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 .
From the last communication regarding restriction and election, applicant elects to prosecute claims 9-14 (group II), without traverse.
Claims 1-8, 15-20 are non-elected.
Claim 9-14 are pending.
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.
Use of the word “means” (or “step for”) in a claim with functional language creates a rebuttable presumption that the claim element is to be treated in accordance with 35 U.S.C. 112(f) (pre-AIA 35 U.S.C. 112, sixth paragraph). The presumption that 35 U.S.C. 112(f) (pre-AIA 35 U.S.C. 112, sixth paragraph) is invoked is rebutted when the function is recited with sufficient structure, material, or acts within the claim itself to entirely perform the recited function.
Absence of the word “means” (or “step for”) in a claim creates a rebuttable presumption that the claim element is not to be treated in accordance with 35 U.S.C. 112(f) (pre-AIA 35 U.S.C. 112, sixth paragraph). The presumption that 35 U.S.C. 112(f) (pre-AIA 35 U.S.C. 112, sixth paragraph) is not invoked is rebutted when the claim element recites function but fails to recite sufficiently definite structure, material or acts to perform that function.
Claim elements in this application that use the word “means” (or “step for”) are presumed to invoke 35 U.S.C. 112(f) except as otherwise indicated in an Office action. Similarly, claim elements that do not use the word “means” (or “step for”) are presumed not to invoke 35 U.S.C. 112(f) except as otherwise indicated in an Office action.
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: a steady state range determination module…to… estimate a range of values… in claim 9, a batch processing module… divide...the time series data in claim 9, a batch processing module… compute a first score for the respective batches… in claim 9, a batch processing module… compute a second score for the respective batches… in claim 9, a batch processing module… assign a composite score to the respective batches…in claim 9, a training period selection module identify… a set of consecutive batches… in claim 9, a training period selection module…. provide the time series data… in claim 9, a preprocessing module to filter the anomalous data in claim 10, preprocessing module is to compute data missing in the time series data in claim 11, preprocessing module is to extract… a subset of operating parameters in claim 12, steady state range determination module is to estimate the range of values in claim 13, batch processing module is to scale the values of each of the plurality of operating parameters in claim 14.
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, for example paragraph [0049] indicating the structures of the modules, 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 § 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.
Claim 9 rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1: The claim recites a system, which fall within a statutory category.
Step 2A Prong one: claim 9 recites steps of “for a time series data corresponding to a plurality of operating parameters of an industrial process recorded over a time period, estimate a range of values for each of the plurality of operating parameters corresponding to at least one mode of operation of the industrial process when the industrial process is exhibiting a normal operation behavior”, “divide, into plurality of batches the time series data, each batch comprising the time series data corresponding to a time frame of predetermined duration in the time period”,“ compute a first score for the respective batches based on a number of operating parameters, from amongst the plurality of operating parameters, having values within the corresponding estimated range of values”,“ compute a second score for the respective batches based on a number of transient values of plurality of the operating parameters in the corresponding time series data, a transient value of an operating parameter being indicative of a change in values of the operating parameter from the ranges corresponding to one mode of operation of the industrial process to the ranges corresponding to another mode of operation of the industrial process”,“ assign a composite score to the respective batches as a weighted sum of the first score and the second score”,“ identify, based on the composite score, a set of consecutive batches in the plurality of batches”. As is evident from the background, the claimed calculation falls into the “mental process” group of abstract ideas, because the recited steps of “estimate”, “divide”,“ compute”,“ compute”,“ assign”,“ identify”, can be practically performed in the human mind. Note that even if most humans would use a physical aid (e.g., pen and paper, a slide rule, or a calculator) to help them complete the recited calculation, the use of such physical aid does not negate the mental nature of this limitation. If a claim limitation under its broadest reasonable interpretation covers performance of the limitation in the mind but for the recitation of generic computer components then it falls within the “Mental Processes” grouping of abstract ideas. Accordingly, the claims recite an abstract idea.
Step 2A Prong two: Besides the abstract ideas, the claim recites additional limitation “provide the time series data of the plurality of operating parameters, corresponding to the identified set of consecutive batches, as training data to an AI model to be trained to monitor normal operation behavior of the industrial process”. The additional limitations represent mere data outputting that is necessary for use of the recited judicial exception and is recited at a high level of generality. Limitation “provide the time series data of the plurality of operating parameters…” in the claim is thus insignificant extra-solution activity. The additional elements “processor”, “steady state range determination module”, “batch processing module”, and “training period selection module” in both steps is recited at a high-level of generality (i.e., as a generic component performing a generic computing function) such that it amounts no more than mere instructions to apply the exception using a generic computer component. Accordingly, the additional elements do 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.
Step 2B: The claim as a whole does not amounts to significantly more than the recited exception. The additional limitation of “provide the time series data of the plurality of operating parameters…” represent mere data outputting is recited at a high level of generality, and, as disclosed in the specification, is also well-known. This limitation therefore remains insignificant extra-solution activity even upon reconsideration. Thus, limitation “provide the time series data of the plurality of operating parameters…” do not amount to significantly more. The additional elements “processor”, “steady state range determination module”, “batch processing module”, and “training period selection module” in both steps is recited at a high-level of generality (i.e., as a generic component performing a generic computer function) such that it amounts 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. Even when considered in combination, these additional elements represent mere instructions to apply an exception and insignificant extra-solution activity, which do not provide an inventive concept. The claim is not eligible.
Claim 10 rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. Dependent Claim 10 recites step of “filter the anomalous data from the time series data”, the step cover performance of the limitation in the mind but for the recitation of generic computer components. If a claim limitation under its broadest reasonable interpretation covers performance of the limitation in the mind but for the recitation of generic computer components then it falls within the “Mental Processes” grouping of abstract ideas. Accordingly, the claims recite an abstract idea. The claim lacks any additional elements which may serve to integrate it into a practical application and amount to significantly more than the abstract idea itself. The claim is not eligible subject matter under 35 U.S.C. 101.
Claim 11 rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. Dependent Claim 11 recites step of “compute data missing in the time series data”, the step cover performance of the limitation in the mind but for the recitation of generic computer components. If a claim limitation under its broadest reasonable interpretation covers performance of the limitation in the mind but for the recitation of generic computer components then it falls within the “Mental Processes” grouping of abstract ideas. Accordingly, the claims recite an abstract idea. The claim lacks any additional elements which may serve to integrate it into a practical application and amount to significantly more than the abstract idea itself. The claim is not eligible subject matter under 35 U.S.C. 101.
Claim 12 rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. Dependent Claim 12 recites step of “extract, from amongst the plurality of operating parameters, a subset of operating parameters that are non-colinear”, the step cover performance of the limitation in the mind but for the recitation of generic computer components. If a claim limitation under its broadest reasonable interpretation covers performance of the limitation in the mind but for the recitation of generic computer components then it falls within the “Mental Processes” grouping of abstract ideas. Accordingly, the claims recite an abstract idea. The claim lacks any additional elements which may serve to integrate it into a practical application and amount to significantly more than the abstract idea itself. The claim is not eligible subject matter under 35 U.S.C. 101.
Claim 13 rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. Dependent Claim 13 recites step of “estimate the range of values corresponding to at least one mode of operation of the industrial process based on the subset of operating parameters”, the step cover performance of the limitation in the mind but for the recitation of generic computer components. If a claim limitation under its broadest reasonable interpretation covers performance of the limitation in the mind but for the recitation of generic computer components then it falls within the “Mental Processes” grouping of abstract ideas. Accordingly, the claims recite an abstract idea. The claim lacks any additional elements which may serve to integrate it into a practical application and amount to significantly more than the abstract idea itself. The claim is not eligible subject matter under 35 U.S.C. 101.
Claim 14 rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. Dependent Claim 14 recites step of “scale the values of each of the plurality of operating parameters for the respective batches based on the steady state range of values of the respective parameter”, the step cover performance of the limitation in the mind but for the recitation of generic computer components. If a claim limitation under its broadest reasonable interpretation covers performance of the limitation in the mind but for the recitation of generic computer components then it falls within the “Mental Processes” grouping of abstract ideas. Accordingly, the claims recite an abstract idea. The claim lacks any additional elements which may serve to integrate it into a practical application and amount to significantly more than the abstract idea itself. The claim is not eligible subject matter under 35 U.S.C. 101.
Allowable Subject Matter
Claims 9-14 would be allowable if rewritten or amended to overcome the rejection(s) under 35 U.S.C. 101, set forth in this Office action.
The following is a statement of reasons for the indication of allowable subject matter:
LI et al., US 20220092430 A1, teaches a method and system for time series deep survival analysis combined with active learning, such that selects a part of right censored data to label a survival time (the time experienced from a beginning event to an ending event), putting all the survival data in a preparatory training set pool, in which time series features, survival times and whether censoring of all the objects to be analyzed are stored; using a cox risk proportional regression model to perform cox regression analysis on a preparatory training set, so as to calculate a concordance index; putting all the right censored data in a censored data pool, in which the time series features and censoring times of all the objects to be analyzed are stored; combined with the active learning method, according to a novel sampling strategy, sorting the data in the censored data pool; and selecting the most important batch of right censored data ranked first, labeling a survival time of selected right censored data, updating the labeled data into the preparatory training set pool, and recording whether censoring as NOT; the time series deep survival analysis module constructs a time series deep survival analysis neural network model, and takes the uncensored data and the right censored data as model inputs, so as to obtain survival time prediction results of the objects to be analyzed.
Wang et al., US 20190347570 A1, teaches a method and system for generating a base model by training with a first dataset of data pairs and generating an adapted model by training the base model on a second dataset of data pairs, by determining a contrastive score for each data pair of a third dataset of data pairs using the base model and the adapted model. The contrastive score is indicative of a probability of quality of the respective data pair. The method also includes training a target model using the data pairs of the third dataset and the contrastive scores.
Gendron-Bellemare et al., US 20190332938 A1, teaches a method and system for training a machine learning model, includes receiving training data for training the machine learning model on a plurality of tasks, where each task includes multiple batches of training data. A task is selected in accordance with a current task selection policy. A batch of training data is selected from the selected task. The machine learning model is trained on the selected batch of training data to determine updated values of the model parameters. A learning progress measure that represents a progress of the training of the machine learning model as a result of training the machine learning model on the selected batch of training data is determined. The current task selection policy is updated using the learning progress measure.
Erenrich et al., US 20180330280 A1, teaches a method and system for training a machine learning model includes obtaining, by the computer system, a machine learning model and a training dataset, the training dataset including a plurality of training examples; determining, by the computer system, uncertainty scores for the plurality of training examples according to the machine learning model; selecting, by the computer system, a first example batch from the plurality of training examples according to uncertainty scores of the plurality of training examples; updating, by the computer system, the machine learning model according to at least one labeled training example of the example batch; determining, by the computer system, updated uncertainty scores for the plurality of training examples according to the updated machine learning model; and selecting, by the computer system, a second example batch from the plurality of training examples according to the updated uncertainty scores of the plurality of training examples.
The prior art of record do not teach or suggest, individually or in combination, for a time series data corresponding to a plurality of operating parameters of an industrial process recorded over a time period, estimate a range of values for each of the plurality of operating parameters corresponding to at least one mode of operation of the industrial process when the industrial process is exhibiting a normal operation behavior; divide, into plurality of batches the time series data, each batch comprising the time series data corresponding to a time frame of predetermined duration in the time period; compute a first score for the respective batches based on a number of operating parameters, from amongst the plurality of operating parameters, having values within the corresponding estimated range of values; compute a second score for the respective batches based on a number of transient values of plurality of the operating parameters in the corresponding time series data, a transient value of an operating parameter being indicative of a change in values of the operating parameter from the ranges corresponding to one mode of operation of the industrial process to the ranges corresponding to another mode of operation of the industrial process; assign a composite score to the respective batches as a weighted sum of the first score and the second score; identify, based on the composite score, a set of consecutive batches in the plurality of batches; provide the time series data of the plurality of operating parameters, corresponding to the identified set of consecutive batches, as training data to an AI model to be trained to monitor normal operation behavior of the industrial process.
Any comments considered necessary by applicant must be submitted no later than the payment of the issue fee and, to avoid processing delays, should preferably accompany the issue fee. Such submissions should be clearly labeled “Comments on Statement of Reasons for Allowance.”
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to ZHIPENG WANG whose telephone number is (571)272-5437. The examiner can normally be reached Monday-Friday 10-7.
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/ZHIPENG WANG/Primary Examiner, Art Unit 2115
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