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 .
Claims 1-22 are pending in this application.
Information Disclosure Statement
The information disclosure statement(s) (IDS) submitted on 07/03/2024, 09/26/2025, and 11/24/2025 is/are in compliance with the provisions of 37 C.F.R. § 1.97. Accordingly, the IDS has/have been considered by the examiner.
Claim Objections
Claim 10 is objected to because of the following informalities: Claim 10 recites the limitation “thermal smart monitoring system” in line 1 of the claim. This appears to mean “smart monitoring system” for consistency with the claims 9 and 11-14. Appropriate correction is required.
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.
Claim(s) 1, 2, 8, 9, 15, 16, 18, and 19 is/are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Gundel et al. U.S. Patent Application 2021/0373063 (hereinafter “Gundel”).
Regarding claim 1, Gundel teaches a method of thermal and vibration smart monitoring of electrical distribution equipment (refer to [0003]), the electrical distribution equipment including one or more rigid electric load carrying components (i.e. cable accessories 34A-34J and 340)(figs.1 and 3)(refer also to [0032]), the method comprising: receiving sensor data from a plurality of sensors (refer to [0080]), wherein the sensor data comprises temperature data associated with the one or more rigid electric load carrying components (refer to [0080]); receiving load data representative of an electrical load of the electrical distribution equipment (refer to [0080]); processing the sensor data and the load data as inputs to a trained machine learning model (refer to [0080] and [0142]) to determine a prediction whether a predefined temperature alarm for the electrical distribution equipment (refer to [0113] and [0125]) will be exceeded at a subsequent moment in time (refer to [0113] and [0125]); and generating a notification based on the prediction (refer to [0115]).
Regarding claim 2, Gundel teaches the method of claim 1, wherein the sensor data further comprises vibration data associated with the one or more rigid electric load carrying components (refer to [0080] and [0082]).
Regarding claim 8, Gundel teaches a thermal and vibration smart monitoring system (refer to [0003], [0080] and [0082]) for electrical distribution equipment (refer to [0003]), the electrical distribution equipment including one or more rigid electric load carrying components (i.e. cable accessories 34A-34J and 340)(figs.1 and 3)(refer also to [0032]), the system comprising: a plurality of sensors (refer to [0080]) configured to provide sensor data (refer to [0080]), the sensor data comprising temperature data associated with the one or more rigid electric load carrying components (refer to [0080]); a diagnostics processor (refer to abstract, [0080], and [0142]) receiving and responsive to the sensor data and to load data (refer to [0080] and [0142]), the load data representative of an electrical load of the electrical distribution equipment (refer to [0080] and [0142]); and a memory (refer to abstract, [0080], and [0142]) coupled to the diagnostics processor (refer to abstract, [0080], and [0142]), the memory storing processor- executable instructions that, when executed, configure the diagnostics processor (refer to [0104]) for: processing the sensor data and the load data as inputs to a trained machine learning model (refer to abstract, [0080], and [0142]) to determine a prediction whether a predefined temperature alarm for the electrical distribution equipment (refer to [0113] and [0125]) will be exceeded at a subsequent moment in time (refer to [0113] and [0125]); and generating a notification based on the prediction (refer to [0115]).
Regarding claim 9, Gundel teaches the smart monitoring system of claim 8, wherein the sensor data further comprises vibration data associated with the one or more rigid electric load carrying components (refer to [0080] and [0082]).
Regarding claim 15, Gundel teaches an electrical distribution system (refer to [0003]) comprising: one or more rigid electric load carrying components (i.e. cable accessories 34A-34J and 340)(figs.1 and 3)(refer also to [0032]) configured for supplying power to an electrical load (i.e. cable accessories 34A-34J and 340)(figs.1 and 3)(refer also to [0032]); a plurality of sensors (refer to [0080]) configured to provide sensor data (refer to [0080]), the sensor data comprising temperature data associated with the one or more rigid electric load carrying components (refer to [0080]); a diagnostics processor (refer to abstract, [0080], and [0142]) receiving and responsive to the sensor data and to load data (refer to [0080] and [0142]), the load data representative of the electrical load (refer to [0080] and [0142]); and a memory (refer to abstract, [0080], and [0142]) coupled to the diagnostics processor (refer to abstract, [0080], and [0142]), the memory storing a machine learned model (refer to [0104] and [0112]) that, when executed by the diagnostics processor: processes the sensor data and the load data as inputs to the machine learned model (refer to abstract, [0080], and [0142]) to determine a prediction whether a predefined temperature alarm for the electrical distribution system (refer to [0113] and [0125]) will be exceeded at a subsequent moment in time (refer to [0113] and [0125]); and causes a notification to be generated based on the prediction (refer to [0115]).
Regarding claim 16, Gundel teaches the electrical distribution system of claim 15, wherein at least one of the plurality of sensors is located at a joint between rigid electric load carrying components (refer to [0032]) and the sensor data further comprises vibration data associated with the joint (refer to [0032] and [0080]).
Regarding claim 18, Gundel teaches the electrical distribution system of claim 15, further comprising a gateway of a wireless communications network, wherein the diagnostics processor receives the sensor data from the sensors wirelessly via the gateway (refer to [0029]).
Regarding claim 19, Gundel teaches the electrical distribution system of claim 18, wherein the diagnostic processor is accessible through a cloud connection, linked to a separated offer, or available as a single service (refer to [0055]).
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.
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 nonobviousness.
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.
Claim(s) 3-7, 10-14, 17, and 20-22 is/are rejected under 35 U.S.C. 103 as being unpatentable over Gundel as applied to claims 1, 8, and 15 above, and further in view of Benke et al. U.S. Patent Application 2017/0045481 (hereinafter “Benke”).
Regarding claim 3, Gundel teaches the method of claim 1; however, Gundel does not teach wherein the electrical distribution equipment includes one or more circuit breakers and wherein the sensor data further comprises temperature data associated with the one or more circuit breakers. However, Benke teaches wherein the electrical distribution equipment includes one or more circuit breakers (refer to [0093], [0124], and [0163]) and wherein the sensor data further comprises temperature data associated with the one or more circuit breakers (refer to [0093], [0124], and [0163]). It would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to modify the method of Gundel to include the circuit breakers of Benke to provide the advantage of ensuring protection of all of the electrical distribution equipment.
Regarding claim 4, Gundel and Benke teach the method of claim 3, wherein the sensor data further comprises vibration data associated with the one or more circuit breakers (refer to Benke [0093], [0124], and [0163]).
Regarding claim 5, Gundel teaches the method of claim 1, wherein the sensor data further comprises environmental data associated with ambient conditions of the electrical distribution equipment (refer to [0142] to [0144]), and wherein processing the sensor data and the load data comprises modeling temperature and vibration of the electrical distribution equipment as a function of the ambient conditions and the electrical load (refer to [0142] to [0144]); however, Gundel does not teach wherein processing the sensor data and the load data comprises modeling vibration of the electrical distribution equipment as a function of the ambient conditions and the electrical load. However, Benke teaches wherein processing the sensor data and the load data comprises modeling vibration of the electrical distribution equipment as a function of the ambient conditions and the electrical load (refer to [0093], [0124], and [0163]). It would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to modify the method of Gundel to include modeling vibration of the electrical distribution equipment of Benke to provide the advantage of increasing the accuracy of estimate of the health and longevity of the electrical distribution equipment to minimize downtime.
Regarding claim 6, Gundel and Benke teach the method of claim 5, wherein modeling the temperature and vibration of the electrical distribution equipment comprises defining, based on the received sensor data and load data, a predicted temperature and vibration response of the electrical distribution equipment over time in response to the ambient conditions and the electrical load (refer to Gundel [0142] to [0144])(refer also to Benke [0093], [0124], ands [0163]).
Regarding claim 7, Gundel and Benke teach the method of claim 6, further comprising defining a normality space associated with the electrical distribution equipment from the predicted temperature and vibration response (refer to Gundel [0080], [0125], and [0142] to [0144])(refer also to Benke [0093], [0124], ands [0163]), and wherein processing the sensor data and the load data comprises comparing the normality space to a temperature threshold corresponding to the predefined temperature alarm (refer to Gundel [0080], [0125], and [0142] to [0144])(refer also to Benke [0093], [0124], ands [0163]).
Regarding claim 10, Gundel teaches the thermal smart monitoring system of claim 8; however, Gundel does not teach the thermal smart monitoring system further comprising one or more circuit breakers electrically connected to the electrical distribution equipment, and wherein the sensor data further comprises temperature data associated with the one or more circuit breakers. However, Benke teaches the thermal smart monitoring system further comprising one or more circuit breakers electrically connected to the electrical distribution equipment (refer to [0093], [0124], and [0163]), and wherein the sensor data further comprises temperature data associated with the one or more circuit breakers (refer to [0093], [0124], and [0163]). It would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to modify the system of Gundel to include the circuit breakers of Benke to provide the advantage of ensuring protection of all of the electrical distribution equipment.
Regarding claim 11, Gundel and Benke teach the smart monitoring system of claim 10, wherein the sensor data further comprises vibration data associated with the one or more circuit breakers (refer to Benke [0093], [0124], and [0163]).
Regarding claim 12, Gundel teaches the smart monitoring system of claim 8, wherein the sensor data further comprises environmental data associated with ambient conditions of the electrical distribution equipment (refer to [0142] to [0144]), and wherein the processor-executable instructions, when executed, further configure the diagnostics processor for generating a temperature model of the electrical distribution equipment as a function of the ambient conditions and the electrical load (refer to [0142] to [0144]); however, Gundel does not teach wherein the processor-executable instructions, when executed, further configure the diagnostics processor for generating a temperature and vibration model of the electrical distribution equipment as a function of the ambient conditions and the electrical load. However, Benke teaches wherein the processor-executable instructions, when executed, further configure the diagnostics processor for generating a temperature and vibration model of the electrical distribution equipment as a function of the ambient conditions and the electrical load (refer to [0093], [0124], and [0163]). It would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to modify the system of Gundel to include modeling temperature and vibration of the electrical distribution equipment of Benke to provide the advantage of increasing the accuracy of estimate of the health and longevity of the electrical distribution equipment to minimize downtime.
Regarding claim 13, Gundel and Benke teach the smart monitoring system of claim 12, wherein the temperature and vibration model of the electrical distribution equipment defines, based on the sensor data and the load data, a predicted temperature and vibration response of the electrical distribution equipment over time in response to the ambient conditions and the electrical load (refer to Gundel [0142] to [0144])(refer also to Benke [0093], [0124], ands [0163]).
Regarding claim 14, Gundel and Benke teach the smart monitoring system of claim 13, wherein the predicted temperature and vibration response defines a normality space associated with the electrical distribution equipment (refer to Gundel [0080], [0125], and [0142] to [0144])(refer also to Benke [0093], [0124], ands [0163]), and wherein the processor-executable instructions, when executed, further configure the diagnostics processor for comparing the normality space to a temperature threshold corresponding to the predefined temperature alarm (refer to Gundel [0080], [0125], and [0142] to [0144])(refer also to Benke [0093], [0124], ands [0163]).
Regarding claim 17, Gundel teaches the electrical distribution system of claim 15; however, Gundel does not teach the system further comprising one or more circuit breakers electrically connected to the electrical distribution equipment, and wherein the sensor data further comprises temperature data and vibration data associated with the one or more circuit breakers. However, Benke teaches the system further comprising one or more circuit breakers electrically connected to the electrical distribution equipment (refer to [0093], [0124], and [0163]), and wherein the sensor data further comprises temperature data and vibration data associated with the one or more circuit breakers (refer to [0093], [0124], and [0163]). It would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to modify the system of Gundel to include the circuit breakers of Benke to provide the advantage of ensuring protection of all of the electrical distribution equipment.
Regarding claim 20, Gundel teaches the electrical distribution system of claim 15, wherein the sensor data further comprises environmental data associated with ambient conditions of the electrical distribution system (refer to [0142] to [0144]), and wherein the machine learned model comprises a predicted temperature response over time in response to the ambient conditions and the electrical load (refer to [0142] to [0144]); however, Gundel does not teach wherein the machine learned model comprises a predicted vibration response over time in response to the ambient conditions and the electrical load. However, Benke teaches wherein the machine learned model comprises a predicted vibration response over time in response to the ambient conditions and the electrical load (refer to [0093], [0124], and [0163]). It would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to modify the system of Gundel to include the predicted temperature and vibration response of Benke to provide the advantage of increasing the accuracy of estimate of the health and longevity of the electrical distribution equipment to minimize downtime.
Regarding claim 21, Gundel and Benke teach the electrical distribution system of claim 20, wherein the predicted temperature and vibration response defines a normality space associated with the electrical distribution system (refer to Gundel [0080], [0125], and [0142] to [0144])(refer also to Benke [0093], [0124], ands [0163]), and wherein the predefined temperature alarm is based on comparing the normality space to a temperature threshold (refer to Gundel [0080], [0125], and [0142] to [0144])(refer also to Benke [0093], [0124], ands [0163]).
Regarding claim 22, Gundel and Benke teach the electrical distribution system of claim 21, wherein a specific pattern of temperature or vibration evolution is used to complete the predefined alarm thresholds (refer to Gundel [0083]).
Conclusion
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/KEVIN J COMBER/Primary Examiner, Art Unit 2838