DETAILED ACTION
Claims 1 – 20, which are currently pending, are fully considered below.
Priority
Receipt is acknowledged of certified copies of papers required by 37 CFR 1.55.
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
The information disclosure statement (IDS) submitted on September 18, 2024 is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
Claim Rejections - 35 USC § 102
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
Claims 1, 3, 14, and 20 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Jhelum Chakravorty et al. (U.S. Patent Publication 20230306079).
With respect to claim 1, 14, and 20, Chakravorty teaches:
generate synthetic labelled training data based on a set of labelled sensor data associated with one or more industrial automation devices (see paragraphs [0006] and [0012], for generating synthetic data on labeled samples); and
train, based at least on the synthetic labelled training data, one or more machine learning models for fault classification associated with the one or more industrial automation devices (see paragraph [0108], where machine learning is used to train models for fault classification).
With respect to claim 3, Chakravorty teaches:
train the generative artificial intelligence model based on the set of labelled sensor data (see paragraph [0108], where machine learning is used to train models for fault classification).
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 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 2, 4 – 13, 15 – 19 are rejected under 35 U.S.C. 103 as being unpatentable over Jhelum Chakravorty et al. (U.S. Patent Publication 20230306079) in view of Joshua Thomas Maher (U.S. Patent Publication 20230281439 ).
With respect to claims 2 and 15, Maher teaches:
wherein the synthetic labelled training data is generated by using a generative artificial intelligence model (see paragraph [0027], for generative training).
With respect to claims 4 and 16, Maher teaches:
update the generative artificial intelligence model based on new sensor data associated with at least one industrial automation device with a confirmed health condition, wherein the new sensor data includes sensor data received after the training of the generative artificial intelligence model (see paragraph [0015], where machine learning models may receive input as sensor data).
With respect to claim 5, Maher teaches:
wherein the generative artificial intelligence model includes a variational autoencoder or a generative adversarial network (see paragraph [0027], for generative training).
With respect to claims 6 and 18, Maher teaches:
wherein the one or more machine learning models are trained by using an extreme gradient boosting, XGBoost, algorithm (see paragraph [0093], for gradient).
With respect to claim 7, Maher teaches:
wherein the set of labelled sensor data includes at least one of: real sensor data, synthetic sensor data, or simulated sensor data (see paragraph [0015], where machine learning models may receive input as sensor data).
With respect to claim 8, Maher teaches:
wherein the set of labelled sensor data includes a plurality of health conditions and at least one of: speed data, temperature data, vibration data, load data, rating data, magnetic flux data, or electrical current data of the one or more industrial automation devices (see paragraph [0034], where sensors ascertain health data).
With respect to claim 9, Maher teaches:
wherein the synthetic labelled training data includes a plurality of health conditions under a plurality of operating conditions for multiple different sizes and ratings of the one or more industrial automation devices (see paragraph [0034], where sensors ascertain health data).
With respect to claims 10 and 17, Maher teaches:
wherein the plurality of health conditions include at least one of: a healthy status, a bearing failure, a rotor imbalance, a phase imbalance, an overheating, stator eccentricity, rotor eccentricity, a broken rotor bar, a misaligned shaft, misaligned bearings, or stator winding faults (see paragraph [0034], where sensors ascertain health data).
With respect to claims 11 and 19, Maher teaches:
detect and classify one or more health conditions associated with at least one of the one or more industrial automation devices by using the one or more machine learning models trained based at least on the synthetic labelled training data (see paragraph [0034], where sensors ascertain health data).
With respect to claim 12, Chakravorty teaches:
wherein the one or more industrial automation devices include at least one of: one or more electric motors, or one or more variable speed drives (see paragraph [0056], for drives).
With respect to claim 13, Chakravorty teaches:
wherein the apparatus comprises, or is comprised in, a cloud platform (see paragraph [0040], for the cloud).
Conclusion/Contact Information
Any inquiry concerning this communication or earlier communications from the examiner should be directed to ALEXANDRIA Y BROMELL whose telephone number is (571)270-3034. The examiner can normally be reached M-F 8-4.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Ajay Bhatia can be reached at 571-272-3906. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/ALEXANDRIA Y BROMELL/Primary Examiner, Art Unit 2156 September 29, 2026
