DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Priority
Acknowledgment is made of applicant’s claim for foreign priority under 35 U.S.C. 119 (a)-(d). The certified copy has been filed in the instant Application, filed on 11/21/2022. The effective filing date of claims 1-15 is 5/22/2020.
Claim Status
Claims 3, 5-12, 14, and 16 are pending.
Claims 1-2, 4, 13, and 15 are cancelled.
Claims 3, 5-12, 14, and 16 are rejected.
Withdrawn Rejections/Objections
The rejection of claims 1-15 under 35 U.S.C. 112(b) for indefiniteness in the Office Action mailed 5/07/2026 are withdrawn in view of the amendments filed 7/21/2026.
The rejection of claims 14-15 under 35 U.S.C. 101 for being directed to non-statutory subject matter in the Office Action mailed 5/07/2026 are withdrawn in view of the amendments filed 7/21/2026.
The rejection of claims 1-15 under 35 U.S.C. 101 for being directed to abstract ideas without significantly more, in the Office Action mailed 5/07/2026 are withdrawn in view of the amendments filed 7/21/2026.
Claim Interpretation
The following is a quotation of 35 U.S.C. 112(f):
(f) Element in Claim for a Combination. – An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The following is a quotation of pre-AIA 35 U.S.C. 112, sixth paragraph:
An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The claims in this application are given their broadest reasonable interpretation using the plain meaning of the claim language in light of the specification as it would be understood by one of ordinary skill in the art. The broadest reasonable interpretation of a claim element (also commonly referred to as a claim limitation) is limited by the description in the specification when 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is invoked.
As explained in MPEP § 2181, subsection I, claim limitations that meet the following three-prong test will be interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph:
(A) the claim limitation uses the term “means” or “step” or a term used as a substitute for “means” that is a generic placeholder (also called a nonce term or a non-structural term having no specific structural meaning) for performing the claimed function;
(B) the term “means” or “step” or the generic placeholder is modified by functional language, typically, but not always linked by the transition word “for” (e.g., “means for”) or another linking word or phrase, such as “configured to” or “so that”; and
(C) the term “means” or “step” or the generic placeholder is not modified by sufficient structure, material, or acts for performing the claimed function.
Use of the word “means” (or “step”) in a claim with functional language creates a rebuttable presumption that the claim limitation is to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites sufficient structure, material, or acts to entirely perform the recited function.
Absence of the word “means” (or “step”) in a claim creates a rebuttable presumption that the claim limitation is not to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is not interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites function without reciting sufficient structure, material or acts to entirely perform the recited function.
Claim limitations in this application that use the word “means” (or “step”) are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action. Conversely, claim limitations in this application that do not use the word “means” (or “step”) are not being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action.
This application includes one or more claim limitations that use the word “means” or “step” but are nonetheless not being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph because the claim limitation(s) recite(s) sufficient structure, materials, or acts to entirely perform the recited function. Such claim limitation(s) is/are: “a training module” and “a prediction module” in claim 12. While these limitations invoke 112(f) sufficient structure for both is found on pages 18-19 of the specification so as to provide sufficient definition of the limitations to a person skilled in the art.
Because this/these claim limitation(s) is/are not being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, it/they is/are not being interpreted to cover only the corresponding structure, material, or acts described in the specification as performing the claimed function, and equivalents thereof.
If applicant intends to have this/these limitation(s) interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, applicant may: (1) amend the claim limitation(s) to remove the structure, materials, or acts that performs the claimed function; or (2) present a sufficient showing that the claim limitation(s) does/do not recite sufficient structure, materials, or acts to perform the claimed function.
Claim Rejections - 35 USC § 112
Response to Amendment
In view of applicant’s amendments to the claims, previous rejections of claims 1-15 under 35 U.S.C. 112(b) for indefiniteness have been withdrawn.
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 3, 5-12, 14, and 16 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Regarding claim 3, the phrases "wherein" and “is used for” renders the claim indefinite because it is unclear whether the limitation following the phrase are part of the claimed invention, i.e. it is unclear whether the “wherein” clause combined with the statement "is used for" is an intended use or the actual generating of the chemical mixture. Since all subsequent claims are dependent from claim 3, claims 5-12, 14, and 16 are so too rejected.
Claim Rejections - 35 USC § 101
Response to Amendment
In view of applicant’s amendments to the claims, previous rejections of claims 14 and 15 under 35 U.S.C. 101 for being directed to non-statutory subject matter have been withdrawn.
In view of applicant’s amendments to the claims, previous rejections of claims 1-15 under 35 U.S.C. 101 for being directed to abstract ideas without significantly more, have been reviewed, updated, and provided below.
Response to Arguments
Applicant’s arguments, see page 6 of the Remarks, filed 7/21/2026, with respect to the rejection of claims 1-15 under 35 U.S.C. 101 for being directed to abstract ideas without significantly more, have been fully considered and are persuasive. Specifically, the argument that the invention is directed to a practical application wherein the method produces an improved chemical mixture that is then produced, is persuasive. The rejection of claims 1-15 has been withdrawn.
Claim Rejections - 35 USC § 103
Response to Amendment
In view of applicant’s amendments to the claims, previous rejections under 35 U.S.C. 103 for obviousness over the prior art, have been reviewed, updated, and provided below.
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.
Claims 3, 5, 10, 12, 14 and 16 are rejected under 35 U.S.C. 103 as being unpatentable over Eden et al. (Computer Aided Chemical Engineering (2003) 1175-1180) and Goh et al. (Proceedings of the 24th ACM SIGKDD international conference on knowledge discovery & data mining (2018) 302-310).
Claim 3 is directed to a computer method for clustering mixtures and their ingredients to revise recipes by replacing ingredients and then training a model to predict mixture properties.
Claim 12 is directed to a training device which performs claim 1.
Claim 14 is directed to a computer program product that performs the method of claim 3.
Eden et al. teaches in the abstract “In this work, a new simple, yet effective, systematic method to synthesize and design formulations is presented. Visualization of the problem is achieved by employing recently developed property clustering techniques, which allows a high-dimensional problem to be visualized in two or three dimensions. The clusters by definition satisfy intra-stream and inter-stream conservation through linear "mixing" rules and are therefore applicable in formulation design”, on page 1176, paragraph 1 “Given a set of candidate compounds (constituents) NC, that are mutually totally miscible, determine a set of mixtures with 2 to NC compounds that, in addition to being totally miscible with a specified product, also satisfy a set of property targets”, Tables 1 and 2 provide targets, characterization variables, candidate components and property values for which clustering was performed to identify various mixture combinations depicted in Figure 1, reading on a computer-implemented method for generating a chemical mixture with property performance targets, the method comprising: - obtaining data comprising history and/or calibration data of a plurality of chemical mixture recipes and properties of each chemical mixture recipe, wherein each chemical mixture recipe comprises two or more ingredients; - assigning at least one ingredient in each chemical mixture recipe to one of pre-defined substance clusters, wherein each pre-defined substance cluster represents a single ingredient or a group of ingredients having similar chemistry as defined by a chemical clustering algorithm; - revising each chemical mixture recipe by replacing the at least one ingredient with the assigned pre-defined substance cluster. Eden et al. teaches on page 1177, paragraph 2 “The objective of this investigation is to identify binary and ternary mixtures of pure components resulting in a target mixture with a solubility parameter of approximately 18.5 MPa ~ and properties defined by the values given in Table 1”, reading on further comprising:- comparing the predicted property measurements to property performance targets; and - adjusting the chemical mixture recipe to meet the property performance targets.
Goh et al. teaches in the abstract “In this work, we develop an approach of using rule-based knowledge for training ChemNet, a transferable and generalizable deep neural network for chemical property prediction that learns in a weak-supervised manner…When coupled with transfer learning approaches to predict other smaller datasets for chemical properties that it was not originally trained on, we show that ChemNet’s accuracy outperforms contemporary DNN models that were trained using conventional supervised learning. Furthermore, we demonstrate that the ChemNet pre-training approach is equally effective on both CNN (Chemception) and RNN (SMILES2vec) models, indicating that this approach is network architecture agnostic and is effective across multiple data modalities”, on page 303, column 1, paragraph 2 “Our work addresses the small and fragmented data landscape in chemistry. This is achieved by leveraging rule-based knowledge obtained from prior feature engineering research in chemistry to perform weak supervised learning and combining it with transfer learning methods used in modern deep learning research…We demonstrate that ChemNet learns chemistry-relevant internal representations. ChemNet may be coupled with a transfer learning approach that can be used to predict the novel chemical properties that were not part of the original list of properties”, reading on providing the revised chemical mixture recipes, together with the properties of the chemical mixture recipes, to a machine learning process in order to train a data-driven model, which is usable for predicting properties of a new chemical mixture as well as reading on a device, computer program, instructions, processors, prediction module, and CRM. Furthermore, Goh et al. teaches on page 305, column 2, paragraph 3 “We used a 5-fold cross validation protocol for training and evaluated the performance and early stopping criterion of the model using the validation set”, reading on training a data-driven model for predicting properties of a chemical mixture and providing the revised sample chemical mixture recipes, together with the properties of the chemical mixture recipes, to a machine learning process to train the data-driven mode.
It would have been obvious at the time of first filing to have modified the teachings of Eden et al. for the use of clustering in grouping chemical mixture ingredients and properties to alter the underlying recipe with the teachings of Goh et al. for the use of machine learning methods, specifically RBML, in predicting chemical properties as the latter teaches in the abstract “we demonstrate that the ChemNet pre-training approach is equally effective on both CNN (Chemception) and RNN (SMILES2vec) models, indicating that this approach is network architecture agnostic and is effective across multiple data modalities. Our results indicate a pretrained ChemNet that incorporates chemistry domain knowledge and enables the development of generalizable neural networks for more accurate prediction of novel chemical properties”. One would have had a reasonable expectation of success given that use of unsupervised or supervised techniques does not preclude the use of the other, and the latter teaches “this approach is network architecture agnostic and is effective across multiple data modalities”. Therefore, it would have been obvious at the time of first filing to have modified the teachings of each and to be successful.
Claim 5 is directed to the method of claim 3 but further specifies that the properties comprise a respective performance score indicative of a performance evaluation of the recipe.
Eden et al. teaches in the abstract “In this work, a new simple, yet effective, systematic method to synthesize and design formulations is presented. Visualization of the problem is achieved by employing recently developed property clustering techniques, which allows a high-dimensional problem to be visualized in two or three dimensions. The clusters by definition satisfy intra-stream and inter-stream conservation through linear "mixing" rules and are therefore applicable in formulation design”, on page 1176, paragraph 1 “Given a set of candidate compounds (constituents) NC, that are mutually totally miscible, determine a set of mixtures with 2 to NC compounds that, in addition to being totally miscible with a specified product, also satisfy a set of property targets”, Tables 1 and 2 provide targets, characterization variables, candidate components and property values for which clustering was performed to identify various mixture combinations depicted in Figure 1, and on page 1177, paragraph 2 “The objective of this investigation is to identify binary and ternary mixtures of pure components resulting in a target mixture with a solubility parameter of approximately 18.5 MPa ~ and properties defined by the values given in Table 1”, reading on wherein the properties of each chemical mixture recipe further comprise, for each measured property, a respective performance score indicative of a performance evaluation of the respective chemical mixture recipe.
Claim 10 is directed to the method of claim 3 but further specifies that the data-driven model comprises a rule-based machine learning model.
Goh et al. teaches in the abstract “In this work, we develop an approach of using rule-based knowledge for training ChemNet, a transferable and generalizable deep neural network for chemical property prediction that learns in a weak-supervised manner…’, reading on wherein the data-driven model comprises a rule-based machine learning model.
Claim 16 is directed to the method of claim 3 but further specifies identifying a correlation between a substance cluster and one or more characteristics.
Eden et al. teaches in the abstract “In this work, a new simple, yet effective, systematic method to synthesize and design formulations is presented. Visualization of the problem is achieved by employing recently developed property clustering techniques, which allows a high-dimensional problem to be visualized in two or three dimensions. The clusters by definition satisfy intra-stream and inter-stream conservation through linear "mixing" rules and are therefore applicable in formulation design”, on page 1176, paragraph 1 “Given a set of candidate compounds (constituents) NC, that are mutually totally miscible, determine a set of mixtures with 2 to NC compounds that, in addition to being totally miscible with a specified product, also satisfy a set of property targets”, Tables 1 and 2 provide targets, characterization variables, candidate components and property values for which clustering was performed to identify various mixture combinations depicted in Figure 1, the use of clustering to chemical mixtures, particularly clustering ingredients, would inherently identify correlations between the clustered ingredients and their properties, thereby reading on identifying, based on the training, a correlation between at least one pre-defined substance cluster and one or more characteristics of properties.
Claims 6-9 are rejected under 35 U.S.C. 103 as being unpatentable over Eden et al. (Computer Aided Chemical Engineering (2003) 1175-1180) and Goh et al. (Proceedings of the 24th ACM SIGKDD international conference on knowledge discovery & data mining (2018) 302-310) as applied to claims 3, 5, 10, 12, 14 and 16 above, and further in view of Gupta et al. (Journal of Applied Statistics (2001) 199-213).
Claim 6 is directed to the method of claim 3 but further specifies that one ingredient be a resin and/or additive.
Eden et al. and Goh et al. teach the method of claim 1 as previously described.
Eden et al. and Goh et al. do not teach that the one ingredient be a resin and/or additive.
Gupta et al. teaches in the abstract “paint manufacturing company was facing the problem of Vehicle Separation and Settling in one of its prime products. These two abnormalities are, in general, opposing in nature. The manufacturer tried several modifications in the existing recipe for the product but failed to control them. Experimentation was carried out using mixture design, a special type of designed experiment, and quadratic response surface models were fitted for both the responses. Finally, optimum formulation was obtained by simultaneously optimizing the two response surface models. During the determination of optimal formulation, different methods were compared. The optimum formulation is currently being used for regular manufacturing”, and on page 199, paragraphs 2-3 “Stoving Primer Surfacer is a widely used primer. It is a mixture of extenders (such as Barrytes, Forcal S, etc), wetting agents (such as Soyalicithin, etc), antisettling agents (such as Nilset 117, Smaketon Gel, etc), colour pigments, resin, solvent, and additives… Several modifications to the existing recipe were tried out to overcome this problem. Under this circumstance, the manufacturer decided to initiate a study to obtain a primer formulation, which would minimize the occurrence of Vehicle Separation and Settling simultaneously”, reading on wherein at least one ingredient selected from a resin and/or an additive is represented by a substance cluster.
It would have been obvious at the time of first filing to have modified the teachings of Eden et al. and Goh et al. for the method of claim 1, with the teachings of Goh et al. for the formulation of a new paint recipe as the former is directed to improved chemical recipes and Gupta et al. is attempting to formulate a new chemical paint recipe. One would have had a reasonable expectation of success given that the latter is an application well within the boundaries of the former references and is also trying to model the recipe albeit with a different model. Therefore, it would have been obvious at the time of first filing to have modified the teachings of each and to be successful.
Claim 7 is directed to the method of claim 3 but further specifies the chemical mixture being a paint formulation.
Eden et al. and Goh et al. teach the method of claim 1 as previously described.
Eden et al. and Goh et al. do not teach that the chemical mixture being a paint formulation.
Gupta et al. teaches in the abstract “paint manufacturing company was facing the problem of Vehicle Separation and Settling in one of its prime products. These two abnormalities are, in general, opposing in nature. The manufacturer tried several modifications in the existing recipe for the product but failed to control them. Experimentation was carried out using mixture design, a special type of designed experiment, and quadratic response surface models were fitted for both the responses. Finally, optimum formulation was obtained by simultaneously optimizing the two response surface models. During the determination of optimal formulation, different methods were compared. The optimum formulation is currently being used for regular manufacturing”, and on page 199, paragraphs 2-3 “Stoving Primer Surfacer is a widely used primer. It is a mixture of extenders (such as Barrytes, Forcal S, etc), wetting agents (such as Soyalicithin, etc), antisettling agents (such as Nilset 117, Smaketon Gel, etc), colour pigments, resin, solvent, and additives… Several modifications to the existing recipe were tried out to overcome this problem. Under this circumstance, the manufacturer decided to initiate a study to obtain a primer formulation, which would minimize the occurrence of Vehicle Separation and Settling simultaneously”, reading on wherein the chemical mixture comprises a paint formulation.
Claim 8 is directed to the method of claim 3 but further specifies the paint formulation comprises wet paint and/or properties of coating.
Eden et al. and Goh et al. teach the method of claim 1 as previously described.
Eden et al. and Goh et al. do not teach that the paint formulation comprises wet paint and/or properties of coating.
Gupta et al. teaches in the abstract “paint manufacturing company was facing the problem of Vehicle Separation and Settling in one of its prime products. These two abnormalities are, in general, opposing in nature. The manufacturer tried several modifications in the existing recipe for the product but failed to control them. Experimentation was carried out using mixture design, a special type of designed experiment, and quadratic response surface models were fitted for both the responses. Finally, optimum formulation was obtained by simultaneously optimizing the two response surface models. During the determination of optimal formulation, different methods were compared. The optimum formulation is currently being used for regular manufacturing”, and on page 199, paragraphs 2-3 “Stoving Primer Surfacer is a widely used primer. It is a mixture of extenders (such as Barrytes, Forcal S, etc), wetting agents (such as Soyalicithin, etc), antisettling agents (such as Nilset 117, Smaketon Gel, etc), colour pigments, resin, solvent, and additives… Several modifications to the existing recipe were tried out to overcome this problem. Under this circumstance, the manufacturer decided to initiate a study to obtain a primer formulation, which would minimize the occurrence of Vehicle Separation and Settling simultaneously”, reading on wherein the properties of a paint formulation comprise properties of a wet paint and/or properties of coating formed therefrom.
Claim 9 is directed to the method of claim 3 but further specifies the mixture comprises one of those specified in the group provided.
Eden et al. and Goh et al. teach the method of claim 1 as previously described.
Eden et al. and Goh et al. do not teach that the mixture comprises one of those specified in the group provided.
Gupta et al. teaches in the abstract “paint manufacturing company was facing the problem of Vehicle Separation and Settling in one of its prime products. These two abnormalities are, in general, opposing in nature. The manufacturer tried several modifications in the existing recipe for the product but failed to control them. Experimentation was carried out using mixture design, a special type of designed experiment, and quadratic response surface models were fitted for both the responses. Finally, optimum formulation was obtained by simultaneously optimizing the two response surface models. During the determination of optimal formulation, different methods were compared. The optimum formulation is currently being used for regular manufacturing”, and on page 199, paragraphs 2-3 “Stoving Primer Surfacer is a widely used primer. It is a mixture of extenders (such as Barrytes, Forcal S, etc), wetting agents (such as Soyalicithin, etc), antisettling agents (such as Nilset 117, Smaketon Gel, etc), colour pigments, resin, solvent, and additives… Several modifications to the existing recipe were tried out to overcome this problem. Under this circumstance, the manufacturer decided to initiate a study to obtain a primer formulation, which would minimize the occurrence of Vehicle Separation and Settling simultaneously”, reading on wherein the chemical mixture comprises at least one selected from the group consisting of:- an agricultural multi-component mixture; - a pharmaceutical multi-component mixture; - a nutrition multi-component mixture; - an ink multi-component mixture; - a chemical mixture for construction purposes; and - a chemical mixture used inside oil production.
Claim 11 is rejected under 35 U.S.C. 103 as being unpatentable over Eden et al. (Computer Aided Chemical Engineering (2003) 1175-1180) and Goh et al. (Proceedings of the 24th ACM SIGKDD international conference on knowledge discovery & data mining (2018) 302-310) as applied to claims 3, 5, 10, 12, 14 and 16 above, and further in view of Urbanowicz et al. (Evolutionary intelligence (2015) 89-116).
Claim 11 is directed to the method of claim 10 and thus claim 3, but further specifies the rule-based model comprise one of those specified.
Eden et al. and Goh et al. teach the method of claim 1 as previously described.
Eden et al. and Goh et al. do not teach that the rule-based model comprises one of those specified.
Urbanowicz et al. teaches in the abstract “A large number of potentially predictive attributes is emblematic of problems in bioinformatics, genetic epidemiology, and many other fields. Previously, ExS-TraCS was introduced as an extended Michigan-style supervised learning classifier system that combined a set of powerful heuristics to successfully tackle the challenges of classification, prediction, and knowledge discovery in complex, noisy, and heterogeneous problem domains… this paper presents a complete description of the ExS-TraCS algorithm and introduces an effective strategy to dramatically improve learning classifier system scalability. ExSTraCS 2.0 addresses scalability with (1) a rule specificity limit, (2) new approaches to expert knowledge guided covering and mutation mechanisms, and (3) the implementation and utilization of the TuRF algorithm for improving the quality of expert knowledge discovery in larger datasets”, reading on wherein the rule-based machine learning model comprises at least one selected from the group consisting of:- learning classifier systems; - association rule learning; and - artificial immune systems.
It would have been at the time of first filing to have modified the teachings of Eden et al. and Goh et al. for the method of claim 1, with the teachings of Urbanowicz et al. for the use of an LCS model as the latter teaches in the abstract “Performance over a complex spectrum of simulated genetic datasets demonstrated that these new mechanisms dramatically improve nearly every performance metric on datasets with 20 attributes and made it possible for ExSTraCS to reliably scale up to perform on related 200 and 2000-attribute datasets. ExSTraCS 2.0 was also able to reliably solve the 6, 11, 20, 37, 70, and 135 multiplexer problems, and did so in similar or fewer learning iterations than previously reported, with smaller finite training sets, and without using building blocks discovered from simpler multiplexer problems. Furthermore, ExS-TraCS usability was made simpler through the elimination of previously critical run parameters”. One would have had a reasonable expectation of success as the latter teaches in the abstract “While Michigan-style learning classifier systems are powerful and flexible learners, they are not considered to be particularly scalable. For the first time, this paper presents a complete description of the ExS-TraCS algorithm and introduces an effective strategy to dramatically improve learning classifier system scalability”, while simultaneously providing source code for the program itself. Therefore, it would have been obvious at the time of first filing to have modified the teachings of each and to be successful.
Response to Arguments
Applicant's arguments filed 7/21/2026 have been fully considered but they are not persuasive.
Applicant asserts on page 9 of the Remarks filed 7/21/2026 that the cited art does notread on the amended limitations of independent claim 3. Specifically, applicant claims the disclosure does not read on assigning at least one ingredient in each sample recipe to one of a plurality of pre-defined substance clusters and that there are no pre-defined substance clusters whatsoever in the disclosure. However, examiner reminds applicant that Eden et al. teaches in the abstract “For any formulation design problem, the target and the raw materials are identified on a ternary property cluster diagram”, Section 2 describes their “PROPERTY CLUSTER BASED FORMULATION DESIGN METHOD”, Tables 1 and 2 show their Mixture Formulation, with property variables and candidate components respectively, Figure 1 displaying the “Ternary visualization”, and page 1187, paragraph 1 stating “By employing the procedure presented in Section 2, the property data given in Tables 1 and 2 is converted to property cluster values for ternary representation”. Ternary visualization and clustering is exactly a pre-defined cluster, and since the clusters are based on the substances of Table 2 and values of Table 1, they are definitionally pre-defined substance clusters.
Applicant asserts on page 9 of the Remarks filed 7/21/2026 that Eden et al. does not teach revising each sample chemical mixture recipe by replacing the ingredient with the substance cluster. However, Eden et al. teaches on page 1177, paragraph 3 “To illustrate the use of the property cluster based formulation design method, a simple visual "mixture" design is presented. The objective of this investigation is to identify binary and ternary mixtures of pure components resulting in a target mixture with a solubility parameter of approximately 18.5 MPa1/2 and properties defined by the values given in Table 1…In order to identify candidate components that would be feasible constituents matching the target formulation, a database search was performed…The components were selected solely based on their relative placement on the ternary diagram to ensure a reasonable spread in order to illustrate the formulation synthesis methodology”. Furthermore, Figures 2 and 3 show potential combinations of different ingredients within the cluster to achieve different candidate formulations.
Applicant asserts on page 10 of the Remarks that Eden et al. fails to disclose comparing the predicted property measurements to the performance targets. However, examiner reminds applicant that Eden et al. teaches in the abstract “In this work, a new simple, yet effective, systematic method to synthesize and design formulations is presented. Visualization of the problem is achieved by employing recently developed property clustering techniques, which allows a high-dimensional problem to be visualized in two or three dimensions. The clusters by definition satisfy intra-stream and inter-stream conservation through linear "mixing" rules and are therefore applicable in formulation design”, on page 1176, paragraph 1 “Given a set of candidate compounds (constituents) NC, that are mutually totally miscible, determine a set of mixtures with 2 to NC compounds that, in addition to being totally miscible with a specified product, also satisfy a set of property targets”, Tables 1 and 2 provide targets, characterization variables, candidate components and property values for which clustering was performed to identify various mixture combinations depicted in Figure 1, and on page 1177, paragraph 2 “The objective of this investigation is to identify binary and ternary mixtures of pure components resulting in a target mixture with a solubility parameter of approximately 18.5 MPa ~ and properties defined by the values given in Table 1”, determining a set of mixtures with 2 to NC compounds that…also satisfy a set of property targets inherently compares the predicted property measurements to performance targets.
Conclusion
No claims are allowed.
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
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/K.N.A./Examiner, Art Unit 1687
/LARRY D RIGGS II/ Supervisory Patent Examiner, Art Unit 1686
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