DETAILED ACTION
1. The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
2. this action is in response to the application filed on 1/10/2024, in which claims 1 – 4 was presented for examination.
3. A preliminary amended was filed on 1/10/2024, in which claims 1 – 4 was amended.
4. Claims 1 – 4 are now pending in the application.
Information Disclosure Statement
5. The information disclosure statement (IDS) submitted on 1/10/2024 has been reviewed and entered into the record. The submission is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
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.
6. Claims 1 - 4 are directed are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
As per claim 1,
Step 1: Claim 1 recites a device, which is one of the four statutory categories of eligible matter.
Step 2A Prong 1: The claim recites the limitation of
a discrimination unit, comprising one or more processors, configured to discriminate whether a variation pattern in the workload over time is only a linear pattern, only a non-linear pattern, or a linear/non-linear pattern in which the linear pattern and the non-linear pattern are mixed (Mathematical Process performed in human mind using a pen and paper (i.e. evaluation)).
a prediction unit, comprising one or more processors, configured to select a plurality of prediction models for linearity when the variation pattern is only the linear pattern (Mathematical Process performed in human mind using a pen and paper (i.e. evaluation)).
select a plurality of prediction models for non-linearity when the variation pattern is only the non-linear pattern (Mathematical Process performed in human mind using a pen and paper (i.e. evaluation)).
select the prediction model for linearity and the prediction model for non-linearity when the variation pattern is the linear/non-linear pattern (Mathematical Process performed in human mind using a pen and paper (i.e. evaluation)).
and derive a future prediction value for the workload by performing an ensemble prediction combining the selected plurality of prediction models (Mathematical Process performed in human mind using a pen and paper (i.e. judgmental))
Step 2A Prong 2: The judicial exceptions are not integrated into a practical application. The claim recites the additional elements of
a prediction unit, comprising one or more processors, configured to select a plurality of prediction models for linearity when the variation pattern is only the linear pattern (the step is directed to obtaining information, which is understood to be significant extra-solution activity, see MPEP 2106.05(g)).
select a plurality of prediction models for non-linearity when the variation pattern is only the non-linear pattern (the step is directed to obtaining information, which is understood to be significant extra-solution activity, see MPEP 2106.05(g)).
select the prediction model for linearity and the prediction model for non-linearity when the variation pattern is the linear/non-linear pattern (the step is directed to obtaining information, which is understood to be significant extra-solution activity, and is well understood, routine, and conventional activity of preparing data for preprocessing, see MPEP 2106.05(g)).
Although the additional element limits the identified judicial exceptions. The limitation merely confines the use of the abstract idea to a particular technological environment and thus fails to add an inventive concept to the claims. See MPEP 2106.05(h).
Even when viewed in combination, these additional elements do not integrate the recited judicial exception into a practical application and the claim is directed to the judicial exception.
Step 2B: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
Additional element
a prediction unit, comprising one or more processors, configured to select a plurality of prediction models for linearity when the variation pattern is only the linear pattern (the step is directed to evaluating information, which is understood to be significant extra-solution activity, see MPEP 2106.05(g))
select a plurality of prediction models for non-linearity when the variation pattern is only the non-linear pattern (this step is directed to evaluating information, which is understood to be significant extra-solution activity and is well understood, routine, and conventional activity of evaluating data).
select the prediction model for linearity and the prediction model for non-linearity when the variation pattern is the linear/non-linear pattern (this step is directed to evaluating information, which is understood to be significant extra-solution activity and is well understood, routine, and conventional activity of evaluating data).
As explained above, the additional element are recited at a high level of generality. These elements amount to collecting, evaluating, and generating information are well-understood, routine, conventional activity. See MPEP 2106.05(d), subsection II. The recitation of a computer to perform these limitations amounts to no more than mere instructions to apply the exception using a generic computer. Even when considered in combination, these additional elements represent mere instructions to implement an abstract idea or other exception on a computer and insignificant extra-solution activity, which do not provide an inventive concept.
Thus, the claim is ineligible.
As per claim 2, the rejection of claim 1 is incorporated.
Step 1: The claim recites a device, which is one of the four statutory categories of eligible matter.
Step 2A Prong 1: The judicial exceptions of claim 1 are incorporated, the limitation of
wherein the prediction unit is configured to apply different weights to the plurality of prediction models respectively, and perform the ensemble prediction (Mathematical Process performed in human mind using a pen and a paper (i.e. evaluation)).
Step 2A Prong 2: the judicial exceptions are not integrated into a practical application. The claim recites additional elements of
wherein the prediction unit is configured to apply different weights to the plurality of prediction models respectively, and perform the ensemble prediction (the step is directed to evaluating information, which is understood to be significant extra-solution activity, see MPEP 2106.05(g)).
The limitation recited at high level of generality and thus are insignificant extra-solution activity. Even when viewed in combination, these additional elements do not integrate the recited judicial exception into a practical application.
Step 2B: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exceptions. Mere instructions to implement an abstract idea or other exception on a computer and insignificant extra-solution activity, which do not provide an inventive concept.
The claim is not patent eligible.
As per claim 3,
Step 1: Claim 3 recites a method, which is one of the four statutory categories of eligible matter.
As per other analysis, claim 3 is a method claim correspond to a device claim 1, thus the rationale discussed above regarding claim 1 is applied to claim 3.
As per claim 4,
Step 1: Claim 4 recites a non-transitory computer readable-medium, which is one of the four statutory categories of eligible matter.
As per other analysis, claim 4 is a non-transitory computer readable-medium claim correspond to a device claim 1, thus the rationale discussed above regarding claim 1 is applied to claim 4.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
7. Claims 1 – 4 are rejected under 35 U.S.C. 103 as being unpatentable over Shekhawat et al (Datacenter Workload Classification and Characterization: An Empirical Approach), in view of Zhou et al (An Accurate Ensemble Forecasting Approach for Highly Dynamic Cloud Workload With VMD and R-Transformer).
As per claim 1, Shekhawat et al (Datacenter Workload Classification and Characterization: An Empirical Approach) discloses,
A prediction device predicting a workload of a server (pg.2 col. 1 lines 23 – 25; “workloads comprising of applications like web servers, mail servers, app servers etc. The workload characterization” and pg.3 col.2 lines 47 – 48; “classification is important in workload analysis and further in prediction and provisioning of resources”).
the prediction device comprising: a discrimination unit, comprising one or more processors, configured to discriminate whether a variation pattern in the workload over time is only a linear pattern, only a non-linear pattern (pg.7 col.1 lines 35 – 37; “Google Cluster datacenter applications generate more heterogeneous and non-linear workload as compared to Bit Brain datacenter applications ….. Bit Brain datacenter applications generate linear workload”).
and a prediction unit, comprising one or more processors, configured to select a plurality of prediction models for linearity when the variation pattern is only the linear pattern (pg.7 col.2 lines 38 – 41; “Bit Brain datacenter applications generate linear workload. Therefore, algorithms like KNN, SVM, and DT with linear models are more suitable”).
select a plurality of prediction models for non-linearity when the variation pattern is only the non-linear pattern (pg.7 col.1 lines 35 – 38; “datacenter applications generate more heterogeneous and non-linear workload as compared to Bit Brain datacenter applications. Therefore, nonlinear machine learning models such as MLP and ensembles work better”).
and derive a future prediction value for the workload by performing an ensemble prediction combining the selected plurality of prediction models (pg.1 col.2 lines 3 – 6; “Workload characterization can be used to predict future resource requirements which help in capacity planning, task scheduling and resource”, pg.7 col.1 line 27; “workload classification and characterization”, pg.7 lines 36 – 39; “non-linear workload as compared to Bit Brain datacenter applications. Therefore, nonlinear machine
learning models such as MLP and ensembles work better. Bit Brain datacenter applications generate linear workload”).
Shekhawat does not specifically disclose a linear/non-linear pattern in which the linear pattern and the non-linear pattern are mixed, select the prediction model for linearity and the prediction model for non-linearity when the variation pattern is the linear/non-linear pattern.
However, Zhou et al (An Accurate Ensemble Forecasting Approach for Highly Dynamic Cloud Workload With VMD and R-Transformer) discloses,
or a linear/non-linear pattern in which the linear pattern and the non-linear pattern are mixed (pg.6 col.2 lines 11 – 16; “R-Transformer uses LocalRNN and the multi-head attention mechanism to obtain the local and global nonlinear relation-ship of the IMFs, and the AR model is used to obtain the linear relationship of the IMFs. The combination of these components effectively improves the accuracy of forecasting highly dynamic workload in cloud environment”).
select the prediction model for linearity and the prediction model for non-linearity when the variation pattern is the linear/non-linear pattern (pg.6 col.2 lines 11 – 16; “R-Transformer uses LocalRNN and the multi-head attention mechanism to obtain the local and global nonlinear relation-ship of the IMFs, and the AR model is used to obtain the linear relationship of the IMFs. The combination of these components effectively improves the accuracy of forecasting highly dynamic workload in cloud environment”)
Therefore, it would have been obvious to one of ordinary skill in the art before the invention was filed to incorporate management of cloud center workload of the system of Zhou into workload classification of the system of Shekhawat to manage the cloud resources, improve the quality of service, and avoid the violation of service-level agreement.
As per claim 2, the rejection of claim 1 is incorporated and further Shekhawat et al (Datacenter Workload Classification and Characterization: An Empirical Approach) discloses,
wherein the prediction unit is configured to apply different weights to the plurality of prediction models respectively, and perform the ensemble prediction (pg.3 lines 45 – 46; “decoder to obtain the weight of predictions at different historical time” and pg.7 lines 36 – 39; “non-linear workload as compared to Bit Brain datacenter applications. Therefore, nonlinear machine learning models such as MLP and ensembles work better. Bit Brain datacenter applications generate linear workload”).
Claim 3 is a method claim corresponding to a device claim 1, and rejected under the same reason set forth in connection to the rejection of claim 1 above.
Claim 4 is a non-transitory computer readable medium claim corresponding to a device claim 1, and rejected under the same reason set forth in connection to the rejection of claim 1 above.
Conclusion
8. The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
TITLE: Adaptive ensemble workload prediction model based on machine learning algorithms, US 10,402,733 B1 authors: Li et al.
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/AUGUSTINE K. OBISESAN/
Primary Examiner
Art Unit 2156
6/16/2026
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