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 are pending.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 4, 6, 12, 14, and 19 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
In claims 4 and 12 and taking claim 4 as an example, the limitations “aggregating …”, “filtering …”, and “providing …” are not interconnected/inter-related and therefore, are indefinite.
In claims 6, 14, and 19, limitation “the feedback data comprises one or more instructions by human intervention” (emphasis added) is indefinite since it is not clear what is meant by “by human intervention” as it relates to the one or more instructions. Does the human provide the one or more instructions? If so, the limitation can be amended to read “instructions provided by human intervention” to overcome this rejection.
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 (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 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) 1-3, 5, 8-11, 13, and 16-18 is/are rejected under 35 U.S.C. 103 as being unpatentable over Caswell, US 2021/0410017, (“Caswell”), in view of Hirzallah et al., US 2025/0350332, (“Hirzallah”).
Independent Claims
Regarding claim 1, Caswell teaches “A method of end-to-end (E2E) network observability and automation in a cellular network, the method comprising:
receiving a plurality of sets of management data from a plurality of systems associated with the cellular network (Fig. 5, step 520; paragraph no. 0051, “At block 520, status data may be obtained from some or all core cellular network nodes. The status data may be received by a CNMS that monitors the entirety of the cellular network or the core network of the cellular network. The status data can include: the number of UEs being serviced; the amount of bandwidth used between each node; the amount of bandwidth available between each node; the latency between nodes of the core network; and the connections available in the core network”);
standardizing the plurality of sets of management data to generate standardized data (Fig. 5, step 530; paragraph no. 0052, “At block 530, a cellular network model may be created or updated based on the status data received”; the “standardized data” reads on the model since the model is based on the status data received and as the term “standardized” is broadly construed in accordance with BRI);
analyzing the standardized data to generate analytics data (Fig. 5, step 540; paragraph no. 0053, “At block 540, an analysis may be performed by the CNMS to determine what modifications should be made to the core cellular network. The analysis can be performed using the current status data, the cellular network model (reflective of trends over time) … The analysis performed at block 540 may use a machine-learning model that has been trained to determine whether and what modifications should be made to the cellular network to improve performance (e.g., meeting the desired QoS metrics of the cellular network)”);
Caswell does not teach but Hirzallah teaches “comparing the analytics data with a target state to generate automation data (Fig.5, automation data associated with the model update provided by model training host 502 to model inference host 504; paragraph no. 0107, “Data can be collected from data sources 506, and may be used as training data 516 for training an ML model, or as inference data 512 for feeding an ML model inference operation. Data sources 506 may collect data from various subject of action 510 entities (such as the UE or the network entity) and provide the collected data to a model training host 502 for ML model training. In some examples, if output 514 provided to agent 508 is inaccurate (or the accuracy is below an accuracy threshold), model training host 502 may provide feedback to model inference host 504 to modify or retrain the ML model used by model inference host 504, such as via an ML model deployment update”; the claimed “automation data” reads on at least the data associated with or constituting the modified ML model output by the model training host 502);
processing the automation data to generate feedback data (Fig. 5, model inference host 504 uses the updated model and generates model performance feedback provided to model training host 520; the claimed “feedback data” reads on the model performance feedback provided by model inference host 504 to model training host 502); and
adjusting the automation data based on the feedback data” (Fig. 5, model training host 502 adjusts/updates the model or at least the data associated with or constituting the updated model based on the model performance feedback; paragraph no. 0107, “model training host 502 may provide feedback to model inference host 504 to modify or retrain the ML model used by model inference host 504, such as via an ML model deployment update”) of claim 1.
It would have been obvious to one of ordinary skill in the art before the effective filing date of this claimed invention to modify Caswell by incorporating the teachings of Hirzallah to measure, assess, and rate the performance of ML models, compare it to expected performance benchmarks, and refine the ML models based on the comparison, as suggested by Hirzallah in paragraph no. 0109.
Regarding independent claims 9 and 17, these independent claims are corresponding apparatus and computer readable medium claims of the method claim 1 and recite similar subject matter. As such, the rationale behind the above rejection of claim 1 applies with equal force to these independent claims and as further amplified below to highlight the minor differences between the claims.
Regarding further independent claims 9 and 17, see Caswell, Fig. 4 and paragraph no. 0041 for the claimed structural elements of the claims.
Dependent Claims
Regarding claims 2, 10, and 18, see Caswell, paragraph no. 0051 for “performance management data.”
Regarding claims 3 and 11, see Caswell, paragraph no. 0051 for a “network element.”
Regarding claims 5 and 13, see Caswell, paragraph no. 0053 for “prescriptive analytics data” and the use of a machine-learning model using historical data.
Regarding claims 8 and 16, Caswell does not teach but Hirzallah teaches “wherein the adjusted automation data reduces a discrepancy between an actual state represented by the analytics data and the target state” (paragraph no. 0107, “Data can be collected from data sources 506, and may be used as training data 516 for training an ML model, or as inference data 512 for feeding an ML model inference operation. Data sources 506 may collect data from various subject of action 510 entities (such as the UE or the network entity) and provide the collected data to a model training host 502 for ML model training. In some examples, if output 514 provided to agent 508 is inaccurate (or the accuracy is below an accuracy threshold), model training host 502 may provide feedback to model inference host 504 to modify or retrain the ML model used by model inference host 504, such as via an ML model deployment update”; the claimed “adjusted automation data” reads on at least the data associated with or constituting the modified ML model output by the model training host 502 where the “target state” reads on the accuracy threshold).
It would have been obvious to one of ordinary skill in the art before the effective filing date of this claimed invention to modify Caswell and Hirzallah by incorporating the additional teachings of Hirzallah to further measure, assess, and rate the performance of ML models, compare it to expected performance benchmarks, and further refine the ML models based on the comparison, as suggested by Hirzallah in paragraph no. 0109.
Claim(s) 7, 15, 20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Caswell and Hirzallah as applied to claims 1, 9, 17 above, and further in view of Rady, US 2024/0220349, (“Rady”).
Regarding claims 7, 15, and 20, Caswell does not teach but Rady teaches “wherein the automation data comprises an auto-healing instruction, and wherein the feedback data comprises a testing result associated with the auto-healing instruction” (Fig. 4, steps 414, 418, and 420; step 414 outputs an auto-healing instruction (“related action(s)”), step 418 performs a test associated with the auto-healing instruction, and step 420 provides feedback).
It would have been obvious to one of ordinary skill in the art before the effective filing date of this claimed invention to modify Caswell and Hirzallah by incorporating the teachings of Rady to facilitate the management of the network health of the cellular network.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. See the abstract of the NPL document entitled “A Unified Framework for Self-Healing in Wireless Networks”.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to WON TAE C. KIM whose telephone number is (571)270-1812. The examiner can normally be reached Monday-Friday 8:00 am - 5:00 pm.
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/WON TAE C KIM/Primary Examiner, Art Unit 2414
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