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
This action is in response to an application filed July 22, 2025. Claims 1-20 are pending in this application.
Claim Rejections - 35 USC § 102
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.
Claim(s) 1-20 is/are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Jeuk et al. (US 2021/0392049 A1).
With respect to claim 1, Jeuk discloses a benchmarking method for enabling communication between data sources and an edge computing device via a gateway system ([0033]), the method comprising:
receiving data from a plurality of sources, the plurality of data sources comprising data associated with a resource site (Abstract and [0014], receive operational data from various nodes of network topology);
provisioning a computing model configured to characterize one or more features or properties associated with the resource site, the computing model being adaptable or configurable to be used on one of a first edge computing device or a cloud computing device ([0018] and [0054], generate a logical model for deployment);
determining a first computational tool associated with the first edge computing device, the first computational tool being operable to:
automatically extract device data associated with the first edge computing device ([0048], using service level agreements to extract information and provide it as input parameters for machine-learning engine), and
quantize the computing model to optimally perform on the first edge computing device ([0050], receive performance level feedback of individual nodes within network topology);
quantizing, using the first computational tool, the computing model and thereby generate a first quantized model with attendant benchmark data that indicates an efficacy of the computing model on the first edge computing device when the computing model is successfully deployed via the gateway system coupling the plurality of sources to the first edge computing device ([0020], [0022]-[0023], and [0050], obtain performance of deployed trained machine-learning models within network topology); and
generating a report indicating image or textual information associated with how the computing model performs on the first edge computing device after deployment to the first edge computing device, the report being visualized on a graphical display device ([0014]. [0020], [0042], [0050], and [0067], output deployment performance levels to determine whether model should be updated; machine-learning engine may include visualization tool for data visualization).
With respect to claim 2, Jeuk discloses the method of Claim 1, wherein the computing model is a computer vision model ([0067]).
With respect to claim 3, Jeuk discloses the method of Claim 1, wherein the device data comprises one or more of:
hardware data of the first edge computing device; software data of the first edge computing device; or firmware data of the first edge computing device ([0016]).
With respect to claim 4, Jeuk discloses the method of Claim 3, wherein the hardware data comprises one of: graphical display unit data associated with the first edge computing device; or central processing unit data associated with the first edge computing device ([0070]).
With respect to claim 5, Jeuk discloses the method of Claim 1, wherein quantizing the computing model comprises stripping or trimming parameters of the computing model that contribute to model inefficiencies of the computing model on the first edge computing device ([0021]).
With respect to claim 6, Jeuk discloses the method of Claim 1, wherein quantizing the computing model comprises optimizing the computing model to be compatible with one or more hardware accelerators of the first edge computing device ([0018]).
With respect to claim 7, Jeuk discloses the method of Claim 1, wherein quantizing the computing model comprises:
determining a computation float point for the computing model based on the extracted device data ([0108]); and
automatically configuring the computing model to quickly execute computing operations associated with the resource site on the first edge computing device ([0068]).
With respect to claim 8, Jeuk discloses the method of Claim 1, wherein the first computational tool comprises one of an application programming interface (API) or a compiler ([0047]).
With respect to claim 9, Jeuk discloses the method of Claim 1, wherein the first computational tool comprises a computing platform configured for developing deep learning models ([0076]).
With respect to claim 10, Jeuk discloses the method of Claim 1, wherein the computing model comprises a machine learning model or an artificial intelligence model ([0070]).
With respect to claim 11, Jeuk discloses the method of Claim 1, wherein the gateway system comprises an Agora gateway system ([0029]).
With respect to claim 12, Jeuk discloses the method of Claim 1, wherein the gateway system is configured or coupled to sensor systems associated with collecting data at the resource site ([0043]).
With respect to claim 13, Jeuk discloses the method of Claim 1, wherein the resource site comprises a resource site associated with energy development ([0104]).
With respect to claim 14, Jeuk discloses the method of Claim 1, wherein the attendant benchmark data indicates baseline performance data associated with implementing the computing model on the cloud computing device ([0020]).
With respect to claim 15, Jeuk discloses the method of Claim 1, further comprising:
determining a second computational tool associated with a second edge computing device ([0041]);
quantizing, using the second computational tool, the computing model and thereby generate a second quantized model ([0050]); and
deploying the second quantized model via the gateway system to the second edge computing device, the second edge computing device being operable to rapidly execute a plurality of computing operations associated with the resource site using the second quantized model (Abstract).
With respect to claim 16, Jeuk discloses the method of Claim 15, wherein the first edge computing device and the second edge computing device are distinct from each other based on at least one of:
first hardware of the first edge computing device being different from second hardware of the second edge computing device ([0033]-[0034]);
first firmware of the first edge computing device being different from second firmware of the second edge computing device ([0033]-[0034]); or
first software of the first edge computing device being different from second software of the second edge computing device ([0033]-[0034]).
With respect to claim(s) 17-20, the system of claim(s) 17-20 does/do not limit or further define over the method of claim(s) 1-16. The limitations of claim(s) 17-20 is/are essentially similar to the limitations of claim(s) 1-16. Therefore, claim(s) 17-20 is/are rejected for the same reasons as claim(s) 1-16. Please see rejection above.
Conclusion
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/ESTHER B. HENDERSON/Primary Examiner, Art Unit 2458 September 17, 2026
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