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 § 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.
Claim(s) 1-4, 10-13, and 17-20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Nulty (US 10041968) in view of Gundel et al. (US 2021/0190850).
Regarding to claims 1, 10, 17:
Nulty discloses a computer program product for facilitating processing within a computing environment, the computer program product comprising at least one computer-readable storage medium having program instructions embodied therewith, the program instructions being readable by a processing circuit to cause the processing circuit to perform a method comprising:
providing a model trained to, at least in part, facilitate minimizing downtime within a network (FIG. 3: “RULES ENGINE” broadly reads on the claimed machine learning model because it is trained/programmed in advance for performing the same function as claimed);
obtaining tensile-related data for the overhead line, the tensile-related data including peak tension data for the overhead line for an interval of time (Abstract: The network is the power distribution system having an overhead power line, wherein rules (FIG. 3, element 330) applied to detect ice on the power line, sag and stretch of the power line, the effects of wind on the power line, the ‘galloping’ of the power line due to wind in order to identify or predict conditions requiring maintenance (column 8, lines 8-50. FIG. 3, element 340), wherein the data indicating the sag and stretch of the power line reads on the tensile-related data, wherein the amount of sag and stretch indicates the amplitude/peak of the tension data in the period of time when such amount of sag and stretch appears);
correlating, by the machine learning model, relevant data for the overhead line and the tensile-related data for the overhead line, and generating by the machine learning model a probability of breakage score for the overhead line based on the correlating (column 9, lines 20-38: Rules based on a correlation between inertial/inclination (reads on the tension data) and electrical/temperature (read on the relevant data) is used for predicting a degree of sag or generating a score of sag, in other words); and
initiating, using the machine learning model, an action to minimize downtime within the network based, at least in part, on the generated probability of breakage score for the overhead line exceeding a specified threshold indicative of the likelihood of breakage of the overhead line occurring within the specified interval of time (column 9, lines 20-38: A high degree of inclination may indicate that one or more line segments has sagged to the point of breaking, wherein the dgree of sag reads on the claimed score, and the point of breaking reads on the claimed threshold. Column 2, lines 54-67: The system predicts power outages or identify conditions within the power distribution system requiring maintenance schedule to avoid a power outage, avoid unsafe conditions, such as downed cables, thereby avoiding service disruption).
Nulty however does not teach wherein the model is a remote cloud-based machine learning model trained, using a neural network, to predict a likelihood breakage of an overhead line in a network within a specific interval of time.
Gundel et al. discloses a method in a system for monitoring a power grid to predict likelihood failure events within a predetermined amount of time (paragraph [0185]), comprising obtaining event data from a plurality of monitoring devices and training a model utilizing machine learning techniques including artificial neural networks for performing the prediction (Abstract; FIG. 6, steps 600 and 602; paragraphs [0082] and [0135]), wherein the system is a cloud-based platform (paragraph [0060]).
Therefore, it would have been obvious for one having ordinary skill in the art before the effective filing date of the claimed invention to modify the rule engine in Nulty to be the trained machine learning model as disclosed by Gundel et al. to achieve more accurate prediction the health status (paragraph [0006]).
Nulty further discloses the following claims:
Regarding to claims 2, 11, 18: wherein the tensile-related data for the overheard line is for a geographical location, and the method further comprises: obtaining weather data for the geographical location, wherein the generating includes generating the probability of breakage score of the overhead line using the tensile-related data and the weather data (column 8, line 66 to column 9, line13: Identifying or predicting conditions of the power line system may be based on outputs from one or more sensors from one or more locations to gather weather-related conditions for each location).
Regarding to claims 3-4, 12-13, 19-20: further comprising: obtaining additional data for the overhead line from a line sensor assembly coupled to the overhead line, the additional data being selected from the group consisting of temperature data, humidity data and accelerometer data; and wherein the generating includes generating the probability of breakage score of the overhead line using the tensile-related data and the additional data, further comprising predicting, based on the probability of breakage score, a likelihood of breakage of the overhead line within a defined time interval (column 3, lines 6-23: The sensor units such as acceleration are coupled to the cables at one or more locations. Other sensors within the sensor unit may measure temperature).
Response to Arguments
Applicant’s arguments with respect to the claim(s) 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.
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to LAM S NGUYEN whose telephone number is (571)272-2151.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, DOUGLAS RODRIGUEZ, can be reached on 571-431-0716. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/LAM S NGUYEN/ Primary Examiner, Art Unit 2853
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