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 .
Double Patenting
The nonstatutory double patenting rejection is based on a judicially created doctrine grounded in public policy (a policy reflected in the statute) so as to prevent the unjustified or improper timewise extension of the “right to exclude” granted by a patent and to prevent possible harassment by multiple assignees. A nonstatutory double patenting rejection is appropriate where the conflicting claims are not identical, but at least one examined application claim is not patentably distinct from the reference claim(s) because the examined application claim is either anticipated by, or would have been obvious over, the reference claim(s). See, e.g., In re Berg, 140 F.3d 1428, 46 USPQ2d 1226 (Fed. Cir. 1998); In re Goodman, 11 F.3d 1046, 29 USPQ2d 2010 (Fed. Cir. 1993); In re Longi, 759 F.2d 887, 225 USPQ 645 (Fed. Cir. 1985); In re Van Ornum, 686 F.2d 937, 214 USPQ 761 (CCPA 1982); In re Vogel, 422 F.2d 438, 164 USPQ 619 (CCPA 1970); In re Thorington, 418 F.2d 528, 163 USPQ 644 (CCPA 1969).
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Claims 1-20 are provisionally rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1-18 of US Patent No. 12,033,006, hereinafter Nair, in view of Rosenstein et al. (US 2022/0060455, hereinafter Rosenstein).
Regarding claim 1,
Claim 1 of Instant Application
Claim 1 of Nair
An apparatus of a containerized edge data center unit, comprising:
at least one memory; and
at least one processor coupled to the at least one memory, the at least one processor configured to:
A method comprising:
transmit, from the containerized edge data center unit, a request corresponding to a pre-
trained machine learning model;
transmitting, from a containerized edge data center unit, a request corresponding to a pre-trained machine learning model;
receive, from a cloud management platform and by the containerized edge data center
unit, the pre-trained machine learning model, wherein the containerized edge data center unit is deployed to an edge location and configured to obtain one or more sensor data streams at the edge location;
receiving, from a cloud management platform and by the containerized edge data center unit, the pre-trained machine learning model, wherein the containerized edge data center unit is deployed to an edge location and configured to obtain one or more sensor data streams at the edge location;
transmit, from the containerized edge data center unit, one or more batch uploads of
information associated with inference performed by the containerized edge data center unit using the pre-trained machine learning model and the one or more sensor data streams, wherein the one or more batch uploads of information are transmitted to the cloud management platform;
transmitting, from the containerized edge data center unit, one or more batch uploads of information associated with inference performed by the containerized edge data center unit using the pre-trained machine learning model and the one or more sensor data streams, wherein the one or more batch uploads of information are transmitted to the cloud management platform;
receive, by the containerized edge data center unit, an updated machine learning model generated by the cloud management platform responsive to the one or more batch uploads of information, wherein the updated machine learning model is based on retraining or finetuning of the pre-trained machine learning model with the one or more batch uploads of information; and
receiving, by the containerized edge data center unit, an updated machine learning model generated by the cloud management platform responsive to the one or more batch uploads of information, wherein the updated machine learning model is based on retraining or finetuning of the pre-trained machine learning model with the one or more batch uploads of information; and
perform, by the containerized edge data center unit, localized instruction tuning of the updated machine learning model to a specific task of the inference performed by the containerized edge data center unit, wherein the instruction tuning is based on domain-specific information obtained from a local data store of the containerized edge data center unit.
performing, by the containerized edge data center unit, localized instruction tuning of the updated machine learning model to a particular task of the inference performed by the containerized edge data center, wherein the instruction tuning is based on domain-specific information obtained from a local data store of the containerized edge data center unit.
Nair does not disclose an apparatus of a containerized edge data center unit, comprising: at least one memory; and at least one processor coupled to the at least one memory. Rosenstein discloses an apparatus of a containerized edge data center unit, comprising: at least one memory; and at least one processor coupled to the at least one memory (Fig. 1 edge device including processor and memory). It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the teachings of Nair by implementing processor and memory of Rosenstein. The motivation would have been to provide the necessary computational resources and storage capacity to execute software instructions, process data locally, and manage the operations of the containerized edge data center unit.
The dependent claims 2-20 of the instant application discloses the exact same functional limitations as the dependent method claims 2-20 of Nair, merely drafted in an apparatus format.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant’s disclosure.
Duggal et al. (US 2024/0354567) discloses “performing local tuning of an AI model using domain-specific information obtained from a local data store (e.g., tuning a local LLM using code samples and logs from a local code repository)” (paragraphs [0182] and [0250]). This is pertinent to the claimed concepts of containerized edge deployments and performing localized tuning using a local data store as in claims 1, 9, and 20.
Rosenstein et al. (US 2022/0060455) discloses “an edge computing device that collects sensor data, packages it as retraining data, and sends it to a cloud server; the cloud server then retrains the AI model, packages the updated model into a container, and securely transmits it back to the edge device for execution” (paragraphs [0027] and [0030]). This is pertinent to the claimed sequence of sending batch uploads to a cloud management platform and receiving an updated, retrained model back at the edge as in claims 1 and 12-13.
Wu et al. (US 2023/0153622) discloses “detecting when the data distribution of an inference data set drifts from the original training data set, and triggering a model update using the newly collected inference data” (paragraphs [0007], [0116]-[0122]). This is pertinent to the claimed features of transmitting information associated with inference performed at the edge to trigger model retraining/finetuning as in claims 1 and 14-15.
Dey et al. (US 2022/0375199) discloses “partitioning and configuring neural network layers across different processing elements to balance latency and accuracy” (paragraphs [007] and [0059]). This is pertinent to the claimed features of qualifying or modifying ML/AI applications based on computational hardware configuration requirements as in Claims 4-8 and 18-19.
Zhang et al. (US 2020/0175334) discloses “reducing the computational complexity of a neural network (e.g., using depthwise and pointwise layers) to accommodate the memory and processing limits of an edge device” (paragraphs [0003] and [0030]). This is pertinent to the claimed concept of performing model pruning on an updated machine learning model based on the respective computational hardware deployment of the edge unit as in claims 18-19.
Song et al. (US 2020/0160207) discloses “an edge device that monitors the predictive output of an ML model, determines when the performance/accuracy degrades below a threshold, and transmits information regarding the degraded performance to a central cloud server to trigger an update” (paragraphs [0005] and [0044]). This is pertinent to the claimed features of receiving updated models based on a determination that the inference performance metrics of the edge unit have fallen below a configured threshold as in claims 14-17.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to SISLEY N. KIM whose telephone number is (571)270-7832. The examiner can normally be reached M-F 11:30AM -7:30PM.
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/SISLEY N KIM/Primary Examiner, Art Unit 2196 9/8/2026
