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 StatusClaims 1-20 are pending and have been rejected.
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 07/01/2025 & 10/27/2025 is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
Double Patenting
The nonstatutory double patenting rejection is based on a judicially created doctrine grounded in public policy (a policy reflected in the statute) so as to prevent the unjustified or improper timewise extension of the "right to exclude" granted by a patent and to prevent possible harassment by multiple assignees. A nonstatutory obviousness-type double patenting rejection is appropriate where the conflicting claims are not identical, but at least one examined application claim is not patentably distinct from the reference claim(s) because the examined application claim is either anticipated by, or would have been obvious over, the reference claim(s). See, e.g., In re Berg, 140 F.3d 1428, 46 USPQ2d 1226 (Fed. Cir. 1998); In re Goodman, 11 F.3d 1046, 29 USPQ2d 2010 (Fed. Cir. 1993); In re LongL 759 F.2d 887, 225 USPQ 645 (Fed. Cir. 1985); In re Van Omum, 686 F.2d 937, 214 USPQ 761 (CCPA 1982); In re Vogel, 422 F.2d 438, 164 USPQ 619 (CCPA 1970); and In re Thorington, 418 F.2d 528, 163 USPQ644 (CCPA 1969).
A timely filed terminal disclaimer in compliance with 37 CFR 1.321 (c) or 1.321 (d) may be used to overcome an actual or provisional rejection based on a nonstatutory double patenting ground provided the conflicting application or patent either is shown to be commonly owned with this application, or claims an invention made as a result of activities undertaken within the scope of a joint research agreement. Effective January 1, 1994, a registered attorney or agent of record may sign a terminal disclaimer. A terminal disclaimer signed by the assignee must fully comply with 37 CFR 3.73(b).
Claim 1, 3, 5, 7, 8, 10, 12, 15 & 17 are provisionally rejected on the ground of nonstatutory obviousness-type double patenting as being unpatentable over claims 1, 2, 10, 11, 18 & 19 of U.S. Patent No. 12,265,526. Although the conflicting claims are not identical, they are not patentably distinct from each other because all elements of instant Application No. 19/042189 correspond to elements the U.S. Patent No. 12,265,526. The above claim of the present application would have been obvious over claims of the U.S. Patent No. 12,265,526 because each element of the claims of the present application is anticipated by the claims of the U.S. Patent No. 12,265,526. This is a provisional obviousness-type double patenting rejection because the conflicting claims have not in fact been patented.
Instant Application 19/068835
U.S. Patent No. 12,265,526
Claims 1, 8 & 15 An apparatus, comprising: a memory; and a processor operatively coupled to the memory, the processor configured to: receive, via an interface, natural language data associated with a user request for performing an identified computational task associated with a cybersecurity management system; provide the natural language data as input to a machine learning (ML) model, the ML model configured to automatically infer a query based on the natural language data; cause the query to be displayed via the interface; receive, via the interface, user input indicating feedback for the query; generate, based on the user input, an updated query associated with the identified computational task; re-train the ML model based on the feedback; provide the updated query as input to a system configured to perform the identified computational task; and modify a security setting in the cybersecurity management system based on the performance of the identified computational task.
Claims 1, 12 & 17 An apparatus, comprising: a memory; and a processor operatively coupled to the memory, the processor configured to: receive, via an interface, natural language data associated with a user request for performing an identified computational task associated with a cybersecurity management system; provide the natural language data as input to a first machine learning (ML) model, the first ML model configured to automatically infer a template query based on the natural language data; evaluate a performance of the first ML model; generate a recommendation indicating a type of training data based on the performance; train a second ML model to provide augmented training data, the augmented training data being the type of training data; re-train the first ML model based on the augmented training data; cause the template query to be displayed, via the interface, the template query being editable by a user; receive, via the interface, user input indicating a finalized query associated with the identified computational task; provide the finalized query as input to a system configured to perform the identified computational task; and modify a security setting in the cybersecurity management system based on the performance of the identified computational task.
Claim 2
wherein the ML model is configured to infer the query by parsing the natural language data associated with a user request into a set of portions, each portion from the set of portions being associated with a parameter from a set of parameters, the processor configured to cause the query to be displayed with the set of parameters via the interface.
Claim 3
wherein the first ML model is configured to generate the template query by parsing the natural language data associated with a user request into a set of portions, each portion from the set of portions being associated with a parameter from a set of parameters, the processor configured to cause the display of the template query including the set of parameters via the interface.
Claim 3
wherein the processor is configured to cause the display of the set of parameters such that each portion from the set of portions is displayed as an option from a plurality of options associated with a parameter from the set of parameters, the processor configured to: provide control tools, via the interface, the control tools configured to receive, from a user, a selection of at least one option from the plurality of options.
Claim 5
wherein the processor is configured to cause the display of the set of parameters such that each portion from the set of portions is displayed as one option from a plurality of options associated with a parameter from the set of parameters, the processor configured to: provide control tools, via the interface, the control tools configured to receive, from the user, a selection of at least one option from the plurality of options associated with each parameter from the set of parameters.
Claim 4 wherein the ML model is configured to receive a partially complete portion of the natural language data associated with the user request for performing the identified computational task and automatically infer, based on the partially complete portion, at least a part of a remaining portion of the natural language data associated with the user request.
Claim 7
wherein the first ML model is configured to receive a partially complete portion of the natural language data associated with the user request for performing the identified computational task and automatically infer, based on the partially complete portion, at least a part of a remaining portion of the natural language data associated with the user request.
Claim 5
wherein the identified computational task is associated with implementing measures for malware detection and mitigation, the ML model being trained to receive natural language data associated with a user request and automatically infer a corresponding query based on training the ML model using natural language data related to cybersecurity.
Claim 8
the identified computational task is associated with implementing measures for malware detection and mitigation, the first ML model being trained to receive natural language data associated with a user request and automatically infer a corresponding template query based on training the first ML model using natural language data related to cybersecurity.
Claim 6
wherein the identified computational task includes at least one of blocking a communication or a host, applying a patch to a set of hosts, rebooting a machine, or executing a rule at an identified endpoint.
Claim 10
wherein the identified computational task includes at least one of blocking a communication or a host, applying a patch to a set of hosts, rebooting a machine, or executing a rule at an identified endpoint.
Claim 18
wherein the ML model is configured to receive an incomplete portion of the natural language data, and based on the incomplete portion infer a context associated with the natural language data, the inferring of the query being based on the context.
Claim 15
wherein the first ML model is configured to receive an incomplete portion of the natural language request, and based on the incomplete portion infer a context associated with the natural language request, the prediction of the template query being based on the inferred context.
Claims 1, 3, 5, 7, 8, 10, 12, 15 & 17 provisionally rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1, 2, 7 & 16 of U.S. Patent No. 12,130,923. Although the claims at issue are not identical, they are not patentably distinct from each other because the claims are directed towards using machine learning (ML) models to perform cybersecurity tasks.
Instant Application 19/068835
U.S. Patent No. 12,265,526
Claims 1, 8 & 15 An apparatus, comprising: a memory; and a processor operatively coupled to the memory, the processor configured to: receive, via an interface, natural language data associated with a user request for performing an identified computational task associated with a cybersecurity management system; provide the natural language data as input to a machine learning (ML) model, the ML model configured to automatically infer a query based on the natural language data; cause the query to be displayed via the interface; receive, via the interface, user input indicating feedback for the query; generate, based on the user input, an updated query associated with the identified computational task; re-train the ML model based on the feedback; provide the updated query as input to a system configured to perform the identified computational task; and modify a security setting in the cybersecurity management system based on the performance of the identified computational task.
Claims 1, 7 & 16 A method, comprising: receiving, via an interface, a natural language request for performing an identified task in a management system, the management system being associated with a set of system commands, the set of system commands being configured to perform one or more computational tasks associated with the management system, the identified task being from the one or more computational tasks, and the management system operating within an identified context; extracting, based on the identified context, a set of features from the natural language request; providing the set of features to a first machine learning (ML) model to infer a template command based on the natural language request, the first ML model trained using first training data, the first training data including a set of natural language phrases associated with the identified context, the first ML model trained to receive features based on the set of natural language phrases associated with the identified context as input, the template system command being associated with the set of system commands; receiving, as output from the first ML model, the template command associated with the natural language request; displaying, via the interface, the template command in an editable form to be edited or approved by a user; receiving, via the interface, a final command, based on the template command, the final command approved by the user; providing the final command as a reference input to a second ML model, the second ML model configured to generate a set of natural language phrases semantically related to the reference input; receiving, from the second ML model, the set of natural language phrases semantically related to the reference input; generating a second training data based on the set of natural language phrases semantically related to the reference input; augmenting the first training data by adding the second training data to the first training data to generate third training data; and providing the final command to the management system to implement the identified task.
Claim 2
wherein the ML model is configured to infer the query by parsing the natural language data associated with a user request into a set of portions, each portion from the set of portions being associated with a parameter from a set of parameters, the processor configured to cause the query to be displayed with the set of parameters via the interface.
Claim 2
wherein the providing the set of features to the first ML model to infer the template command based on the natural language request includes: parsing the natural language request into a set of portions to predict, using the first ML model, the template command based on the natural language request, the template command including the set of portions, each portion from the set of portions being associated with a parameter from a set of parameters.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant’s disclosure. This includes: U.S. Publication 2019/0034540, which describes natural classification with semantic mapping and classification.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to TANIA M PENA-SANTANA whose telephone number is (571)270-0627. The examiner can normally be reached Monday - Friday 8am to 4pm EST.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Nicholas R Taylor can be reached at 5712723889. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/TANIA M PENA-SANTANA/Examiner, Art Unit 2443
/CHRISTOPHER B ROBINSON/Primary Examiner, Art Unit 2443
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