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 communication is responsive to Amendment filed 03/12/2026.
Claims 1-20 are pending in this application. Claims 1, 15, and 20 are independent claims. In Amendment, claims 1, 9, 15, and 20 are amended. This Office Action is made final.
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 non-statutory subject matter. The claim(s) does/do not fall within at least one of the four categories of patent eligible subject matter because the claimed invention is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more. Claim(s) 1, 15, and 20 is/are directed to an abstract idea under the mental process wherein under Prong I step 2A the limitations “determine….”; “predict device power…”; “determine a test dataset…”; and “predict....” can be mentally done in human mind with pen and paper given the data set(s). In another words, if the proper data set is given/available, one can determine/evaluate to determine which data set can be used for training the model and predict the device power consumption based on some evaluations/calculation on the data with some judgements. The other limitations including “a processor…”, “a network interface…”, “a memory…”, “collecting…”, “receive…”, “provide…”, “training…”; and “control…” are considered as additional elements under Prong II step 2A. However, these claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the additional elements “a processor…”, “a network interface…”, “a memory…” are well known in the technology as generic high level of computer system and the additional elements “collecting…”, “receive…”, “provide…”, and “training…” are merely considered as extra-activities solutions for collecting/gathering the data over the network and merely high level of utilizing the software tools of the ML model and “control…” is considered as apply it. These additional elements are insignificantly amount to the judicial exception. Thus, either individually or in combination does not integrate into the practical application. Under step 2B, these claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the additional elements “a processor…”, “a network interface…”, “a memory…” are well known in the technology as generic high level of computer system and the additional elements “collecting…”, “receive…”, “provide…”, and “training…” are merely considered as extra-activities solutions for collecting/gathering the data over the network and merely high level of utilizing the software tools of the ML model as seen in MPEP 2106.05(g) and (h) and “control…” is considered as apply it. These additional elements are insignificantly amount to the judicial exception. Thus, either individually or in combination does not integrate into the practical application.
Re claims 2-14 and 16-19, these claims are similarly rejected as directing to an abstract idea under the mental process as seen above wherein these claims are either further elaborate with additional abstract idea limitations or insignificant amount additional elements to the judicial exception. Thus, individually or in combination of these additional elements does not integrate into the practical application.
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 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.
Claims 1, 7-8, 15-16, and 19-20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Engelberg et al. (U.S. 2023/0021961 A1) in view of Ross et al. (U.S. 2022/0201888 A1).
Re claim 1, Engelberg et al. disclose in Figures 1-6 a device, comprising: a processor; and a memory communicatively coupled to the processor, wherein the memory comprises a power management logic (e.g. abstract and Figures 1-2 as generalized concept of the energy consumption prediction based on ML model) that is configured to: collect a base dataset associated with a network device, wherein the base dataset comprises a plurality of values of power consumption and a set of telemetry parameters associated with the network device collected over a time period (e.g. Figure 2 and paragraphs [0020-0021 and 0024-0025] wherein the telemetry and energy consumption are provided to the system for analysis of devices); determine a training dataset from the base dataset (e.g. paragraphs [0034 and 0037] wherein specific dataset is applied or training the ML model); and train a machine learning model based on the training dataset (e.g. paragraphs [0034 and 0037]), wherein the trained machine learning model is configured to predict device power consumption based on device telemetry data (e.g. Figure 2 with component 232 and paragraphs [0024 and 0033-0036] wherein energy consumption of device can be predicted based on ML model); predict, using the trained machine learning mode, device power consumption based on a set of telemetry values derived from the test dataset (e.g. paragraphs [0033-0035]); and control at least one device feature based on the predicted device power consumption to adjust a device performance state and monitor device health in real-time (e.g. Figure 6 with component 614 and paragraphs [0005, 0023, 0042, 0052, and 0056]). Engelberg et al. fail to disclose the limitation of determine a test dataset from the base dataset, wherein the test dataset includes one or more values of the power consumption and a set of model parameters associated with the network device collected over time period. However, Ross et al. disclose the limitation of determine a test dataset from the base dataset, wherein the test dataset includes one or more values of the power consumption and a set of model parameters associated with the network device collected over time period (e.g. paragraphs [0020-0024 and 0034-0036] wherein the dataset is collected telemetry as seen in Engelberg et al.). Therefore, it would have been obvious to a person having ordinary skill in the art before the effective filing date of claimed invention to add determine a test dataset from the base dataset, wherein the test dataset includes one or more values of the power consumption and a set of model parameters associated with the network device collected over time period as seen in Ross et al.’s invention into Engelberg et al.’s invention because it would enable to produce accurate and optimal configuration for a system in operation.
Re claim 7, Engelberg et al. disclose in Figures 1-6 the power management logic is further configured to: execute one or more processing operations on the base dataset; and generate a processed dataset based on the execution of the one or more processing operations, wherein the training dataset is a subset of the processed dataset (e.g. paragraphs [0034 and 0037).
Re claim 8, Engelberg et al. disclose in Figures 1-6 the power management logic is further configured to: generate one or more engineered parameters based on at least one of the set of telemetry parameters (e.g. Figure 2 with configuration 238 and paragraphs [0031-0032]); and select a set of model parameters that comprises the one or more engineered parameters and a subset of telemetry parameters of the set of telemetry parameters, wherein the training dataset comprises one or more values of the power consumption and the set of model parameters associated with the network device collected over the time period (e.g. paragraphs [0034 and 0037]).
Re claim 15, Engelberg et al. disclose in Figures 1-6 a device, comprising: a processor; a network interface controller configured to provide access to a network; and a memory communicatively coupled to the processor, wherein the memory comprises a power management logic (e.g. abstract and Figures 1-2 as generalized concept of the energy consumption prediction based on ML model) that is configured to: receive device telemetry data (e.g. Figure 2 and paragraphs [0020-0021 and 0024-0025] wherein the telemetry and energy consumption are provided to the system for analysis of devices); determine, based on the device telemetry data, a set of values of one or more telemetry parameters (e.g. paragraphs [0034 and 0037] wherein specific dataset is applied or training the ML model); provide the set of values as an input to a trained machine learning model (e.g. paragraphs [0034 and 0037]); and predict device power consumption based on an output of the trained machine learning model for the set of values(e.g. Figure 2 with component 232 and paragraphs [0024 and 0033-0036] wherein energy consumption of device can be predicted based on ML model).
Re claim 16, Engelberg et al. disclose in Figures 1-6 the device telemetry data is configured to indicate at least one of device performance and device physical condition (e.g. Figures 1-2 and paragraphs [0020-0021 and 0056]).
Re claim 19, Engelberg et al. disclose in Figures 1-6 one or more device features are controlled based on the predicted device power consumption (e.g. Figures 1-2, abstract and paragraph [0005]).
Re claim 20, it is a method claim having similar limitations as cited in claim 1. Thus, claim 20 is also rejected under the same rationale as cited in the rejection of claim 1 above.
Claims 2-6, 9-14, and 17-18 is/are rejected under 35 U.S.C. 103 as being unpatentable over Engelberg et al. (U.S. 2023/0021961 A1) in view of Ross et al. (U.S. 2022/0201888 A1) and further in view of Kannan et al. (U.S. 2023/0205591 A1).
Re claim 2, Engelberg et al. fail to disclose in Figures 1-6 the base dataset is collected based on one or more testing operations. However, Kannan et al. disclose the base dataset is collected based on one or more testing operations (e.g. paragraph [0197] with testing dataset). Therefore, it would have been obvious to a person having ordinary skill in the art before the effective filing date of claimed invention to add the base dataset is collected based on one or more testing operations as conceptually seen in Kannan et al.’s invention into Engelberg et al.’s invention because it would enable to accurately optimize the prediction by selective dataset.
Re claim 3, Engelberg et al. in view of Kannan et al. disclose the one or more testing operations are executed sequentially, and in each testing operation of the one or more testing operations, a set of values of the power consumption and the set of telemetry parameters is collected (e.g. Engelberg et al. – Figure 2 and Kannan et al. – paragraph [0197]).
Re claim 4, Engelberg et al. in view of Kannan et al. disclose at least one of the set of values is timestamped (e.g. Kannan et al. – paragraphs [0113 and 0136]).
Re claim 5, Engelberg et al. in view of Kannan et al. disclose the one or more testing operations are associated with variations in at least one of memory consumption, temperature, central processing unit (“CPU”) load, and power over Ethernet (“PoE”) draw associated with the network device (e.g. Kannan et al. – paragraphs [0197, 0253, 0363, 0366]).
Re claim 6, Engelberg et al. in view of Kannan et al. disclose the set of telemetry parameters comprises at least one of: a motherboard temperature, a CPU temperature, a power sourcing equipment (“PSE”) junction temperature, a total memory capacity, a free memory capacity, an available memory capacity, and a CPU idle percentage, associated with the network device (e.g. Kannan et al. – paragraphs [0197, 0253, 0363, 0366]).
Re claim 9, Engelberg et al. in view of Kannan et al. disclose the training dataset comprises one or more values of the power consumption and at least one of: a motherboard temperature, a CPU temperature, a PSE junction temperature, a free memory capacity, a total CPU non-idle percentage, and a maximum CPU non-idle percentage, associated with the network device collected over the time period (e.g. Kannan et al. – paragraphs [0197, 0253, 0363, 0366]).
Re claim 10, Engelberg et al. in view of Kannan et al. disclose the set of telemetry parameters comprises a CPU idle percentage, and wherein the total CPU non-idle percentage and the maximum CPU non-idle percentage are determined based on the CPU idle percentage (e.g. Kannan et al. – paragraphs [0197, 0253, 0363, 0366]).
Re claim 11, Engelberg et al. in view of Kannan et al. disclose the power management logic is further configured to: determine a test dataset from the base dataset; and validate the trained machine learning model based on the test dataset (e.g. Engelberg et al. – Figure 2 and paragraphs [0034 and 0037] and Kannan et al. – paragraphs [0197-0198]).
Re claim 12, Engelberg et al. in view of Kannan et al. disclose the power management logic is further configured to predict power consumed by the network device based on a set of telemetry values derived from the test dataset, and wherein the trained machine learning model is validated based on the predicted power and a power consumption value of the test dataset (e.g. Engelberg et al. – Figure 2 and paragraphs [0023-0024] and Kannan et al. – paragraph [0197]).
Re claim 13, Engelberg et al. in view of Kannan et al. disclose the power management logic is further configured to determine an error associated with the trained machine learning model based on the validation of the trained machine learning model (e.g. Kannan et al. – paragraphs [0197-0198] with testing its data for convergent).
Re claim 14, Engelberg et al. in view of Kannan et al. disclose the power management logic is further configured to tune the machine learning model based on the determined error (e.g. Kannan et al. – paragraphs [0361-0363] with feedback system).
Re claim 17, Engelberg et al. in view of Kannan et al. disclose the device telemetry data comprises a plurality of values of at least one of: a motherboard temperature, a central processing unit (“CPU”) temperature, a power sourcing equipment (“PSE”) junction temperature, a total memory capacity, a free memory capacity, an available memory capacity, and a CPU idle percentage (e.g. Kannan et al. – paragraphs [0197, 0253, 0363, 0366]).
Re claim 18, Engelberg et al. in view of Kannan et al. disclose the one or more telemetry parameters comprise at least one of: a motherboard temperature, a CPU temperature, a PSE junction temperature, a free memory capacity, a total CPU non-idle percentage, and a maximum CPU non-idle percentage (e.g. Kannan et al. – paragraphs [0197, 0253, 0363, 0366]).
Response to Arguments
Applicant's arguments with respect to claims 1-20 have been considered but are moot in view of the new ground(s) of rejection.
The applicant argues in pages 8-9 for claims rejected under 35 USC 101 as directing to an abstract idea under the mental process that the claim is adjusting a device performance state in real time.
The examiner respectfully submits that the claim currently does not detail how to control or adjust the device performance state in real-time in order to give additional weight, but without specific detail (1) the limitation of adjusting in real-time is analyzed under Prong II as additional element rather the abstract idea limitation as alleged by the applicant and (2) the examiner consider the general controlling/adjusting the device for changing performance state as merely as “apply it” which merely applying the judicial exception or abstract idea. Therefore, this additional element does not integrate the judicial exception into a practical application. See MPEP 2106.05(f).
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.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to PHUOC H NGUYEN whose telephone number is (571)272-3919. The examiner can normally be reached M-F: 7:30 am -3:30 pm.
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/PHUOC H NGUYEN/Primary Examiner, Art Unit 2451
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