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 .
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 03/30/2026 has been entered.
Status of the Claims
Claim 1 has been amended. Claims 1-6, 8, 22-25, and 27 are currently pending and have been considered by the Examiner.
Claim Objections
Claim 27 is objected to because of the following informalities: Line 3 appears to be missing a period. Appropriate correction is required.
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.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claims 1-6, 8, 22-25, and 27 are rejected under 35 U.S.C. 103 as being unpatentable over Morton et al. (US 20170304707 A1, cited in the PTO-892 issued 06/30/2021) in view of Lippmann et al. (US 20050138413 A1, cited in the PTO-892 issued 07/24/2025) and Duell et al. (US 20170064012 A1, cited in the PTO-892 issued 07/24/2025).
Regarding claim 1, Morton teaches: A computer-executable method of automating at least one agent for at least one simulated Information Technology (IT) environment, the at least one agent being an automated and/or IT agent, ([0013] and [0015], lines 1-4 disclose an artificial intelligence opponent (“automated agent”) operating within a realistic virtual environment (“simulated IT environment”))
the method being executed by a computer having a processor and a data storage or a memory and comprising: (Morton’s Claim 1 on p. 25, col. 2, lines 6-9)
generating, via the processor, the at least one simulated IT environment having a plurality of computer-virtualized and/or real networked IT systems interconnected therebetween via a defined network topology, wherein the simulated IT environment models hardware and software components of real-world IT systems; ([0064], [0142] and [0594] disclose generating the simulated IT environment having a defined network topology. [0130], lines 8-13 and [0491] disclose computers (“hardware”) and software sensors.)
deploying and executing, in the simulated IT environment, at least one software-based agent instantiated in a virtualized execution environment, the agent configured to interact with the networked IT systems by transmitting and receiving data packets, executing system configurations, and performing operational tasks; ([0110], last 4 lines, all of [0113], and [0532], lines 1-25 discloses an AI opponent, which is a software-based agent. “Transmitting data packets” includes providing messages to trainers and students and “receiving data packets” includes parsing logs and network messages as disclosed in [0532]. “Executing system configurations, and performing operational tasks” includes making operational changes and making changes automatically to the virtual environment in an attempt to remedy a potential breach, as disclosed in [0532].)
loading, via the processor, at least one action determination model for the at least one agent that indicates a sequence of actions to be executed by the at least one agent, the indication being determined by at least one objective of the at least one agent, the at least one action determination model being structured as a
[0110], last 7 lines and [0122] disclose that an AI engine implements an AI opponent which provides actions/responses to the game engine, and which makes decisions.
With respect to the limitation of “a sequence of actions”, [0532], lines 10-25 disclose the following sequence of actions: the system detects a data change and a set of unexpected messages in a cyber threat scenario, it attempts to deduce from a knowledge database the implications of such a scenario and determine all possible root causes, and then as the AI component gathers additional data to narrow in on the cause, it may perform additional actions.)
executing, via the processor, the determined action to the at least one simulated IT environment; ([0122], lines 1-3)
determining, via the processor, at least one actual result of the executed action; ([0113], sentence in lines 6-9 and [0149] disclose collecting cause and effect assessment data and tracking player actions and responses.)
storing data pertaining to the at least one actual result, if available, in the execution data model; ([0113], sentence in lines 6-9 and [0149] disclose storing cause and effect data and player actions and responses. The “execution data model” includes the cause and effect assessment data.)
repeating, until the at least one simulated IT environment or the at least one agent reaches the at least one end state: (A student must achieve the criteria in [0621]-[0623] to succeed in Offensive Mission 2, and achieve the criteria in [0687]-[0690] to succeed in Defensive Mission 2. The AI opponent plays defense in offensive missions, and it plays offense in defensive missions. A mission continues until the criteria are reached.)
However, Morton does not explicitly teach: the at least one action determination model being structured as a graph-based decision framework that encodes possible execution of the sequence of actions with at least one end state that achieves the at least one objective of the at least one agent, with predicted results for actions based on pre-defined system states and environmental conditions, wherein the graph-based decision framework represents a graph of decision states, each of the decision states corresponding to a respective action in the sequence of actions, and wherein traversal of the graph between decision states is driven by execution of actions;
determining, via the processor, whether the at least one action determination model indicates at least one next action option to be executed by the at least one agent, and if so, …
determining, via the processor, whether an execution data model has already been stored in the data storage or the memory, and if so, obtaining from the execution data model data pertaining to the determined action executed to the at least one simulated IT environment, if the determined action requires at least one precondition;
repeating: determining, via the processor, whether the at least one action determination model indicates the at least one next action option to be executed by the at least one agent, and if so, …
determining, via the processor, whether the execution data model has already been stored, and if so, obtaining from the execution data model the data pertaining to the determined action executed to the at least one simulated IT environment, if the determined action requires the at least one precondition.
But Lippmann teaches: loading,
wherein the graph-based decision framework represents a graph of decision states, each of the decision states corresponding to a respective action in the sequence of actions, and wherein traversal of the graph between decision states is driven by execution of actions; ([0062] and [0063] in col. 2, lines 1-10 discloses that in an attack tree, a node represents an attacker’s access level, and an edge represents an attacker performing an action to transition to a new access level. In Fig. 4, nodes 112, 114a-b, and 116a-c are “decision states” because the attacker decides which action to perform next from those states. Each decision state corresponding to a respective action in the sequence of actions means that a current node in the sequence of actions is the source of a next action/edge.)
determining,
repeating, until the at least one simulated IT environment or the at least one agent reaches the at least one end state: … determining,
It would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to have incorporated Lippmann’s attack tree into Morton. A motivation for the combination is the AI opponent, as attacker, may use the attack tree to determine what action it should perform next (Lippmann, [0065]). In the combination of references, the AI opponent would be loaded with the attack tree when the mission begins. In missions where the AI opponent is a defender, it would have been obvious to have replaced attack actions in the tree with defense actions to generate a defense tree, with a motivation of identifying what the AI opponent may do next (Lippmann, [0065]).
However, Morton and Lippmann do not explicitly teach: determining, via the processor, whether an execution data model has already been stored in the data storage or the memory, and if so, obtaining from the execution data model data pertaining to the determined action executed to the at least one simulated IT environment, if the determined action requires at least one precondition;
repeating: determining, via the processor, whether the execution data model has already been stored, and if so, obtaining from the execution data model the data pertaining to the determined action executed to the at least one simulated IT environment, if the determined action requires the at least one precondition.
But Duell teaches: determiningetc. [0045], lines 6-11 discloses circuitry “retrieves the action selection 310 from the public action queue, and searches an action definition memory, such as the script database 234, to locate an instruction sequence that defines processing steps that implement the action.” The limitation of “an execution data model” is an instruction sequence stored in the script database, and “data” is an individual instruction. Determining whether the instruction sequence has already been stored happens when the database is searched.)
…
determining, via the processor, whether the execution data model has already been stored, and if so, obtaining from the execution data model the data pertaining to the determined action executed to the at least one simulated IT environment, if the determined action requires the at least one precondition. ([0001] discloses Duell relates to executing actions on virtual machines (VMs), which is a computer-virtualized IT system of a simulated IT environment. [0036] discloses building an action selection interface for selecting available actions. A “precondition” as claimed includes the particular resource, the resource requester, etc. [0045], lines 6-11 discloses circuitry “retrieves the action selection 310 from the public action queue, and searches an action definition memory, such as the script database 234, to locate an instruction sequence that defines processing steps that implement the action.” The limitation of “an execution data model” is an instruction sequence stored in the script database, and “data” or “execution data” is an individual instruction. Determining whether the instruction sequence has already been stored happens when the database is searched.)
It would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to have incorporated Duell’s script database into the combination of Morton and Lippmann. A motivation for the combination is to store and locate an instruction sequence for each action to be executed by Morton’s AI opponent (Duell, [0045]). In the combination of references, Duell’s script database would be searched each time the AI opponent determines an action to execute.
Regarding claim 2, the combination of Morton, Lippmann, and Duell teaches: The method according to claim 1,
Morton teaches: wherein the at least one actionApplication No. 16/255,811 Attorney Docket No. 1110/0113PUS1determination model is at least one of a neural network, ([0112])
rule engine, model transformation, model execution, statistical model, stochastic model, probabilistic model, artificial reasoning model, reinforcement learning model, inference model, program, fuzzy logic, or decision tree.
Regarding claim 3, the combination of Morton, Lippmann, and Duell teaches: The method according to claim 1,
However, Morton and Duell do not explicitly teach: wherein the at least one action determination model also defines the at least one next action option to select in at least one given context.
But Lippmann teaches: wherein the at least one action determination model also defines the at least one next action option to select in at least one given context. ([0064] and [0065], lines 15-end. The limitation of “at least one given context” includes an attack that has begun.)
A motivation for the combination is the same as the motivation disclosed in claim 1.
Regarding claim 4, the combination of Morton, Lippmann, and Duell teaches: The method according to claim 1,
Morton teaches: wherein the at least one action determination model determines at least one of an attacker action, an attack action, an exploit action, a defender action, a defending action, a detection action, a mitigation action, a prevention action, an alarm/alert action, a monitoring action, an evaluator action, a tester action, a penetration testing action, a vulnerability assessment action, a recommendation for human users, a configuration action for machines, a policy based action, a rule-based action, a user input action, a user output action, a data ingestion action, a repair action, assembly/disassembly action, a preparing action, a use action, a disposal action, a maintenance action, a directing action, an informational action, an entertaining action, a diagnosing action, transaction action, a purchasing action, a selling action, decision action, training action, education action, buying action, notification action, deception action, distraction action, timing action, delay action, support action, redirect action, or a transfer action. ([0532], lines 20-22 teaches a defender/defending action)
Regarding claim 5, the combination of Morton, Lippmann, and Duell teaches: The method according to claim 1,
Morton teaches: wherein determining from the at least one action option is partly or fully based on success likelihood of the at least one action. ([0114], lines 1-6 discloses the AI engine learns which sequences of events are the most optimal in order to achieve an associated mission goal. Lines 6-10 discloses the AI engine determines the probability of success or failure at any given moment within the mission.)
Regarding claim 6, the combination of Morton, Lippmann, and Duell teaches: The method according to claim 1,
Morton teaches: wherein the at least one predicted result and the at least one execution result of the action each includes at least one of an environment effect, (As a defender, the AI opponent can “make changes automatically to the virtual environment within the training scenario in an attempt to remedy a potential breach” (Morton, [0532], lines 20-22).)
an agent effect, console output, data returned by the action, data returned by the at least one simulated IT environment, context data, metadata, or data returned by the at least one agent.
Regarding claim 8, the combination of Morton, Lippmann, and Duell teaches: The method according to claim 1,
Morton teaches: wherein the at least one objective of the at least one agent includes meeting a predetermined criteria in attacking, (Morton teaches Defensive Mission 2 in [0661]-[0690] and defines criteria for student success in [0687]-[0690]. The agent meets predetermined criteria in attacking when a student fails to successfully defend himself/herself.)
defending, preventing, assessing, testing, evaluating, alarming, monitoring of the IT environment, providing a service, maintaining, updating, analyzing, deceiving, action execution, or action sequence execution.
Regarding claim 22, the combination of Morton, Lippmann, and Duell teaches: The method according to claim 1,
Morton teaches: wherein the at least one end state includes success or failure of the at least one objective of the at least one agent. (A student must achieve the criteria disclosed in [0687]-[0690] to succeed in Defensive Mission 2. The AI opponent plays offense in defensive missions.)
Regarding claim 23, Morton, Lippmann, and Duell teaches: The method according to claim 1,
However, Morton and Duell do not explicitly teach: wherein the action determination model determines whether at least one precondition has been met and determines the next action based on whether preconditions are met.
But Lippmann teaches: wherein the action determination model determines whether at least one precondition has been met and determines the next action based on whether preconditions are met. (In [0064], the action determination model is an attack tree. At least one precondition is a current node (such as node 112), and a next action is an edge between the current node and next node (such as node 114b). A precondition for transitioning from node 112 to 114b requires the attacker to be at node 112.)
A motivation for the combination is the same as the motivation disclosed in claim 1.
Regarding claim 24, Morton, Lippmann, and Duell teaches: The method according to claim 5,
Morton teaches: wherein the success likelihood of the action is determined based on
However, Morton and Lippmann do not explicitly teach: the obtained data [from the execution data model]
But Duell teaches: the obtained data ([0045], lines 6-11 discloses locating an instruction sequence that defines processing steps that implement the action. The limitation of “an execution data model” is an instruction sequence stored in the script database, and “data” is an individual instruction.)
In the combination of references, a success likelihood of Morton’s action is based on Duell’s individual instructions for executing that action. A motivation for the combination is the same as the motivation disclosed in claim 1.
Regarding claim 25, Morton, Lippmann, and Duell teaches: The method according to claim 5,
Morton teaches: wherein the method is performed on a machine-learning system that updates the likelihood of success after each repetition. ([0114], lines 1-6 discloses the AI engine learns which sequences of events are the most optimal in order to achieve an associated mission goal, and lines 6-10 discloses the AI engine determines the probability of success or failure at any given moment within the mission. A sequence of events being the most optimal indicates that taking an action during a game state would have a different likelihood of success than taking the same action during a different game state. Thus, the likelihood is updated after each repetition of determining an action.)
Regarding claim 27, Morton, Lippmann, and Duell teaches: The method according to claim 1,
Morton teaches: wherein executing the action comprises attacking, defending, ([0532], lines 20-22 teaches defending action)
probing, analyzing, hardening, mitigating, updating, downgrading, patching, installing, uninstalling, and/or executing.
Response to Arguments
The following is the Examiner’s response to Applicant’s arguments filed 02/27/2026.
Applicant’s Arguments Under 35 U.S.C. 103: Applicant argues that Lippmann does not explicitly or implicitly disclose or suggest the amended limitations in pending claim 1, lines 20-23.
Examiner’s Response: Applicant's arguments have been fully considered but they are not persuasive. Pending claim 1 has been rejected under 35 U.S.C. 103 over Morton in view of Lippmann and Duell. With respect to the limitation “the graph-based decision framework represents a graph of decision states, each of the decision states corresponding to a respective action in the sequence of actions, and wherein traversal of the graph between decision states is driven by execution of actions” Lippmann at [0062] and [0063] in col. 2, lines 1-10 discloses that in an attack tree, a node represents an attacker’s access level, and an edge represents an attacker performing an action to transition to a new access level. In Fig. 4, nodes 112, 114a-b, and 116a-c are “decision states” because the attacker decides which action to perform next from those states. Each decision state corresponding to a respective action in the sequence of actions means that a current node in the sequence of actions is the source of a next action/edge.
It would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to have incorporated Lippmann’s attack tree into Morton. A motivation for the combination is the AI opponent, as attacker, may use the attack tree to determine what action it should perform next (Lippmann, [0065]). In the combination of references, the AI opponent would be loaded with the attack tree when the mission begins. In missions where the AI opponent is a defender, it would have been obvious to have replaced attack actions in the tree with defense actions to generate a defense tree, with a motivation of identifying what the AI opponent may do next (Lippmann, [0065]).
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Asher H. Jablon whose telephone number is (571)270-7648. The examiner can normally be reached Monday - Friday, 9:00 am - 6:00 pm.
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, Abdullah Al Kawsar can be reached at (571)270-3169. 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.
/A.H.J./Examiner, Art Unit 2127
/ABDULLAH AL KAWSAR/Supervisory Patent Examiner, Art Unit 2127
Prosecution Insights