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 .
DETAILED ACTION
The following NON-FINAL Office Action is in response to application 18/683,801 filed on 02/15/2024. This communication is the first action on the merits.
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 02/15/2024 and 06/17/2025 has been considered by the examiner.
Drawings
The drawings were received on 02/15/2024. These drawings are acceptable.
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 1-18 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception without significantly more. A subject matter eligibility analysis is set forth below. See MPEP 2106.
Specifically, representative Claim 1 recites:
A method for estimating at least one quality parameter (Q) related to a product or a feed of processing of organic substances, the method comprising:
repeatedly measuring density (p) and temperature (T) of the product or the
feed;
repeatedly computing an estimate for the at least one quality parameter based on an estimation formula whose input variables include the measured density and the measured temperature;
repeatedly receiving laboratory test results (QLab) indicative of the at least one quality parameter; and
repeatedly updating model parameters (p1, p2, ... ) of the estimation formula based on: i) the received laboratory test results, and on ii) measured values of the input variables of the estimation formula corresponding to the received laboratory test results, the model parameters defining a dependence of changes of the estimate on changes of the input variables including the density and the temperature.
The claim limitations in the abstract idea have been highlighted in bold above; the remaining limitations are “additional elements.”
Claim 15 comprises similar limitations that performs the abstract idea of claim 1 and comprises:
supplying a feed containing the organic substances into processing equipment where at least one product is obtained from the organic substances;
repeatedly estimating at least one quality parameter related to the at least one product or to the feed; and
controlling conditions within the processing equipment based on the estimated at least one quality parameter;
Claim 16 comprises similar limitations that performs the abstract idea of claim 1.
Claim 17 comprises similar limitations that performs the abstract idea of claim 1 and comprises:
processing equipment configured to receive a feed containing the organic substances and obtain at least one product from the organic substances;
an apparatus for estimating, the apparatus for estimating being configured to repeatedly estimate a least one quality parameter related to the at least one product or to the feed; and
the process controller configured to control conditions within the processing equipment based on the estimated at least one quality parameter;
Claim 18 comprises similar limitations that performs the abstract idea of claim 1.
Under Step 1 of the analysis, claim 1 belongs to a statutory category, namely it is a method claim. Likewise, claim 15 is a method claim, claim 16 is an apparatus claim, claim 17 is a system claim, Claim 18 is directed towards a non-volatile computer readable medium.
Claim 18 is rejected because it does not sufficiently recite a non-transitory computer readable storage medium. The United States Patent and Trademark Office (USPTO) is obliged to give claims their broadest reasonable interpretation consistent with the specification during proceedings before the USPTO. See In re Zletz, 893 F.2d 319(Fed. Cir. 1989) (during patent examination the pending claims must be interpreted as broadly as their terms reasonably allow). The broadest reasonable interpretation of a claim drawn to a computer readable medium (also called machine readable medium and other such variations) typically covers forms of non-transitory tangible media and transitory propagating signals per se in view of the ordinary and customary meaning of computer readable media, particularly when the specification is silent. See MPEP 2111.01. The specification at page 5, lines 11-16 states that the invention provides a “non-volatile computer readable medium, e.g. a compact disc ‘CD’ that is encoded with a computer program according to the invention,” and further states that the computer program product comprises a non-volatile computer readable medium. However, the specification does not clearly limit the term “computer readable medium” to only non-volatile or tangible media and does not expressly exclude transitory propagating signals.
When the broadest reasonable interpretation of a claim covers a signal per se, the claim must be rejected under 35 U.S.C. §101 as covering non-statutory subject matter. See In re Nuijten, 500 F.3d 1346, 1356-57 (Fed. Cir. 2007) (transitory embodiments are not directed to statutory subject matter) and Interim Examination Instructions for Evaluating Subject Matter Eligibility Under 35 U.S.C. §101, Aug. 24, 2009; p. 2.
The USPTO recognizes that applicants may have claims directed to computer readable media that cover signals per se, which the USPTO must reject under 35 U.S.C. §101 as covering both non-statutory subject matter and statutory subject matter. In an effort to assist the patent community in overcoming a rejection or potential rejection under 35 U.S.C. §101 in this situation, the USPTO suggests the following approach. A claim drawn to such a computer readable medium that covers both transitory and non-transitory embodiments may be amended to narrow the claim to cover only statutory embodiments to avoid a rejection under 35 U.S.C. §101 by adding the limitation "non-transitory" to the claim. Cf. Animals – Patentability, 1077 Off. Gaz. Pat. Office 24 (April 21, 1987) (suggesting that applicants add the limitation "non-human" to a claim covering a multi-cellular organism to avoid a rejection under 35 U.S.C. §101). Such an amendment would typically not raise the issue of new matter, even when the specification is silent because the broadest reasonable interpretation relies on the ordinary and customary meaning that includes signals per se. The limited situations in which such an amendment could raise issues of new matter occur, for example, when the specification does not support a non-transitory embodiment because a signal per se is the only viable embodiment such that the amended claim is impermissibly broadened beyond the supporting disclosure. See, e.g., Gentry Gallery, Inc. v. Berkline Corp., 134 F.3d 1473 (Fed. Cir. 1998). In furtherance of compact prosecution, Examiner will further consider the claims under 35 USC § 101 as if the claims were amended to be directed towards a non-transitory computer-readable medium and not signal per se.
Under Step 2A, prong 1: This part of the eligibility analysis evaluates whether the claim recites a judicial exception. As explained in MPEP 2106.04, subsection II, a claim “recites” a judicial exception when the judicial exception is “set forth” or “described” in the claim.
In the instant case, claim 1 is found to recite at least one judicial exception (i.e. abstract idea), that being a Mental Process and a Mathematical Concept. This can be seen in the claim limitations of “A method for estimating at least one quality parameter (Q) related to a product or a feed of processing of organic substances”, “repeatedly computing an estimate for the at least one quality parameter based on an estimation formula whose input variables include the measured density and the measured temperature”, and “repeatedly updating model parameters (p1, p2, ... ) of the estimation formula based on: i) the received laboratory test results, and on ii) measured values of the input variables of the estimation formula corresponding to the received laboratory test results, the model parameters defining a dependence of changes of the estimate on changes of the input variables including the density and the temperature” which is the judicial exception of a mental process because these limitations are merely data observations, evaluations, and/or judgements in order to estimate at least one quality parameter based on measured density and temperature and update model parameters of the estimation formula using received laboratory test results and is capable of being performed mentally and/or with the aid of pen and paper. Additionally, the aforementioned limitations recite mathematical calculations, e.g. see Spec. [Page 9]-[Page 13] describing the use of mathematical calculations, including estimation formulas, regression analysis, optimization (e.g., least-squares optimization), and correlation calculations, in order to estimate at least one quality parameter and update model parameters of the estimation formula.
Similar limitations comprise the abstract ideas of Claim 15, 16, 17, 18. Claim 15 found to recite at least one judicial exception (i.e. abstract idea), that being a Mental Process and a Mathematical Concept. This can be seen in the claim limitations of “repeatedly estimating at least one quality parameter related to the at least one product or to the feed”, which is the judicial exception of a mental process because these limitations are merely data observations, evaluations, and/or judgements in order to estimate at least one quality parameter related to at least one product or a feed and control conditions within the processing equipment based on at least one quality parameter and is capable of being performed mentally and/or with the aid of pen and paper.
Step 2A, prong 2 of the eligibility analysis evaluates whether the claim as a whole integrates the recited judicial exception(s) into a practical application of the exception. This evaluation is performed by (a) identifying whether there are any additional elements recited in the claim beyond the judicial exception, and (b) evaluating those additional elements individually and in combination to determine whether the claim as a whole integrates the exception into a practical application.
In addition to the abstract ideas recited in claim 1, the claimed method recites additional elements including “repeatedly measuring density (p) and temperature (T) of the product or the feed” and “repeatedly receiving laboratory test results (QLab) indicative of the at least one quality parameter” however these elements are found to be data gathering and output steps, which are recited at a high level of generality, and thus merely amount to “insignificant extra-solution” activity(ies). See MPEP 2106.05(g) “Insignificant Extra-Solution Activity,”.
Moreover, similar limitations comprise the additional elements of Claim 15, 16, 17 and 18. In addition, claim 15 recites the same additional elements and also recites “supplying a feed containing the organic substances into processing equipment where at least one product is obtained from the organic substances” and “controlling conditions within the processing equipment based on the estimated at least one quality parameter”. Moreover, Claim 17 also recites “processing equipment configured to receive a feed containing the organic substances and obtain at least one product from the organic substances”, “an apparatus for estimating, the apparatus for estimating being configured to repeatedly estimate a least one quality parameter related to the at least one product or to the feed”, and “the process controller configured to control conditions within the processing equipment based on the estimated at least one quality parameter” however these elements are found to be data gathering and output steps, which are recited at a high level of generality, and thus merely amount to “insignificant extra-solution” activity(ies). See MPEP 2106.05(g) “Insignificant Extra-Solution Activity,”. Furthermore, the claim recites that the steps, e.g. “computing”, are performed by a controller however this is found to be equivalent to adding the words “apply it” and mere instructions to apply a judicial exception on a general purpose computer does not integrate the abstract idea into a practical application. See MPEP 2106.05(f).
The generic data gathering, processing, and output steps, are recited at such a high level of generality that it represents no more than mere instructions to apply the judicial exceptions on a computer. It can also be viewed as nothing more than an attempt to generally link the use of the judicial exceptions to the technological environment of a computer. Noting MPEP 2106.04(d)(I): “It is notable that mere physicality or tangibility of an additional element or elements is not a relevant consideration in Step 2A Prong Two. As the Supreme Court explained in Alice Corp., mere physical or tangible implementation of an exception does not guarantee eligibility. Alice Corp. Pty. Ltd. v. CLS Bank Int’l, 573 U.S. 208, 224, 110 USPQ2d 1976, 1983-84 (2014) ("The fact that a computer ‘necessarily exist[s] in the physical, rather than purely conceptual, realm,’ is beside the point")”.
Thus, under Step 2A, prong 2 of the analysis, even when viewed in combination, these additional elements do not integrate the recited judicial exception into a practical application and the claim is directed to the judicial exception. No specific practical application is associated with the claimed system.
Under Step 2B, the claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the additional elements, as described above with respect to Step 2A Prong 2, merely amount to a general purpose computer system that attempts to apply the abstract idea in a technological environment, limiting the abstract idea to a particular field of use, and/or merely performs insignificant extra-solution activit(ies) (claims 1, 16, 17, and 18). Such insignificant extra-solution activity, e.g. data gathering and output, when re-evaluated under Step 2B is further found to be well-understood, routine, and conventional as evidenced by MPEP 2106.05(d)(II) (describing conventional activities that include transmitting and receiving data over a network, electronic recordkeeping, storing and retrieving information from memory, and electronically scanning or extracting data from a physical document).
Therefore, similarly the combination and arrangement of the above identified additional elements when analyzed under Step 2B also fails to necessitate a conclusion that claim 1, as well as claim 15, 16, 17 and 18, amount to significantly more than the abstract idea.
With regards to the dependent claims, claims 2-14, merely further expand upon the algorithm/abstract idea and do not set forth further additional elements that integrate the recited abstract idea into a practical application or amount to significantly more. Therefore, these claims are found ineligible for the reasons described for claims 1, 15, 16, 17, and 18. Specifically:
With respect to dependent claims 2-6 specifically, the claims further recite limitations directed to additional mathematical analysis used to improve the estimation model, including repeatedly updating model parameters using received laboratory test results, performing constrained optimization such as constrained least-squares optimization, selecting a sliding time-window for laboratory results, and performing sanity checks on received laboratory test results before updating the estimation formula. These limitations merely refine the mathematical model by specifying additional mathematical calculations and evaluation criteria for updating the estimation formula and therefore merely expand upon the mathematical concepts and mental processes. Such limitations amount to insignificant extra solution activity and/or merely refine the abstract mathematical analysis without improving any technology or computer functionality. Accordingly, these limitations fail to integrate the abstract idea into a practical application or amount to significantly more. See MPEP 2106.05(g)(h).
With respect to dependent claims 7-9 specifically, the claims further recite limitations directed to performing additional mathematical analysis through auxiliary estimation formulas, calculating correlations between measured variables, generating axillary estimates, and replacing one estimate with another based on a calculated correlation threshold. These limitations merely recite additional mathematical calculations and statistical evaluations that further process collected data and therefore merely expand upon the mathematical concepts and mental processes. Additionally, limiting the mathematical analysis to a particular statical techniques or auxiliary models merely limits the abstract idea to a particular mathematical implementation. Accordingly, these limitations fail to integrate the abstract idea into a practical application or amount to significantly more. See MPEP 2106.05(g)(h).
With respect to dependent claims 10-12 specifically, the claims further recite limitations directed to particular types of products, feeds, quality parameters, and measurement intervals used by the estimation process. These limitations merely specific the particular filed of use and the type of data utilized by the mathematical analysis without changing the nature of the claimed abstract idea. Restricting the mathematical analysis to organic substances processing or to particular quality parameters merely limits the abstract idea to a particular technology environment and does not improve the technology or computer functionality. Accordingly, these limitations fail to integrate the abstract idea into a practical application or amount to significantly more. See MPEP 2106.05(h).
With respect to dependent claims 13 and 14 specifically, the claims further recite limitations directed to particular update frequencies, measurement intervals, and timing relationships between measurements and estimation operations. These limitations merely define when data is collected and when the recited mathematical calculations are performed and therefore amount to insignificant extra solution activity related to data gathering and scheduling of the mathematical analysis. Such limitations do not improve any technology and therefore fail to integrate the abstract idea into a practical application or amount to significantly more. See MPEP 2106.05(g).
Accordingly, for the reasons above and those discussed in relation to independent claims 1, 15, 16,17, and 18, the dependent claims are insufficient to integrate the claimed abstract ideas into a practical application or significant more.
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 1, 2, 4, 5, 6, 16, and 18 are rejected under 35 U.S.C. 103 as being unpatentable over WO 2022171788 A1, Helterhoff et al. (hereinafter Helterhoff) in view of US 5446681 A, Gethner et al. (hereinafter Gethner).
Regarding Claim 1, 16, and 18, Helterhoff discloses a method for estimating at least one quality parameter (Q) related to a product or a feed of processing of organic substances (Helterhoff, [Page 2] Embodiments include a method for training a machine learning module of a computer-implemented prediction model for predicting product quality parameter values for one or more quality parameters of a chemical product manufactured by a chemical production facility, wherein the production facility comprises a plurality of sensors, each of which is configured to do so operation of the production facility process parameter values for one or more process parameters of a chemical process carried out by the production facility to produce the chemical product), the method comprising:
repeatedly (Helterhoff, [Page 8] The process data X 100 are recorded, for example continuously or periodically, during the operation of a production plant) measuring density (p) (Helterhoff, [Page 2] parameters such as product composition, proportions, pH value, phase distribution/proportions, hardness, (grain) size distribution and/or density can also playa role… process parameters are measured by various sensors in the plant during operation to manufacture a product and can include, for example, concentrations and flows of raw materials and additives, temperatures, pressures, valve settings, rotational speeds, energies, volumes, weights and so on. In addition, mass flows, volume flows, filling levels, density and/or masses can also be important process parameter values) and temperature (T) of the product or the feed (Helterhoff, [Page 8] The process data X 100 include, for example, sensor values, concentration values of basic components and additives, and flow values of basic components and additives, temperatures, pressures, valve positions, aggregated and/or calculated data values from the plant control system, etc.);
repeatedly receiving laboratory test results (QLab) indicative of the at least one quality parameter (Helterhoff, [Page 11] The result of the training procedure is a trained prediction model f .sub.M 120, i.e. a prediction model with precise algorithms and model parameters foreach of the blocks 121-127 of the corresponding prediction model, so that for each new data set of process parameter values X as input data, a data set of product quality parameter values Q as Output data can be calculated); and
repeatedly updating model parameters (p1, p2, ... ) of the estimation formula based on: i) the received laboratory test results (Helterhoff, [Page 11] This trained prediction model f .sub.M 120 can be used, for example, during operation of the production plant to continuously predict quality properties to be expected in real time and to monitor them without having to wait for laboratory measurements), and on ii) measured values of the input variables of the estimation formula corresponding to the received laboratory test results, the model parameters defining a dependence of changes of the estimate on changes of the input variables including the density and the temperature (Helterhoff, [Page 12] the new data {X .sub.T (t), Q .sub.j (t)} or, in the case of mini-batch training methods, the collection or batch of new data is used as input data 462 and output data 464 for training the existing model f .sub.M 120 . The result of the retraining is an updated prediction model f .sub.M 420, ie a prediction model with precise algorithms and model parameters in each of the blocks 421-426. In this way, a prediction model can continuously learn from new observations and can thus automatically adapt to future operating conditions of the production plan)
Helterhoff does not disclose repeatedly computing an estimate for the at least one quality parameter based on an estimation formula whose input variables include the measured density and the measured temperature;
However, Gethner teaches repeatedly computing (Gethner, [Col. 27 Line 50-55] Parameters are calculated in real-time which are diagnostic of process operation and which can be used for control and/or optimization of the process and/or diagnosis of unusual or unexpected process operation conditions) an estimate for the at least one quality parameter (Gethner, [Col. 24 Line 33-36] The steps comprising the methodology are performed in an integrative manner so as to provide continuous estimates for method adjustment, operations diagnosis and automated sample collection) based on an estimation formula whose input variables include the measured density and the measured temperature (Gethner, [Col. 27 Line 55-68] Examples of parameters which are based on the spectral measurement of a single process stream include chemical composition measurements (such as the concentration of individual chemical components as, for example, benzene, toluene, xylene, or the concentration of a class of compounds as, for example, paraffins); physical property measurements (such as density, index of refraction, hardness, viscosity, flash point, pour point, vapor pressure); performance property measurement (such as octane number, cetane number, combustibility); and perception (such as smell/odor, color) [Col. 28 Line 8-14] Parameters which are based on one or more spectral measurements along with other process operational measurements (such as temperatures, pressures, flow rates) are used to calculate a multi-parameter (multivariate) process model);
Before the effective filing date of the claimed invention, It would have been obvious to one of ordinary skill in the art to combine Helterhoff in view of Gethner teachings because Gethner teaches that physical property measurements together with process operational measurements including temperature, may be used to calculate a multivariable process model and provide continuous estimates of process parameters, while Helterhoff teaches continuously predicting quality parameters of a product or feed based on measured process parameter values. A person of ordinary skill in the art would have been motivated to integrate Gethner’s density measurement and estimation teachings into the production model of Heltherhoff in order to provide additional measured process information for continuous real-time estimates of quality parameters of the product or feed.
Regarding Claim 2, Helterhoff discloses a method according to claim 1, the model parameters are updated with a regression analysis (Helterhoff, [Page 14] A multilayer neural perceptron-neuron network is used as an artificial neural network, for example, which consists of two hidden layers (“hidden layer”) with a maximum of 200 nodes and a rectified activation function for the input and the hidden layers delle, specifically lasso regression and ridge regression are applied. The prediction model is trained iteratively using the Adam optimizer by minimizing a mean square error of the neural network output compared to the truth values of the training data set For example, iterative minimization is performed for a maximum number of 1000 epochs, however, an early stop criterion is enforced to ensure that the loss function of the validation sample evaluated at the same time is minimal and learning of statistical variations in the training sample is avoided) where the laboratory test results represent a scalar response (Helterhoff, [Page 15] In block 1000, training data for training the machine learning module is provided. This training data includes product quality parameter values determined as training product quality parameter values for a plurality of product units produced by the production plant for one or more quality parameters of the respective product unit [Page 13] The target specification, ie the desired quality of the end product, is described by a vector Q .sup.* 580. A scalar function f .sub.R 560, which represents a measure of the difference between the predicted quality and the target quality, can be specified in various ways) and the measured values of the input variables of the estimation formula corresponding to the laboratory test results represent explanatory variables (Helterhoff, [Page 8] predictive model 120 uses the process parameter values X 100 as input values and predicts resulting product quality parameter values Q 130).
Helterhoff does not disclose wherein the estimation formula is a first order polynomial of each of the model parameters
However, Gethner teaches wherein the estimation formula is a first order polynomial of each of the model parameters (Gethner, [Col. 11 Line 65-68] A regression of the form V.sub. =VZ+R is calculated to establish the correlation between the dot products and the scores of the principal components)
Before the effective filing date of the claimed invention, It would have been obvious to one of ordinary skill in the art to combine Helterhoff in view of Gethner teachings because Gethner teaches using a first order regression to establish a correlation between measured variables, while Helterhoff teaches updating model parameters using regression analysis to predict product quality parameter values. A person of ordinary skill in the art would have been motivated to integrate Gethner’s first order regression teachings into the regression analysis of Helterhoff in order to provide a straightforward mathematical relationship between the model parameters and the predicted response.
Regarding Claim 4, Helterhoff in view of Gethner teaches a method according to claim 1, comprising:
comparing each of the laboratory test results to one or more predetermined criteria to sanity check the laboratory test result (Helterhoff, [Page 3] According to embodiments, the method further includes cleaning the provided training process parameter values, wherein the cleaning includes one or more of the following data processing steps:
• removing outlier values from the training process parameter values,
• removing non-physical values from the training process parameter values, and/or
• Complementing missing training process parameter values, with the training data being checked for completeness using a priori completeness information to identify missing training process parameters); and
using the laboratory test result updating the model parameters of the estimation formula only if the one or more predetermined criteria are fulfilled by the laboratory test result (Helterhoff, [Page 10] cross-sensor plausibility checks can be carried out, implausible values can be identified and removed from the process data values X .sub.T 200. For example, sensor values from neighboring sensors should not differ from one another or only to a limited extent if no process steps, ie physical and/or chemical processes, occur between or in the area of the corresponding sensors that could lead to a significant change in the corresponding sensor values. A significant change in this case means a change which lies outside of a predefined range of fluctuation, as can be caused, for example, by tolerances in the design of the production plant and/or tolerances in the measuring accuracy of the sensors used for measuring. Furthermore, certain developments can be assumed for the measured sensor values. Thus, a temperature should decrease in the absence of exothermic reactions, ie in the case of no or purely endothermic reactions, for example without energy being supplied to the system due to the heat dissipation that generally occurs. If successive temperature sensors measure a temperature increase, although a temperature decrease is to be expected for these sensors, a plausibility test can result in the rejection of a sensor value which, contrary to expectations, indicates a temperature increase).
Regarding Claim 5, Helterhoff in view of Gethner teaches a method according to claim 4, wherein the one or more predetermined criteria comprise one or more of the following:
i) a requirement that the laboratory test result is within a predetermined range (Helterhoff, [Page 3] Training process parameter values that are outside of these expected value ranges are considered unphysical, for example. In the case of such non-physical values, it can be assumed that they are based, for example, on errors in the acquisition of the training process parameter values. The system design can, for example, define value ranges for parameter values or training process parameter values for which the system is designed and which can be achieved in the system. If training process parameter values lie outside of these expected value ranges for which the system is designed, the corresponding training process parameter values can be rejected as unphysical), ii) a requirement that a deviation between the laboratory test result and a previous laboratory test result is less than a first limit value (Helterhoff, [Page 6] Embodiments can have the advantage that anomalies can be detected effectively. Corresponding anomalies can be caused by the production plant or by the prediction model. Embodiments may have the advantage of being able to detect abnormal and faulty plant operations, such as reactor fouling, decomposition processes, failures of certain components, etc., and identifying probable root causes for the detected anomalies. For example, deviations between model predictions and retrospectively measured product quality data are evaluated. If the deviations, for example individually or in combination, exceed a predefined threshold value, this indicates the presence of an anomaly or error in system operation. Likewise, for example, shortcomings of the prediction model can be identified),
and iv) a requirement that a deviation between the laboratory test result and a corresponding statistical value based on empirical cases is at most a predetermined factor times a standard deviation of the statistical value (Helterhoff, [Page 13] The target specification, ie the desired quality of the end product, is described by a vector Q .sup.* 580. A scalar function f .sub.R 560, which represents a measure of the difference between the predicted quality and the target quality, can be specified in various ways. [Page 13-14] Characteristic values are extracted for each of these groups. Feature values extracted include, for example, mean, standard deviation, and in some cases group-to-group difference. All characteristic values are normalized. For example, z-normalization is used for normally distributed feature values, ie expectation value 0 and its variance/standard deviation 1, while for binary distributed features, for example, min-max normalization is used)
Helterhoff does not disclose iii) a requirement that a rate of change from the previous laboratory test result to the laboratory test result is less than a second limit value
However, Gethner teaches iii) a requirement that a rate of change from the previous laboratory test result to the laboratory test result is less than a second limit value (Gethner, [Col 27 Line 42-50] experienced-based diagnostics commonly obtained by control chart techniques, frequency distribution analysis, or any similar techniques which evaluate the current measurement (either spectral or parameter) in terms of the past experience available either from the calibration sample set or the past on-line sample measurements, [Col 28 Line 19-20] process diagnosis and/or optimization by observing process response and trends)
Before the effective filing date of the claimed invention, It would have been obvious to one of ordinary skill in the art to combine Helterhoff in view of Gethner teachings because Gethner teaches evaluating a current measurement in view of past measurements and observing process response and trends, while Helterhoff teaches comparing deviations to a predetermined threshold value to identify an anomaly. A person of ordinary skill in the art would have been motivated to integrate the measurement trend teachings of Gethner into the method of Helterhoff in order to determine whether a change between successive laboratory test results exceeds an acceptable limit and to identify abnormal laboratory test results before using the laboratory test results to update the model parameters.
Regarding Claim 6, Helterhoff in view of Gethner teaches a method according to claim 1, comprising:
updating the model parameters with constrained optimization in which constraining is applied to avoid abrupt changes between successively computed estimates of the at least one quality parameter (Q) (Helterhoff, [Page 13] a Nelder-Mead method or downhill simplex method, a simplex method or an L-BFGS method (limited memory Broyden-Fletcher-Goldfarb-Shanno method) is used as the optimization algorithm f .sub.0 590 . [Page 13] the parameter space for C .sub.Q 504, in which the optimization algorithm f .sub.0 searches for an optimized solution, is restricted such that the optimization algorithm can only obtain process parameter values that are physically meaningful and can be set in the production plant. These constraints and other hyperparameters of the optimization model are provided by definitions 593 based on information about general physical constraints, manufacturing plant constraints, and/or parameter ranges in historical data).
Claims 7 and 8 are rejected under 35 U.S.C. 103 as being unpatentable over WO 2022171788 A1, Helterhoff et al. (hereinafter Helterhoff) in view of US 5446681 A, Gethner et al. (hereinafter Gethner), in further view of US 20200104737 A1, Abaci et al. (hereinafter Abaci).
Regarding Claim 7, Helterhoff teaches that process parameters may be related by correlation (Helterhoff, [Page 13] the entire set of available process parameters X 500 can be divided into process parameters C 502 that can be directly controlled by the operator and process parameters P that cannot be directly controlled by the operator. The set of controllable process parameters C 502 can be divided into two groups: the control parameters C .sub.Q 504, which have a direct influence on the product quality, and the remaining control parameters C \ C .sub.Q , which either do not affect the product quality or only via its correlation with parameters already defined in C .sub.Q.);
Helterhoff does not disclose computing an auxiliary estimate for the at least one quality parameter based on an auxiliary estimation formula in which at least one input variable is the measured density and which has auxiliary model parameters (q1, q2 ... )
updating the auxiliary model parameters of the auxiliary estimation formula based on i) the laboratory test results and ii) measured values of the at least one input variable of the auxiliary estimation formula corresponding to the laboratory test results;
computing a correlation between the measured density and the measured temperature;
replacing the estimate for the at least one quality parameter with the auxiliary estimate in response to a situation in which an absolute value of the computed correlation exceeds a threshold.
However, Gethner teaches updating the auxiliary model parameters of the auxiliary estimation formula based on i) the laboratory test results and ii) measured values of the at least one input variable of the auxiliary estimation formula corresponding to the laboratory test results (Gethner, Fig. 1A, [Col. 23 Line 45-51] Model updating consists of operations [2], [3], and [4]. Any or all of the operations may be performed on the analyzer computer or may be performed off-line on a separate computer. In the latter case, the results of the updated model must be transferred to the analyzer control computer).
Before the effective filing date of the claimed invention, It would have been obvious to one of ordinary skill in the art to combine Helterhoff in view of Gethner teachings because Gethner teaches updating model parameters based on measured input values and laboratory test results, while Helterhoff teaches estimating product quality based on measured process parameters. A person of ordinary skill in the art would have been motivated to integrate the model updating teachings of Gethner into the method of Heltherhoff in order to provide an updated estimation model based on measured process data and corresponding laboratory test results.
In addition, Helterhoff in view of Gethner does not disclose computing an auxiliary estimate for the at least one quality parameter based on an auxiliary estimation formula in which at least one input variable is the measured density and which has auxiliary model parameters (q1, q2 ... )
computing a correlation between the measured density and the measured temperature;
replacing the estimate for the at least one quality parameter with the auxiliary estimate in response to a situation in which an absolute value of the computed correlation exceeds a threshold.
However, Abaci teaches computing an auxiliary estimate for the at least one quality parameter based on an auxiliary estimation formula in which at least one input variable is the measured density and which has auxiliary model parameters (q1, q2 ... ) (Abaci, [0016] The regression modeler 110 receives the data inputs 105 and generates one or more of the self-intelligent entities 115 using one or more regression analysis techniques on the data inputs 105. Using selection information stored in one or more configuration data, or by random or heuristic selection, the regression modeler 110 selects one or more data inputs 105 to be independent variables, and one data input 105 to be a dependent variable. These selected data inputs 105 may be selected from the same data source or group of related data sources. Data sources are related if the information they describe have some relationship to each other (e.g., the information describes different aspects of the same element or group of elements))
computing a correlation between the measured density and the measured temperature (Abaci, [0057] The model assessor 130 computes 530 a correlation score between an actual output and a predicted output of the candidate model 520 to determine 530 if the correlation score exceeds a threshold criteria)
replacing the estimate for the at least one quality parameter with the auxiliary estimate in response to a situation in which an absolute value of the computed correlation exceeds a threshold (Abaci, [0057] The model assessor 130 generates 520 a candidate model, that includes as input: 1) data inputs of a selected model and 2) predictive output of one of the models or other data inputs. The model assessor 130 computes 530 a correlation score between an actual output and a predicted output of the candidate model 520 to determine 530 if the correlation score exceeds a threshold criteria. The criteria, as noted above, may be a numerical value, or may be a categorization of the candidate model over multiple runs through multiple sets of validation data. If the correlation score exceeds the threshold criteria, the model assessor 130 replaces 550 the selected model with the candidate model. Otherwise, the model assessor 130 discards 560 the candidate model (and starts over again))
Before the effective filing date of the claimed invention, It would have been obvious to one of ordinary skill in the art to combine Helterhoff in view of Gethner and Abaci teachings because Abaci teaches generating a candidate regression model using selected data inputs, computing a correlation score between an actual output and the predicted output, and replacing a selected model with the candidate model when the correlation score exceeds a threshold, while Helterhoff in view of Gethner teaches estimating product quality parameters using measured process parameters and updating the model based on measured values and laboratory test results. A person of ordinary skill in the art would have been motivated to integrate the model selection and replacement teachings of Abaci into the method of Heltherhoff in view of Gethner in order to provide an alternative estimation model and selectively use the model having an acceptable correlation.
Regarding Claim 8, Helterhoff in view of Gethner discloses a method according to claim 7, wherein where the laboratory test results represent a scalar response (Gethner, [Col. 24 Line 9-18] A regression is performed using the scores for the updated cailbration set and the laboratory measurements of composition and/or property parameters to obtain regression coefficients which will be used to perform the parameter and confidence interval estimation of operation [13]. The regression step is identical to that described above for CPSA (equations 2.9a and b in the section on the development of an emperical model hereinabove))
Helterhoff in view of Gethner does not disclose the auxiliary estimation formula is a first order polynomial with respect to each of the auxiliary model parameters and the auxiliary model parameters are updated with a regression analysis and the measured values of the at least one input variable of the auxiliary estimation formula corresponding to the laboratory test results represent an explanatory variable
However, Abaci teaches the auxiliary estimation formula is a first order polynomial with respect to each of the auxiliary model parameters (Abaci, [0018] The regression techniques that may be used include, but are not limited to: 1) linear regression) and the auxiliary model parameters are updated with a regression analysis (Abaci, [0038] When generating a new candidate model, the candidate model generator 210 may perform a regression analysis, using as the dependent variable the actual output 135 corresponding to the predicted output 125 that the existing SIE 115 is predicting, and using as independent variables the data inputs 105 already used by the SIE, as well as the predicted outputs 125 of other SIEs) and the measured values of the at least one input variable of the auxiliary estimation formula corresponding to the laboratory test results represent an explanatory variable (Abaci, [0038] the candidate model generator 210 may perform a regression analysis, using as the dependent variable the actual output 135 corresponding to the predicted output 125 that the existing SIE 115 is predicting, and using as independent variables the data inputs 105 already used by the SIE, as well as the predicted outputs 125 of other SIEs. For the regression analysis to be successful, each example of independent and dependent values should share the same, or similar timestamp)
Before the effective filing date of the claimed invention, It would have been obvious to one of ordinary skill in the art to combine Helterhoff in view of Gethner teachings because Abaci teaches using linear regression to generate and update a model based on measured input values as independent variables, and an actual output as a dependent variable, while Helterhoff in view of Gethner teaches estimating a quality parameter based on measured process values and laboratory test results. A person of ordinary skill in the art would have been motivated to integrate the regression analysis teachings of Abaci into the method of Helterhoff in view of Gethner in order to update the model parameters based on measured input values and laboratory test results.
Claims 10, 11, 12, 15, and 17 are rejected under 35 U.S.C. 103 as being unpatentable over WO 2022171788 A1, Helterhoff et al. (hereinafter Helterhoff) in view of US 5446681 A, Gethner et al. (hereinafter Gethner), in further view of US 5680321 A, Helmer et al. (hereinafter Helmer).
Regarding Claim 10, Helterhoff in view of Gethner in further view of Helmer teaches a method according to claim 1, wherein the feed comprises;
organic substances whose molecules have at least 10 carbon atoms (Helmer, [Col. 5 Line 30-35] Tall oil rosin is obtained by distillation of acidified black liquor from the kraft pulping of soft wood. The main components are the so called rosin acids, all tricyclic acids. Abietic acid and levo pimaric acid are very important and have one carboxyl group each)
Before the effective filing date of the claimed invention, It would have been obvious to one of ordinary skill in the art to combine Helterhoff in view of Gethner and Helmer teachings because Helmer teaches processing tall oil rosin comprising rosin acids having at least 10 carbon atoms, while Heltherhoff in view of Gethner teaches processing organic substances and estimating quality parameters associated with the product or feed. A person of ordinary skill in the art would have been motivated to utilize the organic substances taught by Helmer as the feed in the method of Heltherhoff in view of Gethner in order to apply the process monitoring and quality estimation method to the feed comprising substances having at least 10 carbon atoms.
Regarding Claim 11, Helterhoff in view of Gethner in further view of Helmer teaches a method according to claim 10, wherein the processing of the organic substances is a tall oil distillation process, a heavy gas oil fractionation process, or a base oil fractionation process (Helmer, [Col. 5 Line 29-35] There are different ways to produce the rosin acids from softwood. Current economics favour tall oil as a source. Tall oil rosin is obtained by distillation of acidified black liquor from the kraft pulping of soft wood. The main components are the so called rosin acids, all tricyclic acids)
Before the effective filing date of the claimed invention, It would have been obvious to one of ordinary skill in the art to combine Helterhoff in view of Gethner and Helmer teachings because Helmer teaches processing tall oil by distillation, while Helterhoff in view of Gethner teaches estimating quality parameters associated with the processing of organic substances. A person of ordinary skill in the art would have been motivated to apply the quality parameter estimation teachings of Heltherhoff in view of Gethner to the tall oil distillation process taught by Helmer in order to monitor and estimate quality parameters associated with the tall oil distillation process.
Regarding Claim 12, Helterhoff in view of Gethner in further view of Helmer teaches a method according to claim 10, wherein the at least one quality parameter is indicative of one of the following: i) rosin product softening point, i) rosin product rosin acid content (Helmer, [Col. 5 Line 29-35] There are different ways to produce the rosin acids from softwood. Current economics favour tall oil as a source. Tall oil rosin is obtained by distillation of acidified black liquor from the kraft pulping of soft wood. The main components are the so called rosin acids, all tricyclic acids), iii) rosin acid content of a fatty acid product, iv) rosin acid content of crude fatty acid, v) rosin acid content of crude tall oil, vi) a color index of heavy gas oil, and vii) weight fractions of compounds in polyalphaolefin products or intermediates.
Before the effective filing date of the claimed invention, It would have been obvious to one of ordinary skill in the art to combine Helterhoff in view of Gethner and Helmer teachings because Helmer teaches that rosin acid is a component of tall oil rosin and that tall oil rosin is obtained by distillation, while Helterhoff in view of Gethner teaches estimating quality parameters associated with the processing of organic substances. A person of ordinary skill in the art would have been motivated to apply the quality parameter estimation teachings of Helterhof in view of Gethner to the rosin acid content taught by Helmer in order to determine and monitor the quality of the product during processing.
Regarding Claim 15 and 17, Helterhoff discloses a method for processing organic substances (Helterhoff, [Page 2] Embodiments include a method for training a machine learning module of a computer-implemented prediction model for predicting product quality parameter values for one or more quality parameters of a chemical product manufactured by a chemical production facility, wherein the production facility comprises a plurality of sensors, each of which is configured to do so operation of the production facility process parameter values for one or more process parameters of a chemical process carried out by the production facility to produce the chemical product), the method comprising:
repeatedly (Helterhoff, [Page 8] The process data X 100 are recorded, for example continuously or periodically, during the operation of a production plant) measuring density (p) (Helterhoff, [Page 2] parameters such as product composition, proportions, pH value, phase distribution/proportions, hardness, (grain) size distribution and/or density can also playa role… process parameters are measured by various sensors in the plant during operation to manufacture a product and can include, for example, concentrations and flows of raw materials and additives, temperatures, pressures, valve settings, rotational speeds, energies, volumes, weights and so on. In addition, mass flows, volume flows, filling levels, density and/or masses can also be important process parameter values) and temperature (T) of the product or the feed (Helterhoff, [Page 8] The process data X 100 include, for example, sensor values, concentration values of basic components and additives, and flow values of basic components and additives, temperatures, pressures, valve positions, aggregated and/or calculated data values from the plant control system, etc.);
repeatedly receiving laboratory test results (QLab) indicative of the at least one quality parameter (Helterhoff, [Page 11] The result of the training procedure is a trained prediction model f .sub.M 120, i.e. a prediction model with precise algorithms and model parameters foreach of the blocks 121-127 of the corresponding prediction model, so that for each new data set of process parameter values X as input data, a data set of product quality parameter values Q as Output data can be calculated); and
repeatedly updating model parameters (p1, p2, ... ) of the estimation formula based on: i) the received laboratory test results (Helterhoff, [Page 11] This trained prediction model f .sub.M 120 can be used, for example, during operation of the production plant to continuously predict quality properties to be expected in real time and to monitor them without having to wait for laboratory measurements), and on ii) measured values of the input variables of the estimation formula corresponding to the received laboratory test results, the model parameters defining a dependence of changes of the estimate on changes of the input variables including the density and the temperature (Helterhoff, [Page 12] the new data {X .sub.T (t), Q .sub.j (t)} or, in the case of mini-batch training methods, the collection or batch of new data is used as input data 462 and output data 464 for training the existing model f .sub.M 120 . The result of the retraining is an updated prediction model f .sub.M 420, ie a prediction model with precise algorithms and model parameters in each of the blocks 421-426. In this way, a prediction model can continuously learn from new observations and can thus automatically adapt to future operating conditions of the production plan)
Helterhoff does not disclose supplying a feed containing the organic substances into processing equipment where at least one product is obtained from the organic substances;
repeatedly estimating, at least one quality parameter related to the at least one product or to the feed; and
controlling conditions within the processing equipment based on the estimated at least one quality parameter;
repeatedly computing an estimate for the at least one quality parameter based on an estimation formula whose input variables include the measured density and the measured temperature;
However, Gethner teaches repeatedly estimating, at least one quality parameter related to the at least one product or to the feed (Gethner, [Col. 4 Line 40-46] The method disclosed herein finds particular application to on-line estimation of property and/or composition data of hydrocarbon test samples. Conveniently and suitably, all or most of the above-described steps are performed by a computer system of one or more computers with minimal or no operator interaction required); and
repeatedly computing (Gethner, [Col. 27 Line 50-55] Parameters are calculated in real-time which are diagnostic of process operation and which can be used for control and/or optimization of the process and/or diagnosis of unusual or unexpected process operation conditions) an estimate for the at least one quality parameter (Gethner, [Col. 24 Line 33-36] The steps comprising the methodology are performed in an integrative manner so as to provide continuous estimates for method adjustment, operations diagnosis and automated sample collection) based on an estimation formula whose input variables include the measured density and the measured temperature (Gethner, [Col. 27 Line 55-68] Examples of parameters which are based on the spectral measurement of a single process stream include chemical composition measurements (such as the concentration of individual chemical components as, for example, benzene, toluene, xylene, or the concentration of a class of compounds as, for example, paraffins); physical property measurements (such as density, index of refraction, hardness, viscosity, flash point, pour point, vapor pressure); performance property measurement (such as octane number, cetane number, combustibility); and perception (such as smell/odor, color) [Col. 28 Line 8-14] Parameters which are based on one or more spectral measurements along with other process operational measurements (such as temperatures, pressures, flow rates) are used to calculate a multi-parameter (multivariate) process model);
Before the effective filing date of the claimed invention, It would have been obvious to one of ordinary skill in the art to combine Helterhoff in view of Gethner teachings because Gethner teaches repeatedly estimating product or feed quality parameters using measured process parameters, including density and temperature, while Helterhoff teaches a prediction model that uses measured process parameters and laboratory test results to predict product quality parameters and continuously updates the model based on new observations. A person of ordinary skill in the art would have been motivated to integrate Gethner’s real-time estimation using measured density and temperature into Helterhoff’s prediction method in order to provide continuous estimates of product quality for monitoring, control, and optimization of the process.
In addition, Helterhoff in view of Gethner does not disclose supplying a feed containing the organic substances into processing equipment where at least one product is obtained from the organic substances;
controlling conditions within the processing equipment based on the estimated at least one quality parameter;
However, Helmer teaches supplying a feed containing the organic substances into processing equipment where at least one product is obtained from the organic substances (Helmer, [Col. 4 Line 38-40] The fluff pulp is produced on a paper machine as a thick paper and the debonding agents are added to the stock as ordinary paper chemicals. There has to be no destruction of the fibers during the defibration process and the energy needed to defibrate the fibers in a fluff pulp should be as low as possible);
controlling conditions within the processing equipment based on the estimated at least one quality parameter (Helmer, [Col. 4 Line 4-8] It is another object of the invention to provide a method of maintaining an effective process control program wherein the above-mentioned properties are quantified to detect any change and provide control input, assuring optimum dosage levels for the different chemical additives);
Before the effective filing date of the claimed invention, It would have been obvious to one of ordinary skill in the art to combine Helterhoff in view of Gethner and Helmer teachings because Helmer teaches supplying a feed containing organic substances to processing equipment to obtain a product and controlling the processing condition based on quantified product properties, while Helterhoff in view of Gethner teaches continuously estimating quality parameters from measured process variables. A person of ordinary skill in the art would have been motivated to apply the quality-parameter estimation teaching of Helterhoff in view of Gethner to Helmer’s processing equipment in order to use the estimated product quality as control input for adjusting processing conditions and maintaining desired product properties.
Claims 13 are rejected under 35 U.S.C. 103 as being unpatentable over WO 2022171788 A1, Helterhoff et al. (hereinafter Helterhoff) in view of US 5446681 A, Gethner et al. (hereinafter Gethner), in further view of US 20130179092 A1, Martin et al. (hereinafter Martin).
Regarding Claim 13, Helterhoff in view of Gethner in further view of Martin teaches a method according to claim 1, wherein comprising:
receiving the laboratory test results are received from two or more laboratories (Martin, [0107] A procedure to ensure consistency of the spectroscopic, physical property, and inspection tests results is required since numerous laboratories will be involved in the data generation); and
forming a value used for updating the model parameters with a predetermined rule from two or more of the received laboratory test results in response to a situation in which: i) the two or more of the received laboratory test results are received from different laboratories (Martin, [0103] A primary objective of a crude oil monitoring program is to identify grades which require a Recommended Assay update. Once a determination has been made that an assay update is required, the monitoring information may be used to: [0104] Implement the Virtual Assay information as the new Recommended Assay [0105] Signal the need to obtain a crude oil sample for a new wet assay to develop the new Recommended Assay [0106] Define the acceptable range of the crude oil sample to be used for the new wet assay), and ii) the two or more of the received laboratory test results represent a same quantity and are based on a same sample of the product or the feed (Martin, [0049] When the same sample is tested independently by different laboratories, the results are expected to agree with R 19 times out of 20 (95% of the time)).
Before the effective filing date of the claimed invention, It would have been obvious to one of ordinary skill in the art to combine Helterhoff in view of Gethner and Martin teachings because Martin teaches obtaining test results for the sample from multiple laboratories and evaluating those results for consistency, while Heltherhoff in view of Gethner teaches using laboratory test results to update model parameters for estimating product quality. A person of ordinary skill in the art would have been motivated to integrate Martin’s multi-laboratory testing and consistency teachings into the model updating method of Helterhoff in view of Gethner in order to provide reliable laboratory data for updating the model when test results are obtained from different laboratories.
Claims 14 are rejected under 35 U.S.C. 103 as being unpatentable over WO 2022171788 A1, Helterhoff et al. (hereinafter Helterhoff) in view of US 5446681 A, Gethner et al. (hereinafter Gethner), in further view of US 20070250292 A1, Alagappan et al. (hereinafter Alagappan).
Regarding Claim 14, Helterhoff in view of Gethner discloses A method according to claim 1, wherein:
the model parameters are updated based on the received laboratory test results in response to a situation in which the received laboratory test results fulfil one or more predetermined criteria (Helterhoff, [Page 12-13] The new data {X .sub.T (t), Q .sub.j (t)} or, in the case of mini-batch training methods, the collection or batch of new data is used as input data 462 and output data 464 for training the existing model f .sub.M 120 . The result of the retraining is an updated prediction model f .sub.M 420, ie a prediction model with precise algorithms and model parameters in each of the blocks 421-426. In this way, a prediction model can continuously learn from new observations and can thus automatically adapt to future operating conditions of the production plant)
Helterhoff does not disclose each time-interval between two successive measurements of the density and the temperature is at most 10 seconds;
each time-interval between two successive computations of the estimate is at most 10 minutes;
each time-interval between two successive receptions of the laboratory test results is at most 10 days; and
However, Alagappan teaches each time-interval between two successive measurements of the density and the temperature is at most 10 seconds (Alagappan, [0358] In order to accurately perform this transform, the data should be gathered at the sample frequency that matches the on-line system, often every minute or faster. This will result in collecting 525,600 samples for each measurement to cover one year of operating data. Once this transformation has been calculated, the dataset is resampled to get down to a more manageable number of samples, typically in the range of 30,000 to 50,000 samples);
each time-interval between two successive computations of the estimate is at most 10 minutes (Alagappan, [0302] The developer should gather several months of process data using the site's process historian, preferably getting one minute spot values. If this is not available, the highest resolution data, with the least amount of averaging should be used);
each time-interval between two successive receptions of the laboratory test results is at most 10 days (Alagappan, [0303] Quality measurements (analyzers and lab samples) have a much slower sample frequency than other process measurements, ranging from tens of minutes to daily. In order to include these measurements in the model a continuous estimate of these quality measurements needs to be constructed. FIG. 8 shows the online calculation of a continuous quality estimate); and
Before the effective filing date of the claimed invention, It would have been obvious to one of ordinary skill in the art to combine Helterhoff in view of Gethner and Alagappan teachings because Alagappan teaches collecting process measurements at a high sampling frequency, computing continuous quality estimates process data, and receiving laboratory quality measurements at a comparatively slower sampling frequency, while Helterhoff in view of Gethner teaches using measured process variables and received laboratory test results to estimate product quality and update model parameters. A person of ordinary skill in the art would have been motivated to integrate Alagappan’s sampling and computation intervals into the estimation and model updating method of Helterhoff in view of Gethner in order to provide frequent process measurements, thereby enabling continuous monitoring and updating of product quality during operation of the production process.
Subject Matter Free of the Prior Art
The following is an examiner’s statement of subject matter free of the prior art:
Regarding claim 3, the ordered combination of limitations reciting the estimation formula:
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The following is free of the prior art. The broadest reasonable interpretation of the claim language requires the quality parameter estimate to be calculated using dentisty and temperature as separate input variable shaving respective model parameters p1 and p2, together with p3. The most relevant prior art of record includes Helterhoff and Gethner but neither teaches the claimed estimation formula. Therefore, claim 3 is free of the prior art of record.
Regarding claim 9, the ordered combination of limitations reciting the auxiliary estimation formula:
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The following is free of prior art. The broadest reasonable interpretation of the claim language requires the auxiliary quality parameter estimate to be calculated using measured density, auxiliary model parameter q1 and q2. The most relevant prior art of record includes Helterhoff and Gethner but neither teaches the claimed auxiliary estimation formula. Therefore, claim 9 is free of the prior art of record.
Pertinent Prior Art
The prior art made of record and not relied upon is considered pertinent to applicant’s disclose:
-WO 2020058237 A2, describing a system and method for model-based prediction of quality attributes of a chemical compound or formulation produced by a production process using process information to predict product quality.
-CN 102736570 A, describing an on-line estimation system and methods for estimating quality indexes and operating constraints of a gas-phase polyethylene production process.
-EP 1484118 A1, describing a method for estimating quality-related properties of wood chips used in a pulp and paper production process and combining the properties in a quality model for predicting and controlling product quality.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to IBRAHIM NAGI SHOHATEE whose telephone number is (571)272-6612. The examiner can normally be reached 8am-5pm.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Shelby Turner can be reached at (571) 272-6334. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/IBRAHIM NAGI SHOHATEE/Examiner, Art Unit 2857
/JORDAN L JACKSON/Primary Examiner, Art Unit 2857
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