CTNF 18/186,059 CTNF 84695 Notice of Pre-AIA or AIA Status 07-03-aia AIA 15-10-aia 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 § 101 07-04-01 AIA 07-04 35 U.S.C. 101 reads as follows: Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title. Claims 1-27 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. Step 2A Prong 1: Claim 19 recites, outside of the brackets: A method to predict power consumption in a server, the method comprising: dividing, [by executing an instruction with programmable circuitry], a training data set into a first sub-range of data and a second sub-range of the data, a data point in the training data set representative of resource utilization of a workload and a corresponding power consumption metric of the workload; [training, by executing an instruction with the programmable circuitry, first candidate models based on the first sub-range of the data and second candidate models based on the second sub-range of the data]; selecting, [by executing an instruction with the programmable circuitry], a first prediction model from the first candidate models; and selecting, [by executing an instruction with the programmable circuitry], a second prediction model from the second candidate models, outputs of the first prediction model and the second prediction model to predict the power consumption of the server. The above limitations, but for the additional elements as described below, covers performance of the limitations in the human mind or on pen and paper, including observation, evaluation, judgment, opinion. Therefore, the limitations fall into the “mental processes” grouping of abstract ideas. Claims 1 and 10 recite similar limitations. Step 2A Prong 2: This judicial exception is not integrated into a practical application. Claim 19 recites, “by executing an instruction with the programmable circuitry,” and “training . . . first candidate models based on the first sub-range of the data and second candidate models based on the second sub-range of the data.” Claim 1 recites “interface circuitry,” “processor circuitry including . . . “, “range determiner circuitry,” “model trainer circuitry,” “to train . . .”, and “prediction selector circuitry.” Claim 10 recites “A non-transitory machine readable storage medium” and “train . . .” The generically claimed training of various models generally applies the abstract idea without placing any limits on how the training is accomplished, and amounts to “apply it.” The remaining limitations (i.e. various circuitry, including the enumerated types of “processor circuitry” in claim 1, and non-transitory machine readable storage medium) amount to mere instructions to apply the exception via generic computer components. The combination of these additional elements is no more than mere instructions to apply the exception using generic computer implementation. Accordingly, even in combination, these additional elements do not integrate the abstract idea into a practical application. The claim is directed to an abstract idea. Step 2B: The claims does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception because. As discussed with respect to Step 2A Prong 2, the generically claimed training of a model generally applies the abstract idea without placing any limits on how the training is accomplished, and amounts to “apply it.” The remaining limitations (i.e. various circuitry and non-transitory machine readable storage medium) amount to mere instructions to apply the exception via generic computer components. Accordingly, even in combination, these additional elements are not significantly more than the abstract idea. For these reasons, there is no inventive concept. The claims are not patent eligible. Dependent claims 2-9, 11-18, 20-27 merely narrow the abstract idea, above. Accordingly, the when viewed in combination, the claim does not amount to a practical application or significantly more than the abstract idea. Claim Rejections - 35 USC § 103 07-06 AIA 15-10-15 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. 07-20-aia AIA 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. 07-23-aia AIA The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows: 1. Determining the scope and contents of the prior art. 2. Ascertaining the differences between the prior art and the claims at issue. 3. Resolving the level of ordinary skill in the pertinent art. 4. Considering objective evidence present in the application indicating obviousness or nonobviousness. 07-21-aia AIA Claim (s) 1, 3, 7, 9, 10, 12, 16, 18, 19, 21, 25, 27 are rejected under 35 U.S.C. 103 as being unpatentable over Uskudar et al. (US 2022/0311675 A1) in view of Geffin et al. (US 2012/0053925 A1) in view of Liu et al. (US 2021/0390256 A1) . Claim 1: Uskudar discloses: An apparatus to predict power consumption in a server (Uskudar [0041], [0053]), the apparatus comprising: With respect to the limitation: interface circuitry to obtain a power prediction request corresponding to the server; and Uskudar, in Fig. 1, [0038], [0139] discloses a server node that includes an interface used for power prediction, but does not explicitly disclose interface circuitry that obtains a power prediction request. However, Geffin, [0046], Fig. 2, teaches an interface to obtain a power prediction request of a server. One of ordinary skill in the art would have recognized that applying the known technique of Geffin to Uskudar would have yielded predictable results and resulted in an improved system. It would have been recognized that applying the technique of Geffin to the teaching of Uskudar would have yielded predictable results because the level of ordinary skill in the art demonstrated by the references applied shows the ability to incorporate such an interface. Further, applying an interface for requesting data to Uskudar which already has data would have been recognized by one of ordinary skill in the art as resulting in an improved system that would allow more efficient and specific requests for data. Uskudar further discloses: processor circuitry including one or more of: at least one of a central processor unit, a graphics processor unit, or a digital signal processor, the at least one of the central processor unit, the graphics processor unit, or the digital signal processor having control circuitry to control data movement within the processor circuitry, arithmetic and first logic circuitry to perform one or more first operations corresponding to instructions, and one or more registers to store a first result of the one or more first operations, the instructions in the apparatus; a Field Programmable Gate Array (FPGA), the FPGA including second logic gate circuitry, a plurality of configurable interconnections, and storage circuitry, the second logic gate circuitry and the plurality of the configurable interconnections to perform one or more second operations, the storage circuitry to store a second result of the one or more second operations; or Application Specific Integrated Circuitry (ASIC) including logic gate circuitry to perform one or more third operations (Uskudar [0036-0037], [0126], [0139] showing a computer processor and memory, which a PHOSITA would understand includes a CPU. See alternatively Geffin [0032] also showing a memory and a CPU) the processor circuitry to perform at least one of the first operations, the second operations, or the third operations to instantiate (Uskudar [0036], [0126], [0139]), With respect to the limitation: range determiner circuitry to divide a training data set into a first sub-range of data and a second sub-range of the data, a data point in the training data set representative of resource utilization of a workload and a corresponding power consumption metric of the workload; Uskudar, in [0035] discloses the system utilized to determine a server’s power consumption. Uskudar, in [0064], [0105-0107] further discloses dividing training data into sub-range slices. Uskadar, in [0041-0052], [0080-0081] further discloses correlating the power consumption based on various event correlations. Uskudar does not explicitly disclose that the data set is indicative of a workload to correlate with the power consumption metric. However, Geffin, in [0048] and [0090] explicitly teaches modeling a server’s power consumption based on its workload. One of ordinary skill in the art would have recognized that applying the known technique of Geffin to Uskudar would have yielded predictable results and resulted in an improved system. It would have been recognized that applying the technique of Uskudar to the teaching of Geffin would have yielded predictable results because the level of ordinary skill in the art demonstrated by the references applied shows the ability to incorporate such workload data. Further, applying workload data to Uskudar which already correlates events and power consumption, would have been recognized by one of ordinary skill in the art as resulting in an improved system that would allow more robust data analysis variables. With respect to the limitation: model trainer circuitry to train first candidate models based on the first sub- range of the data and second candidate models based on the second sub-range of the data; and Uskudar, as above in [0064], [0105-0107], teaches slicing data and, in [0101-0102] discloses that each trained model may be achieved via training a plurality of models. As such, Uskudar highly suggests training different models for each data slice, but does not explicitly disclose doing so. However, Liu, in [0024], [0081], Fig. 5, teaches each slice will be trained on its own model. Accordingly, the combination of Uskudar with Liu would result in a plurality of models trained for each slice. One of ordinary skill in the art would have recognized that applying the known technique of Liu to Uskudar would have yielded predictable results and resulted in an improved system. It would have been recognized that applying the technique of Liu to the teaching of Uskudar would have yielded predictable results because the level of ordinary skill in the art demonstrated by the references applied shows the ability to incorporate such slice-based modeling. Further, applying slice-based modeling to Uskudar which already incorporates data slices and a plurality of models, would have been recognized by one of ordinary skill in the art as resulting in an improved system that would allow more granular data modeling. Uskudar, as modified above, teaches the remaining limitations: prediction selector circuitry to: select a first prediction model from the first candidate models (Uskudar [0102-0103]); and select a second prediction model from the second candidate models (Uskudar [0102-0103] (in view of Liu, above teaching discrete models for each data slice), outputs of the first prediction model and the second prediction model to predict the power consumption of the server (Uskudar [0082], [0105-0107]). Claim 3: Uskudar discloses: baseline model circuitry to generate the training data set (Uskudar [0041-0052], [0080-0081]); and workload model circuitry to modify the training data set with workload data (Uskudar [0108] showing modifying training data with updated data). Claim 7: Uskudar discloses: wherein the workload data includes: a description of a historical workload executed by the server [Uskudar [0041-0046], [0080]); and a recorded power consumption value corresponding to the execution of the historical workload (Uskudar [0041-0052], [0080]). Examiner notes Geffin, in e.g. [0044], [0049], alternatively teaches power consumption values may be correlated with particular workflows. Claim 9: Uskudar discloses: wherein: the first candidate models includes the first prediction model and a third model (Uskudar [0101-0104]); the apparatus further includes model executor circuitry (Uskudar [0139] to: execute the first prediction model to produce a first output (Uskudar [0101-0104]); and execute the third model to produce a third output (Uskudar [0101-0104]); and to select the first prediction model from the first candidate models, the prediction selector circuitry is to: compare the first output to an expected output to determine a first model error (Uskudar [0101-0104]); compare the third output to the expected output to determine a third model error (Uskudar [0101-0104]); and determine the first model error is less than or equal to the third model error (Uskudar [0101-0104]). Claims 10, 12, 16, 18: See above relevant rejections of claims 1, 3, 7, 9. In addition, Uskudar, in [0139] describes a non-transitory computer readable medium for performing the method. Claims 19, 21, 25, 27: See above relevant rejections of claims 1, 3, 7, 9. In addition, Uskudar, in [0139] discloses use of programmable circuitry for performing the method . 07-21-aia AIA Claim (s) 2, 11, 20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Uskudar/Geffin/Liu and further in view of Chickering et al. (US 2007/0055477 A1) . Claim 2, 11, 20: With respect to the limitation: wherein the range determiner circuitry is further to divide the training data set into a plurality of sub-ranges, ones of the sub-ranges corresponding to respective numbers of data points greater than or equal to a threshold value. Uskudar, as above in [0064], [0105-0107], discloses slicing training data into a plurality of sub-ranges based on the data characteristics, but does not explicitly disclose doing so based on data points being greater than or equal to a threshold value. However, Chickering, in [0032], teaches slicing data based on time periods to optimize modeling, of which the time periods include data points that are greater than or equal to a threshold value of that time period (e.g. the 1pm to 2pm on Thursday example described in Chickering). One of ordinary skill in the art would have recognized that applying the known technique of Chickerign to Uskudar would have yielded predictable results and resulted in an improved system. It would have been recognized that applying the technique of Chickering to the teaching of Uskudar would have yielded predictable results because the level of ordinary skill in the art demonstrated by the references applied shows the ability to incorporate such time based data slices. Further, applying threshold time slices to Uskudar which already incorporates time data and data slicing, would have been recognized by one of ordinary skill in the art as resulting in an improved system that would allow additional ways to analyze data . 07-21-aia AIA Claim (s) 4-6, 13-15, 22-24 is/are rejected under 35 U.S.C. 103 as being unpatentable over Uskudar/Geffin/Liu and further in view of Ubert et al. (US 2022/0326666 A1) Claim 4, 13, 22: With respect to the limitations: wherein to generate the training data set, the baseline model circuitry is to: obtain power consumption data points from a manufacturer, the power consumption data points to describe expected power consumption based on a type of the server and a workload description, the workload description to include one or more of central processing unit (CPU) utilization and memory utilization; and extrapolate the power consumption data points to produce additional data points based on the type of the server, the CPU utilization, and the memory utilization, the power consumption data points and the additional data points to form the training data set. Uskudar, in [0107], discloses use of a type of server for generating training data, but does not disclose 1) that the workload data includes CPU utilization and memory utilization, or 2) extrapolating data points from manufacturer data points. With respect to 1), Geffin, in [0048], [0090] teaches that server training data may include server type, workload type, CPU usage, memory usage and their associated power consumption. For similar reasons described above with respect to specific workload data in claim 1, adding additional variables (CPU/memory usage) would have been obvious to one of ordinary skill in the art leading to a more flexible system. With respect to 2), Ubert, in [0008], [0113], [0120], [0126-0127], [0131], [0165-0166] teaches that manufacturer data may be extrapolated into additional data to generate a training data set. One of ordinary skill in the art would have recognized that applying the known technique of Ubert to Uskudar would have yielded predictable results and resulted in an improved system. It would have been recognized that applying the technique of Ubert to the teaching of Uskudar would have yielded predictable results because the level of ordinary skill in the art demonstrated by the references applied shows the ability to incorporate such manufacturer data extrapolation. Further, applying manufacturer data extrapolation to Uskudar already utilizing training data, would have been recognized by one of ordinary skill in the art as resulting in an improved system that would allow more accurate initial training data. Claim 5, 14, 23: With respect to the limitations: wherein the server is an implementation of a first configuration of a device, the power consumption data points include one or more configurations of the device, the baseline model circuitry is to: determine whether the one or more configurations from the power consumption data points includes a second configuration corresponding to the first configuration; and extrapolate, after a determination the power consumption data points include the second configuration, data points corresponding to the second configuration to produce the additional data points. Uduskar, in [0105], discloses that each server may have multiple configurations each of which incorporates its own training data. [Examiner notes Geffin, in e.g. Fig. 2, similarly shows customizable server configurations]. Accordingly, for the reasons set forth above, Uduskar modified by Ubert would result in a system that extrapolates training data for multiple configurations of each server. The above rationale to combine persists. Claim 6, 15, 24: With respect to the limitations: wherein the server is an implementation of a first configuration of a device, the power consumption data points include one or more configurations of the device, the baseline model circuitry is to: determine whether the one or more configurations from the power consumption data points includes a second configuration corresponding to the first configuration; and after a determination the power consumption data points do not include the second configuration: identify a third configuration in the one or more configurations that is similar to the first configuration; select historical workload data points from the server that correspond to data points in the third configuration; shift the data points in the third configuration based on the workload data; and extrapolate the shifted data points to produce the additional data points. Uduskar, in [0105], discloses that each server may have multiple configurations, each of which incorporates its own training data. Uduskar, in [0064] further discloses that if no data exists for a particular configuration, the system may instead choose another similar configuration and utilize its training data. Accordingly, for the reasons set forth above, Uduskar modified by Ubert would result in a system that extrapolates relevant training data for available relevant configurations. The above rationale to combine persists . 07-21-aia AIA Claim (s) 8, 17, 26 is/are rejected under 35 U.S.C. 103 as being unpatentable over Uskudar/Geffin/Liu as applied above, and further in view of Zhang et al. (US 2022/0292074 A1) . Claim 8, 17, 26: With respect to the limitations: wherein to modify the training data set, the workload model circuitry is to: identify a data point in the training data set with a workload description that matches the historical workload; and replace a first power consumption value corresponding to the data point with the recorded power consumption value. Uskudar, in e.g. [0108], generally discloses that the model may be retrained as new data is obtained (which, as per [0041-0046], [0080], would be mapped to particular workloads). Examiner again notes Geffin, in e.g. [0044], [0049], alternatively teaches power consumption values may be correlated with particular workflows. However, neither Uskudar nor Geffin discloses replacing training data set values with relevant recorded data values. However, Zhang, in [0019], [0071], [0081] teaches that observed data values may replace other training data values. One of ordinary skill in the art would have recognized that applying the known technique of Zhang to Uskudar would have yielded predictable results and resulted in an improved system. It would have been recognized that applying the technique of Zhang to the teaching of Uskudar would have yielded predictable results because the level of ordinary skill in the art demonstrated by the references applied shows the ability to incorporate such data replacement. Further, applying data replacement to Uskudar with a data updates would have been recognized by one of ordinary skill in the art as resulting in an improved system that would result in more relevant data. Furthermore, although the manual matching described in Zhang teaches the limitations of the claim, Examiner further notes that mere automation of a manual activity at a high level is an obvious modification. See MPEP 2144.04(III) Conclusion Any inquiry concerning this communication or earlier communications from the examiner should be directed to KEVIN H FLYNN whose telephone number is (571)270-3108. The examiner can normally be reached Monday-Friday, 8:00 am - 5:00 pm. Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice. If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Beth Boswell can be reached at 571-272-6737. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300. Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. 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If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000. /KEVIN H FLYNN/Primary Examiner, Art Unit 3600 Application/Control Number: 18/186,059 Page 2 Art Unit: 3600 Application/Control Number: 18/186,059 Page 3 Art Unit: 3600 Application/Control Number: 18/186,059 Page 4 Art Unit: 3600 Application/Control Number: 18/186,059 Page 5 Art Unit: 3600 Application/Control Number: 18/186,059 Page 6 Art Unit: 3600 Application/Control Number: 18/186,059 Page 7 Art Unit: 3600 Application/Control Number: 18/186,059 Page 8 Art Unit: 3600 Application/Control Number: 18/186,059 Page 9 Art Unit: 3600 Application/Control Number: 18/186,059 Page 10 Art Unit: 3600 Application/Control Number: 18/186,059 Page 11 Art Unit: 3600 Application/Control Number: 18/186,059 Page 12 Art Unit: 3600 Application/Control Number: 18/186,059 Page 13 Art Unit: 3600 Application/Control Number: 18/186,059 Page 14 Art Unit: 3600 Application/Control Number: 18/186,059 Page 15 Art Unit: 3600 Application/Control Number: 18/186,059 Page 16 Art Unit: 3600 Application/Control Number: 18/186,059 Page 17 Art Unit: 3600 Application/Control Number: 18/186,059 Page 18 Art Unit: 3600
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