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 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 1-10 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (abstract idea) without significantly more.
Under Step 1 of the 2019 Revised Patent Subject Matter Eligibility Guidance, the claims are directed to a machine (claim 1, a system), which is statutory category.
However, evaluating claim 1, under Step 2A, Prong One, the claim is directed to the judicial exception of an abstract idea using the grouping of a mathematical relationship/mental process. The limitations include:
receive the rotational equipment condition sensor data; determine at least one data scope parameter; apply the at least one data scope parameter to the rotational equipment condition sensor data; analyze the rotational equipment condition sensor data to determine a plurality of rotational equipment condition data features associated with the plurality of rotational equipment peer devices; compare each rotational equipment condition data feature with each of the other rotational equipment condition data features to determine a plurality of rotational equipment condition data feature values associated with the plurality of rotational equipment peer devices; and analyze the plurality of rotational equipment condition data feature values to identify, from the plurality of rotational equipment condition data feature values, at least a first rotational equipment condition data feature value satisfying an anomaly threshold, wherein the first rotational equipment condition data feature value corresponds to a first rotational equipment peer device of the plurality of rotational equipment peer devices.
These limitations describe collecting data, performing mathematical comparisons and applying a threshold to identify an anomaly. Such operations constitute mathematical concepts and metal processes (i.e., evaluation, comparison, and judgment based on data), which fall within the abstract idea grouping of mathematical relationships and data analysis.
Next, Step 2A, Prong Two evaluates whether additional elements of the claim “integrate the abstract idea into a practical application” in a manner that imposes a meaningful limit on the judicial exception, such that the claim is more than a drafting effort designed to monopolize the exception. The claim does not recite additional elements that integrate the judicial exception into a practical application.
This judicial exception is not integrated into a practical application because the remaining elements amount to no more than general purpose computer components programmed to perform the abstract ideas. As set forth in the 2019 Eligibility Guidance, 84 Fed. Reg. at 55 “merely include[ing] instructions to implement an abstract idea on a computer” is an example of when an abstract idea has not been integrated into a practical application.
Although the claim recites rotational equipment sensors, a processor, and a non-transitory storage medium, these elements are recited at a high level of generality and merely perform routine functions of data acquisition, data processing, and storage. The claim does not recite any improvement to sensor technology, signal acquisition techniques, rotating machinery operation, or computer functionality itself. Rather additional elements simply implement the abstract data analysis using generic computing components.
Therefore, the claims are directed to an abstract idea.
At Step 2B, consideration is given to additional elements that may make the abstract idea significantly more. Under Step 2B, there are no additional elements that make the claim significantly more than the abstract idea.
The claim does not include additional elements that amount to “significantly more” that the abstract idea.
The additional elements of “rotational equipment condition sensors” are considered insignificant extra-solution activity of collecting data that is not sufficient to integrate the claim into a particular practical application. The sensors merely collect and communicate the data to a controller, without adding anything novel or transformative to the system itself. The act of data gathering by the sensors is considered insufficient to elevate the claim to a practical application.
The additional elements of “a processor” and “non-transitory tangible storage medium” are recited at a high level of generality and are recited as performing generic computer functions routinely used in computer applications. Generic computer components recited as performing generic computer functions that are well-understood, routine and conventional activities amount to no more than implementing the abstract idea with a computerized system (Alice Corp. Pty. Ltd. v. CLS Bank Int’l 573 U.S. __, 134 S. Ct. 2347, 110 U.S.P.Q.2d 1976 (2014)).
Accordingly, claim 1 is directed to an abstract idea and does not recite significantly more than abstract idea. Therefore, claim 1 is not eligible for patent protection under 35 U.S.C. § 101.
Dependent claims 2-8 do not add anything which would render the claimed invention a patent eligible application of the abstract idea. The claim merely extends (or narrow) the abstract idea which do not amount for "significant more" because it merely adds details to the algorithm which forms the abstract idea as discussed above.
Dependent claims 9 and 10, further recite operating one or more actuators to inspect or modify the first rotational equipment peer device. However, the claims recite the actuators at a high level of generality and merely state that they are “automatically operated” to inspect or modify the device. The claim does not specify what modification is performed, how inspection is conducted, what physical parameter is changed, or how such operation improves the functioning of the rotational equipment or the control system. As drafted, the actuator limitations amount to applying the abstract anomaly detection result in a generic field-of-use environment. Merely appending generic actuator control to an otherwise abstract data analysis process does not meaningfully limit the abstract or integrate it into a practical technological improvement.
Therefore, claims 2-10 are directed to an abstract idea and do not recite significantly more than the abstract idea. The claims are not eligible for patent protection under 35 U.S.C. § 101.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 1-8 are rejected under 35 U.S.C. 103 as being unpatentable over
Senturk-Doganaksoy et al. (Pub. No. US 2009/0030752) (hereinafter Senturk-Doganaksoy) in view of Iben et al. (Pub. No. US 2020/0386841) (hereinafter Iben).
As per claim 1, Senturk-Doganaksoy teaches a plurality of rotational equipment condition sensors configured to detect one or more rotational equipment conditions associated with the plurality of rotational equipment peer devices and generate rotational equipment condition sensor data corresponding to the one or more rotational equipment conditions, a rotational equipment peer anomaly processor, and a non-transitory tangible storage medium storing rotational equipment peer anomaly processor executable instructions that, when executed by the rotational equipment peer anomaly processor, causes the rotational equipment peer anomaly processor to: (see ¶¶ [0025]-[0034] and [0037]-[0038], i.e., a rotational equipment monitoring system for a plurality of rotating machines including a plurality of condition sensors configured to detect operational conditions (e.g., vibration, temperature) and generate corresponding sensor data); receive the rotational equipment condition sensor data (see ¶¶ [0033]-[0034]); determine at least one data scope parameter, apply the at least one data scope parameter to the rotational equipment condition sensor data; analyze the rotational equipment condition sensor data to determine a plurality of rotational equipment condition data features associated with the plurality of rotational equipment peer devices (see ¶¶ [004]-[009] and [0036]-[0043], i.e., derives diagnostic indicators/feature representative of equipment health, ¶¶ [0039] and [0045]-[0058], i.e., compares such indicators to thresholds to detect abnormal conditions).
Senturk-Doganaksoy fails to explicitly teach comparing each rotational equipment condition data feature with each of the other rotational equipment condition data features to determine a plurality of rotational equipment condition data feature values associated with the plurality of rotational equipment peer devices and analyze the plurality of rotational equipment condition data feature values to identify, from the plurality of rotational equipment condition data feature values, at least a first rotational equipment condition data feature value satisfying an anomaly threshold, wherein the first rotational equipment condition data feature value corresponds to a first rotational equipment peer device of the plurality of rotational equipment peer devices.
However, Iben teaches performing outlier detection across a plurality of similar elements by calculating feature values for each element (see ¶¶ [0096]-[0100]), determining a group statistic such as median across the plurality of similar elements (see ¶¶ [0007] and [0035]), computing residual/deviation values for each element relative to the group (i.e., Δ value = value – median {value}) (see ¶¶ [0165]-[0166]), performing nearest-neighbor comparisons among elements (see ¶¶ [0111]-[0116]), and comparing the resulting deviation values to predefined limits to identify outliers (see ¶¶ [0101], [0122]-[0124] and [0167]-[-172]); under BRI, computing residuals or neighbor deviations necessarily entails comparing each feature value against other feature values in the group to determine a plurality of feature comparison values and analyzing those values to identify at least a first value satisfying an anomaly threshold. It would have been obvious to one having ordinary skill in the art before the effective filling date of the claimed invention to modify the anomaly detection of Senturk-Doganaksoy to incorporate the peer-based residual and nearest-neighbor comparison techniques of Iben because Iben teaches that evaluating each element relative to corresponding feature values of other similar elements improves anomaly detection accuracy by accounting for baseline variation and reducing bias from outliers (see ¶¶ [0115], and [0169]), thereby improving the robustness and sensitivity of detecting abnormal operating conditions across a plurality of rotational equipment peer devices.
As per claim 2, the combination of Senturk-Doganaksoy and Iben teach the system as stated above.
Senturk-Doganaksoy fails to explicitly teach determine whether a quantity of the rotational equipment condition sensor data satisfies a data threshold; and analyze the rotational equipment condition sensor data to determine the plurality of rotational equipment condition data features associated with the plurality of rotational equipment peer devices on condition that the quantity of the rotational equipment condition sensor data satisfies the data threshold.
However, Iben teaches statistical feature computation across multiple devices, including calculation of slopes, residuals, medians, and standard deviations, see ¶¶ [0096]-[0103] and [0120]-[0123]) (the examiner notes that this inherently require a sufficient number of data samples before meaningful feature extraction and outlier determination can occur). It would have been obvious to one having ordinary skill in the art before the effective filling date of the claimed invention to incorporate into the threshold-based alerting system of Senturk-Doganaksoy and explicit determination that a sufficient quantity of sensor data has been collected before performing feature analysis because reliable peer-based feature extraction and anomaly detection depend on adequate sample size, thereby improving robustness and preventing anomaly determinations based on incomplete or insufficient data.
As per claim 3, the combination of Senturk-Doganaksoy and Iben teach the system as stated above.
While Senturk-Doganaksoy teaches acquiring condition sensor data from monitored equipment, processing tag values derived from that data, and applying customized rule-based thresholds and alert logic (see ¶¶ [0036]-[0043]), thereby disclosing a system in which processing and evaluation parameters are configurable and while Iben teaches that expected values and model parameters are device-specific and may vary across monitored devices (see ¶¶ [0100] and [0103]), thereby demonstrating that data evaluation may be performed in a manner that depends on device-specific configuration or operational parameters.
Although neither prior art uses the precise limitation “identify one or more configurations”, it would have been obvious to one having ordinary skill in the art before the effective filling date of the claimed invention to identify operational or device configurations (e.g., equipment type, sensor arrangement, or device characteristics) and to determine condition data features based on those configurations, because configurable monitoring and device-specific evaluation allow monitoring parameters to be selected per monitored device, thereby enabling anomaly detection to be performed using parameters appropriate to the monitored device.
As per claims 4 and 5, the combination of Senturk-Doganaksoy and Iben teach the system as stated above. Senturk-Doganaksoy further teaches identify a period of time or event; and compare the rotational equipment condition data features corresponding to the period of time or event (see ¶¶ [0036]-[0043], i.e., acquiring and processing rotational equipment condition sensor data over defined monitoring intervals and operational contexts, and evaluating tag values within those time-based monitoring periods to generate alerts).
As per claim 6, the combination of Senturk-Doganaksoy and Iben teach the system as stated above. Senturk-Doganaksoy further teaches determine, from the plurality of rotational equipment condition data feature values, one or more inlying feature values and one or more outlying feature values; and compare the one or more outlying feature values to the anomaly threshold to identify the at least the first rotational equipment condition data feature value satisfying the anomaly threshold (see ¶¶ [0036]-[0043], in particular ¶ [0039], i.e., monitoring condition tag values for a plurality of peer rotational devices and generating alerts when values deviates from customized thresholds, thereby distinguishing normal (inlying) values from anomalous (outlying) values).
As per claims 7 and 8, the combination of Senturk-Doganaksoy and Iben teach the system as stated above. Senturk-Doganaksoy further teaches generating a list including the at least the first rotational equipment condition data feature value satisfying the anomaly threshold ((see ¶¶ [0036]-[0043], in particular ¶ [0039], i.e., monitoring a plurality of condition tags associated with peer rotational equipment and generating alerts when tag values satisfy customized thresholds (the examiner notes that this necessarily requires identifying and recording the tag values that exceed the threshold for reporting or operator notification).
Claims 9 and 10 are rejected under 35 U.S.C. 103 as being unpatentable over
Senturk-Doganaksoy in view of Iben and further in view of Karpman et al. (Pub. No. US 2019/0226407) (hereinafter Karpman).
As per claims 9 and 10, the combination of Senturk-Doganaksoy and Iben teach the system as stated above.
the combination of Senturk-Doganaksoy and Iben teach fails to explicitly teach that the system comprises one or more actuators and automatically operating the one or more actuators to inspect the first rotational equipment peer device.
However, Karpman teaches an engine control system including an electronic controller in signal communication with an actuator and configured, upon detecting a fault, to automatically adjust/command actuator position based on a synthesized/estimated actuator position, including invoking a “safe mode” so the system can be brought to a desired condition/location for inspection (see Abstract, ¶¶ [0003], [0017]-[0019], [0041]-[0043], [0052], and [0058]-[0059]). It would have been obvious to one having ordinary skill in the art before the effective filling date of the claimed invention to incorporate Karpman’s teaching into the combination of Senturk-Doganaksoy and Iben because using residual/outlier comparisons to expected values provides a known, predictable way to robustly identify anomalous devices/conditions and once an anomaly/fault is detected, automatically commanding an actuator response (including safe/inspection-oriented accommodation) is a known, predictable control action, thereby improving reliability and responsiveness of the anomaly system by (i) identifying candidate anomalies via expected-value residuals and (ii) automatically driving the system/device to an inspection-enabled or accommodated state upon detecting an anomalous condition.
Prior art
The prior art made record and not relied upon is considered pertinent to applicant’s
disclosure:
Adhikari [‘832] discloses a method for anomaly detection, the method including identifying a chain of operations for one or more systems, the chain of operations including a plurality of consecutive operations for the one or more systems; clustering operation phases in the chain of operations based on one or more operating conditions, using sensor data for the chain of operations; computing statistical values for one or more parameters across the clustered operation phases; identifying one or more outlier parameters in the sensor data based on the computed statistical values, and excluding the one or more outlier parameters from the sensor data; computing one or more nominal values for one or more parameters of a first sub-system, of a plurality of sub-systems in the one or more systems, using the sensor data with the one or more outlier parameters excluded, and detecting an anomaly in the first sub-system based on the computed one or more nominal values.
Taguchi et al. [‘278] discloses an anomaly detection apparatus includes a processing circuit. The processing circuit is configured to: acquire measured values from sensors installed in a system, a first function, a first threshold, and a second function to output a second threshold; generate the predicted values based on the measured value and the first function; detect that a deviation between the measured values and the predicted values exceeds the first threshold; calculate the feature quantities based on the measured values; and determine whether a number of consecutive times is equal to or larger than the second threshold to detect an anomaly or a sign of the anomaly.
Ardel et al. [‘292] discloses a method of monitoring behavior of a device includes obtaining, at a computing device, first data based on first sensor data from a first sensor device coupled to the device. The method includes processing, at the computing device, the first data at a first anomaly detection model and at a second anomaly detection model of multiple anomaly detection models trained to detect anomalous behavior of the device. The method also includes determining, based on outputs of the multiple anomaly detection models, whether to generate an alert.
Contact information
Any inquiry concerning this communication or earlier communications from the
examiner should be directed to MOHAMED CHARIOUI whose telephone number is (571)272-2213. The examiner can normally be reached Monday through Friday, from 9 am to 6 pm.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Andrew Schechter can be reached on (571) 272-2302. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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Mohamed Charioui
/MOHAMED CHARIOUI/Primary Examiner, Art Unit 2857