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 .
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 (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 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.
Claims 1, 3, 4, 6, 7, 8, 11, 13, 14, 16-18, 20-23, 26-27 and 29-31 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Cui et al. (2018/0299933).
As in claims 1 and 18, Cui teaches a method comprising:
receiving from a scheduler one or more server indicators indicating an upcoming task to be executed by at least one component of a server, wherein the upcoming task has not yet been executed (Figs. 2, 3 and 7, par. 23 – teaches that a host serve blade/node (scheduler) is associated with one or more computer server blades/nodes, and is configured to determine and distribute tasks to each of the computer nodes; Fig. 7, pars. 18, 23 and 47 also teach that the tasks have not yet been executed since prior to distributing the tasks, the host node estimates the workload for the computer nodes; The fact that the host node knows the about the upcoming tasks (task distribution), teaches that “server indicators” have been received, the tasks themselves have just not been distributed yet. The “server indicators” could easily be interpreted as operating temperatures of the processors, workload information, etc. – see pars. 18 and 40);
predicting, via a processor, an expected cooling demand for the at least one component based on the one or more server indicators (Fig. 7, pars.18, 23 and 47 – teaches that the host node estimates (predicts) the workload (power requirements) of the computer nodes, which configures a liquid pump to modify a liquid flow rate of the heat removal liquid to generate sufficient cooling liquid volume); and
proactively adjusting a cooling amount provided by a cooling mechanism before a task begins execution based on the expected cooling demand of the at least one component (Fig. 7, pars 18, 23 and 47 – teach that after a pump speed of the liquid pump has been adjusted, the tasks associated with the workload are distributed to the computer nodes. This is proactively adjusting the cooling mechanism before a task begins execution).
As in claim 11, see Figs. 1-4 and analysis in claim 1 above.
As in claims 3, 13 and 20, Cui teaches the method of Claim 1, wherein the at least one of the one or more server indicators received from the scheduler indicates an expected usage of at least one of the at least one component for the task (Fig. 7, pars. 18, 23 and 47 – teaches that the host node estimates the workload (power requirements) for the computer nodes and sends the workload information to a workload calculator).
As in claims 4, 14 and 21, Cui teaches the method of Claim 1, wherein at least one of the one or more server indicators is received from a switch connected to the server, wherein the at least one of the one or more server indicators comprises information relating to traffic routing via the switch (Fig. 4, par. 40 – teaches workload information is obtained via a switch).
As in claims 6 and 22, Cui teaches the method of Claim 1, wherein at least two of the one or more server indicators are received from at least two of the scheduler, a switch, or a temperature sensor, and wherein the server indicator received from the scheduler comprises a total capacity of the at least one component (Figs. 2 and 4 show that at least two server indicators (from servers 203C…203E) are received from at least two of the scheduler (host server (203A)) and a switch. Pars. 18, 21, 35, 40 and 42 also teach that operating temperatures (GPU card’s temperature) of the computer nodes are monitored, so it is clearly implied that they also include temperature sensors. Pars. 23 and 33 teach that the workload calculator is configured to calculate the total workload of the computer nodes to generate sufficient cooling).
As in claim 7, Cui teaches the method of Claim 1, wherein the one or more server indicators comprise at least one server indicator received from the scheduler (Fig. 4, server 203A (host server), and par. 40), at least one server indicator received from a switch (Fig. 4, element 401, and par. 40), and at least one server indicator received from a temperature sensor (Figs. 3 and 4, pars. 18, 21, 35, 40 and 42 – teaches that operating temperatures (GPU card’s temperature) of the computer nodes are monitored, so it is clearly implied that they also include temperature sensors), wherein each of the at least one server indicator received from the scheduler, the at least one server indicator received from the switch, and the at least one server indicator received from the temperature sensor is used to predict the expected cooling demand (Figs. 4 and 7, and pars. 18 and 23 – teaches that the host node estimates the workload and sends the provided workload information to a workload calculator).
As in claims 8 and 23, Cui teaches the method of Claim 1, further comprising monitoring a temperature of the at least one component during execution of the task, wherein the temperature of the component during the execution is compared to a target temperature to determine whether the cooling amount was correct (Par. 24 – teaches that the temperatures are actively monitored during the task operations, and the liquid flow rate is dynamically adjusted as needed. Par. 23 clearly teaches that the computer nodes operate in a “proper temperature” (target temperature). This clearly implies that some type of comparison is taking place).
As in claim 16, Cui teaches the system of Claim 11, further comprising a temperature sensor positioned adjacent to the at least one component, wherein at least one of the one or more server indicators is based on a temperature measured by the temperature sensor (Pars. 18, 21, 35, 40 and 42 teach that operating temperatures (GPU card’s temperature) of the computer nodes are monitored, so it is clearly implied that they also include temperature sensors. The fact that the GPU card for each computer node is what includes the temperature (Figs. 3 and 4, par. 18), clearly implies that a temperature sensor is “adjacent” to the computer node).
As in claim 17, Cui teaches the system of Claim 11, wherein at least one of the one or more server indicators indicates a temperature of one or more servers adjacent to the server (Figs, 3 and 4, pars. 18, 21, 35, 40 and 42 – servers 203B, D…F are adjacent to servers 203 A, C…E).
As in claim 26, Cui teaches the method of Claim 7 further comprising:
ranking, via the processor, a source of each of the at least one server indicators based on importance (Pars. 46 and 48 teach that processing logic selects the workload of a computer node having the highest workload amongst all of the computer nodes…to ensure cooling is sufficient. This clearly implies ranking, and based on importance); and weighting each of the at least one server indicators based on its ranking (Pars. 46 and 48 teach that it clearly knows the “highest” workload, so that implies weighting), wherein predicting, via the processor, the expected cooling demand for the at least one component based on the one or more server indicators comprises predicting the expected cooling demand for the at least one component based on the one or more weighted server indicators (Fig. 7, pars.18, 23 and 47 – teaches that the host node estimates (predicts) the workload (power requirements) of the computer nodes, which configures a liquid pump to modify a liquid flow rate of the heat removal liquid to generate sufficient cooling liquid volume. Pars 46 and 48 teach that this can based on the highest (weighted) temperature amongst the temperatures collected from al of the computer nodes).
As in claim 27, see analysis in claims 16 and 17 above.
As in claim 29, Cui teaches the method of Claim 1, wherein adjusting the cooling amount comprises providing additional cooling as compared to a non-adjusted cooling amount (Fig. 7, pars. 18, 23 and 47 teach that the liquid flow rate is modified before the GPU’s power increases. The fact that it is modified would be in addition to “non-adjusting” the liquid flow rate).
As in claim 30, Cui teaches the method of Claim 1, wherein the cooling amount is provided proximate at least one of a graphics processing unit, a central processing unit, a data processing unit, a switch, or an optical component (Figs. 1-3).
As in claim 31, see analysis in claims 1, 6 and 26 above.
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 9, 10, 24 and 25 are rejected under 35 U.S.C. 103 as being unpatentable over Cui et al. in view of Heydari (2022/0206547).
As in claims 9 and 24, although Cui teaches that the host servers can execute deep data learning algorithms or modeling, they do not describe teaching a training set to predict the expected cooling demand via a machine learning model.
However, Heydari teaches of a system for cooling servers in a data center, specifically using liquid flow rates (Abstract), wherein it also includes a neural network enabled approach to liquid cool the servers where the neural network can predict the expected cooling demands to maintain desired operating conditions (Pars. 60-66).
It would have been obvious to a person skilled in the art at the time the invention was filed to include the teaching of Heydari into the system taught by Cui above. This would have been obvious because both Cui and Heydari teach of systems for cooling servers in a data center using liquid flow rates, and further, by using neural networks it can provide accurate predictive modeling with various agents running in equipment across an entire environment without the need of an administrator (Heydari – par. 60).
As in claims 10 and 25, Cui and Heydari teach the method of Claim 9, further comprising monitoring the server during execution of the task and updating the training set based on the monitored task execution (Cui teaches monitoring the server during execution of the tasks – Par. 24; Heydari teaches updating the training set over time – Pars. 61 and 65).
Response to Arguments
Applicant’s arguments with respect to claims 1, 3, 4, 6-11, 13-14, 16-18, 20-27 and 29-31 have been considered but are moot because the new ground of rejection does not rely on any reference applied in the prior rejection of record for any teaching or matter specifically challenged in the argument.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. See PTO-892.
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/SCOTT T BADERMAN/Supervisory Patent Examiner, Art Unit 2118
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