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 .
Response to Arguments
Applicant’s arguments with respect to claims 1-3, 5-12, 14-16 and 18-23 have been considered but are moot because the new ground of rejection does not rely on any reference applied in the prior rejection of record for any teaching or matter specifically challenged in the argument.
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, 5-12, 14-16 and 18-23 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Stubbs et al., U.S. Patent Application Publication No. 2021/0133607 A1 (‘607).
As per claim 1, ‘607 discloses a sensor adapted to perform industrial machinery monitoring based on sensor data processing by a machine learning algorithm (e.g., See ‘607; [0010] and [0034], which disclose a sensor for monitoring industrial machines using sensor data that is processed by machine learning), the sensor comprising:
at least one communication interface (e.g., See ‘607; [0053] and [0060] – [0061], which disclose a communication interface for sending sensor data);
memory storing a predictive model of the machine learning algorithm (e.g., See ‘607; [0095] and [0119], which disclose storing and downloading a machine learning model on the device for use by the machine learning inference engine);
at least one sensing component adapted to generate measurements, the measurements comprising a sound intensity related to an industrial machine (e.g., See ‘607; [0054] and [0075], which disclose microphones generating measurements including a magnitude of a sound signal related to an industrial machine), and
a processing unit comprising one or more processor configured to receive from the at least one sensing component the measurements (e.g., See’607; [0053] and [0068], which disclose a microprocessor in communication with sensing components receiving sensors measurements), and execute the machine learning algorithm, the machine learning algorithm using the predictive model for inferring one or more output based on inputs, the one or more output comprising at least one predicted operating condition of the industrial machine, the inputs comprising at least a portion of the measurements, the portion comprising the sound intensity related to the industrial machine (e.g., See ‘607; [0088], [0107] – [0109], and [0119] – [0120], which disclose executing a machine learning inference engine using a stored machine learning model to predict anomalies or operating conditions from sensor measurements, including sound signal measurements, related to the industrial machine).
As per claim 2, ‘607 further discloses that the at least one predicted operating condition of the industrial machine comprises at least one of the following a general failure prediction of the industrial machine and a failure prediction of a component of the industrial machine (e.g., See ‘607; [0010] and [0043], which disclose the predicted operating condition as a predicted service event or failure of the industrial machine).
As per claim 3, ‘607 further discloses that the at least one predicted operating condition of the industrial machine comprises at least one of the recited predictions, such as a prediction of a failure of the industrial machine or a component of it, and/or a prediction of an occurrence of an event related to the industrial machine (e.g., See ‘607; [0043] and [0049] – [0052], which disclose predicting some of the machine failures and operational anomalies, such as worn bearings, tool wear, leaks , pressure loss and other failure conditions).
As per claim 5, ‘607 further discloses that the inputs of the machine learning algorithm further comprise at least one of the following an identification of a type of machinery to which the industrial machine belongs and an identification of a type of measurement point of the sensor (e.g., See ‘607; [0083] – [0084], which disclose identifying the type of industrial machine and characteristics of the industrial machine for predictive analysis).
As per claim 6, ‘607 further discloses that the processing unit further receives additional data from another device via the at least one communication interface, the additional data being used as inputs of the machine learning algorithm (e.g., See ‘607; [0077] and [0079] – [0081], which disclose multiple sensors communicating sensor data and combining data from various sensors as input to a machine learning inference engine).
As per claim 7, ‘607 further discloses that the other device is another sensor adapted to generate measurements, the additional data used as inputs of the machine learning algorithm comprising at least one of the following measurements: a temperature related to the industrial machine generated by the other sensor, a measurement representative of a vibration related to the industrial machine generated by the other sensor, a sound intensity related to the industrial machine generated by the other sensor, an air pressure related to the industrial machine generated by the other sensor, a water pressure related to the industrial machine generated by the other sensor, an oil pressure related to the industrial machine generated by the other sensor, an air particles concentration generated by the other sensor and a carbon dioxide (CO2) level generated by the other sensor (e.g., See ‘607; [0054], [0057] and [0077], which disclose sensors including temperature sensors, accelerometers, microphones, pressure sensors, and CO2 sensors, and combining sensor data as input to a machine learning inference engine).
As per claim 8, ‘607 further discloses that the processing unit further transmits to a remote monitoring device via the at least one communication interface information based on the at least one predicted operating condition generated by the machine learning algorithm (e.g., See ‘607; [0040], which discloses transmitting information based on the predicted operating conditions to a remote user device).
As per claim 9, ‘607 further discloses that the processing unit further transmits to a remote training server executing a machine learning training algorithm via the at least one communication interface training data based on at least some of the measurements, and receiving from the remote training server via the at least one communication interface the predictive model or an update of the predictive model (e.g., See ‘607; [0087] – [0088] and [0093] - [0095], which disclose transmitting sensor data, for model training, and receiving the trained predictive model at the sensor for execution by the processing unit).
As per claim 10, ‘607 further discloses that the processing unit updates the predictive model based on feedback, the feedback being received from another device or the feedback being generated by the processing unit based on measurements performed by the sensor or measurements received from another sensor (e.g., See ‘607; [0104] – [0105] and [0123] – [0125], which disclose updating the predictive model based on feedback about whether a detected anomaly was correct or not).
As per claim 11, this claim is rejected under the rationale as set forth above with respect to claim 1, which is incorporated herein by reference, since claim 11 recites the method steps corresponding to the sensor operations recited in claim 1.
As per claim 12, this claim is rejected under the rationale as set forth above with respect to claim 3, which is incorporated herein by reference, since claim 12 recites corresponding method limitations.
As per claim 14, this claim is rejected under the rationale as set forth above with respect to claim 5, which is incorporated herein by reference, since claim 14 recites a corresponding method limitation.
As per claim 15, this claim is rejected under the rationale as set forth above with respect to claim 6, which is incorporated herein by reference, since claim 15 recites a corresponding method limitation.
As per claim 16, this claim is rejected under the rationale as set forth above with respect to claim 7, which is incorporated herein by reference, since claim 16 recites a corresponding method limitation.
As per claim 18, this claim is rejected under the rationale as set forth above with respect to claim 9, which is incorporated herein by reference, since claim 18 recites a corresponding method limitation.
As per claim 19, this claim is rejected under the rationale as set forth above with respect to claim 10, which is incorporated herein by reference, since claim 19 recites a corresponding method limitation.
As per claim 20, this claim is rejected under the rationale as set forth above with respect to claim 1, which is incorporated herein by reference, since claim 20 recites the non-transitory computer-readable medium having instructions for the sensor operations recited in claim 1. Also See ‘607; [0088] and [0112].
As per claim 21, ‘607 further discloses that the measurements further comprise a temperature related to the industrial machine, and the portion of the measurements used as inputs of the machine learning algorithm further comprises the temperature related to the industrial machine (e.g., See ‘607; [0054], [0056] and [0077], which disclose temperature sensors generating temperature measurements and combining that data as input to a machine learning inference engine).
As per claim 22, ‘607 further discloses that the measurements further comprise a measurement representative of a vibration related to the industrial machine, and the portion of the measurements used as inputs of the machine learning algorithm further comprises the measurement representative of a vibration related to the industrial machine (e.g., See ‘607; [0054] and [0077], which disclose accelerometers sensing vibrations and combining vibration sensor data as input to a machine learning inference engine).
As per claim 23, this claim is rejected under the rationale as set forth above with respect to claims 21 and 22, which are incorporated herein by reference, since claim 23 recites corresponding method limitations requiring temperature and vibration measurements as inputs.
References Considered but Not Relied Upon
The following references were considered but were not relied upon with respect to any prior art rejections:
(1) US 11,688,415 B2, which discloses using machine sound with neural network processing to identify abnormal equipment behavior;
(2) US 2017/0031329 A1, which discloses using machine learning on industrial machine sensor data to predict faults;
(3) US 2021/0341901 A1, which discloses using sensor signals and machine learning to detect motor fault conditions; and
(4) US 2022/0026879 A1, which discloses using machine sensor data and neural networks to detect abnormal patterns.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to RONALD D HARTMAN JR whose telephone number is (571)272-3684. The examiner can normally be reached M-F 8:30 - 4:30 EST.
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/RONALD D HARTMAN JR/Primary Patent Examiner, Art Unit 2119 May 26, 2026
/RDH/
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