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 .
This action is responsive to the claims filed 3/27/2023.
Claims 1-20 are presented for examination.
Information Disclosure Statement
The information disclosure statement (IDS) submitted 9/27/2023 has been considered by the examiner.
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.
Claims 1-2, 5, 7-9, 12, 14-15, 18 and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Chopra et al. (hereinafter Chopra), US 2021/0385233 A1, in view of Woerner et al. (hereinafter Woerner), US 2020/0394276 A1.
Regarding independent claim 1, Chopra teaches a system for dynamically analyzing and configuring nodes of a distributed network ([0011] system for anomaly detection and deploying customized anomaly detection on edge devices through a distributed communication network), the system comprising ([0007], [0049] present invention provides for systems such as computing platform 500): a memory device with computer-readable program code stored thereon ([0050] memory 502 storing instructions 510); a communication device ([0051] communications module); and a processing device operatively coupled to the memory device and the communication device ([0050]-[0051] processing device 504 coupled to memory 502 and communications module on computing platform 500), wherein the processing device is configured to execute the computer-readable program code to ([0050] processing device 504 configured to execute the instructions 510): identify one or more nodes of a distributed register network ([0011] communicating with and identifying "edge devices" (nodes) of a distributed network where the edge devices are identified (of a distributed register network)); extract metadata from the one or more nodes ([0008] present invention provides for fetching configure-related information from edge devices); continuously monitor the one or more nodes to detect changes to the one or more nodes ([0007]-[0011] a system configured to "detect anomalies occurring at a device" using models. It describes an "adaptive application" that monitors "attributes captured on the edge device" to detect these anomalies); parse the metadata and the changes to a deep learning network ([0007]-[0011] utilizing "Deep Learning (DL) anomaly detection models". It describes converting (parsing) "master DL anomaly detection models" based on the "device configuration information" (metadata)); determine, via the deep learning network, anomalies associated with the one or more nodes based on the changes ([0007]-[0011] the Deep Learning models are "configured to detect anomalies occurring at a device"); determine target metrics ([0025] teaches selecting these factors based on "ability to influence a prediction" (target metrics)) and factors associated with the anomalies ([0025] teaches determining "Factors" by "selecting predictors" (factors) in DL anomaly detection based on their ability to influence predictions, FIG. 2 explicitly lists "Reduction in Predictors/Variables Factors"); and determine configuration for the one or more nodes ([0007]-[0011] teaches determining "customized... models" (configurations) specific to the device).
Chopra does not expressly teach a system leveraging photonic quantum computing; create simulation test scenarios based on the target metrics, factors, and the anomalies, wherein the simulation test scenarios are associated with a plurality of configurations of the one or more nodes; execute the simulation test scenarios in parallel, via a quantum computer; and determine an optimal configuration for the one or more nodes from the plurality of configurations based on executing the simulation test scenarios.
However, Woerner teaches a system leveraging photonic quantum computing ([0001]-[0003] teaches a system for "simulation-based optimization on a quantum computer" that operates on quantum physics and suggests the broader class of computing to which photonic quantum computing belongs); create simulation test scenarios (ABSTRACT, [0022]-[0023] teaches a "simulator" that simulates a "decision-making problem" (scenario)) based on target metrics, factors ([0019]-[0021] teaches simulating the problem "associated with a set of parameters" (factors) and estimating an "objective function" (metrics)), and anomalies ([0039] estimates an unknown parameter associated with the decision-making problem), wherein the simulation test scenarios are associated with a plurality of configurations ([0029]-[0031] describes optimizing a "configuration of a quantum state" or using parameters to simulate a problem); execute the simulation test scenarios in parallel ([0016]-[0021] teaches that the quantum processor operates on the "superposition principle," allowing qubits to represent multiple values "at the same time" (parallelism), and achieves a "quadratic speedup" over classical simulation), via a quantum computer (ABSTRACT explicitly teaches "simulation-based optimization on a quantum computer" and using a "quantum processor"); and determine an optimal configuration from the plurality of configurations based on executing the simulation test scenarios ([0037]-[0039], [0044]-[0050] teaches performing an optimization process to determine an "optimal parameter" or "continuous parameter" that maximizes/minimizes the objective function).
Because Chopra and Woerner address the issue of utilizing factors and metrics to solve for unknowns/anomalies and to determining configurations, accordingly, it would have been obvious to one of ordinary skill in the art before the effective filing data of the claimed invention to incorporate the teaches of a system leveraging photonic quantum computing; create simulation test scenarios based on target metrics, factors, and anomalies, wherein the simulation test scenarios are associated with a plurality of configurations; execute the simulation test scenarios in parallel, via a quantum computer; and determine an optimal configuration from the plurality of configurations based on executing the simulation test scenarios as suggested by Woerner into Chopra’s system, with a reasonable expectation of success, such that the determined factors and metrics become the objective function and parameters used by quantum amplitude estimation and classical optimization loop, allowing the process to select the appropriate remedy, e.g. optimal configuration, for anomaly detection to teach a system leveraging photonic quantum computing; create simulation test scenarios based on the target metrics, factors, and the anomalies, wherein the simulation test scenarios are associated with a plurality of configurations of the one or more nodes; execute the simulation test scenarios in parallel, via a quantum computer; and determine an optimal configuration for the one or more nodes from the plurality of configurations based on executing the simulation test scenarios. This modification would have been motivated by the desire to optimize a quantum computer and quantum computing process such as a quantum amplitude estimation to achieve a quadratic speedup as compared to other classical simulation techniques (Woerner [0018]-[0019]).
Regarding claim 2, Chopra, in view of Woerner the system according to claim 1, wherein the processing device is further configured to execute the computer-readable program code to: route the optimal configuration to the distributed register network (see Chopra FIG. 3 block 640 “Translate, for Each Edge Device, the one or more customized DL Anomaly Detection Models (configuration) to an Edge Device-Specific Format Executable on the Corresponding Edge Device” on the distributed network where the edge nodes are identified (route to the distributed register network) wherein Woerner [0045] teaches determining optimal parameters for a solution (optimal configuration)); and deploy the optimal configuration to the one or more nodes of the distributed register network (see Chopra FIG. 3 block 650 “Deploy, on Each of the Edge Devices, the One or More Translated and Customized DL Anomaly Detection Models/Application for Execution on the Corresponding Edge Device” on the distributed network where edge nodes are identified (and deploy the configuration to the one or more nodes of the distributed register network) wherein Woerner [0045] teaches determining optimal parameters for a solution (optimal configuration)).
Regarding dependent claim 5, Chopra, in view of Woerner, teach the system according to claim 1, wherein the processing device is configured to determine type of the one or more nodes based on the metadata extracted from the one or more nodes (see Chopra [0011]-[0015], [0053] teaches that the system communicates with edge devices to retrieve "information from each edge device that identifies configuration of a corresponding edge device", this information (metadata) includes make and model information, the system uses this information to identify the device configuration/capabilities, which implicitly and explicitly identifies the device type (e.g., mobile device, PC/laptop, router) or “the customized DL anomaly detection application may be identified by an Internet Protocol or the like”. The selection of the appropriate master DL model (the first step in creating the configuration) is based on this information).
Regarding dependent claim 7, Chopra, in view of Woerner, teach the system according to claim 1, wherein the quantum computer is a photonic quantum computer (see Woerner ABSTRACT, [0016]-[0018] teaches the core elements of using a "quantum computer" or "quantum processor" for simulation-based optimization. The quantum processor utilizes principles like superposition and entanglement. Since photonic quantum computing is a specific physical implementation of the broader "quantum computer", the specific term “photonic quantum computer” is suggested to one of ordinary skill in the art as an applicable alternative hardware platform).
Regarding claims 8-9 and 12, these are computer program product claims that are substantially the same as the system of claims 1-2 and 5, respectively. Thus, claims 8-9 and 12 are rejected for the same reasons as claims 1-2 and 5. In addition, Chopra teaches a computer program product dynamically analyzing and configuring nodes of a distributed network ([0011], [0050]-[0051] instructions 510 executed by processing device 501 to implement system for anomaly detection and deploying customized anomaly detection on edge devices of a distributed communication network), the computer program product comprising at least one non-transitory computer readable medium having computer-readable program code portions embodied therein ([0050] the instructions 510 stored in memory 502), the computer-readable program code portions comprising executable portions for ([0050] processing device 504 configured to execute the instructions 510).
Regarding claims 14-15, 18, and 20, these are computer-implemented method claims that are substantially the same as the system of claims 1-2, 5, and 7, respectively. Thus, claims 14-15, 18, and 20 are rejected for the same reasons as claims 1-2, 5, and 7.
Claims 3, 10, and 16 are rejected under 35 U.S.C. 103 as being unpatentable over Chopra in view of Woerner, as applied in the rejections of claims 1, 8, and 14 above, and further in view of Friman et al. (hereinafter Friman), “Service exposure: a critical capability in a 5G world”, Ericsson Technology Review (2019) pages 18-27.
Regarding dependent claim 3, Chopra, in view of Woerner, teach the system according to claim 1, wherein the processing device is further configured to execute the computer-readable program code to determine the optimal configuration (see Chopra [0011], [0050]-[0051] wherein processing device 501 to execute instructions 510 to implement deploying customized anomaly detection (determine the configuration) wherein Woerner [0045] teaches determining optimal parameters for a solution (optimal configuration)).
Chopra and Woerner do not express teach determine the optimal configuration based at least on determining an exposure rating associated with the plurality of configurations.
However, Friman teaches determine an optimal configuration based at least on determining an exposure rating associated with the plurality of configurations (pages 4, 6, 9 suggest the necessary constraint that a configuration optimization must specifically assess and control the security risk associated with exposed network configurations, e.g. “There are three main types of service exposure in a telecon environment”. This supports using an “exposure rating” for selecting configurations).
Because Chopra, in view of Woerner, and Friman address the issue of determining optimal configurations, accordingly, it would have been obvious to one of ordinary skill in the art before the effective filing data of the claimed invention to incorporate the teachings to determine an optimal configuration based at least on determining an exposure rating associated with the plurality of configurations as suggested by Friman into Chopra and Woerner’s system, with a reasonable expectation of success, such that the when determining the optimal parameters for the solution service exposure types in a telecon environment are considered. This modification would have been motivated by the desire to add service exposure making it possible to interact with the data streams through local breakout for optimization functions (Friman page 6).
Regarding claim 10, this is a computer program product claim that is substantially the same as the system of claim 3. Thus, claim 10 is rejected for the same reason as claim 3.
Regarding claims 16, this is a computer-implemented method claim that is substantially the same as the system of claim 3. Thus, claim 16 is rejected for the same reason as claim 3.
Claims 4, 11, and 17 are rejected under 35 U.S.C. 103 as being unpatentable over Chopra in view of Woerner, as applied in the rejections of claims 1, 8, and 14 above, and further in view of Jia et al. (hereinafter Jia), “Semi-analytical configuration optimization of geocentric gravitational wave observatory”, Acta Astronautica (2023 Jan 1) 202:522-34.
Regarding dependent claim 4, Chopra, in view of Woerner, teach the system according to claim 1, wherein the processing device is further configured to execute the computer-readable program code to determine the optimal configuration (see Chopra [0011], [0050]-[0051] wherein processing device 501 to execute instructions 510 to implement deploying customized anomaly detection (determine the configuration) wherein Woerner [0045] teaches determining optimal parameters for a solution (optimal configuration)).
Chopra and Woerner do not express teach determine the optimal configuration based at least on determining a stability rating associated with the plurality of configurations.
However, Jia teaches determine the optimal configuration based at least on determining a stability rating associated with the plurality of configurations (Abstract, Section 1, Section 2.2, Section 3 emphasizes that long-term stability of the configuration is key to success in missions like TianQin. The overall configuration optimization problem is defined as finding orbital elements such that performance indexes related to geometric stability satisfy mission constraints and defines specific parameters of stability used to evaluate configurations thus, providing a technical measurement framework for defining a "stability rating" in the context of configuration optimization for distributed satellite missions).
Because Chopra, in view of Woerner, and Jia address the issue of determining optimal configurations for distributed objects, accordingly, it would have been obvious to one of ordinary skill in the art before the effective filing data of the claimed invention to incorporate the teachings to determine the optimal configuration based at least on determining a stability rating associated with the plurality of configurations as suggested by Jia into Chopra and Woerner’s system, with a reasonable expectation of success, to provide technical measurement framework for defining a "stability rating" in the context of configuration optimization for distributed satellite missions (which are analogous to distributed network nodes, such as those discussed in Chopra). This modification would have been logically driven by the necessity of ensuring that the deployed configuration (found via the optimal parameter in Woerner's simulation) is not only anomalous-free but also stable over time, as quantified by metrics relevant to the physical components and network operation (Jia Section 1).
Regarding claim 11, this is a computer program product claim that is substantially the same as the system of claim 4. Thus, claim 11 is rejected for the same reason as claim 4.
Regarding claims 17, this is a computer-implemented method claim that is substantially the same as the system of claim 4. Thus, claim 17 is rejected for the same reason as claim 4.
Claims 6, 13, and 19 are rejected under 35 U.S.C. 103 as being unpatentable over Chopra in view of Woerner, as applied in the rejections of claims 1, 8, and 14 above, and further in view of Faical et al. (hereinafter Faical), “A Cyber-Physical System's Roadmap to Last-Mile Delivery Drones”, IEEE Aerospace and Electronic Systems Magazine (2023 Jan 26) 38(5):6-16.
Regarding dependent claim 6, Chopra, in view of Woerner, teach all the elements of claim 1.
Chopra and Woerner do not expressly teach wherein the processing device is configured to control the one or more nodes based on at least one of geo fencing and temporal fencing.
However, Faical teaches wherein the processing device is configured to control the one or more nodes based on at least one of geo fencing and temporal fencing (pages 13-14 system relies on the Tradable Permit Model (wherein the processing device is configured to), where a permit is defined as an authorization to use a specific volume in the airspace for a specific timespan, that is, a fencing block. The drones are controlled (control the one or more nodes) by the necessity of adhering to the permits they acquire the configurations they are allowed to use. Pages 7-8 describes a Geofencing Service as one of the six main concerns of the proposed Cyber-Physical System (based on at least one of geo fencing)).
Because Chopra, in view of Woerner, and Faical address the issue of a processing device configured with one or more nodes, accordingly, it would have been obvious to one of ordinary skill in the art before the effective filing data of the claimed invention to incorporate the teachings wherein the processing device is configured to control the one or more nodes based on at least one of geo fencing and temporal fencing as suggested by Faical into Chopra and Woerner’s system, with a reasonable expectation of success, such that the processing device includes a tradable permit model that controls the edge devices based on a geofencing service. This modification would have been motivated by the desire to enable the system to address issues with usage of drones for delivery purposes (Faical page 6).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Yellick et al. (US 2022/0069976 A1) (Mar. 3, 2022) (ABSTRACT A node in a blockchain network may generate a configuration override for the blockchain network, approve the configuration override by the blockchain network, transmit the approval for the configuration override to peers in the blockchain network, and submit the configuration override to the blockchain network for validation. The validation will be based on the configuration override matching the approval).
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/KC CHEN/Primary Patent Examiner, Art Unit 2143