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 .
This action is responsive to the application filed 01/25/2024. Claims 1-20 are presented for examination.
Information Disclosure Statement
The information disclosure statements (IDS) submitted 01/25/2024, 02/16/2024, 12/16/2024, 04/21/2025, 07/23/2025, 11/27/2025, & 01/30/2026 have been considered by the examiner.
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
Step 1: The claim recites “A method for generating synthetic artificial intelligence (AI)-implemented computer network behavioral model training data, the method comprising:”; therefore, it is directed to the statutory category of a process.
Step 2A Prong 1: The claim recites, inter alia:
generating, based on the input, a test case definition for configuring and controlling components of an instrumented testbed environment to execute at least one network test: These limitations recite a mentally performable process with the aid of pen and paper of using observation and judgement to generate a test case definition for configuring and controlling components of an instrumented testbed environment to execute at least one network test, based on the input.
and generating, as output and based on the network performance and operational data, synthetic AI-implemented computer network behavioral model training data, wherein synthetic AI-implemented computer network behavioral model training data includes at least one parameter not included or defined in the AI-implemented computer network behavioral model training data or the AI-implemented computer network behavioral model training data definition: These limitations recite a mentally performable process with the aid of pen and paper of using observation, judgement, and evaluation to generate synthetic AI-implemented computer network behavioral model training data as output, wherein this data includes at least one parameter not included or defined in the AI-implemented computer network behavioral model training data or AI-implemented computer network behavioral model training data definition.
Step 2A Prong 2: This judicial exception is not integrated into a practical application. The additional elements of the claim are as follows:
A method for generating synthetic artificial intelligence (AI)-implemented computer network behavioral model training data, the method comprising: These additional elements are recited at a high level of generality and merely indicate a field of use or technological environment in which to apply a judicial exception, e.g. a method, to a particular technological environment or field of use, e.g. for generating synthetic artificial intelligence (AI)-implemented computer network behavioral model training data. See MPEP 2106.05(h). Elements that use or interact with the judicial exception do not integrate the judicial exception into a practical application.
receiving, as input, sample AI-implemented computer network behavioral model training data or an AI-implemented computer network behavioral model training data definition: These additional elements amount to insignificant extra-solution activity in the form of mere data gathering per MPEP § 2106.05(g).
executing the at least one network test within the instrumented testbed environment: These additional elements recite only the idea of executing at least one network test within the instrumented testbed environment and attempts to cover any implementation of a network test without any restriction as to how the execution of the network test is completed or the type of network test that is used. These additional elements do not meaningfully limit the claim and does not integrate the judicial exception into a practical application because this type of recitation is equivalent to the words “apply it”. See MPEP 2106.05(f).
recording network performance and operational data generated from the execution of the at least one network test: These additional elements amount to insignificant extra-solution activity in the form of mere data gathering per MPEP § 2106.05(g).
Thus, the way in which the additional elements use or interact with the judicial exception do not integrate the judicial exception into a practical application.
Step 2B: The additional elements from Step 2A Prong 2 include generally linking the use of the judicial exception to indicate a field of use or technological environment, adding words equivalent to "apply it" with the judicial exception, and insignificant extra-solution activity of data gathering recited by “receiving, as input, sample AI-implemented computer network behavioral model training data or an AI-implemented computer network behavioral model training data definition” and “recording network performance and operational data generated from the execution of the at least one network test” which are well-understood routine and conventional activities similar to presenting offers and gathering statistics per MPEP 2106.05(d)(II). Thus, the additional elements, viewed individually or in combination, do not provide an inventive concept or otherwise amount to significantly more than the abstract idea itself. See MPEP § 2106.05.
Claim 2
Step 1: A process, as above.
Step 2A Prong 1: The claim recites the abstract ideas as the judicial exception of claim 1.
Step 2A Prong 2: This judicial exception is not integrated into a practical application. The additional elements of the claim are as follows:
wherein receiving, as input, sample AI-implemented computer network behavioral model training data or an AI-implemented computer network behavioral model training data definition includes receiving the sample AI-implemented computer network behavioral model training data as input: These additional elements amount to insignificant extra-solution activity in the form of mere data gathering per MPEP § 2106.05(g).
Step 2B: The additional elements from Step 2A Prong 2 include insignificant extra-solution activity of data gathering recited by “wherein receiving, as input, sample AI-implemented computer network behavioral model training data or an AI-implemented computer network behavioral model training data definition includes receiving the sample AI-implemented computer network behavioral model training data as input” which are well-understood routine and conventional activities similar to presenting offers and gathering statistics per MPEP 2106.05(d)(II). Thus, the additional elements, viewed individually or in combination, do not provide an inventive concept or otherwise amount to significantly more than the abstract idea itself. See MPEP § 2106.05.
Claim 3
Step 1: A process, as above.
Step 2A Prong 1: The claim recites the abstract ideas as the judicial exception of claim 1.
Step 2A Prong 2: This judicial exception is not integrated into a practical application. The additional elements of the claim are as follows:
wherein receiving, as input, sample AI-implemented computer network behavioral model training data or an AI-implemented computer network behavioral model training data definition includes receiving the AI-implemented computer network behavioral model training data definition as input: These additional elements amount to insignificant extra-solution activity in the form of mere data gathering per MPEP § 2106.05(g).
Step 2B: The additional elements from Step 2A Prong 2 include insignificant extra-solution activity of data gathering recited by “wherein receiving, as input, sample AI-implemented computer network behavioral model training data or an AI-implemented computer network behavioral model training data definition includes receiving the AI-implemented computer network behavioral model training data definition as input” which are well-understood routine and conventional activities similar to presenting offers and gathering statistics per MPEP 2106.05(d)(II). Thus, the additional elements, viewed individually or in combination, do not provide an inventive concept or otherwise amount to significantly more than the abstract idea itself. See MPEP § 2106.05.
Claim 4
Step 1: A process, as above.
Step 2A Prong 1: The claim recites the abstract ideas of claim 1 as well as, inter alia:
wherein generating the test case definition includes generating instructions for configuring the components of the instrumented testbed environment to implement a network topology: These limitations recite a mentally performable process with the aid of pen and paper of using judgement to generate instructions for configuring the components of the instrumented testbed environment to implement a network topology.
Thus, the claim recites a judicial exception.
Step 2A Prong 2 & Step 2B: There are no additional elements recited so the claim does not provide a
practical application and is not considered to be significantly more. As such, the claim is patent
ineligible.
Claim 5
Step 1: A process, as above.
Step 2A Prong 1: The claim recites the abstract ideas as the judicial exception of claim 4.
Step 2A Prong 2: This judicial exception is not integrated into a practical application. The additional elements of the claim are as follows:
wherein executing the at least one network test includes transmitting network traffic within the network topology: These additional elements amount to insignificant extra-solution activity in the form of selecting a particular data source or type of data to be manipulated per MPEP § 2106.05(g).
Thus, the way in which the additional elements use or interact with the judicial exception do not integrate the judicial exception into a practical application.
Step 2B: The additional elements from Step 2A Prong 2 include insignificant extra-solution activity of data gathering recited by “wherein executing the at least one network test includes transmitting network traffic within the network topology” which are well-understood routine and conventional activities similar to receiving or transmitting data over a network per MPEP 2106.05(d)(II). Thus, the additional elements, viewed individually or in combination, do not provide an inventive concept or otherwise amount to significantly more than the abstract idea itself. See MPEP § 2106.05.
Claim 6
Step 1: A process, as above.
Step 2A Prong 1: The claim recites the abstract ideas as the judicial exception of claim 5.
Step 2A Prong 2: This judicial exception is not integrated into a practical application. The additional elements of the claim are as follows:
wherein recording the network performance and operational data includes recording network-traffic-related statistics resulting from the execution of the at least one network test and network conditions that resulted in the generation of the network-traffic-related statistics: These additional elements amount to insignificant extra-solution activity in the form of mere data gathering per MPEP § 2106.05(g).
Thus, the way in which the additional elements use or interact with the judicial exception do not integrate the judicial exception into a practical application.
Step 2B: The additional elements from Step 2A Prong 2 include insignificant extra-solution activity of data gathering recited by “wherein recording the network performance and operational data includes recording network-traffic-related statistics resulting from the execution of the at least one network test and network conditions that resulted in the generation of the network-traffic-related statistics” which are well-understood routine and conventional activities similar to presenting offers and gathering statistics per MPEP 2106.05(d)(II). Thus, the additional elements, viewed individually or in combination, do not provide an inventive concept or otherwise amount to significantly more than the abstract idea itself. See MPEP § 2106.05.
Claim 7
Step 1: A process, as above.
Step 2A Prong 1: The claim recites the abstract ideas as the judicial exception of claim 1.
Step 2A Prong 2: This judicial exception is not integrated into a practical application. The additional elements of the claim are as follows:
wherein generating the synthetic AI-implemented computer network behavioral model training data includes generating synthetic AI-implemented computer network behavioral model training dataset records: These additional elements amount to insignificant extra-solution activity in the form of mere data gathering per MPEP § 2106.05(g).
Thus, the way in which the additional elements use or interact with the judicial exception do not integrate the judicial exception into a practical application.
Step 2B: The additional elements from Step 2A Prong 2 include insignificant extra-solution activity of data gathering recited by “wherein generating the synthetic AI-implemented computer network behavioral model training data includes generating synthetic AI-implemented computer network behavioral model training dataset records” which are well-understood routine and conventional activities similar to presenting offers and gathering statistics per MPEP 2106.05(d)(II). Thus, the additional elements, viewed individually or in combination, do not provide an inventive concept or otherwise amount to significantly more than the abstract idea itself. See MPEP § 2106.05.
Claim 8
Step 1: A process, as above.
Step 2A Prong 1: The claim recites the abstract ideas as the judicial exception of claim 2.
Step 2A Prong 2: This judicial exception is not integrated into a practical application. The additional elements of the claim are as follows:
configuring the instrumented testbed environment to implement a network topology of a fidelity higher than a fidelity used to generate the sample AI-implemented computer network behavioral model training data: These additional elements recite only the idea of configuring the instrumented testbed environment to implement a network topology of a fidelity higher than a fidelity used to generate the sample AI-implemented computer network behavioral model training data and attempts to cover any implementation of configuring the instrumented testbed environment without any restriction as to how the instrumented testbed environment is specifically configured or how a network topology of a higher fidelity is specifically achieved. These additional elements do not meaningfully limit the claim and does not integrate the judicial exception into a practical application because this type of recitation is equivalent to the words “apply it”. See MPEP 2106.05(f). Thus, the way in which the additional elements use or interact with the judicial exception do not integrate the judicial exception into a practical application.
Step 2B: The additional elements from Step 2A Prong 2 include adding words equivalent to "apply it" with the judicial exception. Thus, the additional elements, viewed individually or in combination, do not provide an inventive concept or otherwise amount to significantly more than the abstract idea itself. See MPEP § 2106.05.
Claim 9
Step 1: A process, as above.
Step 2A Prong 1: The claim recites the abstract ideas of claim 1 as well as, inter alia:
wherein generating the test case definition includes using the scaling instructions to generate a network topology of a desired scale within the instrumented testbed environment: These limitations recite a mentally performable process with the aid of pen and paper of using judgement to generate a network topology of a desired scale within the instrumented testbed environment, using the scaling instructions.
Step 2A Prong 2: This judicial exception is not integrated into a practical application. The additional elements of the claim are as follows:
receiving, as input, scaling instructions: These additional elements amount to insignificant extra-solution activity in the form of mere data gathering per MPEP § 2106.05(g).
executing the at least one network test includes executing the at least one network test in the network topology of the desired scale: These additional elements recite only the idea of executing at least one network test in the network topology of the desired scale and attempts to cover any implementation of a network test without any restriction as to how the execution of the network test is completed or the type of network test that is used. These additional elements do not meaningfully limit the claim and does not integrate the judicial exception into a practical application because this type of recitation is equivalent to the words “apply it”. See MPEP 2106.05(f).
Thus, the way in which the additional elements use or interact with the judicial exception do not integrate the judicial exception into a practical application.
Step 2B: The additional elements from Step 2A Prong 2 include adding words equivalent to "apply it" with the judicial exception and insignificant extra-solution activity of data gathering recited by “receiving, as input, scaling instructions” which are well-understood routine and conventional activities similar to presenting offers and gathering statistics per MPEP 2106.05(d)(II). Thus, the additional elements, viewed individually or in combination, do not provide an inventive concept or otherwise amount to significantly more than the abstract idea itself. See MPEP § 2106.05.
Claim 10
Step 1: A process, as above.
Step 2A Prong 1: The claim recites the abstract ideas of claim 2 as well as, inter alia:
computing an error metric indicating a difference between the synthetic AI-implemented computer network behavioral model training data and the sample AI-implemented computer network behavioral model training data: These limitations recite mathematical calculations similar to calculating the difference between local and average data values per MPEP 2106.04(a)(2)(I)(C)(vi).
generating at least one updated network test in response to the error metric exceeding a threshold: These limitations recite a mentally performable process with the aid of pen and paper of using observation ad judgement to generate the configurations for at least one updated network test in response to the error metric exceeding a threshold.
and generating, as output and based on the network performance and operational data, updated synthetic AI-implemented computer network behavioral model training data: These limitations recite a mentally performable process with the aid of pen and paper of using observation, judgement, and evaluation to generate updated synthetic AI-implemented computer network behavioral model training data as output, based on the network performance and operational data.
Step 2A Prong 2: This judicial exception is not integrated into a practical application. The additional elements of the claim are as follows:
executing the at least one updated network test within the instrumented testbed environment: These additional elements recite only the idea of executing at least one updated network test within the instrumented testbed environment and attempts to cover any implementation of a network test without any restriction as to how the execution of the network test is completed or the type of network test that is used. These additional elements do not meaningfully limit the claim and does not integrate the judicial exception into a practical application because this type of recitation is equivalent to the words “apply it”. See MPEP 2106.05(f).
recording network performance and operational data generated by the execution of the at least one updated network test: These additional elements amount to insignificant extra-solution activity in the form of mere data gathering per MPEP § 2106.05(g).
Thus, the way in which the additional elements use or interact with the judicial exception do not integrate the judicial exception into a practical application.
Step 2B: The additional elements from Step 2A Prong 2 include adding words equivalent to "apply it" with the judicial exception and insignificant extra-solution activity of data gathering recited by “recording network performance and operational data generated by the execution of the at least one updated network test” which are well-understood routine and conventional activities similar to presenting offers and gathering statistics per MPEP 2106.05(d)(II). Thus, the additional elements, viewed individually or in combination, do not provide an inventive concept or otherwise amount to significantly more than the abstract idea itself. See MPEP § 2106.05.
Claims 11-19
Step 1: These claims are directed to “A system for generating synthetic artificial intelligence (AI)-implemented computer network behavioral model training data, the system comprising:”; therefore, it is directed to the statutory category of a machine.
Step 2A Prong 1: Claims 11-19 recite the same judicial exception as Claims 1-10, respectively.
Step 2A Prong 2: The judicial exception recited in these claims are not integrated into a practical application. The analysis at this step for 11-19 mirrors that of Claims 1-10, respectively.
Step 2B: The additional elements from Step 2A Prong 2 do not contain significantly more than the judicial exception for these claims. The analysis at this step for Claims 11-19 mirrors that of Claims 1-10, respectively.
Claim 20
Step 1: This claim recites "A non-transitory computer readable medium having stored thereon executable instructions that when executed by a processor of a computer control the computer to perform steps comprising:"; therefore, it is directed to the statutory category of an article of manufacture.
Step 2A Prong 1: Claim 20 recites the same judicial exception as Claim 1.
Step 2A Prong 2: The judicial exception recited in these claims are not integrated into a practical application. The only difference between Claim 20 and Claim 1, is that Claim 20 is directed to "A non-transitory computer readable medium having stored thereon executable instructions that when executed by a processor of a computer control the computer to perform steps comprising”. However, mere recitation that a judicial exception is to be performed using generic computer equipment in their ordinary capacity, i.e. a non-transitory computer readable medium having stored thereon executable instructions that when executed by a processor of a computer control the computer to perform steps comprising, cannot meaningfully integrate the judicial exception into a practical application. See MPEP 2106.05(f). With that exception, the analysis at this step for Claim 20 mirrors that of Claim 1.
Step 2B: The additional elements from Step 2A Prong 2 do not contain significantly more than the judicial exception for these claims. The only difference between Claim 20 and Claim 1, is that Claim 20 is directed to "A non-transitory computer readable medium having stored thereon executable instructions that when executed by a processor of a computer control the computer to perform steps comprising”. However, mere recitation that a judicial exception is to be performed using generic computer equipment in their ordinary capacity, i.e. a non-transitory computer readable medium having stored thereon executable instructions that when executed by a processor of a computer control the computer to perform steps comprising, cannot amount to significantly more than the judicial exception. See MPEP 2106.05(f). With that exception, the analysis at this step for Claim 20 mirrors that of Claim 1.
Claim Rejections - 35 USC § 103
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 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 1-20 are rejected under 35 U.S.C. 103 as being unpatentable over Pastor Perales et al. (US 11301778 B2, published 04/12/2022), hereafter Pastor, in view of Thai et al. (US 9600386 B2, published 03/21/2017), hereafter Thai.
Thai was cited in the IDS submitted 07/23/2025.
Regarding independent claim 1, Pastor teaches a method for generating synthetic artificial intelligence (AI)-implemented computer network behavioral model training data ([Abstract] teaches generating synthetic training data for machine learning algorithms applied to network behavioral modeling), the method comprising:
receiving, as input, sample AI-implemented computer network behavioral model training data or an AI-implemented computer network behavioral model training data definition ([Col. 10, Lines 60-66 & Col. 11, Lines 29-31] discusses selecting real or synthetic training data and further loading the labeled dataset as input for training and thus, represents receiving sample AI-implemented computer network behavioral model training data);
recording network performance and operational data ([Col. 9, Lines 9-12] discusses a probe module recording metadata from the received traffic during training, that includes real and synthetic traffic);
and generating, as output and based on the network performance and operational data, synthetic AI-implemented computer network behavioral model training data, wherein synthetic AI-implemented computer network behavioral model training data includes at least one parameter not included or defined in the AI-implemented computer network behavioral model training data or the AI-implemented computer network behavioral model training data definition ([Col. 4, Lines 12-15] discusses generating an output dataset based on each network flow, comprising information defined by the metadata and thus, represents the network performance and operational data; [Col. 10, Lines 27-31 & 35-42] discusses new fields can be added and included in the network flow, that were not previously defined or included in the input data; thus, the synthetic AI-implemented computer network behavioral model training data that is generated as output can include at least one parameter not previously included or defined).
Pastor does not explicitly teach generating, based on the input, a test case definition for configuring and controlling components of an instrumented testbed environment to execute at least one network test; executing the at least one network test within the instrumented testbed environment; and recording network performance and operational data generated from the execution of the at least one network test.
However, in a similar field of endeavor, Thai teaches a method for generating a test case definition for configuring and controlling components of an instrumented testbed environment to execute at least one network test ([Col. 3, Lines 24-31 & Col. 6, Lines 18-21] discusses using an input for generating a test case definition to configure and control testbed components); executing a network test within the instrumented testbed environment ([Col. 10, Lines 1-10 & Lines 36-47] discusses executing network tests within an instrumented testbed environment comprising real and emulated components); and recording network performance and operational data generated from the execution of the network test ([Col. 10-11, Lines 53-2] discusses recording network performance and operational data during test execution through the use of a data collection subsystem).
Because Pastor teaches receiving sample AI-implemented computer network behavioral model training data as input, recording network performance and operational data, and generating synthetic AI-implemented computer network behavioral model training data based on network performance and operational data and the training data includes at least one parameter not included or defined in the AI-implemented computer network behavioral model training data; and Thai teaches generating a test case definition for configuring and controlling components of an instrumented testbed environment to execute at least one network test, executing a network test within the instrumented testbed environment, and recording network performance and operational data generated from the execution of the network test, accordingly, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate generating a test case definition for configuring and controlling components of an instrumented testbed environment to execute at least one network test, executing a network test within the instrumented testbed environment, and recording network performance and operational data generated from the execution of the network test as taught by Thai into Pastor’s computer-implemented method, with a reasonable expectation of success, to teach receiving, as input, sample AI-implemented computer network behavioral model training data or an AI-implemented computer network behavioral model training data definition; generating, based on the input, a test case definition for configuring and controlling components of an instrumented testbed environment to execute at least one network test; executing the at least one network test within the instrumented testbed environment; recording network performance and operational data generated from the execution of the at least one network test; and generating, as output and based on the network performance and operational data, synthetic AI-implemented computer network behavioral model training data, wherein synthetic AI-implemented computer network behavioral model training data includes at least one parameter not included or defined in the AI-implemented computer network behavioral model training data or the AI-implemented computer network behavioral model training data definition. This combination would have been motivated by the desire to employ a virtual testbed which is a replicated environment that is used to validate the performance or effects of a target network or an aspect of its design—e.g., proposed networked computer platforms, software applications, network protocols and topologies, routing, application Quality of Service (QoS), etc. As described below, network testbed 106 is configured via network testbed configuration logic 104 to completely (or near completely) replicate the design, scale and complexity of target network (Thai [Col. 2, Lines 40-48]).
Regarding dependent claim 2, the combination of Pastor and Thai teaches the claimed invention as claimed in claim 1, including wherein receiving, as input, sample AI-implemented computer network behavioral model training data or an AI-implemented computer network behavioral model training data definition includes receiving the sample AI-implemented computer network behavioral model training data as input (Pastor [Col. 10, Lines 60-66 & Col. 11, Lines 29-31] discusses selecting real or synthetic training data and further loading the labeled dataset as input for training and thus, represents receiving sample AI-implemented computer network behavioral model training data).
Regarding dependent claim 3, the combination of Pastor and Thai teaches the claimed invention as claimed in claim 1, including wherein receiving, as input, sample AI-implemented computer network behavioral model training data or an AI-implemented computer network behavioral model training data definition includes receiving the AI-implemented computer network behavioral model training data definition as input (Thai [Col. 4, Lines 45-53] discusses receiving a training data definition as input, in the form of a structured description of what the testbed should produce).
Regarding dependent claim 4, the combination of Pastor and Thai teaches the claimed invention as claimed in claim 1, including wherein generating the test case definition includes generating instructions for configuring the components of the instrumented testbed environment to implement a network topology (Thai [Col. 6, Lines 6-8] discusses provisioning logic ensures that the testbed nodes replicate the topology and runtime characteristics of the target network; Thai [Col. 6, Lines 30-35] discusses the logic receiving testbed information which includes the design of the environment and information to be executed and thus, instructions are generated for configuring the components of the instrumented testbed environment).
Regarding dependent claim 5, the combination of Pastor and Thai teaches the claimed invention as claimed in claim 4, including wherein executing the at least one network test includes transmitting network traffic within the network topology (Thai [Col. 10, Lines 38-44] discusses testbed nodes acting as network traffic generators during a network test, and can generate random data streams to create a noisy testbed network and thus, executing a network test includes transmitting network traffic within the topology).
Regarding dependent claim 6, the combination of Pastor and Thai teaches the claimed invention as claimed in claim 5, including wherein recording the network performance and operational data includes recording network-traffic-related statistics resulting from the execution of the at least one network test and network conditions that resulted in the generation of the network-traffic-related statistics (Thai [Col. 10, Lines 53-63] discusses monitoring the network traffic to generate and record network and device status information, and the experiment data for the execution of testbed node sets is generated based on the status information; Thai [Col. 10-11, Lines 65-2] also discusses the data collection system recording network-traffic-related statistics).
Regarding dependent claim 7, the combination of Pastor and Thai teaches the claimed invention as claimed in claim 1, including wherein generating the synthetic AI-implemented computer network behavioral model training data includes generating synthetic AI-implemented computer network behavioral model training dataset records (Pastor [Col. 10, Lines 42-47] discusses the network flow is transformed into a suitable dataset record, usually in matrix or table format, to be used for ML algorithms).
Regarding dependent claim 8, the combination of Pastor and Thai teaches the claimed invention as claimed in claim 2, including configuring the instrumented testbed environment to implement a network topology of a fidelity higher than a fidelity used to generate the sample AI-implemented computer network behavioral model training data (Thai [Col. 3, Lines 46-57] discusses configuring a high density of virtual testbed network devices per physical node which allows for a highly scalable environment with less physical hardware than prior art and manually built environments; Thai [Abstract] discusses these virtual testbed network devices allow for large and topologically complex test networks and thus, allows for network testing at a higher fidelity than a fidelity used to generate the sample AI-implemented computer network behavioral model training data).
Regarding dependent claim 9, the combination of Pastor and Thai teaches the claimed invention as claimed in claim 1, including receiving, as input, scaling instructions and wherein generating the test case definition includes using the scaling instructions to generate a network topology of a desired scale within the instrumented testbed environment and executing the at least one network test includes executing the at least one network test in the network topology of the desired scale (Thai [Col. 4, Lines 61-64] discusses receiving a description of topology as an input, and further, Thai [Col. 6, Lines 6-8] discusses the logic ensures that the testbed nodes replicate the inputted topology and runtime characteristics of the target network for subsequent execution and thus, the method receives scaling instructions as input to generate a network topology of a desired scale and executes a network test in the generated topology).
Regarding dependent claim 10, the combination of Pastor and Thai teaches the claimed invention as claimed in claim 2, including computing an error metric indicating a difference between the synthetic AI-implemented computer network behavioral model training data and the sample AI-implemented computer network behavioral model training data, generating at least one updated network test in response to the error metric exceeding a threshold, executing the at least one updated network test within the instrumented testbed environment, recording network performance and operational data generated by the execution of the at least one updated network test; and generating, as output and based on the network performance and operational data, updated synthetic AI-implemented computer network behavioral model training data (Pastor [Col. 11, Lines 40-47] discusses computing an error metric between synthetic training outputs against known labeled data; Pastor [Col. 4, Step (v)] discusses if the metric comparison is negative, generate another trained model with the collected information and update the data after executing a test within the redesigned instrumented testbed environment; and this threshold is further defined by Pastor [Col. 8, Lines 54-55] discussing that a ML model redesign is required when negative results are gathered; thus, this constitutes generating updated network tests; Thai [Col. 10, Lines 64-67] discusses recording network performance and operational data of each network test; Pastor [Col. 4, Lines 12-15] discusses generating an output dataset based on each network flow, comprising information defined by the metadata and thus, represents the network performance and operational data; and Pastor [Col. 11, Lines 47-50] discusses this process may continue until the threshold is met; thus, the method can iteratively generate output updated synthetic AI-implemented computer network behavioral model training data based on the network performance and operational data).
Regarding claims 11-19, claims 11-19 are system claims that are substantially the same as the method of claims 1-10, respectively. Therefore, claims 11-19 are rejected for the same reasons as claims 1-10.
Regarding dependent claim 20, claim 20 is a non-transitory computer-readable storage medium claim that is substantially the same as the method of Claim 1. Therefore, claim 20 is rejected for the same reasons as Claim 1.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Rangappagowda et al. (US 2018/00662972, published 03/01/2018) ([Abstract] The method further includes configuring, by the test controller, the first test agent to execute a network test. The method further includes executing, by the first test agent, the network test. The method further includes reporting results of execution of the network test to the test controller)
Bothe et al. (US 2025/0007813 A1, filed 06/27/2023) ([Abstract] Application and network tests executed for paths between endpoints and an application are proxied to reduce network traffic sent to application as part of synthetic testing; [0002] Network or application performance testing may be achieved through monitoring of actual network traffic (e.g., with packet capture) and/or through synthetic monitoring. Synthetic monitoring, also referred to as synthetic testing, refers to generation of network traffic that targets a specific network-accessible destination to be tested, such as a network element or an application or service, and analysis of performance metrics and/or responses captured or observed as a result).
Any inquiry concerning this communication or earlier communications from the examiner should be directed to RILEY S ACOSTA whose telephone number is (571)272-8714. The examiner can normally be reached Monday-Thursday 6am-4pm.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Jennifer N Welch can be reached at (571)272-7212. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/RILEY S ACOSTA/Examiner, Art Unit 2143
/JENNIFER N WELCH/Supervisory Patent Examiner, Art Unit 2143
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