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
Claims 1-20 are rejected under 35 U.S.C. 101 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 1
Step 2A Prong One: This part of the eligibility analysis evaluates whether the claim recites a judicial exception. As explained in MPEP 2106.04, subsection II, a claim "recites" a judicial exception when the judicial exception is "set forth" or "described" in the claim.
Limitation “determining, using a machine-learning model based on a clustering algorithm, an action responsive to the indication.” This limitation recites a judicial exception because it encompasses mathematical concepts. Specifically, a “clustering” algorithm” is a mathematical operation used to group data points based mathematical similarity. Because the claim relies on the “clustering algorithm” to determine action, it falls within the mathematical concepts grouping.
Limitation “determining that the utilization of the computing resource no longer exceeds the preset threshold.” This limitation recites a judicial exception because it encompasses mental processes (i.e., concepts performed in the human mind, including observations, evaluations, judgments, and opinions). This limitation describes a cognitive act of comparing utilization against a preset threshold and making a judgment on the status of the system.
“Unless it is clear that a claim recites distinct exceptions, such as a law of nature and an abstract idea, care should be taken not to parse the claim into multiple exceptions, particularly in claims involving abstract ideas." MPEP 2106.04, subsection II.B. However, if possible, the examiner should consider the limitations together as a single abstract idea rather than as a plurality of separate abstract ideas to be analyzed individually. "For example, in a claim that includes a series of steps that recite mental steps as well as a mathematical calculation, an examiner should identify the claim as reciting both a mental process and a mathematical concept for Step 2A, Prong One to make the analysis clear on the record." MPEP 2106.04, subsection II.B. Here, the mentioned steps fall within the Mathematical Concepts and Mental Processes groupings of abstract ideas and are considered together as a single abstract idea for further analysis. (Step 2A, Prong One: YES).
Step 2A Prong Two: The claim recites the additional elements:
receiving an indication that a utilization of a computing resource has exceeded a preset threshold
outputting a first command to execute the action responsive to the indication
generic computer components: machine-learning model, computing resource
MPEP § 2106.05(a) Improvements to the Functioning of a Computer or to Any Other Technology or Technical Field.
The claim does not recite any specific improvement in computer functionality or any other technology. The limitations of “receiving an indication” and “outputting a first command” do not describe a novel technical solution for improving the hardware or software infrastructure.
MPEP § 2106.05(b) Particular Machine.
The claim does not recite a particular machine or manufacturer-specific hardware. It is directed to a generic “machine learning model” which does not provide a meaningful limit on the abstract idea by tying it to a specific technical architecture.
MPEP § 2106.05(c) Particular Transformation.
The claim does not recite a particular transformation of an article or a reduction in physical property. The steps describe data processing and command output, which do not result in a physical change to an object.
MPEP § 2106.05(e) Other Meaningful Limitations.
The limitations “receiving an indication” and “outputting a first command” are merely data input and data output. They are generic steps that do not impose a meaningful limit on the practice of the abstract ideas of clustering and judgment.
MPEP § 2106.05(g) Insignificant Extra-Solution Activity.
The limitations describes monitoring a threshold and sending a command to execute an action do not provide an unconventional solution to the problem of resource management.
MPEP § 2106.05(h) Field of Use and Technological Environment.
The claim is directed to a generic “computing resource” and a general “machine-learning model.” It does not recite a specific, non-generic technological environment that provides a meaningful limit on the abstract idea.
Accordingly, the additional limitations do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. The claim is directed to an abstract idea.
Step 2B, the claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. The ordered combination of receiving an indication, applying a clustering algorithm to determine an action, outputting a command, and verifying the result does not describe a specific, non-generic solution to a technical problem, as required by Berkheimer v. HP Inc. Instead, the limitations describe the use of generic computer functions to execute the abstract idea of mathematical clustering and human-like judgment. The claim lacks the specific, non-generic technical configuration necessary to amount to significantly more than the recited abstract idea. Therefore, the claim does not amount to significantly more than the recited abstract idea. The claim is not patent eligible.
Claim 2 recites “wherein receiving the indication that the utilization of the computing resource has exceeded the preset threshold comprises: monitoring utilization of a plurality of computing resources including at least one of disk space, memory, CPU utilization, database memory allocations, or JVM memory allocations; determining that the utilization of a first computing resource has exceeded a first preset threshold; and generating a message including information about the utilization of the first computing resource exceeding the first preset threshold.” Monitoring a list of standard hardware resources and generating an informational message are well-understood, routine, and conventional activities in server administration. The claim does not have any additional limitation that amount to significantly more than the abstract idea.
Claim 3 recites “wherein receiving the indication that the utilization of the computing resource has exceeded the preset threshold comprises: receiving, from a database, a message generated by the database in response to identifying a lack of disk space condition.” Receiving a message from a database does not provide a concrete technical solution or a specific, practical application that transforms the underlying mathematical clustering and judgment into a patent-eligible invention.
Claim 4 recites “wherein determining the action responsive to the indication comprises: applying the clustering algorithm to group candidate files into clusters according to at least one property including file type, date added, size, or retention duration; and designating a cluster for deletion based on the at least one property.” The claim applies a mathematical concept (clustering) to a set of data (files) remains an abstract idea.
Claim 5 recites “wherein applying the clustering algorithm comprises: calculating, for each candidate file, a similarity measure relative to other candidate files; comparing the similarity measures to a predetermined clustering threshold; and identifying one or more clusters of candidate files based on the comparisons.” This limitation details the underlying mathematical operations of the clustering algorithm, such as calculating a similarity measure and comparing it to a threshold.
Claim 6 recites “wherein the predetermined clustering threshold is based on a hyperparameter configured for the machine-learning model.” This is a standard configuration step for any machine-learning model and is considered a conventional tool.
Claim 7 recites “wherein the machine-learning model based on the clustering algorithm is trained using training data comprising commands manually generated in response to prior utilization conditions and results of corrective actions documented in a ticketing system.” This limitation specifies the data source for training the model. It merely describes how the machine-learning model is prepared.
Claim 8 recites “wherein training the machine-learning model based on the clustering algorithm comprises: inputting unlabeled examples of utilization conditions into the clustering algorithm; and grouping the unlabeled examples into clusters according to a similarity measure.” This claim describes the process of unsupervised learning. It merely describes a method for training a model.
Claim 9 recites “wherein training the machine-learning model based on the clustering algorithm further comprises: refining the clusters using feedback from results of actions executed in response to utilizations exceeding preset thresholds or results of actions executed on a test system.” This limitation simply uses feedback to retrain the model. It does not provide a non-conventional, unique workflow or a specific technical solution that rescues the claim from the abstract idea of clustering and judgment.
Claim 10 recites “wherein outputting the first command to execute the action responsive to the indication comprises: executing the first command to cause execution of a script configured to execute the action.” Executing a script is a generic computer function.
Claim 11 recites “wherein the script is configured using configurations determine A1d by the machine-learning model based on the utilization exceeding the preset threshold.” This limitation describes the link between the ML model's output and the script's configuration.
Claim 12 recites “wherein the script comprises one or more commands authored in a scripting language that is one of bash, sh, zsh, bat, or PowerShell.” This limitation merely lists common scripting languages.
Claim 13 recites “wherein determining that the utilization of the computing resource no longer exceeds the preset threshold comprises: monitoring the computing resource to detect that the utilization has fallen below the preset threshold comprising executing one or more commands to measure the utilization of the computing resource.” Executing commands to measure resource utilization does not provide an unconventional use or a specific technical improvement to the computer's functionality.
Claim 14 recites “responsive to determining that the utilization of the computing resource no longer exceeds the preset threshold, outputting a second command to record the action taken in response to the utilization for use in training the machine-learning model.” This limitation describes a data logging step for training purposes. It does not provide a specific, non-generic solution to a technical problem.
Claim 15 recites “responsive to receiving the indication of the utilization, outputting instructions to create an incident; and responsive to determining that the utilization of the computing resource no longer exceeds the preset threshold, outputting a second command to close the incident.” This limitation describes an incident management (creating and closing tickets). It does not provide a technical solution to the abstract idea of clustering and judgment.
Claim 16 recites “wherein outputting the instructions to create the incident comprises assigning a severity level to the incident corresponding to an extent to which the preset threshold was exceeded.” This is a "judgment" step and does not provide a specific, non-generic technical solution.
Claim 17 recites “wherein outputting the second command to close the incident comprises: recording, in association with the incident, the action taken in response to the utilization, the recording being usable as training data for the machine-learning model.” This is a data recording and logging step for the purpose of model training. It does not provide an inventive concept or transform the abstract idea into a patent-eligible application.
Claim 18 recites “outputting the instructions to create the incident comprises transmitting the instructions to a remote execution module configured to execute commands on a server configured for incident creation; and outputting the second command to the remote execution module to record the action taken in response to the utilization.” This limitation describes a remote execution architecture. It utilizes "generic computer components" (remote execution modules and servers) to perform the incident management. It does not provide a specific, non-generic solution to a technical problem.
Claims 19-20 are like claim 1. The claims are rejected based on the same reason.
Claim Rejections - 35 USC § 112
The following is a quotation of the first paragraph of 35 U.S.C. 112(a):
(a) IN GENERAL.—The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor or joint inventor of carrying out the invention.
The following is a quotation of the first paragraph of pre-AIA 35 U.S.C. 112:
The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor of carrying out his invention.
Claim 8, 13 are rejected under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA ), first paragraph, as failing to comply with the written description requirement. The claim(s) contains subject matter which was not described in the specification in such a way as to reasonably convey to one skilled in the relevant art that the inventor or a joint inventor, or for applications subject to pre-AIA 35 U.S.C. 112, the inventor(s), at the time the application was filed, had possession of the claimed invention.
Claim 8: “wherein training the machine-learning model based on the clustering algorithm comprises: inputting unlabeled examples of utilization conditions into the clustering algorithm; and grouping the unlabeled examples into clusters according to a similarity measure” The claim is not supported by the original specification – 18/061165
[0049] The processing device 204 may train the machine-learning model 106 using a machine-learning algorithm using the training data 130 and the second plurality of commands. The machine-learning algorithm may include, for example, classifying labeled examples using a neural network or clustering unlabeled examples using a clustering algorithm according to a similarity measure. Any other suitable machine-learning algorithm may be used including artificial intelligence technologies such as deep learning, natural language processing, expert systems, inference engines, or knowledge bases. Other sources of training data may be used in addition to the two examples given here. For example, the machine-learning model 106 may be trained using a test system 134. The test system 134 may mirror the operating of the system 100. For example, the test system 134 may include filesystems or databases that are populated according to the populating of the corresponding components in the system 100. The test system 134 may include a portion or subset of the operations performed on the system 100. For example, a designated percentage of files written to the filesystems and database of the system 100 may be written to the test system, to make the test system 134 cost effective. The machine-learning model 106 may determine actions to correct utilization conditions on the test system 134, which may then execute commands to perform the actions. The machine-learning model 106 can use the result of executing the commands as labeled examples for supervised training. The test system 134 may include the benefit of lower risk since the actions take on the test system 134 may not affect the system 100.
Claim 13: “… wherein determining that the utilization of the computing resource no longer exceeds the preset threshold comprises: monitoring the computing resource to detect that the utilization has fallen below the preset threshold comprising executing one or more commands to measure the utilization of the computing resource…” The underlined phrase is not supported by the original specification – 18/061165
[0051] In block 310, the processing device 204 may determine that the utilization no longer exceeds the preset threshold. For example, the processing device 204 may use a monitoring system to detect that disk space utilization has fallen below 90% or some other preset threshold. In some examples, the processing device 204 may run one or more commands or run one or more scripts 108 to make the determination.
Claim Rejections - 35 USC § 112
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(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. Claim 1 does not mention the step of “training”
Applicant may cancel the claim(s), amend the claim(s) to place the claim(s) in proper dependent form, rewrite the claim(s) in independent form, or present a sufficient showing that the dependent claim(s) complies with the statutory requirements.
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.
Claim 8 recites the limitation " training the machine-learning model. There is insufficient antecedent basis for this limitation in the claim.
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.
Claim(s) 1-2, 4-9, and 13-20 are rejected under 35 U.S.C. 103 as being unpatentable over Prakash (U.S. Pub 2021/0397495 A1), in view of Schneider (U.S. Pub 2010/0223497 A1)
Claim 1
Prakash discloses a computer-implemented method, comprising:
receiving an indication that a utilization of a computing resource has exceeded a preset threshold ([0081], “… the processor can detect that the CPU load of the CPU is exceeding a temporally proximate CPU load by more than 10%…”);
determining, using a machine-learning model based on a clustering algorithm, an action responsive to the indication ([0083], “…if the current anomaly is categorized as a CPU anomaly, the processor can compare the current anomaly to prior anomalies that were also categorized as the CPU anomaly. The processor can compare a temporal window surrounding the anomaly and a prior temporal window surrounding the prior anomaly. The temporal window can be measured in seconds, minutes, and/or hours. The processor can calculate a difference between data points in the temporal window and data points in the prior temporal window… When the difference is within a predetermined threshold, the processor can determine that the anomaly and the prior anomaly are similar…” <examiner note: the current anomaly is in the same group/cluster of previous anomaly> [0085], “… Once the processor identifies a similar prior anomaly… the processor can obtain a resolution…”);
outputting a first command to execute the action responsive to the indication ([0039], “… The solution, if the CPU load is high or the memory use is high, can be to restart the system. The solution to the slow network can be to spawn additional daemons to listen to network ports…”)
However, Prakash does not explicitly disclose determining that the utilization of the computing resource no longer exceeds the preset threshold.
Schneider discloses determining that the utilization no longer exceeds the preset threshold ([0016], “… Once the steady-state for each of the processes has been identified, then the monitor and correction module continues with the monitoring of the metrics to detect deviations in the determined steady-state values…” [0021] After a set of corrective measures has been initiated, the monitor and correction module attempts to track the effect by continuing monitoring of the metrics (Block 213). If the metrics begin trending back towards the previous steady-state, then a determination of the success of the corrective measures can be made in the affirmative (Block 215) …” <examiner note: steady-state values [Wingdings font/0xF3] preset threshold>)
Prakash discloses steps of predicting and correcting the anomaly in the system. However, Prakash does not explicitly disclose to follow up the remedial action. Schneider discloses the monitoring continues after the application of corrective measures to decide of the successful of the corrective measures. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporating the follow up of the corrective measures as disclosed by Schneider into Wang to determine whether the corrective measures are successful or not and to implement additional corrective measures to correct the problem.
Claim 2
Claim 1 is included, Prakash discloses wherein receiving the indication that the utilization of the computing resource has exceeded the preset threshold comprises ([0081], “… the processor can detect that the CPU load of the CPU is exceeding a temporally proximate CPU load by more than 10%…”):
monitoring utilization of a plurality of computing resources including at least one of disk space, memory, CPU utilization, database memory allocations, or JVM memory allocations ([0082], “… the processor can categorize the anomaly into a category… The category can further be subdivided into one or more categories of CPU anomaly, memory anomaly, ethernet traffic anomaly, GPU anomaly, disk anomaly, software application anomaly, and/or hardware application anomaly…”);
determining that the utilization of a first computing resource has exceeded a first preset threshold ([0081], “… the processor can detect that the CPU load of the CPU is exceeding a temporally proximate CPU load by more than 10%…”); and
generating a message including information about the utilization of the first computing resource exceeding the first preset threshold ([0085], “… The processor can provide a notification of the anomaly… The processor can provide notification before the hardware error occurs, or shortly after the hardware error occurs…”)
Claim 4
Claim 1 is included, Prakash discloses wherein determining the action responsive to the indication comprises: applying the clustering algorithm to group candidate files into clusters according to at least one property including file type, date added, size, or retention duration; and designating a cluster for deletion based on the at least one property ([0044], “…the machine learning model 170 receives a current issue, and identifies prior issues that are similar to the current issue. The machine learning model can identify a qualitative issue associated with the current issue, such as a category to which the current issue belongs. For example, the category can be network bandwidth, memory usage, CPU load. To identify a similar prior issue, the machine learning model 170 can first identify the prior issues having the same qualitative issue as the current issue. Once the prior issues having the same qualitative issue have been identified, the machine learning model 170 can examine how well the quantitative data of the prior issue matches the quantitative data of the current issue…”)
Claim 5
Claim 4 is included, Prakash discloses wherein applying the clustering algorithm comprises: calculating, for each candidate file, a similarity measure relative to other candidate files; comparing the similarity measures to a predetermined clustering threshold; and identifying one or more clusters of candidate files based on the comparisons.
Claim 6
Claim 5 is included, Prakash discloses wherein the predetermined clustering threshold is based on a hyperparameter configured for the machine-learning model ([0061] Machine learning model 780 can use the data from the database 740 for training. For example, the machine learning model 780 can take as input data from the database 740 and data from the database 770, and then it can correlate the logs from the database 740 with the issue ticket from the database 770 to determine what kind of patterns, e.g., anomalies, occurring in the logs are associated with issue tickets. The next time the machine learning model receives the logs containing similar patterns associated with an issue ticket, the machine learning model 780 can predict that a problem will occur, prior to the issue ticket generation.)
Claim 7
Claim 1 is included, Prakash discloses wherein the machine-learning model based on the clustering algorithm is trained using training data comprising commands manually generated in response to prior utilization conditions and results of corrective actions documented in a ticketing system ([0037], FIG. 3 a method to generate data used in training a machine learning model to resolve an issue. In step 300… a user… creates an issue ticket, which is stored in a database, and describes the issue that needs to be resolved… and the issue ticket can be created to address the issue…” [0038] In step 310, an engineer… can troubleshoot the issue…” [0039] The solution, if the CPU load is high or the memory use is high, can be to restart the system. The solution to the slow network can be to spawn additional daemons to listen to network ports…”)
Claim 8
Claim 1 is included, Prakash discloses wherein training the machine-learning model based on the clustering algorithm comprises: inputting unlabeled examples of utilization conditions into the clustering algorithm; and grouping the unlabeled examples into clusters according to a similarity measure ([0042] The database storing the issue tickets and issue ticket resolutions can be used to train a machine learning model 170 in FIG. 1 that can determine a resolution to an issue detected in the wireless telecommunication network. For example, the machine learning model 170 can receive an issue ticket as input and produce an issue ticket resolution as output. When the output issue ticket resolution matches the issue ticket resolution stored in the database, the machine learning model 170 receives a positive feedback, and when they do not match, the machine learning model 170 receives a negative feedback. Once trained, the machine learning model 170 can be deployed…”)
Claim 9
Claim 8 included, Prakash discloses wherein training the machine-learning model based on the clustering algorithm further comprises: refining the clusters using feedback from results of actions executed in response to utilizations exceeding preset thresholds or results of actions executed on a test system ([0043] Even when the machine learning model 170 is deployed, in step 350, an issue ticket resolution listener can monitor the issue ticket resolutions coming into the database, and can determine which issue ticket resolutions are new. If a new issue ticket resolution occurs, the issue ticket resolution listener can pass the new issue ticket and the new issue ticket resolution to the machine learning model 170 for further training in step 360. This reinforcement learning process can help improve the accuracy and confidence level of the machine learning model 170 to automatically detect and resolve the issues without human intervention. The newly trained model can be deployed in a production environment to detect and resolve production issues…”)
Claim 13
Claim 1 is included, Schneider discloses wherein determining that the utilization of the computing resource no longer exceeds the preset threshold comprises: monitoring the computing resource to detect that the utilization has fallen below the preset threshold comprising executing one or more commands to measure the utilization of the computing resource ([0016], “… Once the steady-state for each of the processes has been identified, then the monitor and correction module continues with the monitoring of the metrics to detect deviations in the determined steady-state values (Block 205)…” [0018] If the deviation is found to be problematic and not a routine transition (Block 209), then corrective measures may be initiated (Block 211)…”)
Claim 14
Claim 1 is included, Schneider discloses further comprising: responsive to determining that the utilization of the computing resource no longer exceeds the preset threshold, outputting a second command to record the action taken in response to the utilization for use in training the machine-learning model ([0022] In one embodiment, if the corrective measures have successfully addressed the performance impact of the process, then the process can continue by resumption of monitoring for deviations (Block 205) or identify a new steady-state (Block 203) dependent on the corrective measure utilized and similar factors….”)
Claim 15
Claim 1 is included, Prakash discloses further comprising: responsive to receiving the indication of the utilization, outputting instructions to create an incident, and responsive to determining that the utilization of the computing resource no longer exceeds the preset threshold, outputting a second command to close the incident. ([0037, “… FIG. 3 is a flowchart of a method to generate data used in training a machine learning model to resolve an issue. In step 300, an initial creator, such as a user or a processor, creates an issue ticket, which is stored in a database, and describes the issue that needs to be resolved. The issue can include a slow access to a service running on a remote device. For example, normally the service takes about 5 seconds or less to respond, but when the service takes more than 10 seconds to respond, the slow response is indicative of an issue, and the issue ticket can be created to address the issue…”)
Claim 16
Claim 15 is included, Prakash discloses wherein outputting the instructions to create the incident comprises assigning a severity level to the incident corresponding to an extent to which the preset threshold was exceeded ([0082], “… the machine learning model can determine whether the anomaly is similar to a prior anomaly indicating a prior hardware error. To determine similarity, the processor can categorize the anomaly into a category where the anomaly occurred such as the application log and the system performance indicator. The category can further be subdivided into one or more categories of CPU anomaly, memory anomaly, ethernet traffic anomaly, GPU anomaly, disk anomaly, software application anomaly, and/or hardware application anomaly…”)
Claim 17
Claim 16 is included, Prakash discloses wherein outputting the second command to close the incident comprises: recording, in association with the incident, the action taken in response to the utilization, the recording being usable as training data for the machine-learning model ([0022] In one embodiment, if the corrective measures have successfully addressed the performance impact of the process, then the process can continue by resumption of monitoring for deviations (Block 205) or identify a new steady-state (Block 203) dependent on the corrective measure utilized and similar factors….”)
Claim 18
Claim 15 is included, Prakash discloses wherein: outputting the instructions to create the incident comprises transmitting the instructions to a remote execution module configured to execute commands on a server configured for incident creation; and outputting the second command to close the incident comprises transmitting the second command to the remote execution module to record the action taken in response to the utilization. ([0082], “… the machine learning model can determine whether the anomaly is similar to a prior anomaly indicating a prior hardware error. To determine similarity, the processor can categorize the anomaly into a category where the anomaly occurred such as the application log and the system performance indicator. The category can further be subdivided into one or more categories of CPU anomaly, memory anomaly, ethernet traffic anomaly, GPU anomaly, disk anomaly, software application anomaly, and/or hardware application anomaly…”)
Claim 19 and 20 are similar to claim 1. The claims are rejected based on the same reasons.
Claim(s) 3 is/are rejected under 35 U.S.C. 103 as being unpatentable over Prakash (U.S. Pub 2021/0397495 A1), in view of Schneider (U.S. Pub 2010/0223497 A1), as applied to claim 1, and further in view of “cannot be opened due to inaccessible files or insufficient memory or disk space”. See the SQL Server errorlog for details, Ashvin Ajagiya, https://www.experts-exchange.com/questions/28576940/cannot-be-opened-due-to-inaccessible-files-or-insufficient-memory-or-disk-space-See-the-SQL-Server-errorlog-for-details.html
Claim 3
Claim 1 is included, however, Prakash does not explicitly disclose wherein receiving the indication that the utilization of the computing resource has exceeded the preset threshold comprises: receiving, from a database, a message generated by the database in response to identifying a lack of disk space condition.
Ajagiya discloses wherein receiving the indication that the utilization of the computing resource has exceeded the preset threshold comprises: receiving, from a database, a message generated by the database in response to identifying a lack of disk space condition. (this is in my one of live database issue"Msg 945, Level 14, State 2, Line 2 Database 'BD_NAME' cannot be opened due to inaccessible files or insufficient memory or disk space. See the SQL Server errorlog for details.")
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to include a database notification of insufficient space as disclosed by Ajagiya into Prakash to allow the Prakash’system to identify resolution to resolve the database problem.
Claim(s) 10-12 are rejected under 35 U.S.C. 103 as being unpatentable over Prakash (U.S. Pub 2021/0397495 A1), in view of Schneider (U.S. Pub 2010/0223497 A1), as applied to claim 1, and further in view of Gandi (U.S. Patent 11379442 B2)
Claim 10
Claim 1 is included, however, Prakash does not explicitly disclose wherein outputting the first command to execute the action responsive to the indication comprises: executing the first command to cause execution of a script configured to execute the action.
Gandi discloses wherein outputting the first command to execute the action responsive to the indication comprises: executing the first command to cause execution of a script configured to execute the action (abstract, “… identify… one or more issues affecting the performance of the database server… identify one or more candidate issues… generates a script that, when executed, remediates the candidate issues of the one or more candidate issues classified by the classification algorithm as the issues of the one or more issues. The processor further executes the script…”)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to generate a script based on the identified issue as disclosed by Gandi so that the script is executed to remediate the issue.
Claim 11
Claim 10 is included, Gandi further discloses wherein the script is configured using configurations determined by the machine-learning model based on the utilization exceeding the preset threshold. (col 6, line 54-60, “… training data 155 may also include program code blocks used by system administrators to remediate the historical issues. Based on the program code blocks used to remediate the historical issues, training data 155 may be used to train machine learning algorithm 170 to learn from the application of the program code blocks to remediate the historical issues, such that it may create and suggest future program codes to remediate future issues….”)
Claim 12
Claim 10 is included, Gandi discloses wherein the script comprises one or more commands authored in a scripting language that is one of bash, sh, zsh, bat, or PowerShell. (col 6, line 61-65, “… Program code blocks 160 include pieces of program code that may be used to generate issue remediation scripts to remediate issues identified by machine learning algorithm 170. For example, database issue remediation tool 105 may select a subset of program code blocks from the full set of program code blocks 160, to generate a remediation script. Program code blocks 160 may include adjustable parameters, whose values may be set by database issue remediation tool 105 to address a given identified issue….”)
Double Patenting
The nonstatutory double patenting rejection is based on a judicially created doctrine grounded in public policy (a policy reflected in the statute) so as to prevent the unjustified or improper timewise extension of the “right to exclude” granted by a patent and to prevent possible harassment by multiple assignees. A nonstatutory double patenting rejection is appropriate where the conflicting claims are not identical, but at least one examined application claim is not patentably distinct from the reference claim(s) because the examined application claim is either anticipated by, or would have been obvious over, the reference claim(s). See, e.g., In re Berg, 140 F.3d 1428, 46 USPQ2d 1226 (Fed. Cir. 1998); In re Goodman, 11 F.3d 1046, 29 USPQ2d 2010 (Fed. Cir. 1993); In re Longi, 759 F.2d 887, 225 USPQ 645 (Fed. Cir. 1985); In re Van Ornum, 686 F.2d 937, 214 USPQ 761 (CCPA 1982); In re Vogel, 422 F.2d 438, 164 USPQ 619 (CCPA 1970); In re Thorington, 418 F.2d 528, 163 USPQ 644 (CCPA 1969).
A timely filed terminal disclaimer in compliance with 37 CFR 1.321(c) or 1.321(d) may be used to overcome an actual or provisional rejection based on nonstatutory double patenting provided the reference application or patent either is shown to be commonly owned with the examined application, or claims an invention made as a result of activities undertaken within the scope of a joint research agreement. See MPEP § 717.02 for applications subject to examination under the first inventor to file provisions of the AIA as explained in MPEP § 2159. See MPEP § 2146 et seq. for applications not subject to examination under the first inventor to file provisions of the AIA . A terminal disclaimer must be signed in compliance with 37 CFR 1.321(b).
The filing of a terminal disclaimer by itself is not a complete reply to a nonstatutory double patenting (NSDP) rejection. A complete reply requires that the terminal disclaimer be accompanied by a reply requesting reconsideration of the prior Office action. Even where the NSDP rejection is provisional the reply must be complete. See MPEP § 804, subsection I.B.1. For a reply to a non-final Office action, see 37 CFR 1.111(a). For a reply to final Office action, see 37 CFR 1.113(c). A request for reconsideration while not provided for in 37 CFR 1.113(c) may be filed after final for consideration. See MPEP §§ 706.07(e) and 714.13.
The USPTO Internet website contains terminal disclaimer forms which may be used. Please visit www.uspto.gov/patent/patents-forms. The actual filing date of the application in which the form is filed determines what form (e.g., PTO/SB/25, PTO/SB/26, PTO/AIA /25, or PTO/AIA /26) should be used. A web-based eTerminal Disclaimer may be filled out completely online using web-screens. An eTerminal Disclaimer that meets all requirements is auto-processed and approved immediately upon submission. For more information about eTerminal Disclaimers, refer to www.uspto.gov/patents/apply/applying-online/eterminal-disclaimer.
A later patent claim is not patentably distinct from an earlier patent claim if the later claim is obvious over, or anticipated by, the earlier claim. In re Longi, 759 F.2d at 896, 225 USPQ at 651 (affirming a holding of obviousness-type double patenting because the claims at issue were obvious over claims in four prior art patents); In re Berg, 140 F.3d at 1437, 46 USPQ2d at 1233 (Fed. Cir. 1998) (affirming a holding of obviousness-type double patenting where a patent application claim to a genus is anticipated by a patent claim to a species within that genus). ELI LILLY AND COMPANY v BARR LABORATORIES, INC., United States Court of Appeals for the Federal Circuit, ON PETITION FOR REHEARING EN BANC (DECIDED: May 30, 2001).
Claim 1 is rejected on the ground of nonstatutory double patenting as being unpatentable over claim 1 of U.S. Patent No. 12455857. Although the claims at issue are not identical, they are not patentably distinct from each other because claim 1 of patent # 12455857 contain(s) every element of claim(s) 1 of the instant application and as such anticipate(s) claim(s) 1 of the instant application.
Instant Application: 19345827
Patent: 12455857
Claim 1
A computer-implemented method, comprising:
receiving an indication that a utilization of a computing resource has exceeded a preset threshold;
determining, using a machine-learning model based on a clustering algorithm, an action responsive to the indication;
outputting a first command to execute the action responsive to the indication; and
determining that the utilization of the computing resource no longer exceeds the preset threshold.
Claim 1
A computer-implemented method, comprising:
receiving an indication of a utilization condition for a computing resource, wherein the utilization condition is a utilization of the computing resource exceeding a preset threshold;
determining, using a machine-learning model comprising a clustering algorithm, an action responsive to the utilization condition based on the utilization condition, comprising:
identifying a candidate computing resource utilization from among a plurality of candidate computing resource utilizations;
determining a similarity measure for each candidate computing resource utilization of the plurality of candidate computing resource utilizations using a property of the candidate computing resource utilization;
determining a candidate cluster including at least the candidate computing resource utilization based on the similarity measure, wherein the similarity measure is based on a clustering threshold that is based on one or more hyperparameters; and
designating the candidate cluster for the action;
outputting a first command to execute the action responsive to the utilization condition; and
determining that the utilization condition no longer exceeds the preset threshold.
The nonstatutory double patenting rejection is based on a judicially created doctrine grounded in public policy (a policy reflected in the statute) so as to prevent the unjustified or improper timewise extension of the “right to exclude” granted by a patent and to prevent possible harassment by multiple assignees. A nonstatutory double patenting rejection is appropriate where the conflicting claims are not identical, but at least one examined application claim is not patentably distinct from the reference claim(s) because the examined application claim is either anticipated by, or would have been obvious over, the reference claim(s). See, e.g., In re Berg, 140 F.3d 1428, 46 USPQ2d 1226 (Fed. Cir. 1998); In re Goodman, 11 F.3d 1046, 29 USPQ2d 2010 (Fed. Cir. 1993); In re Longi, 759 F.2d 887, 225 USPQ 645 (Fed. Cir. 1985); In re Van Ornum, 686 F.2d 937, 214 USPQ 761 (CCPA 1982); In re Vogel, 422 F.2d 438, 164 USPQ 619 (CCPA 1970); In re Thorington, 418 F.2d 528, 163 USPQ 644 (CCPA 1969).
A timely filed terminal disclaimer in compliance with 37 CFR 1.321(c) or 1.321(d) may be used to overcome an actual or provisional rejection based on nonstatutory double patenting provided the reference application or patent either is shown to be commonly owned with the examined application, or claims an invention made as a result of activities undertaken within the scope of a joint research agreement. See MPEP § 717.02 for applications subject to examination under the first inventor to file provisions of the AIA as explained in MPEP § 2159. See MPEP § 2146 et seq. for applications not subject to examination under the first inventor to file provisions of the AIA . A terminal disclaimer must be signed in compliance with 37 CFR 1.321(b).
The filing of a terminal disclaimer by itself is not a complete reply to a nonstatutory double patenting (NSDP) rejection. A complete reply requires that the terminal disclaimer be accompanied by a reply requesting reconsideration of the prior Office action. Even where the NSDP rejection is provisional the reply must be complete. See MPEP § 804, subsection I.B.1. For a reply to a non-final Office action, see 37 CFR 1.111(a). For a reply to final Office action, see 37 CFR 1.113(c). A request for reconsideration while not provided for in 37 CFR 1.113(c) may be filed after final for consideration. See MPEP §§ 706.07(e) and 714.13.
The USPTO Internet website contains terminal disclaimer forms which may be used. Please visit www.uspto.gov/patent/patents-forms. The actual filing date of the application in which the form is filed determines what form (e.g., PTO/SB/25, PTO/SB/26, PTO/AIA /25, or PTO/AIA /26) should be used. A web-based eTerminal Disclaimer may be filled out completely online using web-screens. An eTerminal Disclaimer that meets all requirements is auto-processed and approved immediately upon submission. For more information about eTerminal Disclaimers, refer to www.uspto.gov/patents/apply/applying-online/eterminal-disclaimer.
Claim 1 is rejected on the ground of nonstatutory double patenting as being unpatentable over claim 1 of U.S. Patent No. 11914550 in view of Prakash (U.S. 2021/0397495)
Instant Application: 19345827
Patent 11914550
Claim 1
A computer-implemented method, comprising:
receiving an indication that a utilization of a computing resource has exceeded a preset threshold;
determining, using a machine-learning model based on a clustering algorithm, an action responsive to the indication;
outputting a first command to execute the action responsive to the indication; and
determining that the utilization of the computing resource no longer exceeds the preset threshold.
Claim 1
A computer-implemented method, comprising:
receiving an indication of a utilization exceeding a preset threshold;
responsive to the indication of the utilization, outputting instructions to create an incident;
determining, using a machine-learning model, an action responsive to the utilization;
outputting a first command to execute the action responsive to the utilization;
determining that the utilization no longer exceeds the preset threshold; and
outputting a second command to close the incident.
However, ‘550 does not disclose determining, using a machine-learning mode based on a clustering algorithm, an action responsive to the utilization
Prakash discloses determining, using a machine-learning mode based on a clustering algorithm, an action responsive to the utilization ([0083], “…if the current anomaly is categorized as a CPU anomaly, the processor can compare the current anomaly to prior anomalies that were also categorized as the CPU anomaly. The processor can compare a temporal window surrounding the anomaly and a prior temporal window surrounding the prior anomaly. The temporal window can be measured in seconds, minutes, and/or hours. The processor can calculate a difference between data points in the temporal window and data points in the prior temporal window… When the difference is within a predetermined threshold, the processor can determine that the anomaly and the prior anomaly are similar…” <examiner note: the current anomaly is in the same group/cluster of previous anomaly> [0085], “… Once the processor identifies a similar prior anomaly… the processor can obtain a resolution…”)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to use a machine-learning model based on clustering algorithm as disclosed by Prakash into ‘500 to determine the current issue is similar to previous issues to obtain the solution for the current issue.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to HAU HAI HOANG whose telephone number is (571)270-5894. The examiner can normally be reached 1st biwk: Mon-Thurs 7:00 AM-5:00 PM; 2nd biwk: Mon-Thurs: 7:00 am-5:00pm, Fri: 7:00 am - 4:00pm.
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HAU HAI. HOANG
Primary Examiner
Art Unit 2154
/HAU H HOANG/Primary Examiner, Art Unit 2154
Prosecution Insights