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 .
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, 8-13 and 16-18 is/are rejected under 35 U.S.C. 103 as being unpatentable over US 2024/0338193 to Bhupati et al. (Bhupati) in view of US 2024/0155025 to Akdeniz et al. (Akdeniz).
Claim 1, 11 and 16: Bhupati discloses a system, comprising:
at least one processor; and
at least one memory that stores executable instructions that, when executed by the at least one processor, facilitate performance of operations, the operations comprising:
adjusting parameters of a machine learning model (par. [0025] “reinforcement learning”, e.g. par. [0026] “compute a penalty”); and
in response to the adjusting, generating a firmware upgrade schedule for a system deployment by applying the machine learning model to deployment data associated with the second system deployment and upgrade data associated with a firmware upgrade to be applied to at least one device of the second telecommunications system deployment (par. [0023] “discovering an optimal schedule by which specific actions are to occur in the software update process”).
Bhupati does not disclose:
adjusting parameters of a first machine learning model based on model parameter data representative of at least one model parameter usable to configure at least one model, the model parameter data having been received from a first telecommunications system deployment, and the model parameter data having been generated by a second machine learning model maintained by the first telecommunications system deployment
Akdeniz teaches:
adjusting parameters of a first machine learning model based on model parameter data representative of at least one model parameter usable to configure at least one model, the model parameter data having been received from a first telecommunications system deployment, and the model parameter data having been generated by a second machine learning model maintained by the first telecommunications system deployment (par. [0115] “computing nodes can locally computer partial gradients from their respective local data sets and communicate the computed partial gradients back to the central node for aggregation”).
It would have been obvious before the effective filing date of the claimed invention to adjust parameters of the first model based on parameter data received from a first telecommunications system deployment. Those of ordinary skill in the art would have been motivated to do so to provide “learning that is collaborative, hierarchical , and that uses distributed datasets” (Akdeniz par. [0023]).
Claims 2, 12 and 17: Bhupati and Akdeniz teach claims 1, 11 and 16, wherein the firmware upgrade schedule comprises an ordered list of devices of the second telecommunications system deployment to be upgraded during the firmware upgrade and a time window for application of the firmware upgrade (Bhupati par. [0023] “discovering an optimal schedule by which specific actions are to occur in the software update process”, e.g. par. [0056] “the schedule indicates … a specific future time window”).
Claims 3, 13 and 18: Bhupati and Akdeniz teach claims 2, 12 and 17, wherein the operations further comprise:
upgrading, during the time window, firmware associated with respective devices of the second telecommunications system deployment in an order defined by the ordered list (Bhupati par. [0026] “At the conclusion of the rollout”).
Claim 8: Bhupati and Akdeniz teach claim 1, wherein the deployment data is of at least one data type selected from a group of data types comprising
a server telemetry type corresponding to server telemetry data representative of performance of a server (Bhupati par. [0063] “the percentage of overall network traffic”),
a server hardware type corresponding to server hardware configuration data representative of a hardware configuration of the server,
a network performance type corresponding to network performance data representative of performance of network equipment of a network (Bhupati par. [0063] “CPU utilization”), and
a network usage pattern type corresponding to network usage pattern data representative of a pattern associated with usage of the network equipment of the network.
Claim 9: Bhupati and Akdeniz teach claim 1, wherein the model parameter data is first model parameter data, and wherein the operations further comprise:
repeating the adjusting of the parameters of the first machine learning model based on second model parameter data generated by a third machine learning model maintained by the second telecommunications system deployment, the second model parameter data being generated by the third machine learning model based on a result of applying the firmware upgrade to the at least one device of the second telecommunications system deployment (Akdeniz par. [0115] “each client … obtains a global model 1204” , par. [0116] “used for future implementations of the NN”).
Claim 10: Bhupati and Akdeniz teach claim 1, wherein the operations further comprise:
receiving the model parameter data from the first telecommunications system deployment without receiving any other data, other than the model parameter data, from the first telecommunications system deployment (Akdeniz par. [0124] “data collected by clients remain at clients”).
Claim(s) 4-7, 14-15 and 19-20 is/are rejected under 35 U.S.C. 103 as being unpatentable over US 2024/0338193 to Bhupati et al. (Bhupati) in view of US 2024/0155025 to Akdeniz et al. (Akdeniz) in view of US 2022/0075613 to Ramachandran et al. (Ramachandran).
Claims 4, 14 and 19: Bhupati and Akdeniz teach claims 2, 12 and 17, wherein the devices of the ordered list are target devices of the second telecommunications system deployment (e.g. Akdeniz par. [0003] “telecommunications”),
Bhupati and Akdeniz do not teach:
the firmware upgrade schedule further comprises a list of respective backup devices of the second telecommunications system deployment to which workloads associated with respective corresponding ones of the target devices are to be offloaded during the firmware upgrade.
Ramachandran teaches:
an upgrade schedule comprising a respective backup devices of a second system deployment to which workloads associated with respective corresponding ones of the target devices are to be offloaded during the firmware upgrade (par. [0094] “migrating all guest virtual machines from the particular node to another node”).
It would have been obvious before the effective filing date of the claimed invention to provide a list of respective backup devices. Those of ordinary skill in the art would have been motivated to do so to avoid interruption of service.
Claims 5, 15 and 20: Bhupati, Akdeniz and Ramachandran teach claims 4, 14 and 19, wherein the operations further comprise:
redirecting, during the time window, the workloads associated with the target devices of the second telecommunications system deployment to respective ones of the backup devices that correspond to the target devices (Ramachandran par. [0094] “migrating all guest virtual machines from the particular node to another node”).
Claim 6: Bhupati, Akdeniz and Ramachandran teach claim 4, wherein the operations further comprise:
selecting, as a backup device of the backup devices corresponding to a target device of the target devices, a computing device located within a same cluster as the target device (e.g. Ramachandran par. [0089] “to be applied to a particular cluster (e.g., the cluster 310)”).
Claim 7: Bhupati, Akdeniz and Ramachandran teach claim 4, wherein a target device of the target devices is associated with a radio access network site (par. [0038] “radio access network (RAN) capable endpoint devices”), and wherein the operations further comprise:
selecting, as a backup device of the backup devices corresponding to the target device, a computing device associated with a data center communicatively coupled to the radio access network site (Ramachandran par. [0019] ”a distributed datacenter”).
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to JASON D MITCHELL whose telephone number is (571)272-3728. The examiner can normally be reached Monday through Thursday 7:00am - 4:30pm and alternate Fridays 7:00am 3:30pm.
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/JASON D MITCHELL/Primary Examiner, Art Unit 2199
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