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 . Claims 1-20 have been examined and are pending.
Priority
Acknowledgment is made of applicant's claim for foreign priority based on an application filed in India on 30 March 2024.
Pertinent Prior Art
Prior art that is considered pertinent to applicant's disclosure but not currently relied upon:
20230319083
Pars. 68-70
Natural language queries directed to anomalous metric values, relative changes, absolute changes, aggregate behavior, etc.
20250190213
Pars. 22-26
Natural language capabilities and error knowledge graphs for indicating errors and corresponding solutions
20220108274
Abstract, pars. 46-48
Natural language processing engine to generate information about changes, conflicts, exposures, etc.
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. Claims 1-20 are directed to one of the eligible categories of subject matter.
With respect to independent claims 1 and 11, the identifying, processing, generate, cover performance of the limitations manually and/or in the mind (mental processes abstract idea). The receiving, outputting limitations are recited at a high level of generality and do not add meaningful limitations to the abstract idea; these limitations are directed to insignificant extra solution activities. The claims as a whole merely describe how to generally “apply” the exception in a computer environment using generic computer functions or components. Even when viewed in combination, these additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. The claims are not patent eligible.
With respect to dependent claim 2, 8, 12, 18 the converting, exploring, analyzing, updating cover performance of the limitations manually and/or in the mind (mental processes abstract idea). The retrieving, receiving is recited at a high level of generality and do not add meaningful limitations to the abstract idea; these limitations are directed to insignificant extra solution activities. The claims as a whole merely describe how to generally “apply” the exception in a computer environment using generic computer functions or components. Even when viewed in combination, these additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. The claims are not patent eligible.
With respect to dependent claims 4, 5, 6, 7, 9, 10, 14, 15, 16, 17, 19, 20 the analyzing, identify, perform, segment, determine, assigning, tracking, initiating cover performance of the limitations manually and/or in the mind (mental processes abstract idea). No additional elements are recited and so the claims do not provide a practical application and are not considered to be significantly more. The claims are not eligible.
With respect to dependent claims 3, 13 retrieving are recited at a high level of generality and do not add meaningful limitations to the abstract idea. The claims as a whole merely describe how to generally “apply” the exception in a computer environment using generic computer functions or components. Even when viewed in combination, these additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. The claims are not patent eligible.
Claim Rejections - 35 USC § 102
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
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)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claims 1-20 are rejected under 35 U.S.C. 102(a)(2) as being anticipated by Erlingsson et al., Patent No.: US 11973784 B1, hereinafter Erlingsson.
As per claim 1, Erlingsson discloses A method comprising:
receiving, in a natural language interface of a computer system, a query in natural language from a user (col. 93, line 34-40 disclose receiving natural language inputs), the query specifying at least one change in resources associated with deployment of an application in a target environment (at least col. 17, lines 64-66, col. 25, line 24, col. 94, lines 24-39, col. 95, lines 40-55, and col. 96, lines 15-28, fig. 2M disclose that the natural language input queries specify information about resources in a deployment and relate to changes in resources) ;
processing the query, by the natural language interface, to: identify user intent from the query (col. 94, last full par.; col. 97, line 5, col. 100, lines 50-61); and
identify entities related to the at least one change from the query (see rejection of first limitation above and note that resources, users, machines, etc. are various entities; see also, col. 8, lines 55-63, col. 17, lines 40-55, col. 92, lines 29-44);
generating, by the natural language interface, a natural language response to the user query, the natural language response including data associated with the change (see rejection above including at least col. 98, lines 12-27); and
outputting, by the natural language interface, the natural language response to the user (see rejection above including at least col. 98, lines 12-27).
As per claim 2, Erlingsson discloses The method of claim 1, further comprising: converting the natural language query into a structured query that conforms to a preestablished data model (col. 94, lines 40-50, col. 98, first full par., col. 53, last par., col. 54, lines 5-15, col. 55, last par., col.’s 60-61), the structured query including at least one of: a declarative query for retrieving tabular data (see rejection above for multiple examples of SQL (i.e. declarative) queries that retrieve tabular data); a graph traversal query for exploring dependency relationships (col. 27, lines 40-46 disclose graph structures and the queries to these graph structures are graph traversal queries that explore cloud deployment dependency relationships; see also, col. 28, first full par., col. 40, lines 61-62, col. 54, lines 20-25); and a time-series query for analyzing performance metrics over a specified time range (col. 95, lines 50-52, col. 96, line 59-65, col. 101, line 65 to col. 102, line 5).
As per claim 3, Erlingsson discloses The method of claim 2, further comprising: retrieving, by the natural language interface, data associated with the structured query from at least one database in connection with the computer system (see at least col. 98, first full par.), the at least one database being selected from a graph storage containing a dependency graph associated with the deployment of the application (see at least col. 27, lines 40-46, col. 28, first full par., col. 40, lines 61-62, col. 54, lines 20-25), a historical impact database containing historical change data (see rejection of claims 1-2 and col. 95, lines 35-40, col. 97, lines 34-51), and a monitoring database containing performance metrics data (see rejection of claims 1-2 and col. 72, last full par., col. 76, lines 46-55, col. 96, lines 9-13, col. 98, lines 15-17, col. 65, line 7, ).
As per claim 4, Erlingsson discloses The method of claim 3, further comprising: analyzing, by the natural language interface, the data associated with the structured query to: identify the at least one change in resources associated with the deployment of the application and change impact associated with the at least one change; identify presence or absence of an anomaly; and perform comparisons on performance metrics before and after the change (see rejection of above claims as well as col. 95, second full par., col. 28, lines 32-36 and last par., col. 74, first par., col. 8, lines 45-50).
As per claim 5, Erlingsson discloses The method of claim 4, wherein the further comprising: segmenting the data associated with the structured query into time slices; and determining temporal correlations between the at least one change and the change impact based on the time slices (col. 28, first par., col. 95, lines 45-55, col. 28, lines 61-65).
As per claim 6, Erlingsson discloses The method of claim 4, wherein the at least one change in resources comprises a plurality of changes, and the method further comprises: identifying correlations and interdependencies between the plurality of changes; and determining a cumulative impact caused the plurality of changes (col. 23, lines 50-57, col. 27, lines 40-47, col. 28, first full par. and last par.).
As per claim 7, Erlingsson discloses The method of claim 6, wherein the at least one change comprises a plurality of changes, and the method further comprises: determining causal relationships between the plurality of changes in resources and the performance metrics (see rejection of claims 4-6 above).
As per claim 8, Erlingsson discloses The method of claim 2, further comprising: receiving, in the natural language interface, feedback from users; analyzing, by the natural language interface, the feedback to identify one or more machine learning models employed by the natural language interface; and updating the identified machine learning models (col. 96, last par., col. 97, lines 36-51, col. 99, lines 50-63, claim 12).
As per claim 9, Erlingsson discloses The method of claim 8, wherein updating the identified machine learning models further comprises: assigning an identifier to each one of the machine learning models; assigning a version number to the machine learning model before and after updating, the version number indicating metadata describing training datasets used for training the machine learning model; and tracking the performance metrics for each one of the machine learning models (col. 97, lines 35-51; col. 99, lines 50-63, col. 100, lines 25-27).
As per claim 10, Erlingsson discloses The method of claim 9, wherein updating the identified machine learning models further comprises: initiating rollback after updating the machine learning model upon a determination of performance degradation caused by the updated machine learning model (col. 99, lines 50-64 disclose rolling back by means of model retraining due to performance feedback wherein if a retrained model degrades performance, it would be reverted and retrained back to a previous version).
As per claims 11-20, they are analogous to above claims and therefore likewise rejected.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to SYED HASAN whose telephone number is (571)270-5008. The examiner can normally be reached M-F 8am - 5 pm.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Boris Gorney can be reached at (571)270-5626. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/SYED H HASAN/Primary Examiner, Art Unit 2154