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 .
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 1-20 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 pre-AIA the applicant regards as the invention.
Regarding Claims 1-4, 6-7, 10, 13, and 15 the term "rapid" in Claims 1-4, 6-7, 10, 13, and 15 is a relative term which renders the claim indefinite. The term "rapid" is not defined by the claim, the specification does not provide a standard for ascertaining the requisite degree, and one of ordinary skill in the art would not be reasonably apprised of the scope of the invention.
Regarding Claims 1 and 12 the term "low-latency" in Claims 1 and 12 is a relative term which renders the claim indefinite. The term "low-latency" is not defined by the claim, the specification does not provide a standard for ascertaining the requisite degree, and one of ordinary skill in the art would not be reasonably apprised of the scope of the invention.
Regarding Claims 5, 8-9, 11, 14, 16-20, Dependent Claims 5, 8-9, 11, 14, 16-20 are rejected under 35 U.S.C. 112(b) for inheriting the deficiencies of Claim 1.
Allowable Subject Matter
Claims 1-20 would be allowable if rewritten or amended to overcome the rejection(s) under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), 2nd paragraph, set forth in this Office action.
The following is a statement of reasons for the indication of allowable subject matter:
In interpreting the currently amended claims, in light of the specification, the Examiner finds the claimed invention to be patentably distinct from the prior art of record.
Regarding Claims 1-20, the closest prior art of record Zhu et al (US 20240031235) in view of BOGINENI et al (US 20200196155) in further view of Zheng et al (US 20200267053) and in even further view of Cmielowski et al (US 20230289650) does not teach a rapid response containerized edge data center apparatus for deployment to an edge location associated with an emergency or rapid response event, the apparatus comprising: a container housing defining an enclosed interior volume, wherein the container housing comprises a shipping container having a configured length; a power control module configured to generate and provide electrical power to the rapid response containerized edge data center apparatus, the power control module including one or more electrical generators and one or more battery arrays; a low-latency edge compute engine comprising one or more server racks included within the enclosed interior volume of the container housing, wherein the low-latency edge compute engine utilizes computational hardware of the one or more server racks to implement local inference for one or more machine learning (ML) or artificial intelligence (AI) applications deployed to the rapid response containerized edge data center apparatus; a local networking communications node configured to create one or more local networks at the edge location using one or more local network wireless communication modalities; a plurality of satellite transceivers coupled to the container housing and configured for communications with one or more satellite internet constellations, wherein the apparatus includes a link bonding engine configured to create a bonded satellite uplink and a bonded satellite downlink using a plurality of individual satellite uplinks and downlinks associated with the plurality of satellite transceivers; and a communications hub engine configured to provide communications relay between devices on the one or more local networks, and configured to connect the devices on the one or more local networks to one or more remote endpoints reachable over the bonded satellite uplink and bonded satellite downlink.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure:
Zhu et al (US 20240031235), Abstract - A method implements a network slicing controller to manage network slicing instances in an edge cloud platform. The method includes receiving at least one policy change from an artificial intelligence powered smart traffic controller (APSTC) or an artificial intelligence powered edge traffic controller (APETC), determining whether the at least one policy change is valid based on local monitoring information, and sending the at least one policy change to a common control network function in a 5G mobile network.
BOGINENI et al (US 20200196155), Abstract - A device receives analytics data associated with management of a network associated with the device, core data associated with a core domain of the network, edge data associated with an edge domain of the network, and radio access network (RAN) data for a RAN associated with the network. The device processes the analytics data, the core data, the edge data, and the RAN data, with a machine learning model, to determine actions to be performed with respect to the core domain of the network, the edge domain of the network, and/or the RAN. The device causes the actions to be performed by one or more core devices associated with the core domain of the network, one or more edge devices associated with the edge domain of the network, and/or one or more RAN devices associated with the RAN.
Zheng et al (US 20200267053), Abstract - Provided are systems, methods, and apparatuses for latency-aware edge computing to optimize network traffic. A method can include: determining network parameters associated with a network architecture, the network architecture comprising a data center and an edge data center; determining, using the network parameters, a first programmatically expected latency associated with the data center and a second programmatically expected latency associated with the edge data center; and determining, based at least in part on a difference between the first programmatically expected latency or the second programmatically expected latency, a distribution of a workload to be routed between the data center and the edge data center.
Cmielowski et al (US 20230289650), Abstract - A continuous machine learning system includes a data generator module, a pipeline search module, a pipeline refinement module, and a pipeline training module. The data generator module obtains raw training data defining a total data size and generates a plurality of data batches from the raw training data. The pipeline search module obtains an initial data batch from among the plurality of data batches and determines a best machine learning model pipeline among a plurality of machine learning model pipelines based on the initial data batch. The pipeline refinement module receives the best machine learning model pipeline and refines the best machine learning model pipeline to generate a refined pipeline that consumes the plurality of data batches. The pipeline training module incrementally trains the refined pipeline using remaining data batches among the plurality of data batches generated after the initial data batch.
MOATTI et al (US 20240104368), Abstract - A hub of a computing environment obtains a training set from an edge of the computing environment. The training set that is obtained includes data from the edge and is used to train a neural network at the hub. The training of the neural network provides a detector and a generator at the hub. A determination is made as to whether the training of the neural network is complete. Based on determining that the training of the neural network is complete, the detector is sent to the edge. The detector at the edge is to facilitate suppression of additional edge data to the hub based on the detector at the edge determining that the additional edge data is statistically similar, based on one or more selected criteria, to data used to train the neural network.
Hari (US 20200089515), Abstract - A method that involves receiving budget information of a containerized application deployed with a set of containers to a first cloud provider service of a set of cloud provider services; receiving pricing information from each cloud provider service of the set of cloud provider services, wherein the set of cloud provider services includes the first cloud provider service and a second cloud provider service; receiving performance information of the containerized application from the first cloud provider service; generating an output vector from a machine learning model, wherein the machine learning model uses the pricing information and the performance information to generate the output vector; determining a first cloud provider service cost and a second cloud provider service cost based on the output vector and the pricing information; migrating the containerized application from the first cloud provider service to the second cloud provider service.
Aftab et al (US 20190325353), Abstract - A method may include a processing system having at least one processor for receiving a first machine learning model, the first machine learning model in a first format associated with a first development environment, adapting the first machine learning model to a containerized environment, validating the first machine learning model according to at least one validation criterion associated with a repository, and publishing the first machine learning model to the repository.
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/RAQIUL A CHOUDHURY/Examiner, Art Unit 2444
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