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 .
This office action is in response to applicant’s amendment filed on 05/27/2026.
Claims 1-20 are pending and examined.
Response to Arguments
Applicant’s arguments filed on 05/27/2026 have been fully considered. However, the amended claims are rejected under new grounds of rejection with a new reference applied.
Applicant also argued that cited prior art do not teach the amended claim limitations of “perform, via a machine learning operation system of the container orchestration platform, one or more cluster tests of the container orchestration platform using respective test pipelines from the one or more test pipelines”. The examiner respectfully disagrees. Lopes suggests the above (paragraphs [0019][0046][0048][0049][0050][0090]; claims 1-4; the system can orchestrate the machine learning pipeline stages in a workflow that can be deployed into production data infrastructures; test pipelines are executed in parallel; runtime environments allow applications to run on operating systems that can support containers, and applications can be in the form of containers; the tests are performed in the container orchestration platform).
The examiner is available for a phone interview with applicant.
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 1-8, 10-18 and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Lopes et al. (US PGPUB 2024/0168855) hereinafter Lopes, in view of Patel et al. (US PGPUB 2019/0065345) hereinafter Patel, in view of Zhang et al. (US PGPUB 2020/0250074) hereinafter Zhang.
Per claim 1, Lopes discloses a system: one or more memories; and one or more processors, communicatively coupled to the one or more memories, configured to:” (Fig. 11; a computer system with processors and memories); obtain, via a notebook repository, one or more training notebooks that are associated with respective pipeline types of the container orchestration platform, wherein the one or more training notebooks are interactive computational documents that include executable code and plain-text-formatted information (claims 1, 8; paragraphs [0019][0031][0043]; a system for orchestrating machine learning pipeline stages in a workflow that can be deployed into production data infrastructures using notebooks; a notebook collector can be configured to continuously scan internal and external repositories for notebooks with topics on ML; collecting different machine learning notebook data structures and different machine learning pipeline data indicating different machine learning pipeline stages associated with the different machine learning notebook data structures; i.e. the different ML notebooks are associated with different pipeline stages/types; lines of codes are extracted from the notebooks; an automated framework can extract topics (textual information) from a data science workspace such as a machine learning notebook); extract, from the one or more training notebooks, one or more executable code elements, to obtain one or more sets of executable code for respective training notebooks of the one or more training notebooks, using respective sets of executable code of the one or more sets of executable code to generate one or more test pipelines (paragraphs [0042]-[0046]; the lines of codes are extracted from the notebooks, they are assigned cells, labeled to different stages of testing pipelines, test pipelines are formed from the labeled cells); perform, via a machine learning operation system of the container orchestration platform, one or more cluster tests of the container orchestration platform using respective test pipelines from the one or more test pipelines; and provide, for display, result information indicating the result of the cluster test (paragraphs [0019][0046][0048][0049][0050][0090]; claims 1-4; the system can orchestrate the machine learning pipeline stages in a workflow that can be deployed into production data infrastructures; test pipelines are executed in parallel; runtime environments allow applications to run on operating systems that can support containers, and applications can be in the form of containers; the tests are performed in the container orchestration platform; results (success/failure) are output to the user).
While Lopes discloses using respective sets of executable code of the one or more sets of executable code to generate one or more test pipelines, Lopes does not explicitly teach insert testing information into respective sets of executable code of the one or more sets of executable code to generate one or more test pipelines. However, Patel suggests inserting testing information into executable code (paragraphs [0117]-0119]; using a generic test case to test different applications, by replacing placeholder fields in test execution methods of a test case with regular expressions (such as URLs); i.e. testing information (URLs) are inserted into executable code in a test case). It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine Lopes and Patel to insert testing information into executable code to generate one or more test pipelines, this allows a generic test template (executable code) be used to test different applications, thereby increase reusability of test code.
Lopes does not explicitly teach determine whether a result of a cluster test, of the one or more cluster tests, passed by comparing one or more metrics indicated by the result of the cluster test to a threshold. However, Zhang suggests determine whether a result of a cluster test, of the one or more cluster tests, passed by comparing one or more metrics indicated by the result of the cluster test to a threshold, provide, for display, result information indicating the result of the cluster test (claim 1, paragraphs [0156][0158][0110][0162][0163]; performing tests in a container orchestration platform, running verification tests to confirm the outputs, if output of a component (microservice) crossed a threshold value, an action is triggered; action, when triggered, causes the microservice application to provide, store, or display the output; test results are stored for data analysis). Thus, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine Lopes, Patel and Zhang to display a test result of the container orchestration system when the test result crossed a threshold value, so the user is informed of the anomaly in application performance, to further diagnose the application.
Per claim 2, Lopes further suggests provide, for display via a user device, at least one training notebook of the one or more training notebooks (paragraphs [0031][0036]0093]; retrieving training notebooks from repository, the system also includes a display for displaying data; thus, it would have been obvious to display the retrieved notebooks for user’s inspection).
Per claim 3, Patel further discloses wherein the testing information includes at least one of:
one or more arguments, or one or more configurable code elements (paragraphs [0117]-0119]; using a generic test case to test different applications, by replacing placeholder fields in test execution methods of a test case with regular expressions (configurable code elements)).
Per claim 4, Patel further discloses wherein the one or more processors, to insert the testing information, are configured to: detect, in a set of executable code from the one or more sets of executable code, a placeholder element; and replace the placeholder element with a configurable code element of the one or more configurable code elements, wherein the configurable code element corresponds to the placeholder element (paragraphs [0117]-0119]; using a generic test case to test different applications, by replacing placeholder fields in test execution methods of a test case with regular expressions (configurable code elements)).
Per claim 5, Lopes further suggests wherein the one or more cluster tests are associated with a testing event, and wherein the one or more processors, to obtain the one or more training notebooks, are configured to: obtain the one or more training notebooks based on an occurrence of the testing event (paragraphs [0036][0048]; a public source collector, such as a Github application programming interface (API), can be used periodically (such as daily) to retrieve notebooks from repositories with relevant topics; i.e. a periodic trigger (testing event) is associated with obtaining the training notebooks).
Per claim 6, Lopes further suggests obtain configuration information indicating the notebook repository (paragraphs [0036][0048]; a public source collector, such as a Github application programming interface (API), can be used periodically (such as daily) to retrieve notebooks from repositories with relevant topics; i.e. the address of a repository must be obtained in order to access the repository). Patel further suggests obtain configuration information indicating the testing information (paragraphs [0117]-0119]; using a generic test case to test different applications, by replacing placeholder fields in test execution methods of a test case with obtained regular expressions).
Per claim 7, Lopes further suggests wherein the one or more test pipelines indicate respective workflows for the machine learning operation system, and wherein the one or more processors, to perform the one or more cluster tests, are configured to: execute, via the machine learning operation system, the respective workflows, wherein the result information indicates whether the respective workflows were successfully executed (paragraphs [0019][0034][0046][0048]; an automated framework can extract topics from a data science workspace such as a machine learning notebook, transform and annotate cells of the machine learning notebook to various machine learning pipeline stages, and orchestrate the machine learning pipeline stages in a workflow that can be deployed into production data infrastructures; after the pipelines are generated, artifacts can be sent to a queue for testing and verifying if the stages of the pipelines/workflows are accurate or not; the experiments can be configured validate if a potential pipeline meets an end goal (SUCCESS) or not (FAILURE)).
Claims 8, 11-15 recite similar limitations as claims 1-5, 7. Therefore, claims 8, 11-15 are rejected under similar rationales as claims 1-5, 7.
Per claim 10, Lopes further suggests wherein the one or more training notebooks include training information for the respective pipeline types associated with the container orchestration platform (claims 1, 8; paragraphs [0019][0031][0043]; a system for orchestrating machine learning pipeline stages in a workflow that can be deployed into production data infrastructures using notebooks; a notebook collector can be configured to continuously scan internal and external repositories for notebooks with topics on ML; collecting different machine learning notebook data structures and different machine learning pipeline data indicating different machine learning pipeline stages associated with the different machine learning notebook data structures; i.e. the different ML notebooks are associated with different pipeline stages/types).
Claims 16 recites similar limitations as claims 1+2. Therefore, claim 16 is rejected under similar rationales as claims 1+2.
Claim 20 recites similar limitations as claim 5. Therefore, claim 20 is rejected under similar rationales as claim 5.
Per claim 17, Lopes further suggests extract, from the one or more training notebooks, one or more executable code elements included in the executable code (paragraphs [0042]-[0046]; the lines of codes are extracted from the notebooks, they are assigned cells, labeled to different stages of testing pipelines, test pipelines are formed from the labeled cells).
Per claim 18, Patel further suggests insert testing information into one or more executable code elements to generate the one or more test pipelines (paragraphs [0117]-0119]; using a generic test case to test different applications, by replacing placeholder fields in test execution methods of a test case with regular expressions).
Claims 9 and 19 are rejected under 35 U.S.C. 103 as being unpatentable over Lopes, in view of Patel, in view of Zhang, in view of Clement et al. (US PGPUB 2023/0177261) hereinafter Clement.
Per claim 9, Lopes does not explicitly teach wherein the one or more training notebooks are interactive computational documents that include executable code and plain-text-formatted information, and wherein extracting the one or more executable code elements comprises: removing, from the one or more training notebooks, any information that is presented via a plain-text formatting syntax. However, Clement suggests the above (paragraphs [0004][0020][0023]; providing an interactive digital notebook that contains computer code and rich text elements, the notebook is organized by cells, containing rich markdown cells (text elements) and code cells (executable code elements); the content of each markdown cell is masked (filtered out)). It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine Lopes, Patel, Zhang and Clement to remove text information from the training notebooks, because they are not needed to generate the pipelines.
Claim 19 recites similar limitations as claim 9. Therefore, claim 19 is rejected under similar rationales as claim 9.
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). 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 HANG PAN whose telephone number is (571)270-7667. The examiner can normally be reached 9 AM to 5 PM.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Chat Do can be reached at 571-272-3721. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/HANG PAN/Primary Examiner, Art Unit 2193
Prosecution Insights