DETAILED ACTION
This office action is in response to the application filed on December 14, 2023.
Claims 1-14 are pending and have been examined. Claims 1-14 are rejected.
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 .
Information Disclosure Statement
Acknowledgment is made of the information disclosure statements filed December 14, 2023 and April 30, 2025, which comply with 37 CFR 1.97. As such, the information disclosure statements have been placed in the application file and the information referred to therein has been considered by the examiner.
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) are: “evaluation unit” in Claims 13 and 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 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.
Claim 7 is 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 joint inventor (or for applications subject to pre-AIA 25 U.S.C. 112, the applicant), regards as the invention.
Regarding Claim 7, the ending of the limitation “a machine-learning algorithm, the machine-learning algorithm” does not clearly set the metes and bounds of the patent protection desired. There is insufficient antecedent basis for the limitation in the claim, rendering the claim indefinite because the claim does not recite a further limitation associated with the stated machine-learning algorithm. Therefore, the scope of the claim is unclear.
The following is a quotation of 35 U.S.C. 112(d):
(d) REFERENCE IN DEPENDENT FORMS.—Subject to subsection (e), a claim in dependent form shall contain a reference to a claim previously set forth and then specify a further limitation of the subject matter claimed. A claim in dependent form shall be construed to incorporate by reference all the limitations of the claim to which it refers.
The following is a quotation of pre-AIA 35 U.S.C. 112, fourth paragraph:
Subject to the following paragraph [i.e., the fifth paragraph of pre-AIA 35 U.S.C. 112], a claim in dependent form shall contain a reference to a claim previously set forth and then specify a further limitation of the subject matter claimed. A claim in dependent form shall be construed to incorporate by reference all the limitations of the claim to which it refers.
Claim 14, which is dependent on Claim 12, is rejected under 35 U.S.C. 112(d)
or pre-AIA 35 U.S.C. 112, 4th paragraph, as being of improper dependent form for failing to further limit the subject matter of the claim upon which it depends, or for failing to include all the limitations of the claim upon which it depends. Claim 14 recites “A machine installation, comprising a machine that is equipped with sensors for measuring multiple measured values relating to an operation of the machine, and an evaluation unit that is connected to the sensors and configured to process measured values from the sensors, the evaluation unit being equipped with a computer program product according to claim 12”.
The additional limitations in Claim 14 do not further limit the subject matter of Claim 12 because they merely change the statutory category and restate the limitations already recited in Claim 12. Applicant may cancel the claim(s), amend the claim(s) to place the claim(s) in proper dependent form, rewrite the claim(s) in independent form, or present a sufficient showing that the dependent claim(s) complies with the statutory requirements.
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 1-14 are rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter.
According to the USPTO guidelines, a claim is directed to non-statutory subject matter if:
Step 1: The claim does not fall within one of the four statutory categories of invention (process, machine, manufacture, or composition of matter) – see MPEP 2106.03, or,
Step 2: The claim recites a judicial exception, e.g. an abstract idea, without reciting additional elements that amount to significantly more than the judicial exception, as determined using the following analysis – see MPEP 2106.04:
Step 2A, Prong 1: Does the claim recite an abstract idea, law of nature, or natural phenomenon?
Step 2A, Prong 2: Does the claim recite additional elements that integrate the judicial exception into a practical application?
Step 2B: Does the claim recite additional elements that amount to significantly more than the judicial exception? - see MPEP 2106.05
MPEP 2106.04(a)(2)(I) states: “The mathematical concepts grouping is defined as mathematical relationships, mathematical formulas or equations, and mathematical calculations.”
MPEP 2106.04(a)(2)(III) states: “Accordingly, the “mental processes” abstract idea grouping is defined as concepts performed in the human mind, and examples of mental processes include observations, evaluations, judgements, and opinions.
Further, the MPEP states: “The courts do not distinguish between mental processes that are performed entirely in the human mind and mental processes that require a human to use a physical aid (e.g. pen and paper or a slide run) to perform the claim limitation.
Using the two-step inquiry, it is clear that Claims 1-14 are each directed to non-statutory subject matter as shown below:
Please note the following:
The following groups of claims are expressed in different statutory categories:
Claims 1-11 are directed to a computer-implemented method for monitoring a prediction system (process).
Claim 12 is directed to a computer program product comprising a computer-readable program code embodied on a non-transitory storage medium, which when loaded into a memory of an evaluation unit, which is configured to receive and process measured values measured at a running machine, causes the evaluation unit to monitor a prediction system of the machine (apparatus).
Claim 13 is directed to an evaluation unit for monitoring a prediction system of a machine, comprising a non-transitory memory and a processor for running a computer program product, the evaluation unit being configured to perform a set of processes (apparatus).
Claim 14 is directed to a machine installation, comprising a machine that is equipped with sensors for measuring multiple measured values relating to an operation of the machine, and an evaluation unit that is connected to the sensors and configured to process measured values from the sensors, the evaluation unit being equipped with a computer program product that performs a set of processes (apparatus).
With respect to Claim 1:
Step 1: The claim is directed to a method, also known as a process, which is one of the four statutory categories of patentable subject matter.
Step 2A, Prong 1: A judicial exception is recited in the claim as it recites a mental process, which is an abstract idea:
“Detecting an unreliable state of the prediction system when the anomaly parameter exceeds a threshold;” (Detecting an unreliable state of a prediction system when an anomaly parameter exceeds a threshold covers mental concepts that could be practically performed in the human mind or through the use of a pencil and paper, including observation, evaluation, and judgement.)
Step 2A, Prong 2: The claims do not recite additional elements that integrate the judicial exception into a practical application:
“Computer-implemented method for monitoring a prediction system, the prediction system being configured to predict at least one process variable of a machine based on at least one measured value, the method comprising: Running the machine, receiving the at least one measured value and substantially simultaneously feeding the received at least one measured value into the prediction system and into an anomaly detection algorithm;” (Receiving at least one measured value and feeding the received at least one measured value into the prediction system and anomaly detection algorithm is regarded as a generic computer function of inputting and outputting data. Inputting and outputting data is considered insignificant extra-solution activity – see MPEP 2106.05(g).)
“Obtaining an anomaly parameter from the anomaly detection algorithm;” (Obtaining an anomaly parameter from an anomaly detection algorithm is regarded as a generic computer function of receiving/outputting data. Mere data gathering is considered insignificant extra-solution activity – see MPEP 2106.05(g).)
“Outputting a warning.” (Outputting a warning is regarded as a generic computer function of outputting data. Outputting data is considered insignificant extra-solution activity – see MPEP 2106.05(g).)
Step 2B: The claims do not recite additional elements that amount to significantly more than the judicial exception. Receiving at least one measured value and feeding the received at least one measured value into the prediction system and anomaly detection algorithm is regarded as a generic computer function of inputting and outputting data. Inputting and outputting data is considered insignificant extra-solution activity – see MPEP 2106.05(g). Obtaining an anomaly parameter from an anomaly detection algorithm is regarded as a generic computer function of receiving/outputting data. Mere data gathering is considered insignificant extra-solution activity – see MPEP 2106.05(g). Outputting a warning is regarded as a generic computer function of outputting data. Outputting data is considered insignificant extra-solution activity – see MPEP 2106.05(g).
With respect to Claim 2:
Step 2A, Prong 1: Inherits the limitations and abstract ideas from Claim 1.
Step 2A, Prong 2: The claim does not recite additional elements that integrate the judicial exception into a practical application:
“wherein the prediction system comprises a prediction algorithm that is trained based on substantially the same training data as the anomaly detection algorithm.” (A prediction system comprised of a predication algorithm that is trained based on substantially the same training data as an anomaly detection algorithm generally links the use of the abstract idea to a particular technological environment or field of use – see MPEP 2106.05(h).)
Step 2B: The claim does not recite additional elements that amount to significantly more than the judicial exception. A prediction system comprised of a predication algorithm that is trained based on substantially the same training data as an anomaly detection algorithm generally links the use of the abstract idea to a particular technological environment or field of use – see MPEP 2106.05(h).
With respect to Claim 3:
Step 2A, Prong 1: Inherits the limitations and abstract ideas from Claim 1. An additional judicial exception is recited in the claim as it recites a mental process, which is an abstract idea:
“wherein the unreliable state of the prediction system is detected when the anomaly parameter exceeds the threshold at least for a predefined time interval.” (Detecting an unreliable state of a prediction system when an anomaly parameter exceeds a threshold at least for a predefined time internal covers mental concepts that could be practically performed in the human mind or through the use of a pencil and paper, including observation, evaluation, and judgement.)
Step 2A, Prong 2: The claim does not recite additional elements that integrate the judicial exception into a practical application.
Step 2B: The claim does not recite additional elements that amount to significantly more than the judicial exception.
With respect to Claim 4:
Step 2A, Prong 1: Inherits the limitations and abstract ideas from Claim 1.
Step 2A, Prong 2: The claim does not recite additional elements that integrate the judicial exception into a practical application:
“wherein the anomaly prediction algorithm is also used to detect anomalies of the machine during its operation.” (An anomaly prediction algorithm used to detect anomalies of a machine during its operation only amounts to “apply it” and mere instructions to implement an abstract idea on a computer – see MPEP 2106.05(f)(1).)
Step 2B: The claim does not recite additional elements that amount to significantly more than the judicial exception. An anomaly prediction algorithm used to detect anomalies of a machine during its operation only amounts to “apply it” and mere instructions to implement an abstract idea on a computer – see MPEP 2106.05(f)(1).
With respect to Claim 5:
Step 2A, Prong 1: Inherits the limitations and abstract ideas from Claim 1.
Step 2A, Prong 2: The claim does not recite additional elements that integrate the judicial exception into a practical application:
“wherein the prediction system comprises a machine-learning algorithm” (A prediction system comprised of a machine-learning algorithm generally links the use of the abstract idea to a particular technological environment or field of use – see MPEP 2106.05(h).)
Step 2B: The claim does not recite additional elements that amount to significantly more than the judicial exception. A prediction system comprised of a machine-learning algorithm generally links the use of the abstract idea to a particular technological environment or field of use – see MPEP 2106.05(h).
With respect to Claim 6:
Step 2A, Prong 1: Inherits the limitations and abstract ideas from Claim 5.
Step 2A, Prong 2: The claim does not recite additional elements that integrate the judicial exception into a practical application:
“wherein the machine-learning algorithm is at least one of a gradient boosting machine, a support vector machine, a k-nearest neighbors algorithm, a recurrent neural network, a long short-term memory neural network and a combination thereof.” (A machine-learning algorithm that is at least one of a gradient boosting machine, a support vector machine, a k-nearest neighbors algorithm, a recurrent neural network, a long short-term memory neural network, and a combination thereof generally links the use of the abstract idea to a particular technological environment or field of use – see MPEP 2106.05(h).)
Step 2B: The claim does not recite additional elements that amount to significantly more than the judicial exception. A machine-learning algorithm that is at least one of a gradient boosting machine, a support vector machine, a k-nearest neighbors algorithm, a recurrent neural network, a long short-term memory neural network, and a combination thereof generally links the use of the abstract idea to a particular technological environment or field of use – see MPEP 2106.05(h).
With respect to Claim 7:
Due to containing similar claim language as Claim 5, refer to the 101 rejection for Claim 5 above.
With respect to Claim 8:
Step 2A, Prong 1: Inherits the limitations and abstract ideas from Claim 1. An additional judicial exception is recited in the claim as it recites mental processes, which are abstract ideas:
“Setting a margin of error parameter based on the anomaly parameter and obtaining a prediction value from the prediction system;” (Setting a margin of error parameter based on an anomaly parameter covers mental concepts that could be practically performed in the human mind or through the use of a pencil and paper, including observation, evaluation, and judgement.)
“Applying the margin of error parameter to the prediction value and outputting the prediction value with an indication of its margin of error.” (Applying a margin of error parameter to a prediction value covers mental concepts that could be practically performed in the human mind or through the use of a pencil and paper, including observation, evaluation, and judgement.)
Step 2A, Prong 2: The claim does not recite additional elements that integrate the judicial exception into a practical application:
“Setting a margin of error parameter based on the anomaly parameter and obtaining a prediction value from the prediction system;” (Obtaining a prediction value from a prediction system is regarded as a generic computer function of obtaining/outputting data. Mere data gathering is considered insignificant extra-solution activity – see MPEP 2106.05(g).)
“Applying the margin of error parameter to the prediction value and outputting the prediction value with an indication of its margin of error.” (Outputting a prediction value with an indication of its margin of error is regarded as a generic computer function of receiving/outputting data. Mere data gathering is considered insignificant extra-solution activity – see MPEP 2106.05(g).)
Step 2B: The claim does not recite additional elements that amount to significantly more than the judicial exception. Obtaining a prediction value from a prediction system is regarded as a generic computer function of obtaining/outputting data. Mere data gathering is considered insignificant extra-solution activity – see MPEP 2106.05(g). Outputting a prediction value with an indication of its margin of error is regarded as a generic computer function of receiving/outputting data. Mere data gathering is considered insignificant extra-solution activity – see MPEP 2106.05(g).
With respect to Claim 9:
Step 2A, Prong 1: Inherits the limitations and abstract ideas from Claim 1. An additional judicial exception is recited in the claim as it recites a mental process, which is an abstract idea:
“Disengaging a control routine of the machine that is configured to receive the prediction value from the prediction system as an input when an unreliable state of the prediction system is detected.” (Disengaging a control routine of a machine, that is configured to receive a prediction value from a prediction system, when an unreliable state of a prediction system is detected covers mental concepts that could be practically performed in the human mind or through the use of a pencil and paper, including observation, evaluation, and judgement.)
Step 2A, Prong 2: The claim does not recite additional elements that integrate the judicial exception into a practical application.
Step 2B: The claim does not recite additional elements that amount to significantly more than the judicial exception.
With respect to Claim 10:
Step 2A, Prong 1: Inherits the limitations and abstract ideas from Claim 1.
Step 2A, Prong 2: The claim does not recite additional elements that integrate the judicial exception into a practical application:
“wherein the machine is at least one of a turbo machine and a reciprocating engine and the prediction system is configured to predict the emissions of the machine.” (A machine that is at least one of a turbo machine and a reciprocating engine, and a prediction system that is configured to predict the emissions of the machine generally links the use of the abstract idea to a particular technological environment or field of use – see MPEP 2106.05(h).)
Step 2B: The claim does not recite additional elements that amount to significantly more than the judicial exception. A machine that is at least one of a turbo machine and a reciprocating engine, and a prediction system that is configured to predict the emissions of the machine generally links the use of the abstract idea to a particular technological environment or field of use – see MPEP 2106.05(h).
With respect to Claim 11:
Step 2A, Prong 1: Inherits the limitations and abstract ideas from Claim 1.
Step 2A, Prong 2: The claim does not recite additional elements that integrate the judicial exception into a practical application:
“wherein a multitude of anomaly parameters are obtained based on multiple measured values, and wherein the anomaly parameters and/or the measured values are fed into an outlier detection algorithm, wherein a measured value or an anomaly parameter is identified as an outlier.” (Obtaining a multitude of anomaly parameters based on multiple measured values and feeding the anomaly parameters and/or measured values into an outlier detection algorithm is regarded as a generic computer function of obtaining/receiving and inputting data. Mere data gathering is considered insignificant extra-solution activity – see MPEP 2106.05(g). A measured value or an anomaly parameter identified as an outlier generally links the use of the abstract idea to a particular technological environment or field of use – see MPEP 2106.05(h).)
Step 2B: The claim does not recite additional elements that amount to significantly more than the judicial exception. Obtaining a multitude of anomaly parameters based on multiple measured values and feeding the anomaly parameters and/or measured values into an outlier detection algorithm is regarded as a generic computer function of obtaining/receiving and inputting data. Mere data gathering is considered insignificant extra-solution activity – see MPEP 2106.05(g). A measured value or an anomaly parameter identified as an outlier generally links the use of the abstract idea to a particular technological environment or field of use – see MPEP 2106.05(h).
With respect to Claims 12, 13, and 14:
Due to containing similar claim language as Claim 1, refer to the 101 rejection for Claim 1 above.
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.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
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)(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, 4-7, and 11-14 are rejected under 35 U.S.C. 102 as being unpatentable over Sjogren et al., (US Patent Application Number US12086701B2 filed on September 5, 2019, hereinafter “Sjogren”).
With respect to Claim 1:
Sjogren teaches:
“Computer-implemented method for monitoring a prediction system, the prediction system being configured to predict at least one process variable of a machine based on at least one measured value, the method comprising: Running the machine, receiving the at least one measured value (Column 7, Lines 7-12 recite a method for predictive maintenance of an entity (running a machine) comprising obtaining at least one sensor data measurement (receiving at least one measured value).)
and substantially simultaneously feeding the received at least one measured value into the prediction system and into an anomaly detection algorithm;” (Column 5, Lines 58-63 and Column 6, Lines 4-7 recite inputting the obtained sensor data measurement to a deep neural network, which determines whether or not the inputted new observation is an outlier with respect to a training dataset based on a latent variable model and a second set of projected values (akin to feeding the received measured value into a prediction system containing an anomaly detection algorithm).
“Obtaining an anomaly parameter from the anomaly detection algorithm;” (Column 5, Lines 58-63 and Column 6, Lines 4-7 recite inputting the obtained sensor data measurement to a deep neural network (anomaly detection algorithm), which then determines whether or not the inputted new observation is an outlier with respect to a training dataset based on a latent variable model and a second set of projected values (akin to obtaining an anomaly parameter (set of projected values) from an anomaly detection algorithm).)
“Detecting an unreliable state of the prediction system when the anomaly parameter exceeds a threshold;” (Column 11, Lines 34-37 recite determining that the received sensor data measurement is an outlier with respect to a training dataset if the calculated distance is larger than a threshold value for the distance (anomaly parameter exceeds a threshold). Column 29, Lines 23-28 recite the system may report the model prediction as an unreliable one (detecting an unreliable state of the prediction system).
“Outputting a warning.” (Column 8, Lines 51-53 recite the anomaly detection results may include a warning to the user (outputting a warning).)
With respect to Claim 2:
Sjogren teaches:
“wherein the prediction system comprises a prediction algorithm that is trained based on substantially the same training data as the anomaly detection algorithm.” (Column 5, Lines 58-63 and Column 6, Lines 4-7 recite inputting the obtained sensor data measurement to a deep neural network (prediction system comprised of a prediction algorithm), which determines whether or not the inputted new observation is an outlier (anomaly) using a second algorithm akin to an anomaly detection algorithm. Column 6, Lines 49-60 further recite how a second set of intermediate output values are obtained from at least one of the plurality of hidden layers from the initial deep neural network (prediction algorithm), which are then mapped to the second set of projected values and then used to determine whether or not the received observation is an outlier (akin to an anomaly detection algorithm) with respect to the initial training dataset fed into the deep neural network (trained based on the same training data as the anomaly detection algorithm) based on the latent variable model and second set of projected values.)
With respect to Claim 4:
Sjogren teaches:
“wherein the anomaly prediction algorithm is also used to detect anomalies of the machine during its operation” (Column 5, Lines 58-63 and Column 6, Lines 4-7 recite inputting the obtained sensor data measurement to a deep neural network, which determines whether or not the inputted new observation is an outlier with respect to a training dataset based on a latent variable model and a second set of projected values (anomaly prediction algorithm). Column 7, Lines 3-9, Lines 22-25, and Lines 44-46 further recite the deep neural network (anomaly prediction algorithm) is used to determine a result indicating an occurrence of at least one anomaly (real-time measurements) in an entity (piece of equipment such as a machine or machine component) based on the prediction, akin to detecting anomalies of the machine during its operation.)
With respect to Claim 5:
Sjogren teaches:
“wherein the prediction system comprises a machine-learning algorithm.” (Column 5, Lines 58-63 and Column 6, Lines 4-7 recite inputting the obtained sensor data measurement to a deep neural network, which determines whether or not the inputted new observation is an outlier with respect to a training dataset based on a latent variable model and a second set of projected values (prediction system comprised of a deep neural network, which is a type of machine-learning algorithm).
With respect to Claim 6:
Sjogren teaches:
“wherein the machine-learning algorithm is at least one of a gradient boosting machine, a support vector machine, a k-nearest neighbors algorithm, a recurrent neural network, a long short-term memory neural network and a combination thereof” (Column 10, Lines 10-15 recite the deep neural network present may be a recurrent neural network, or more specifically, a Long-Short Term Memory (LSTM) deep neural network.)
With respect to Claim 7:
Due to containing similar claim language as Claim 5, refer to the 102 rejection for Claim 5 above.
With respect to Claim 11:
Sjogren teaches:
“wherein a multitude of anomaly parameters are obtained based on multiple measured values, and wherein the anomaly parameters and/or the measured values are fed into an outlier detection algorithm, wherein a measured value or an anomaly parameter is identified as an outlier.” (Column 9, Lines 23-31 recite the multiple types of data (multiple measured values) processed by the deep neural network (anomaly detection algorithm), such as temperature sensor data, pressure sensor data, and force sensor data from multiple sensors. Column 20, Lines 63-66 further recite multiple outlier metrics obtained (multitude of anomaly parameters). Column 19, Lines 1-6 recite the measured values (multitude of anomaly parameters) are fed into an outlier detection module (outlier detection algorithm) configured to determine whether or not an observation is identified as an outlier, where it is inherently understood a measured anomaly parameter or value is used to determine this.)
With respect to Claims 12, 13, and 14:
Due to containing similar claim language as Claim 1, refer to the 102 rejection for Claim 1 above.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. § 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
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 non-obviousness.
Claim(s) 3 and 9 are rejected under 35 U.S.C. 103 as being unpatentable over Sjogren et al., (US Patent Application Number US12086701B2 filed on September 5, 2019, hereinafter “Sjogren”).
With respect to Claim 3:
Sjogren teaches:
“wherein the unreliable state of the prediction system is detected when the anomaly parameter exceeds the threshold at least for a predefined time interval.” (Column 11, Lines recite determining that the received sensor data measurement is an outlier with respect to a training dataset if the calculated distance is larger than a threshold value for the distance (anomaly parameter exceeds a threshold). Column 29, Lines 23-28 recite the system may report the model prediction as an unreliable one (the unreliable state of the prediction system is detected).)
Sjogren does not appear to explicitly disclose:
“wherein the unreliable state of the prediction system is detected when the anomaly parameter exceeds the threshold at least for a predefined time interval.”
However, a person having ordinary skill in the art (PHOSITA) would have been motivated to modify the threshold comparison process from Sjogren so that the unreliable state is declared only after the anomaly parameter remains above the threshold for a predefined time interval in order to prevent unnecessary corrective actions based on temporary sensor fluctuations or disturbances (Sjogren, Column 30, Lines 66-69).
With respect to Claim 9:
Sjogren teaches:
“Disengaging a control routine of the machine that is configured to receive the prediction value from the prediction system as an input when an unreliable state of the prediction system is detected.” (Column 29, Lines 23-26 recite the system (machine) may report the prediction value as an unreliable prediction if the new observation is determined to be an anomaly (akin to an entity configured to receive a prediction value from a prediction system as an input which can also detect an unreliable state of the prediction system.))
Sjorgen does not appear to explicitly disclose:
“Disengaging a control routine of the machine that is configured to receive the prediction value from the prediction system as an input when an unreliable state of the prediction system is detected.”
However, a person having ordinary skill in the art (PHOSITA) would have been motivated to disengage the machine’s control routine from Sjogren when an unreliable state of the prediction system was detected in order to prevent equipment damaged or degraded performance of the machine. Column 22, Lines 26-30 recite performing safety routines to avoid catastrophic failure if the system identifies an outlier (anomaly). It would then be predictable to disengage the control routine of the machine in order to use known fault-management and safety techniques to improve system reliability.
Claim 8 is rejected under 35 U.S.C. 103 as being unpatentable over Sjogren et al., (US Patent Application Number US12086701B2 filed on September 5, 2019, hereinafter “Sjogren”), in view of Arnold et al., (US Patent Application Number US11989626B2 filed on April 7, 2020, hereinafter “Arnold”).
With respect to Claim 8:
Sjogren teaches:
“Setting a margin of error parameter based on the anomaly parameter and obtaining a prediction value from the prediction system;” (Column 5, Lines 58-63 and Column 6, Lines 4-7 recite inputting the obtained sensor data measurement to a deep neural network (anomaly detection algorithm), which then determines whether or not the inputted new observation is an outlier with respect to a training dataset based on a latent variable model and a second set of projected values (akin to obtaining an anomaly parameter (set of projected values) from an anomaly detection algorithm). Column 28, Lines 50-53 recite transforming these observations with a deep neural network, such as calculating predictions for the new observation (obtaining a prediction value from a prediction system).)
Sjogren does not appear to explicitly disclose:
“Setting a margin of error parameter based on the anomaly parameter and obtaining a prediction value from the prediction system;”
“Applying the margin of error parameter to the prediction value and outputting the prediction value with an indication of its margin of error”
However, Arnold teaches:
“Setting a margin of error parameter based on the anomaly parameter and obtaining a prediction value from the prediction system;” (Column 7, Lines 24-26 recite the use of an uncertainty interval (margin of error parameter) regarding the performance prediction.)
“Applying the margin of error parameter to the prediction value and outputting the prediction value with an indication of its margin of error” (Column 5, Lines 51-55 recite generating an uncertainty interval (margin of error parameter) that represents the uncertainty of the prediction value. Column 11, Lines 14-16 further recite a prediction engine outputting an indication of the performance of the base model (outputting the prediction value) and one or more uncertainty intervals (indication of its margin of error).)
It would have been obvious to a PHOSITA before the effective filing date of the present application to implement a method like Claim 8 that utilized the teachings of Sjogren with the teachings of Arnold, which are both in the same field of invention. A PHOSITA would have been motivated to modify Sjogren’s anomaly and prediction monitoring system with the uncertainty interval (margin of error) techniques from Arnold in order to provide a quantitative margin of error metric for prediction outputs based on detected anomaly information. This improves the usability and interpretability of the prediction system before taking action based on the output.
Claim 10 is rejected under 35 U.S.C. 103 as being unpatentable over Sjogren et al., (US Patent Application Number US12086701B2 filed on September 5, 2019, hereinafter “Sjogren”), in view of Sepulveda et al., (US Patent Application Number US20230304664A1 filed on March 24, 2022, hereinafter “Sepulveda”).
With respect to Claim 10:
Sjogren does not appear to explicitly disclose:
“wherein the machine is at least one of a turbo machine and a reciprocating engine and the prediction system is configured to predict the emissions of the machine.”
However, Sepulveda teaches:
“wherein the machine is at least one of a turbo machine and a reciprocating engine and the prediction system is configured to predict the emissions of the machine.” (Paragraph 0005 recites a method for monitoring emissions of a turbo machine configured to predict emissions of the turbomachine based on one or more parameters (prediction system configured to predict the emissions of the machine).)
It would have been obvious to a PHOSITA before the effective filing date of the present application to implement a method like Claim 10 that utilized the teachings of Sjogren and the teachings of Sepulveda, which are both in the same field of invention. A PHOSITA would have been motivated to apply the monitoring and anomaly detection system from Sjogren to the gas turbine emissions prediction system of Sepulveda since emissions prediction models used for turbomachinery are subject to changing environmental conditions that can degrade prediction reliability. This would improve the overall reliability of the emissions system and enable corrective action when needed.
Conclusion
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/Vibha Bhat/Examiner
Art Unit 2142
/Mariela Reyes/Supervisory Patent Examiner, Art Unit 2142
Prosecution Insights