DETAILED ACTION
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Claims 1-16 and 18-21 are pending.
Claims 1, 8, and 15 have been amended.
Claim 21 is new.
This action is Final.
Claim Rejections - 35 USC § 103
The text of those sections of Title 35, U.S. Code not included in this action can be found in a prior Office action.
Claim(s) 1-3, 6-15, and 18-21 is/are rejected under 35 U.S.C. 103 as being unpatentable over Frantz et al. (hereinafter as Frantz) PGPUB 2011/0173475, and further in view of Yu et al. (hereinafter as Yu) Non-Patent Literature “A YANG Data Model for Network Hardware Inventory” and Marinelli et al. (hereinafter as Marinelli) PGPUB 2017/0201425.
As per claim 1, Frantz teaches a method, comprising:
identifying, by the device and from the components, a component capable of powering off based on the model [0005, 0022, 0032, 0059, 0064: (a resource may be placed in a low power state based upon the resource dependencies; resource is defined as circuit components (such as ports clocks, buses, oscillators) inside a computing device (network device))]; and
instructing, by the device, the network device to place the component included in the network device in a power save state based at least in part on at least one of the operational dependencies of the components [0005, 0035, 0064: (disabling resources based on the interdependencies of resources)].
Frantz does not explicitly teach utilizing, by a device, a data modeling language to generate a request to identify components included in a network device and operational dependencies of the components; providing, by the device, the request to the network device; receiving, by the device and based on the request, data identifying the components and the operational dependencies of the components; receiving, by the device, power consumptions by the components and power off capabilities of the components; generating, by the device, a model of power consumptions by the components based on the power consumptions by the components and the power off capabilities of the components;
Yu teaches management of resources in a network device. Yu is thus similar to Frantz. Yu further teaches utilizing, by a device, a data modeling language to generate a request to identify components included in a network device and operational dependencies of the components [Introduction section paragraph 1-2: (operator’s management and control systems (device) request inventory management as a data model) and section 2.1 third and second paragraphs from the bottom: (inventory hardware is obtained from a single network element (e.g. server))]; providing, by the device, the request to the network device [introduction section paragraph 4: (operator acquires new asset; thus operator sends request to model to obtain new information)]; receiving, by the device and based on the request, data identifying the components and the operational dependencies of the components [third and second paragraphs from the bottom: (YANG data model used to retrieve hardware inventory information in a network element like single server)]. Yu teaches generating a data model for internal components of a network device.
The combination of Frantz with Yu leads to generating data model of internal components in a network device to map and discover dependencies of the internal components in Frantz.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to use Yu’s teachings of using a data model to map the inventory and dependencies of internal components in Frantz. One of ordinary skill in the art would have been motivated to build a data model mapping of inventory inside a network device in Frantz because it allows for faster referencing and determination of dependent components.
Frantz and Yu do not teach receiving, by the device and based on the request, data identifying the components and the operational dependencies of the components; receiving, by the device, power consumptions by the components and power off capabilities of the components; generating, by the device, a model of power consumptions by the components based on the power consumptions by the components and the power off capabilities of the components. Yu suggests obtaining of power consumption from components in the network element [Appendix A.1 table shows used-power, and thus power is received], but does not describe power off capabilities and modeling of power consumptions.
Marinelli teaches obtaining inventory of components and their power consumption. Although Marinelli obtains the inventory of components across a data center rather than inside a particular network device, the idea is similar to Frantz and Yu in that they collect inventory and form a data model with it to show the dependencies of components. Marinelli further teaches receiving, by the device and based on the request, data identifying the components and the operational dependencies of the components [0316 and 0319: (data center gateway determines the data center assets based on hierarchical relationships (operational dependencies) and outputs the data to the requesting computing device which provides a graphical depiction)]; receiving, by the device, power consumptions by the components and power off capabilities of the components [0247-0248, 0318-0319, 0321: (provide reports or real-time power data detailing power draw (power consumptions by components)) and 0047 and 0321-0322: (notifies which specific assets are offline or not connected (power off capabilities)]; generating, by the device, a model of power consumptions by the components based on the power consumptions by the components and the power off capabilities of the components [0247, 0328, 0331, and FIG. 19-26: (graphical depictions are made based on the data, including which assets are available or not available, and the power draw of such assets)]. Marinelli obtains power consumption of components and determines which components are on or off, and forms a graphical depiction to show what components are available or not available and their power draw.
The combination of Frantz and Yu with Marinelli leads to the data model in Frantz and Yu obtaining power consumption and power capabilities of each component in the network device, and providing and showing on the data model the power draw and power off capabilities of components.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to use Marinelli’s teachings of having the data model obtain power consumption and power off capabilities of each component in the network device in Frantz and Yu. One of ordinary skill in the art would have been motivated to have the data model in Frantz and Yu collect power off capabilities and power consumption of each component in the network device and place it in the data model because it allows for a controller to quickly determine what components are currently off and which components related to it can be turned off as a result, thus leading to faster power state transitions while using less processing power to make such determination.
As per claim 2, Frantz, Yu, and Marinelli teach the method of claim 1, wherein the power save state causes the network device to power off the component [Frantz 0026: (resources in a network device may be turned off when it is no longer needed)].
As per claim 3, Frantz, Yu, and Marinelli teach the method of claim 1, wherein the power save state causes the network device to maintain the component on standby [Frantz 0065: (sleep states)].
As per claim 6, Frantz, Yu, and Marinelli teach the method of claim 1, wherein the data modeling language is a yet another next generation data modeling language [Marinelli 0059 or Yu title].
As per claim 7, Frantz, Yu, and Marinelli teach the method of claim 1, wherein the operational dependencies of the components include information indicating components that require other components and components that are used by other components [Frantz 0005, 0026-0027, 0032-0035 and 0057-0061].
Claim 8 is similar in scope to claim 1 as addressed above and is thus rejected under the same rationale.
As per claim 9, Frantz, Yu, Marinelli, and Mondal teach the device of claim 8, wherein the power consumptions by the components are periodically reported by the components [Marinelli 0140: (data points may be measured at a given frequency such as every second))].
As per claim 10, Frantz, Yu, Marinelli, and Mondal teach the device of claim 8, wherein each of the power consumptions by each of the components do not include power consumptions by components dependent on each of the components [Marinelli 0121, 0302-0303, and 0309].
As per claim 11, Frantz, Yu, and Marinelli teach the device of claim 8, wherein the power off capabilities of the components indicate that the components are capable of being powered off or are incapable of being powered off [Marinelli 0047 and 0321-0322 or Frantz 0033: (some components are incapable of being powered off if dependent component is running)].
As per claim 12, Frantz, Yu, and Marinelli teach the device of claim 8, wherein the one or more processors are further to: identify, from the components, a plurality of components capable of powering off based on the model [Frantz 0035: (determines interdependencies of various resources (plurality of components)]; and instruct the network device to place the plurality of components in the power save state [Frantz 0035: (disabling resources (plural)].
As per claim 13, Frantz, Yu, and Marinelli teach the device of claim 8, wherein each of the components of the network device include one or more of a hardware component of the network device, a software component of the network device, or a combined hardware and software component of the network device [Frantz 0022 and 0025: (resources include circuits and hardware components)].
As per claim 14, Frantz, Yu, and Marinelli teach the device of claim 8, wherein the component is associated with one or more components in the power save state [Frantz 0032-0035: (voltage regulator may be turned off if it determines clock driver is off)].
Claim 15 is similar in scope to claim 1 as addressed above and is thus rejected under the same rationale.
Claim 18 is similar in scope to claim 7 as addressed above and is thus rejected under the same rationale.
Claim 19 is similar in scope to claim 10 as addressed above and is thus rejected under the same rationale.
Claim 20 is similar in scope to claim 12 as addressed above and is thus rejected under the same rationale.
Claim 21 is similar in scope to claim 14 as addressed above and is thus rejected under the same rationale.
Claim(s) 4-5 and 16 is/are rejected under 35 U.S.C. 103 as being unpatentable over Frantz et al. (hereinafter as Frantz) PGPUB 2011/0173475 in view of Yu et al. (hereinafter as Yu) Non-Patent Literature “A YANG Data Model for Network Hardware Inventory” and Marinelli et al. (hereinafter as Marinelli) PGPUB 2017/0201425, and further in view of Mondal et al. (hereinafter as Mondal) PGPUB 2024/0381259.
As per claim 4, Frantz, Yu, and Marinelli teach the method of claim 1.
Frantz, Yu, and Marinelli do not teach further comprising: receiving a traffic load associated with the network device; and instructing the network device to remove the power save state for the component based on the traffic load. Frantz, Yu, and Marinelli do not appear to describe monitoring traffic.
Mondal uses a YANG data model for power management of a network device [0057 and 0061-0062]. Mondal is thus similar to Frantz, Yu, and Marinelli. Mondal further teaches receiving a traffic load associated with the network device [0020, 0109: (receive predicted or monitored downlink traffic information)]; and instructing the network device to remove the power save state for the component based on the traffic load [0020, 0109, and 0114: (switch from the low power state to the normal power state based on the monitored downlink traffic; returning to the normal power state is a removal of a power saving state)]. Mondal teaches observing or predicting traffic in a part of the network and switching a device from a low power state to the normal power state based on the traffic.
The combination of Frantz, Yu, and Marinelli with Mondal leads to monitoring or predicting traffic in a different part of the network, and selectively controlling a component such as a port in the network device in Frantz, Yu, and Marinelli to be powered up to return to the normal mode based on the traffic.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to use Mondal’s teachings of monitoring traffic and controlling a power state of the device to return to the normal mode based on the traffic in Frantz, Yu, and Marinelli. One of ordinary skill in the art would have been motivated to observe the traffic and return a component to the normal power mode based on the traffic in Frantz, Yu, and Marinelli because it anticipates the upcoming usage of the device and proactively returns a component to the normal state to handle possible traffic or workloads, thus preventing impact to performance when there is traffic while still providing power savings when there is no traffic.
As per claim 5, Frantz, Yu, Marinelli, and Mondal teach the method of claim 4, wherein instructing the network device to remove the power save state for the component causes the network device to remove the power save state for the component [Mondal 0020, 0109-0110, and 0113-0114: (send indication to switch from the low power state to the normal state, and then the switch to the normal state is performed)].
Claim 16 is similar in scope to claim 4 as addressed above and is thus rejected under the same rationale.
Response to Arguments
Applicant’s arguments with respect to claim(s) 1, 8, and 15 have been considered but are moot because the new ground of rejection does not rely on the combination of references applied in the prior rejection of record for any teaching or matter specifically challenged in the argument.
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Chertov et al. (PGPUB 2024/0275676) teaches using a data model of a network device such as YANG [0001].
Moreno et al. (PGPUB 2020/0257540) teaches equipment configuration from devices and services within the system are collected and uses YANG.
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/DANNY CHAN/Primary Examiner, Art Unit 2175
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