DETAILED ACTION
Response to Amendment
Applicant’s amendment, filed 07/28/26, for application number 18/594,978 has been received and entered into record. Claim 1 has been amended and Claims 16-18 were previously cancelled. Therefore, Claims 1-15 are presented for examination.
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Claim Objections
Claim 1 is objected to because of the following informalities:
Claim 1, line 24 recites, “software-executing hardware devices” (emphasis added) and should instead read, “software executing hardware devices” to maintain consistency with the remaining instances of “software executing” throughout the claims. Appropriate correction is required.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
The following is a quotation of 35 U.S.C. 112(d):
(d) REFERENCE IN DEPENDENT FORMS.—Subject to subsection (e), a claim in dependent form shall contain a reference to a claim previously set forth and then specify a further limitation of the subject matter claimed. A claim in dependent form shall be construed to incorporate by reference all the limitations of the claim to which it refers.
The following is a quotation of pre-AIA 35 U.S.C. 112, fourth paragraph:
Subject to the following paragraph [i.e., the fifth paragraph of pre-AIA 35 U.S.C. 112], a claim in dependent form shall contain a reference to a claim previously set forth and then specify a further limitation of the subject matter claimed. A claim in dependent form shall be construed to incorporate by reference all the limitations of the claim to which it refers.
Claim 8 is rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Claim 8, lines 4-5 recite, “determine, by the one or more processors, if the predicted failure time is before the scheduled maintenance time.” (emphasis added) However, there is insufficient antecedent basis for this limitation in the claim, as there does not appear to be any mention of a maintenance time elsewhere in Claim 8 preceding the limitation, nor in Claims 7, 6, nor 1, on which Claim 8 directly or indirectly depends.
Claim 6 is rejected under 35 U.S.C. 112(d) or pre-AIA 35 U.S.C. 112, 4th paragraph, as being of improper dependent form for failing to further limit the subject matter of the claim upon which it depends, or for failing to include all the limitations of the claim upon which it depends.
Regarding Claim 6, the claim recites, “determine, by the one or more processors, a predicted failure of the software executing hardware,” which appears to simply be a broader recitation of the newly-amended limitation of Claim 1, which reads, “determine, by the one or more processors, based on historical power-consumption data and hardware failure-data, a predicted failure time for each of the plurality of software-executing hardware devices” (emphasis added). As such, Claim 6 does not appear to further limit Claim 1.
Applicant may cancel the claim, amend the claim to place the claim in proper dependent form, rewrite the claim in independent form, or present a sufficient showing that the dependent claim complies with the statutory requirements.
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.
The factual inquiries 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.
Claims 1-9 and 12 are rejected under 35 U.S.C. 103 as being unpatentable over Lee et al., US 2019/0163250 A1, in view of Kern et al., US 2021/0081266 A1, and further in view of Seo et al., US 2019/0196430 A1.
Regarding Claim 1, Lee discloses a system comprising for measuring energy usage of software [electronic device 201, Fig. 2] comprising:
one or more computers comprising one or more processors and one or more non-transitory computer readable media [processor 210, memory 230], the one or more non-transitory computer readable media including program instructions stored thereon that when executed cause the one or more computers to:
receive, by the one or more processors, a baseline power measurement from software executing hardware device of a plurality of software executing hardware devices, the baseline power measurement comprising a power usage of the software executing hardware component during an idle time period [the power consumption of the CPU/GPU, the electronic device may estimate power consumption separately for an active time and an inactive time. The electronic device may estimate active power consumption and base power consumption in the active time on the basis of the use time of each application and the stay time rate of each frequency, and estimate power consumption in the corresponding frequency by the estimated active power consumption and base power consumption. In the inactive time, the electronic device may estimate power consumption on the basis of an idle (sleep) time, an awake (state 0) time log or idle power in an idle mode, or an awake power profile, i.e. determining a baseline power consumption during idle; the electronic device may estimate power consumption according to each hardware component or each hardware combination corresponding to the determined application category in step 620, par 194, 193];
receive, by the one or more processors, a loaded power measurement of the software executing hardware, the loaded power measurement including the baseline power measurement and an additional power measurement, the additional power measurement comprising power used by the software executing hardware during the execution of one or more programs [the power profile may define the power consumption of each hardware component per unit time (hereinafter, referred to as “unit power consumption”) according to each application category. In this case, power consumption of a particular hardware component may be estimated by the total time during which the particular hardware component operates and unit power consumption of the particular hardware component. The total time corresponds to a time during which the particular hardware component operates while the target application is in use, i.e. power profile is the time when application is in use, i.e. includes base and additional power, par 114];
determine, by the one or more processors, the additional power measurement by determining a difference between the baseline power measurement and the loaded power measurement [equation 6 determining the loaded power (PCPU) from the active power (Pa(fi)) (additional power) combined with the base power value (Pb(fi)); Equation 6 can be used to determine difference as additional power as the value of power consumption value of based power value are measured, par 83, 194]; and
store, by the one or more processors, a software power usage, the software power usage comprising the additional power measurement and an association of the additional power measurement to the one or more programs [the initial power profile on the basis of measured power consumption of combinations of driven hardware components involved in executing one or a plurality of applications newly installed. Furthermore, the power profile may be continuously updated or tuned on the basis of estimated and measured power consumption of the newly installed applications; different power consumption levels associated and grouped into corresponding categories, power profiles are necessarily stored, and categorizing the power consumption is based on the hardware utilized by the corresponding applications; for the update of the power profile, the electronic device may separately record power consumption estimated for each hardware component corresponding to an application category to be estimated in step 630, Tables 1-3, par 170, 87, 198].
However, Lee does not explicitly teach determining, by the one or more processors, based on historical power-consumption data and hardware-failure data, a predicted failure time for each of the plurality of software-executing hardware devices; and causing, by the one or more processors, one or more programs on a selected software- executing hardware device to be executed based on the software power usage and the predicted failure times of the plurality of software-executing hardware devices such that the selected software-executing hardware device has an adjusted failure time that is greater than the predicted failure time.
Kern teaches determining, by the one or more processors, based on historical power-consumption data and hardware-failure data, a predicted failure time for each of the plurality of software-executing hardware devices [the method for predicting failures includes gathering server failure data of servers of the same type as the remote server from the health alerts and constructing the server failure model using server failure data, the trends in the power consumption data, and the health alerts (i.e. historical data); predicting a failure in the remote server is further based on the current total power consumption of the remote server (software runs on server, i.e. the software executing hardware), par 35]
It would have been obvious to one of ordinary skill in the art, having the teachings of Lee and Kern before him before the effective filing date of the claimed invention, to incorporate the predictions as taught by Kern, into the system as disclosed by Lee, to avoid loss of computing resources by predicting upcoming component failures [Kern, par 3].
However, the combination of references does not explicitly teach causing, by the one or more processors, one or more programs on a selected software- executing hardware device to be executed based on the software power usage and the predicted failure times of the plurality of software-executing hardware devices such that the selected software-executing hardware device has an adjusted failure time that is greater than the predicted failure time.
Seo teaches causing, by the one or more processors, one or more programs on a selected software-executing hardware device to be executed based on the software power usage and the predicted failure times of the plurality of software-executing hardware devices such that the selected software-executing hardware device has an adjusted failure time that is greater than the predicted failure time [the appliance operates according to the normal operation maintenance mode, so the appliance may delay the occurrence of a failure to a time point after a predicted failure time point and a user may more leisurely use a repair service; software and hardware components involved in executing functions of an appliance (i.e. selecting an operating mode based on the power usage of associated software and hardware components and predicted failure times to delay the predicted failure), par 109, 86].
It would have been obvious to one of ordinary skill in the art, having the teachings of Lee, Kern, and Seo before him before the effective filing date of the claimed invention, to incorporate the failure prediction as taught by Seo, into the system as disclosed by Lee and Kern, to provide convenience to the user by allowing continued use of the device according to the user’s schedule based on predicted failure of the device [Seo, par 12].
Regarding Claim 2, Lee, Kern, and Seo disclose the system of Claim 1. Kern further teaches receiving, by the one or more processors, a maintenance prediction for a hardware component of the software executing hardware [the method for predicting failures includes gathering server failure data of servers of the same type as the remote server from the health alerts and constructing the server failure model using server failure data, the trends in the power consumption data, and the health alerts; predicting a failure in the remote server is further based on the current total power consumption of the remote server (software runs on the server, i.e. software executing hardware), par 35].
Regarding Claim 3, Lee, Kern, and Seo disclose the system of Claim 2. Kern further teaches wherein the maintenance prediction includes a failure prediction for the hardware component [the method for predicting failures includes gathering server failure data of servers of the same type as the remote server from the health alerts and constructing the server failure model using server failure data, the trends in the power consumption data, and the health alerts; predicting a failure in the remote server is further based on the current total power consumption of the remote server, par 35].
Regarding Claim 4, Lee, Kern and Seo disclose the system of Claim 3. Kern further teaches wherein the maintenance prediction includes a correlation between total power usage and the failure prediction [the method for predicting failures includes combining the power consumption data of the remote server with previous power consumption data to determine a current total power consumption of the remote server, where predicting a failure in the remote server is further based on the current total power consumption of the remote server; the controller 112, in some embodiments, monitors overall power consumption of the remote server 102 in which it resides, such as server input power. In other embodiments, the controller 112 monitors power consumption of various components of the remote server 102. For example, the controller 112 may measure CPU power, a memory power, graphical processor unit (“GPU”), fan power, and the like and stores the power consumption data, par 35, 46].
Regarding Claim 5, Lee, Kern, and Seo disclose the system of Claim 3. Kern further teaches wherein the maintenance prediction includes a correlation between the software power usage and the failure prediction [the method for predicting failures includes combining the power consumption data of the remote server with previous power consumption data to determine a current total power consumption of the remote server, where predicting a failure in the remote server is further based on the current total power consumption of the remote server; the controller 112 monitors power consumption of various components of the remote server 102. For example, the controller 112 may measure CPU power, a memory power, graphical processor unit (“GPU”), fan power, and the like and stores the power consumption data, i.e. software running on the servers, and thus monitoring the hardware power consumption indirectly monitors the software power consumption; , par 35, 46].
Regarding Claim 6, Lee, Kern, and Seo disclose the system of Claim 1. Kern further teaches determining, by the one or more processors, a predicted failure time of the software executing hardware [the method for predicting failures includes gathering server failure data of servers of the same type as the remote server from the health alerts and constructing the server failure model using server failure data, the trends in the power consumption data, and the health alerts; predicting a failure in the remote server is further based on the current total power consumption of the remote server (software runs on server, i.e. the software executing hardware), par 35].
Regarding Claim 7, Lee, Kern, and Seo disclose the system of Claim 6. Kern further teaches wherein the predicted failure time is determined from a historical analysis of total power usage and hardware component failures in similar software executing hardware [the method for predicting failures includes gathering server failure data of servers of the same type as the remote server from the health alerts and constructing the server failure model using server failure data, the trends in the power consumption data, and the health alerts; predicting a failure in the remote server is further based on the current total power consumption of the remote server (software runs on server, i.e. software executing hardware), par 35].
Regarding Claim 8, Lee, Kern, and Seo disclose the system of Claim 7. Seo further teaches determining, by the one or more processors, if the predicted failure time is before the scheduled maintenance time [the appliance operates according to the normal operation maintenance mode, so the appliance may delay the occurrence of a failure to a time point after a predicted failure time point and a user may more leisurely use a repair service; software and hardware components involved in executing functions of an appliance, par 109, 86].
Regarding Claim 9, Lee, Kern, and Seo disclose the system of Claim 8. Seo further teaches determining, by the one or more processors, a power usage plan for the software executing hardware that results in a failure prediction time that is greater than or equal to the scheduled maintenance time [the appliance operates according to the normal operation maintenance mode, so the appliance may delay the occurrence of a failure to a time point after a predicted failure time point and a user may more leisurely use a repair service; software and hardware components involved in executing functions of an appliance, par 109, 86].
Regarding Claim 12, Lee, Kern, and Seo disclose the system of Claim 1. Seo further teaches assigning, by the one or more processors, one or more programs to the software executing hardware that includes a software power usage that results in an adjusted failure time for the software executing hardware that is greater than a predicted failure time [the appliance operates according to the normal operation maintenance mode, so the appliance may delay the occurrence of a failure to a time point after a predicted failure time point and a user may more leisurely use a repair service; software and hardware components involved in executing functions of an appliance, par 109, 86].
Claims 10, 11, and 13-15 are rejected under 35 U.S.C. 103 as being unpatentable over Lee, Kern, and Seo, and further in view of Blankenburg, US 2021/0089364 A1.
Regarding Claim 10, Lee, Kern, and Seo disclose the system of Claim 7. However, the combination of references does not explicitly teach generating, by the one or more processors, a power usage plan for the software executing hardware and/or the hardware component.
Blankenburg teaches generating, by the one or more processors, a power usage plan for the software executing hardware and/or the hardware component [Fig. 2 illustrates power usage plan for software (and associated software executing hardware) based on power limit properties of the hardware and the power demands of each of the plurality of applications].
It would have been obvious to one of ordinary skill in the art, having the teachings of Lee, Kern, Seo, and Blankenburg before him before the effective filing date of the claimed invention, to incorporate the execution of software based on power usage, as taught by Blankenburg, into the system as disclosed by Lee, Kern, and Seo, to ensure execution of applications while meeting the power demands of the system [Blankenburg, par 2].
Regarding Claim 11, Lee, Kern, Seo, and Blankenburg disclose the system of Claim 10. Blankenburg further teaches wherein the power usage plan comprises a power limit for the software executing hardware [Fig. 2 illustrates power usage plan for software (and associated software executing hardware) based on power limit properties of the hardware and the power demands of each of the plurality of applications].
Regarding Claim 13, Lee, Kern, and Seo disclose the system of Claim 1. However, the combination of references does not explicitly teach determining, by the one or more processors, software power usage for each of a plurality of programs.
Blankenburg teaches determining, by the one or more processors, software power usage for each of a plurality of programs [Fig. 2 illustrates power usage plan for software (and associated software executing hardware) based on power limit properties of the hardware and the power demands of each of the plurality of applications].
It would have been obvious to one of ordinary skill in the art, having the teachings of Lee, Kern, Seo, and Blankenburg before him before the effective filing date of the claimed invention, to incorporate the determination of software power usage, as taught by Blankenburg, into the system as disclosed by Lee, Kern, and Seo, to ensure execution of applications while meeting the power demands of the system [Blankenburg, par 2].
Regarding Claim 14, Lee, Kern, Seo, and Blankenburg disclose the system of Claim 13. Blankenburg further teaches generating, by the one or more processors, an execution schedule for the software executing hardware based on the software power usage for each of a plurality of programs [Fig. 2 illustrates power usage plan for software (and associated software executing hardware) based on power limit properties of the hardware and the power demands of each of the plurality of applications].
Regarding Claim 15, Lee, Kern, Seo, and Blankenburg disclose the system of Claim 14. Blankenburg further teaches wherein the execution schedule comprises one or more of: a program list, a software power usage for each of the programs on the program list, a current power usage for the software executing hardware, a predicted power usage for the software executing hardware, and a maintenance prediction [Fig. 2 illustrates power usage plan for software (and associated software executing hardware) based on power limit properties of the hardware and the power demands of each of the plurality of applications; i.e. predicted/expected power usage for the software executing hardware].
Response to Arguments
Applicant’s arguments filed 07/28/26 have been considered but are moot due to the new rejection based on the newly cited portions of the references previously presented.
Conclusion
Applicant is reminded that in amending a response to a rejection of claims, the patentable novelty must be clearly shown in view of the state of the art disclosed by the references cited and the objections made. Applicant must also show how the amendments avoid such references and objections. See 37 CFR §1.111(c).
Any inquiry concerning this communication or earlier communications from the examiner should be directed to PAUL J YEN whose telephone number is (571)270-5047. The examiner can normally be reached M-F 8-5 PT.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Andrew J Jung can be reached at (571) 270-3779. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/Paul Yen/Primary Examiner, Art Unit 2175
