DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Claim Interpretation
The following is a quotation of 35 U.S.C. 112(f):
(f) Element in Claim for a Combination. – An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The following is a quotation of pre-AIA 35 U.S.C. 112, sixth paragraph:
An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The claims in this application are given their broadest reasonable interpretation using the plain meaning of the claim language in light of the specification as it would be understood by one of ordinary skill in the art. The broadest reasonable interpretation of a claim element (also commonly referred to as a claim limitation) is limited by the description in the specification when 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is invoked.
As explained in MPEP § 2181, subsection I, claim limitations that meet the following three-prong test will be interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph:
(A) the claim limitation uses the term “means” or “step” or a term used as a substitute for “means” that is a generic placeholder (also called a nonce term or a non-structural term having no specific structural meaning) for performing the claimed function;
(B) the term “means” or “step” or the generic placeholder is modified by functional language, typically, but not always linked by the transition word “for” (e.g., “means for”) or another linking word or phrase, such as “configured to” or “so that”; and
(C) the term “means” or “step” or the generic placeholder is not modified by sufficient structure, material, or acts for performing the claimed function.
Use of the word “means” (or “step”) in a claim with functional language creates a rebuttable presumption that the claim limitation is to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites sufficient structure, material, or acts to entirely perform the recited function.
Absence of the word “means” (or “step”) in a claim creates a rebuttable presumption that the claim limitation is not to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is not interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites function without reciting sufficient structure, material or acts to entirely perform the recited function.
Claim limitations in this application that use the word “means” (or “step”) are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action. Conversely, claim limitations in this application that do not use the word “means” (or “step”) are not being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action.
This application includes one or more claim limitations that do not use the word “means,” but are nonetheless being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, because the claim limitation(s) uses a generic placeholder that is coupled with functional language without reciting sufficient structure to perform the recited function and the generic placeholder is not preceded by a structural modifier. Such claim limitation(s) is/are:
first unit and second unit in claim 11;
third unit in claim 12;
fourth unit in claim 13.
Because this/these claim limitation(s) is/are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, it/they is/are being interpreted to cover the corresponding structure described in the specification as performing the claimed function, and equivalents thereof.
If applicant does not intend to have this/these limitation(s) interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, applicant may: (1) amend the claim limitation(s) to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph (e.g., by reciting sufficient structure to perform the claimed function); or (2) present a sufficient showing that the claim limitation(s) recite(s) sufficient structure to perform the claimed function so as to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 2, 3, 9, 12, and 13 rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Claims 2, 3, 12, and 13 recite “machine learning values”. It is not clear whether the “machine learning values” are used for training an ML algorithm, the result of running an ML algorithm, or an intermediate step.
Regarding claim 9, the use of "e.g." and “such as” renders the claim indefinite because it is unclear whether the limitation(s) following the phrase are part of the claimed invention. See MPEP § 2173.05(d).
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 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more.
Specifically, representative Claim 1 recites:
A method for estimating disturbances and giving recommendations for process performance of a water intensive industrial process having steps for measuring variables of the process and collecting process data, and pre-processing measurement and process data of the measuring and collecting step, wherein the method comprises steps for estimating disturbances and forming recommendations,
for each disturbance estimation of a parameter of the process the step of estimating the disturbances comprising the sub steps for receiving the pre-processed measurement and process data from a pre-selected group of the variables of the process,
normalizing the received pre-processed measurement and process data,
operating the normalized data, and
scaling the operated normalized data,
an output of the scaling step being the disturbance estimation of the parameter of the process,
for each recommendation forming the step of forming the recommendations comprises sub steps for receiving the disturbance estimations from a pre-selected group of the outputs of the scaling step,
mapping each received disturbance estimation to one of status categories and forming each recommendation utilizing the mapped disturbance estimations.
The claim limitations in the abstract idea have been highlighted in bold above; the remaining limitations are “additional elements”.
Under the Step 1 of the eligibility analysis, we determine whether the claims are to a statutory category by considering whether the claimed subject matter falls within the four statutory categories of patentable subject matter identified by 35 U.S.C. 101: Process, machine, manufacture, or composition of matter. The above claim is considered to be in a statutory category (process).
Under the Step 2A, Prong One, we consider whether the claim recites a judicial exception (abstract idea). In the above claim, the highlighted portion constitutes an abstract idea because, under a broadest reasonable interpretation, it recites limitations that fall into/recite an abstract idea exceptions. Specifically, under the 2019 Revised Patent Subject matter Eligibility Guidance, it falls into the grouping of subject matter when recited as such in a claim limitation, that covers mathematical concepts (mathematical relationships, mathematical formulas or equations, mathematical calculations) and mental processes – concepts performed in the human mind including an observation, evaluation, judgement, and/or opinion.
For example, steps of “for each disturbance estimation of a parameter of the process the step of estimating the disturbances comprising the sub steps for receiving the pre-processed measurement and process data from a pre-selected group of the variables of the process,
normalizing the received pre-processed measurement and process data (normalizing data),
operating the normalized data (performing a mathematical operation on the data), and
scaling the operated normalized data (scaling the data)” are treated by the Examiner as belonging to mathematical concept grouping, while the steps of “an output of the scaling step being the disturbance estimation of the parameter of the process (assigning/recognizing output),
for each recommendation forming the step of forming the recommendations comprises sub steps for receiving the disturbance estimations from a pre-selected group of the outputs of the scaling step,
mapping each received disturbance estimation to one of status categories and forming each recommendation utilizing the mapped disturbance estimations (formatting output and providing output analysis)” are treated as belonging to mental process grouping.
Similar limitations comprise the abstract ideas of Claim 11.
Next, under the Step 2A, Prong Two, we consider whether the claim that recites a judicial exception is integrated into a practical application.
In this step, we evaluate whether the claim recites additional elements that integrate the exception into a practical application of that exception.
The above claims comprise the following additional elements:
Claim 1: A method for estimating disturbances and giving recommendations for process performance of a water intensive industrial process having steps for measuring variables of the process and collecting process data, and pre-processing measurement and process data of the measuring and collecting step, wherein the method comprises steps for estimating disturbances and forming recommendations;
Claim 11: An arrangement to estimate disturbances and to give recommendations in order to improve process performance, the arrangement having measurement devices and receiving interfaces to measure variables of a process, and receive process data, and a pre- processing arrangement to pre-process measurement and process data from the measuring devices and the receiving interfaces, wherein the arrangement comprises: a first unit, to estimate disturbances, and a second unit to form recommendations.
The additional element in the preamble of “A method/arrangement for estimating disturbances and giving recommendations for process performance of a water intensive industrial process having steps for measuring variables of the process and collecting process data, and pre-processing measurement and process data of the measuring and collecting step, wherein the method comprises steps for estimating disturbances and forming recommendations” is not qualified for a meaningful limitation because it only generally links the use of the judicial exception to a particular technological environment or field of use. Measurement devices and receiving interfaces represents a mere data gathering step and only adds an insignificant extra-solution activity to the judicial exception. A first unit and a second unit (generic processors) are generally recited and are not qualified as particular machines.
In conclusion, the above additional elements, considered individually and in combination with the other claim elements do not reflect an improvement to other technology or technical field, and, therefore, do not integrate the judicial exception into a practical application. Therefore, the claims are directed to a judicial exception and require further analysis under the Step 2B.
However, the above claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception (Step 2B analysis).
The claims, therefore, are not patent eligible.
With regards to the dependent claims, claims 2-10, 12-18 provide additional features/steps which are part of an expanded algorithm, so these limitations should be considered part of an expanded abstract idea of the independent claims.
Claim Rejections - 35 USC § 102
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claim(s) 1, 2, 4, 5, 7-12, 14, 15, 17, and 18 is/are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Gaffoor et al. (US 20210080914 A1).
Regarding Claim 1, Gaffoor teaches a method for estimating disturbances and giving recommendations for process performance of a water intensive industrial process (Gaffoor [0017] A system may then use the generated schedules of setpoints to make recommendations for controlling infrastructure components of the water resource infrastructure in real-time. ) having steps for measuring variables of the process and collecting process data (Gaffoor [0021] The monitoring system 160 communicates with sensors which collect operating data related to the infrastructure components of the WRI 110 through links 152. This operating data may be transmitted from the sensors through communication links 152 and/or network 150 to the monitoring system 160. ), and pre-processing measurement and process data of the measuring and collecting step (Gaffoor [0030] The data processor 304 cleans or ingests the collected data in preparation for further analysis. The cleaning may include by extract, transform, load (ETL) methods, involving statistical imputation techniques for handling missing and erroneous data, smoothing noisy signals, and alarms for indicating sensor faults.), wherein the method comprises steps for estimating disturbances and forming recommendations,
for each disturbance estimation of a parameter of the process the step of estimating the disturbances comprising the sub steps for receiving the pre-processed measurement and process data from a pre-selected group of the variables of the process,
normalizing the received pre-processed measurement and process data (Gaffoor [0030] In some examples, the cleaned dataset may be standardized into Z notation so that effects are properly scaled regardless of the units and magnitudes of each datum received.),
operating the normalized data (Gaffoor [0030] In some examples, the cleaned dataset may be standardized into Z notation so that effects are properly scaled regardless of the units and magnitudes of each datum received.), and
scaling the operated normalized data (Gaffoor [0030] In some examples, the cleaned dataset may be standardized into Z notation so that effects are properly scaled regardless of the units and magnitudes of each datum received. The purpose of standardizing/normalizing the data is to scale it relatively),
an output of the scaling step being the disturbance estimation of the parameter of the process (Gaffoor [0031] The disturbance variables from the historical data are extracted and consumed by pattern recognizing engine 242. The pattern recognizing engine employs a pattern recognition algorithm to generate unique classes corresponding to patterns recognized in the disturbance data, indicated as data patterns 308. The data patterns 308 may be stored in pattern tables store 236. These classes are used to recognize disturbance signals when they are observed in the future.),
for each recommendation forming the step of forming the recommendations comprises sub steps for receiving the disturbance estimations from a pre-selected group of the outputs of the scaling step (Gaffoor [0031] The disturbance variables from the historical data are extracted and consumed by pattern recognizing engine 242. The pattern recognizing engine employs a pattern recognition algorithm to generate unique classes corresponding to patterns recognized in the disturbance data, indicated as data patterns 308. The data patterns 308 may be stored in pattern tables store 236. These classes are used to recognize disturbance signals when they are observed in the future.),
mapping each received disturbance estimation to one of status categories (Gaffoor [0016] The system may apply a pattern recognition algorithm to the received disturbance variables to generate a unique class for the disturbance signal into which similar future disturbance signals can be classified.) and forming each recommendation utilizing the mapped disturbance estimations (Gaffoor [0017] A system may then use the generated schedules of setpoints to make recommendations for controlling infrastructure components of the water resource infrastructure in real-time. The system may receive new disturbance data, generate a predicted disturbance signal, and attempt to classify the predicted disturbance signal into a predetermined class of disturbance signals for which a predetermined schedule of setpoints has already been generated. ).
Regarding Claim 2, Gaffoor further teaches wherein the method comprises a further step for forming machine learning values from the pre- processed measurement and process data, which machine learning values are also used with the pre-processed process and measuring data when estimating disturbances, so that the receiving step also receives the machine learning values from a pre-selected group of the machine learning values, the normalization step also normalizes the received machine learning values, the operation step also operates the normalized machine learning values, and the scaling step also scales the operated normalized machine learning values, an output of the scaling step being the disturbance estimation of the parameter of the process (Gaffoor [0035] The historical data may also be used for initial training of the prediction engine 244. In some examples, the prediction engine 244 may include a hierarchical learning model having a recursive hierarchical layered design whereby each infrastructure component of WRI 110 is represented by a machine learning driven regression estimator describing operating parameters of an infrastructure component of the WRI 110. Machine learning is used to improve predictions and estimations.).
Regarding Claim 4, Gaffoor further teaches wherein the normalization step comprises normalization functions, which are specific for each received data or value (Gaffoor [0030] In some examples, the cleaned dataset may be standardized into Z notation so that effects are properly scaled regardless of the units and magnitudes of each datum received.).
Regarding Claim 5, Gaffoor further teaches the operation step comprises one or more operations (Gaffoor [0030] The data processor 304 cleans or ingests the collected data in preparation for further analysis. The cleaning may include by extract, transform, load (ETL) methods, involving statistical imputation techniques for handling missing and erroneous data, smoothing noisy signals, and alarms for indicating sensor faults. In some examples, the cleaned dataset may be standardized into Z notation so that effects are properly scaled regardless of the units and magnitudes of each datum received.).
Regarding Claim 7, Gaffoor further teaches the scaling of the scaling step is individual for each disturbance estimation (Gaffoor [0030] In some examples, the cleaned dataset may be standardized into Z notation so that effects are properly scaled regardless of the units and magnitudes of each datum received. The purpose of standardizing/normalizing the data is to scale the individual values).
Regarding Claim 8, Gaffoor further teaches the formed recommendations are used for adjusting setpoints of different control arrangements of the process and/or for changing raw material of the process (Gaffoor [0017] A system may then use the generated schedules of setpoints to make recommendations for controlling infrastructure components of the water resource infrastructure in real-time. The system may receive new disturbance data, generate a predicted disturbance signal, and attempt to classify the predicted disturbance signal into a predetermined class of disturbance signals for which a predetermined schedule of setpoints has already been generated. The appropriate schedule of setpoints may then be retrieved and outputted as a recommended schedule of setpoints for review by operating personnel, or directly executed by active infrastructure component control systems.).
Regarding Claim 9, Gaffoor further teaches wherein the setpoint recommendations comprises recommendations for dosing of chemicals, such as retention chemicals, sizing agents, deposit control chemicals, charge control chemicals, strength chemicals, defoamers, dispersing agents, biocides, coagulants, flocculants; for tower levels/tower filling/emptying, for adjusting the amount of dilution water to pulp washers, for improving washing efficiency of pulp(s), for adjusting pH value of a process stream(s), for delay times in storage towers, surface level(s) in storage towers, or aeration, circulation or mixing of a process stream in in storage towers, e.g storage towers of fibrous suspensions (Gaffoor [0014] Water resource infrastructures, such as municipal water distribution networks and water treatment plants, experience disturbances which may impact the functioning of the water resource infrastructure. Control mechanisms in infrastructure components in the water resource infrastructure may be manually actuated to compensate for such disturbances. For example, additional pumping may be brought online to tap into water reservoirs to provide water to a municipality during a particularly hot day. Also see [0077] It is contemplated that, in other examples, other modules pertaining to other key infrastructure components (e.g. other processing equipment) may be employed in similar or other WRIs, such as drinking water treatment plants or sewage collection systems.).
Regarding Claim 10. Gaffoor further teaches the process is a pulp making process, papermaking process, board making process, tissue making process, paper machine, pulp mill, tissue machine, board machine, water treatment process, waste water treatment process, raw water treatment process, water re-use process, any industrial water treatment process, municipal water, municipal waste water treatment process, sludge treatment process, mining process, or oil recovery process (Gaffoor [0014] Water resource infrastructures, such as municipal water distribution networks and water treatment plants, experience disturbances which may impact the functioning of the water resource infrastructure. Control mechanisms in infrastructure components in the water resource infrastructure may be manually actuated to compensate for such disturbances. For example, additional pumping may be brought online to tap into water reservoirs to provide water to a municipality during a particularly hot day. Also see [0077] It is contemplated that, in other examples, other modules pertaining to other key infrastructure components (e.g. other processing equipment) may be employed in similar or other WRIs, such as drinking water treatment plants or sewage collection systems.).
Regarding Claim 11, Gaffoor teaches an arrangement to estimate disturbances and to give recommendations in order to improve process performance (Gaffoor [0017] A system may then use the generated schedules of setpoints to make recommendations for controlling infrastructure components of the water resource infrastructure in real-time. ), the arrangement having measurement devices and receiving interfaces to measure variables of a process, and receive process data (Gaffoor [0021] The monitoring system 160 communicates with sensors which collect operating data related to the infrastructure components of the WRI 110 through links 152. This operating data may be transmitted from the sensors through communication links 152 and/or network 150 to the monitoring system 160. ), and a pre-processing arrangement to pre-process measurement and process data from the measuring devices and the receiving interfaces (Gaffoor [0030] The data processor 304 cleans or ingests the collected data in preparation for further analysis. The cleaning may include by extract, transform, load (ETL) methods, involving statistical imputation techniques for handling missing and erroneous data, smoothing noisy signals, and alarms for indicating sensor faults.), wherein the arrangement comprises:
a first unit to estimate disturbances, and a second unit to form recommendations (Gaffoor [0020] The system 100 further includes a monitoring system 160, disturbance data providers 170, and control mechanism scheduler 200, which are in communication over network 150. The monitoring system 160 includes one or more computing devices running a server application with storage, communication, and processing means. Similarly, the disturbance data providers 170 includes one or more computing devices running a server application with storage, communication, and processing means. The system is computer implemented to perform the various tasks),
which first unit, in order to estimate the disturbances for each disturbance estimation of a parameter of the process, is arranged to receive the pre-processed process and measuring data from a pre-selected group of the variables of the process,
normalize the received pre-processed measuring data (Gaffoor [0030] In some examples, the cleaned dataset may be standardized into Z notation so that effects are properly scaled regardless of the units and magnitudes of each datum received.),
operate the normalized data (Gaffoor [0030] In some examples, the cleaned dataset may be standardized into Z notation so that effects are properly scaled regardless of the units and magnitudes of each datum received.), and
scale the operated normalized data (Gaffoor [0030] In some examples, the cleaned dataset may be standardized into Z notation so that effects are properly scaled regardless of the units and magnitudes of each datum received. The purpose of standardizing/normalizing the data is to scale it relatively),
an output of the scaling being the disturbance estimation of the parameter of the process (Gaffoor [0031] The disturbance variables from the historical data are extracted and consumed by pattern recognizing engine 242. The pattern recognizing engine employs a pattern recognition algorithm to generate unique classes corresponding to patterns recognized in the disturbance data, indicated as data patterns 308. The data patterns 308 may be stored in pattern tables store 236. These classes are used to recognize disturbance signals when they are observed in the future.),
which wherein second unit, for each recommendation forming, is arranged to receive the disturbance estimations from a pre-selected group of the outputs of the first unit (Gaffoor [0031] The disturbance variables from the historical data are extracted and consumed by pattern recognizing engine 242. The pattern recognizing engine employs a pattern recognition algorithm to generate unique classes corresponding to patterns recognized in the disturbance data, indicated as data patterns 308. The data patterns 308 may be stored in pattern tables store 236. These classes are used to recognize disturbance signals when they are observed in the future.),
map each received disturbance estimation to one of status categories (Gaffoor [0016] The system may apply a pattern recognition algorithm to the received disturbance variables to generate a unique class for the disturbance signal into which similar future disturbance signals can be classified.), and form each recommendation utilizing the mapped disturbance estimations (Gaffoor [0017] A system may then use the generated schedules of setpoints to make recommendations for controlling infrastructure components of the water resource infrastructure in real-time. The system may receive new disturbance data, generate a predicted disturbance signal, and attempt to classify the predicted disturbance signal into a predetermined class of disturbance signals for which a predetermined schedule of setpoints has already been generated. ).
Regarding Claim 12, Gaffoor further teaches a third unit to form machine learning values from the pre-processed process and measurement data, which machine learning values are also used with the pre- processed process and measuring data in the first unit, and the first unit is arranged to also receive the machine learning values from a pre-selected group of the machine learning values, to also normalize the received machine learning values, to also operate the normalized machine learning values, and to also scale the operated normalized machine learning values, an output of the scaling being the disturbance estimation of the parameter of the process (Gaffoor [0035] The historical data may also be used for initial training of the prediction engine 244. In some examples, the prediction engine 244 may include a hierarchical learning model having a recursive hierarchical layered design whereby each infrastructure component of WRI 110 is represented by a machine learning driven regression estimator describing operating parameters of an infrastructure component of the WRI 110. Machine learning is used to improve predictions and estimations.).
Regarding Claim 14, Gaffoor further teaches normalization comprises normalization functions, which are specific for each received data or value (Gaffoor [0030] In some examples, the cleaned dataset may be standardized into Z notation so that effects are properly scaled regardless of the units and magnitudes of each datum received.).
Regarding Claim 15, Gaffoor further teaches said operation comprises one or more operations (Gaffoor [0030] The data processor 304 cleans or ingests the collected data in preparation for further analysis. The cleaning may include by extract, transform, load (ETL) methods, involving statistical imputation techniques for handling missing and erroneous data, smoothing noisy signals, and alarms for indicating sensor faults. In some examples, the cleaned dataset may be standardized into Z notation so that effects are properly scaled regardless of the units and magnitudes of each datum received.).
Regarding Claim 17, Gaffoor further teaches said scaling is individual for each disturbance estimation (Gaffoor [0030] In some examples, the cleaned dataset may be standardized into Z notation so that effects are properly scaled regardless of the units and magnitudes of each datum received. The purpose of standardizing/normalizing the data is to scale the individual values).
Regarding Claim 18. Gaffoor further teaches the process is a pulp making process, papermaking process, board making process, tissue making process, paper machine, pulp mill, tissue machine, board machine, water treatment process, waste water treatment process, raw water treatment process, water re-use process, any industrial water treatment process, municipal water, municipal waste water treatment process, sludge treatment process, mining process, or oil recovery process (Gaffoor [0014] Water resource infrastructures, such as municipal water distribution networks and water treatment plants, experience disturbances which may impact the functioning of the water resource infrastructure. Control mechanisms in infrastructure components in the water resource infrastructure may be manually actuated to compensate for such disturbances. For example, additional pumping may be brought online to tap into water reservoirs to provide water to a municipality during a particularly hot day. Also see [0077] It is contemplated that, in other examples, other modules pertaining to other key infrastructure components (e.g. other processing equipment) may be employed in similar or other WRIs, such as drinking water treatment plants or sewage collection systems.).
The Examiner notes that there are currently no prior art rejections for Claims 3, 6, 13, and 16. Determining and analyzing “Explanation Values”, such as Shapley values, as best understood by the Examiner, is a relatively recently developed technique applied in the field of systems disturbance recommendation.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Lundberg et al. (“A Unified Approach to Interpreting Model Predictions”. In Proceedings of the 31st Conference on Neural Information Processing Systems, Long Beach, CA, USA, 4-9 December 2017; pp. 4768–4777) discloses a unified framework for interpreting predictions, SHAP (SHapley Additive exPlanations).
Any inquiry concerning this communication or earlier communications from the examiner should be directed to CHRISTIAN T BRYANT whose telephone number is (571)272-4194. The examiner can normally be reached Monday-Thursday and Alternate Fridays 7:00-4:30.
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/CHRISTIAN T BRYANT/Examiner, Art Unit 2863