Prosecution Insights
Last updated: April 19, 2026
Application No. 18/170,037

ESTIMATING WORKLOAD ENERGY CONSUMPTION

Non-Final OA §101§103
Filed
Feb 16, 2023
Examiner
WAI, ERIC CHARLES
Art Unit
2195
Tech Center
2100 — Computer Architecture & Software
Assignee
International Business Machines Corporation
OA Round
1 (Non-Final)
82%
Grant Probability
Favorable
1-2
OA Rounds
3y 9m
To Grant
99%
With Interview

Examiner Intelligence

Grants 82% — above average
82%
Career Allow Rate
529 granted / 644 resolved
+27.1% vs TC avg
Strong +27% interview lift
Without
With
+27.2%
Interview Lift
resolved cases with interview
Typical timeline
3y 9m
Avg Prosecution
27 currently pending
Career history
671
Total Applications
across all art units

Statute-Specific Performance

§101
15.7%
-24.3% vs TC avg
§103
50.0%
+10.0% vs TC avg
§102
11.4%
-28.6% vs TC avg
§112
14.4%
-25.6% vs TC avg
Black line = Tech Center average estimate • Based on career data from 644 resolved cases

Office Action

§101 §103
CTNF 18/170,037 CTNF 82218 DETAILED ACTION Claims 1-20 are presented for examination. 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. Specification 06-11 AIA The title of the invention is not descriptive. A new title is required that is clearly indicative of the invention to which the claims are directed. 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 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (abstract idea) without significantly more. As per claim 1, in step 1 of the 101 analysis, the examiner has determined that the claim is directed to a method. Therefore, the claim is directed to one of the four statutory categories of invention. In step 2A prong 1 of the 101 analysis, the examiner has determined that the claim recites a judicial exception. Specifically, the limitations “creating a first model based on the energy consumption data corresponding to a first duration; creating a second model based on the energy consumption data corresponding to a second duration, wherein the second duration is longer than the first duration” and “calculating a first estimated energy consumption of the workload during the time period based on the first model; calculating a second estimated energy consumption of the workload during the time period based on the second model; and calculating a combined estimated energy consumption of the workload based on the first estimated energy consumption and the second estimated energy consumption” recite mental processes. The limitations encompass a human mind carrying out the functions through observation, evaluation, judgment and /or opinion, or even with the aid of pen and paper. Thus, these limitations recite and fall within the “Mental Processes” grouping of abstract ideas under Prong 1 Step 2A. In step 2A prong 2 of the 101 analysis, the examiner has determined that the additional elements, alone or in combination do not integrate the judicial exceptions into a practical application for the following rationale: The limitation “a cloud computing system” apply judicial exceptions on a generic computer. "Alappat 's rationale that an otherwise ineligible algorithm or software could be made patent-eligible by merely adding a generic computer to the claim was superseded by the Supreme Court's Bilski and Alice Corp. decisions" so therefore applying judicial exceptions on a management entity which are generic computers does not integrate the judicial exceptions into a practical application (MPEP 2106.05(b)). The limitations “periodically collecting an energy consumption data for the workload” and “receiving a request for an estimated energy consumption of the workload during a time period” represent insignificant, extra-solution activities. The term "extra-solution activity" can be understood as "activities incidental to the primary process or product that are merely a nominal or tangential addition to the claim" (MPEP 2106.05(g)). The examiner has determined that the limitations “periodically collecting an energy consumption data for the workload” and “receiving a request for an estimated energy consumption of the workload during a time period” are directed to mere data gathering activities which is a category of insignificant extra-solution activities (MPEP 2106.05(g)). In step 2B of the 101 analysis, the examiner has determined that the additional elements, alone or in combination do not recite significantly more than the abstract ideas identified above for the following rationale: The limitation “a cloud computing system” apply judicial exceptions on a generic computer and therefore do not provide significantly more. The limitations “periodically collecting an energy consumption data for the workload” and “receiving a request for an estimated energy consumption of the workload during a time period” represent insignificant, extra-solution activities and are well-understood, routine, or conventional because they are directed to "receiving or transmitting data" (MPEP 2106.05(d)). These are additional elements that the courts have recognized as well understood, routine, or conventional (MPEP 2106.05(d)). The citation of court cases in the MPEP meets the Berkheimer evidentiary burden since citation of a court case in the MPEP is one of the 4 types of evidentiary support that can be used to prove that the additional elements are well-understood, routine, or conventional (see 125 USPQ2d 1649 Berkheimer v. HP, Inc.). Thus, the limitations do not amount to significantly more than the abstract idea. Considering the additional elements individually and in combination and the claim as a whole, the additional elements do not provide significantly more than the abstract idea. The claim is not patent eligible. As per claim 9, it is a system claim of claim 1, so it is rejected for the same reasons as claim 1. Additionally, claim 9 recites “a memory having computer readable instructions and one or more processors” which recite generic computing components that do not integrate the judicial exceptions into a practical application and do not provide significantly more and recite intended use limitations that do not have patentable weight. As per claim 17, it is a media/product type claim of claim 1, so it is rejected for the same reasons as claim 1. Additionally, claim 17 recites “a computer readable storage medium having program instructions” which are generic computing components that do not integrate the judicial exceptions into a practical application and do not provide significantly more. As per claim 2 (and similarly for claims 10 and 18), it recites “wherein creating the first model based on the energy consumption data includes filtering outlier data from the energy consumption data” which further describes the abstract idea. As per claim 3 (and similarly for claims 11 and 19), it recites “wherein creating the second model based on the energy consumption data includes filtering outlier data from the energy consumption data” which further describes the abstract idea. As per claim 4 (and similarly for claims 12 and 20), it recites “wherein the combined estimated energy consumption of the workload is calculated based on a weighted combination of the first estimated energy consumption and the second estimated energy consumption” which further describes the abstract idea. As per claim 5 (and similarly for claim 13), it recites “wherein a weight assigned to the first estimated energy consumption and the second estimated energy consumption are based on the first duration, the second duration and a length of the time period” which further describes the abstract idea. As per claim 6 (and similarly for claim 14), it recites “wherein the energy consumption data includes a workload identifier, a pod identifier, and a current energy usage level for a portion of the identified workload being executing on the identified pod” which further describes the abstract idea. As per claim 7 (and similarly for claim 15), it recites “the combined estimated energy consumption of the workload is further based on workload scheduling information for the workload” which further describes the abstract idea. As per claim 8 (and similarly for claim 16), it recites “wherein the first model and the second model are regression models” which further describes the abstract idea. Claim Rejections - 35 USC § 103 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. 07-21-aia AIA Claim (s) 1, 6-9, and 14-17 is/are rejected under 35 U.S.C. 103 as being unpatentable over Chaudhari et al. (US PG Pub No. 2025/0093393 A1) in view of Li et al. (US PG Pub No. 2020/0364211 A1), further in view of Huang et al. (UG PG Pub No. 2018/0046924 A1) . Regarding claim 1, Chaudhari teaches a method for estimating energy consumption of a workload in a cloud computing system, comprising: periodically collecting an energy consumption data for the workload ([0050], wherein power consumption is transmitted to the cluster managing device); creating a first model based on the energy consumption data corresponding to a first duration ([0031-32], wherein a new power model is built based on actual power consumption of the deployed application); receiving a request for an estimated energy consumption of the workload during a time period ([0051]; [0071]); calculating a first estimated energy consumption of the workload during the time period based on the first model ([0092]). Chaudhari does not teach creating a second model based on the energy consumption data corresponding to a second duration, wherein the second duration is longer than the first duration; calculating a second estimated energy consumption of the workload during the time period based on the second model. Li teaches collecting data over different time periods to train multiple predictive models, each being trained to predict performance over different time periods of interest ([0049]). It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to create a second model based on the energy consumption data corresponding to a second duration, wherein the second duration is longer than the first duration; calculating a second estimated energy consumption of the workload during the time period based on the second model. One would be motivated by the desire to perform predictions over the course of an hour, a day or a week as taught by Li ([0049]). Chaudhari and Li do not teach calculating a combined estimated energy consumption of the workload based on the first estimated energy consumption and the second estimated energy consumption. Huang teaches combining models for different prediction types and different time periods ([0050]). It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to calculating a combined estimated energy consumption of the workload based on the first estimated energy consumption and the second estimated energy consumption. One would be motivated by the desire to increase accuracy by combining models as taught by Huang ([0050]). Regarding claim 6, Chaudhari teaches wherein the energy consumption data includes a workload identifier, a pod identifier, and a current energy usage level for a portion of the identified workload being executing on the identified pod ([0050]). Regarding claim 7, Chaudhari teaches the combined estimated energy consumption of the workload is further based on workload scheduling information for the workload ([0092]). Regarding claim 8, Chaudhari teaches wherein the first model and the second model are regression models ([0010]). Regarding claims 9 and 14-17, they are the system and medium claims of claims 1 and 6-8 above. Therefore, they are rejected for the same reasons as claims 1 and 6-8 above . 07-21-aia AIA Claim (s) 2-3, 10-11 and 18-19 is/are rejected under 35 U.S.C. 103 as being unpatentable over Chaudhari et al. (US PG Pub No. 2025/0093393 A1) in view of Li et al. (US PG Pub No. 2020/0364211 A1), in view of Huang et al. (UG PG Pub No. 2018/0046924 A1), further in view of Mitterhofer (US PG Pub No. 2019/0310393 A1) . Regarding claim 2, Chaudhari, Li, and Huang do not teach wherein creating the first model based on the energy consumption data includes filtering outlier data from the energy consumption data. It is old and well known to filter outlier data from statistical data when generating models. For example, Mitterhofer teaches using a dynamic energy model to applying statistical modeling to the harvested data utilizing filtering to remove outliers in the stored energy metrics providing improved energy metric ([0037]). It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to filter outlier data from the energy consumption data. One would be motivated by the desire to improve the data metrics by removing anomalies. Regarding claim 3, Chaudhari, Li, and Huang do not teach wherein creating the second model based on the energy consumption data includes filtering outlier data from the energy consumption data. It is old and well known to filter outlier data from statistical data when generating models. For example, Mitterhofer teaches using a dynamic energy model to applying statistical modeling to the harvested data utilizing filtering to remove outliers in the stored energy metrics providing improved energy metric ([0037]). It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to filter outlier data from the energy consumption data. One would be motivated by the desire to improve the data metrics by removing anomalies. Regarding claims 10-11 and 18-19, they are the system and medium claims of claims 2-3 above. Therefore, they are rejected for the same reasons as claims 2-3 above . 07-21-aia AIA Claim (s) 4-5, 12-13, and 20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Chaudhari et al. (US PG Pub No. 2025/0093393 A1) in view of Li et al. (US PG Pub No. 2020/0364211 A1), in view of Huang et al. (UG PG Pub No. 2018/0046924 A1), further in view of Thomas et al. (US PG Pub No. 2021/0295987 A1) . Regarding claim 4, Chaudhari, Li, and Huang do not teach wherein the combined estimated energy consumption of the workload is calculated based on a weighted combination of the first estimated energy consumption and the second estimated energy consumption. It is old and well known to utilize weights when combining model outputs. For example, Thomas teaches determining appropriate weights for the respective models and combine the outputs of the respective models using the weights to generate a final prediction ([0101]). It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to using a weighted combination. One would be motivate by the desire use weights to account for model accuracy of the respective models as taught by Thomas ([0101]). Regarding claim 5, Thomas teaches wherein a weight assigned to the first estimated energy consumption and the second estimated energy consumption are based on the first duration, the second duration and a length of the time period ([0101]). Regarding claims 12-13, and 20, they are the system and medium claims of claims 4-5 above. Therefore, they are rejected for the same reasons as claims 4-5 above. Conclusion Any inquiry concerning this communication or earlier communications from the examiner should be directed to ERIC C WAI whose telephone number is (571)270-1012. The examiner can normally be reached Monday - Friday 9-5. 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, Aimee Li can be reached at (571) 272-4169. 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. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000. /Eric C Wai/ Primary Examiner, Art Unit 2195 Application/Control Number: 18/170,037 Page 2 Art Unit: 2195 Application/Control Number: 18/170,037 Page 3 Art Unit: 2195 Application/Control Number: 18/170,037 Page 4 Art Unit: 2195 Application/Control Number: 18/170,037 Page 5 Art Unit: 2195 Application/Control Number: 18/170,037 Page 8 Art Unit: 2195 Application/Control Number: 18/170,037 Page 9 Art Unit: 2195 Application/Control Number: 18/170,037 Page 10 Art Unit: 2195 Application/Control Number: 18/170,037 Page 11 Art Unit: 2195
Read full office action

Prosecution Timeline

Feb 16, 2023
Application Filed
Nov 29, 2023
Response after Non-Final Action
Mar 17, 2026
Non-Final Rejection — §101, §103 (current)

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Study what changed to get past this examiner. Based on 5 most recent grants.

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Prosecution Projections

1-2
Expected OA Rounds
82%
Grant Probability
99%
With Interview (+27.2%)
3y 9m
Median Time to Grant
Low
PTA Risk
Based on 644 resolved cases by this examiner. Grant probability derived from career allow rate.

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