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 .
Claim Interpretation
Claim(s) 1-20 do not use “means for” (or “step for”) language, or generic placeholders for "means” coupled with functional language without recitation of sufficient structure for carrying out the claimed functions and therefore do not invoke 35 U.S.C. 112(f) (pre-AIA 35 U.S.C. 112, sixth paragraph).
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 (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 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.
Claim(s) 1, 2, 4, 5, 8-12, 14, 15 and 18-20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Billet et al. (US 2016/0234972 A1) in view of Kochov et al. “Machine Learning Accelerated Computational Fluid Dynamics” (cited on IDS).[claim 11]
Regarding claim 11, Billet discloses an electronic device (Figure 13), comprising:
at least one processor (Figure 13, 510); and
a memory, coupled to the at least one processor and having instructions stored therein (Figure 13, 518; Paragraph 0146), the instructions when executed by the at least one processor causing the electronic device to perform actions (Figure 1) comprising:
acquiring a heating parameter and a cooling parameter (Paragraph 0047, 0098-0099); and
determining, based on the heating parameter and the cooling parameter, a flow field parameter at a target location in an object utilizing network model (Figure 1, 105; Paragraph 0050),
wherein the flow field parameter comprises at least one of temperature, pressure, and flow rate at the target location in the object (Paragraph 0050), the model based on computational fluid dynamics (e.g. Paragraph 0018, 0061)
However, Billet does not disclose a trained neural network model, wherein the trained neural network model is trained based on computational fluid dynamics (CFD) simulation sample data.
Kochov discloses a trained neural network model for performing CFD simulation which is trained based on CFD simulation sample data (Section II.B.3 Training). The neural network of Kochov improves CFD modeling while providing computational speedups (Abstract).
Therefore, it would have been obvious to use a trained neural network as taught by Kochov to perform CFD simulations in the system of Billet to provide faster CFD simulation.[claim 12]
Regarding claim 12, Billet discloses wherein the object comprises a computing device, and the heating parameter comprises at least one of:
a first power associated with a processor in the computing device; a second power associated with a storage device in the computing device; or a third power associated with a power supply unit in the computing device (Paragraph 0043, 0097-0098, 0102; rack power is “associated with” the processors/storage devices/power supplies of computing devices located in the rack).[claim 14]
Regarding claim 14, Billet discloses herein the object comprises a computing device, and the cooling parameter comprises at least one of:
ambient temperature; or rotational speed of a fan in the computing device (Paragraphs 0044, 0055-0056, 0098-0099).[claim 15]
Regarding claim 15, Billet in view of Kochov discloses wherein the instructions, when executed by the at least one processor, further cause the electronic device to perform actions:
connecting an output of the trained neural network model to a calibrator (Paragraph 0090, 0097-0099); and
calibrating, by the calibrator, the flow field parameter determined by the trained neural network model (Paragraph 0097-0099).
Note that it would have been obvious to utilize the same calibration technique of Billet with the trained neural network model so that the results may be similarly optimized.[claim 18]
Regarding claim 18, Kochov discloses wherein the CFD simulation sample data comprises: a CFD simulation condition parameter; an input sample parameter and an output sample parameter at a location with the CFD simulation condition parameter (Section II.B.3, comparing model for inputs/outputs and calculating loss compared to simulation sample data model).[claim 19]
Regarding claim 19, Billet in view of Kochov discloses wherein the trained neural network model is used for determining a flow field parameter at one or more of a plurality of target locations in the object, and the flow field parameter comprises at least one of temperature, pressure, and flow rate (Paragraph 0050).[claims 1, 2, 4, 5, 8 and 10]
Claims 1, 2, 4, 5, 8 and 10 are method claims corresponding to apparatus claims 11, 12, 14, 15, 18 and 19. Therefore, claims 1, 2, 4, 5, 8 and 10 are analyzed and rejected as previously discussed with respect to claims 11, 12, 14, 15, 18 and 19.[claim 9]
Regarding claim 9, Kochov discloses wherein the CFD simulation sample data is selected from a plurality of sets of parameters for use in a CFD simulator, and each of the plurality of sets of parameters comprises a simulation condition parameter of the CFD simulator, an input sample parameter, and an output sample parameter at the location with the simulation condition parameter (Section II.B.3, plurality of sets of simulation data, each starting from different conditions to produce outputs to simulate particular parameters used for training).[claim 20]
Regarding claim 20, Billet in view of Kochov discloses a computer program product, the computer program product being tangibly stored on a non-transitory computer-readable medium and comprising machine-executable instructions (Figure 13, 518; Paragraph 0146), wherein the machine-executable instructions, when executed by a machine, cause the machine to perform the claimed actions (see rejection of claim 11 above).
Claim(s) 3 and 13 is/are rejected under 35 U.S.C. 103 as being unpatentable over Billet et al. (US 2016/0234972 A1) in view of Kochov et al. “Machine Learning Accelerated Computational Fluid Dynamics” (cited on IDS).[claim 13]
Regarding claim 13, Billet discloses obtaining power information (e.g. Paragraphs 0098-0099) but does not disclose wherein the instructions, when executed by the at least one processor, cause the electronic device to perform actions:
obtaining the first power by querying a power diagram based on an operating frequency of the processor and a working voltage of the processor.
However, Official Notice is taken that it is well known in the art to calculate power consumption of a processor based on frequency and voltage, e.g. P = C*V2*f where C is the switched load capacitance of the processor, V is the voltage and f is the frequency. By performing such a calculation the power of devices could be estimated without the need for a physical power meter.
Therefore, it would have been obvious to include instructions for obtaining the first power through a calculation based on operating frequency and working voltage so that power could be estimated and the system cost could be reduced by removing the need for a physical power meter.[claim 3]
Claim 3 is a method claim corresponding to apparatus claim 13. Therefore, claim 3 is analyzed and rejected as previously discussed with respect to claim 13.
Allowable Subject Matter
Claims 6, 7, 16 and 17 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.[claims 6, 7, 16 and 17]
Regarding claims 6 and 16, the prior art does not teach or reasonably suggest acquiring a preset calibration graph, the calibration graph corresponding to at least one parameter of the flow field parameter; and calibrating the at least one parameter based on the calibration graph.
Regarding claims 7 and 17, the prior art does not teach or reasonably suggest acquiring a preset calibration graph, the calibration graph corresponding to at least one parameter of the flow field parameter; acquiring a measured parameter obtained by measurement at least one reference location in the object; comparing the at least one parameter with the measured parameter; updating the calibration graph based on a comparison result; and calibrating the at least one parameter based on the updated calibration graph.
While Billet discloses a calibration function (see rejections above), Billet does not disclose acquiring a preset calibration graph for calibrating the at least one parameter as claimed.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. The following references show additional systems for simulating temperature/pressure/airflow:
Kim US 2026/0087213 A1
Abarham et al. US 12,566,909 B1
Ratcliff US 2025/0261344 A1
Ramsperger et al. US 2025/0041942 A1
Cili et al. US 11,656,664 B2
VanGilder et al. US 2021/0185858 A1
Civilini US 2015/0057828 A1
Hamann et al. US 2014/0257740 A1
Liang et al. US 2011/0301778 A1
Cox et al. US 2010/0094582 A1
Any inquiry concerning this communication or earlier communications from the examiner should be directed to TIMOTHY J HENN whose telephone number is (571)272-7310. The examiner can normally be reached Monday-Friday ~10-6.
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/Timothy J Henn/ Primary Examiner, Art Unit 2639
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