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
Claims 1-20 are pending.
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 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.
Claim Objections
The first claim is listed as “A1.” It should be labeled as “1.” and will be treated as such (i.e. claim 1, not A1). Appropriate correction is required.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter.
Claims 1-20 are directed systems that when interpreted in light of the specification may read on software alone which is non-statutory. In order to comply, the claimed systems must explicitly comprise hardware (e.g. a processor, memory) so they may not be reasonably be interpreted as software alone.
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 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.
Claim 1 recites, “based on the outcomes of decisions…the decisions” but this lacks clear and proper antecedent basis as the claim only previous recites, “dynamic membership functions for decision-making” and does not recite any outcomes or even decisions; only the functions to make decisions.
Claims 1, 5, 6, 12, and 16-20 use the relative terms “optimize” and/or “optimal” that render the claims indefinite. In other words, the terms “optimize” and “optimal” may have different meanings in different contexts (e.g. optimal weather for skiing is different than the optimal weather for swimming) and differing scopes based on the reader (e.g. one person’s optimal swimming weather may be different than another person’s anyways). Therefore, the claim language cannot be given a consistent broadest reasonable interpretation as currently constructed.
Claim Rejections - 35 USC § 103
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 1-11 and 13-15 are rejected under 35 U.S.C. 103 as being unpatentable over Pedersen (US Pub. No. 2020/0364583) in view of Chung-Ju et al (US Pat. 6,067,287), hereafter, “Chung-Ju.”
As to claim 1, Pedersen disclose a system for managing Internet of Things (IoT) networks (Abstract), comprising:
a network performance monitor configured to collect real-time data on network performance metrics and contextual information ([0224]-[0228] and [0237], particularly, “As also indicated at 1102 of FIG. 11, telecommunication network sensor signal analysis may include multiple communication parameters as discussed above. FIG. 11 identifies, without limitation, multiple such parameters including communication links error rates, signal-to-noise ratios, traffic congestion delays, lack of telecommunication system response, reported link outages, reported processing node outages, reported storage outages and reported subnetwork failures as discussed above.”);
an adaptive fuzzy logic engine (AFLE) configured to utilize membership functions for decision-making regarding network management tasks based on input from the network performance monitor ([0248]-[0251], particularly, “In the above example of FIG. 13, the composite warning and control index depends on the degrees of membership of the audio signal analysis “or” the video signal analysis. The conjunctive relation “or” corresponds to the logical intersection of the two sets corresponding to the audio and video variables. In this case the appropriate DOM is the maximum DOM for each of the sets at the specified time.” and [0256], particularly, “For example, while the above example is limited to two variables, audio and video, clearly, for some embodiments, additional tables may be constructed to include other important variables in the decision process. Multidimensional tables may be constructed with more than two variables to reflect additional indices. Exemplary other parameters may include, the results of analysis for medical, process, followed IoT sensor monitor units and telecommunication network analysis as described above” and [0278]-[0279], particularly, “As shown in FIG. 22, the operations of fuzzy logic inference engine 2201 include access to the artificial intelligence expert system knowledge base 2205 which may include the fuzzy logic rules discussed above. The fuzzy logic operations include the fuzzifier 2202 used to establish degree of memberships DOMs as discussed above.”);
a learning module configured to adapt the rules of the AFLE based on the outcomes of decisions to optimize future network performance ([0257], “However, clearly a simpler artificial intelligence expert system implementation is desirable. In another embodiment of this invention, a hierarchical artificial expert and/or fuzzy logic system is disclosed that reduces the increased size of the inference rule data base with addition of more variables from exponential growth to linear growth. Hierarchical fuzzy system designs are discussed, for example, in the G. Raju, L. Wang and D. Wang references cited above in the identification of prior art in this patent. In addition, the hierarchical systems and methods of this invention implement MIMO (Multiple input-Multiple Output) operations with intermediate evaluation of dangerous situations permitting response to such situations in addition to providing evaluation of the levels of concern or dangerous situations for the combination of considered variables. In some embodiments, adaptive feedback control is provided to further improve hierarchical system control and processing of input signals.”); and
an action executor configured to implement the decisions made by the AFLE to adjust network configurations ([0279]; see also [0205]-[0206] describing “corrective actions” i.e. adjusting network configurations).
However, Pedersen does not explicitly disclose the member functions are dynamic and necessarily adapting the membership functions.
But, Chung-Ju discloses an adaptive fuzzy logic engine (AFLE) configured to utilize dynamic membership functions (column 3, lines 2-49, particularly, “The proposed NFCAC is an integrated connection admission control (CAC) that combines their benefits and solves their difficulties. It can automatically construct the rule structure and the membership functions by learning the training examples itself. Moreover, NFCAC provides rich information about the way it works and is easy to be trained. Simulation results show that the proposed NFCAC saves a large amount of training time, simplifies the design procedure, and provides a superior system utilization, while keeping the QoS construct, over both the neural network and the fuzzy logic system.”) and
a learning module configured to adapt membership functions and rules of the AFLE based on outcomes of decisions to optimize future network performance (column 3, lines 2-49, particularly, “The proposed NFCAC is an integrated connection admission control (CAC) that combines their benefits and solves their difficulties. It can automatically construct the rule structure and the membership functions by learning the training examples itself. Moreover, NFCAC provides rich information about the way it works and is easy to be trained. Simulation results show that the proposed NFCAC saves a large amount of training time, simplifies the design procedure, and provides a superior system utilization, while keeping the QoS construct, over both the neural network and the fuzzy logic system.”).
Therefore it would have been obvious to one of ordinary skill in the art prior to the effective filing date of the application to combine the teachings of Pedersen and Chung-Ju in order to provide a system with greater adaptability so that it could be used in a broader and larger variety of systems thereby increasing market size.
As to claim 2, the teachings of Pedersen and Chung-Ju as combined for the same reasons set forth in claim 1’s rejection further disclose the network performance metrics include at least one metric selected from the group consisting of latency, bandwidth utilization, packet loss rates, and device connectivity status (Pedersen, [0224]-[0228] and [0237] and Chung-Ju, Abstract).
As to claim 3, the teachings of Pedersen and Chung-Ju as combined for the same reasons set forth in claim 1’s rejection further disclose the contextual information includes information selected from the group consisting of device density, time-of-day usage patterns, and historical network performance data (Pedersen, [0224]-[0229] and [0237] and Chung-Ju, Abstract).
As to claim 4, the teachings of Pedersen and Chung-Ju as combined for the same reasons set forth in claim 1’s rejection further disclose the dynamic membership functions are configured to adjust shapes and parameters based on real-time data collected by the network performance monitor (Chung-Ju, column 3, lines 2-49).
As to claim 5, the teachings of Pedersen and Chung-Ju as combined for the same reasons set forth in claim 1’s rejection further disclose the learning module employs online learning algorithms to optimize the parameters of the dynamic membership functions (Chung-Ju, column 3, lines 2-49).
As to claim 6, the teachings of Pedersen and Chung-Ju as combined for the same reasons set forth in claim 1’s rejection further disclose the learning module employs evolutionary strategies to optimize the parameters of the dynamic membership functions (Chung-Ju, column 3, lines 2-49).
As to claim 7, the teachings of Pedersen and Chung-Ju as combined for the same reasons set forth in claim 1’s rejection further disclose the adaptive fuzzy logic engine (AFLE) is further configured to evaluate network conditions using a set of fuzzy logic rules that adjust dynamically based on the adaptive membership functions (Chung-Ju, column 3, lines 2-49).
As to claim 8, the teachings of Pedersen and Chung-Ju as combined for the same reasons set forth in claim 1’s rejection further disclose the action executor is configured to adjust at least one item selected from the group consisting of routing of data packets, allocation of bandwidth, and prioritization of devices, based on their needs and the overall state of the network (Pedersen, [0205]-[0206] and Chung-Ju, column 3, lines 2-49).
As to claim 9, the teachings of Pedersen and Chung-Ju as combined for the same reasons set forth in claim 1’s rejection further disclose a user interface module configured to present the network performance data and decisions made by the AFLE in a user-accessible format (Pedersen, [0200]-[0202]).
As to claim 10, the teachings of Pedersen and Chung-Ju as combined for the same reasons set forth in claim 1’s rejection further disclose the AFLE further incorporates neural network components to form a neuro-fuzzy system for enhanced decision-making capability (Pedersen, Abstract, and Chung-Ju, Abstract).
As to claim 11, the teachings of Pedersen and Chung-Ju as combined for the same reasons set forth in claim 1’s rejection further disclose the adaptive fuzzy logic engine (AFLE) is configured to perform cross-layer data analysis by integrating data from the physical layer, data link layer, network layer, transport layer, and application layer to inform decision-making processes, thereby enabling a comprehensive understanding of network conditions across multiple layers (Pedersen, [0113]).
As to claim 13, the teachings of Pedersen and Chung-Ju as combined for the same reasons set forth in claim 1’s rejection further disclose the learning module utilizes predictive analytics models that forecast future network conditions and potential issues by analyzing historical and real-time data across the physical layer, data link layer, network layer, transport layer, and application layer, thereby allowing for proactive adjustments to network configurations (Pedersen, [0113] and [0276]-[0277]).
As to claim 14, the teachings of Pedersen and Chung-Ju as combined for the same reasons set forth in claim 1’s rejection further disclose a cross-layer security management feature, wherein the system identifies and mitigates security threats by analyzing anomalies and patterns of behavior across a plurality of network layers to ensure comprehensive network security (Pedersen, [0113] and [0205]-[0206]).
As to claim 15, the teachings of Pedersen and Chung-Ju as combined for the same reasons set forth in claim 1’s rejection further disclose said plurality of network layers are selected from the group consisting of the physical layer, data link layer, network layer, and application layer (Pedersen, [0113]).
Claim 12 and 16 are rejected under 35 U.S.C. 103 as being unpatentable over Pedersen and Chung-Ju in view of Smith (US Pub. No. 2025/0292091).
As to claim 12, the teachings of Pedersen and Chung-Ju disclose the parent claim but do not explicitly disclose a mechanism for dynamic adjustment of network configurations based on cross-layer feedback, wherein said adjustments include changes to routing protocols, bandwidth allocation, and Quality of Service (QoS) parameters to optimize network performance and resilience based on integrated feedback from multiple network layers.
However, Smith discloses a mechanism for dynamic adjustment of network configurations based on cross-layer feedback, wherein said adjustments include changes to routing protocols, bandwidth allocation, and Quality of Service (QoS) parameters to optimize network performance and resilience based on integrated feedback from multiple network layers ([0178], particularly, “The processing system may also modify operational roles of network nodes by transitioning specific nodes between active and standby states based on predicted utilization levels. The processing system may also modify access and gateway rules. Resource scaling may include adjusting bandwidth allocations, activating additional computing instances, or modifying Quality of Service (QOS) rules to address projected demand” and [0237]-[0238]).
Therefore it would have been obvious to one of ordinary skill in the art prior to the effective filing date of the application to combine the teachings of Pedersen and Chung-Ju with Smith in order to provide a system with greater adaptability that can operate more efficiently in a greater variety of situations and conditions.
As to claim 16, the teachings of Pedersen and Chung-Ju disclose the parent claim but does not disclose the system operates to improve energy efficiency across multiple network layers through adjustments to power output at the physical layer, optimization of data link layer protocols for low-energy operation, energy-efficient routing at the network layer, and management of application layer processes to reduce unnecessary data transmissions, thereby enhancing the overall energy efficiency of the IoT network.
However, Smith discloses a system operates to improve energy efficiency across multiple network layers through adjustments to power output at the physical layer, optimization of data link layer protocols for low-energy operation, energy-efficient routing at the network layer, and management of application layer processes to reduce unnecessary data transmissions, thereby enhancing the overall energy efficiency of the IoT network ([0187], [0263], and [0314]).
Therefore it would have been obvious to one of ordinary skill in the art prior to the effective filing date of the application to combine the teachings of Pedersen and Chung-Ju with Smith in order to provide a system with greater adaptability that can operate more efficiently and thereby reduce energy consumption.
Claims 17-20 are rejected under 35 U.S.C. 103 as being unpatentable over Pedersen and Chung-Ju in view of Lambotte (US Pat. 11,829,486).
As to claim 17, the teachings of Pedersen and Chung-Ju disclose the parent claim and a learning module incorporates algorithms to optimize the parameters of the dynamic membership functions across multiple network layers, including the physical layer, data link layer, network layer, transport layer, and application layer, based on a comprehensive objective function that assesses network performance, energy efficiency, and security posture (Pedersen, [0113] and [0205]-[0206] and Chung-Ju, column 3, lines 2-49) but does not disclose the algorithms incorporate Particle Swarm Optimization (PSO) algorithms.
However, Lambotte discloses a learning module incorporates Particle Swarm Optimization (PSO) algorithms to optimize the parameters of the dynamic membership functions across multiple network layers, including the physical layer, data link layer, network layer, transport layer, and application layer, based on a comprehensive objective function that assesses network performance, energy efficiency, and security posture (column 18, line 58-column 19, line 36 and column 39, line 6-column 40, line 4, particularly, “In some embodiments, cybersecurity threat classification model may include a particle swarm optimization model. In some embodiments, determining the cybersecurity threat classification of an entity data may include using a fuzzy inference engine. A fuzzy inference engine may be configured to map one or more entity data elements using fuzzy logic. In some embodiments, entity data may be arranged by a logic comparison program into cybersecurity threat classification arrangement. An “cybersecurity threat classification arrangement” as used in this disclosure is any grouping of objects and/or data based on skill level and/or output score. This step may be implemented as described above in FIGS. 1-4. Membership function coefficients and/or constants as described above may be tuned according to classification and/or clustering algorithms.”)
Therefore it would have been obvious to one of ordinary skill in the art prior to the effective filing date of the application to combine the teachings of Pedersen and Chung-Ju with Lambotte in order to provide a system with greater adaptability that can operate more efficiently in a greater variety of situations and conditions.
As to claim 18, the teachings of Pedersen and Chung-Ju as combined for the same reasons set forth in claim 1’s rejection further disclose dynamically adjusting network configurations in real-time, where algorithms analyze the collective impact of changes across multiple network layers to identify optimal configurations that meet predefined network performance goals (Pedersen, [0113] and [0205]-[0206] and Chung-Ju, column 3, lines 2-49) but does not disclose utilizing PSO and PSO algorithms.
However, Lambotte discloses utilizing PSO for dynamically adjusting network configurations in real-time, where PSO algorithms analyze the collective impact of changes across multiple network layers to identify optimal configurations that meet predefined network performance goals (column 18, line 58-column 19, line 36 and column 39, line 6-column 40, line 4, particularly, “In some embodiments, cybersecurity threat classification model may include a particle swarm optimization model. In some embodiments, determining the cybersecurity threat classification of an entity data may include using a fuzzy inference engine. A fuzzy inference engine may be configured to map one or more entity data elements using fuzzy logic. In some embodiments, entity data may be arranged by a logic comparison program into cybersecurity threat classification arrangement. An “cybersecurity threat classification arrangement” as used in this disclosure is any grouping of objects and/or data based on skill level and/or output score. This step may be implemented as described above in FIGS. 1-4. Membership function coefficients and/or constants as described above may be tuned according to classification and/or clustering algorithms.”)
Therefore it would have been obvious to one of ordinary skill in the art prior to the effective filing date of the application to combine the teachings of Pedersen and Chung-Ju with Lambotte in order to provide a system with greater adaptability that can operate more efficiently in a greater variety of situations and conditions.
As to claim 19, the teachings of Pedersen and Chung-Ju as combined for the same reasons set forth in claim 1’s rejection further disclose enhance cross-layer security measures, dynamically adjusting security protocols and configurations across the network layers in response to detected threats and vulnerabilities, based on risk assessments (Pedersen, [0113] and [0205]-[0206] and Chung-Ju, column 3, lines 2-49), but does not disclose PSO is employed and the risk assessments are calculated through PSO algorithms,
However, Lambotte discloses PSO is employed to enhance cross-layer security measures, dynamically adjusting security protocols and configurations across the network layers in response to detected threats and vulnerabilities, based on risk assessments calculated through PSO algorithms (column 18, line 58-column 19, line 36 and column 39, line 6-column 40, line 4, particularly, “In some embodiments, cybersecurity threat classification model may include a particle swarm optimization model. In some embodiments, determining the cybersecurity threat classification of an entity data may include using a fuzzy inference engine. A fuzzy inference engine may be configured to map one or more entity data elements using fuzzy logic. In some embodiments, entity data may be arranged by a logic comparison program into cybersecurity threat classification arrangement. An “cybersecurity threat classification arrangement” as used in this disclosure is any grouping of objects and/or data based on skill level and/or output score. This step may be implemented as described above in FIGS. 1-4. Membership function coefficients and/or constants as described above may be tuned according to classification and/or clustering algorithms.”)
Therefore it would have been obvious to one of ordinary skill in the art prior to the effective filing date of the application to combine the teachings of Pedersen and Chung-Ju with Lambotte in order to provide a system with greater adaptability that can operate more efficiently in a greater variety of situations and conditions.
As to claim 20, the teachings of Pedersen and Chung-Ju disclose the parent claim but do not disclose PSO is applied to optimize energy consumption across IoT devices and network infrastructure, leveraging cross-layer data to dynamically adjust power settings, operational modes, and routing protocols to achieve optimal energy efficiency without compromising network performance or reliability.
However, Lambotte discloses PSO is applied to optimize energy consumption across IoT devices and network infrastructure, leveraging cross-layer data to dynamically adjust power settings, operational modes, and routing protocols to achieve optimal energy efficiency without compromising network performance or reliability (column 18, line 58-column 19, line 36 and column 39, line 6-column 40, line 4, particularly, “In some embodiments, cybersecurity threat classification model may include a particle swarm optimization model. In some embodiments, determining the cybersecurity threat classification of an entity data may include using a fuzzy inference engine. A fuzzy inference engine may be configured to map one or more entity data elements using fuzzy logic. In some embodiments, entity data may be arranged by a logic comparison program into cybersecurity threat classification arrangement. An “cybersecurity threat classification arrangement” as used in this disclosure is any grouping of objects and/or data based on skill level and/or output score. This step may be implemented as described above in FIGS. 1-4. Membership function coefficients and/or constants as described above may be tuned according to classification and/or clustering algorithms.”)
Therefore it would have been obvious to one of ordinary skill in the art prior to the effective filing date of the application to combine the teachings of Pedersen and Chung-Ju with Lambotte in order to provide a system with greater adaptability that can operate more efficiently in a greater variety of situations and conditions.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to THOMAS J DAILEY whose telephone number is (571)270-1246. The examiner can normally be reached 9:30am-6:00pm.
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/THOMAS J DAILEY/ Primary Examiner, Art Unit 2458
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