Detailed Action
1. This office action is in response to communication filed May 1, 2024. Claims 1-20 are currently pending and claims 1, 12, and 20 are the independent claims.
Notice of Pre-AIA or AIA Status
2. 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 § 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.
3. Claims 1-5, 7, 9-17, and 19-20 are rejected under 35 U.S.C. 103 as being unpatentable over Ford et al. (U.S. Patent No. 8,898,520) – hereinafter “Ford” in view of Jayaraman et al. (U.S. Patent No. 10,558,921) – hereinafter “Jayaraman”.
Regarding independent claim 1, Ford discloses:
A computer-implemented method, comprising:
determining, by the processor set, evaluation data from at least one message queue; (Col. 7, Lines 1-7 “The active log may indicate events information that may be needed for recovering the queue, the queue manager, and related message queue system software (or middleware) objects for operating the queue and queue manager. Such information may include the depth of the queue, i.e., the amount of data or messages in the queue, and the status history of the queue and queue manager.”) The citation is interpreted to read on the claimed invention because under broadest reasonable interpretation, the “active log” corresponds to the recited “evaluation data”.
performing, by the processor set, synchronous message restoration based on the targeted number of messages … (Col. 11, Lines 21-25 “In box 412, one of a plurality of failure recovery procedures may be executed based on the current status of the message queue and the queue manager, the active log, the message recovery log, and optionally the current status of the system resources.”) The citation is interpreted to read on the claimed invention because under broadest reasonable interpretation, the message queue is restored based on the status of the message queue and its active log and message recovery log.
performing, by the processor set, remaining system restart functions in response to the synchronous message restoration being completed. (Col. 4, Lines 23-31 “In another scenario, when both the message queue and the queue manager become non-responsive and the active log and message recovery log indicate a relatively recent previous restart of the message queue manager, a second recovery procedure may be selected to shut down and restart the hosting server. This procedure may have relatively slower recovery time than the first recovery procedure since the server's operations may be interrupted.”) The citation is interpreted to read on the claimed invention because under broadest reasonable interpretation, the secondary recovery procedure is performed to perform hosting server restart.
Ford does not explicitly disclose:
determining, by the processor set, a targeted number of messages included in the at least one message queue and a confidence value using a trained machine learning model with the evaluation data;
determining, by the processor set, that the confidence value is greater than a predetermined threshold;
… and the confidence value being greater than the predetermined threshold;
However, Jayaraman discloses:
determining, by the processor set, a targeted number of messages included in the at least one message queue and a confidence value using a trained machine learning model with the evaluation data; (Col. 1, Lines 54-59 “ … receive test data, wherein the test data includes j observations, each associated with a respective ground truth category, where the ground truth categories are from the i categories, and produce output that provides, for each particular observation of the j observations, a set of i probabilities, one probability for each of the i categories.”) The citation is interpreted to read on the claimed invention because under broadest reasonable interpretation, the “j observations” correspond to the recited “determining number of messages” and the “set of I probabilities” corresponds to the recited “confidence value”.
determining, by the processor set, that the confidence value is greater than a predetermined threshold; (Col. 1, Lines 42-45 “Further, the efficacy of the classifier may be measured in terms of both its accuracy for the observations it does classify—i.e., those with a confidence of at least the confidence threshold …”) The citation is interpreted to read on the claimed invention because under broadest reasonable interpretation, the “confidence of at least the confidence threshold” corresponds to the recited “confidence value is greater than a predetermined threshold”.
… and the confidence value being greater than the predetermined threshold; (Col. 1, Lines 42-45 “Further, the efficacy of the classifier may be measured in terms of both its accuracy for the observations it does classify—i.e., those with a confidence of at least the confidence threshold …”) The citation is interpreted to read on the claimed invention because under broadest reasonable interpretation, the “confidence of at least the confidence threshold” corresponds to the recited “confidence value is greater than a predetermined threshold”.
Therefore, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to add determining, by the processor set, a targeted number of messages included in the at least one message queue and a confidence value using a trained machine learning model with the evaluation data, determining, by the processor set, that the confidence value is greater than a predetermined threshold, and the confidence value being greater than the predetermined threshold as seen in Jayaraman's invention into Ford's invention because these modifications allow the use of a known technique to improve similar devices in the same way such that the confidence value attached to evaluation data is essential to determining that the message restoration should be performed.
Regarding claim 2, Ford discloses the computer-implemented method of claim 1, but does not explicitly disclose:
determining the targeted number of messages based on coded rules.
However, Jayaraman discloses:
determining the targeted number of messages based on coded rules. (Col. 1, Lines 54-59 “ … receive test data, wherein the test data includes j observations, each associated with a respective ground truth category, where the ground truth categories are from the i categories, and produce output that provides, for each particular observation of the j observations, a set of i probabilities, one probability for each of the i categories.” and Col. 25, Lines 64-67 “Alternatively or additionally, a step or block that represents a processing of information can correspond to a module, a segment, or a portion of program code (including related data).”) The citation is interpreted to read on the claimed invention because under broadest reasonable interpretation, the “j observations” correspond to the recited “determining number of messages” and the “set of I probabilities” corresponds to the recited “confidence value” based on program code.
Therefore, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to add determining the targeted number of messages based on coded rules as seen in Jayaraman's invention into Ford's invention because these modifications allow combining prior art elements according to known methods to yield predictable results such that the messages to restore are determined based on rules coded in by a user or system’s creator.
Regarding claim 3, Ford discloses the computer-implemented method of claim 1, wherein the performing the remaining system restart functions comprises restoring remaining messages of the at least one message queue. (Fig. 4, Box 414 and Col. 11, Lines 27-35 “In box 414, a plurality of messages assigned to the failed message queue or queue manager may be redistributed to other message queues, queue managers, or servers. The messages may be redistributed based on the analysis of the data above to account for and prevent future failures in the recovered message queue or queue manager and to improve overall load balancing in the message queue system. Improving the load balancing in the system may also improve the system robustness or resilience to future failures.”) The citation is interpreted to read on the claimed invention because under broadest reasonable interpretation, the redistribution of a plurality of messages assigned to the failed message queue restores the messages to a proper queue.
Regarding claim 4, Ford discloses the computer-implemented method of claim 3, but does not explicitly disclose:
wherein the remaining messages comprise messages of the at least one message queue other than the targeted number of messages.
However, Jayaraman discloses:
wherein the remaining messages comprise messages of the at least one message queue other than the targeted number of messages. (Col. 1, Lines 54-59 “ … receive test data, wherein the test data includes j observations, each associated with a respective ground truth category, where the ground truth categories are from the i categories, and produce output that provides, for each particular observation of the j observations, a set of i probabilities, one probability for each of the i categories.” and Col. 25, Lines 64-67 “Alternatively or additionally, a step or block that represents a processing of information can correspond to a module, a segment, or a portion of program code (including related data).”) The citation is interpreted to read on the claimed invention because under broadest reasonable interpretation, the “j observations” correspond to the recited “determining number of messages” and the “set of I probabilities” corresponds to the recited “confidence value” based on program code. The different observations belong to different message purposes such that they are not all counted toward the targeted number of messages.
Therefore, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to add wherein the remaining messages comprise messages of the at least one message queue other than the targeted number of messages as seen in Jayaraman's invention into Ford's invention because these modifications allow combining prior art elements according to known methods to yield predictable results such that a plurality of messages can be in a queue on top of the critical messages that need to be restored.
Regarding claim 5, Ford discloses the computer-implemented method of claim 1, wherein the evaluation data is selected from the group consisting of a dequeue rate, a type of system outage, a duration of the system outage, a current time of data, remote system states, an anticipated time of a next remote system maintenance, a queue priority, a queue depth, an enqueue rate, an average message size, and a network bandwidth utilization. (Col. 10, Lines 35-43 “In box 308, the current status of system resources associated with the message queue and queue manager may be examined. Examining the current status of system resources (at the time of the failure) in addition to the status and log information may provide more in-depth analysis of the circumstances that led to the failure. This may provide a better prediction of the cause(s) of the failure and hence selecting a more suitable recovery procedure, which may further improve system robustness.”) The citation is interpreted to read on the claimed invention because under broadest reasonable interpretation, the examination of the current status of the system resources provides in-depth analysis of the type of system outage that occurred.
Regarding claim 7, Ford discloses the computer-implemented method of claim 1, wherein the synchronous message restoration is completed in response to restoring the targeted number of messages. (Col. 11, Lines 21-25 “In box 412, one of a plurality of failure recovery procedures may be executed based on the current status of the message queue and the queue manager, the active log, the message recovery log, and optionally the current status of the system resources.”) The citation is interpreted to read on the claimed invention because under broadest reasonable interpretation, the message queue is restored based on the status of the message queue and its active log and message recovery log.
Regarding claim 9, Ford discloses the computer-implemented method of claim 1, but does not explicitly disclose:
training the machine learning model with the evaluation data.
However, Jayaraman discloses:
training the machine learning model with the evaluation data. (Col. 16, Lines 33-40 “As described above, a classifier is a particular type of machine learning model that classifies observations (input values) into one or more of a number of categories (output values). A classifier can be trained by providing a training data set including observations mapped to ground truth categories thereof. With enough training data, the classifier can be expected to make reasonably accurate predictions of the categories of new observations.”) The citation is interpreted to read on the claimed invention because under broadest reasonable interpretation, the classifier is trained to be able to place observations (messages) into categories based on a confidence threshold.
Therefore, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to add training the machine learning model with the evaluation data as seen in Jayaraman's invention into Ford's invention because these modifications allow applying a known technique to a known device ready for improvement to yield predictable results such that a machine learning model is trained to make the restoration decisions for queued messages.
Regarding claim 10, Ford discloses the computer-implemented method of claim 9, but does not explicitly disclose:
wherein the training the machine learning model with the evaluation data further comprises a neural network using the evaluation data to solve a defined regression problem.
However, Jayaraman discloses:
wherein the training the machine learning model with the evaluation data further comprises a neural network using the evaluation data to solve a defined regression problem. (Col. 16, Lines 41-45 “Examples of machine learning classifiers include Bayesian classifiers, support vector machines, linear classifiers, k-nearest-neighbor classifiers, decision trees, random forests, and neural networks. Other types of classifiers may be possible.”) The citation is interpreted to read on the claimed invention because under broadest reasonable interpretation, the classifier is a trained neural network.
Therefore, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to add wherein the training the machine learning model with the evaluation data further comprises a neural network using the evaluation data to solve a defined regression problem as seen in Jayaraman's invention into Ford's invention because these modifications allow applying a known technique to a known device ready for improvement to yield predictable results such that a neural network is trained to make the restoration decisions for queued messages.
Regarding claim 11, Ford discloses the computer-implemented method of claim 1, but does not explicitly disclose:
wherein the targeted number of messages represents a targeted number of critical messages.
However, Jayaraman discloses:
wherein the targeted number of messages represents a targeted number of critical messages. (Col. 1, Lines 54-59 “ … receive test data, wherein the test data includes j observations, each associated with a respective ground truth category, where the ground truth categories are from the i categories, and produce output that provides, for each particular observation of the j observations, a set of i probabilities, one probability for each of the i categories.” and Col. 19, Lines 53-57 “Therefore, the IT professionals might not want VPN issues to be miscategorized as email issues, because email issues could be given a lower priority and critical VPN issues that are misclassified as email issues might be ignored for hours.”) The citation is interpreted to read on the claimed invention because under broadest reasonable interpretation, the “j observations” correspond to the recited “determining number of messages” and the “set of I probabilities” corresponds to the recited “confidence value”. Certain types of messages like VPN issues are considered critical in comparison to email issues.
Therefore, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to add wherein the targeted number of messages represents a targeted number of critical messages as seen in Jayaraman's invention into Ford's invention because these modifications allow combining prior art elements according to known methods to yield predictable results such that a plurality of messages can be in a queue on top of the critical messages that need to be restored, so the critical messages must be specifically chosen to be restored whether by a user or machine learning model.
Regarding independent claim 12, it is a computer program product claim having the same limitations as cited in computer-implemented method claim 1. Thus, claim 12 is also rejected under the same rationale as addressed in the rejection of claim 1 above.
Regarding claim 13, it is a computer program product claim having the same limitations as cited in computer-implemented method claim 2. Thus, claim 13 is also rejected under the same rationale as addressed in the rejection of claim 2 above.
Regarding claim 14, it is a computer program product claim having the same limitations as cited in computer-implemented method claim 3. Thus, claim 14 is also rejected under the same rationale as addressed in the rejection of claim 3 above.
Regarding claim 15, it is a computer program product claim having the same limitations as cited in computer-implemented method claim 4. Thus, claim 15 is also rejected under the same rationale as addressed in the rejection of claim 4 above.
Regarding claim 16, it is a computer program product claim having the same limitations as cited in computer-implemented method claim 5. Thus, claim 16 is also rejected under the same rationale as addressed in the rejection of claim 5 above.
Regarding claim 17, it is a computer program product claim having the same limitations as cited in computer-implemented method claim 7. Thus, claim 17 is also rejected under the same rationale as addressed in the rejection of claim 7 above.
Regarding claim 19, it is a computer program product claim having the same limitations as cited in computer-implemented method claim 11. Thus, claim 19 is also rejected under the same rationale as addressed in the rejection of claim 11 above.
Regarding independent claim 20, it is a system claim having the same limitations as cited in computer-implemented method claim 1. Thus, claim 20 is also rejected under the same rationale as addressed in the rejection of claim 1 above.
4. Claim 6 is rejected under 35 U.S.C. 103 as being unpatentable over Ford et al. (U.S. Patent No. 8,898,520) – hereinafter “Ford” in view of Jayaraman et al. (U.S. Patent No. 10,558,921) – hereinafter “Jayaraman” further in view of Bartleywood (U.S. Pub. No. 2017/0048120).
Regarding claim 6, Ford discloses the computer-implemented method of claim 1, but does not explicitly disclose:
wherein the evaluation data is selected from the group consisting of a dequeue rate and an average message size.
However, Bartleywood discloses:
wherein the evaluation data is selected from the group consisting of a dequeue rate and an average message size. ([0007] “The middleware data may comprise message counts, enqueue rates, or dequeue rates. The centralized harness service may be configured to send a specified number of messages at a specified size over a specified number of threads through the computer network architecture.” and [0046] “Generally, the put and get rate testing quantifies the put and get rates of the MQ Queue Managers. Test results can be mapped according to message size for example, 1 KB, 10 KB, 50 KB, 100 KB, 250 KB, 500 KB, etc. … the message size may be chosen for the average message size and type per an application stack.”) The citation is interpreted to read on the claimed invention because under broadest reasonable interpretation, the dequeue rate and average message size are evaluated for the computer network architecture.
Therefore, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to add wherein the evaluation data is selected from the group consisting of a dequeue rate and an average message size as seen in Bartleywood's invention into Ford's invention because these modifications allow combining prior art elements according to known methods to yield predictable results such that computer network architecture evaluates dequeue rate and average message size similar to how message queue restoration will need this data to make determinations as well.
5. Claims 8 and 18 are rejected under 35 U.S.C. 103 as being unpatentable over Ford et al. (U.S. Patent No. 8,898,520) – hereinafter “Ford” in view of Jayaraman et al. (U.S. Patent No. 10,558,921) – hereinafter “Jayaraman” further in view of Yeung et al. (U.S. Pub. No. 2007/0220308) – hereinafter “Yeung”.
Regarding claim 8, Ford discloses the computer-implemented method of claim 1, but does not explicitly disclose:
sending a restart success output message in response to the performing the remaining restart functions.
However, Yeung discloses:
sending a restart success output message in response to the performing the remaining restart functions. ([0070] “The user may subsequently start the restore process, and status information for the restore may be displayed for each session being restored. When the restore cannot complete for any reason, a message may be displayed warning the user of the incompletion. Accordingly, the user may check or save a log file to identify an error and possibly take corrective action. When the restore completes successfully, a message may be displayed indicating such success, and client 104 may be automatically rebooted (if necessary).”) The citation is interpreted to read on the claimed invention because under broadest reasonable interpretation, the restore completes successfully, so a success message is displayed.
Therefore, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to add sending a restart success output message in response to the performing the remaining restart functions as seen in Yeung’s invention into Ford's invention because these modifications allow the use of a known technique to improve similar devices in the same way such that a restart success for any system can lead to an output message to let the system know of the successful restart.
Regarding claim 18, it is a computer program product claim having the same limitations as cited in computer-implemented method claim 8. Thus, claim 18 is also rejected under the same rationale as addressed in the rejection of claim 8 above.
Conclusion
6. The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. Such prior art includes Shilane et al. (U.S. Pub. No. 2021/0365296) which discloses pushing tasks onto a message queue that were not completed before an interruption.
Examiner has cited particular columns/paragraphs/sections and line numbers in the references applied and not relied upon to the claims above for the convenience of the applicant. Although the specified citations are representative of the teachings of the art and are applied to specific limitations within the individual claim, other passages and figures may apply as well. It is respectfully requested from the applicant in preparing responses, to fully consider the references in entirety as potentially teaching all or part of the claimed invention, as well as the context of the passage as taught by the prior art or disclosed by the Examiner.
When responding to the Office action, applicant is advised to clearly point out the patentable novelty the claims present in view of the state of the art disclosed by the reference(s) cited or the objections made. A showing of how the amendments avoid such references or objections must also be present. See 37 C.F.R. 1.111(c).
When responding to this Office action, applicant is advised to provide the line and page numbers in the application and/or reference(s) cited to assist in locating the appropriate paragraphs.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to DANIEL B TRAINOR whose telephone number is (571)272-3710. The examiner can normally be reached Monday-Friday 9AM-5PM.
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/D.T./Examiner, Art Unit 2198
/PIERRE VITAL/Supervisory Patent Examiner, Art Unit 2198
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