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 .
Priority
Applicant’s claim for domestic benefit under 35 U.S.C. 119(e) is acknowledged.
Information Disclosure Statement
The information disclosure statement submitted has been considered by the Examiner and made of record in the application file.
Election/Restrictions
Applicant’s election without traverse of group I in the reply filed on 12/22/2025 is acknowledged. Claims 1-11 and 19-20 are hereby examined on the merits.
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.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claims 1-4, 7-8 and 19-20 are rejected under 35 U.S.C. 103 as being unpatentable over McGrane USPN 2008/0178019 in view of Fithritama et al. "Modeling fuzzy rules for managing power consumption of ethernet switch." 2015 23rd International Conference on Software, Telecommunications and Computer Networks (SoftCOM). IEEE, 2015.
Consider claim 1, McGrane discloses a network node (see fig. 2), comprising: a processor; at least one network interface controller configured to provide access to a network; and a memory communicatively coupled to the processor, wherein the memory comprises a power consumption management logic that is configured to: priority of the power consuming functions, based on stored user preferences indicating priority of the power consuming functions… power control module ranks the plurality of power consuming functions. In some implementations, the power control module ranks the plurality of power consuming functions by providing a ranking value to each of the plurality of power consuming functions indicative of a desirability to enable (or not disable) the power consuming function…”); determine a power budget (see [0065]: “…power control module also selects the power consuming functions based on the determined power consumption values. In some implementations, the sum of the power consumption values of the selected one or more power consuming functions is less than a difference between a first value indicative of an amount of available power and a second value indicative of amount of power consumed. As an example, based on a first value indicating that the device has 14.3 W of power available and a second value indicative that the device is consuming 12.1 W, the power control module may determine that 1.2 W (e.g., 14.3−12.1) worth of power consuming functions are to be enabled. In some implementations, the power control module selects the highest ranked power consuming function having a power consumption value less than 1.2 W. If this leaves additional power to be used, in some implementations, the power control module additionally selects the highest ranked remaining power consuming function having a power consumption value less than the remaining additional power. As another example, the power control module may determine that 0.5 W worth of power consuming functions are to be disabled…”); and disable at least one feature of the plurality of features based on the power budget, the priority associated with the at least one feature, and power consumption value less than 1.2 W. If this leaves additional power to be used, in some implementations, the power control module additionally selects the highest ranked remaining power consuming function having a power consumption value less than the remaining additional power. As another example, the power control module may determine that 0.5 W worth of power consuming functions are to be disabled…”).
However, McGrane does not explicitly disclose access feature-to-power association data, the feature-to-power association data being derived from a machine learning process and disable based on the feature-to-power association data.
In the related field of endeavor, Fithritama disclose access feature-to-power association data, the feature-to-power association data being derived from a machine learning process and disable based on the feature-to-power association data (see Table 1: functions “Hibernate” “Bandwidth” “PC” “Traffic load” are associated with “Power consumption”; section VI: “…a given input values, a power consumption can be estimated using the ”IF..THEN...” fuzzy rules as described in Table 1 … idea is to choose appropriate rules which can be applied as a strategy to determine the level of bandwidth, number of connection, hibernation mode, and input bytes that provides expected level of power consumption”).
Therefore, it would have been obvious to one of ordinary skill in the art at a time before the effective filing date of the claimed subject matter to improve the network power management of McGrane with the model for prediction/estimation when deploying new switches of Fithritama in order to arrive at the instant recitation to provide more network power savings.
Consider claim 2 as applied to respective claim, McGrane as modified discloses the power consumption management logic is configured to determine a real-time power consumption level of the network node based on reading at least one sensor of the network node, and wherein the at least one feature is enabled or disabled based further on the determined real-time power consumption level (see [0034]: “…power control module 240 receives information from a database of live, real-time information on the status and history of devices in operation such as a database of a cloud-based controller architecture. For example, the power control module 240 may receive information indicating that a particular power consuming function designed to consume approximately 4.0 W typically consumes between 2.1-2.4 W of power…”).
Consider claim 3 as applied to respective claim, McGrane as modified discloses the feature-to-power association data is indicative of an index of an impact that each of the plurality of features has on power consumption at the network node (see Fithritama Table 1: “high” “med” “low”).
Consider claim 4 as applied to respective claim, McGrane as modified discloses the at least one feature is disabled in response to an increased power consumption by a second feature of the plurality of features, the second feature being associated with a greater priority than the at least one feature (see [0051]: “…selected one or more power consuming functions to be disabled may include at least one of the power consuming functions previously enabled by an earlier iteration through the method 300. The method 300 may include enabling (in block 345) or disabling (in block 355) the update-selected power consuming function…”).
Consider claim 7 as applied to respective claim, McGrane as modified discloses the power consumption management logic is further configured to reenable the at least one feature in response to a real-time power consumption level of the network node falling below the power budget (see fig. 3: feedback loop enabling and disabling with “A”; [0034;0051]: “…selected one or more power consuming functions to be disabled may include at least one of the power consuming functions previously enabled by an earlier iteration through the method 300. The method 300 may include enabling (in block 345) or disabling (in block 355) the update-selected power consuming function…” “…power control module 240 receives information from a database of live, real-time information on the status and history of devices in operation such as a database of a cloud-based controller architecture…”).
Consider claim 8 as applied to respective claim, McGrane as modified discloses the network node comprises at least one of a router, a switch, or a line card (see fig. 1: 111 switching hub).
Examiner Note: See detailed rejection of independent claim 1 for any remaining independent claim rejections.
Consider claim 19, McGrane discloses a method for network node feature shedding, comprising: figs. 2 and 5; [0047;0062;0065]); and disabling at least one feature of the plurality of features based on the power budget, the priority associated with the at least one feature, and highest ranked remaining power consuming function having a power consumption value less than the remaining additional power. As another example, the power control module may determine that 0.5 W worth of power consuming functions are to be disabled…”).
However, McGrane does not explicitly disclose access feature-to-power association data, the feature-to-power association data being derived from a machine learning process and disable based on the feature-to-power association data.
In the related field of endeavor, Fithritama disclose access feature-to-power association data, the feature-to-power association data being derived from a machine learning process and disable based on the feature-to-power association data (see Table 1: functions “Hibernate” “Bandwidth” “PC” “Traffic load” are associated with “Power consumption”; section VI: “…a given input values, a power consumption can be estimated using the ”IF..THEN...” fuzzy rules as described in Table 1 … idea is to choose appropriate rules which can be applied as a strategy to determine the level of bandwidth, number of connection, hibernation mode, and input bytes that provides expected level of power consumption”).
Therefore, it would have been obvious to one of ordinary skill in the art at a time before the effective filing date of the claimed subject matter to improve the network power management of McGrane with the model for prediction/estimation when deploying new switches of Fithritama in order to arrive at the instant recitation to provide more network power savings.
20. The method of claim 19, wherein the feature-to-power association data is indicative of an index of an impact that each of the plurality of features has on power consumption at the network node (see Table 1: “high” “med” “low”).
Claims 9-11 are rejected under 35 U.S.C. 103 as being unpatentable over McGrane USPN 2008/0178019 in view of Fithritama et al. "Modeling fuzzy rules for managing power consumption of ethernet switch." 2015 23rd International Conference on Software, Telecommunications and Computer Networks (SoftCOM). IEEE, 2015 in view of Kuo USPN 2025/0384650.
Consider claim 9 as applied to respective claim, McGrane as modified does not explicitly disclose the machine learning process is associated with a logistic regression model.
In the related field of endeavor, Kuo discloses the machine learning process is associated with a logistic regression model (see [0125]: “… trained machine learning model(s) 1732 can include one or more models of one or more machine learning algorithms 1720. Machine learning algorithm(s) 1720 may include, but are not limited to: an artificial neural network (e.g., a herein-described convolutional neural networks, a recurrent neural network, a Bayesian network, a hidden Markov model, a Markov decision process, a logistic regression function, a support vector machine, a suitable statistical machine learning algorithm, and/or a heuristic machine learning system). Machine learning algorithm(s) 1720 may be supervised or unsupervised, and may implement any suitable combination of online and offline learning…”).
Therefore, it would have been obvious to one of ordinary skill in the art at a time before the effective filing date of the claimed subject matter to improve the network power management of McGrane as modified with machine learning training models of Kuo in order to arrive at the instant recitation to follow market trends of machine learning for automating systems.
Consider claim 10 as applied to respective claim, McGrane as further modified by Kuo discloses the logistic regression model is trained online (see [0125]: “… trained machine learning model(s) 1732 can include one or more models of one or more machine learning algorithms 1720. Machine learning algorithm(s) 1720 may include, but are not limited to: an artificial neural network (e.g., a herein-described convolutional neural networks, a recurrent neural network, a Bayesian network, a hidden Markov model, a Markov decision process, a logistic regression function, a support vector machine, a suitable statistical machine learning algorithm, and/or a heuristic machine learning system). Machine learning algorithm(s) 1720 may be supervised or unsupervised, and may implement any suitable combination of online and offline learning…”).
Consider claim 11 as applied to respective claim, McGrane as further modified by Kuo discloses the logistic regression model is trained offline (see [0125]: “… trained machine learning model(s) 1732 can include one or more models of one or more machine learning algorithms 1720. Machine learning algorithm(s) 1720 may include, but are not limited to: an artificial neural network (e.g., a herein-described convolutional neural networks, a recurrent neural network, a Bayesian network, a hidden Markov model, a Markov decision process, a logistic regression function, a support vector machine, a suitable statistical machine learning algorithm, and/or a heuristic machine learning system). Machine learning algorithm(s) 1720 may be supervised or unsupervised, and may implement any suitable combination of online and offline learning…”).
Allowable Subject Matter
Claims 5-6 are objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims.
The following is a statement of reasons for the indication of allowable subject matter: See respective instant recitaitons.
Conclusion
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Fayyaz Alam
January 10, 2026
/FAYYAZ ALAM/
Primary Examiner, Art Unit 2674