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 .
DETAILED ACTION
2. This action is in response to the amendment filed September 11, 2026.
3. Claims 1, 7-8, 10, 14-15 and 20 have been amended.
4. The information Disclosure Statement filed September 11, 2026 has been considered.
5. Claims 1-20 have been examined and are pending with this action.
Response to Arguments
6. Applicant's arguments filed September 11, 2026 with respect to the rejection of claims 1-20, previously rejected under 35 U.S.C. 103 as being unpatentable over Giokas (US 2015/0128274 A1) in view of Singh et al. (US 2022/0287038 A1), have been fully considered and are persuasive. Therefore, the rejection has been withdrawn. However, upon further consideration, a new ground(s) of rejection is made in view of Ibrahim (US 2026/0105308 A1).
Ibrahim has been cited to better teach the claimed invention as presently amended. Please see rejections below.
For these reason and the rejections set forth below, claims 1-20 have been rejected and remain pending.
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 (i.e., changing from AIA to pre-AIA ) 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)(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.
7. Claims 1-20 are rejected under 35 U.S.C. 102(a)(1) and 102(a)(2) as being anticipated by Ibrahim (US 2026/0105308 A1)
INDEPENDENT:
As per claim 1, Ibrahim teaches a method of implementing open radio access network (O-RAN) standardized log management services in a cellular network, the method comprising:
aggregating, by a processing device specific to a first vendor, via one or more standardized interfaces of an O-RAN of the cellular network, log data associated with a plurality of network elements in the O-RAN of cellular network (see Ibrahim, [0003]: “Over time, the GCN-LSTM model 138 undergoes continuous training and validation, as the real-time data logs 320 used for prediction purposes are aggregated with the historical training data 305, and a replicated model is trained and evaluated using the newly arrived data.”; [0004]: “These blocks are interconnected by open and standardised interfaces and managed by RAN intelligent controllers (RICs) in the cloud.”; and [0055]: “Over time, the GCN-LSTM model 138 undergoes continuous training and validation, as the real-time data logs 320 used for prediction purposes are aggregated with the historical training data 305, and a replicated model is trained and evaluated using the newly arrived data.”),
wherein the plurality of radio access network elements are specific to various vendors (see Ibrahim, [0047]: “In general, O-RAN architecture disaggregates hardware and software elements, separating them into distinct layers with interfaces between them to allow for integration of equipment from different vendors. O-RAN elements are designed as virtualised software-based components that can be deployed on an O-Cloud, which is a cloud computing platform that provides flexible, scalable infrastructure and computing resources for the different components of the O-RAN”);
storing the log data (see Ibrahim, [0013]: “Time series network log data in the form of historical network logs are also received”; and [0077]: “The forecasted data is generated by the trained model using historical data as input. The similarity between the forecasted data and the real data indicates how well the model predicts future network traffic patterns based on historical data.”);
analyzing the log data (see Ibrahim, [0011]: “An objective of the current invention is therefore to provide an AI or ML model that can integrate with the new 5G architecture, and perform cellular traffic analysis and load predictions for thousands of connected O-RUs within the 5G network.”; and [0055]: “In order to predict cellular traffic load, real-time network logs 320 are input to the prediction model 150, and the prediction model 150 outputs a network traffic prediction in the form of predicted logs 325 for the network. The predicted logs 325 may undergo further processing for visualisation purposes. Over time, the GCN-LSTM model 138 undergoes continuous training and validation, as the real-time data logs 320 used for prediction purposes are aggregated with the historical training data 305, and a replicated model is trained and evaluated using the newly arrived data.”); and
outputting a notification based on a result of the analyzing (see Ibrahim, Abstract: “outputting a trained hybrid neural network for network traffic load prediction”; and [0055]: “In order to predict cellular traffic load, real-time network logs 320 are input to the prediction model 150, and the prediction model 150 outputs a network traffic prediction in the form of predicted logs 325 for the network.”).
As per claim 8, Ibrahim teaches a computing system to facilitate a cellular network, the computing system comprising:
one or more processing devices specific to a first vendor (see Ibrahim, [0051]: “It typically contains digital signal processors and other specialized hardware to perform these functions.”); and
memory communicatively coupled with and readable by the one or more processing devices and having stored therein processor-readable instructions which, when executed by the one or more processing devices, cause the one or more processing devices to perform operations (see Ibrahim, [0030]: “The present invention extends to a computer program comprising instructions which, when the program is executed by a computer, cause the computer to carry out the method of the first or second aspects of the present invention. The present invention also extends to a computer-readable medium comprising instructions which, when executed by a computer, cause the computer to carry out the first or second aspects of the present invention.”) comprising:
aggregating, via one or more standardized interfaces of an open radio access network (O-RAN) of the cellular network, log data associated with a plurality of radio access network elements in the O-RAN of the cellular network, wherein the plurality of radio access network elements are specific to various different vendors (see Claim 1 rejection above);
storing the log data (see Claim 1 rejection above);
analyzing the log data (see Claim 1 rejection above); and
outputting a notification based on a result of the analyzing (see Claim 1 rejection above).
As per claim 15, Ibrahim teaches one or more non-transitory, computer-readable storage media having computer-readable instructions thereon which, when executed by one or more processing devices, cause the one or more processing devices to perform operations (see Ibrahim, [0030]: “The present invention extends to a computer program comprising instructions which, when the program is executed by a computer, cause the computer to carry out the method of the first or second aspects of the present invention. The present invention also extends to a computer-readable medium comprising instructions which, when executed by a computer, cause the computer to carry out the first or second aspects of the present invention.”) comprising:
aggregating, via one or more standardized interfaces of an open radio access network (O-RAN) of a cellular network, log data associated with a plurality of radio access network elements in the O-RAN of the cellular network, wherein the one or more processing devices are specific to a first vendor, and wherein the plurality of radio access network elements are specific to various vendors (see Claim 1 rejection above);
storing the log data (see Claim 1 rejection above);
analyzing the log data (see Claim 1 rejection above); and
outputting a notification based on a result of the analyzing (see Claim 1 rejection above).
DEPENDENT:
As per claims 2, 9, and 16, which respectively depend on claims 1, 8, and 15, Ibrahim teaches further comprising:
monitoring the log data (see Ibrahim, [0008]: “This process involves monitoring traffic at various points in the network, using sampling techniques to identify heavy hitters, and encoding and storing them in a data structure for further analysis and prediction.”; [0050]: “Control plane functions include tasks such as managing the configuration and control of the O-DU 230 and O-RU 235, as well as monitoring their performance and coordinating the communication between them. The O-CU 225 also provides the interface for communication with other network elements, such as the RIC 215.”; and [0055]: “Over time, the GCN-LSTM model 138 undergoes continuous training and validation, as the real-time data logs 320 used for prediction purposes are aggregated with the historical training data 305, and a replicated model is trained and evaluated using the newly arrived data.”);
determining that a parameter associated with the log data satisfies a threshold criterion (see Ibrahim, [0054]: “The optimisation process and hyper-parameters will be discussed later in more detail with reference to FIG. 7. The model training 330, model validation 335, and hyper-parameter optimisation 340 process is configured to repeat until the model validation module 335 determines that the model accuracy is greater than the threshold, and so the model is optimised. Model validation 335 is carried out using mean square errors and total training loss. It should be noted that the training process occurs ‘offline’using the training data 305 until the model is optimised.”; and [0065]: “a default GCN/LSTM model GCNLSTM, network logs L and a threshold accuracy At”); and
outputting an alert regarding the parameter associated with the log data (see Ibrahim, [0005]: “For example, a network prediction model allows a network to effectively allocate network resources, manage task scheduling, turn base stations off when the cellular traffic is below a certain threshold (base station sleeping), and perform admission control (a check performed before establishing a connection to determine if the current resources are sufficient for the proposed connection)”).
As per claims 3, 10, and 17, which respectively depend on claims 1, 8, and 15, Ibrahim further teaches wherein analyzing the log data comprises: identifying a pattern, a trend, or a potential issue in the O-RAN based on the log data (see Ibrahim, [0014]: “The hybrid nature of the neural network is suited to predicting cellular network traffic as the GCN layers are designed to learn the spatiotemporal correlation between the different sites in a cellular network, while the recurrent neural network (e.g. LSTM) layers then learn the time series periodic pattern, for example seasonality or stationery of the traffic loads”; and [0067]: “The combination of the two types of neural networks is particularly suited to this application (i.e. the prediction of cellular network traffic), as the GCN layers are designed to learn the spatiotemporal correlation between the different sites in a cellular network, such as handover patterns, which reflect the dynamic change of the required services. The LSTM layers then learn the time series periodic pattern, for example seasonality or stationery of the traffic loads”; and Claim 1 rejection above).
As per claims 4, 11, and 18, which respectively depend on claims 1, 8, and 15, Ibrahim teaches further comprising: performing a remedy action responsive to the notification (see Ibrahim, Abstract: “”; and [00]: “”).
As per claims 5 and 12, which respectively depend on claims 1 and 8, Ibrahim further teaches wherein the log data comprises at least one of: application data or event data (see Ibrahim, Abstract: “receiving time series network log data of traffic loads within the telecommunications network”).
As per claims 6, 13, and 19, which respectively depend on claims 1, 8, and 15, Ibrahim further teaches wherein the plurality of radio access network elements comprise: one or more open radio units (O-RUs), one or more open distributed units (O-DUs), and one or more open centralized units (O-CUs) (see Ibrahim, [0046]: “The network 200 comprises several entities including a service management and orchestration (SMO) framework 210, an O-RAN intelligent controller (RIC) 215, 220, open central units (O-CUs) 225, an open distributed unit (O-DU) 230, an open radio unit (O-RU) 235, and an open evolved Node B (O-eNB) 240 (the hardware element of the network 200).”).
As per claims 7, 14, and 20, which respectively depend on claims 1, 8, and 15, Ibrahim further teaches wherein the processing device comprises a service management and orchestration (SMO) or an element management system (EMS) (see Ibrahim, [00]: “The network 200 comprises several entities including a service management and orchestration (SMO) framework 210, an O-RAN intelligent controller (RIC) 215, 220, open central units (O-CUs) 225, an open distributed unit (O-DU) 230, an open radio unit (O-RU) 235, and an open evolved Node B (O-eNB) 240 (the hardware element of the network 200)”).
Conclusion
8. For the reasons above, claims 1-20 have been rejected and remain pending.
9. 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.
10. Any inquiry concerning this communication or earlier communications from the examiner should be directed to MICHAEL Y WON whose telephone number is (571)272-3993. The examiner can normally be reached on Wk.1: M-F: 8-5 PST & Wk.2: M-Th: 8-7 PST.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Nicholas R Taylor can be reached on 571-272-3889. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/Michael Won/Primary Examiner, Art Unit 2443
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