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 .
Status of Claims
This action is a responsive to the application filed on 05/12/2026.
Claims 1-20 are pending.
Claims 1-2, 4-5, 10, 13-14, 16-17, 20 have been amended.
Response to Arguments
Applicant’s arguments, with respect to the rejection(s) of claim(s) 1-20 under 35 U.S.C. 101, have been fully considered and are persuasive. Therefore, the objections set forth in the previous office action have been withdrawn.
Applicant’s arguments, with respect to the rejection(s) of claim(s) 1 and 13 under 35 U.S.C. 103, have been considered but they are not persuasive. Applicant argues that no reference teaches the amended limitations now stating “for an identified change to the relationship between the energy usage and the associated characteristics ... displaying, to a datacenter operator via an output interface, an indication of the identified change and a recommendation for remediation of the identified change; collecting, based on the identified change, third data for a third time period; and generating a second machine-trained model based on the third data”, since “Li's approach does not teach detecting changes in the relationship between energy usage and associated characteristics that trigger collecting new data and generating a replacement mode”. The examiner respectfully disagrees.
Due to the broadness of the claim language, it is maintained that Li teaches the argued limitations, since sections 3-4 and section 6, paragraphs 1-4 teach training the Q network (first machine-trained model) on state data including the ambient temperature and load factor variables (between the energy usage and the outside air temperature) to compute a cost of state-action pair in order to minimize the “optimization problem” (accounts for a dependency).
See 35 U.S.C 103 section for full mapping of claim limitations necessitated by applicant amendments.
Applicant’s arguments, with respect to the rejection(s) of claim(s) 1 and 13 under 35 U.S.C. 103, for Baig not teaching the claim limitations now stating “for an identified change to the relationship between the energy usage and the associated characteristics ... displaying, to a datacenter operator via an output interface, an indication of the identified change and a recommendation for remediation of the identified change; collecting, based on the identified change, third data for a third time period; and generating a second machine-trained model based on the third data”, have been considered have been considered but are moot because the arguments do not apply to the current combination of references being used in the current rejection.
Applicant’s arguments, with respect to the rejection(s) of claim(s) 1 and 13 under 35 U.S.C. 103, have been considered but they are not persuasive. Applicant argues that no reference teaches the amended limitations now stating “the first machine-trained model accounts for a dependency between the energy usage and the outside air temperature to distinguish changes to the energy usage attributable to the outside air temperature from changes not explainable by the outside air temperature”, since neither Li nor Baig teach a “dependency-aware modeling approach”. The examiner respectfully disagrees.
Due to the broadness of the claim language, Li has been found to teach the limitations, since sections 3-4 and section 6, paragraphs 1-4 teach training the Q network (first machine-trained model) on state data including the ambient temperature and load factor variables (between the energy usage and the outside air temperature) to compute a cost of state-action pair in order to minimize the “optimization problem” (accounts for a dependency).
See 35 U.S.C 103 section for full mapping of claim limitations necessitated by applicant amendments.
Applicant’s arguments, with respect to the rejection(s) of claim(s) 4, 9-12, and 20 under 35 U.S.C. 103, have been considered but they are not persuasive. Applicant argues that no reference teaches the amended limitations, since “Cambron does not teach using detected changes to trigger collecting new training data and generating a replacement machine-trained model” after detecting a change. The examiner respectfully disagrees.
Due to the broadness of the claim language, it is maintained that the combination teaches the limitations, since Baig is cited as teaching the multiple sampling sliding window of time-series data, and Cambron is cited as teaching, in sections 3 and 4.3, “EWMA” upper and lower control limit exponential calculations including “We choose k = 3, corresponding to a 3σ control limits” (EWM standard deviation); wherein the upper limit is an addition calculation and the lower limit is a subtraction calculation; further the data for validating the model being a time period before the “EWMA calculation”.
See 35 U.S.C 103 section for full mapping of claim limitations necessitated by applicant amendments.
Applicant’s arguments, with respect to the rejection(s) of claim(s) 10 under 35 U.S.C. 103, have been considered but they are not persuasive. Applicant argues that no reference teaches the amended limitations now stating “the scaling factor is configured based on a desired sensitivity of a change-detection operation for detecting changes to the relationship between the energy usage and the associated characteristics of the datacenter”, since “Cambron does not teach using these control limits for detecting changes in datacenter energy usage relationships that trigger model replacement”. The examiner respectfully disagrees.
Due to the broadness of the claim language, it is maintained that Cambron teaches the limitations, since section 3-section 4, paragraph 1 teach “EWMA” upper and lower control limit exponential calculations including “We choose k = 3 (scaling factor), corresponding to a 3σ control limits” (EWM standard deviation); wherein the upper limit is an addition calculation and the lower limit is a subtraction calculation. Further, “The main principle of the proposed method is to compare measured power to expected power corresponding to measured wind speed with the use of a model. Residual power is then monitored using an EWMA or GWMA chart”.
See 35 U.S.C 103 section for full mapping of claim limitations necessitated by applicant amendments.
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.
The factual inquiries set forth in Graham v. John Deere Co., 383 U.S. 1, 148 USPQ 459 (1966), that are applied 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.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claims 1-8 and 13-19 are rejected under 35 U.S.C. 103 as being unpatentable over Li et al (“Transforming Cooling Optimization for Green Data Center via Deep Reinforcement Learning”, 2018) hereinafter Li, in view of Stein et al (US Pub 20140058572) hereinafter Stein.
Regarding claims 1 and 13, Li teaches a method comprising; and an apparatus comprising: a memory: and at least one processor coupled to the memory and, based at least in part on information stored in the memory (sections 5-6 teach using a “CPU” for executing the embodiments of the disclosure, known to be included in a computer system and communicatively coupled to one or memories), the at least one processor is configured to:
receiving, from a set of sensors associated with a datacenter, a data stream comprising measurements of energy usage for, and associated characteristics of, the datacenter, wherein the associated characteristics comprise at least an outside air temperature (sections 3, 6, and Table 3 teach “We are given a time-varying tuple of the ambient air temperature Tamb and the load factor Hite” (associated characteristics comprise at least an outside air temperature), and multiple PCU temperatures (associated characteristics), power consumptions (energy usage), and air/water flow rate readings (first and second energy usage data). “With the above data, we utilize the proposed algorithm to train the Q and μ network. In this case, as the power consumption can be directly computed by the fan law from the airflow rate, we will only rely on the Q to approximate the inlet temperature which we use as the thermal indicator (modeling a relationship).”);
collecting, from the data stream, first data for a first time period and second data for a second time period wherein the first data and the second data are associated with rolling windows of the data stream (sections 5-5A, 6, and Table 3 teach PCU temperatures, power consumptions, and air/water flow rate readings; and further “We collected these data entries for every 3 minutes (rolling windows of the data stream) from March 1 to 15 of 2017. For these data, we use the first 85% as the training data (first data for a first time period) and the last 15% as the test data (second data for a second time period)”);
generating, based on the first data for the first time period, a first machine-trained model modeling a relationship between the energy usage and the associated characteristics (section 6 teaches “With the above data (based on the first data for the first time period), we utilize the proposed algorithm to train the Q and μ network. In this case, as the power consumption can be directly computed by the fan law from the airflow rate, we will only rely on the Q (first machine-trained model) to approximate the inlet temperature which we use as the thermal indicator (modeling a relationship).”), wherein the first machine-trained model accounts for a dependency between the energy usage and the outside air temperature to distinguish changes to the energy usage attributable to the outside air temperature from changes not explainable by the outside air temperature (sections 3-4 and section 6, paragraphs 1-4 teach training the Q network (first machine-trained model) on state data including the ambient temperature and load factor variables (between the energy usage and the outside air temperature) to compute a cost of state-action pair in order to minimize the “optimization problem” (accounts for a dependency)); and
for an identified change to the relationship between the energy usage and the associated characteristics based on a first prediction error associated with the second time period that measures a difference between a first predicted energy usage for the second time period based on the first machine-trained model and a first actual energy usage for the second time period indicated in the second data being one of greater than a first value or less than a second value (sections 4D, 6, and Algorithm 1 teach using a validation set for computing “validation error” (first prediction error associated with the second time period) of the model; wherein “Q outputs the predicted energy and temperature data, concatenated as yr”, and “minimizing the error between the predicted yr (measures a difference between a first predicted energy usage for the second time period based on the first machine-trained model) and the real data (and a first actual energy usage for the second time period indicated in the second data)” used in the error computation that is further compared to predetermined values, “EQval” (being one of greater than a first value or less than a second value)):
collecting, based on the identified change, third data for a third time period (section 5D teaches “different designs of the neural network” including a trained LSTM output states and actions (third data) are input into a trained Q network); and
generating a second machine-trained model based on the third data (section 5D teaches different designs of the neural network including a trained LSTM output states and actions (third data) are input into a trained Q network for further turning as a new “training episode” (generating a second machine-trained model)).
However, Li does not teach displaying, to a datacenter operator via an output interface, an indication of the identified change and a recommendation for remediation of the identified change.
Stein teaches displaying, to a datacenter operator via an output interface, an indication of the identified change and a recommendation for remediation of the identified change (paragraphs 0113-0114, 0120-0122, and 0152-0153 teach giving recommendations for curtailment to “end users” for resolving “issues” between factors including energy usage and weather data for a “data center”).
Thus, it would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to implement Stein’s teachings of datacenter resource utilization results and recommendations for fixing poor results output to a user operator into Li‘s teaching of using machine learning algorithms for optimizing datacenter power usage and error metric calculations in order to maximize “savings and make appropriate recommendations to optimize the return on the building manager's effort” (Stein, paragraph 0120).
Regarding claims 2 and 14, the combination of Li and Stein teach all the claim limitations of claims 1 and 13 above; and further teach wherein the generating the second machine-trained model is further based on the second data; and
using the second machine-trained model to predict energy usage for a subsequent time period following the third time period to continue monitoring the datacenter for subsequent changes to the relationship between the energy usage and the associated characteristics (Li, section 5D teaches “different designs of the neural network” including a trained LSTM output states and actions (third data) are iteratively input into a trained Q network for further tuning as a new “training episode” (generating the second machine-trained model); wherein sections 4D, 6, and Algorithm 1 teach the Q network primarily trained and validated with “predicted yr and the real data (second data)” for predicting “energy and temperature data, concatenated as yr”, and “minimizing the error between the predicted yr and the real data”).
Regarding claims 5 and 17, the combination of Li and Stein teach all the claim limitations of claims 1 and 13 above; and further teach wherein the associated characteristics comprise at least the outside air temperature and the energy usage comprises at least a first energy usage data associated with a first power consumed by equipment providing information technology (IT) functions at the datacenter and a second energy usage data associated with a second power consumed by the datacenter, wherein the relationship between the energy usage and the associated characteristics comprises a particular relationship between the second power, the first power, and the associated characteristics (Li, sections 3, 6, and Table 3 teach “We are given a time-varying tuple of the ambient air temperature Tamb and the load factor Hite” (outside air temperature), and multiple power consumptions (first and second energy usage data). “With the above data, we utilize the proposed algorithm to train the Q and μ network. In this case, as the power consumption can be directly computed by the fan law from the airflow rate, we will only rely on the Q to approximate the inlet temperature which we use as the thermal indicator (modeling a relationship).”).
Regarding claim 6, the combination of Li and Stein teach all the claim limitations of claim 5 above; and further teach wherein the particular relationship between the second power, the first power, and the associated characteristics comprises a function for calculating a power usage effectiveness (PUE) based on the first power and the associated characteristics, wherein the PUE is calculated by dividing the second power by the first power (Li, sections 4-5 teach error calculations involving the different powers and temperatures, and further calculating PUE from total power divided by equipment power).
Regarding claims 7 and 18, the combination of Li and Stein teach all the claim limitations of claims 5 and 17 above; and further teach wherein the first energy usage data and the second energy usage data comprise one or more of energy usage data at a first set of two or more levels of granularity in space or energy usage data at a second set of two or more levels of granularity in time, wherein the first set of two or more levels of granularity in space comprises one or more of an IT device-level granularity, a rack-level granularity, a group-of-racks level granularity, a room level granularity, a group-of-rooms level granularity, a floor level granularity, a building level granularity, or a datacenter level granularity, wherein the second set of two or more levels of granularity in time comprises one or more of seconds, minutes, hours, days, weeks, months, quarters, or years (Li, sections 5-5A, 6, and Table 3 teach PCU temperatures, power consumptions per rack grouping (first energy usage data and the second energy usage data comprise one or more of energy usage data at a first set of two or more levels of granularity in space/group-of-racks level granularity), and air/water flow rate readings; and further “We collected these data entries for every 3 minutes (second set of two or more levels of granularity in time comprises one or more of…minutes) from March 1 to 15 of 2017. For these data, we use the first 85% as the training data and the last 15% as the test data”).
Regarding claims 8 and 19, the combination of Li and Stein teach all the claim limitations of claims 7 and 18 above; and further teach receiving a selection of a first level of granularity in time and a second level of granularity in space, wherein generating the first machine-trained model is further based on the first level of granularity in time and the second level of granularity in space (Li, sections 5-5A, 6, and Table 3 teach PCU temperatures, power consumptions per rack grouping (second level of granularity in space), and air/water flow rate readings; and further “We collected these data entries for every 3 minutes (selection of a first level of granularity in time) from March 1 to 15 of 2017. For these data, we use the first 85% as the training data and the last 15% as the test data” to train the Q network).
Claims 3 and 15 are rejected under 35 U.S.C. 103 as being unpatentable over Li et al (“Transforming Cooling Optimization for Green Data Center via Deep Reinforcement Learning”, 2018) hereinafter Li, in view of Stein et al (US Pub 20140058572) hereinafter Stein, in view of Baig et al (“Adaptive sliding windows for improved estimation of data center resource utilization”, 2019) hereinafter Baig.
Regarding claims 3 and 15, the combination of Li and Stein teach all the claim limitations of claims 1 and 13 above; however, the combination does not explicitly teach refraining from using the first machine-trained model until the second machine-trained model is generated based on the third data, wherein the third time period comprises at least a threshold amount of time for collecting data to generate the second machine-trained model after the identified change.
Baig teaches refraining from using the first machine-trained model until the second machine-trained model is generated based on the third data (Baig, sections 4 and 6.3 teach using different observation window sizes to train machine learning models (until the second machine-trained model is generated based on the third data) without using the MLP to select the window sizes for data collection in Experiments 1-3 (refraining from using the first machine-trained model) until Experiment 4), wherein the third time period comprises at least a threshold amount of time for collecting data to generate the second machine-trained model after the identified change (Baig, sections 6.3 and 7 teach different window sizes covering different time periods (third time period comprises at least a threshold amount of time for collecting data) for collecting a dataset for iteratively training the model (to generate the second machine-trained model) and iteratively determining error minimization (after the identified change)).
Thus, it would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to modify Li‘s teaching of using machine learning algorithms for optimizing datacenter power usage and error metric calculations, as modified by Stein’s teachings of datacenter resource utilization results and recommendations for fixing poor results output to a user operator, to include prediction result observation plots in user VMs for datacenter resource utilization as taught by Baig in order to “improve prediction accuracy” (Baig, section 8).
Claims 4 and 16 are rejected under 35 U.S.C. 103 as being unpatentable over Li et al (“Transforming Cooling Optimization for Green Data Center via Deep Reinforcement Learning”, 2018) hereinafter Li, in view of Stein et al (US Pub 20140058572) hereinafter Stein, in view of Cambron et al (“Power curve monitoring using weighted moving average control charts”, 2019) hereinafter Cambron.
Regarding claims 4 and 16, the combination of Li and Stein teach all the claim limitations of claims 1 and 13 above; and further teach wherein the identified change is further based on the difference being one of greater than the first value or less than the second value at least a threshold number of times (Li, sections 4D, 6, and Algorithm 1 teach using a validation set for computing “validation error” of the model; wherein “Q outputs the predicted energy and temperature data, concatenated as yr”, and “minimizing the error between the predicted yr and the real data (difference)” used in the error computation that is further compared to a predetermined value, “EQval” at least once (being one of greater than the first value or less than the second value at least a threshold number of times)), and wherein the second machine-trained model is generated to replace the first machine-trained model after the identified change (Li, sections 5B and D teach “we train the same evaluation network like CCA to replace the original neural network designed for modeling chiller efficiency”; wherein “different designs of the neural network” including a trained LSTM output states and actions are iteratively input into a trained Q network for further tuning as a new “training episode” (second machine-trained model is generated to replace the first machine-trained model after the identified change). Further, sections 4D, 6, and Algorithm 1 teach the Q network primarily trained and validated with “predicted yr and the real data (second data)”.).
However, the combination does not explicitly teach and wherein the first value and the second value are control limits derived from an exponentially weighted moving average (EWMA) and an exponentially weighted moving standard deviation (EWMSTD) of prediction errors from a validation time period preceding the second time period, and wherein the second machine-trained model is generated to replace the first machine-trained model after the identified change.
Combron teaches and wherein the first value and the second value are control limits derived from an exponentially weighted moving average (EWMA) and an exponentially weighted moving standard deviation (EWMSTD) of prediction errors from a validation time period preceding the second time period (sections 3 and 4.3 teach “EWMA” upper and lower control limit exponential calculations including “We choose k = 3, corresponding to a 3σ control limits” (EWM standard deviation); wherein the upper limit is an addition calculation and the lower limit is a subtraction calculation; further the data for validating the model being a time period before the “EWMA calculation”).
Thus, it would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to modify Li‘s teaching of using machine learning algorithms for optimizing datacenter power usage and error metric calculations, as modified by Stein’s teachings of datacenter resource utilization results and recommendations for fixing poor results output to a user operator, to include EMWA and standard deviation calculations for prediction error analysis as taught by Cambron in order to reduce model “underperformances” and accomplish more accurate prediction accuracy (Cambron, sections 3-4.3).
Claims 9-12 and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Li et al (“Transforming Cooling Optimization for Green Data Center via Deep Reinforcement Learning”, 2018) hereinafter Li, in view of Stein et al (US Pub 20140058572) hereinafter Stein, in view of Baig et al (“Adaptive sliding windows for improved estimation of data center resource utilization”, 2019) hereinafter Baig, in view of Cambron et al (“Power curve monitoring using weighted moving average control charts”, 2019) hereinafter Cambron.
Regarding claim 9, the combination of Li and Stein teach all the claim limitations of claim 1 above; however, the combination does not explicitly teach collecting fourth data for a fourth time period following the first time period and preceding the second time period; determining an average of a second prediction error for the fourth time period based on a second predicted energy usage for the fourth time period predicted by the first machine-trained model and a second actual energy usage for the fourth time period indicated in the fourth data; and determining a standard deviation of the second prediction error, wherein the first value and the second value are based on the average of the second prediction error and the standard deviation of the second prediction error.
Baig teaches collecting fourth data for a fourth time period following the first time period and preceding the second time period (sections 3-4 and 6.3 teach a sliding window for collecting training time period datasets in a time-series that are each input into the model; and “For validation purposes, the final data-set is randomly split 80/20 for training vs. testing subsets (preceding the second time period)”);
determining an average of a second prediction error for the fourth time period based on a second predicted energy usage for the fourth time period predicted by the first machine-trained model and a second actual energy usage for the fourth time period indicated in the fourth data (section 6.1.2-6.2 and 7 teach determining “MSE” for the different window training sets (fourth time period) for the “true and estimated values” of “resource estimation prediction” via the prediction model);
Thus, it would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to modify Li‘s teaching of using machine learning algorithms for optimizing datacenter power usage and error metric calculations, as modified by Stein’s teachings of datacenter resource utilization results and recommendations for fixing poor results output to a user operator, to include prediction result observation plots in user VMs for datacenter resource utilization as taught by Baig in order to “improve prediction accuracy” (Baig, section 8).
However, the Baig does not explicitly teach determining a standard deviation of the second prediction error, wherein the first value and the second value are based on the average of the second prediction error and the standard deviation of the second prediction error.
Cambron teaches determining a standard deviation of the second prediction error, wherein the first value and the second value are based on the average of the second prediction error and the standard deviation of the second prediction error (sections 3-4.2 and 5.1 teach “EWMA” of the predictions, computing the standard deviation of the error, and upper and lower control limit (the first value and the second value) using the EWMA and standard deviation).
Thus, it would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to modify Li‘s teaching of using machine learning algorithms for optimizing datacenter power usage and error metric calculations, as modified by Stein’s teachings of datacenter resource utilization results and recommendations for fixing poor results output to a user operator, as modified by Baig’s teachings of prediction result observation plots in user VMs for datacenter resource utilization, to include EMWA and standard deviation calculations for prediction error analysis as taught by Cambron in order to reduce model “underperformances” and accomplish more accurate prediction accuracy (Cambron, sections 3-4.2).
Regarding claim 10, the combination of Li and Stein teach all the claim limitations of claim 9 above; however, the combination does not explicitly teach wherein the average of the second prediction error is an exponentially weighted moving average (EWMA) and the standard deviation of the second prediction error is an exponentially weighted moving standard deviation (EWM standard deviation), wherein the first value is an upper control limit (UCL) calculated as the EWMA plus the EWM standard deviation times a scaling factor and the second value is a lower control limit (LCL) calculated as the EWMA minus the EWM standard deviation times the scaling factor, wherein the scaling factor is configured based on a desired sensitivity of a change-detection operation for detecting changes to the relationship between the energy usage and the associated characteristics of the datacenter.
Cambron teaches wherein the average of the second prediction error is an exponentially weighted moving average (EWMA) and the standard deviation of the second prediction error is an exponentially weighted moving standard deviation (EWM standard deviation), wherein the first value is an upper control limit (UCL) calculated as the EWMA plus the EWM standard deviation times a scaling factor and the second value is a lower control limit (LCL) calculated as the EWMA minus the EWM standard deviation times the scaling factor, wherein the scaling factor is configured based on a desired sensitivity of a change-detection operation for detecting changes to the relationship between the energy usage and the associated characteristics of the datacenter (section 3-section 4, paragraph 1 teach “EWMA” upper and lower control limit exponential calculations including “We choose k = 3 (scaling factor), corresponding to a 3σ control limits” (EWM standard deviation); wherein the upper limit is an addition calculation and the lower limit is a subtraction calculation. Further, “The main principle of the proposed method is to compare measured power to expected power corresponding to measured wind speed with the use of a model. Residual power is then monitored using an EWMA or GWMA chart”.).
Thus, it would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to modify Li‘s teaching of using machine learning algorithms for optimizing datacenter power usage and error metric calculations, as modified by Stein’s teachings of datacenter resource utilization results and recommendations for fixing poor results output to a user operator, as modified by Baig’s teachings of prediction result observation plots in user VMs for datacenter resource utilization, to include EMWA and standard deviation calculations for prediction error analysis as taught by Cambron in order to reduce model “underperformances” and accomplish more accurate prediction accuracy (Cambron, sections 3-4.2).
Regarding claim 11, the combination of Li and Stein teach all the claim limitations of claim 9 above; and further teach collecting fifth data for a fifth time period following the first time period and preceding the fourth time period (Baig, section 6.1.2-6.2 and 7 teach determining “MSE” for the different window training sets (fifth time period) for the “true and estimated values” of “resource estimation prediction” via the prediction model);
determining at least an additional average or an additional standard deviation for a third prediction error based on a third predicted energy usage for the fifth time period predicted by the first machine-trained model and a third actual energy usage for the fifth time period indicated in the fifth data (Baig, section 6.1.2-6.2 and 7 teach determining “MSE” (additional average) for the different window training sets (fifth time period) for the “true and estimated values” of “resource estimation prediction” via the prediction model); and
for an identified absence of a change to the relationship between the energy usage and the associated characteristics beyond a threshold based on the second prediction error being within a range between a third value and a fourth value (Li, sections 4D, 6, and Algorithm 1 teach iteratively using a validation set for computing “validation error” (first prediction error associated with the second time period) of the model; wherein “Q outputs the predicted energy and temperature data, concatenated as yr”, and ensuring the minimum error is the same “between the predicted yr (relationship between the energy usage and the associated characteristics) and the real data” used in the error computation that is further compared to predetermined values, “EQval” (beyond a threshold based on the second prediction error being within a range between a third value and a fourth value)), wherein the third value and the fourth value are based on at least one of the additional average for the third prediction error or the additional standard deviation for the third prediction error:
using the first machine-trained model to predict the first predicted energy usage (Li, sections 4D, 6, and Algorithm 1 teach iteratively using a validation set for computing “validation error” of the model (first machine-trained model) that can be an MAE calculations compared to predetermined values (the third value and the fourth value are based on at least one of the additional average for the third prediction error); wherein “Q outputs the predicted energy and temperature data, concatenated as yr”, and ensuring).
Li, Stein, Baig, and Cambron are combinable for the same rationale as set forth above with respect to claim 9.
Regarding claim 12, the combination of Li and Stein teach all the claim limitations of claim 11 above; and further teach updating the first machine-trained model based on the fifth data and at least a subset of the first data, wherein the first predicted energy usage for the second time period is based on the first machine-trained model after the updating of the first machine-trained model based on the fifth data and at least the subset of the first data (Baig, sections 4, 6.1.2-6.3, and 7 teach determining “MSE” (additional average) for the different window training sets (first/fifth time period) for the “true and estimated values” of “resource estimation prediction” via the prediction model and used for training the model (updating of the first machine-trained model)).
Li, Stein, Baig, and Cambron are combinable for the same rationale as set forth above with respect to claim 11.
Regarding claim 20, claims 9-10 are analogous and the combination of Li, Stein, Baig, and Cambron teach all the claim limitations of claims 9-10 above.
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
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/C.M./Examiner, Art Unit 2123
/ALEXEY SHMATOV/Supervisory Patent Examiner, Art Unit 2123
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