DETAILED ACTION
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Claims 1-20 have been submitted for examination and are pending further prosecution by the United States Patent & Trademark Office.
Allowable Subject Matter
With respect to independent claim 1, the prior art of record does not teach or suggest, either solely or in combination, the limitations "predicting, by the server, based on the first set of elements and using a first machine learning (ML) model, a first impact score indicative of an impact of the first set of updates on a set of performance metrics associated with the first webpage, wherein the first impact score is predicted prior to deployment of an [[the]] updated first webpage; and selecting, by the server, a first deployment strategy from a plurality of pre-defined deployment strategies based on the predicted first impact score and a set of rules, wherein the first deployment strategy is executed for the updating and the deployment of the first webpage based on the first set of updates." when considered in combination with the other limitations of claim 1.
With respect to independent claim 10, the prior art of record does not teach or suggest, either solely or in combination, the limitations "predict, based on the first set of elements and using a first machine learning (ML) model, a first impact score indicative of an impact of the first set of updates on a set of performance metrics associated with the first webpage, wherein the first impact score is predicted prior to deployment of an [[the]] updated first webpage; and select a first deployment strategy from a plurality of pre-defined deployment strategies based on the predicted first impact score and a set of rules, wherein the first deployment strategy is executed for the updating and deployment of the first webpage based on the first set of updates." when considered in combination with the other limitations of claim 10.
With respect to independent claim 19, the prior art of record does not teach or suggest, either solely or in combination, the limitations "predicting based on the first set of elements and using a first machine learning (ML) model, a first impact score indicative of an impact of the first set of updates on a set of performance metrics associated with the first webpage, wherein the first impact score is predicted prior to deployment of an [[the]] updated first webpage; and selecting a first deployment strategy from a plurality of pre-defined deployment strategies based on the predicted first impact score and a set of rules, wherein the first deployment strategy is executed for the updating and the deployment of the first webpage based on the first set of updates." when considered in combination with the other limitations of claim 19.
Note, however, that claims 1-20 are rejected under 35 USC § 101 as being directed to an abstract idea.
Claim Objections
The following claims are objected to because of antecedence issues. It is suggested Applicants amend these claims as follows:
Claim 1
-- predicting, by the server, based on the first set of elements and using a first machine learning (ML) model, a first impact score indicative of an impact of the first set of updates on a set of performance metrics associated with the first webpage, wherein the first impact score is predicted prior to deployment of an [[the]] updated first webpage; --
Claim 2
-- the first plurality of features include at least two or more of a page type of a webpage, a code structure of a webpage, a count of code snippets associated with a webpage, a locale associated with a webpage, a caching arrangement for loading a webpage, a versioning history of a webpage, a current average time taken for a webpage to load on a user device, a current average time taken for a webpage to activate interactive features post loading, a previous average time taken for a webpage to load on a user device, a previous average time taken for a webpage to activate interactive features post loading; --
-- training, by the server, using the first training dataset, the first ML model to predict an impact of updates on a performance of a webpage, wherein the trained first ML model is used for the prediction of the first impact score. --
Claim 7
-- the second plurality of features include at least two or more of a page type of a webpage, a type of content associated with a webpage, a locale associated with a webpage, an average time taken for a webpage to load on a user device, an average time taken for a webpage to activate interactive features post loading; --
-- training, by the server, using the second training dataset, the second ML model to predict an impact of updates on conversion corresponding to a webpage, wherein the trained second ML model is used for the prediction of the second impact score. --
Claim 9
-- an average time taken for a webpage to load on a user device and an average time taken for a webpage to activate interactive features post loading. --
Claim 10
-- predict, based on the first set of elements and using a first machine learning (ML) model, a first impact score indicative of an impact of the first set of updates on a set of performance metrics associated with the first webpage, wherein the first impact score is predicted prior to deployment of an [[the]] updated first webpage; and --
Claim 11
-- the first plurality of features include at least two or more of a page type of a webpage, a code structure of a webpage, a count of code snippets associated with a webpage, a locale associated with a webpage, a caching arrangement for loading a webpage, a versioning history of a webpage, a current average time taken for a webpage to load on a user device, a current average time taken for a webpage to activate interactive features post loading, a previous average time taken for a webpage to load on a user device, a previous average time taken for a webpage to activate interactive features post loading; --
-- train, using the first training dataset, the first ML model to predict an impact of updates on a performance of a webpage, wherein the trained first ML model is used for the prediction of the first impact score. --
Claim 16
-- the second plurality of features include at least two or more of a page type of a webpage, a type of content associated with a webpage, a locale associated with a webpage, a positioning of content in a webpage, an average time taken for a webpage to load on a user device, an average time taken for a webpage to activate interactive features post loading; --
-- train, using the second training dataset, the second ML model to predict an impact of updates on conversion corresponding to a webpage, wherein the trained second ML model is used for the prediction of the second impact score. --
Claim 18
-- an average time taken for a webpage to load on a user device and an average time taken for a webpage to activate interactive features post loading. --
Claim 19
-- predicting based on the first set of elements and using a first machine learning (ML) model, a first impact score indicative of an impact of the first set of updates on a set of performance metrics associated with the first webpage, wherein the first impact score is predicted prior to deployment of an [[the]] updated first webpage; and --
Claims 2-9 and 11-18 are additionally objected to due to their dependence on objected parent claim(s).
Appropriate correction is required.
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 a method for managing updates to webpages. Under a broadest reasonable interpretation, claim 1 would fall under the category of mental processes as the claim features limitations performable as mental steps, with the assistance of pen & paper, but without additional elements that integrate the abstract idea into a practical application or amount to significantly more than the abstract idea. An analysis of claim 1 according to the 2019 Revised Patent Subject Matter Eligibility test follows:
Step 1: Is the claim directed to a process, machine, manufacture or composition of matter?
Yes, claim 1 is directed to a method and, therefore, a process.
Step 2A Prong 1: Does the claim recite an Abstract Idea, Law of Nature, or Natural Phenomenon?
Yes, claim 1 recites an abstract idea as the following limitations are performable as mental processes with the assistance of pen & paper: receiving- A software developer can receive a printout of proposed updates, containing program code, for updating a deployed webpage; predictingan [[the]] updated first webpage; - The software developer can manually analyze the proposed updates and derive, using a formula, an impact score reflecting the impact the proposed updates could have on performance metrics if the updates were applied to the webpage. selectingThe developer can decide not to apply the proposed updates to the webpage upon determining that application of the updates would degrade performance metrics associated with the webpage beyond acceptable thresholds.
Step 2A Prong 2: Does the Claim Recite Additional Elements That Integrate The Judicial Exception Into A Practical Application?
Claim 1 recites the additional element of a server for performing the receiving, predicting and performing steps. However, as recited, the server is merely used as a tool for performing the abstract idea and, therefore, does not integrate the abstract idea into a practical application.
Claim 1 also recites the additional element of using a first machine learning (ML) model to predict the first impact score. However, since the claim does not provide any detail regarding how the ML model predicts the impact score, the additional element can be considered a generic machine learning technique for performing the abstract idea. Consequently, the additional element does not integrate the abstract idea into a practical application.
Step 2B: Does the Claim Recite Additional Elements That Amount To Significantly More Than The Judicial Exception?
Claim 1 recites the additional element of a server for performing the receiving, predicting and performing steps. However, as recited, the server is merely used as a tool for performing the abstract idea and, therefore, is not significantly more than the abstract idea.
Claim 1 also recites the additional element of using a first machine learning (ML) model to predict the first impact score. However, since the claim does not provide any detail regarding how the ML model predicts the impact score, the additional element can be considered a generic machine learning technique for performing the abstract idea. Consequently, the additional element is not significantly more than the abstract idea.
Claim 10 is rejected for the same reasons given for analogous claim 1. While claim 10 recites other additional elements, including a processor, and a memory storing instructions executable by the processor, these additional elements merely constitute generic computing components for performing the abstract idea and, therefore, do not integrate the abstract idea into a practical application or amount to significantly more than the abstract idea.
Claim 19 is rejected for the same reasons given for analogous claim 1. While claim 19 recites other additional elements, including a non-transitory computer readable medium, computer executable instructions, and a computer, these additional elements merely constitute generic computing components for performing the abstract idea and, therefore, do not integrate the abstract idea into a practical application or amount to significantly more than the abstract idea.
Claims 2 and 11 are also directed to the abstract idea as determining two or more features, and corresponding feature values, for training the first ML model, and generating a training dataset, can be performed as mental steps with the assistance of pen & paper. While the claims also recite the additional element of training, by the server, using the first training dataset, the first ML model to predict an impact of updates on a performance of a webpage, wherein the trained first ML model is used for the prediction of the first impact score (claim 2), since the claims provide no detail regarding how the first ML model is trained by the server, the additional element can be considered a generic machine learning technique for performing the abstract idea. Consequently, the additional element does not integrate the abstract idea into a practical application or amount to significantly more than the abstract idea.
Claims 3 and 12 are also directed to the abstract idea as generating a second plurality of feature values can be performed as mental steps with the assistance of pen & paper. While the claims also recite the additional element of providing, by the server, the second plurality of feature values as input to the first ML model, wherein the first impact score is outputted by the first ML model in response to the provided input (claim 3), since the claim provides no detail regarding how the first ML model outputs the first impact score responsive to the server providing the second plurality of feature values, the additional element can be considered a generic machine learning technique for performing the abstract idea. Consequently, the additional element does not integrate the abstract idea into a practical application or amount to significantly more than the abstract idea.
Claims 4, 13 and 20 recite the additional element of wherein the plurality of deployment strategies corresponds to a plurality of integration/continuous deployment (CI/CD) pipelines and wherein a first CI/CD pipeline of the plurality of CI/CD pipelines is executed for the updating and the deployment of the first webpage (claim 4). However, the additional element merely links use of the abstract idea to the particular technological environment of CI/CD pipelines and, therefore, does not integrate the abstract idea into a practical application or amount to significantly more than the abstract idea.
Claims 5 and 14 recite an additional iteration of the receiving, predicting and selecting steps from claims 1 and 10, respectively, using a second set of updates. Since the claims do not recite further additional elements that integrate the abstract idea into a practical application or amount to significantly more than the abstract idea, the claims are rejected for the same reasons given for claims 1 and 10.
Claims 6 and 15 recite an additional iteration of the predicting step and an elaboration of the selecting step from claims 1 and 10, respectively, using a second ML model to predict a second impact score, from which the first deployment strategy is additionally based upon. Since the claims do not recite further additional elements that integrate the abstract idea into a practical application or amount to significantly more than the abstract idea, the claims are rejected for the same reasons given for claims 1 and 10.
Claims 7 and 16 are also directed to the abstract idea as determining two or more features, and corresponding feature values, for training the second ML model, and generating a second training dataset, can be performed as mental steps with the assistance of pen & paper. While the claims also recite the additional element of training, by the server, using the second training dataset, the second ML model to predict an impact of updates on conversion corresponding to a webpage, wherein the trained second ML model is used for the prediction of the second impact score (claim 7), since the claims provide no detail regarding how the second ML model is trained by the server, the additional element can be considered a generic machine learning technique for performing the abstract idea. Consequently, the additional element does not integrate the abstract idea into a practical application or amount to significantly more than the abstract idea.
Claims 8 and 17 are also directed to the abstract idea as the developer can manually assign weights to the impact scores, determine an aggregate impact score and use the aggregated impact score for determining the first deployment strategy. While the claims recite the additional element of using a server or processor for performing the determining steps, as recited, the additional element simply amounts to using a computer as a tool to perform the abstract idea. Consequently, the additional element does not integrate the abstract idea into a practical application or amount to significantly more than the abstract idea.
Claim 9 and 18 are also directed to the abstract idea as they merely elaborate upon limitations found abstract in independent claims 1 and 10, respectively. Since the claims lack additional elements that integrate the abstract idea into a practical application or amount to significantly more than the abstract idea, the claims are ineligible.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. US 20200034135 A1 discloses a machine learning-based method to measure and evaluate the impact of software changes on components of cloud computing platforms. US 20200134457 A1 discloses a method for using machine learning to determine indications of change in software. US 20230088784 A1 discloses a method and system for using a machine learning model to predict a risk associated with deploying an impending code change. US 20250085967 A1 discloses a method and system for using a machine learning-based impact model for determining whether a code change impacts a documentation set requiring an update to the documentation set. The NPL document "Predicting Software Maintenance Type, Change Impact, and Maintenance Time Using Machine Learning Algorithms" proposes a method for predicting maintenance type, impacts of changes, and maintenance time for requested software changes using machine learning. The NPL document "A Prediction Model for Software Requirements Change Impact" discusses research to use a combination of Machine Learning and Natural Language Processing for performing change impact analysis on software requirements.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to GEOFFREY R ST LEGER whose telephone number is (571)270-7720. The examiner can normally be reached M-F (IFP) ~9:00-5:00 pm.
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/GEOFFREY R ST LEGER/Primary Examiner, Art Unit 2192