See Figure 1,
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The reference teaches that observation data from the environment, in the form of natural language text, constitutes a dataset that is processed and converted into first-order logical facts and input into a Logical Neural Network (LNN), as shown in Figure 1 and described in Section 3.2. The LNN learns logical policy rules that are input into the reinforcement learning computing environment and directly governs the RL agent’s action selection. The RL agent makes decisions that comply with the LNN policy rules and is rewarded based on the outcome of those decisions.).
Kimura does not teach a system, an interface for entry of (i) at least one dataset comprising information relating to existing polymers, and a dataset relating to the discovery of new polymer materials.
Ristoski, in the same field of endeavor, teaches relating to the discovery of new polymer materials (Paragraph 0080, “FIG. 8 is a diagram illustrating a method of polymer discovery in one embodiment… At 802, the method can include generating a set of material candidates expected to yield materials with target properties. Examples of material candidates include, but not limited to, molecules, monomers, and/or polymer repeat units.”)
(1) an interface for entry of (i) at least one dataset comprising information relating to existing polymers (Paragraph 0091, “For instance, at least one hardware processor 1102 may receive training data set which can include candidate material for polymerization, identify one or more desired features in the candidate material, and train a machine learning model to generate a new material having one or more of the desired features.”, Paragraph 0088, “In an embodiment, e.g., optionally, the identified desired features can be presented or caused to be presented on a user interface, e.g., for user interaction or view. In an embodiment, a user (e.g., an SME) may select from the desired features, a desired feature to include in the new material being generated.”
The reference teaches an interface or GUI that allows for the user to select desired features, which is considered an entry due to the fact that the user is entering constraints into the model.)
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date to combine Kimura’s teaching of integrating a neuro-symbolic Logical Neural Network (LNN) framework with a reinforcement learning environment for decision making with Ristoski’s teaching of providing a user interface for entering polymer related constraints in order to apply the reinforcement learning and LNN to the discovery of new polymer materials, thereby improving the efficiency and guidance of polymer discovery (Paragraph 2 of Ristoski).
Kimura in view of Ristoski does not teach subject-matter-expert-defined constraining rules.
Triplet, in the same field of endeavor, teaches subject matter expert (SME) defined constraining rules (Paragraph 97 of Triplet, "rules manually defined by SME to map each policy to a remediation template", Paragraph 41, "AI may be used to discover these rules and policies... Configuring policies and rules may be part of an ML training process."). Triplet teaches rules defined by a subject matter expert that constrain machine-learning decision-making, corresponding to the claimed subject-matter-expert-defined constraining rules. Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date to combine Kimura in view of Ristoski’s teaching with Triplet's teaching of SME-defined constraining rules in order to allow an SME to establish policy constraints that guardrail machine learning decisions toward goal advancing outcomes (Paragraph 2 of Triplet).
Kimura in view of Ristoski in further view of Triplet does not teach the information including polymer-synthesis experiments and corresponding outcomes; the rules specifying lower and upper limits for polymer-synthesis reaction parameters; and the at least one dataset including polymer-synthesis experiments satisfying the lower and upper limits.
Knox, in the same field of endeavor, teaches the information including polymer-synthesis experiments and corresponding outcomes (Page 3 Results and discussion Section of Knox, “The rich dataset (Fig. 2a), generated with no human inter action comprises conversion and data over several reaction times, across a broad temperature range.”, Page 4 Fig. 2 Caption, "Conditions and subsequent monomer conversion and molar mass dispersity, for the automated polymerisation", Page 8 Conclusions, "The experiments produced rich datasets regarding the reaction.", Page 1 Introduction, “Some initial multi-objective work in the field of polymer synthesis includes the in silico and sub sequent manual optimization…”, See Figure 1 Knox's platform autonomously runs RAFT polymerizations and records the result of each run. Each polymerization is a polymer-synthesis experiment, and the monomer conversion and molar mass dispersity measured on-line for that run are its corresponding outcomes. The rich dataset is composed of experiment and outcome records, corresponding to the information that includes polymer-synthesis experiments and corresponding outcomes.)
and the rules specifying lower and upper limits for polymer-synthesis reaction parameters (Page 2 Results and discussion Section, “Programmed RAFT polymerisation… was performed… at pre-defined reaction temperatures between 80 °C and 116 °C and reaction times between 4 and 20 minutes.”, Page 2 Introduction, “…we present an automated polymer synthesis plat form, combining orthogonal online NMR spectroscopy and GPC which enables closed-loop multi-objective optimisation of RAFT polymerisations.”
Before Knox's platform runs, the user fixes bounded ranges for the polymerization variables where these condition limits govern which reactions the platform is permitted to perform. Temperature and time are reaction parameters of the RAFT polymerization, and Knox assigns each an explicit lower and upper bound (e.g., 80–116 °C and 4–20 min).)
the at least one dataset including polymer-synthesis experiments satisfying the lower and upper limits (Page 4 Results and discussion, “The system used Latin hypercube (LHC) sampling to generate ten experimental conditions distributed throughout the reaction parameter space. The reactor executed these experiments autonomously…”, Page 5 Results and discussion, “These results do not discount the possibility to obtain greater conversions using TTC-3 as the RAFT agent, but instead explore that which is achievable within the conditions explored (4–20 minutes residence time, 80–120 °C)” Knox generates the experimental conditions by sampling within the user-defined condition limits and then physically executes those reactions on the flow reactor. Since every executed reaction is drawn from and confined to the defined limits, each experiment in the resulting dataset falls within the specified bounds.) Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date to combine Kimura in view of Ristoski's teaching with Knox's teaching of a dataset of polymer-synthesis experiments and outcomes bounded by user-set lower and upper reaction parameter limits in order to constrain the polymer dataset to synthesis experiments within defined reaction-parameter limits and thereby enable efficient, targeted exploration of the polymer reaction parameter space with a reduced experimental workload (Introduction of Knox).
Claim 11 is a process claim that recites similar limitations to method claim 2. Therefore, claim 11 is rejected using the same rationale as claim 2.
Claim 12 is a process claim that recites similar limitations to method claim 3. Therefore, claim 12 is rejected using the same rationale as claim 3.
Claim 13 is a process claim that recites similar limitations to method claim 4. Therefore, claim 13 is rejected using the same rationale as claim 4.
Claim 14 is a process claim that recites similar limitations to method claim 5. Therefore, claim 14 is rejected using the same rationale as claim 5.
Regarding claim 15,
Kimura teaches [a] computer-implemented method comprising (Page 2 Paragraph 2 in Introduction, “In this paper, we propose an action knowledge acquisition method featuring a neuro-symbolic LNN framework for the RL algorithm.”):
programming a reinforcement learning (RL) algorithm to act as a reward-based RL agent within an RL computing environment (See Algorithm 1 RL by FOL-LNN,
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wherein the RL agent makes decisions in furtherance of a goal… (Pg. 2 Paragraph 1 of Section 3.1, “As text-based games are sequential decision-making problems, they can naturally be applied to RL. These games are partially observable… where the observation text does not include the entire information of the environment… The objective for the agent is to maximize the expected discounted reward E”);
and integrating a logical optimal action (LOA) computing framework with the RL computing environment, wherein the LOA computing framework comprises (Page 1 Under the Figure Caption of the Introduction, “In order to train logical rules, a recent neuro-symbolic framework called the Logical Neural Network (LNN) (Riegel et al., 2020) has been proposed to simultaneously provide key properties of both the neural network (learning) and the symbolic logic (reasoning). The LNN can train the symbolic rules with logical functions in the neural networks by having an end-to-end differentiable network minimizes a contradiction loss.”, Page 2 Paragraph 2 of Introduction, “In this paper, we propose an action knowledge acquisition method featuring a neuro-symbolic LNN framework for the RL algorithm.”
The specification of the instant case states that the logical optimal action refers to neuro-symbolic LNN that is capable of optimizing the LNN. In this case, the reference teaches using a neuro-symbolic framework using Logical Neural Network integrated with the reinforced learning environment.)
and (2) a logical neural network (LNN) that is trained on the at least one dataset… and establishes LNN policy rules based on the training (Page 1 of Introduction Under Figure 1 Caption, “The LNN can train the symbolic rules with logical functions in the neural networks by having an end-to-end differentiable network minimizes a contradiction loss… At the same time, the trained LNN can extract obtained logical rules by selecting high weighted connections that represent the important rules for an action policy.”, See Figure 1,
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Here, the dataset corresponds to the observation in the Figure, the constraining rules correspond to the symbolic logical rules generated from the FOL converter, and the LNN policy rules corresponds to the output of the logical neural network which provides the rules used for the agent to make decisions. The observation and the rules are fed into the LNN to generate the policy rules for the agent to perform the actions.),
(ii) …rules comprising predicates and logical operations … that constrain the scope of the at least one dataset… (Pg. 1 Under Figure 1 Caption In Introduction Section, “…the Logical Neural Network (LNN)… Every neuron in the LNN has a component for a formula of weighted real-valued logics from a unique logical conjunction, disjunction, or negation nodes”, Page 3 of Section 3.2.1, “The FOL converter converts a given natural observation text ot and observation history (ot−1, ot−2, ...) into first-order logic facts…", Page 2 Introduction, “The method then converts first-order logical (predicates) facts from the propositional logics and categories of each word, such as ∃x ∈ {south, north},⟨find x⟩ = True and ∃x ∈ {east, west},⟨find x⟩ = False.”, Page 4 Section 4, “We attached the extracted rules from the medium level games here: ∃x ∈ W_direction (⟨find x⟩∧¬⟨visited x⟩ ∧¬⟨initial x⟩)∨ (⟨find x⟩∧⟨all are visited⟩ ∧ ⟨initial x⟩) → ⟪go x⟫,”
The "rules comprising predicates and logical operations that constrain the scope of the information of the at least one dataset" correspond to the symbolic logical rules used in the Logical Neural network (LNN). The FOL facts are explicitly predicates (e.g., find x, visited x, initial x) combined via logical operations (AND, OR, NOT) within the LNN’s neuron structure. The rules are constrained to logical predicate rules, thus limiting the scope of the information in the dataset based on the results of the FOL converter and the predicate logic of the LNN.)
wherein the at least one dataset and the policy rules are input into the RL computing environment where the RL agent makes decisions on the information in the at least one dataset that comply with the LNN policy rules and the RL agent is rewarded for decisions that advance the goal (Page 5 Paragraph 1 Under Discussion about ethics, “The data set used in our experiment does not contain any sensitive information.”, Page 3 of Section 3.2.1, “The FOL converter converts a given natural observation text ot and observation history (ot−1, ot−2, ...) into first-order logic facts… the agent understands an opened direction from the current room. The agent then retrieves the class type c of the word meaning in propositional logic l_i,t by using Concept Net… or the network of another word’s definition.”, Page 3 Paragraph 1 of Section 3.2.2, “The LNN training component is for obtaining an action policy from the given FOL logics.”, See Figure 1,
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The reference teaches that observation data from the environment, in the form of natural language text, constitutes a dataset that is processed and converted into first-order logical facts and input into a Logical Neural Network (LNN), as shown in Figure 1 and described in Section 3.2. The LNN learns logical policy rules that are input into the reinforcement learning computing environment and directly governs the RL agent’s action selection. The RL agent makes decisions that comply with the LNN policy rules and is rewarded based on the outcome of those decisions.).
wherein the decisions that advance the goal are used to refine the LNN policy rules (Page 3 Section 3.2.2, “The LNN trains by this fact inputs and reward; that means it forwards from input facts through LNN, calculates a loss value from the reward value, and optimizes weights in LNN.”, See Algorithm 1 on Page 3,
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Kimura teaches that goal-advancing decisions are stored in the replay buffer are used to update the LNN weights via gradient updates (line 14 of Algorithm 1). The reward signal, which corresponds to decisions that advance the goal of maximizing expected discounted reward, drives the optimization of the LNN weights, which in turn refines the policy rules extracted from the LNN. Specifically, Kimura shows that the reward from goal advancing decisions is used to update the LNN weights at line 14 of Algorithm 1. The LNN weights encode the logical connections that define the LNN policy rules. Thus, updating the weights based on goal advancing decisions directly refines those policy rules.)
Kimura does not teach relating to the discovery of new polymer materials, an interface for entry of (i) at least one dataset comprising information relating to existing polymers, rules… that constrain the scope of the at least one dataset to available materials and laboratory equipment, rules defined by a subject matter expert (SME), and train[ing] on… the SME-defined constraining rules.
Ristoski, in the same field of endeavor, teaches relating to the discovery of new polymer materials (Paragraph 0080, “FIG. 8 is a diagram illustrating a method of polymer discovery in one embodiment… At 802, the method can include generating a set of material candidates expected to yield materials with target properties. Examples of material candidates include, but not limited to, molecules, monomers, and/or polymer repeat units.”)
(1) an interface for entry of (i) at least one dataset comprising information relating to existing polymers (Paragraph 0091, “For instance, at least one hardware processor 1102 may receive training data set which can include candidate material for polymerization, identify one or more desired features in the candidate material, and train a machine learning model to generate a new material having one or more of the desired features.”, Paragraph 0088, “In an embodiment, e.g., optionally, the identified desired features can be presented or caused to be presented on a user interface, e.g., for user interaction or view. In an embodiment, a user (e.g., an SME) may select from the desired features, a desired feature to include in the new material being generated.”
The reference teaches an interface or GUI that allows for the user to select desired features, which is considered an entry due to the fact that the user is entering constraints into the model.),
and (ii) rules… that constrain the scope of the at least one dataset to available materials and laboratory equipment (Paragraph 42, “The general model tries to learn general rules for synthesizing new materials that are shared across all labs and SMEs, e.g., each monomer suitable for polymerization must contains a polymerizable group and should not contain pendant groups that will be active under polymerization conditions.”, Paragraph 0050, “By way of example only, a training data set to train or build the classification model 106 can include a random sample from a combinatorial library of polymerizable components suitable for the preparation of polyimides, including dianhydrides, dicarboxylic acids, and diamines.”),
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date to combine Kimura’s teaching of integrating a neuro-symbolic Logical Neural Network (LNN) framework with a reinforcement learning environment for decision making with Ristoski’s teaching of providing a user interface for entering polymer related constraints in order to apply the reinforcement learning and LNN to the discovery of new polymer materials, thereby improving the efficiency and guidance of polymer discovery (Paragraph 0002 of Ristoski).
Kimura in view of Ristoski do not teach rules defined by a subject matter expert (SME) and train[ing] on… the SME-defined constraining rules.
Triplet, in the same field of endeavor, teaches rules defined by a subject matter expert (SME) (Paragraph 97 of Triplet, “The methods may detect compliance violations in real-time and predict remediation actions to non-compliant events, such as by using a) rules manually defined by SME to map each policy to a remediation template, b) collaborative filtering algorithms to predict which template can best remediate a given violation based on historical data, c) supervised ML to predict which template can best remediate a given violation, d) reinforcement learning algorithms for more complex scenarios, to predict a sequence of remediations actions on one or several devices, among others.”)
train[ing] on… the SME-defined constraining rules (Paragraph 97, “The methods may detect compliance violations in real-time and predict remediation actions to non-compliant events, such as by using a) rules manually defined by SME to map each policy to a remediation template… c) supervised ML to predict which template can best remediate a given violation, d) reinforcement learning algorithms for more complex scenarios, to predict a sequence of remediations actions on one or several devices, among others.”, Paragraph 41, “In some embodiments, AI may be used to discover these rules and policies and/or generate the rules to improve some area of rules compliance. Configuring policies and rules may be part of an ML training process.”)
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date to combine Kimura and Ristoski’s teaching with Triplet’s teaching of SME defined constraining rules in order to allow an SME to establish policy constraints that guardrail machine learning decisions toward goal-advancing outcomes (Paragraph 2 of Triplet).
Kimura in view of Ristoski in further view of Triplet does not teach the information including polymer-synthesis experiments and corresponding outcomes; the rules specifying lower and upper limits for polymer-synthesis reaction parameters; and the at least one dataset including polymer-synthesis experiments satisfying the lower and upper limits.
Knox, in the same field of endeavor, teaches the information including polymer-synthesis experiments and corresponding outcomes (Page 3 Results and discussion Section of Knox, “The rich dataset (Fig. 2a), generated with no human inter action comprises conversion and data over several reaction times, across a broad temperature range.”, Page 4 Fig. 2 Caption, "Conditions and subsequent monomer conversion and molar mass dispersity, for the automated polymerisation", Page 8 Conclusions, "The experiments produced rich datasets regarding the reaction.", Page 1 Introduction, “Some initial multi-objective work in the field of polymer synthesis includes the in silico and sub sequent manual optimization…”, See Figure 1 Each automated RAFT polymerization constitutes a polymer-synthesis experiment, and the measured monomer conversion and molar mass dispersity recorded for each experiment corresponds to the outcomes forming the dataset.)
and the rules specifying lower and upper limits for polymer-synthesis reaction parameters (Page 2 Results and discussion Section, “Programmed RAFT polymerisation… was performed… at pre-defined reaction temperatures between 80 °C and 116 °C and reaction times between 4 and 20 minutes.”, Page 2 Introduction, “…we present an automated polymer synthesis plat form, combining orthogonal online NMR spectroscopy and GPC which enables closed-loop multi-objective optimisation of RAFT polymerisations.”
The pre-defined lower and upper bounds on reaction temperature (e.g., 80 °C to 116 °C) and reaction/residence time (e.g., 4 to 20 minutes) correspond to constraining rules specifying lower and upper limits for polymer-synthesis reaction parameters.)
the at least one dataset including polymer-synthesis experiments satisfying the lower and upper limits (Page 4 Results and discussion, “The system used Latin hypercube (LHC) sampling to generate ten experimental conditions distributed throughout the reaction parameter space. The reactor executed these experiments autonomously…”, Page 5 Results and discussion, “These results do not discount the possibility to obtain greater conversions using TTC-3 as the RAFT agent, but instead explore that which is achievable within the conditions explored (4–20 minutes residence time, 80–120 °C)” The experimental conditions are sampled within, and executed within, the defined lower and upper condition limits, such that the resulting dataset of executed polymer-synthesis experiments comprises experiments satisfying those lower and upper limits.) Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date to combine Kimura in view of Ristoski's teaching with Knox's teaching of a dataset of polymer-synthesis experiments and outcomes bounded by user-set lower and upper reaction parameter limits in order to constrain the polymer dataset to synthesis experiments within defined reaction-parameter limits and thereby enable efficient, targeted exploration of the polymer reaction parameter space with a reduced experimental workload (Introduction of Knox).
Claim 16 is a process claim that recites similar limitations to method claim 7. Therefore, claim 16 is rejected using the same rationale as claim 7.
Claim 17 is a process claim that recites similar limitations to method claim 8. Therefore, claim 17 is rejected using the same rationale as claim 8.
Claim 18 is a process claim that recites similar limitations to method claim 9. Therefore, claim 18 is rejected using the same rationale as claim 9.
Regarding claim 19,
Kimura teaches establishing a reinforcement learning (RL) computing environment comprising an RL algorithm acting as a reward-based RL agent programmed to make decisions in furtherance of a goal… (Pg. 2 Paragraph 1 of Section 3.1, “As text-based games are sequential decision-making problems, they can naturally be applied to RL. These games are partially observable… where the observation text does not include the entire information of the environment… The objective for the agent is to maximize the expected discounted reward E”);
establishing a logical optimal action (LOA) computing framework integrated with the RL computing environment, wherein the LOA computing framework comprises (Page 1 Under the Figure Caption of the Introduction, “In order to train logical rules, a recent neuro-symbolic framework called the Logical Neural Network (LNN) (Riegel et al., 2020) has been proposed to simultaneously provide key properties of both the neural network (learning) and the symbolic logic (reasoning). The LNN can train the symbolic rules with logical functions in the neural networks by having an end-to-end differentiable network minimizes a contradiction loss.”, Page 2 Paragraph 2 of Introduction, “In this paper, we propose an action knowledge acquisition method featuring a neuro-symbolic LNN framework for the RL algorithm.”
The specification of the instant case states that the logical optimal action refers to neuro-symbolic LNN that is capable of optimizing the LNN. In this case, the reference teaches using a neuro-symbolic framework using Logical Neural Network integrated with the reinforced learning environment.)
(ii) …constraining rules comprising predicates and logical operations, that limit the scope of the information in the at least one dataset (Page 5 Paragraph 1 Under Discussion about ethics, “The data set used in our experiment does not contain any sensitive information.”, Pg. 1 Under Figure 1 Caption In Introduction Section, “…the Logical Neural Network (LNN)… has been proposed to simultaneously provide key properties of both the neural network (learning) and the symbolic logic (reasoning). The LNN can train the symbolic rules with logical functions in the neural networks by having an end-to-end differentiable network minimizes a contradiction loss. Every neuron in the LNN has a component for a formula of weighted real-valued logics from a unique logical conjunction, disjunction, or negation nodes… the trained LNN can extract obtained logical rules by selecting high weighted connections that represent the important rules for an action policy”, Page 3 of Section 3.2.1, “The FOL converter converts a given natural observation text ot and observation history (ot−1, ot−2, ...) into first-order logic facts…", Page 2 Introduction, “The method then converts first-order logical (predicates) facts from the propositional logics and categories of each word, such as ∃x ∈ {south, north},⟨find x⟩ = True and ∃x ∈ {east, west},⟨find x⟩ = False.”, Page 4 Section 4, “We attached the extracted rules from the medium level games here: ∃x ∈ W_direction (⟨find x⟩∧¬⟨visited x⟩ ∧¬⟨initial x⟩)∨ (⟨find x⟩∧⟨all are visited⟩ ∧ ⟨initial x⟩) → ⟪go x⟫,”
The "constraining rules comprising predicates and logical operations that limit the scope of the information in the at least one dataset" correspond to the symbolic logical rules used in the Logical Neural network (LNN). The FOL facts are explicitly predicates (e.g., find x, visited x, initial x) combined via logical operations (AND, OR, NOT) within the LNN’s neuron structure. The rules are constrained to logical predicate rules, thus limiting the scope of the information in the dataset based on the results of the FOL converter and the predicate logic of the LNN.),
and (2) a logical neural network (LNN) that is trained on the at least one dataset and the constraining rules and establishes LNN policy rules based on the training (Page 1 of Introduction Under Figure 1 Caption, “The LNN can train the symbolic rules with logical functions in the neural networks by having an end-to-end differentiable network minimizes a contradiction loss… At the same time, the trained LNN can extract obtained logical rules by selecting high weighted connections that represent the important rules for an action policy.”, See Figure 1,
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Here, the dataset corresponds to the observation in the Figure, the constraining rules correspond to the symbolic logical rules generated from the FOL converter, and the LNN policy rules corresponds to the output of the logical neural network which provides the rules used for the agent to make decisions. The observation and the rules are fed into the LNN to generate the policy rules for the agent to perform the actions.),
wherein the at least one dataset and the LNN policy rules are input into the RL computing environment where the RL agent makes decisions on the information in the at least one dataset that comply with the LNN policy rules and the RL agent is rewarded for decisions that advance the goal (Page 5 Paragraph 1 Under Discussion about ethics, “The data set used in our experiment does not contain any sensitive information.”, Page 3 of Section 3.2.1, “The FOL converter converts a given natural observation text ot and observation history (ot−1, ot−2, ...) into first-order logic facts… the agent understands an opened direction from the current room. The agent then retrieves the class type c of the word meaning in propositional logic l_i,t by using ConceptNet… or the network of another word’s definition.”, Page 3 Paragraph 1 of Section 3.2.2, “The LNN training component is for obtaining an action policy from the given FOL logics.”, See Figure 1,
The reference teaches that observation data from the environment, in the form of natural language text, constitutes a dataset that is processed and converted into first-order logical facts and input into a Logical Neural Network (LNN), as shown in Figure 1 and described in Section 3.2. The LNN learns logical policy rules that are input into the reinforcement learning computing environment and directly governs the RL agent’s action selection. The RL agent makes decisions that comply with the LNN policy rules and is rewarded based on the outcome of those decisions.).
wherein the decisions that advance the goal are used to refine the LNN policy rules (Page 3 Section 3.2.2, “The LNN trains by this fact inputs and reward; that means it forwards from input facts through LNN, calculates a loss values from the reward value, and optimizes weights in LNN.”, See Algorithm 1 on Page 3,
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Kimura teaches that goal-advancing decisions are stored in the replay buffer are used to update the LNN weights via gradient updates (line 14 of Algorithm 1). The reward signal, which corresponds to decisions that advance the goal of maximizing expected discounted reward, drives the optimization of the LNN weights, which in turn refines the policy rules extracted from the LNN. Specifically, Kimura shows that the reward from goal advancing decisions is used to update the LNN weights at line 14 of Algorithm 1. The LNN weights encode the logical connections that define the LNN policy rules. Thus, updating the weights based on goal advancing decisions directly refines those policy rules.).
Kimura does not teach [a] computer program product for discovery of polymers comprising: program instructions on or more computer readable storage media to cause a processor set to perform operations comprising…, relating to the discovery of new polymer materials, (1) an interface for entry of (i) at least one dataset comprising information relating to existing polymers, and (ii) Subject Matter Expert defined constraining rules… that limit the scope of the information in the at least one dataset.
Ristoski, in the same field of endeavor, teaches [a] computer program product for discovery of polymers comprising: program instructions on or more computer readable storage media to cause a processor set to perform operations comprising (Paragraph 9 of Ristoski, “A system, in one aspect, can include a hardware processor and a memory device coupled with the hardware processor. The hardware processor can be configured to generate a set of material candidates expected to yield materials with target properties. The hardware processor can also be configured to receive subject matter expert's decision indicating accepted and rejected material candidates from the set of material candidates. The hardware processor can also be configured to, based on the subject matter expert's input, train a machine learning model to replicate the subject matter expert's decision.”, Paragraph 12, “A computer readable storage medium storing a program of instructions executable by a machine to perform one or more methods described herein also may be provided.”, Paragraph 12, “A computer readable storage medium storing a program of instructions executable by a machine to perform one or more methods described herein also may be provided.”)
relating to the discovery of new polymer materials (Paragraph 80, “FIG. 8 is a diagram illustrating a method of polymer discovery in one embodiment… At 802, the method can include generating a set of material candidates expected to yield materials with target properties. Examples of material candidates include, but not limited to, molecules, monomers, and/or polymer repeat units.”)
(1) an interface for entry of (i) at least one dataset comprising information relating to existing polymers (Paragraph 0091, “For instance, at least one hardware processor 1102 may receive training data set which can include candidate material for polymerization, identify one or more desired features in the candidate material, and train a machine learning model to generate a new material having one or more of the desired features.”, Paragraph 0088, “In an embodiment, e.g., optionally, the identified desired features can be presented or caused to be presented on a user interface, e.g., for user interaction or view. In an embodiment, a user (e.g., an SME) may select from the desired features, a desired feature to include in the new material being generated.”
The reference teaches an interface or GUI that allows for the user to select desired features, which is considered an entry due to the fact that the user is entering constraints into the model.)
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date to combine Kimura’s teaching of integrating a neuro-symbolic Logical Neural Network (LNN) framework with a reinforcement learning environment for decision making with Ristoski’s teaching of providing a user interface for entering polymer related constraints used in a computer program in order to apply the reinforcement learning and LNN to the discovery of new polymer materials in a computer program, thereby improving the efficiency and guidance of polymer discovery (Paragraph 0002 of Ristoski).
Kimura in view of Ristoski does not teach ii) Subject Matter Expert defined constraining rules.
Triplet, in the same field of endeavor, teaches (ii) Subject Matter Expert defined constraining rules… that limit the scope of the information in the at least one dataset (Paragraph 0097 of Triplet, “The methods may detect compliance violations in real-time and predict remediation actions to non-compliant events, such as by using a) rules manually defined by SME to map each policy to a remediation template, b) collaborative filtering algorithms to predict which template can best remediate a given violation based on historical data, c) supervised ML to predict which template can best remediate a given violation, d) reinforcement learning algorithms for more complex scenarios, to predict a sequence of remediations actions on one or several devices, among others.”)
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date to combine Kimura in view of Ristoski’s teaching with Triplet’s teaching of SME defined constraining rules in order to allow an SME to establish policy constraints that guardrail machine learning decisions toward goal-advancing outcomes (Paragraph 2 of Triplet).
Kimura in view of Ristoski in further view of Triplet does not teach the information including polymer-synthesis experiments and corresponding outcomes; the rules specifying lower and upper limits for polymer-synthesis reaction parameters; and the at least one dataset including polymer-synthesis experiments satisfying the lower and upper limits.
Knox, in the same field of endeavor, teaches the information including polymer-synthesis experiments and corresponding outcomes (Page 3 Results and discussion Section of Knox, “The rich dataset (Fig. 2a), generated with no human inter action comprises conversion and data over several reaction times, across a broad temperature range.”, Page 4 Fig. 2 Caption, "Conditions and subsequent monomer conversion and molar mass dispersity, for the automated polymerisation", Page 8 Conclusions, "The experiments produced rich datasets regarding the reaction.", Page 1 Introduction, “Some initial multi-objective work in the field of polymer synthesis includes the in silico and sub sequent manual optimization…”, See Figure 1 Each automated RAFT polymerization constitutes a polymer-synthesis experiment, and the measured monomer conversion and molar mass dispersity recorded for each experiment corresponds to the outcomes forming the dataset.)
and the rules specifying lower and upper limits for polymer-synthesis reaction parameters (Page 2 Results and discussion Section, “Programmed RAFT polymerisation… was performed… at pre-defined reaction temperatures between 80 °C and 116 °C and reaction times between 4 and 20 minutes.”, Page 2 Introduction, “…we present an automated polymer synthesis plat form, combining orthogonal online NMR spectroscopy and GPC which enables closed-loop multi-objective optimisation of RAFT polymerisations.”
The pre-defined lower and upper bounds on reaction temperature (e.g., 80 °C to 116 °C) and reaction/residence time (e.g., 4 to 20 minutes) correspond to constraining rules specifying lower and upper limits for polymer-synthesis reaction parameters.)
the at least one dataset including polymer-synthesis experiments satisfying the lower and upper limits (Page 4 Results and discussion, “The system used Latin hypercube (LHC) sampling to generate ten experimental conditions distributed throughout the reaction parameter space. The reactor executed these experiments autonomously…”, Page 5 Results and discussion, “These results do not discount the possibility to obtain greater conversions using TTC-3 as the RAFT agent, but instead explore that which is achievable within the conditions explored (4–20 minutes residence time, 80–120 °C)” The experimental conditions are sampled within, and executed within, the defined lower and upper condition limits, such that the resulting dataset of executed polymer-synthesis experiments comprises experiments satisfying those lower and upper limits.) Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date to combine Kimura in view of Ristoski's teaching with Knox's teaching of a dataset of polymer-synthesis experiments and outcomes bounded by user-set lower and upper reaction parameter limits in order to constrain the polymer dataset to synthesis experiments within defined reaction-parameter limits and thereby enable efficient, targeted exploration of the polymer reaction parameter space with a reduced experimental workload (Introduction of Knox).
Claim 20 is an article of manufacture claim that recites similar limitations to method claim 2. Therefore, claim 20 is rejected using the same rationale as claim 2.
Claim 21 is an article of manufacture claim that recites similar limitations to method claim 3. Therefore, claim 21 is rejected using the same rationale as claim 3.
Claim 22 is an article of manufacture claim that recites similar limitations to method claim 4. Therefore, claim 22 is rejected using the same rationale as claim 4.
Claim 23 is an article of manufacture claim that recites similar limitations to method claim 5. Therefore, claim 23 is rejected using the same rationale as claim 5.
Conclusion
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/M.M.H./Examiner, Art Unit 2125
/KAMRAN AFSHAR/Supervisory Patent Examiner, Art Unit 2125
