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 .
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.
This office action is in response to the communication filed on 7/12/2023.
Claims 1-10 are pending.
Claim Objections
Claims 3, 5, 9, 10 are objected to because of the following informalities:
In claims 3, 5, 9, “the plurality of forecasting models” lacks antecedent basis.
In claim 10, “the at least one N-dimensional tensor” lacks antecedent basis.
Appropriate correction is required.
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.
(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.
Claim(s) 1-7 is/are rejected under AIA 35 U.S.C. 102(a)(2) as being anticipated by Vasas et al. (WO 2023/179871 A1, “Vasas”).
As to claim 1, Vasas discloses a method of providing warnings in a telecom network based on forecasting a Key Performance Indicator (KPI), the method comprising:
receiving, at a data processing and preparation service, data; transforming, by the data processing and preparation service, the data (p. 4, l. 4-5, RAN information is collected by a data collector and converted to KPIs; fig. 7, p. 16, l. 16-20, inputting PRB or physical resource block, RRC or radio resource control data, THP or throughput to a cell-level congestion prediction service);
feeding the transformed data to a forecasting model (fig. 3, data collect and sharing modules to feed data to a correlation apparatus (with AI models for predicting congestion as in fig. 2), fig. 7, p. 16, l. 16-20, inputting RAN information such as PRB or physical resource block, RRC or radio resource control data, THP or throughput to a cell-level congestion evaluation/prediction service 710);
predicting, by the forecasting model, a future KPI value for each cell (fig. 7, p. 16, l. 22-29, predict at least one quality indicator or KPI per cell), wherein the KPI has a pre-trained model for prediction that covers all cells (fig. 5, evaluate for all cells to identify cells prone to congestion);
sending, by the forecasting model, predictions to a notification component; receiving, by the notification component, predicted KPI values (fig. 5, step 530, trigger mitigation action based on KPIs for the cell); and
matching, by the notification component, the predicted KPI value against an individual KPI threshold specific to the KPI (fig. 6, p. 11, l. 15-24, PRB utilization threshold predicted to exceed 90% threshold at 20:55) to generate warnings for a predicted KPI value that exceeds the individual KPI threshold (p. 14, l. 5-11, a congestion mitigation action is triggered, which can involve a human confirmation or warning; p. 16, l. 30-37, Dependent on an analytics result in regard to the one or more quality indicators (e.g., depending on a thresholding based one or more degradation criteria), a congestion mitigation action is triggered).
As to claim 2, Vasas discloses training a plurality of forecasting models, one per KPI (fig. 6, p. 4, ML models for predicted KPIs, p. 17, par. 2, p. 18, par. 2, short term and long term prediction models require different variables or KPIs).
As to claim 3, Vasas discloses training the plurality of forecasting models at a non-real time radio access network intelligent controller (non-RT RIC) in an OpenRAN compatible deployment architecture (fig. 2, 3, non-RT O-RAN correlation (Artificial Intelligence) apparatus).
As to claim 4, Vasas discloses performing the method for multiple KPIs (page 23, scenario 1, KPIs such as number of users and PRB utilization).
As to claim 5, Vasas discloses training the plurality of forecasting models to be specific to individual cells (p. 24, certain cell or cell set congestion forecasting).
As to claim 6, Vasas discloses training the plurality of forecasting models for individual cells at a near-real time radio access network intelligent controller (near-RT RIC) (p. 2, last par., p. 4, l. 1-14, 28-32).
As to claim 7, Vasas discloses the KPIs are 4G or 5G networking metrics (fig. 1, p. 2, par. 2, next-generation NG-core is 5G).
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) 8 is/are rejected under AIA 35 U.S.C. 103 as being unpatentable over Vasas in view of Chen et al. (US 2021/0241090, “Chen”).
As to claim 8, Vasas does not disclose the KPIs are 2G or 3G networking metrics.
Chen discloses KPIs such as PRB utilization (of Vasas) are 2G or 3G networking metrics ([0047], [0049], PRB utilization is a 2G or 3G RAN KPI or metric)
It would have been obvious to one skilled in the art before the effective filing date of the claimed invention to apply Vasas’ teachings of RAN congestion prediction based on KPI(s) to known and earlier 2G or 3G networks in order to provide support for these legacy networks.
Claim(s) 9 is/are rejected under AIA 35 U.S.C. 103 as being unpatentable over Vasas in view of Bellenguez (US 2022/0156667).
As to claim 9, Vasas does not disclose the plurality of forecasting models are one of convolutional neural networks (CNNs) or long short term memory networks (LSTMs).
ZZ discloses the plurality of forecasting models are one of convolutional neural networks (CNNs) or long short term memory networks (LSTMs) ([0024], [0025], [0028], KPI prediction in a 5G network using CNN and LSTM neural networks).
It would have been obvious to one skilled in the art before the effective filing date of the claimed invention to apply any of ZZ’s known machine learning techniques such as CNNs and LSTMs to Vasas’ teachings of congestion prediction and mitigation based on KPIs in order to produce possible alternative machine learning solution(s) to Vasas’ (ZZ, [0028]).
Claim(s) 10 is/are rejected under AIA 35 U.S.C. 103 as being unpatentable over Vasas in view of Ryan et al. (US 2020/0387797, “Ryan”).
As to claim 10, Vasas does not disclose the at least one N-dimensional tensor is used for training N-dimensional models which can provide context for context-aware predictions.
Ryan discloses the at least one N-dimensional tensor is used for training N-dimensional models which can provide context for context-aware predictions ([0107], multi-dimensional tensor used to forecast congestion of contexts such as metrics of a first group of specific buffers and another specific buffer).
It would have been obvious to one skilled in the art before the effective filing date of the claimed invention to apply any of Ryan’s multi-dimensional tensor machine learning techniques to Vasas’ teachings of congestion prediction and mitigation based on KPIs in order to discover patterns that exist across multiple time-series, for example, in cases that congestion occurs if two or more related or dependent actions occur at the same time (Ryan, [0107]).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure is included in form PTO 892.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to HIEU T HOANG whose telephone number is (571) 270-1253. The examiner can normally be reached Mon-Fri 9 AM -5 PM.
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/HIEU T HOANG/Primary Examiner, Art Unit 2449
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