DETAILED ACTION
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 .
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.
The text of those sections of Title 35, U.S. Code not included in this action can be found in a prior Office action.
As per claims 17-20, they are rejected because the applicant has provided evidence that the applicant intends the term "computer readable storage medium” to include non-statutory matter. The applicant describes a computer-readable storage medium as including open ended language and thus it is reasonable to interpret it to include all possible mediums, including non-statutory mediums (see paragraph 0018). The words "storage" and/or "recording" are insufficient to convey only statutory embodiments to one of ordinary skill in the art absent an explicit and deliberate limiting definition or clear differentiation between storage media and transitory media in the disclosure. As such, the claim(s) is/are drawn to a form of energy. Energy is not one of the four categories of invention and therefore this/these claim(s) is/are not statutory. Energy is not a series of steps or acts and thus is not a process. Energy is not a physical article or object and as such is not a machine or manufacture. Energy is not a combination of substances and therefore not a composition of matter.
The Examiner suggests amending the claim(s) to read as a “non-transitory computer-readable storage medium”.
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.
Claims 5 and 13 are 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 1 recites “issuing a second command to shut down the first subsystem.” Claim 5, which depends on claim 1 recites “issuing a second command to shut down a second subsystem.” It is unclear to the examiner whether the “a second command” recited in claim 5 is the same as, or different from, the “a second command” recited in claim 1.
Claim 9 recites “issuing a second command to shut down the first subsystem.” Claim 13, which depends on claim 9 recites “issuing a second command to shut down a second subsystem.” It is unclear to the examiner whether the “a second command” recited in claim 13 is the same as, or different from, the “a second command” recited in claim 9.
Allowable Subject Matter
Claims 2-3,10-11 and 18-19 are objected to as being dependent upon a rejected base claim but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims.
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 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 1,4,7-9,12-13,15-17 and 19-20 are rejected under 35 U.S.C. 103 as being unpatentable over Sachs et al. (Pub No. US 2020/0033932) in view of Smith et al. (Smith) (Pub No. US 2021/0064431)
Regrading claim 1 Sachs teaches a method [Fig.4] for performing shutdown of a computing system [Fig.1 and Fig.2] using custom shutdown intervals, [Fig.4] comprising: receiving, by the computing system, a command to shutdown the computing system; [Fig.4, item 410, a first command is received to shut down the primary processor for a first operating platform] issuing a first command to shutdown a first subsystem of the computing system;[Fig.4, item 410 and [0052] command may be sent to shut down the AP during transition.. to force AP to shut down.] determining, after a first interval since issuing the first command, that the first subsystem has not shutdown; [[0052] At block 430, it may be determined whether the first timer expired relative to a first timeout value……. When the AP fails to respond to the setting of AP shutdown, such as AP shutdown 242 as described for FIG. 2, and the AP does not enter a shutdown state ] and issuing a second command to shutdown the first subsystem of the computing system, [[0052] the power management subsystem may force the AP PMIC, such as AP PMIC 208 as described for FIG. 2, to turn off the supply of power to the AP by re-asserting AP shutdown for an extended period of time. [0056] where a third command is sent to the secondary processor to shut down,]
Sachs does not teach wherein the first interval is obtained from a trained machine learning model.
However, Smith teaches wherein the first interval is obtained from a trained machine learning model. [a trained ML model receive system/activity data as input and its output is used to generate and adjust shutdown time, [0130] the computing system 110 can take one or more additional steps to improve the prediction of the machine learning model 308. The computing system 110 can adjust shut down times 312, adjust power on times 314, retrain the machine learning model 316, adjust threshold levels, and determine activity during off period 320. For example, adjusting shut down times 312 can include adjusting when to shut down the server environment 106 and how long to shut down the server environment 106. The computing system 110 can adjust the shut down times in response to determining how often the computing system 110 has shut down the server environment 106 in the past]
Therefore, it would have been obvious to one of the ordinary skilled in the art to which this invention pertains before the effective filing date of the invention to use the ML learning model of Smith to adjust the shutdown time in Sachs’s system.
A person with ordinary skill in the art would have been motivated to combine Smith and Sachs to reduce unnecessary waiting/forced-shutdown risk through prediction using ML.
Regarding claim 4 combination of Sachs and smith teaches determining, after a second interval since issuing the second command, that the first subsystem has not shutdown; and issuing a third command to shutdown the first subsystem of the computing system, wherein the second interval is obtained from the trained machine learning model. [[0052] hen the first timer expires, method 400 may proceed to block 435. Otherwise, method 400 may return to block 425. At block 435, a third command may be sent to the secondary processor to shut down. The third command may be sent in response to a determination that the first timer expired. For example, a command may be sent to shut down the AP during transition 376 as described for FIG. 3B. The command may set the AP status, such as AP status 238 as described for FIG. 2, to indicate that the AP is being shut down. As another example, the command may be sent to shut down the AP during transition 334 in FIG. 3B. The command may set AP shutdown, such as AP shutdown 242 as described for FIG. 2, to force the AP to shut down. Smith teaches ML derived intervals]
Regarding claim 7 smith teaches the trained machine learning model is trained using training data that includes shutdown timing data collected from a plurality of computing systems, a configuration of each of the plurality of computing systems, and operational characteristics for each of the plurality of computing systems that correspond to the shutdown timing data. [[0133] computing system 110 may train a separate machine learning model for different server environments 106. For example, a machine learning model 308 may be trained for a first server environment and another machine learning model may be trained for a second server environment. By training a particular machine learning model for a corresponding server environment, the machine learning model can be more accurate in predicting when to shut down or power on the corresponding server environment. Alternatively, if the computing system 110 trains a single machine learning model for various server environments, the single machine learning model may require a longer training period. Additionally, the single machine learning model may not generate accurate predictions for various server environments.]
Regrading claim 8 smith teaches the operational characteristics include a resource utilization rate of a computing resource for each of the plurality of computing systems, a workload level for each of the plurality of computing systems, and a date and time systems that correspond to the shutdown timing data. [[0124] The table 224 can include a task ID, a user ID, a task type, CPU utilization, memory utilization, a priority ranking, and a classification. [[0014] he system can store activity data that indicates the way that environments are used over time, e.g., which environments are run, the times and durations that they are run, the number of users logged on, the number and type of tasks performed, the amount and types of computing resources they use, and so on. This information can be tracked for users who generate or manage environments, e.g., indicating how many environments a user typically has running and which environments those are, how frequently the user creates new environments, the times and durations that the environments run, the computing resources used by that user's environments, and so on. [0083] historical activity data 122 for each client device that connected to the server environment 106 over a particular time period. The time periods for retrieving historical activity data 122 can range over particular intervals, such as during 6:00 AM to 6:00 PM over each day of the month of October 2017. In some implementations, the computing system 110 can retrieve historical activity data 122 during time periods in which the server environment 106 previously received a high number of connection requests or a low number of connection requests from client devices.]
Regrading claim 9 Sachs teaches A computing system having a memory having computer readable instructions and one or more processors for executing the computer readable instructions, the computer readable instructions controlling the one or more processors to perform [[0063] an embodiment can be implemented as a computer-readable storage medium having computer readable code stored thereon for programming a computer (e.g., comprising a processor) to perform a method as described and claimed herein. Examples of such computer-readable storage mediums include, but are not limited to, a hard disk, a CD-ROM, an optical storage device, a magnetic storage device, a ROM (Read Only Memory), a PROM (Programmable Read Only Memory), an EPROM (Erasable Programmable Read Only Memory), an EEPROM (Electrically Erasable Programmable Read Only Memory) and a flash memory.] operations comprising: receiving a command to shutdown the computing system; [Fig.4, item 410, a first command is received to shut down the primary processor for a first operating platform] issuing a first command to shutdown a first subsystem of the computing system; ;[Fig.4, item 410 and [0052] command may be sent to shut down the AP during transition.. to force AP to shut down.] determining, after a first interval since issuing the first command, that the first subsystem has not shutdown; [[0052] At block 430, it may be determined whether the first timer expired relative to a first timeout value……. When the AP fails to respond to the setting of AP shutdown, such as AP shutdown 242 as described for FIG. 2, and the AP does not enter a shutdown state ] and issuing a second command to shutdown the first subsystem of the computing system, [[0052] the power management subsystem may force the AP PMIC, such as AP PMIC 208 as described for FIG. 2, to turn off the supply of power to the AP by re-asserting AP shutdown for an extended period of time. [0056] where a third command is sent to the secondary processor to shut down,]
Sachs does not teach wherein the first interval is obtained from a trained machine learning model.
However, Smith teaches wherein the first interval is obtained from a trained machine learning model. [a trained ML model receive system/activity data as input and its output is used to generate and adjust shutdown time, [0130] the computing system 110 can take one or more additional steps to improve the prediction of the machine learning model 308. The computing system 110 can adjust shut down times 312, adjust power on times 314, retrain the machine learning model 316, adjust threshold levels, and determine activity during off period 320. For example, adjusting shut down times 312 can include adjusting when to shut down the server environment 106 and how long to shut down the server environment 106. The computing system 110 can adjust the shut down times in response to determining how often the computing system 110 has shut down the server environment 106 in the past]
Therefore, it would have been obvious to one of the ordinary skilled in the art to which this invention pertains before the effective filing date of the invention to use the ML learning model of Smith to adjust the shutdown time in Sachs’s system.
A person with ordinary skill in the art would have been motivated to combine Smith and Sachs to reduce unnecessary waiting/forced-shutdown risk through prediction using ML.
Claims 12 and 20 have similar limitations to that of the apparatus of claim 4. Accordingly, claims 12 and 20 are rejected under a similar rational as that of claim 4 above.
Claim 15 has similar limitations to that of the apparatus of claim 7. Accordingly, claims 15 is rejected under a similar rational as that of claim 7 above.
Claim 16 has similar limitations to that of the apparatus of claim 8. Accordingly, claims 16 is rejected under a similar rational as that of claim 8 above.
Regrading claim 17 Sachs A computer program product comprising a computer readable storage medium having program instructions embodied therewith, the program instructions executable by a processor to cause the processor to perform operations [[0063] an embodiment can be implemented as a computer-readable storage medium having computer readable code stored thereon for programming a computer (e.g., comprising a processor) to perform a method as described and claimed herein. Examples of such computer-readable storage mediums include, but are not limited to, a hard disk, a CD-ROM, an optical storage device, a magnetic storage device, a ROM (Read Only Memory), a PROM (Programmable Read Only Memory), an EPROM (Erasable Programmable Read Only Memory), an EEPROM (Electrically Erasable Programmable Read Only Memory) and a flash memory.] comprising: receiving, by a computing system, a command to shutdown the computing system; [Fig.4, item 410, a first command is received to shut down the primary processor for a first operating platform] issuing a first command to shutdown a first subsystem of the computing system; [Fig.4, item 410 and [0052] command may be sent to shut down the AP during transition.. to force AP to shut down.] determining, after a first interval since issuing the first command, that the first subsystem has not shutdown; [[0052] At block 430, it may be determined whether the first timer expired relative to a first timeout value……. When the AP fails to respond to the setting of AP shutdown, such as AP shutdown 242 as described for FIG. 2, and the AP does not enter a shutdown state ] and issuing a second command to shutdown the first subsystem of the computing system, , [[0052] the power management subsystem may force the AP PMIC, such as AP PMIC 208 as described for FIG. 2, to turn off the supply of power to the AP by re-asserting AP shutdown for an extended period of time. [0056] where a third command is sent to the secondary processor to shut down,]
Sachs does not teach wherein the first interval is obtained from a trained machine learning model.
However, Smith teaches wherein the first interval is obtained from a trained machine learning model. [a trained ML model receive system/activity data as input and its output is used to generate and adjust shutdown time, [0130] the computing system 110 can take one or more additional steps to improve the prediction of the machine learning model 308. The computing system 110 can adjust shut down times 312, adjust power on times 314, retrain the machine learning model 316, adjust threshold levels, and determine activity during off period 320. For example, adjusting shut down times 312 can include adjusting when to shut down the server environment 106 and how long to shut down the server environment 106. The computing system 110 can adjust the shut down times in response to determining how often the computing system 110 has shut down the server environment 106 in the past]
Therefore, it would have been obvious to one of the ordinary skilled in the art to which this invention pertains before the effective filing date of the invention use the ML learning model of Smith to adjust the shutdown time in Sachs’s system.
A person with ordinary skill in the art would have been motivated to combine Smith and Sachs to reduce unnecessary waiting/forced-shutdown risk through prediction using ML.
Claims 5-6 and 13-14 and are rejected under 35 U.S.C. 103 as being unpatentable over Sachs et al. (Pub No. US 2020/0033932) in view of Smith et al. (Smith) (Pub No. US 2021/0064431) further in view of Xue et al. (Patent No. US 12,461,755)
Regarding Claim 5, Sachs teaches: issuing a second command to shutdown a second subsystem of the computing system;[Fig.4, item 410 and [0052] command may be sent to shut down the AP during transition.. to force AP to shut down.] determining, after a second interval since issuing the second command, that the second subsystem has not shutdown; [[0052] At block 430, it may be determined whether the first timer expired relative to a first timeout value……. When the AP fails to respond to the setting of AP shutdown, such as AP shutdown 242 as described for FIG. 2, and the AP does not enter a shutdown state ] and issuing a third command to shutdown the second subsystem of the computing system, [[0052] the power management subsystem may force the AP PMIC, such as AP PMIC 208 as described for FIG. 2, to turn off the supply of power to the AP by re-asserting AP shutdown for an extended period of time. [0056] where a third command is sent to the secondary processor to shut down,]
Sachs does not teach wherein the second interval is obtained from the trained machine learning model.
However, Smith teaches wherein the first interval is obtained from a trained machine learning model. [a trained ML model receive system/activity data as input and its output is used to generate and adjust shutdown time, [0130] the computing system 110 can take one or more additional steps to improve the prediction of the machine learning model 308. The computing system 110 can adjust shut down times 312, adjust power on times 314, retrain the machine learning model 316, adjust threshold levels, and determine activity during off period 320. For example, adjusting shut down times 312 can include adjusting when to shut down the server environment 106 and how long to shut down the server environment 106. The computing system 110 can adjust the shut down times in response to determining how often the computing system 110 has shut down the server environment 106 in the past]
Therefore, it would have been obvious to one of the ordinary skilled in the art to which this invention pertains before the effective filing date of the invention to use the ML learning model of Smith to adjust the shutdown time in Sachs’s system.
A person with ordinary skill in the art would have been motivated to combine Smith and Sachs to reduce unnecessary waiting/forced-shutdown risk through prediction using ML.
The combination of Sachs and Smith does not teach determining that the first subsystem has shut down;
However, Xue teaches determining that the first subsystem has shut down; [shutdown according to the priority order]
Therefore, it would have been obvious to one of the ordinary skilled in the art to which this invention pertains before the effective filing date of the invention to combine Sachs, Smith and Xue to shutdown Sach’s system using on ML data based on priority order as taught by Xue to prevent physical damage to hardware and prevent data loss.
Regrading Claim 6 Xue teaches first subsystem of the computing system is dependent on the second subsystem of the computing system. ; [shutdown according to the priority order. Node being unable to shut down because the dependency node has not been shut down.]
Claim 13 has similar limitations to that of the apparatus of claim 5. Accordingly, claims 13 is rejected under a similar rational as that of claim 5 above.
Claim 14 has similar limitations to that of the apparatus of claim 6. Accordingly, claims 14 is rejected under a similar rational as that of claim 6 above.
Citation of Relevant Prior Art
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure:
-Prior art Bernstein et al. (Patent No. US 7,533,277) teaches receiving information from an application regarding a task that the application is configured to perform; receiving a command to initiate operating system shut down while the application is running; determining that the operating system shut down should be delayed due to a status of the application; and displaying the information received from the application on a graphical user interface during a period in which the operating system shut down is being delayed, the graphical user interface showing that the application is running.
-Prior art LeCrone et al. (Pub No. US 2021/0064470) teaches Adapting a storage system in response to operation of a corresponding host includes determining whether the host is performing a boot up operation, determining whether the host is performing a shutdown operation, and adapting operation of the storage system in response to the host performing one of: a boot up operation or a shutdown operation. Adapting operation of the storage system may include suspending low-priority housekeeping tasks, decreasing work queue scan times to be more responsive to incoming work, moving cores from other emulations, increasing thread counts, and/or preloading specified files into cache memory of the storage system. Determining whether the host is performing a boot up operation may include making a call from the storage system to the host that causes the host to return an indication thereof. Operations that are characteristic of booting up may be determined by machine learning.
Prio art Gardner et al. (Patent No. US 11,714,658) teaches a computer system can use machine learning to monitor characteristics of server environments and produce instructions to shut down or power up on the server environments. The computer system can monitor aspects of a particular server environment over a period of time to determine usage patterns for that particular server environment. The usage patterns can be determined from an assessment of, for example, currently active user sessions in the computer environment, a number of users logged in to the computer environment, a number of active users logged in, scheduled application activity, user-initiated application activity, historical user activity on the computer environment, and a priority ranking of active CPU tasks. The computer system can additionally monitor reports or logs generated by the server environment that indicate activities performed using the server environment. These reports can be used to determine whether one or more users are currently logged in or are currently active, and to determine whether tasks being performed were scheduled tasks or were initiated based on a user action, such as by a user clicking on an icon of the application or a client device sending a request to the server environment. User activity for individual server environments can be monitored over a predetermined period of time, such as an hour, a day, a week, or longer, in order to determine the manner and extent that users make use of each individual environment. Data indicative of current and/or prior usage of a computing environment can be input to a trained machine learning model, which can produce output that indicates a prediction of how likely the environment is to have at least a threshold level of demand or utilization in a future time period. If the trained machine learning model predicts low usage of environment, the computer system can send instructions causing the environment to be automatically shut down, which can help to conserve or reallocate computing resources and increase power efficiency.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to ZAHID CHOUDHURY whose telephone number is (571)270-5153. The examiner can normally be reached Monday-Friday.
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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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/ZAHID CHOUDHURY/Primary Examiner, Art Unit 2175
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