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 .
This office action is in response to communication filed on June 05, 2026.
Status of claims within the present application:
Claims 1 – 20 are pending.
Claims 1 – 2, 5, 10, 12 – 13, 16 and 20 are amended.
Response to Amendment
Applicant’s arguments with respect to claims 1 – 20 have been considered but are moot because the new ground of rejection does not rely on any reference applied in the prior rejection of record for any teaching or matter specifically challenged in the argument.
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 US 20240330482 A1 to Agarwwal in view of US 20250348593 A1 to Taneja et al., (hereinafter, “Taneja”) and US 10740469 B2 to Zheng et al., (hereinafter, “Zheng”).
Regarding claim 1, Agarwwal teaches a system for measuring threat levels of applications, the system comprising: one or more processors; and a non-transitory computer-readable storage medium storing instructions, [Agarwwal, para. 84 discloses a representative example of a threat modeling process (process) (method) 200 includes generating a threat model (model) 208 for any application, process, or system under consideration. By non-limiting example, this could include modeling the possible threats to commuting to work safely, modeling the possible threats to preventing the spread of an infectious disease, or modeling the possible attacks on a computing environment (cybersecurity). Model 208 is used to generate an original threat report (report) 214 which in implementations includes identified threats, the status of identified threats (threat status), and the source(s) of identified threats, among other things.] which when executed by the one or more processors cause the one or more processors to: receiving a code base and an infrastructure diagram associated with an application, [Agarwwal, para. 10 discloses providing one or more first data stores communicatively coupled with the processor; analyzing the code file to identify one or more properties, of the plurality of properties, associated with the one or more resources included in the code file;] wherein the code base comprises computer code for the application and the infrastructure diagram comprises a representation of infrastructure components for the application; [Agarwwal, para. 14 discloses generating a mapping of the one or more resources included in the code file to one or more components of the plurality of threat model components; and generating the threat model based on the generated mapping and the information stored in the one or more second data stores. Para. 208 discloses The system 100, in implementations, includes an infrastructure as a code deployment system that generates a threat model from a code file. The system comprises one or more first data stores (knowledge base 1325) configured to store information on a plurality of properties to be configured for one or more resources included in the code file and a plurality of security threats associated with one or more values of the plurality of properties] generating, based on the plurality of potential threats, risk data for the plurality of potential threats, wherein the risk data identifies for each potential threat one or more potential risks associated with a corresponding potential threat; [Agarwwal, para. 10 discloses a method for generating a threat model from a code file comprises providing one or more first data stores communicatively coupled with the processor; analyzing the code file to identify one or more properties, of the plurality of properties, associated with the one or more resources included in the code file; for each property of the identified one or more properties, identifying a value for the property defined in the code file and determining one or more security threats based on the identified value for the property, using the information stored in the one or more first data stores; and generating a threat model for the one or more resources based on the determined one or more security threats.] retrieving risk mitigation data associated with the code base and the infrastructure diagram, wherein the risk mitigation data represents one or more potential threats that were addressed for the code base and the infrastructure diagram; [Agarwwal, para. 13 discloses the method further comprises identifying one or more dubious properties that generated each security threat of the one or more security threats and identifying, using the information stored in the one or more first data stores, a modified value for each of the identified one or more dubious properties that generated each security threat to mitigate the one or more security threats.] and generating a threat level for the application based on the unmitigated risks [Agarwwal, para. 86 discloses the system also retrieves from the database relevant threats 210 that were previously correlated with the chosen components or combinations of components through the database, to form the threat model 208 (this is representatively illustrated by the arrow between “threats” and “relevant threats”). The threat model thus includes relevant threats and the relevant sources of those threats. The threat model is used to generate a threat report 214.], but Agarwwal does not teach determining risk parameters within the risk data that are not within the risk mitigation data to determine one or more risk identifiers corresponding to unmitigated risks indicated by the risk parameters; inputting the code base and the infrastructure diagram into a machine learning model to obtain a plurality of potential threats associated with the application, wherein the machine learning model has been trained to identify threats within code bases and infrastructure diagrams;
However, Taneja does teach inputting the code base and the infrastructure diagram into a machine learning model to obtain a plurality of potential threats associated with the application, wherein the machine learning model has been trained to identify threats within code bases and infrastructure diagrams; [Taneja, para. 45 discloses the processing engine 144 may execute the one or more generative machine-learning models 168, such as one or more of a language model (LM), a large language model (LLM), one or more transformer-based machine-learning models, one or more sequence-to-sequence (Seq2Sec) models, or other one or more generative machine-learning models 168. In particular embodiments, the interactions 164 may include user interaction data captured in relation to a live natural language exchange session conducted electronically between the user 102 and an external user. In particular embodiments, the processing engine 144 may further train the one or more generative machine-learning models 168 based on the user activity and interaction data 153, interactions 164, and the architecture and characteristics associated with respective software applications 151, and cyberthreat intelligence (CTI) data.]
Therefore, it would have been obvious to one of ordinary skill within the art before the effective filling date to combine Taneja’s system with Agarwwal’s system, with a motivation for the workflow of the dynamic remote based isolation and threat detection system 200 may then proceed with executing one or more generative machine-learning models 204 trained to generate a prediction of one or more cyber threat scenarios based the set of application environment parameters 202 and the data set of raw cyberthreat data 218. For example, in accordance with the presently disclosed embodiments, the prediction of the one or more cyber threat scenarios may be specific to the particular software application 151. [Taneja, para. 61]
However, Agarwwal in view of Taneja does not teach determining risk parameters within the risk data that are not within the risk mitigation data to determine one or more risk identifiers corresponding to unmitigated risks indicated by the risk parameters;, but Zheng does teach determining risk parameters within the risk data that are not within the risk mitigation data to determine one or more risk identifiers corresponding to unmitigated risks indicated by the risk parameters; [Zheng, col. 3 lines 49 – 59 discloses determining an initial set of one or more security requirements for the software application under development based upon the plurality of technical attributes, each security requirement associated with a mitigation plan that, when implemented in the software application under development, resolves a security threat; correlating the initial set of security requirements to one or more of the security threats in the threat model to determine which of the security threats are mitigated by the mitigation plan; and generating a final set of security requirements based upon the security threats left unmitigated.]
Therefore, it would have been obvious to one of ordinary skill within the art before the effective filling date to combine Zheng’s system with modified Agarwwal’s system, with a motivation for Each security requirement is associated with a mitigation plan which, when implemented in the software application under development, will resolve or otherwise address a security risk raised by the security requirement. The security requirements can be correlated to one or more threats as defined by the threat model, so that the system can analyze which requirement, when implemented, will mitigate which threat, and therefore determine the applicable requirements for a given application (e.g., using the mitigation plan). Each requirement can be rated with priority such as High/Medium/Low, or Required/Desired. [Zheng, col. 7 lines 14 – 25]
Regarding claim 2, it recites features similar to features within claim 1, therefore, they are rejected in a similar manner.
Regarding claim 3, modified Agarwwal teaches the method of claim 2, further comprising: determining, based on the code base, an infrastructure diagram associated with the application, wherein the infrastructure diagram comprises a representation of infrastructure components for the application; [Agarwwal, para. 208 discloses the system comprises one or more first data stores (knowledge base 1325) configured to store information on a plurality of properties to be configured for one or more resources included in the code file and a plurality of security threats associated with one or more values of the plurality of properties. The system also includes one or more memories configured to store instructions and one or more computing devices communicatively connected to the one or more first data stores and the one or more memories.], but Agarwwal does not teach inputting the infrastructure diagram into the machine learning model, wherein the machine learning model is further trained to identify the threats based on both a corresponding code base and a corresponding infrastructure diagram.
However, Taneja does teach inputting the code base and the infrastructure diagram into a machine learning model to obtain a plurality of potential threats associated with the application, wherein the machine learning model has been trained to identify threats within code bases and infrastructure diagrams; [Taneja, para. 45 discloses the processing engine 144 may execute the one or more generative machine-learning models 168, such as one or more of a language model (LM), a large language model (LLM), one or more transformer-based machine-learning models, one or more sequence-to-sequence (Seq2Sec) models, or other one or more generative machine-learning models 168. In particular embodiments, the interactions 164 may include user interaction data captured in relation to a live natural language exchange session conducted electronically between the user 102 and an external user. In particular embodiments, the processing engine 144 may further train the one or more generative machine-learning models 168 based on the user activity and interaction data 153, interactions 164, and the architecture and characteristics associated with respective software applications 151, and cyberthreat intelligence (CTI) data.]
Therefore, it would have been obvious to one of ordinary skill within the art before the effective filling date to combine Taneja’s system with Agarwwal’s system, with a motivation for the workflow of the dynamic remote based isolation and threat detection system 200 may then proceed with executing one or more generative machine-learning models 204 trained to generate a prediction of one or more cyber threat scenarios based the set of application environment parameters 202 and the data set of raw cyberthreat data 218. For example, in accordance with the presently disclosed embodiments, the prediction of the one or more cyber threat scenarios may be specific to the particular software application 151. [Taneja, para. 61]
As per claim 4, modified Agarwwal teaches the method of claim 2, wherein generating the risk data for the plurality of potential threats further comprises: receiving, from the machine learning model, a plurality of risk identifiers associated with a plurality of risks; [Agarwwal, para. 163 discloses Step 1404 includes system generation of a threat report for each component or component group and step 1406 includes system combination of the individual threat reports into a comprehensive threat report for the overall diagrammed system/process (this would include, for example, including threat report elements for nested or chained threat models, as has been explained above), and then steps 1408-1414 include steps which may occur in any order. In step 1408, once a user has selected an asset to analyze, the system summarizes data to show all attack vectors associated with threats which may compromise that asset.] transmitting, to a database, a request for a plurality of risk parameters associated with the plurality of risks, wherein the request comprises the plurality of risk identifiers; [Agarwwal, para. 109 discloses If any specific threat model is selected it will be highlighted and its associated threat report (threat report interface) 1302 will be displayed, which will be discussed hereafter. From the top menu items the user may select the new selector to create a new threat model, the edit selector to edit the name, version, risk level, an “internal” toggle, and labels associated with the selected threat model, a delete selector to delete the selected threat model, a diagram selector to view the diagram for the selected threat model, a report selector to export to PDF the threat report (which shows for each threat the threat name, source, risk level, status, and creation date), a threat tree selector to view a diagrammed threat tree, showing threats of the threat model, and other selectors already described.] and receiving, from the database, the risk data, wherein the risk data comprises the plurality of risk parameters. [Agarwwal, para. 163 discloses once a user has selected an asset to analyze, the system summarizes data to show all attack vectors associated with threats which may compromise that asset. At step 1410 the user analyzes the various attack vectors to determine what compensating controls may be included to protect the asset. At step 1412 the user adds or removes compensating controls to/from the diagram and/or toggles compensating controls between ON/OFF states. At step 1414 the user determines the effectiveness of the compensating controls or other risk management methods (such as changing communication protocols, changing the relative location of the asset within the modeled environment, adding non-compensating control elements between the asset and attack locations, and so forth).]
Regarding claim 5, modified Agarwwal teaches the method of claim 2, wherein receiving the risk mitigation data comprises: receiving natural language input describing one or more risk mitigation mechanisms associated with the application; [Agarwwal, para. 13 discloses the method further comprises identifying one or more dubious properties that generated each security threat of the one or more security threats and identifying, using the information stored in the one or more first data stores, a modified value for each of the identified one or more dubious properties that generated each security threat to mitigate the one or more security threats.] and generating the risk mitigation data based on the plurality of risk mitigation parameters. [Agarwwal, para. 86 discloses the system also retrieves from the database relevant threats 210 that were previously correlated with the chosen components or combinations of components through the database, to form the threat model 208 (this is representatively illustrated by the arrow between “threats” and “relevant threats”). The threat model thus includes relevant threats and the relevant sources of those threats. The threat model is used to generate a threat report 214.], but Agarwwal does not teach inputting the natural language input, into a natural language processing model to obtain a plurality of risk mitigation parameters associated with the application;
However, Taneja does teach inputting the natural language input, into a natural language processing model to obtain a plurality of risk mitigation parameters associated with the application; [Taneja, para. 45 discloses the processing engine 144 may execute the one or more generative machine-learning models 168, such as one or more of a language model (LM), a large language model (LLM), one or more transformer-based machine-learning models, one or more sequence-to-sequence (Seq2Sec) models, or other one or more generative machine-learning models 168. In particular embodiments, the interactions 164 may include user interaction data captured in relation to a live natural language exchange session conducted electronically between the user 102 and an external user. In particular embodiments, the processing engine 144 may further train the one or more generative machine-learning models 168 based on the user activity and interaction data 153, interactions 164, and the architecture and characteristics associated with respective software applications 151, and cyberthreat intelligence (CTI) data.]
Therefore, it would have been obvious to one of ordinary skill within the art before the effective filling date to combine Taneja’s system with Agarwwal’s system, with a motivation for the workflow of the dynamic remote based isolation and threat detection system 200 may then proceed with executing one or more generative machine-learning models 204 trained to generate a prediction of one or more cyber threat scenarios based the set of application environment parameters 202 and the data set of raw cyberthreat data 218. For example, in accordance with the presently disclosed embodiments, the prediction of the one or more cyber threat scenarios may be specific to the particular software application 151. [Taneja, para. 61]
As per claim 6, modified Agarwwal teaches the method of claim 5, further comprising generating, based on the risk data and the risk mitigation data, a threat model for the application, wherein the threat model comprises risk parameters associated with the risk data, the plurality of risk mitigation parameters, and a description associated with the code base. [Agarwwal, para. 86 discloses the system also retrieves from the database relevant threats 210 that were previously correlated with the chosen components or combinations of components through the database, to form the threat model 208 (this is representatively illustrated by the arrow between “threats” and “relevant threats”). The threat model thus includes relevant threats and the relevant sources of those threats. The threat model is used to generate a threat report 214. Para. 14 discloses generating the threat model further comprises providing one or more second data stores communicatively coupled with the processor, the one or more second data stores storing information on a plurality of threat model components, and a plurality of threats, wherein each threat of the plurality of threats is associated with at least one of the components of the plurality of threat model components; generating a mapping of the one or more resources included in the code file to one or more components of the plurality of threat model components; and generating the threat model based on the generated mapping and the information stored in the one or more second data stores.]
As per claim 7, modified Agarwwal teaches the method of claim 6, further comprising: providing the threat model to an operator with a prompt to edit the threat model, wherein the prompt comprises a plurality of user interface elements that enable the operator to edit a plurality of components of the threat model, and wherein the plurality of components of the threat model comprises the risk parameters, the plurality of risk mitigation parameters, the code base, or an infrastructure diagram; [Agarwwal, para. 90 discloses A modified data store 302 includes data store 206 but also includes compensating controls 304 stored in the database. The stored compensating controls include, by non-limiting example, a title, definition, image, and/or other items for each compensating control. Each compensating control may be associated with one or more threats and/or with one or more components and/or with one or more security requirements through the database (security requirements may in turn be associated with one or more components and/or one or more threats through the database). Method 300 includes user selection of one or more compensating controls (relevant compensating controls 308) from among all compensating controls 304 stored in the database, and the relevant compensating controls together with the threat model 208 previously discussed (in other words the relevant threats 210 and relevant sources 212) are included in the modified threat model 306. Modified threat model is used to generate modified threat report 310.] receiving, from the operator, a plurality of changes to the threat model; [Agarwwal, para. 90 discloses method 300 includes user selection of one or more compensating controls (relevant compensating controls 308) from among all compensating controls 304 stored in the database, and the relevant compensating controls together with the threat model 208 previously discussed (in other words the relevant threats 210 and relevant sources 212) are included in the modified threat model 306.] and recalculating the threat level based on the plurality of changes. [Agarwwal, para. 90 discloses modified threat model is used to generate modified threat report 310.]
Regarding claim 8, modified Agarwwal teaches the method of claim 7, wherein generating the threat level for the application comprises: receiving the threat level from the machine learning model [Agarwwal, para. 163 discloses once a user has selected an asset to analyze, the system summarizes data to show all attack vectors associated with threats which may compromise that asset. At step 1410 the user analyzes the various attack vectors to determine what compensating controls may be included to protect the asset. At step 1412 the user adds or removes compensating controls to/from the diagram and/or toggles compensating controls between ON/OFF states. At step 1414 the user determines the effectiveness of the compensating controls or other risk management methods (such as changing communication protocols, changing the relative location of the asset within the modeled environment, adding non-compensating control elements between the asset and attack locations, and so forth).], but Agarwwal does not teach wherein generating the threat level for the application comprises: inputting the risk parameters and the plurality of risk mitigation parameters into a threat level generation machine learning model, wherein the threat level generation machine learning model has been trained to generate threat levels based on the risk data and the plurality of risk mitigation parameters;.
However, Taneja does teach wherein generating the threat level for the application comprises: inputting the risk parameters and the plurality of risk mitigation parameters into a threat level generation machine learning model, wherein the threat level generation machine learning model has been trained to generate threat levels based on the risk data and the plurality of risk mitigation parameters; [Taneja, para. 45 discloses the processing engine 144 may execute the one or more generative machine-learning models 168, such as one or more of a language model (LM), a large language model (LLM), one or more transformer-based machine-learning models, one or more sequence-to-sequence (Seq2Sec) models, or other one or more generative machine-learning models 168. In particular embodiments, the interactions 164 may include user interaction data captured in relation to a live natural language exchange session conducted electronically between the user 102 and an external user. In particular embodiments, the processing engine 144 may further train the one or more generative machine-learning models 168 based on the user activity and interaction data 153, interactions 164, and the architecture and characteristics associated with respective software applications 151, and cyberthreat intelligence (CTI) data.]
Therefore, it would have been obvious to one of ordinary skill within the art before the effective filling date to combine Taneja’s system with Agarwwal’s system, with a motivation for the workflow of the dynamic remote based isolation and threat detection system 200 may then proceed with executing one or more generative machine-learning models 204 trained to generate a prediction of one or more cyber threat scenarios based the set of application environment parameters 202 and the data set of raw cyberthreat data 218. For example, in accordance with the presently disclosed embodiments, the prediction of the one or more cyber threat scenarios may be specific to the particular software application 151. [Taneja, para. 61]
As per claim 9, modified Agarwwal teaches the method of claim 2, wherein receiving the risk mitigation data associated with the code base comprises: retrieving, from a risk mitigation database, a plurality of potential risk mitigations; [Agarwwal, para. 13 discloses the method further comprises identifying one or more dubious properties that generated each security threat of the one or more security threats and identifying, using the information stored in the one or more first data stores, a modified value for each of the identified one or more dubious properties that generated each security threat to mitigate the one or more security threats.] determining a plurality of operators associated with the application; [Agarwwal, para. 17 discloses determining one or more security threats based on the identified value for the property, using the information stored in the one or more data stores; identifying one or more dubious properties that generated each security threat of the one or more security threats; and displaying, on a user interface, an indication of the one or more dubious properties that generated each security threat of the one or more security threats.] transmitting a plurality of representations of the plurality of potential risk mitigations to the plurality of operators; [Agarwwal, para. 109 discloses If any specific threat model is selected it will be highlighted and its associated threat report (threat report interface) 1302 will be displayed, which will be discussed hereafter. From the top menu items the user may select the new selector to create a new threat model, the edit selector to edit the name, version, risk level, an “internal” toggle, and labels associated with the selected threat model, a delete selector to delete the selected threat model, a diagram selector to view the diagram for the selected threat model, a report selector to export to PDF the threat report (which shows for each threat the threat name, source, risk level, status, and creation date), a threat tree selector to view a diagrammed threat tree, showing threats of the threat model, and other selectors already described.] and receiving, from the plurality of operators a set of risk mitigation representations representing the risk mitigation data. [Agarwwal, para. 90 discloses A modified data store 302 includes data store 206 but also includes compensating controls 304 stored in the database. The stored compensating controls include, by non-limiting example, a title, definition, image, and/or other items for each compensating control. Each compensating control may be associated with one or more threats and/or with one or more components and/or with one or more security requirements through the database (security requirements may in turn be associated with one or more components and/or one or more threats through the database). Method 300 includes user selection of one or more compensating controls (relevant compensating controls 308) from among all compensating controls 304 stored in the database, and the relevant compensating controls together with the threat model 208 previously discussed (in other words the relevant threats 210 and relevant sources 212) are included in the modified threat model 306. Modified threat model is used to generate modified threat report 310.]
As per claim 10, modified Agarwwal teaches the method of claim 2, wherein generating the threat level for the application further comprises: determining a difference between the risk data and the risk mitigation data, [Agarwwal, para. 10 discloses for each property of the identified one or more properties, identifying a value for the property defined in the code file and determining one or more security threats based on the identified value for the property, using the information stored in the one or more first data stores;] wherein the difference comprises a plurality of difference parameters that are within the risk data and are not within the risk mitigation data; [Agarwwal, para. 10 discloses analyzing the code file to identify one or more properties, of the plurality of properties, associated with the one or more resources included in the code file;] executing a database lookup using an identifier associated with each difference parameter of the plurality of difference parameters to retrieve a plurality of scores corresponding to the plurality of difference parameters; [Agarwwal, para. 219 discloses where the user is expected to define specific parameters that allow communication to and from the resource being deployed. The system uses the knowledge database to determine whether the input parameters “FromPort” and “ToPort” provide secure information and communication flow from and to the resource. Para. 220 discloses when a user is in the process of typing an IAC snippet, the system can dynamically, and in real-time, check the defined property configurations against the knowledge base to identify dubious properties whose defined values may generate security threats, as discussed in the preceding paragraphs. The user also has the ability to make changes as and when they type their code. Para. 221 discloses where the user understands the risk and is willing to accept the security threat, the system will generate a threat model while the user is in the process of typing code. The system will identify security threats (presented by the knowledge database), and present the risk mitigated (of the threat) by each security property. If the user is willing to take the risk of threat, the security property, for the architecture in question will not be applicable and the user typing the IAC will not be blocked by those security parameters.] and generating the threat level based on the plurality of scores corresponding to the plurality of difference parameters. [Agarwwal, para. 10 discloses generating a threat model for the one or more resources based on the determined one or more security threats. Para. 84 discloses generating a threat model (model) 208 for any application, process, or system under consideration. By non-limiting example, this could include modeling the possible threats to commuting to work safely, modeling the possible threats to preventing the spread of an infectious disease, or modeling the possible attacks on a computing environment (cybersecurity). Model 208 is used to generate an original threat report (report) 214 which in implementations includes identified threats, the status of identified threats (threat status), and the source(s) of identified threats, among other things.]
As per claim 11, modified Agarwwal teaches the method of claim 10, further comprising: generating a message comprising the plurality of difference parameters; [Agarwwal, para. 13 discloses the method further comprises identifying one or more dubious properties that generated each security threat of the one or more security threats and identifying, using the information stored in the one or more first data stores, a modified value for each of the identified one or more dubious properties that generated each security threat to mitigate the one or more security threats.] and transmitting the message to one or more operators. [Agarwwal, para. 13 discloses displaying, on the user interface, an indication of the modified value of the one or more dubious properties that generated each security threat of the one or more security threats.]
Regarding claim 12, modified Agarwwal teaches the method of claim 2, further comprising: splitting the code base according to a plurality of functions within the code base; [Agarwwal, para. 10 discloses analyzing the code file to identify one or more properties, of the plurality of properties, associated with the one or more resources included in the code file;] determining a corresponding type associated with each function; [Agarwwal, para. 17 discloses determining one or more security threats based on the identified value for the property, using the information stored in the one or more data stores;], but Agarwwal does not teach inputting the corresponding type associated with each function into the machine learning model.
However, Taneja does teach inputting the corresponding type associated with each function into the machine learning model. [Taneja, para. 45 discloses the processing engine 144 may execute the one or more generative machine-learning models 168, such as one or more of a language model (LM), a large language model (LLM), one or more transformer-based machine-learning models, one or more sequence-to-sequence (Seq2Sec) models, or other one or more generative machine-learning models 168. In particular embodiments, the interactions 164 may include user interaction data captured in relation to a live natural language exchange session conducted electronically between the user 102 and an external user. In particular embodiments, the processing engine 144 may further train the one or more generative machine-learning models 168 based on the user activity and interaction data 153, interactions 164, and the architecture and characteristics associated with respective software applications 151, and cyberthreat intelligence (CTI) data.]
Therefore, it would have been obvious to one of ordinary skill within the art before the effective filling date to combine Taneja’s system with Agarwwal’s system, with a motivation for the workflow of the dynamic remote based isolation and threat detection system 200 may then proceed with executing one or more generative machine-learning models 204 trained to generate a prediction of one or more cyber threat scenarios based the set of application environment parameters 202 and the data set of raw cyberthreat data 218. For example, in accordance with the presently disclosed embodiments, the prediction of the one or more cyber threat scenarios may be specific to the particular software application 151. [Taneja, para. 61]
Regarding claim 13, it recites features similar to features within claim 1, therefore, they are rejected in a similar manner.
Regarding claims 14 – 19, they recite features similar to features within claims 3 – 8, therefore, they are rejected in a similar manner.
Regarding claim 20, it recites features similar to features within claim 10, therefore, they are rejected in a similar manner.
Conclusion
Pertinent prior art made of record however not relied upon:
US 9235493 B2 to Goetsch
“Systems and methods for peer-based code quality analysis reporting are provided. In accordance with an embodiment, a system can include a peer-based code quality analysis tool, executing on a computer, which is configured to receive one or more source code files, and perform an analysis of the one or more source code files based on a plurality of rules. The peer-based code quality analysis tool can further compare results of the analysis to peer results data to determine a percentile based score, and create an interactive report which includes the results of the analysis and the percentile based score.”
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
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/P.P./Patent Examiner, Art Unit 2408
/LINGLAN EDWARDS/Supervisory Patent Examiner, Art Unit 2408
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