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 1-20 have been examined.
Response to Argument
Applicant’s arguments in the Remarks, filed on 1/28/26 have been fully considered but they are moot in view of new grounds of rejections.
Allowable Subject Matter
Claims 3-5, 10-12 and 17-19 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.
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 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.
Claims 1, 7, 8, 14 and 15 are rejected under 35 U.S.C. 103 as being unpatentable over Yen et al, Graph Neural Network based Root Cause Analysis Using Multivariate Time-series KPIs for Wireless Networks, (hereinafter Yen) in view of Liebman, U.S. Patent Application Publication 2023/0085991 (hereinafter Liebman).
As per claim 1, Yen teaches the invention substantially as claimed comprising:
generating multiple embedded features representing operational data of network elements in a wireless communication network (See III, Problem Formulation, first paragraph; IV, A: Input data, first paragraph, e.g., generating features representing KPI of nodes in 5G wireless network);
generating a relationship graph based on the multiple embedded features, the relationship graph representing a behavior of the network elements in the wireless communication network (e.g., building an input graph based on the nodes’ features (see IV, B: Graph Structure Construction, first paragraph), the nodes’ features representing the KPI of the nodes (see IV, A: Input data, first paragraph));
detecting one or more anomalies in the wireless communication network using the relationship graph (e.g., detecting if parts of the networks go wrong using the Input graph (see IV, A: Input data, first paragraph)), the one or more anomalies identifying one or more deviations of one or more network elements from an expected behavior of the one or more network elements (e.g., identifying nodes that are performing normally and nodes that are failing (i.e., nodes that are deviating from expected/normal behavior) (see IV, B: Graph Structure Construction, first paragraph)) that is identified based on a fusing operation (e.g., combing operation Z1=Ɵ1·Xr Z2=Ɵ2·Xr Zˊ1=tanh(α · Z1) Zˊ2=tanh(α · Z2) A=ReLU(tanh(α · (Zˊ1 Zˊ2 – Zˊ2 Zˊ1)))) between the behavior of the network elements and a behavior of neighbor network elements(i.e., combine/fuse nodes’ features/KPIs (including neighboring nodes) to determine nodes that are performing normally and nodes that are failing)) (see IV, B: Graph Structure Construction, first paragraph); and
generating network analytics based on the one or more detected anomalies (e.g., CNN generating analysis of possible root causes (e.g., potential root causes (sources) and the other victim nodes (symptoms) based on the detected root cause identification problem (e.g., detected nodes classification) (see IV, C, “Classification”, 2nd paragraph).
Yen is silent in regards to the operational data comprises non-numerical data, the non-numerical data being encoded based on a variable encoding operation to identify numerical values of the non-numerical data. Liebman teaches wherein the operational data comprises non-numerical data, the non-numerical data being encoded based on a variable encoding operation to identify numerical values of the non-numerical data ([13], e.g., categorical data being encoded based on on-hot encoding to identify numerical values of the categorical data)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate Liebman’s teaching with Yen’s system in order to allow different types of data including categorial data to be transformed for machine learning training, thus improving the machine learning training in Yen’s system.
As per claim 7, Yen and Liebman teach the invention substantially as claimed in claim 1 above. Yen further teach wherein the operational data of the network elements comprises at least one of performance management data, fault management data, and configuration management data (See III, Problem Formulation, first paragraph; IV, A: Input data, first paragraph).
As per claims 8 and 15, they are rejected for the same reason as set forth in claim 1 above. See I, “Introduction”, third paragraph; III. “Problem Formulation” (e.g., must comprised a transceiver for collecting and propagating KPI data of nodes from the 5G networks and must comprised a processor to utilizing the KPI data as inputs to train a RCA model…) for a transceiver; and a processor operably connected to the transceiver, the processor configured to perform the method of claim 1.
As per claim 14, it is rejected for the same reason as set forth in claim 7 above.
Claims 2, 9 and 16 are rejected under 35 U.S.C. 103 as being unpatentable over Yen and Liebman in view of Chawathe et al, U.S. Patent Application Publication 2022/0376970 (hereinafter Chawathe).
As per claim 2, Yen and Liebman teach the invention substantially as claimed in claim 1 above. Yen and Liebman are silent in regards to scoring the one or more anomalies for prioritization of troubleshooting. Chawathe teaches scoring the one or more detected anomalies for prioritization of troubleshooting ([154], e.g., scoring the anomalies and displaying the highest ranked score anomaly for troubleshooting by a user).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate Chawathe’s teaching with Yen’s and Liebman’s system in order to identify highest scored potential cause as the root cause, thus enhancing the root cause analysis in Yen’s and Liebman ’s system.
As per claims 9 and 16, they are rejected for the same reason as set forth in claim 2 above.
Claims 6, 13 and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Yen and Liebman in view of Li et al, U.S. Patent 11,636,090 (hereinafter Li).
As per claim 6, Yen and Liebman teach the invention substantially as claimed in claim 1 above. Although Yen teaches network analytics, however, Yen and Liebman are silent in regards to at least one of an anomaly causal graph and a root cause ranking. Li teaches wherein the network analytics comprise at least one of an anomaly causal graph and a root cause ranking (col. 6, lines 56-64; col. 7, lines 36-39).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate Li’s teaching with Yen’s and Liebman’s system in order to identify the first ranked potential cause as the root cause, thus enhancing the root cause analysis in Yen’s and Liebman’s system.
As per claims 13 and 20, they are rejected for the same reason as set forth in claim 6 above.
Conclusion
THIS ACTION IS MADE FINAL. 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 extension fee 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
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Glenton Burgess can be reached on 571-272-3949. The fax phone number for the organization where this
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/PHILIP C LEE/Primary Examiner, Art Unit 2454