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 .
Claims 1-20 are presented for Examination.
DETAILED ACTION
Claim Rejections - 35 USC § 102
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.
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, 10, 18 is/are rejected under 35 U.S.C. 102(a) (1) as being anticipated by Gupta et.al. (U.S Patent Application Publication 2025/0272205 hereinafter Gupta”).
Regarding claims 1, 10, 18, Gupta discloses, A method comprising:
obtaining a first set of messages generated by a plurality of baseboard management controllers (BMCs), [ “BMC 102 is in communication with BMCs 302-1 to 302-P. “, 0038] the first set of messages indicating statuses of a plurality of hosts associated with the plurality of BMCs, [ “ The service components 132 of the BMC 102 may manage the host computer 180 and is responsible for managing and monitoring the server vitals such as temperature and voltage levels. .. “, 0031;Fig.1” the BMC 102 further includes a data collection configuration manager 331, ..”, 0038; “ the BMC 102 may collect information about the resource utilization (e.g., CPU, memory, I/O) and health of the BMC processes 214-1 to 214-N and host processes 212-1 to 212-M.. By monitoring the CPU and memory usage of the BMC processes 214-1 to 214-N, abnormal behavior can be detected. Thresholds can be set for CPU and memory utilization per process (e.g., 5-10%). If a process exceeds the threshold usage, it can be automatically restarted to avoid potential denial of service situations.”, 0047; (i.e. messages indicating statuses of a plurality of hosts); “the BMC 102 acts as a master BMC to collect information from subordinate BMCs in a group…”, 0063; “a data collection agent, implemented through components such as the BMC process data collector 333-1, the BMC hardware data collector 333-2, and the host data collector 333-3 on the BMC 102, collects heuristic data about various parameters based on the data collection configuration set up through the data collection configuration manager 331. The data collection configuration can specify collection of parameters such as process states, resource utilization, fault statistics, hardware parameters, and network statistics for both BMC processes 214-1 to 214-N and host processes 212-1 to 212-M. This configuration, managed by the data collection configuration manager 331, allows for dynamic adjustments based on external inputs or system administrator preferences.”, 0056; 0194; ( i.e. the master BMC collecting various parameters from the BMC’s and associated host)] wherein the plurality of BMCs generates the first set of messages in accordance with reporting parameters assigned to the plurality of BMCs [“ threshold-based alerts can be configured to detect abnormal resource consumption “, 0094; 0107; “the data collection configuration can specify collection of parameters such as process states, resource utilization, fault statistics, hardware parameters, and network statistics for both BMC processes 214-1 to 214-N and host processes 212-1 to 212-M. This configuration, managed by the data collection configuration manager 331, allows for dynamic adjustments based on external inputs or system administrator preferences.”, 0056[i.e. messages indicating the status/ states of host and BMC processes based on the parameters configured by the data collection configuration manager)] and
based, at least in part, on analyzing the first set of messages, determining updated reporting parameters[ “The management cloud 370 contains various services to enable administrators to customize analysis based on data gathered from entities such as BMCs 402-1, 402-2, . . . , 402-N, which may be the BMC 102 and subordinate BMCs 302-1 to 302-P “Overall, the management cloud 370 enables customizable correlation and analytics by allowing admins to select parameters of interest. Data is aggregated from entities like the BMC 102 and subordinate BMCs 302-1 to 302-P over desired time periods. “, 0122; 0124; “the management system provides a configuration interface to enable configuration of data collection at the BMC. The configuration interface allows configuring the BMC to collect data about particular processes executed on at least one of the BMC and the host.”, 0203; ( i.e determining to update the parameters/ configuration based on the collected data for, Host & BMC’s)] by performing at least one of:(a) adjusting the reporting parameters to alter a frequency that messages are generated by one or more BMCs of the plurality of BMCs, or (b) adjusting the reporting parameters to alter content that is included in the messages generated by the one or more BMCs of the plurality of BMCs “[“ ..The aggregated data can be used to tune performance, optimize resource allocation between host and BMC, debug abnormal behaviors, calculate cost analysis when certain processes run on the host 180, adjust hardware parameters based on software behavior, and enable closed loop remediations, etc.”, 0046; Collection data may be modified from an external entity to enhance trained data model. For example, users can add cost data for a particular master variable to derive cost analysis””, 0075;” the management cloud 370 may allow users to add cost data corresponding to the power consumption of the host 180 when specific processes of the host processes 212-1 to 212-M run. As an illustration, if the host 180 consumes 100 W when running a machine learning application, the user can assign a cost value of $X to this 100 W consumption by entering it into the management cloud 370”, 0076; “ Accordingly, the management cloud 370 provides flexibility for users to incorporate external data like cost as an additional variable in the aggregated data mode..”, 0077; ( i.e updating the reporting requirements based on analyzing the aggregated data. The report content is altered by including cost data parameter based on the power usage)].
wherein the method is performed by at least one device including a hardware processor [“The BMC 102 has, among other components, a main processor 112..”, 0023; “ system can have one master BMC 102”, 0100].
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 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 of this title, 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 2, 8, 9, 11, 17, 19 are rejected under 35 U.S.C. 103 as being unpatentable over Gupta in view of Allen-Ware et.al. (U.S Patent 9,880,599; hereinafter “Allen-Ware”)
Regarding Claims 2, 11,19, Gupta discloses, wherein the first set of messages comprises a first message; wherein the first message is generated by a first BMC of the plurality of BMCs; wherein the first BMC is comprised within a first host of the plurality of hosts; [ “The management system receives a consolidated data set from a BMC, the consolidated data set including at least one of data related to one or more processes executed on the BMC, data related to one or more processes executed on a host coupled to the BMC, data related to resource allocation at the BMC, data related to resource allocation at the host, fault statistics at the BMC, fault statistics at the host, network statistics at the BMC, network statistics at the host, hardware sensor data at the BMC, and hardware sensor data at the host. The management system analyzes the consolidated data set to determine whether to adjust operational parameters of at least one of the BMC and the host. The management system, in response to determining that the operational parameters require adjustment, generates instructions for adjusting the operational parameters. The management system transmits the instructions to the BMC.”, 0007; 0047; 0094; 0107 (i.e it is apparent to indicate when the power consumption of a host exceeds a threshold) ] ;
However, Gupta does not expressly disclose wherein the first message comprises a first value the first value indicating a first amount of power that is being drawn by the first host from an ancestor device.
In the same field of endeavor (e.g. priority-aware power capping for hierarchical power distribution networks), Allen-Ware discloses, wherein the first message comprises a first value the first value indicating a first amount of power that is being drawn by the first host from an ancestor device [ “FIG. 3 depicts a functional block diagram of a mechanism for priority-aware power capping for hierarchical power distribution networks in accordance with an illustrative embodiment. “, ; “As illustrated, data center 300 comprises power distribution network 301 that comprises a set of power consumption devices, i.e. a set of servers 302, 304, 312, 314, 322, 324, 332, and 334. In power distribution network 301, servers 302 and 304 are coupled to overcurrent protection device 306, servers 312 and 314 are coupled to overcurrent protection device 316, servers 322 and 324 are coupled to overcurrent protection device 326, and servers 332 and 334 are coupled to overcurrent protection device 336. As is further illustrated, in power distribution network 301, overcurrent protection devices 306 and 316 are coupled to remote power panel (RPP) 308 and overcurrent protection devices 326 and 336 are coupled to RPP 328. ..”, col 7 lines 50-67;” in power distribution network 301, RPPs 308 and 328 are coupled to transformer 310, which is the main point of entry for electricity into power distribution network 301 of data center 300. While power distribution network 301 is only depicted as comprising a hierarchy of servers, overcurrent protection devices, RPPs, and a transformer. The power demand that the power controller/server exposes to its parent power controller for the priority. “, col 8 lines 50-67; “Demand refers to the power that the device currently consumes without enforcing any power caps “, col 9 line 30 ( i.e . as illustrated in Fig.3. The parent device of the respective downstream/ child devices corresponds to the ancestor device , from which power is controlled to the child devices based on the demand .Each of the child / downstream devices corresponds to the host devices .The power demand of server 302 is 600 and 304 is 600. Indicating the amount of power drawn from the overcurrent protection/ ancestor device.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the teachings of Gupta with Allen-Ware. Allen-Ware’s teaching of assigning power budget to the child device based on the priority and demand will substantially improve Gupta’s system to prevent abnormalities or power consumption exceeding the threshold by allocating unassigned budget to the child device by performing predictive power capping .
Regarding Claims 8, 17, Gupta discloses,
applying one or more trained machine learning models to information comprised within the first set of messages[“In certain configurations, to analyze the consolidated data set, the management system applies at least one machine learning algorithm to the consolidated data set to determine expected values for operational parameters based on historical patterns.”, 0206].
Allen-Ware teaches, determining, an aggregate amount of power that is being drawn from an ancestor device by the plurality of hosts[Fig.3 ; col 12 lines 56-67].
determine at least one of:(a) a level of risk associated with the aggregate amount of power that is being drawn from the ancestor device by the plurality of hosts exceeding a restriction that is applicable to the ancestor device,(b) an available reaction time for responding to the aggregate amount of power that is being drawn from the ancestor device by the plurality of hosts exceeding the restriction that is applicable to the ancestor device,(c) a response time for responding to the aggregate amount of power that is being drawn from the ancestor device by the plurality of hosts exceeding the restriction that is applicable to the ancestor device, or (d) enforcement logic for generating one or more enforcement thresholds for one or more descendant devices of the ancestor device in response to the aggregate amount of power that is being drawn from the ancestor device by the plurality of hosts exceeding the restriction that is applicable to the ancestor device. [Fig.3; “For each power controller 406, 408, 410, 416, 426, 428, and 436, priority-aware power capping mechanism 340 performs a three-step operation to determine the power budget assigned child power controllers/servers: Assign minimum power budget by assigning the determined priority Pcapmin j to each child based on the child's priority. Assign demanded power budget by, from high priority to low priority, at each priority. If the remaining power budget is enough to satisfy the remaining Demand.sub.exposed j of the priority, further assign each child its remaining Demand.sub.exposed j of the priority. If the remaining power budget fails to be enough to satisfy the remaining Demand.sub.exposed j of the priority, priority-aware power capping mechanism 340 utilizes a supplemental algorithm to break up the remaining power budget to each child, with the condition that each priority of each child does not receive a total power budget that exceeds its Demand.sub.exposed j of the priority. The supplemental algorithm may be, for example, an equal proportion algorithm, a high-demand cut-first algorithm, or the like. Equal proportion assigns a same percentage to all children nodes of their (remaining) demanded power (i.e. the amount of demand beyond Pcapmin) when the full demand cannot be satisfied. High-demand cut-first removes budget from the highest power consuming children nodes until they match lower power consuming children nodes, which is repeated until the power budget is acceptable. ….col 13 lines 1-45; lines 56-67Fig.4 ].
Regarding claim 9, Gupta teaches, obtaining feedback regarding an application of a machine learning model of the one or more trained machine learning models; and further training the machine learning model based on the feedback[ “The accuracy of the regression model will depend on the data set collected. The amount of data collected will be based on the number of nodes and the frequency of data collection. The parameters of data collection may be tuned based on the number of nodes it has at its disposal.”, 0062; 0206;” The accuracy of regression models depends on having distinct datasets across parameters and entities over time. Accordingly, the data collection configuration manager 331 allows tuning the collection frequency and duration to ensure model accuracy. (performing continuous tuning based on the parameters configured “, 0072; “In certain configurations, the management system receives related data sets from a plurality of BMCs, aggregates the related data sets from the plurality of BMCs, and analyzes the aggregated data sets to determine whether to adjust operational parameters of at least one of the plurality of BMCs or respective hosts coupled to the plurality of BMCs.”, 0204].
Allowable Subject Matter
Claims 3,4, 5, 6, 7 and 12, 3, 14, 15, 16, 20 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.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Sathyanarayana et al., U.S Patent Application Publication 2024/0354181, teaches A system receives, from a policy command service executing in a control plane of a distributed computing system, a configuration file defining parameters to be monitored at the computing node, a timing factor indicating when the parameters are to be monitored, and a threshold for when the parameters are to be sent from the computing node
Bailey et al., U.S Patent Application Publication 2015/0177814, teaches rack-level predictive power capping and power budget allocation to processing nodes in a rack-based information handling system.
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/GAYATHRI SAMPATH/ Examiner, Art Unit 2176
/JAWEED A ABBASZADEH/ Supervisory Patent Examiner, Art Unit 2176
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