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 .
Information Disclosure Statement
The information disclosure statement (IDS) submitted on December 16, 2025 has been considered. The submission is in compliance with the provisions of 37 CFR 1.97. Form PTO-1449 is signed and attached hereto.
Drawings
The drawings filed on March 07, 2025 are accepted.
Specification
The specification filed March 07, 2025 is accepted.
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 1-3, 6-8, 10 and 12-15 are rejected under 35 U.S.C. 103 as being unpatentable over Agarwal et al. US 2019/0179633 A1 [hereinafter Agarwal] in view of Durvasula et al. US 2023/0186117 A1 [hereinafter Durvasula].
As per claims 1 and 15, Agarwal teaches a computer-implemented method for handling updates to a computing environment, the method comprising:
receiving, from a user, a change request comprising a text description justifying a change in hardware and/or software within the computing environment [paragraph 0013];
applying natural language processing to the text description to extract one or more text features from the text description [paragraphs 0013-0016];
performing a validation check of the received change request by applying a trained classification model to the extracted one or more text features, the classification model having been trained on a dataset of historical change requests [paragraphs 0013-0016]; and
if the received change request fails the validation check giving the request the lowest ranking on update recommendations [paragraph 0016].
In the same field of endeavor, Durvasula teaches a method of handling updates to computing environment comprising:
if the received change request fails the validation check, preventing submission of the change request into a queue of change requests and/or implementation of the change associated with the change request [paragraph 0131]. It would have been obvious to one having ordinary skill in the art to employ the teachings of Durvasula within the system of Agarwal in order to enhance security of the system by applying changes to validated requests.
As per claim 2, Agarwal further teaches the method wherein the extracted one or more text features form part of a feature vector, wherein applying the trained classification model to the extracted one or more text features comprises applying the trained classification model to the feature vector [paragraphs 0013-0016].
As per claim 3, Agarwal further teaches the method wherein a vector representing a change justification category further forms part of the feature vector, the change justification category indicating a reason for the change in hardware and/or software within the computing environment [paragraphs 0013-0016].
As per claim 6, Agarwal further teaches the method wherein each change request in the dataset of historical change requests have has been pre-labelled as either passing or failing the validation check [paragraphs 0013-0016].
As per claim 7, Agarwal further teaches the method wherein the dataset of historical change requests comprises data related to the performance of one or more changes associated with one or more change requests that were submitted previously [paragraphs 0013-0016].
As per claim 8, Agarwal further teaches the method wherein preventing submission of the change request into a queue of change requests and/or implementation of the change associated with the change request comprises at least one of: limiting relevant access control to the user; and requesting manual approval of the change associated with the change request [paragraphs 0013-0016].
As per claim 10, Agarwal further teaches the method, further comprising, if the received change request passes the validation check, enabling the user to submit the change request into the queue of change requests by permitting relevant access control to the user [paragraphs 0013-0016].
As per claim 12, Agarwal further teaches the method wherein applying natural language processing comprises at least one of: applying a regular expression replacer to the text description; performing tokenisation of the text description; performing stopword removal of the text description; performing stemming of the text description; performing text cleaning of the text description; performing part-of-speech tagging of the text description; performing named entity recognition of the text description; performing sentiment analysis of the text description; performing Term Frequency-Inverse Document Frequency analysis of the text description; applying a dictionary counter to the text description; and applying a regular expression counter to the text description [paragraphs 0013-0016].
As per claim 13, Agarwal further teaches the method further comprising, if the received change request passes the validation check, implementing the change associated with the change request [paragraphs 0013-0016].
As per claim 14, Agarwal further teaches the method wherein the method further comprises updating the classification model based on an outcome resulting from implementing the change associated with the change request [paragraphs 0013-0016].
Allowable Subject Matter
Claims 4, 5, 9 and 11 are objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to BEEMNET W DADA whose telephone number is (571)272-3847. The examiner can normally be reached Monday-Friday, 9am-5pm.
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BEEMNET W. DADA
Primary Examiner
Art Unit 2435
/BEEMNET W DADA/Primary Examiner, Art Unit 2435
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