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 .
DETAILED ACTION
This action is responsive to the following communication: Non-Provisional Application filed Feb. 5, 2024.
Claims 1-11 are pending in the case. Claims 1, 6 and 11 are independent claims.
Specification
The title of the invention is not descriptive. A new title is required that is clearly indicative of the invention to which the claims are directed.
Claim Rejections - 35 USC § 102
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.
Claims 1-2, 6-7 and 11 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Reddy et al. (hereinafter Reddy) “Anomaly Detection and Fault Disambiguation in Large Flight Data: A Multi-modal Deep Auto-encoder Approach” Published Oct 3, 2016
With respect to independent claim 1 Reddy teaches a method for training a model configured to monitor a working status of equipment based on sensor data (see e.g., Abstract – “the authors propose leveraging existing unsupervised learning methods based on Deep Auto-encoders (DAE) on raw time series data from multiple sensors to build a robust model for anomaly detection. The anomaly detection algorithm analyzes the reconstruction error of a DAE trained on nominal data scenarios. The reconstruction error of individual sensors is examined to perform fault disambiguation.”), the method comprising: training a model using a training data set including historical sensor data gathered only when the equipment is under normal working conditions (see e.g., Section 4.1 and Fig. 1 – “For training, 51 nominal runs of length ~30s are considered. The sampling rate of all 13 sensor data is 100Hz. They are all performed during a certain operating condition in a laboratory setting.”); testing the model with sensor data causing a false alarm, sensor data of the equipment's historical confirmed failure, and sensor data within pre-defined recent time period when the equipment is under normal working conditions (see e.g., Section 4.1 – “During the testing phase, there are a total of 95 nominal runs and 225 faulty runs.” and Fig. 4 – some of the normal runs cause false alarms); and
activating the model if the model passes test, otherwise rejecting the model (see e.g., see e.g., Section 5 –“ A high fault detection rate (~97:8%) along with zero false alarm are achieved by the 11-layer DAE model.”).
With respect to dependent claim 2 Reddy teaches testing the model comprises: calculating a sensitivity of the model based on the sensor data of the equipment's historical confirmed failure (see e.g., Section 4.2 – “A detection threshold _ is applied on the average NRMS error such that a test run with larger NRMS error than _ would be diagnosed as a fault. Varying _, multiple receiver operating characteristics (ROC) curves are generated and showed in figure 4(a) for different bottleneck layer dimensions and few single hidden-layer DAE models. It is observed that the ROC curve for 14-dimensional bottleneck layer case performs the best in fault detection. For λ = 0:56, the fault detection rate is 97:8% with 0:0% false alarm.”); calculating a specificity of the model based on the sensor data causing false alarm and the sensor data within pre-defined recent time period when the equipment is under normal working conditions; and determining the model passes the test if the sensitivity is not lower than a first predefined threshold and the specificity is not lower than a second predefined threshold, else determining the model fails the test (see e.g., Section 4.2 and Fig. 4 – The examiner notes that predefined a threshold is well-known in the art).
Claim 6 is rejected for the similar reasons discussed above with respect to claim 1.
Claim 7 is rejected for the similar reasons discussed above with respect to claim 2.
Claim 11 is rejected for the similar reasons discussed above with respect to claim 1.
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 3-5, 8-10 are rejected under 35 U.S.C. 103 as being unpatentable over Reddy in view of Zhang et al. (hereinafter Zhang) U.S. Patent Pub. No. 2021/0055984.
With respect to dependent claim 3, Reddy does not expressly show collecting real-time sensor data; monitoring working condition of the equipment by providing the real-time sensor data into the activated model; using the real-time sensor data as the sensor data within predefined recent time period when the equipment is under normal working condition, if no alarm is generated; and conducting failure pattern recognition if an alarm is generated; and using the real-time sensor data as the sensor data causing false alarm if a failure is not recognized. However, Reddy teaches anomaly monitoring from sensor and fault disambiguation from reconstruction error patterns (see e.g., Section 4.1, 4.2) Furthermore, Zhang teaches real-time sensor data and the recited false alarm analysis logic (see e.g., Para [5]-[32] – “after an alarm event is generated, the alarm event is thrown into an alarm delay queue; if a recovery event is generated again in an alarm delay process, and the event previously thrown into the alarm delay queue is deleted, this recovery event is also to be deleted, and the alarm is updated to still an un-alarmed state; … Based on the method, a probability of occurrence of false alarms is reduced, a quantity of invalid alarms is reduced, and storage space overheads are reduced, system CPU consumption is reduced, and workloads of maintenance personnel are reduced.”). Both Reddy and Zhang are directed to analyzing alarm data. Accordingly, it would have been obvious to the skilled artisan before the effective filing date of the claimed invention having Reddy and Zhang in front of them to modify the system of Reddy to include the above feature. The motivation to combine Reddy and Zhang comes from Zhang. Zhang discloses the motivation to reduce false alarm and invalid alarms during monitoring process (see e.g., Para [32]).
With respect to dependent claim 4, the modified Reddy teaches if a failure is recognized, combining the real-time sensor data, sensor data from a first pre-defined previous time point to the start time point of the real-time sensor data, and sensor data from a second pre-defined later time point to the end point of the real-time sensor data as the sensor data of the equipment's historical confirmed failure (see e.g., Zhang Para [8]-[18] and claim 1 – “performing delay processing on an alarm event and a recovery event: buffering a logically generated alarm event and a logically generated recovery event, and controlling, based on an established delay rule, whether to discard a logically generated event … the alarm event and the recovery event are mutually excluded within the delay time … after an alarm event is generated, the alarm event is thrown into an alarm delay queue … when a recovery event is generated after the system actually generates an alarm, the recovery event is to be thrown into a recovery delay queue”).
With respect to dependent claim 5, the modified Reddy teaches adding the real-time sensor data to the training data set if no alarm is generated (see e.g., Zhang Para [45] –“(1) no alarm is generated, and only the alarm logic is executed each time analysis is performed”), and including the sensor data causing false alarm as part of the training data set (see e.g., Zhang teaches recognition of dales alarm (see Para [45] “if a recovery event is generated again in an alarm delay process, and the event previously thrown into the alarm delay queue is deleted, this recovery event is also to be deleted, and the alarm is updated to still an un-alarmed state”) Therefore, in view of Reddy, it would have been obvious for a person of ordinary skill to include the false alarm data as part of training dataset).
Claim 8 is rejected for the similar reasons discussed above with respect to claim 3.
Claim 9 is rejected for the similar reasons discussed above with respect to claim 4.
Claim 10 is rejected for the similar reasons discussed above with respect to claim 5.
It is noted that any citation to specific pages, columns, lines, or figures in the prior art references and any interpretation of the references should not be considered to be limiting in any way. “The use of patents as references is not limited to what the patentees describe as their own inventions or to the problems with which they are concerned. They are part of the literature of the art, relevant for all they contain.” In re Heck, 699 F.2d 1331, 1332-33, 216 USPQ 1038, 1039 (Fed. Cir. 1983) (quoting In re Lemelson, 397 F.2d 1006, 1009, 158 USPQ 275, 277 (CCPA 1968)). Further, a reference may be relied upon for all that it would have reasonably suggested to one having ordinary skill the art, including nonpreferred embodiments. Merck & Co. v. Biocraft Laboratories, 874 F.2d 804, 10 USPQ2d 1843 (Fed. Cir.), cert. denied, 493 U.S. 975 (1989). See also Upsher-Smith Labs. v. Pamlab, LLC, 412 F.3d 1319, 1323, 75 USPQ2d 1213, 1215 (Fed. Cir. 2005); Celeritas Technologies Ltd. v. Rockwell International Corp., 150 F.3d 1354, 1361, 47 USPQ2d 1516, 1522-23 (Fed. Cir. 1998).
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to PEIYONG WENG whose telephone number is (571)270-1660. The examiner can normally be reached on Mon.-Fri. 8 am to 5 pm.
If attempts to reach the examiner by telephone are unsuccessful, the examiner's supervisor, Matthew Ell, can be reached on (571) 270-3264. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/PEI YONG WENG/Primary Examiner, Art Unit 2141
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