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 .
Allowable Subject Matter
Claims 2-3, 10-11 and 18-19 are objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims.
.1.6.150
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 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)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claims 1, 4-6, 9, 12-14, 17 and 20 are rejected under 35 U.S.C. 102(a)(2) as being anticipated by Wozich et al, U.S. Patent Application Publication 2022/0417106 (hereinafter Wozich).
As per claim 1, Wozich teaches the invention as claimed comprising:
obtaining, by a computing system, network data collected by a plurality of network devices in a network ([24][25][27][38], e.g., receiving network data collected by a plurality of network devices of figure 1);
detecting, by the computing system, an anomaly in the network, the anomaly having an associated anomaly time period ([27][28][38], e.g., detecting an alarm of an issue associated with a piece of cellular equipment, wherein the alarm of an issue was detected and collected during a predetermined time period; the received network data includes anomalies in the network, alarms, faults, etc., which provide indications of network characteristics of parameters, components/UEs of the network);
determining, by the computing system and based on the network data, a representative value for each network device of the plurality of network devices based on a time series of statistics for each network device within the anomaly time period ([41][42][31][27][29][13][14][38], e.g., determining KPI values for each of UE operating within a geo-cluster based on time series of statistics for the UEs collected within the predetermined time period);
determining, by the computing system, a representative value for the network based on an aggregated time series of statistics for the network within the anomaly time period ([29][42], e.g., determining an overall KPI average of the KPIs for each UEs for a geo-cluster based on time-series of statistics collected within the predetermined time period); and
determining, by the computing system, that a first network device of the plurality of network devices is related to the anomaly based on the representative value for the first network device being more correlated to the representative value for the network than representative values for other network devices of the plurality of network devices ([42]-[51]; fig. 3; e.g., determining that a UE/cell of a plurality of UEs/cells is related to the anomaly based on KPI data/value for the UE/cell pulling down the overall KPI average for the geo-cluster the most (i.e., greater contribution to the overall average for the geo-cluster) compare to the KPI data/values for the other UEs).
As per claim 4, Wozich teaches the invention as claimed in claim 1 above. Wozich further teach determining an action to take with respect to the first network device of the plurality of network devices ([33][53], e.g., alert action, rollout function, etc.).
As per claim 5, Wozich teaches the invention as claimed in claim 4 above. Wozich further teach wherein determining the action to take includes using one or more supervised machine learning models to determine the action to take ([29][40], e.g., ARIMA).
As per claim 6, Wozich teaches the invention as claimed in claim 4 above. Wozich further teach wherein the action comprises one or more of: changing a configuration of the first network device of the plurality of network devices, changing a software version of the first network device, or restarting the first network device or a component of the first network device ([8][53]).
As per claims 9 and 17, they are rejected for the same reason as set forth in claim 1 above. (See [71][72] of Wozich for a memory and one or more processors coupled to the memory and configured to perform the method of claim 1)
As per claims 12 and 20, they are rejected for the same reason as set forth in claim 4 above.
As per claim 13, it is rejected for the same reason as set forth in claim 5 above.
As per claim 14, it is rejected for the same reason as set forth in claim 6 above.
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.
Claims 7 and 15 are rejected under 35 U.S.C. 103 as being unpatentable over Wozich in view of George et al, U.S. Patent Application Publication 2021/0203563 (hereinafter George).
As per claim 7, Wozich teaches the invention as claimed in claim 1 above. Although Wozich teaches determining, from the network data, the time series of statistics for each network device of the plurality of network devices ([41][42][31][27][29][13][14][38], e.g., determining KPI values for each of UE operating within a geo-cluster based on time series of statistics for the UEs collected within the predetermined time period); aggregating the time series of statistics for each network device to produce the aggregated time series of statistics for the network ([29][42], e.g., determining an overall KPI average of the KPIs for each UEs for a geo-cluster based on time-series of statistics collected within the predetermined time period), however, Wozich is silent in regards to determining that the aggregated time series of statistics is outside a normal range. George teaches wherein detecting the anomaly includes: determining a predicted aggregated time series of statistics using the time series of statistics; and determining that the aggregated time series of statistics is outside a normal range based on the predicted aggregated time series of statistics [62]-[64][94].
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate George’s teaching of determining a predicted aggregated time series of statistics using Wozich’s time series of statistics for each network device in order to allow Wozich’s system to recognize data trend to detect anomaly, thus improving the anomaly detection in Wozich’s system.
As per claim 15, it is rejected for the same reason as set forth in claim 7 above.
Claims 8 and 16 are rejected under 35 U.S.C. 103 as being unpatentable over Wozich.
As per claim 8, Wozich teaches the invention as claimed in claim 1 above. Although Wozich teaches wherein detecting the anomaly includes using one or more neural networks and machine learning models and the like to identify the anomaly [29][40], however, Wozich is silent in regards to unsupervised machine learning models. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to include neural network as unsupervised machine learning models because by doing so it would allow for automated handling of data, thus improving the efficiency of data preparation.
As per claim 16, it is rejected for the same reason as set forth in claim 8 above.
Conclusion
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/PHILIP C LEE/Primary Examiner, Art Unit 2454