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 .
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 4/27/2023 was filed. The submission is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
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.
Claim(s) 1-6, 8-15, 17, 18, and 21-24 is/are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Ruso et al. [Ruso] (US PGPub 2024/0064941).
As to claim 1
Ruso discloses an apparatus (cooling control device 100; see Figs. 1 and 2) comprising:
memory (memory 201, see Fig. 2);
machine readable instructions (program instructions; see paragraph 0036, line 19); and
programmable circuitry (processor 202, see Fig. 2) to at least one of instantiate or execute the machine readable instructions (see paragraph 0036, lines 17-19) to:
input operational data (one or more server indicators; see paragraph 0048, line 2) into a machine-learning model (machine learning model; see paragraph 0049, line 9), the operational data including first information (server indicators relating to a task to be executed by at least one component of the server; see paragraph 0048, lines 2-3) relating to a workload (task) of a server (one or more servers 101, see Fig. 1) and second information (server indicators from the temperature sensors 103; see paragraph 0048, lines 5-6) relating to an ambient condition (temperature) of the server (Step 300, see Fig. 3 and paragraph 0048, lines 1-10);
compare a predicted cooling power requirement (expected cooling demand; see paragraph 0052, line 2) for a time period with a predicted cooling power availability (percentage of total cooling available; see paragraph 0055, lines 7-8) for the time period, the predicted cooling power requirement based on an output of the machine-learning model (Steps 310 and 340, see Fig. 3; also see paragraph 0049, lines 1-12 and paragraph 0059, lines 9); and
generate a cooling plan (cooling amount provided by a cooling mechanism; see paragraph 0055, lines 2-3) based on the comparison, the cooling plan to define operation of at least one of the server or a cooling system (cooling mechanism device 102, see Fig. 1) used to cool the server during the time period (Steps 320 and 350, see Fig. 3 and paragraph 0055, lines 2-10).
As to claim 2
Ruso discloses the apparatus of claim 1, wherein the cooling plan defines temporally segmented cooling plans for different time segments of the time period, the operation of at least one of the server or the cooling system to change between different ones of the time segments (see paragraph 0061, lines 23-29).
As to claim 3
Ruso discloses the apparatus of claim 2, wherein a number of the temporally segmented cooling plans is greater than two (see paragraph 0061, lines 23-29).
As to claim 4
Ruso discloses the apparatus of claim 1, wherein the operation of the server is to be at least one of throttled or deployed to another server when the predicted cooling power requirement exceeds the predicted available cooling power availability (see paragraph 0028, lines 1-10 and paragraph 0029, lines 3-13).
As to claim 5
Ruso discloses the apparatus of claim wherein the operation of the cooling system is to reduce a temperature of the server when the predicted cooling power requirement is less than the predicted available cooling power availability (see paragraph 0022, lines 7-15).
As to claim 6
Ruso discloses the apparatus of claim 1, wherein the operation of the server is to increase a temperature of the server when the predicted cooling power requirement exceeds the predicted available cooling power availability (see paragraph 0022, lines 7-15).
As to claim 8
Ruso discloses the apparatus of claim 1, wherein the second information includes:
sensor data related to a current ambient condition of the server (see paragraph 0053, lines 5-6);
historic records of past ambient conditions of the server (see paragraph 0058, lines 3-5); and
forecasts of future ambient conditions on the server (see paragraph 0056, lines 9-16).
As to claim 9
Ruso discloses a non-transitory machine readable storage medium (memory 201, see Fig. 2) comprising instructions (program instructions; see paragraph 0036, line 19) to cause programmable circuitry (processor 202, see Fig. 2) to at least:
input operational data (one or more server indicators; see paragraph 0048, line 2) into a machine-learning model (machine learning model; see paragraph 0049, line 9), the operational data including first information (server indicators relating to a task to be executed by at least one component of the server; see paragraph 0048, lines 2-3) relating to a workload (task) of a server (one or more servers 101, see Fig. 1) and second information (server indicators from the temperature sensors 103; see paragraph 0048, lines 5-6) relating to an ambient condition (temperature) of the server (Step 300, see Fig. 3 and paragraph 0048, lines 1-10);
compare a predicted cooling power requirement (expected cooling demand; see paragraph 0052, line 2) for a time period with a predicted cooling power availability (percentage of total cooling available; see paragraph 0055, lines 7-8) for the time period, the predicted cooling power requirement based on an output of the machine-learning model (Steps 310 and 340, see Fig. 3; also see paragraph 0049, lines 1-12 and paragraph 0059, lines 9); and
generate a cooling plan (cooling amount provided by a cooling mechanism; see paragraph 0055, lines 2-3) based on the comparison, the cooling plan to define operation of at least one of the server or a cooling system (cooling mechanism device 102, see Fig. 1) used to cool the server during the time period (Steps 320 and 350, see Fig. 3 and paragraph 0055, lines 2-10).
As to claim 10
Ruso discloses the non-transitory machine readable medium of claim 9, wherein the cooling plan defines temporally segmented cooling plans for different time segments of the time period, the operation of at least one of the server or the cooling system to change between different ones of the time segments (see paragraph 0061, lines 23-29).
As to claim 11
Ruso discloses the non-transitory machine readable medium of claim 10, wherein a number of the temporally segmented cooling plans is greater than two (see paragraph 0061, lines 23-29).
As to claim 12
Ruso discloses the non-transitory machine readable medium of claim 9, wherein the operation of the server is to be at least one of throttled or deployed to another server when the predicted cooling power requirement exceeds the predicted available cooling power availability (see paragraph 0028, lines 1-10 and paragraph 0029, lines 3-13).
As to claim 13
Ruso discloses the non-transitory machine readable medium of claim 9, wherein the operation of the cooling system is to reduce a temperature of the server when the predicted cooling power requirement is less than the predicted available cooling power availability (see paragraph 0022, lines 7-15).
As to claim 14
Ruso discloses the non-transitory machine readable medium of claim 9, wherein the operation of the server is to increase a temperature of the server when the predicted cooling power requirement exceeds the predicted available cooling power availability (see paragraph 0022, lines 7-15).
As to claim 15
Ruso discloses the non-transitory machine readable medium of claim 9, wherein the first information includes at least one of an instruction set associated with the workload or a power requirement of an input/output device of the server, the input/output device to be used during the execution of the workload (see paragraph 0040, lines 7-11 and paragraph 0043, lines 11-17).
As to claim 17
Ruso discloses a method comprising:
inputting operational data (one or more server indicators; see paragraph 0048, line 2) into a machine-learning model (machine learning model; see paragraph 0049, line 9), the operational data including first information (server indicators relating to a task to be executed by at least one component of the server; see paragraph 0048, lines 2-3) relating to a workload (task) of a compute device (one or more servers 101; see Fig. 1) and second information (server indicators from the temperature sensors 103; see paragraph 0048, lines 5-6) relating to an ambient condition (temperature) of the compute device (Step 300, see Fig. 3 and paragraph 0048, lines 1-10);
comparing a predicted cooling power requirement (expected cooling demand; see paragraph 0052, line 2) for a time period with a predicted cooling power availability (percentage of total cooling available; see paragraph 0055, lines 7-8) for the time period, the predicted cooling power requirement based on an output of the machine-learning model (Steps 310 and 340, see Fig. 3; also see paragraph 0049, lines 1-12 and paragraph 0059, lines 9); and
generating a cooling plan (cooling amount provided by a cooling mechanism; see paragraph 0055, lines 2-3) based on the comparison, the cooling plan to define operation of at least one of the compute device or a cooling system (cooling mechanism device 102, see Fig. 1) used to cool the compute device during the time period (Steps 320 and 350, see Fig. 3 and paragraph 0055, lines 2-10).
As to claim 18
Ruso discloses the method of claim 17, wherein the cooling plan defines temporally segmented cooling plans for different time segments of the time period, the operation of at least one of the compute device or the cooling system to change between different ones of the time segments (see paragraph 0061, lines 23-29).
As to claim 21
Ruso discloses the method of claim 17, wherein the operation of the cooling system is to reduce a temperature of the compute device when the predicted cooling power requirement is less than the predicted available cooling power availability (see paragraph 0022, lines 7-15).
As to claim 22
Ruso discloses the method of claim 17, wherein the operation of the compute device is to increase a temperature of the compute device when the predicted cooling power requirement exceeds the predicted available cooling power availability (see paragraph 0022, lines 7-15).
As to claim 23
Ruso discloses the method of claim 17, wherein the first information includes at least one of an instruction set associated with the workload or a power requirement of an input/output device of the compute device, the input/output device to be used during the execution of the workload (see paragraph 0040, lines 7-11 and paragraph 0043, lines 11-17).
As to claim 24
Ruso discloses the method of claim 17, wherein the second information includes: sensor data related to a current ambient condition of the compute device (see paragraph 0053, lines 5-6); historic records of past ambient conditions of the compute device (see paragraph 0058, lines 3-5); and forecasts of future ambient conditions on the compute device (see paragraph 0056, lines 9-16).
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Michael J. Brown whose telephone number is (571)272-5932. The examiner can normally be reached Monday-Thursday from 5:30am-4:00pm.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Kamini Shah can be reached at (571)272-2279. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/Michael J Brown/
Primary Examiner, Art Unit 2115