Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Claim Status
Claims 1-15 are pending and examined.
Priority
Receipt is acknowledged of certified copies of papers required by 37 CFR 1.55.
Information Disclosure Statement
The information disclosure statement (IDS) received on 3/11/2024 is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 1-15 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 1, the claim recites, “determining at least one physical-chemical parameter by providing the sensor data to a plant model, wherein the plant model comprises: i. a mechanistic model…ii. a data-driven model associated to the mechanistic model…”. However, the relationship between the mechanistic model and the data-driven model is unclear. How is the data-driven model associated to the mechanistic model? It is unclear how the two models work together to determine at least one physical-chemical parameter absent more detail about their relationship. Further clarification is needed.
Examiner’s Note: Pgs. 10-11 of the instant Specification detail how the data-driven model and mechanistic model can be related, and recite in part, “the output of a data-driven model can bs used as input for an equation of the mechanistic model or the output of an equation of the mechanistic model can be used as input for a data-driven model”. If the claim was amended to recite the relationship between the mechanistic model and data-driven model in a similar manner, this would overcome the above 112(b) rejection.
Claim 11 contains similar issues regarding reciting “determine at least one physical-chemical parameter by providing the sensor data to a plant model, wherein the plant model comprises: i. mechanistic model…ii. at least one data-driven model associated to the mechanistic model…”, and is similarly rejected.
Further regarding claim 1, 4th to Last Ln. to 3rd to Last Ln. recite, “the chemical reaction”. There is insufficient antecedent basis for this limitation in the claim. For purposes of compact prosecution, the above limitation has been examined as, “a chemical reaction”.
Claim 11 contains similar issues regarding the recitation of “the chemical reaction” absent sufficient antecedent basis, and is similarly rejected and examined as, “a chemical reaction”.
Claims 2-10, 12-13, and 15 are rejected at least for depending on a rejected claim.
Regarding claim 14, the claim recites, “training a plant model by adjusting the parameterization according to the training dataset, wherein the plant model comprises: i. a mechanistic model containing at least two equations each representing a part of the physical-chemical process, and ii. at least one data-driven model associated to the mechanistic model…”. However, the relationship between the mechanistic model and the data-driven model is unclear. How is the data-driven model associated to the mechanistic model? It is unclear how the two models work together to result in a model suitable for determining at least one physical-chemical parameter absent more detail about their relationship. Further clarification is needed.
Examiner’s Note: Pgs. 10-11 of the instant Specification detail how the data-driven model and mechanistic model can be related, and recite in part, “the output of a data-driven model can bs used as input for an equation of the mechanistic model or the output of an equation of the mechanistic model can bs used as input for a data-driven model”. If the claim was amended to recite the relationship between the mechanistic model and data-driven model in a similar manner, this would overcome the above 112(b) rejection.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-15 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. The subject matter eligibility test for the claims is shown below:
Subject Matter Eligibility Test, Step 1
Independent claim 1 is drawn to a method, independent claim 11 is drawn to a system, and independent claim 14 is drawn to a method. Each are statutory categories.
Subject Matter Eligibility Test, Step 2A Prong One
In Step 2A Prong One, it is determined if the claims recite an abstract idea, law of nature, or natural phenomenon.
Independent claim 1 recites receiving sensor data, determining at least one physical-chemical parameter by providing the sensor data to a plant model, and outputting the at least one physical-chemical parameter determined by the plant model. The act of determining at least one physical-chemical parameter by providing the sensor data to a plant model is an evaluation/determination type mental process, particularly as the steps are performed at a high level of generality, and do not recite a specialized computer for performing the mental processes. Evaluation/determination-type mental processes are abstract ideas. Further, although independent claim 1 recites that the plant model comprises a mechanistic model containing at least two equations, and a data-driven model that has been trained with a training dataset based on sets of historical data, a model is just an algorithm or a series of mental steps that are performed by a computer. A claim that requires a computer may still recite a mental process, particularly as the recitations “a mechanistic model” and “a data-driven model” are recited at a high level of generality, and amount to a generic computer, where the computer is used as a tool to perform the concept of determining at least one physical-chemical parameter. This is further bolstered by the fact that claim 1 is recited as a computer-implemented method, without providing any details about the computer beyond a generic computer. See MPEP 2106.04(a)(2)(III)(C). Further, both the mechanistic model and at least one data-driven model, when given their broadest reasonable interpretation, are mathematical equations, which are also a judicial exception.
Independent claim 11 recites an input configured to receive sensor data, a processor configured to determine at least one physical-chemical parameter by providing the sensor data to a plant model, and an output configured to output the at least one physical-chemical parameter determined by the plant model. The act of determining at least one physical-chemical parameter by providing the sensor data to a plant model is an evaluation/determination type mental process, particularly as the steps are performed at a high level of generality, and do not recite a specialized computer for performing the mental processes. Evaluation/determination-type mental processes are abstract ideas. Further, although independent claim 11 recites that the plant model comprises a mechanistic model containing at least two equations, and a data-driven model that has been trained with a training dataset based on sets of historical data, a model is just an algorithm or a series of mental steps that are performed by a computer. A claim that requires a computer may still recite a mental process, particularly as the recitations “a mechanistic model” and “a data-driven model” are recited at a high level of generality, and amount to a generic computer, where the computer is used as a tool to perform the concept of determining at least one physical-chemical parameter. See MPEP 2106.04(a)(2)(III)(C). Further, both the mechanistic model and at least one data-driven model, when given their broadest reasonable interpretation, are mathematical equations, which are also a judicial exception.
Independent claim 14 recites receiving a training dataset based on sets of historical data comprising sensor data, training a plant model by adjusting the parameterization according to the training set, and outputting the trained plant model. The plant model comprises a mechanistic model comprising at least two equations and at least one data-driven model. When given their broadest reasonable interpretation, the mechanistic model and at least one data-driven model are mathematical calculations, which is a judicial exception. Further, a model is just an algorithm or a series of mental steps that are performed by a computer. A claim that requires a computer may still recite a mental process, particularly as the recitations “a mechanistic model” and “at least one data-driven model” are recited at a high level of generality, and amounts to a generic computer, where the computer is used as a tool to perform the concept of determining the set of improved fermentation parameters, i.e. the mental process-type abstract idea. See MPEP 2106.04(a)(2)(III)(C).
Subject Matter Eligibility Test, Step 2A Prong Two
In step 2A Prong Two, it is determined if the claims recite additional elements that integrate the judicial exception into a practical application.
The independent claims further recite generating control signals based on the determined set of improved fermentation parameters, and adjusting operating conditions of a fermentation chamber based on the control signals. These additional limitations amount to the recitation of the words “apply it”, and are insignificant extra-solution activity. See MPEP 2106.05(f). Further, although the independent claims recite components of a fermentation system, e.g. a fermentation chamber, and a plurality of sensors, these additional elements merely generally link the abstract idea-type judicial exception to a particular technological environment or field of use (in this case, a fermentation system). See MPEP 2106.05(h). Accordingly, the additional elements recited do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. The claims are directed to an abstract idea.
Independent claim 1 further recites receiving sensor data, and outputting the at least one physical-chemical parameter determined by the plant model. These acts correspond to necessary data gathering and outputting, and are therefore insignificant extra-solution activity. See MPEP 2106.05(g). Further, although independent claim 1 further recites a physical-chemical process in a chemical plant, these elements merely generally link the judicial exception to a particular technological environment or field of use (in this case, a chemical plant). See MPEP 2106.05(h). Accordingly, the additional elements recited do not integrate the judicial exception into a practical application because they do not impose any meaningful limits on practicing the judicial exception. The claims are directed to a judicial exception.
Independent claim 11 further recites an input configured to receive sensor data, a processor, and an output configured to output the at least one physical-chemical parameter determined by the plant model. The input and output correspond to necessary data gathering and outputting, and are therefore insignificant extra-solution activity. See MPEP 2106.05(g). Further, although independent claim 11 further recites the aforementioned processor, and a physical-chemical process in a chemical plant, these elements merely generally link the judicial exception to a particular technological environment or field of use (in this case, a chemical plant), particularly as the processor is recited at a high level of generality. See MPEP 2106.05(h). Accordingly, the additional elements recited do not integrate the judicial exception into a practical application because they do not impose any meaningful limits on practicing the judicial exception. The claims are directed to a judicial exception.
Independent claim 14 further recites receiving a training data set of historical data comprising sensor data, and outputting a trained plant model. These acts correspond to necessary data gathering and outputting, and are therefore insignificant extra-solution activity. See MPEP 2106.05(g). Further, although independent claim 11 further recites a physical-chemical process in a chemical plant, these elements merely generally link the judicial exception to a particular technological environment or field of use (in this case, a chemical plant). See MPEP 2106.05(h). Accordingly, the additional elements recited do not integrate the judicial exception into a practical application because they do not impose any meaningful limits on practicing the judicial exception. The claims are directed to a judicial exception.
Subject Matter Eligibility Test, Step 2B
In step 2B, it is determined if the claim recites additional elements that amount to significantly more than the judicial exception. In this case, the independent claims additionally recite, “receiving sensor data” (in claims 1 and 14, corresponding to an input configured to receive sensor data), “outputting the at least one physical-chemical parameter” (in claim 1, corresponding to an output configured to output the at least one physical-chemical parameter in claim 11), “outputting the trained plant model” (claim 14), “a physical-chemical process in a chemical plant” (all independent claims), and “a processor” (claim 11). The elements “a physical-chemical process in a chemical plant” and “a processor” are well-known and conventional within the art. Further, the application of the judicial exception into a chemical plant is nothing more than generally linking the mental process judicial exception to a particular technological environment or field of use. See MPEP 2106.05(h). Still further, the acts of receiving sensor data related to a physical-chemical process (or, in the case of claim 14, receiving a training dataset based on historical data comprising sensor data and physical-chemical parameters related to the physical-chemical process) and outputting the at least one physical chemical parameter (or, in the case of claim 14, outputting the trained plant model) are necessary data gathering and outputting. See MPEP 2106.05(g).
Further, with regards to the generically recited “receiving sensor data”, “outputting the at least one physical-chemical parameter”, “outputting the trained plant model”, “a physical-chemical process in a chemical plant”, and “a processor” being nothing more than well-understood, routine, and conventional components that are well-known in the art, the following prior art is relied upon to show that the above elements are well-understood, routine, and conventional:
Mrziglod et al. (WO Pub. No. 2020/058237; hereinafter Mrziglod; already of record on the IDS received 3/11/2024) teaches receiving sensor data (Pg. 12 Lns. 3-10), outputting at least one physical-chemical parameter (see Fig. 2, Claim 1, the prediction results for the quality attribute(s) are outputted as a single quality value), outputting a trained plant model (Claim 3), a physical-chemical process in a chemical plant (see Claim 1), and a processor (Claim 1, a computer intrinsically includes a processor).
Hartman et al. (US Pub. No. 2021/0060514; hereinafter Hartman) teaches receiving sensor data ([0079]), outputting a trained plant model ([0031]), outputting at least one physical-chemical parameter ([0089]-[0091]), a physical-chemical process in a chemical plant ([0003]), and a processor ([0094]).
Hou et al. (US Pub. No. 2019/0198136; hereinafter Hou) teaches receiving sensor data ([0036]-[0038], see Fig. 4), outputting a trained plant model ([0036]-[0038]), outputting at least one physical-chemical parameter ([0068]), a physical-chemical process in a chemical plant ([0001], [0068]), and a processor ([0031]).
Claims 2-10, 12-13, and 15 are rejected under 35 U.S.C. 101 as depending on a rejected claim.
Claim Rejections - 35 USC § 102
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 the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claims 1-3 and 5-15 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Mrziglod.
Regarding claim 1, Mrziglod discloses a computer-implemented method for monitoring and/or controlling a physical- chemical process in a chemical plant (Abstract). The method comprises:
(a) receiving sensor data related to the physical-chemical process (Pg. 3 Ln. 16, Pg. 5 Ln. 15, Pg. 7 Lns. 11-28).
(b) determining at least one physical-chemical parameter by providing the sensor data to a plant model (Pg. 2 Ln. 13, Pg. 3 Lns. 9-21, Fig. 2). The plant model comprises:
i. a mechanistic model containing at least two equations each representing a part of the physical-chemical process (Pg. 4 Lns. 22-25, Pg. 4 Lns. 31-35, see Claim 9). and
ii. a data-driven model associated to the mechanistic model, wherein the data-driven model has been trained with a training dataset based on sets of historical data comprising sensor data and physical-chemical parameters related to the chemical reaction and wherein the total number of scalars as output parameters from the data-driven model is lower than the number of equations of the mechanistic model (Pg. 4 Lns. 4-7, Pg. 4 Ln. 31-Pg. 5 Ln. 4, Pg. 5 Ln. 32-Pg. 6 Ln. 3, see Fig. 2, see also Pg. 3 Ln. 9, and Claim 1, which state that the prediction results for the quality attribute(s) is output as a single quality value, which is considered to be a single scalar value).
(c) outputting the at least one physical-chemical parameter determined by the plant model (see Fig. 2 and Claim 1).
Regarding claim 2, Mrziglod discloses the computer-implemented method of claim 1, wherein the at least one physical-chemical parameter comprises at least one of reaction yield, catalyst activity or equipment fouling (Pg. 2 Lns. 26-31, overall process yield).
Regarding claim 3, Mrziglod discloses the computer-implemented method of claim 1, wherein the sensor data comprises temperature, pressure and flow rate of reagents (Pg. 7 Lns. 11-28).
Regarding claim 5, Mrziglod discloses the computer-implemented method of claim 1, wherein the data-driven model is an artificial neural network (Pg. 2 Lns. 10-21, see Fig. 2).
Regarding claim 6, Mrziglod discloses the computer-implemented method of claim 1,wherein the at least one physical-chemical parameter is output to a control system capable of changing settings of equipment in the chemical plant in which the physical-chemical process takes place based on the physical-chemical parameter (Claim 1, see also Claims 12-13, Pg. 9 Lns. 10-20).
Regarding claim 7, Mrziglod discloses the computer-implemented method of claim 1, wherein the output parameters from the at least one data-driven model is selected based on the sensitivity of the output parameters, wherein sensitivity is the relative difference of the physical-chemical parameters when the output parameters of the data-driven model are varied (Pg. 8 Lns. 7-18).
Regarding claim 8, Mrziglod discloses the computer-implemented method of claim 1, wherein the data-driven model uses parts of the sensor data determined by one or more of subset selection, regularization and dimensionality reduction (Pg. 6 Lns. 4-16, Pg. 4 Lns. 27-30).
Regarding claim 9, Mrziglod discloses a non-transitory computer-readable data medium storing a computer program comprising instructions for executing steps of the method according to claim 1 (Pg. 10 Lns. 8-10).
Regarding claim 10, Mrziglod discloses a method for monitoring and/or controlling a chemical plant using the physical-chemical parameter obtained according to claim 1 (Claim 1, see also Claims 12-13, Pg. 9 Lns. 10-20).
Regarding claim 11, Mrziglod discloses a production monitoring and/or control system for monitoring and/or controlling a physical-chemical process in a chemical plant (Abstract, Claim 1, see also Claims 12-13, Pg. 9 Lns. 10-20). The system comprises:
(a) an input configured to receive sensor data related to the physical-chemical process (Pg. 3 Ln. 16, Pg. 5 Ln. 15, Pg. 7 Lns. 11-28).
(b) a processor configured to determine at least one physical-chemical parameter by providing the sensor data to a plant model (Pg. 2 Ln. 13, Pg. 3 Lns. 9-21, Pg. 10 Lns. 8-10, Fig. 2). The plant model comprises:
i. a mechanistic model containing at least two equations each representing a part of the physical-chemical process (Pg. 4 Lns. 22-25, Pg. 4 Lns. 31-35, see Claim 9).
ii. at least one data-driven model associated to the mechanistic model, wherein the data- driven model has been trained with a training dataset based on sets of historical data comprising sensor data and physical-chemical parameters related to the chemical reaction and wherein the total number of scalars as output parameters from the at least one data-driven model is lower than the number of equations of the mechanistic models (Pg. 4 Lns. 4-7, Pg. 4 Ln. 31-Pg. 5 Ln. 4, Pg. 5 Ln. 32-Pg. 6 Ln. 3, see Fig. 2, see also Pg. 3 Ln. 9, and Claim 1, which state that the prediction results for the quality attribute(s) is output as a single quality value, which is considered to be a single scalar value).
(c) an output configured to output the at least one physical-chemical parameter determined by the plant model (see Fig. 2 and Claim 1. See also Claims 12-13, Pg. 9 Lns. 10-20).
Regarding claim 12, Mrziglod discloses the production monitoring and/or control system of claim 11, wherein the system is part of or in connection with a distributed control system of the chemical plant (Claim 1, see also Claims 12-13, Pg. 9 Lns. 10-20).
Regarding claim 13, Mrziglod discloses the production monitoring and/or control system of The production monitoring and/or control system of wherein the sensor data is received from sensors in the chemical plant (Pg. 3 Ln. 16, Pg. 5 Ln. 15, Pg. 7 Lns. 11-28).
Regarding claim 14, Mrziglod discloses a method for training a plant model suitable for determining at least one physical-chemical parameter from sensor data of a physical-chemical process in a chemical plant (Abstract, Pg. 2 Ln. 13, Pg. 3 Lns. 9-21, Fig. 2, Pg. 4 Lns. 4-7, Pg. 4 Ln. 31-Pg. 5 Ln. 4, Pg. 5 Ln. 32-Pg. 6 Ln. 3, see also Pg. 3 Ln. 9, and Claim 1). The method comprises:
(a) receiving a training dataset based on sets of historical data comprising sensor data and physical-chemical parameters related to the physical-chemical process (Pg. 4 Lns. 4-7, Pg. 4 Ln. 31-Pg. 5 Ln. 4, Pg. 5 Ln. 32-Pg. 6 Ln. 3, see Fig. 2, see also Pg. 3 Ln. 9, and Claims 1, 3).
(b) training a plant model by adjusting the parameterization according to the training dataset, wherein the plant model comprises:
i. a mechanistic model containing at least two equations each representing a part of the physical-chemical process (Pg. 4 Lns. 22-25, Pg. 4 Lns. 31-35, see Claim 9).
ii. at least one data-driven model associated to the mechanistic model, wherein the total number of scalars as output parameters from the at least one data-driven model is lower than the number of equations of the mechanistic models (Pg. 4 Lns. 4-7, Pg. 4 Ln. 31-Pg. 5 Ln. 4, Pg. 5 Ln. 32-Pg. 6 Ln. 3, see Fig. 2, see also Pg. 3 Ln. 9, and Claim 1, which state that the prediction results for the quality attribute(s) is output as a single quality value, which is considered to be a single scalar value).
(c) outputting the trained plant model (see Claim 3).
Regarding claim 15, Mrziglod discloses the method of claim 10, wherein the physical-chemical parameter is used to change settings of equipment in the chemical plant (Claim 1, see also Claims 12-13, Pg. 9 Lns. 10-20).
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claim 4 is rejected under 35 U.S.C. 103 as being unpatentable over Mrziglod as applied to claims 1-3 and 5-15 above in view of Nielsen et al., “Hybrid machine learning assisted modelling framework for particle processes”, 23 May 2020, Computers and Chemical Engineering, Vol. 140, Pgs. 1-19 (hereinafter Nielsen; already of record on the IDS received 3/11/2024).
Regarding claim 4, Mrziglod discloses the computer-implemented method of claim 1.
Mrziglod fails to explicitly disclose that the output of the data-driven model is used as input for at least one equation of the mechanistic model.
Nielsen is in the analogous field of chemical process modeling (Nielsen Abstract). Nielsen teaches an output of a data-driven model that is used as input for at least one equation of a mechanistic model (see Nielsen Fig. 1). It would have been obvious to one having ordinary skill in the art before the effective filing date of the invention to modify the method of Mrziglod with the teachings of Nielsen so that the output of the data-driven model is used as input for at least one equation of the mechanistic model, as Nielsen teaches that using the output of a data-driven model as input for an equation of a mechanistic model will allow for the prediction of future states of a chemical process while allowing flexibility toward use of process sensors and model predictions that do not violate physical constraints (Nielsen; Abstract, see Fig. 1).
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to John McGuirk whose telephone number is (571)272-1949. The examiner can normally be reached M-F 8am-530pm.
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/JOHN MCGUIRK/Examiner, Art Unit 1798
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