DETAILED ACTION
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 non-final and is in response to the claims filed December 22, 2023. Claims 1-12 are currently pending, of which claims 1-12 are currently rejected.
Claim Objections
Claims 1, 4-6, 11, and 12 are objected to because of the following informalities:
Claim 1 recites “the process” and each instance should be referred to as “the industrial process” to prevent any potential confusion and have continuity throughout the claim language. This further comes into play in claim 2, which appropriately refers back to “the industrial process”. Additionally, the claim recites “the second point in time” and this should be referred to as “the second, later point in time” to prevent any potential confusion and have continuity throughout the claim language. Finally, the claim recites
Claim 4 recites “the process” and this should be referred to as “the industrial process” to prevent any potential confusion and have continuity throughout the claim language.
Claim 5 recites “the process” and this should be referred to as “the industrial process” to prevent any potential confusion and have continuity throughout the claim language.
Claim 6 recites “the process” and this should be referred to as “the industrial process” to prevent any potential confusion and have continuity throughout the claim language.
Claim 11 recites “the process” and each instance should be referred to as “the industrial process” to prevent any potential confusion and have continuity throughout the claim language. Furthermore, the claim recites “the loss function” and this should be referred to as “the predetermined loss function” to prevent any potential confusion and have continuity throughout the claim language. Finally, the claim recites a rating between two potential options. However, the “optimizing” limitation appears to be incorrectly indented and should be in the same indentation level as the “rating” limitation, and not nested within the “rating” limitation. Otherwise, another “and” statement must be introduced at the end of the “mapping” limitation, indicating that it is the penultimate claim to the “rating” limitation (at that particular indentation level).
Claim 12 recites “the process” and each instance should be referred to as “the industrial process” to prevent any potential confusion and have continuity throughout the claim language.
Appropriate correction is required.
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 1-7 and 10-12 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea(s) without significantly more.
As per claim 1, at Step 1, the claim is directed to the statutory category of invention of a method (process). At Step 2A, Prong 1, the claims are directed to various mental processes. The claim language has been reproduced below:
A computer-implemented method for predicting, based on the state of an industrial process at a first point in time that is described by a process snapshot record with values of a first set of variables, a value of at least one process variable of the industrial process at a second, later point in time (mental process – evaluation), comprising:
mapping using at least one trained machine learning model the process snapshot record to at least one initial state record, wherein this initial state record characterizes a state of the process at the first point in time and contains an estimate of at least one hidden variable that is not comprised in the process snapshot record (mental process – evaluation and judgment);
providing the initial state record to a simulation model of the process (mental process – evaluation and judgment;
simulating using the simulation model a further development of the process (mental process – evaluation and judgment;
obtaining from the simulation model a final state record that characterizes the state of the process at the second point in time (mental process – evaluation and judgment); and
determining based on the final state record the sought value of the process variable at the second point in time (mental process – evaluation and judgment).
That is, a human user/operator, with the assistance of pen and paper, can certainly find best ways to perform a process. Examiner explicitly notes that no process is being explicitly performed either. Just merely general determinations, calculations, and predictions. A user/operator can simply try arrangements a certain number of ways and determine which way is best. At Step 2A, Prong 2, the additional element(s) are bolded above. A “computer” to implement the method is merely a generic component that does not integrate the identified abstract ideas into a practical application. Additionally, even if the high level simulation and models above are not abstract ideas at Step 2A, Prong 1, they are certainly high level recitations and thus mere instructions to apply an exception at Step 2A, Prong 2. See MPEP 2106.05(f).
At Step 2B, there are no additional elements that amount to significantly more than the identified judicial exception(s).
As per claim 2, at Step 2A Prong 1, the claim is directed to the mental process of identifying and recording a process (mental process – observation and evaluation). At Step 2A Prong 2 and Step 2B, the claim does not recite any additional elements that integrate the abstract idea into a practical application, nor do they amount to significantly more than the judicial exception(s).
As per claim 3, at Step 2A Prong 1, the claim is directed to the mental process of identifying and recording a process (mental process – observation and evaluation). At Step 2A Prong 2 and Step 2B, the claim does not recite any additional elements that integrate the abstract idea into a practical application, nor do they amount to significantly more than the judicial exception(s).
As per claim 4, at Step 2A Prong 1, the claim is directed to the mental process of evaluating a process (mental process –evaluation and judgment; mathematical relationships). At Step 2A Prong 2, the control action is recited at a high level of generality and is merely a general linking to a particular technology and/or an “apply it” scenario for the general components. See MPEP 2106.05(f) and (h). At Step 2B, the claim does not recite any additional elements that integrate the abstract idea into a practical application, nor do they amount to significantly more than the judicial exception(s).
As per claim 5, at Step 2A Prong 1, the claim is directed to the mental process of evaluating a process and acting upon said evaluation (mental process –evaluation and judgment). At Step 2A Prong 2, the modifying a set point value is recited at a high level of generality and is merely a general linking to a particular technology and/or an “apply it” scenario for the general components. Examiner notes that the other optional limitations relate to specific physical acts that would integrate the claims into a practical application should the set-point value limitation be removed. See MPEP 2106.05(f) and (h). At Step 2B, the claim does not recite any additional elements that integrate the abstract idea into a practical application, nor do they amount to significantly more than the judicial exception(s).
As per claim 6, at Step 2A Prong 1, the claim is directed to the mental process of evaluating models and ranking choices (mental process –evaluation and judgment; mathematical relationships). At Step 2A Prong 2 and Step 2B, the claim does not recite any additional elements that integrate the abstract idea into a practical application, nor do they amount to significantly more than the judicial exception(s).
As per claim 7, at Step 2A Prong 1, the claim is directed to the mental process of evaluating a process and acting upon said evaluation (mental process –evaluation and judgment). At Step 2A Prong 2, the control action is recited at a high level of generality and is merely a general linking to a particular technology and/or an “apply it” scenario for the general components. See MPEP 2106.05(f) and (h). At Step 2B, the claim does not recite any additional elements that integrate the abstract idea into a practical application, nor do they amount to significantly more than the judicial exception(s).
As per claims 8 and 9, the claims are directed to specific physical instances of control actions and industrial processes. These are sufficiently integrated into a practical application and would be eligible under 35 U.S.C. §101.
As per claim 10, at Step 2A Prong 1, the claim is directed to the mental process of evaluating models over arbitrary periods of time (mental process –evaluation and judgment). At Step 2A Prong 2 and Step 2B, the claim does not recite any additional elements that integrate the abstract idea into a practical application, nor do they amount to significantly more than the judicial exception(s).
As per claim 11, at Step 1 the claim is directed to the statutory category of invention of a method (process). At Step 2A, Prong 1, the claims are directed to various mental processes. The claim language has been reproduced below:
A computer-implemented method for training at least one machine learning model for use in predicting, based on the state of an industrial process at a first point in time that is described by a process snapshot record with values of a first set of variables, a value of at least one process variable of the industrial process at a second, later point in time (mental process – evaluation), comprising:
simulating using a simulation model of an industrial process a development of the process from an initial state characterized by an initial state record over a first time period to an intermediate state at an intermediate point in time characterized by a simulated intermediate state record (mental process – evaluation and judgment);
determining from the simulation up to the intermediate point in time a process snapshot record that would be observable in an industrial plant executing the process according to the simulation (mental process – evaluation and judgment);
mapping by the to-be trained machine learning model the process snapshot record to a candidate intermediate state record (mental process – evaluation and judgment);
rating using a predetermined loss function a difference between the candidate intermediate state record and/or a further processing result obtained based on the candidate intermediate state record, and the simulated intermediate state record and/or the corresponding processing result obtained based on this simulated intermediate state record (mental process – evaluation and judgment; mathematical relationship); and
optimizing parameters that characterize the behavior of the machine learning model such that mapping of further process snapshot records to candidate intermediate state records tends to improve the rating by the loss function (mental process – evaluation and judgment; mathematical relationship).
That is, a human user/operator, with the assistance of pen and paper, can certainly find best ways to perform a process. Examiner explicitly notes that no process is being explicitly performed either. Just merely general determinations, calculations, and predictions. A user/operator can simply try arrangements a certain number of ways and determine which way is best, including using their own rating systems. At Step 2A, Prong 2, the additional element(s) are bolded above. A “computer” to implement the method is merely a generic component that does not integrate the identified abstract ideas into a practical application. Additionally, even if the high level simulation and models above are not abstract ideas at Step 2A, Prong 1, they are certainly high level recitations and thus mere instructions to apply an exception at Step 2A, Prong 2. See MPEP 2106.05(f).
At Step 2B, there are no additional elements that amount to significantly more than the identified judicial exception(s).
As per claim 12, at Step 2A Prong 1, the claim is directed to high level simulation and modeling of candidate records (mental process – evaluation and judgment; mathematical relationships). At Step 2A Prong 2 and Step 2B, the claim does not recite any additional elements that integrate the abstract idea into a practical application, nor do they amount to significantly more than the judicial exception(s).
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 11 and 12 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.
Claim 11 recites “the to-be trained machine learning model” and it is unclear if this is the same machine learning model as introduced in the preamble, or a different machine learning model. This is further exacerbated by the use of “the machine learning model” in the final “optimizing” limitation of the claim, therefore breaking any consistency in the claim language.
Claim 12 is rejected based on its dependency from above-rejected claim 11.
Examiner’s Note
The prior art rejections below cite particular paragraphs, columns, and/or line numbers in the references for the convenience of the applicant. Although the specified citations are representative of the teachings in the art and are applied to the specific limitations within the individual claim, other passages and figures may apply as well. It is respectfully requested that, in preparing responses, the applicant fully consider the references in their entirety as potentially teaching all or part of the claimed invention, as well as the context of the passage as taught by the prior art.
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.
Claim Rejections - 35 USC § 102
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.
Claim(s) 1-3 is/are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Chan et al. (U.S. Publication No. 20200379442; hereinafter, “Chan”).
As per claim 1, Chan teaches a computer-implemented method for predicting, based on the state of an industrial process at a first point in time that is described by a process snapshot record with values of a first set of variables, a value of at least one process variable of the industrial process at a second, later point in time, comprising:
mapping using at least one trained machine learning model the process snapshot record to at least one initial state record, wherein this initial state record characterizes a state of the process at the first point in time and contains an estimate of at least one hidden variable that is not comprised in the process snapshot record (See Chan Fig. 1B and paras. [0051-52]: input variables translated into output variables of a machine learning model for industrial process; para. [0064]: key variables can be included when not measured. “Using the original input data (X) and resulting augmented data (XA), system 100/module 108 trains and develops a machine learning model for the pressure drop (Y) in resultant hybrid model 116 for deployment in process control and/or process modeling and simulation.”);
providing the initial state record to a simulation model of the process (See Chan Fig. 1B and paras. [0052-53] and [0064]: first principle models libraries using original input data at module 106);
simulating using the simulation model a further development of the process (See Chan Fig. 1B and paras. [0052-53] and [0064]: data set of module 106 used to training step at module 108);
obtaining from the simulation model a final state record that characterizes the state of the process at the second point in time (See Chan Fig. 1B and para. [0064]: “logy, Inc.), represented at libraries (databases) 110, 112, 114. Using the original input data (X) and resulting augmented data (XA), system 100/module 108 trains and develops a machine learning model for the pressure drop (Y) in resultant hybrid model 116 for deployment in process control and/or process modeling and simulation”; para. [0068] and Table 3 show various time profiles of augmented data and measured data); and
determining based on the final state record the sought value of the process variable at the second point in time (See Chan paras. [0051-53] and [0064-65]: final output variables (Y) in a model).
As per claim 2, Chan further teaches the method of claim 1, further comprising obtaining the process snapshot record from an industrial plant that is used to execute the industrial process, and/or from a plant historian of the industrial plant (See Chan Fig. 1B and paras. [0051-52], [0071] and [0077]: raw process data used for input).
As per claim 3, Chan further teaches the method of claim 2, wherein the hidden variable is a variable that is not directly obtainable from the industrial plant and/or plant historian (See Chan para. [0064]: key variables can be included when not measured. “Using the original input data (X) and resulting augmented data (XA), system 100/module 108 trains and develops a machine learning model for the pressure drop (Y) in resultant hybrid model 116 for deployment in process control and/or process modeling and simulation.” Additionally, “if the viscosity of the fluid flowing through a pipe is not a measured physical property, then system 100/module 106 calculates these values by using an underlying property mode”).
Claim Rejections - 35 USC § 103
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.
Claims 4 and 5 is/are rejected under 35 U.S.C. 103 as being unpatentable over Chan as applied above, and further in view of Umemoto et al. (U.S. Publication No. 2021/0333768; hereinafter “Umemoto”).
As per claim 4, Chan teaches the method of claim 1. However, while Chan teaches time intervals and the points in time, Chan does not explicitly teach control actions between the first and second points in time.
Umemoto teaches providing at least one candidate input to the simulation model, wherein this candidate input corresponds to a control action performed on the process between the first point in time and the second point in time (See Umemoto paras. [0093-94]: selecting control methods from candidate control methods to “improve the control method of equipment 10 before all the simulations are completed”).
It would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to combine, with a reasonable expectation of success, the control actions and simulation of Chan with the intervals and candidates of Umemoto. One would have been motivated to combine these references because both references disclose control actions as they relate to flow of gas and liquids, and Umemoto enhances the control actions of Chan by further optimizing processes in an overarching manner and further optimizing process objectives, such as those of Chan (See Coward paras. [0003-06]).
As per claim 5, Chan/Umemoto further teaches the method of claim 4, wherein the control action comprises: activating or deactivating at least one piece of equipment that is used to execute the process; and/or opening or closing at least one valve or other device that controls the a of at least one substance during execution of the process; and/or modifying a set-point value of at least one controller that participates in the execution of the process (See Chan paras. [0008] and [0047]: controller to implements settings of equipment of the subject industrial plant, including operating the plant equipment in carrying out the subject process).
Claim 8 is/are rejected under 35 U.S.C. 103 as being unpatentable over Chan as applied above, and further in view of Coward (U.S. Publication No. 2007/0275471).
As per claim 8, Chan teaches the method of claim 4. While Chan simulates/predicts flow of gas and liquids (See Chan paras. [0053[), Chan does not explicitly separate
Coward teaches wherein the industrial process comprises separation of an oil/gas well stream into oil, gas and water, and the control action comprises an action with the goal of improving the purity of the separated oil, gas and water (See Coward paras. [0026-27]: “Water and oil are separated by separators 230, and the water is removed. The remaining oil/gas mixture is then passed to de-gasser 250, which can be controlled by de-gasser process control system 255. Oil is removed for storage or other processing, while any separated lift gas is returned to compressor 260”).
It would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to combine, with a reasonable expectation of success, the control actions and simulation of Chan with the oil/gas separation of Coward. One would have been motivated to combine these references because both references disclose control actions as they relate to flow of gas and liquids, and Coward enhances the control actions of Chan by further optimizing processes in an overarching manner and further optimizing process objectives, such as those of Chan (See Coward paras. [0003-06]).
Claims 9 and 10 is/are rejected under 35 U.S.C. 103 as being unpatentable over Chan as applied above, and further in view of Ganti et al. (U.S. Publication No. 2017/0364043; hereinafter, “Ganti”).
As per claim 9, Chan teaches the method of claim 4. However, while Chan teaches control action modeling and simulations, Chan does not teach electricity generation.
Ganti teaches wherein the industrial process comprises generation of electricity in a fuel-burning power station, and the control action comprises an action with the goal of: reducing an amount of solid waste left over from combustion of the fuel; and/or reducing emission of at least one pollutant; and/or adapting an amount of power generated by the power station to a given power demand profile (See Ganti paras. [0068] and [0119]: energy demands can be used by the plant controller; para. [0200]: control action may be based on emission limits).
It would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to combine, with a reasonable expectation of success, the control actions and simulation of Chan with the timing of Ganti. One would have been motivated to combine these references because both references disclose modeling as they relate to control actions, and Ganti enhances the control actions of Chan by further optimizing processes in an overarching manner and taking into account improvements to efficiency based on the “myriad operating modes available to operators of complex modern power plants and the economic trade-offs associated with each” (See Ganti paras. [0004-07]).
As per claim 10, Chan further teaches the method of claim 1. However, while Chan teaches the first and second points in time, Chan does not explicitly disclose an interval length.
Ganti teaches wherein a time interval between the first point in time and the second point in time is between 10 minutes and 90 minutes (See Ganti para. [0154]: “a 24 hour prediction horizon may be defined as including 24 1-hour time intervals, meaning that the proposed horizon parameter set includes proposed parameter sets for each of the 24 time intervals”. Therefore, these 1 hour (60 minute) intervals would apply to the points in time of Chan).
It would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to combine Chan with the teachings of Ganti for at least the same reasons as discussed above in claim 9.
Allowable Subject Matter
Examiner notes that, should Applicant be able to overcome the other rejections to the claim, claim 6 recites subject matter that, when combined with all of the intervening claims, is not taught by the prior art of record. Harvey (U.S. 2019/0377306) discloses ranking when picking optimal regimes from a series of potential regimes. However, Harvey does not teach or suggest the separate simulations and the different candidate inputs affecting a predetermined criterion for such rankings (See Harvey paras. [0056] and [0069]). Claim 7 is allowable over the prior art merely based on its dependency from claim 6. Claim 11 recites the ratings related to a loss function that can be optimized over the run time of the industrial process. Similar to claim 6, the ratings of the claim are not taught in the specific order and combination as claimed.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
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/NICHOLAS KLICOS/Primary Examiner, Art Unit 2118