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 .
DETAILED ACTION
Status of Claims
This action is a Final action in response to the communications filed on 07/01/2026.
Claims 1, 9, 10 and 17 have been amended. Claims 1 – 20 are currently pending and have been examined in this application.
Response to Amendment
Applicant’s amendment has been considered.
Response to Arguments
Applicant’s remarks have been considered.
Applicant argues, “When considered as a whole, claim 1 is directed to computer-implemented control of software deployment and testing operations, not to a fundamental economic practice or other method of organizing human activity. (pg. 10)
Examiner respectfully disagrees. The claims encompass Certain Methods of Organizing Human Activity related to commercial or legal actions (e.g. business/customer impact of deployment, scheduling) but for the recitation of generic computer components (e.g. a computing device). For example, determining a plurality of products for a product release, determining the number of features, determining a testing for each product, determining using first, second and third ML models various scores and a probabilistic testing timing are related to business relations, processes and impact. Accordingly, the claim recites an abstract idea.
Applicant argues, “…even assuming that claim 1 recites an abstract idea, it does not, the claim integrates any such concept into a practical application.” (pg. 11)
The judicial exception is not integrated into a practical application. Claim 1 recites the additional element of a computing device. Claims 10 and 17 recite the additional elements of non-transitory machine-readable mediums, one or more processor and a computing device. These are generic computer components recited at a high level of generality as performing generic computer functionality (see Spec ¶0045, general purpose computer).
For instance, the step of receiving a request for a recommendation of product deployment sequence is data gathering activity. The steps of determining a plurality of products associated with a product release, a number of features to be deployed, testing to be performed, and generating an optimal product deployment sequence involves collecting and analyzing data (data gathering). The steps of determining using a first machine learning (ML) model trained using historical product release data, a usage signature score and a revenue score for the product release; determining using a second ML model trained using historical product release data, a user impact score and a business impact score for the product release based at least on the usage signature score and the revenue score output by the first ML model and determining using a third ML model trained using historical product release data, a probabilistic time to test the features that are to be deployed for each product of the plurality of products based at least on the user impact score and the business impact score output by the second ML model and product-release parameters including the number of features that are to be deployed and the testing that is to be performed is analyzing data using complex mathematics.
Examiner notes that the use of ML models that have been previously trained to determine values is similar to ‘apply it’. The models are providing an output and input into the next model to produce a result.(a score or value). The step of sending information about the optimal product deployment sequence for automatically controlling deployment and testing of the plurality of products is merely data transmission of the recommendation that a user at another computer can implement from a UI (see Spec ¶0063).
Each of the additional limitations is no more than mere instructions to apply the exception using a generic computer components (e.g. a computing device). The combination of these additional elements is no more than mere instructions to apply the exception using a generic computer component (e.g. a computing device). The additional elements do not integrate the abstract ideas into a practical application because it does not impose meaningful limits on practicing the abstract idea. Therefore, the claims are directed to an abstract idea.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. Claim 1 recites:
receiving, …, a request for a recommendation of an optimal product deployment sequence for a product release from another computing device;
determining, …, a plurality of products associated with the product release, the plurality of products including a product that is being released and one or more interlocks linked to the product release;
determining, ., a number of features that are to be deployed for each product of the plurality of products;
determining, ., a testing that is to be performed for each product of the plurality of products;
determining, by the computing device using a first machine learning (ML) model trained using historical product release data, a usage signature score and a revenue score for the product release;
determining, by the computing device using a second ML model trained using historical product release data, a user impact score and a business impact score for the product release based at least on the usage signature score and the revenue score output by the first ML model;
determining, by the computing device using a third ML model trained using historical product release data, a probabilistic time to test the features that are to be deployed for each product of the plurality of products based at least on the user impact score and the business impact score output by the second ML model and product-release parameters including the number of features that are to be deployed and the testing that is to be performed;
generating,...using the third ML model, the optimal product deployment sequence for the product release to minimize total deployment time and reduce resource contention across testing environments based on a release sequence determined from historical deployments and the probabilistic times to test the features that are to be deployed;
The limitation under its broadest reasonable interpretation covers Certain Methods of Organizing Human Activity related to commercial or legal actions (e.g. business/customer impact of deployment) but for the recitation of generic computer components (e.g. a computing device). For example, determining a plurality of products for a product release, determining number of features, and a probabilistic testing timing are related to business relations/processes. Accordingly, the claim recites an abstract ideas.
Independent Claims 10 and 17 substantially recite the subject matter of Claim 1 and also include the abstract ideas identified above. The dependent claims encompass the same abstract ideas. For instance, Claim 2 is directed to a ridge regression algorithm, Claim 3 is directed to a linear regression model, Claim 4 is directed to XGBoost algorithm, Claims 5-8 are directed to buffer time for testing, Claim 9 is directed to determining test cases. Claims 12-16 and 18-20 substantially recite the subject matter of claims 2-9 and encompass same abstract concept. Thus, the dependent claims further limit the abstract concepts found in the independent claims.
The judicial exception is not integrated into a practical application. Claim 1 recites the additional element of a computing device. Claims 10 and 17 recite the additional elements of non-transitory machine-readable mediums, one or more processor and a computing device. These are generic computer components recited at a high level of generality as performing generic computer functionality (see Spec ¶0045, general purpose computer).
For instance, the step of receiving a request for a recommendation of product deployment sequence is data gathering activity. The steps of determining a plurality of products associated with a product release, a number of features to be deployed, testing to be performed, and generating an optimal product deployment sequence involves collecting and analyzing data (data gathering). The steps of determining using a first machine learning (ML) model trained using historical product release data, a usage signature score and a revenue score for the product release; determining using a second ML model trained using historical product release data, a user impact score and a business impact score for the product release based at least on the usage signature score and the revenue score output by the first ML model and determining using a third ML model trained using historical product release data, a probabilistic time to test the features that are to be deployed for each product of the plurality of products based at least on the user impact score and the business impact score output by the second ML model and product-release parameters including the number of features that are to be deployed and the testing that is to be performed is analyzing data using complex mathematics.
Examiner notes that the use of ML models that have been previously trained to determine values is similar to ‘apply it’. The step of sending information about the optimal product deployment sequence for automatically controlling deployment and testing of the plurality of products is merely data transmission of the recommendation that a user at another computer can implement from a UI (see Spec ¶0063).
Each of the additional limitations is no more than mere instructions to apply the exception using a generic computer components (e.g. a computing device). The combination of these additional elements is no more than mere instructions to apply the exception using a generic computer component (e.g. a computing device). The additional elements do not integrate the abstract ideas into a practical application because it does not impose meaningful limits on practicing the abstract idea. Therefore, the claims are directed to an abstract idea.
The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. As stated above, the additional elements of a computing device, one or more processors and crm are considered generic computer components performing generic computer functions that amount to no more than instructions to implement the judicial exception. Mere, instructions to apply an exception using generic computer components cannot provide an inventive concept.
The dependent claims when analyzed both individually and in combination are also held to be ineligible for the same reason above and the additional recited limitations fail to establish that the claims are not directed to an abstract. The additional limitations of the dependent claims when considered individually and as an ordered combination do not amount to significantly more than the abstract idea.
Looking at these limitations as an ordered combination and individually adds nothing additional that is sufficient to amount to significantly more than the recited abstract idea because they simply provide instructions to use generic computer components, to "apply" the recited abstract idea. Thus, the elements of the claims, considered both individually and as an ordered combination, are not sufficient to ensure that the claim as a whole amounts to significantly more than the abstract idea itself. Therefore, Claims 1-20 are not patent eligible.
Conclusion
The prior art made of record and not relied upon is considered relevant but not applied:
Lu et al. (US 2022/0092668) discloses service deployments of multiple services are grouped together according to common usage history. Services are proposed for a user account where one service of a services group is deployed, and another service of the services group is new or not deployed by the user account.
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 of a general nature or relating to the status of this application or concerning this communication or earlier communications from the Examiner should be directed to Renae Feacher whose telephone number is 571-270-5485. The Examiner can normally be reached Monday-Friday, 9:00 am - 5:00 pm. If attempts to reach the examiner by telephone are unsuccessful, the Examiner's supervisor, Beth Boswell can be reached at 571-272-6737.
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/Renae Feacher/
Primary Examiner, Art Unit 3625
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