DETAILED ACTION
Status of the Claims
The following is a non-final Office Action in response to claims filed 16 January 2024.
Claims 1-20 are pending.
Claims 1-20 have been examined.
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 08 April 2025 and 25 July 2025 are being considered by the Examiner.
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 1-20 is/are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. The claims are directed to a process (an act, or series of acts or steps), a machine (a concrete thing, consisting of parts, or of certain devices and combination of devices), and a manufacture (an article produced from raw or prepared materials by giving these materials new forms, qualities, properties, or combinations, whether by hand labor or by machinery). Thus, each of the claims falls within one of the four statutory categories (Step 1). The claims recite a method (process), system, and apparatus, however, the claim(s) recite(s) predicting forecasted parameters and state periods thereof based upon historical data which is an abstract idea of observing historical data in order to evaluate or predict future values and state periods as well as the abstract idea of organizing human activities such as a fundamental economic practice (forecasting/planning) or a business relation (production planning/key performance indicator management).
The limitations of “identifying a plurality of parameters of the industrial process; receiving historical values for the identified parameters; training a parameter forecast model for each of the identified parameters, wherein each of the parameter forecast models is trained based at least in part on the received historical values for at least some of the identified parameters; predicting a plurality of forecasted parameter values into the future for each of the identified parameters based at least in part on the corresponding parameter forecast model that corresponds to the respective parameter; and predicting a plurality of forecasted steady state periods and a plurality of forecasted transient state periods for each of one or more of the identified parameters based at least in part on the forecasted parameter values of the respective parameter, wherein each of the plurality of forecasted transient state periods corresponds to a period of time when the respective forecasted parameter value is predicted to transition from one forecasted steady state period to another forecasted steady state period” as drafted, is a process that, under its broadest reasonable interpretation, covers a mental process—concepts performed in the human mind (including an observation, evaluation, judgment, opinion) and/or organizing human activities--fundamental economic principles or practices (including hedging, insurance, mitigating risk); commercial or legal interactions (including agreements in the form of contracts; legal obligations; advertising, marketing or sales activities or behaviors; business relations); managing personal behavior or relationships or interactions between people (including social activities, teaching, and following rules or instructions) but for the recitation of generic computer components (Step 2A Prong 1). Method claim 1 is devoid of any structure whatsoever and thus can only amount to the abstract idea(s). Regarding claims 11 and 16, other than reciting “A system for predicting steady states and transient states of predetermined Key Performance Indicators (parameters) of an industrial process, the system comprising: an I/O port; a memory; a controller operatively coupled to the I/O port and the memory, the controller configured to:,” or “A non-transitory computer readably medium storing instructions that when executed by one or more processors causes the one or more processors to:” nothing in the claim element precludes the step from practically being performed in the mind or from the methods of organizing human interactions grouping. For example, but for the “..the controller configured to...” or “...the one or more processors to...” language, “identify(ing),” “receive(ing)” “train(ing),” “predict(ing),” and “predict(ing)” in the context of this claim encompasses the user manually observing historical data in an attempt to organizing and evaluate the data to predict forecasted parameter values and periods which is a mental process/judgement or business relation/fundamental economic practice/commercial interaction. However, if possible, the Examiner should consider the limitations together as a single abstract idea rather than as a plurality of separate abstract ideas to be analyzed individually. “For example, in a claim that includes a series of steps that recite mental steps as well as a mathematical calculation, an examiner should identify the claim as reciting both a mental process and a mathematical concept for Step 2A, Prong One to make the analysis clear on the record.” MPEP 2106.04, subsection II.B. Under such circumstances, however, the Supreme Court has treated such claims in the same manner as claims reciting a single judicial exception. Id. (discussing Bilski v. Kappos, 561 U.S. 593 (2010)). Here, the limitations are considered together as a single abstract idea for further analysis. If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitations as a mathematical concept, while some of the limitations may be performed in the mind after certain limitations are performed, but for the recitation of generic computer components, then it falls within the grouping of abstract ideas. (Step 2A, Prong One: YES). Accordingly, the claim(s) recite(s) an abstract idea.
This judicial exception is not integrated into a practical application (Step 2A Prong Two). Method claim 1 is devoid of any structure whatsoever and thus does not integrate the claims into a practical application. Next, claims 11 and 16 only recites one additional element – using a controller or processor to perform the steps. The controller or processor in the steps is recited at a high-level of generality (i.e., sorting/organizing data for patterns in order to forecast and predict) such that it amounts no more than mere instructions to apply the exception using a generic computer component. Specifically the claims amount to nothing more than an instruction to apply the abstract idea using a generic computer or invoking computers as tools by adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea - see MPEP 2106.04(d)(I) discussing MPEP 2106.05(f). Accordingly, the combination of these additional elements does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. The claim is directed to an abstract idea, even when considered as a whole (Step 2A Prong Two: NO).
The claim does not include a combination of additional elements that are sufficient to amount to significantly more than the judicial exception (Step 2B). Method claim 1 is devoid of any structure whatsoever and thus can only amount to the abstract idea(s). As discussed above with respect to integration of the abstract idea into a practical application (Step 2A Prong 2), the combination of additional elements of using a controller or processor to perform the steps amounts to no more than mere instructions to apply the exception using a generic computer component. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept. Therefore, when considering the additional elements alone, and in combination, there is no inventive concept in the claim. As such, the claim(s) is/are not patent eligible, even when considered as a whole (Step 2B: NO).
Claims 2-3, 12-13, 17-18 recite(s) the additional limitation(s) further performing actions or removing data which is still directed towards the abstract idea previously identified and is not an inventive concept that meaningfully limits the abstract idea. Again, as discussed with respect to claims 1, 11, and 16, the claims are simply limitations which are no more than mere instructions to apply the exception using a computer or with computing components. Accordingly, the additional element(s) does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. Even when considered as a whole, the claims do not integrate the judicial exception into a practical application at Step 2A or provide an inventive concept in Step 2B.
Claims 4-10, 14-15, and 19-20 recite(s) the additional limitation(s) further including how the models are trained and the data is filtered which is not an inventive concept that meaningfully limits the abstract idea. Again, as discussed with respect to claims 1, 11, and 16, the claims are simply limitations which are no more than mere instructions to apply the exception using a computer or with computing components. Accordingly, the additional element(s) does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. Even when considered as a whole, the claims do not integrate the judicial exception into a practical application at Step 2A or provide an inventive concept in Step 2B.
Claims 1-20 are therefore not eligible subject matter, even when considered as a whole.
Claim Rejections - 35 USC § 102
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
Claim(s) 1-7, 9, 11-14, and 16-19 is/are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Zhang et al. (US PG Pub. 2021/0208545).
As per claims 1, 11, and 16, Zhang discloses a method for predicting steady states and transient states of predetermined parameters of an industrial process, a system for predicting steady states and transient states of predetermined Key Performance Indicators (parameters) of an industrial process, the system comprising: an I/O port; a memory; a controller operatively coupled to the I/O port and the memory, the controller configured to: and non-transitory computer readably medium storing instructions that when executed by one or more processors causes the one or more processors to: the method comprising (method, processors, Zhang ¶42; computing device, ¶63; control system, ¶71 and Fig. 4-7; software, ¶74; sensors, ¶75; databases, ¶79):
identifying a plurality of parameters of the industrial process (process control parameters, Zhang ¶8; control parameters and/or operation parameters, ¶65-¶66; see also inputs or constraints, ¶14);
receiving historical values for the identified parameters (the method performs the steps of (1) gathering and processing real-time industrial process data (e.g., as time-series data) of the industrial process to generate current model deployment data for use by the forecast model to predict optimal suggestion for controlling the industrial process, including removing noise, outlier and invalid data, selecting valid ranges or windows of industrial process data across multiple tags, aligning industrial process data across multiple tags, and filling in missing data points (e.g., via interpolation), and (2) using the current model deployment data as forecast model constraint to predict optimal suggestion (e.g., control settings) for controlling the industrial process to achieve one or more operation objectives. In one example, the method uses a genetic algorithm to search through the dynamic space of the forecast model to predict an optimal solution (e.g., optimal suggestion such as optimal control settings) for a set of inputs or constraints (e.g., operation objectives, safe operation zone boundary), Zhang ¶14; historical industrial process data, ¶8; stored historical data, ¶18);
training a parameter forecast model for each of the identified parameters, wherein each of the parameter forecast models is trained based at least in part on the received historical values for at least some of the identified parameters (the machine learning forecast model is trained using historical industrial process data as training data. In one aspect, the present approach provides techniques that enable the forecast model to be automatically and repeatedly trained and deployed while guided only by a configuration file containing a set of machine learning configurations and model deployment configurations without human input. The retraining and re-deployment of the forecast model may be initiated automatically when the forecast model performance degrades, or when a triggering event has occurred (e.g., equipment has been replaced), Zhang ¶8-¶9);
predicting a plurality of forecasted parameter values into the future for each of the identified parameters based at least in part on the corresponding parameter forecast model that corresponds to the respective parameter (The method includes (1) deploying a forecast model with a set of model training configurations to a production environment to predict optimal suggestion for controlling industrial process, where the set of model training configurations is used as a single point of truth for guiding the training of the forecast model using a model training algorithm, and where an optimization search algorithm is used to search through the dynamic space of the forecast model to predict an optimal set of control parameters for controlling the industrial process to achieve one or more operation objectives of the industrial process; (2) deploying a first version of the forecast model trained using the set of model training configurations to predict optimal suggestion for controlling the industrial process, (3) monitoring the performance of the first version of the forecast model on current industrial process data of the industrial process, (4) if the performance of the first version of the forecast model becomes unsatisfactory, retraining a second version of the forecast model on current industrial process data with the set of model training configurations as a single point of truth for guiding the forecast model training using the model training algorithm, and (5) deploying the second version of the forecast model to replace the first version of the forecast model to predict optimal suggestion for controlling the industrial process with the set of model training configurations, Zhang ¶10; The forecast model 404 can be used to predict or inference various parameters of the industrial process, such as optimal suggestion 406, such as the optimal control parameters and/or the operation parameters to achieve one or more operation objective(s), such as improved yield, better product quality, reduced cost, shorter production, and reduced emission, etc. The forecast model 404 may be a neural network-based machine learning model, such as a recurrent neural network such as a LSTM neural network, ¶88); and
predicting a plurality of forecasted steady state periods and a plurality of forecasted transient state periods for each of one or more of the identified parameters based at least in part on the forecasted parameter values of the respective parameter, wherein each of the plurality of forecasted transient state periods corresponds to a period of time when the respective forecasted parameter value is predicted to transition from one forecasted steady state period to another forecasted steady state period (an example control system of automatically providing optimal suggestion for controlling an industrial process is provided, Zhang ¶17; Industrial process data are automatically and continuously gathered and processed from a source database in real-time (e.g., valid data range selected, normalized and denoised, outliers removed, data aligned across tags, missing data filled, and/or unnecessary data removed, etc.) to generate deployment data to be used by the forecast model to perform inferencing. An optimization search algorithm (e.g., genetic algorithm) may be used to search through the dynamic space of the forecast model to find optimal suggestion for a given set of inputs. For example, cleaned and processed real-time valid industrial process data (e.g., valid data range selected, normalized, denoised, outliers removed, data aligned across tags, missing data filled, and/or unnecessary data removed, etc.) are fed into a consumer together with a desired future state (e.g., operation objectives) of the industrial process (e.g., maximizing yield), a genetic algorithm is used to search the entire dynamic space of the forecast model to find an optimal solution (i.e., optimal suggestion) for one or more control parameters or operation parameters (e.g., current air flow setting, temperature setting, and/or pressure setting) for controlling the industrial process, or to find a future state of the industrial process (e.g., yield) given the current control parameters or operation parameters (e.g., current air flow setting, temperature setting, and/or pressure setting), ¶65; see also (2) The operating conditions of industrial process can quickly become dangerous. For example, system pressure can build up quickly (e.g., in less than 10 seconds) to a dangerous point due to rapid increase in gas production as a result of a slight increase in operating temperature. The industrial process control system needs to have low latency and can quickly adapt to fast changing operating conditions. (3) Industrial process sensors often operate in extreme conditions, such as extreme temperature, pressure, and pH. Sensor drift due to degradation is a common problem and can lead to inaccurate sensor reading. Sensor failure can also occur and lead to missing sensor data. The industrial process control system needs to be able to automatically and efficiently remove or mask faulty or missing sensor data and automatically and quickly fine-tune forecast model to adapt to sensor data drift. (4) Industrial process system dynamics can quickly change due to for example rapid equipment degradation or failure. The industrial process control system needs to be able to automatically, quickly, consistently and repeatedly train and retrain forecast model without introducing additional human bias or requiring additional human input, ¶2-¶4; based upon threshold control values, ¶53; see also ¶66) (Examiner notes any rapid change prediction from the optimal states or periods as the ability to predict transient states in between steady states i.e. some sort of change from normal optimized forecasted states to a different or out of control state which is no longer optimal or desired).
As per claims 2, 12, and 17, Zhang discloses as shown above with respect to claims 1, 11, and 16. Zhang further discloses when one or more of the forecasted transient state periods is determined to be unplanned or undesirable, automatically adjusting one or more parameters of the industrial process based at least in part on one or more of the forecasted transient state periods to eliminate one or more of the unplanned or undesirable forecasted transient state periods, reduce a duration of one or more of the unplanned or undesirable forecasted transient state periods and/or reduce a severity of one or more of the unplanned or undesirable forecasted transient state periods (The operating conditions of industrial process can quickly become dangerous. For example, system pressure can build up quickly (e.g., in less than 10 seconds) to a dangerous point due to rapid increase in gas production as a result of a slight increase in operating temperature. The industrial process control system needs to have low latency and can quickly adapt to fast changing operating conditions. (3) Industrial process sensors often operate in extreme conditions, such as extreme temperature, pressure, and pH. Sensor drift due to degradation is a common problem and can lead to inaccurate sensor reading. Sensor failure can also occur and lead to missing sensor data. The industrial process control system needs to be able to automatically and efficiently remove or mask faulty or missing sensor data and automatically and quickly fine-tune forecast model to adapt to sensor data drift. (4) Industrial process system dynamics can quickly change due to for example rapid equipment degradation or failure. The industrial process control system needs to be able to automatically, quickly, consistently and repeatedly train and retrain forecast model without introducing additional human bias or requiring additional human input, Zhang ¶2-¶4; based upon threshold control values, ¶53; see also ¶66) (Examiner notes the indication of rapid degradation or failure as the unplanned or undesirable transient state periods).
In addition, the Examiner asserts that claim scope is not limited by claim language that suggests or makes optional but does not require steps to be performed, or by claim language that does not limit a claim to a particular structure. However, examples of claim language, although not exhaustive, that may raise a question as to the limiting effect of the language in a claim are: (A) "adapted to" or "adapted for" clauses; (B) "wherein" clauses; and (C) "whereby" clauses (See MPEP 2111.04). In the instant case, the recited "when one or more of the forecasted transient state periods is determined to be unplanned or undesirable, automatically adjusting one or more parameters of the industrial process based at least in part on one or more of the forecasted transient state periods to eliminate one or more of the unplanned or undesirable forecasted transient state periods, reduce a duration of one or more of the unplanned or undesirable forecasted transient state periods and/or reduce a severity of one or more of the unplanned or undesirable forecasted transient state periods" is not a positive method step as it do not require any actual positive recited claim steps to be performed; nor does it modify any of the positively claimed method steps. Similarly, the recited wherein clause is not a positive system element since it doesn’t structurally limit the system and merely describes the intended use of the system and/or the intended result of the use of the system.
As per claims 3, 13, and 18, Zhang discloses as shown above with respect to claims 1, 11, and 16. Zhang further discloses when one or more of the forecasted transient state periods is determined to be unplanned or undesirable, outputting a recommendation for adjusting one or more parameters of the industrial process to eliminate one or more of the unplanned or undesirable forecasted transient state periods, reduce a duration of one or more of the unplanned or undesirable forecasted transient state periods and/or reduce a severity of one or more of the unplanned or undesirable forecasted transient state periods (The operating conditions of industrial process can quickly become dangerous. For example, system pressure can build up quickly (e.g., in less than 10 seconds) to a dangerous point due to rapid increase in gas production as a result of a slight increase in operating temperature. The industrial process control system needs to have low latency and can quickly adapt to fast changing operating conditions. (3) Industrial process sensors often operate in extreme conditions, such as extreme temperature, pressure, and pH. Sensor drift due to degradation is a common problem and can lead to inaccurate sensor reading. Sensor failure can also occur and lead to missing sensor data. The industrial process control system needs to be able to automatically and efficiently remove or mask faulty or missing sensor data and automatically and quickly fine-tune forecast model to adapt to sensor data drift. (4) Industrial process system dynamics can quickly change due to for example rapid equipment degradation or failure. The industrial process control system needs to be able to automatically, quickly, consistently and repeatedly train and retrain forecast model without introducing additional human bias or requiring additional human input, Zhang ¶2-¶4; based upon threshold control values, ¶53; see also ¶66) (Examiner notes the indication of rapid degradation or failure as the unplanned or undesirable transient state periods).
In addition, the Examiner asserts that claim scope is not limited by claim language that suggests or makes optional but does not require steps to be performed, or by claim language that does not limit a claim to a particular structure. However, examples of claim language, although not exhaustive, that may raise a question as to the limiting effect of the language in a claim are: (A) "adapted to" or "adapted for" clauses; (B) "wherein" clauses; and (C) "whereby" clauses (See MPEP 2111.04). In the instant case, the recited "when one or more of the forecasted transient state periods is determined to be unplanned or undesirable, outputting a recommendation for adjusting one or more parameters of the industrial process to eliminate one or more of the unplanned or undesirable forecasted transient state periods, reduce a duration of one or more of the unplanned or undesirable forecasted transient state periods and/or reduce a severity of one or more of the unplanned or undesirable forecasted transient state periods" is not a positive method step as it do not require any actual positive recited claim steps to be performed; nor does it modify any of the positively claimed method steps. Similarly, the recited wherein clause is not a positive system element since it doesn’t structurally limit the system and merely describes the intended use of the system and/or the intended result of the use of the system.
As per claim 4, Zhang discloses as shown above with respect to claim 1. Zhang further discloses wherein each of the parameter forecast models is trained based at least in part on the received historical values for at least some of the identified parameters of the industrial process and one or more of the forecasted parameter values (the method performs the steps of (1) gathering and processing real-time industrial process data (e.g., as time-series data) of the industrial process to generate current model deployment data for use by the forecast model to predict optimal suggestion for controlling the industrial process, including removing noise, outlier and invalid data, selecting valid ranges or windows of industrial process data across multiple tags, aligning industrial process data across multiple tags, and filling in missing data points (e.g., via interpolation), and (2) using the current model deployment data as forecast model constraint to predict optimal suggestion (e.g., control settings) for controlling the industrial process to achieve one or more operation objectives. In one example, the method uses a genetic algorithm to search through the dynamic space of the forecast model to predict an optimal solution (e.g., optimal suggestion such as optimal control settings) for a set of inputs or constraints (e.g., operation objectives, safe operation zone boundary), Zhang ¶14; historical industrial process data, ¶8; stored historical data, ¶18).
As per claims 5, 14, and 19, Zhang discloses as shown above with respect to claims 1, 11, and 16. Zhang further discloses wherein predicting the plurality of forecasted steady state periods and the plurality of forecasted transient state periods for each of one or more of the identified parameters comprises: filtering each of the forecasted parameter values of the one or more identified parameters, resulting in respective filtered forecasted parameter values; and predicting the plurality of forecasted steady state periods and the plurality of forecasted transient state periods for each of one or more of the identified parameters based at least in part on the filtered forecasted parameter values of the respective parameter (An optimization search algorithm (e.g., genetic algorithm) may be used to search through the dynamic space of the forecast model to find optimal suggestion for a given set of inputs. For example, cleaned and processed real-time valid industrial process data (e.g., valid data range selected, normalized, denoised, outliers removed, data aligned across tags, missing data filled, and/or unnecessary data removed, etc.) are fed into a consumer together with a desired future state (e.g., operation objectives) of the industrial process (e.g., maximizing yield), a genetic algorithm is used to search the entire dynamic space of the forecast model to find an optimal solution (i.e., optimal suggestion) for one or more control parameters or operation parameters (e.g., current air flow setting, temperature setting, and/or pressure setting) for controlling the industrial process, or to find a future state of the industrial process (e.g., yield) given the current control parameters or operation parameters (e.g., current air flow setting, temperature setting, and/or pressure setting), Zhang ¶65).
As per claim 6, Zhang discloses as shown above with respect to claim 5. Zhang further discloses wherein filtering each of the forecasted parameter values of the one or more identified parameters comprises applying a significance filter to each of the forecasted parameter values of the one or more identified parameters to remove outliers (including removing noise, outlier and invalid data, selecting valid ranges or windows of industrial process data across multiple tags, aligning industrial process data across multiple tags, and filling in missing data points (e.g., via interpolation), and (2) using the current model deployment data as forecast model constraint to predict optimal suggestion (e.g., control settings) for controlling the industrial process to achieve one or more operation objectives. In one example, the method uses a genetic algorithm to search through the dynamic space of the forecast model to predict an optimal solution (e.g., optimal suggestion such as optimal control settings) for a set of inputs or constraints (e.g., operation objectives, safe operation zone boundary), Zhang ¶14).
As per claim 7, Zhang discloses as shown above with respect to claim 6. Zhang further discloses wherein filtering each of the forecasted parameter values of the one or more identified parameters comprises applying a linear regression filter to each of the forecasted parameter values of the one or more identified parameters to remove additional outliers (various regression algorithms, to train and clean data, Zhang ¶126-¶127) (Examiner notes the various regression algorithms as including the linear regression filter).
As per claim 9, Zhang discloses as shown above with respect to claim 6. Zhang further discloses 9. The method of claim 6, wherein filtering each of the forecasted parameter values of the one or more identified parameters comprises applying a peak threshold filter to each of the forecasted parameter values of the one or more identified parameters to remove additional outliers (high frequency threshold for outliers, Zhang ¶69 and ¶84) (Examiner notes the ability to have frequency thresholds for outliers as applying the peak threshold filter).
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claim(s) 8, 10, 15, and 20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Zhang et al. (US PG Pub. 2021/0208545) and further in view of Cross et al. (US PG Pub. 2017/0315523).
As per claim 8, Zhang discloses as shown above with respect to claim 6. While Zhang does disclose the removal of outliers with several different methodologies, Zhang does not expressly disclose wherein filtering each of the forecasted parameter values of the one or more identified parameters comprises applying a gaussian estimator to each of the forecasted parameter values of the one or more identified parameters to remove additional outliers.
However, Cross teaches wherein filtering each of the forecasted parameter values of the one or more identified parameters comprises applying a gaussian estimator to each of the forecasted parameter values of the one or more identified parameters to remove additional outliers (gaussian noise vectors, Cross ¶13-¶14).
Both the Cross and Zhang references are analogous in that both are directed towards/concerned with forecasting of automated control systems. Before the effective filing date of the claimed invention, it would have been obvious to a person of ordinary skill in the art to use Cross’ Gaussian noise vectors in Zhang’s system to improve the system and method with reasonable expectation that this would result in a process control management system that is able to identify noise and remove outliers.
The motivation being that various difficulties exist with existing automated control systems and their underlying architectures and computing technologies, including with respect to managing uncertainty in future values of parameters that can affect operation of the automated control systems (Cross ¶3).
As per claims 10, 15, and 20, Zhang discloses as shown above with respect to claims 5, 14, and 16. Zhang further discloses wherein filtering each of the forecasted parameter values of the one or more identified parameters comprises:
applying a significance filter to each of the forecasted parameter values of the one or more identified parameters (noise removed, denoising, Zhang ¶65 and ¶118) (Examiner notes the removal of noise/denoising as the ability to apply a significance filter for the parameters);
applying a linear regression filter to each of the forecasted parameter values of the one or more identified parameters (various regression algorithms, to train and clean data, Zhang ¶126-¶127) (Examiner notes the various regression algorithms as including the linear regression filter);
applying a peak threshold filter to each of the forecasted parameter values of the one or more identified parameters (high frequency threshold for outliers, Zhang ¶69 and ¶84) (Examiner notes the ability to have frequency thresholds for outliers as applying the peak threshold filter).
While Zhang does disclose the removal of outliers with several different methodologies, Zhang does not expressly disclose applying a gaussian estimator to each of the forecasted parameter values of the one or more identified parameters.
However, Cross teaches applying a gaussian estimator to each of the forecasted parameter values of the one or more identified parameters (gaussian noise vectors, Cross ¶13-¶14).
Both the Cross and Zhang references are analogous in that both are directed towards/concerned with forecasting of automated control systems. Before the effective filing date of the claimed invention, it would have been obvious to a person of ordinary skill in the art to use Cross’ Gaussian noise vectors in Zhang’s system to improve the system and method with reasonable expectation that this would result in a process control management system that is able to identify noise and remove outliers.
The motivation being that various difficulties exist with existing automated control systems and their underlying architectures and computing technologies, including with respect to managing uncertainty in future values of parameters that can affect operation of the automated control systems (Cross ¶3).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant’s disclosure (additional art can be located on the PTO-892):
Bell et al. (US PG Pub. 2017/0102696) Distributed industrial performance monitoring and analytics.
Wichmann et al. (US PG Pub. 2015/0184550) Methods and systems for enhancing control of power plant generating units.
Lundeberg et al. (US Patent No. 8,489,360) Multivariate Monitoring And Diagnostics Of Process Variable Data.
Any inquiry concerning this communication or earlier communications from the Examiner should be directed to ANDREW B WHITAKER whose telephone number is (571)270-7563. The examiner can normally be reached on M-F, 8am-5pm, EST.
If attempts to reach the examiner by telephone are unsuccessful, the Examiner’s supervisor, Lynda Jasmin can be reached on (571) 272-6782. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/ANDREW B WHITAKER/Primary Examiner, Art Unit 3629
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