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 .
DETAILED ACTION
This action is a responsive to the application filed on 01/13/2023.
Claims 1-20 are pending.
Claims 1-20 are rejected.
Claim Rejections - 35 USC § 101
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 an abstract idea without significantly more.
Claims 1 and 13 are respectively drawn to a method and a system, hence each falls under one of four categories of statutory subject matter (Step 1). Nonetheless, the claims are directed to a judicially recognized exception of an abstract idea without significantly more.
Claims 1 and 13 recite the following, or analogous, limitations “collecting first data for a first time period and second data for a second time period regarding energy usage for, and associated characteristics of, a datacenter; generating, based on the first data for the first time period, a first…model modeling a relationship between the energy usage and the associated characteristics; 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…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;…collecting, based on the identified change, third data for a third time period; and generating a second…model based on the third data”. These limitations, as claimed, under its broadest reasonable interpretation, can be evaluated in a human mind/with the aid of pen and paper, except for the recitation of generic computer components (Step 2A). Other than reciting “a memory: and at least one processor coupled to the memory”, “machine-trained”, and “displaying an indication of the identified change” to perform the exceptions, nothing in the claims preclude the steps from practically being performed in the human mind and/or the with aid of pen and paper. For example, a human expert can:
mentally/with the aid of pen and paper collecting first data for a first time period and second data for a second time period regarding energy usage for, and associated characteristics of, a datacenter (e.g. by thinking of/writing out estimations for wattage used and temperature of a datacenter server room),
mentally/with the aid of pen and paper generating, based on the first data for the first time period, a first…model modeling a relationship between the energy usage and the associated characteristics (e.g. by thinking of/writing out a first calculation regarding the wattage use and the temperature within a first time range),
mentally/with the aid of pen and paper 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…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 (e.g. by thinking of/writing out a difference in the calculation’s output and a target value for a next time range and the value within a specified numerical range),
mentally/with the aid of pen and paper collecting, based on the identified change, third data for a third time period (e.g. by thinking of/writing out estimations for wattage used and temperature of a datacenter server room for a third time range),
mentally/with the aid of pen and paper generating a second…model based on the third data (e.g. by thinking of/writing out a second calculation regarding the wattage use and the temperature within the third time range).
Thus, the claims recite a mental process (Step 2A, Prong 1).
Claims 1 and 13 include additional elements, “a memory: and at least one processor coupled to the memory”, “machine-trained”, and “displaying an indication of the identified change”, however the recitations of these elements are at a high level of generality, and amount to adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea (i.e., “a memory: and at least one processor coupled to the memory”) (see MPEP 2106.05(f)); and generally link the use of the judicial exception to a particular technological environment or field of use (i.e., “machine-trained”, and “displaying an indication of the identified change”) (see MPEP 2106.05(h)). Hence, each of the additional limitations or in combination do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea (Step 2A, Prong 2). Furthermore, the claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to the integration of the abstract idea into a practical application, the additional elements of using “a memory: and at least one processor coupled to the memory”, “machine-trained”, and “displaying an indication of the identified change” to perform the steps of the independent claims amount to mere data storing and data outputting, adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea, and generally link the use of the judicial exception to a particular technological environment or field of use. These cannot provide an inventive concept. (STEP 2B). As such, claims 1 and 13 are not patent eligible.
Dependent claims 2-12 and 14-20 are also ineligible for the same reasons given with respect to claims 1 and 13. The dependent claims describe additional mental processes:
mentally/with the aid of pen and paper wherein the generating the second…model is further based on the second data (claims 2 and 14) (e.g. by mentally/writing out the second calculation from the second time range)
mentally/with the aid of pen and paper refraining from using the first…model until the second…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…model after the identified change (claims 3 and 15) (e.g. by mentally/writing out using the second calculation and not the first, and the third time range is at least 2 seconds long and the second calculation is made after the first model’s difference is computed)
mentally/with the aid of pen and paper 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 (claim 4 and 16) (e.g. by mentally/writing out the first model’s difference being higher than a predetermined value at least once)
mentally/with the aid of pen and paper wherein the associated characteristics comprise at least an 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 (claim 5 and 17) (e.g. by mentally/writing out the estimated room temperature being outside a piece of equipment in the server room, and the estimated wattages includes estimated wattage used by a server that are used in the first calculation to determine outputs)
mentally/with the aid of pen and paper 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 (claim 6) (e.g. by mentally/writing out a PUE computation between the estimated wattages and temperatures)
mentally/with the aid of pen and paper 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 (claim 7 and 18) (e.g. by mentally/writing out the estimated wattages being for a device in a specific server room for at least 2 seconds)
mentally/with the aid of pen and paper receiving a selection of a first level of granularity in time and a second level of granularity in space, wherein generating the first…model is further based on the first level of granularity in time and the second level of granularity in space (claim 8 and 19) (e.g. by mentally/writing out the first calculation depending on server room and 2 second range specifications)
mentally/with the aid of pen and paper 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…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 (claims 9 and 20) (e.g. by mentally/writing out estimations for wattage used and temperature of a datacenter server room for a fourth time range after the first time range and before the second time period, calculating EWMA for the fourth time range data and first calculation and second target value. Computing standard deviation for the EWMA, and computing the specified numerical range upper and lower limits based on the standard deviation and EWMA)
mentally/with the aid of pen and paper 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 the EWMA plus the EWM standard deviation and the second value is the EWMA minus the EWM standard deviation (claims 10 and 20) (e.g. by mentally/writing out a EWMA for the error computation and EWM for the standard deviation, and the specified numerical range upper and lower limits based on the standard deviation and EWMA being added and subtracted)
mentally/with the aid of pen and paper collecting fifth data for a fifth time period following the first time period and preceding the fourth time period; 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…model and a third actual energy usage for the fifth time period indicated in the fifth data; 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, 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…model to predict the first predicted energy usage (claim 11) (e.g. by mentally/writing out estimations for wattage used and temperature of a datacenter server room for a fifth time range after the first time range and before the second time period, calculating EWMA for the fifth time range data and first calculation and third target value. Computing standard deviation for the EWMA, and computing a new specified numerical range upper and lower limits based on the standard deviation and EWMA. Computing a PUE between the wattage and temperatures and error values until there is not a change in outputs, and using the first calculation for estimating wattage)
mentally/with the aid of pen and paper updating the first…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…model after the updating of the first…model based on the fifth data and at least the subset of the first data (claim 12) (e.g. by mentally/writing out tuning the first calculations parameters from the first and fifth data time ranges and using the updated model to output wattage estimations for the second time range data)
Again, the dependent claims continued to cover the performance of the limitation in the mind as inherited from the independent claims (Step 2A, Prong 1). The dependent claims 2-3, 8-9, 11-12, 14-15, and 19-20 recitation of “machine-trained”, are again recited at a high level and generally link the use of the judicial exception to a particular technological environment or field of use (see MPEP 2106.05(h)), and do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea (Step 2A, Prong 2). The additional element in the claims do not amount to significantly more than an abstract idea. As discussed above with respect to the integration of the abstract idea into a practical application, the additional elements to perform the steps of in the dependent claims and perform the steps of the claims amount to generally link the use of the judicial exception to a particular technological environment or field of use; which cannot provide an inventive concept. (STEP 2B). As such, dependent claims 2-12 and 14-20 additional elements or combination of elements do not amount to significantly more than an abstract idea nor provide any inventive concept, nor impose a meaningful limit to integrate the elements into a practical application or significantly more than the judicial exceptions; therefore, the dependent claims are not deemed patent eligible.
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 Baig et al (“Adaptive sliding windows for improved estimation of data center resource utilization”, 2019) hereinafter Baig.
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:
collecting first data for a first time period and second data for a second time period regarding energy usage for, and associated characteristics of, a datacenter (sections 5-5A, 6, and Table 3 teach PCU temperatures (associated characteristics), power consumptions (regarding energy usage), and air/water flow rate readings (associated characteristics); and further “We collected these data entries for every 3 minutes 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).”); 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)):
displaying an indication of the identified change (sections 5C teach outputting simulation results by graphing and enable observation capabilities (display));
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)).
Li at least implies displaying an indication of the identified change (see mappings above); however, Baig teaches displaying an indication of the identified change (section 5 teach drawing the results in a box plot for review and observation (display)).
Thus, it would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to implement Baig’s teachings of prediction result observation plots in user VMs for datacenter resource utilization into Li‘s teaching of using machine learning algorithms for optimizing datacenter power usage and error metric calculations in order to “improve prediction accuracy” (Baig, section 8).
Regarding claims 2 and 14, the combination of Li and Baig 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 (Li, 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 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)”).
Regarding claims 3 and 15, the combination of Li and Baig teach all the claim limitations of claims 1 and 13 above; and further teach 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)).
Li and Baig are combinable for the same rationale as set forth above with respect to claims 1 and 13.
Regarding claims 4 and 16, the combination of Li and Baig 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)).
Regarding claims 5 and 17, the combination of Li and Baig teach all the claim limitations of claims 1 and 13 above; and further teach wherein the associated characteristics comprise at least an 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 Baig 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 Baig 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 Baig 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 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 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 Baig teach all the claim limitations of claim 1 above; and further teach collecting fourth data for a fourth time period following the first time period and preceding the second time period (Baig, 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 (Baig, 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);
However, the combination 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 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 Baig teach all the claim limitations of claim 1 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 the EWMA plus the EWM standard deviation and the second value is the EWMA minus the EWM standard deviation.
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 the EWMA plus the EWM standard deviation and the second value is the EWMA minus the EWM standard deviation (section 3 teaches “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).
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 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 Baig 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, 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 Baig 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, 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, Baig, and Cambron teach all the claim limitations of claims 9-10 above.
Prior Art
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Lerer et al (US Pub 20250384341) teach training machine learning models for optimizing datacenter workloads at periods of time.
Conclusion
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/C.M./Examiner, Art Unit 2123
/ALEXEY SHMATOV/Supervisory Patent Examiner, Art Unit 2123