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 .
Status of Claims:
Claims 8-9, 16, and 20 are cancelled.
Claims 1-7, 10-15, and 17-19 are pending.
Regarding 101:
The rejection in light of the amendments is withdrawn. The claims, as understood by the Examiner, are improving the actual computational workflow as opposed to optimizing a general process of the workflow.
Regarding 102/103:
Upon review of the amendments and remarks, the Examiner maintains the rejections. However, the Examiner has updated the rejection below.
Claim Rejections - 35 USC § 102
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
Claims 1-6, 8-11, 14-17 and 18-19 are rejected under 35 U.S.C. 102 (a) (1) as being anticipated by Aichen, et al. (US Pub. No. 2023/0083891) (hereinafter, Aichen).
As per claim 1,
Achin teaches,
a computer-implemented method comprising:
executing one or more computational workflows under varying workflow
configurations of the one or more computational workflows to obtain output metrics of
interest as a result of the executing the one or more computational workflows:
(paragraph 49)
training an artificial intelligence (AI) model using the obtained output metrics of
interest to make predictions, during runtime, of output metrics of executing
computational workflows;
(paragraph 50, noting node 204)
generating, based at least in part on the obtained output metrics of interest, a collection of workflow execution control rules;
(Paragraph 146, noting “… Initially…space exploration engine 610 may automatically select a default modeling methodology. The available modeling methodologies may include, without limitation, selection of modeling techniques based on application of deductive rules…”)
Commencing workflow execution of a computational workflow and monitoring execution of the computational workflow at runtime, the monitoring comprising monitoring intermediate output of the workflow execution, obtained prior to completion of the computational workflow, and predicting, using the trained AI model, output metrics, predicted to be obtained from completing the computational workflow; and
(Paragraphs 41 and 72 & 74, noting on paragraph 72 “… provides tools for monitoring and/or guiding the search of the predictive modeling space. These tools may provide insight into a prediction problem's dataset (e.g., by highlighting problematic variables in the dataset, identifying relationships between variables in the dataset, etc.), and/or insight into the results of the search. In some embodiments, data analysts may use the interface to guide the search, e.g., by specifying the metrics to be used to evaluate and compare modeling solutions, by specifying the criteria for recognizing a suitable modeling solution, etc.…”)
determining one or more workflow execution intervention actions for an automated workflow orchestrator to take based on the predicted output metrics predicted by the trained AI model to be obtained from completing the computational workflow, wherein the one or more workflow execution intervention actions halting, reconfiguring, and re-initiating workflow execution of the computational workflow from a prior point of the computational workflow using a different computational workflow configuration of the computational workflow, based on computational resource cost if the workflow execution were allowed to proceed.
(Paragraph 103, noting “… For example, predictive modeling system 600 may flag potential improvements to methodologies, techniques, and/or tasks, and a user may decide whether to implement those potential improvements…”;paragraphs 100, 170, 176, noting on paragraph 100 “…. Moreover, the modeling tool 700 can provide guidance in the development of high-quality techniques by, for example, providing a checklist of steps for the developer to consider and comparing the task graphs for new techniques to those of existing techniques to, for example, detect missing tasks, detect additional steps, and/or detect anomalous flows among steps…”)
As per claim 2,
Aichen teaches,
The method of claim 1, wherein the generating the collection of workflow execution control rules comprises generating at least one static execution control rule to control workflow execution, the generating the at least one static execution control rule being based on at least one selected from the group consisting of (i) user preferences and (ii) domain expert knowledge that inform workflow execution context
(Paragraph 27, noting “The system may then output the results in accordance with the end user's preferences and instructions…” See also, paragraphs 30 & 31 teaching by example)
As per claim 3,
Aichen teaches,
The method of claim 2, further comprising:
applying a static execution control rule, of the at least one static execution control rule, for evaluation based on a current workflow configuration;
(paragraphs 30 & 31 teaching by example)
determining, as a result of the applying, to commence the workflow execution;
and
initiating commencement of the workflow execution
(paragraphs 32-35, noting Fig. 2 B)
As per claim 4,
Aichen teaches,
the method of claim 2, wherein the generating the collection of workflow execution control rules comprises generating at least one dynamic execution control rule by:
iterating execution of the one or more computational workflows under the varying workflow configurations,
(paragraphs 136 & 137 & 142)
wherein the iterating comprises, at each iteration of the iterating:
predicting, using AI model, output metrics predicted to be obtained from completing the one or more computational workflows at the iteration based on intermediate output of the workflow execution of the one or more computational workflows at the iteration;
(paragraph 143)
And for each given computational workflow of the one or more computational workflows,
comparing, using the predicted output metrics predicted to be obtained from completing the given computational workflow at the iteration,
allowing the workflow execution of the given computational workflow to proceed to an end of the workflow at the iteration, with
halting, reconfiguring, and re-initiating workflow execution of the given computational workflow from a prior point of the computational workflow using a different workflow configuration;
(paragraphs 100 & 176, noting on paragraph 100 “…. Moreover, the modeling tool 700 can provide guidance in the development of high-quality techniques by, for example, providing a checklist of steps for the developer to consider and comparing the task graphs for new techniques to those of existing techniques to, for example, detect missing tasks, detect additional steps, and/or detect anomalous flows among steps…”)
and
building the at least one dynamic execution control rule based on thresholds derived from the iterating
(paragraph 170).
As per claim 5,
Aichen teaches,
the method of claim 4, wherein a dynamic execution control rule of the at least one dynamic execution control rule suggests a changed workflow configuration to use in a subsequent execution of the workflow
(paragraphs 172 & 174, “blend”)
As per claim 6,
Aichen teaches,
the method of claim 4, wherein the predicting the output metrics predicted to be obtained from completing the given computational workflow and the comparing are performed at a plurality of points during workflow execution of the given computational workflow at the iteration
(paragraph 88)
As per claim 10,
Aichen teaches,
the method of claim 1, wherein the automated workflow orchestrator orchestrates execution computational workflows that the one or more computational workflows includes the computational workflow for which execution is commenced by the commencing and other workflows, and wherein the one or more workflow execution intervention actions comprises a decision to change execution priority of at least one of (i) the computational workflow and (ii) one or more of the other workflows
(paragraph 172 & 173).
As per claim 11,
Aichen teaches,
the method of claim 1, wherein the automated workflow orchestrator comprises an observability module configured to perform the monitoring the execution of the workflow, and use the generated collection of workflow execution control rules to determine the one or more workflow execution intervention actions
(Fig. 4, paragraphs 28 and 176).
As per claims 14-15, 17,
Claims 14-15, 17 disclose similar limitations to the claim limitations above, however, in a system form. Aichen teaches the prior art invention in such form, see Aichen Abstract. Therefore, claims 14-15, 17 are rejected under similar rationale as the claims above.
As per claims 18-19,
Claims 18-19 disclose similar limitations to the claim limitations above. However, claims 18-19 are in a computer program product form. Aichen teaches the prior art in such form, therefore, the claims of 18-19 are rejected under similar rationale as the claims above.
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.
Claims 12-13 are rejected under 35 U.S.C. 103 as being unpatentable over Aichen as applied to claims 1, 11 above, and further in view of Bahl, et al. (US Pub. No. 2021/0019194) (Hereinafter, Bahl)
As per claim 12,
Aichen does not teach, however, Bahl does teach,
the method of claim 11, wherein the observability module is implemented as a Stateless sidecar component to a model task executing the workflow, and wherein the sidecar component and model task execute as part of a single pod on a hybrid cloud platform
(paragraphs 65 & 72)
Therefore, it would have been obvious to one of ordinary skill in the art at the time of filing to incorporate the teachings of Bahl within the invention of Aichen with the motivation of taking full advantage of multi-cloud computing.
As per claim 13,
Aichen does not teach, however, Jiang does teach,
the method of claim 12, wherein the sidecar component is configured for automated injection of a user-specified workflow configuration into the observability module for use in workflow execution
(paragraphs 72 & 78).
Therefore, it would have been obvious to one of ordinary skill in the art at the time of filing to incorporate the teachings of Bahl within the invention of Aichen with the motivation of taking full advantage of multi-cloud computing.
Claim 7 is rejected under 35 U.S.C. 103 as being unpatentable over Aichen as applied to claims 1, 4 above, and further in view of Parameswaran (US Pub. No. 20200184376) (hereinafter, Parameswaran).
As per claim 7,
Aichen does not explicitly teach, however, Parameswaran does teach,
the method of claim 4, wherein the method further comprises building at least one knowledge graph based on the intermediate output of the workflow execution at each iteration of the iterating, the intermediate output being represented in a unified intermediate representation implemented using an ontology and further using embedding vectors that define the intermediate output of the workflow execution at each iteration of the iterating and interconnectedness of the intermediate output
(Figs. 1A, 1B, and 1C with paragraphs 31-34).
Therefore, it would have been obvious to one of ordinary skill in the art at the time of filing to incorporate the teachings of Parameswaran within the invention of Aichen with the motivation of making the modification of workflow observation easy to assess. (Parameswaran paragraph 30)
Conclusion
THIS ACTION IS MADE FINAL. Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to ZAHRA ELKASSABGI whose telephone number is (571)270-7943. The examiner can normally be reached Monday through Friday 11:30 to 8:00.
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ZAHRA . ELKASSABGI
Examiner
Art Unit 3623
/RUTAO WU/ Supervisory Patent Examiner, Art Unit 3623