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 § 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.
Claim(s) 1-4, 10-13, and 17-20 is/are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Nulty (US 10041968).
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 machine learning model trained to, at least in part, facilitate minimizing downtime within a network, the network including an overhead line (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 a likelihood of a breakage of the overhead line occurring within the 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).
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. Please see the rejection above for newly citation and explanations.
Conclusion
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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/LAM S NGUYEN/ Primary Examiner, Art Unit 2853
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