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 .
Information Disclosure Statement
The information disclosure statement (IDS) submitted is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
Double Patenting
The nonstatutory double patenting rejection is based on a judicially created doctrine grounded in public policy (a policy reflected in the statute) so as to prevent the unjustified or improper timewise extension of the “right to exclude” granted by a patent and to prevent possible harassment by multiple assignees. A nonstatutory double patenting rejection is appropriate where the conflicting claims are not identical, but at least one examined application claim is not patentably distinct from the reference claim(s) because the examined application claim is either anticipated by, or would have been obvious over, the reference claim(s). See, e.g., In re Berg, 140 F.3d 1428, 46 USPQ2d 1226 (Fed. Cir. 1998); In re Goodman, 11 F.3d 1046, 29 USPQ2d 2010 (Fed. Cir. 1993); In re Longi, 759 F.2d 887, 225 USPQ 645 (Fed. Cir. 1985); In re Van Ornum, 686 F.2d 937, 214 USPQ 761 (CCPA 1982); In re Vogel, 422 F.2d 438, 164 USPQ 619 (CCPA 1970); In re Thorington, 418 F.2d 528, 163 USPQ 644 (CCPA 1969).
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The filing of a terminal disclaimer by itself is not a complete reply to a nonstatutory double patenting (NSDP) rejection. A complete reply requires that the terminal disclaimer be accompanied by a reply requesting reconsideration of the prior Office action. Even where the NSDP rejection is provisional the reply must be complete. See MPEP § 804, subsection I.B.1. For a reply to a non-final Office action, see 37 CFR 1.111(a). For a reply to final Office action, see 37 CFR 1.113(c). A request for reconsideration while not provided for in 37 CFR 1.113(c) may be filed after final for consideration. See MPEP §§ 706.07(e) and 714.13.
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Claims 1 – 20 are rejected on the ground of nonstatutory double patenting as being unpatentable over claims of U.S. Patent No. 12316655. Although the claims at issue are not identical, they are not patentably distinct from each other because the said patent anticipates the instant application.
Instant App. 19218145
Patent #: 12316655
1. An autonomous agent, comprising: one or more processing circuits configured to: receive or identify, from at least one computing system external or internal to a decentralized network, centralized network, or data source (DNCNDS), cyber resilience data, wherein the DNCNDS is registered with a plurality of autonomous agents to allow the plurality of autonomous agents to be discoverable by at least one entity computing system via the DNCNDS; perform at least one cyber resilience operation of a plurality of cyber resilience operations based at least on: selecting at least one function of a dynamic data structure based at least on the cyber resilience data; or generating a new function based at least on one or more frameworks of the dynamic data structure; generate one or more tokens comprising at least one proof of the at least one cyber resilience operation; and update the dynamic data structure based on the at least one cyber resilience operation performed.
2. The autonomous agent of claim 1, wherein performing the at least one cyber resilience operation comprises: generating a data package corresponding to the new function or at least one function of the dynamic data structure, wherein the data package comprises one or more deployable sub-functions of the new function or at least one function; and providing, via the DNCNDS, the data package configured to deploy on at least one entity computing system.
3. The autonomous agent of claim 1, wherein generating the new function comprises: storing the new function in the dynamic data structure; and providing the new function and corresponding performance data to at least one second autonomous agent of the DNCNDS, wherein the new function and the corresponding performance data causes the at least one second autonomous agent of the DNCNDS to update a corresponding dynamic data structure.
4. The autonomous agent of claim 1, wherein the one or more processing circuits are further configured to: apply the at least one cyber resilience operation as input to an artificial intelligence (Al) model to cause the Al model to generate a performance metric corresponding with selecting the at least one function or generating the new function; andupdate the dynamic data structure based at least on the performance metric.
5. The autonomous agent of claim 1, wherein performing the at least one cyber resilience operation comprises:responsive to determining the cyber resilience data corresponds with a verification request, transmitting, by the one or more processing circuits, at least one of the one or more tokens to the DNCNDS for verification of the at least one cyber resilience operation or cyber resilience data.
6. The autonomous agent of claim 1, wherein the one or more processing circuits are further configured to:re-register with the DNCNDS to cause the one or more processing circuits to be discoverable on the DNCNDS with the updated dynamic data structure.
7. The autonomous agent of claim 6, wherein re-registering with the DNCNDS allows the autonomous agent to be discoverable by at least one entity computing system via the DNCNDS, wherein registering and re-registering with the DNCNDS comprises causing the one or more processing circuits and one or more functions or one or more of the one or more frameworks received by the dynamic data structure to be discoverable on the DNCNDS.
8. The autonomous agent of claim 1, wherein the one or more processing circuits are configured to:identify (1) a portion of the at least one cyber resilience operation to perform corresponding to at least one of the at least one functions or one or more frameworks of the dynamic data structureand (2) a portion of the at least one cyber resilience operation to transmit to the DNCNDS for performance by a second agent; andprovide usage statistics and metrics of the dynamic data structure to the DNCNDS for the DNCNDS, the second agent or an entity to track activity levels and resource consumption.
9. The autonomous agent of claim 1, wherein receiving or identifying cyber resilience data from at least one computing system external to the DNCNDS comprises receiving or identifying cyber resilience data from an entity computing system, third-party computing system, or data source and receiving or identifying cyber resilience data from at least one computing system internal to the DNCNDS comprises receiving or identifying cyber resilience data from an agent network, second autonomous agent, or orchestration computing system.
10. A method for executing an autonomous agent, comprising:receiving or identifying, by one or more processing circuits, cyber resilience data, wherein the autonomous agent is discoverable by at least one entity computing system via a decentralized network, centralized network, or data source (DNCNDS);performing, by the one or more processing circuits, at least one cyber resilience operation based at least on:selecting at least one function of a dynamic data structure based at least on the cyber resilience data; or generating a new function based at least on one or more frameworks of the dynamic data structure;generating, by the one or more processing circuits, one or more tokens comprising at least one proof of the at least one cyber resilience operation; andupdating, by the one or more processing circuits, the dynamic data structure based on the at least one cyber resilience operation performed.
11. The method of claim 10, wherein performing the at least one cyber resilience operation comprises: generating a data package corresponding to the new function or at least one function of the dynamic data structure, wherein the data package comprises one or more deployable sub-functions of the new function or at least one function; and providing, via the DNCNDS, the data package configured to deploy on at least one entity computing system.
12. The method of claim 10, wherein generating the new function comprises: storing the new function in the dynamic data structure; and providing the new function and corresponding performance data to at least one second autonomous agent of the DNCNDS, wherein the new function and the corresponding performance data causes the at least one second autonomous agent of the DNCNDS to update a corresponding dynamic data structure.
13. The method of claim 10, further comprising:applying, by the one or more processing circuits, the at least one cyber resilience operation as input to an artificial intelligence (Al) model to cause the Al model to generate a performance metric corresponding with selecting the at least one function or generating the new function; andupdating, by the one or more processing circuits, the dynamic data structure based at least on the performance metric.
14. The method of claim 10, wherein performing the at least one cyber resilience operation comprises:responsive to determining the cyber resilience data corresponds with a verification request, transmitting, by the one or more processing circuits, at least one of the one or more tokens to the DNCNDS for verification of the at least one cyber resilience operation or cyber resilience data.
15. The method of claim 10, further comprising:re-registering, by the one or more processing circuits, with the DNCNDS to cause the one or more processing circuits to be discoverable on the DNCNDS with the updated dynamic data structure.
16. The method of claim 15, wherein re-registering with the DNCNDS allows the autonomous agent to be discoverable by at least one entity computing system via the DNCNDS, wherein registering and re-registering with the DNCNDS comprises causing the one or more processing circuits and one or more functions or one or more of the one or more frameworks received by the dynamic data structure to be discoverable on the DNCNDS.
17. The method of claim 10, further comprising:identifying, by the one or more processing circuits, (1) a portion of the at least one cyber resilience operation to perform corresponding to at least one of the at least one functions or one or more frameworks of the dynamic data structure and (2) a portion of the at least one cyber resilience operation to transmit to the DNCNDS for performance by a second agent; andproviding, by the one or more processing circuits, usage statistics and metrics of the dynamic data structure to the DNCNDS for the DNCNDS, the second agent or an entity to track activity levels and resource consumption.
18. The method of claim 10, wherein receiving or identifying cyber resilience data from at least one computing system external to the DNCNDS comprises receiving or identifying cyber resilience data from an entity computing system, third-party computing system, or data source and receiving or identifying cyber resilience data from at least one computing system internal to the DNCNDS comprises receiving or identifying cyber resilience data from an agent network, second autonomous agent, or orchestration computing system.
19. A non-transitory computer readable medium (CRM) comprising one or more instructions stored thereon and executable by one or more processors to:receive or identify, from at least one computing system external or internal to a decentralized network, centralized network, or data source (DNCNDS), cyber resilience data, wherein the DNCNDS is registered with a plurality of autonomous agents to allow the plurality of autonomous agents to be discoverable by at least one entity computing system via the DNCNDS;perform at least one cyber resilience operation of a plurality of cyber resilience operations based at least on:selecting at least one function of a dynamic data structure based at least on the cyber resilience data; or generating a new function based at least on one or more frameworks of the dynamic data structure; generate one or more tokens comprising at least one proof of the at least one cyber resilience operation; and update the dynamic data structure based on the at least one cyber resilience operation performed.
20. The CRM of claim 19, wherein performing the at least one cyber resilience operation comprises:generating a data package corresponding to the new function or at least one function of the dynamic data structure, wherein the data package comprises one or more deployable sub-functions of the new function or at least one function; andproviding, via the DNCNDS, the data package configured to deploy on at least one entity computing system.
1. An autonomous agent, comprising:
one or more processing circuits configured to: receive or identify a dynamic data structure comprising one or more functions or one or more frameworks for performing a plurality of cyber resilience operations; register the autonomous agent with a decentralized network, centralized network, or data source (DNCNDS) to cause the one or more processing circuits to be discoverable on the DNCNDS;
receive or identify, from at least one computing system external or internal to the DNCNDS, cyber resilience data; [[and]] perform, in real-time, [[the]] at least one cyber resilience operation of the plurality of cyber resilience operations based at least on: selecting at least one function of the one or more functions of the dynamic data structure based at least on the cyber resilience data, wherein the at least one function corresponds to (i) updating a computing environment, (ii) generating and providing an assessment to a plurality of computing systems, (iii) updating a dashboard; or generating a new function based at least on the one or more frameworks of the dynamic data structure, wherein the new function corresponds to generating at least one new cyber resilience operation based on to the cyber resilience data; update the dynamic data structure based on the at least one cyber resilience operation performed; wherein performing the at least one cyber resilience operation comprises generating one or more tokens comprising at least one proof of the at least one cyber resilience operation, wherein the at least one proof corresponds with a verification; and wherein registering with the DNCNDS allows the autonomous agent to be discoverable by at least one entity computing system via the DNCNDS, wherein registering with the DNCNDS comprises causing the one or more processing circuits and one or more of the one or more functions or one or more of the one or more frameworks received by the dynamic data structure to be discoverable on the DNCNDS.
2. The autonomous agent of claim 1, wherein performing the at least one cyber resilience operation comprises: generating a data package corresponding to the new function or at least one function of the one or more functions, wherein the data package comprises one or more deployable sub-functions of the new function or at least one function of the one or more functions; and
providing, via the DNCNDS, the data package configured to deploy on at least one entity computing system.
3. (Original) The autonomous agent of claim 1, wherein generating the new function comprises:
storing the new function in the dynamic data structure; and
providing the new function and corresponding performance data to at least one second autonomous agent of the DNCNDS, wherein the new function and the corresponding performance data causes the at least one second autonomous agent of the DNCNDS to update a corresponding dynamic data structure.
4. The autonomous agent of claim 1, wherein the one or more processing circuits are further configured to:
apply the at [[the]] least one cyber resilience operation as input to an artificial intelligence (AI) model to cause the AI model to generate a performance metric corresponding with selecting the at least one function of the one or more functions or generating the new function; and
update the dynamic data structure based at least on the performance metric.
5. The autonomous agent of claim 1, wherein performing the at least one cyber resilience operation comprises:
responsive to determining the cyber resilience data corresponds with a verification request, transmitting, by the one or more processing circuits, at least one of the one or more tokens to the DNCNDS for verification of the at least one cyber resilience operation or cyber resilience data.
6. The autonomous agent of claim 1, wherein the one or more processing circuits are further configured to:
re-register with the DNCNDS to cause the one or more processing circuits to be discoverable on the DNCNDS with the updated dynamic data structure.
7. The autonomous agent of claim 6, wherein re-registering with the DNCNDS allows the autonomous agent to be discoverable by at least one entity computing system via the DNCNDS, wherein registering and re-registering with the DNCNDS comprises [[(1)]] causing the one or more processing circuits and one or more of the one or more functions or one or more of the one or more frameworks received by the dynamic data structure to be discoverable on the DNCNDS.
8. The autonomous agent of claim 1, wherein the one or more processing circuits are configured to:
identify (1) a portion of the at least one cyber resilience operation to perform corresponding to [[the]] at least one of the one or more functions or one or more frameworks of the dynamic data structure and (2) a portion of the at least one cyber resilience operation to transmit to the DNCNDS for performance by a second agent; and provide usage statistics and metrics of the dynamic data structure to the DNCNDS for the DNCNDS, the second agent or an entity to track activity levels and resource consumption.
9. The autonomous agent of claim 1, wherein receiving or identifying cyber resilience data from at least one computing system external to the DNCNDS comprises receiving or identifying cyber resilience data [[form]] from an entity computing system, third-party computing system, or data source and receiving or identifying cyber resilience data from at least one computing system internal to the DNCNDS comprises receiving or identifying cyber resilience data from an agent network, second autonomous agent, or orchestration computing system.
10. A method for executing an autonomous agent, comprising: receiving or identifying, by one or more processing circuits, from at least one computing system external or internal to a decentralized network, centralized network, or data source DNCNDS), cyber resilience data; [[and]]
performing, by the one or more processing circuits, in real-time, at least one cyber resilience operation of a plurality of cyber resilience operations based at least on:
selecting at least one function of one or more functions of a dynamic data structure based at least on the cyber resilience data, wherein the at least one function corresponds to (i) updating a computing environment, (ii) generating and providing an assessment to a plurality of computing systems, (iii) updating a dashboard; or generating a new function based at least on one or more frameworks of the dynamic data structure, wherein the new function corresponds to generating at least one new cyber resilience operation based on to the cyber resilience data; updating, by the one or more processing circuits, the dynamic data structure based on the at least one cyber resilience operation performed;
wherein performing the at least one cyber resilience operation comprises generating one or more tokens comprising at least one proof of the at least one cyber resilience operation, wherein the at least one proof corresponds with a verification;
wherein the autonomous agent is registered with the DNCNDS to allow the autonomous agent to be discoverable by at least one entity computing system via the DNCNDS, wherein registering with the DNCNDS comprises causing the one or more processing circuits and one or more of the one or more functions or one or more of the one or more frameworks received by the dynamic data structure to be discoverable on the DNCNDS.
11. The method of claim 10, wherein performing the at least one cyber resilience operation comprises: generating a data package corresponding to the new function or at least one function of the one or more functions, wherein the data package comprises one or more deployable sub-functions of the new function or at least one function of the one or more functions; and
providing, via the DNCNDS, the data package configured to deploy on at least one entity computing system.
12. (Original) The method of claim 10, wherein generating the new function comprises: storing the new function in the dynamic data structure; and providing the new function and corresponding performance data to at least one second autonomous agent of the DNCNDS, wherein the new function and the corresponding performance data causes the at least one second autonomous agent of the DNCNDS to update a corresponding dynamic data structure.
13. (Original) The method of claim 10, further comprising: applying, by the one or more processing circuits, the at least one cyber resilience operation as input to an artificial intelligence (AI) model to cause the AI model to generate a performance metric corresponding with selecting the at least one function of the one or more functions or generating the new function; and updating, by the one or more processing circuits, the dynamic data structure based at least on the performance metric.
14. The method of claim 10, wherein performing the at least one cyber resilience operation comprises: responsive to determining the cyber resilience data corresponds with a verification request, transmitting, by the one or more processing circuits, at least one of the one or more tokens to the DNCNDS for verification of the at least one cyber resilience operation or cyber resilience data.
15. The method of claim 10, further comprising: re-registering, by the one or more processing circuits, with the DNCNDS to cause the one or more processing circuits to be discoverable on the DNCNDS with the updated dynamic data structure.
16. The method of claim 15, wherein re-registering with the DNCNDS allows the autonomous agent to be discoverable by at least one entity computing system via the DNCNDS, wherein registering and re-registering with the DNCNDS comprises [[(1)]] causing the one or more processing circuits and one or more of the one or more functions or one or more of the one or more frameworks received by the dynamic data structure to be discoverable on the DNCNDS.
17. The method of claim 10, further comprising: identifying, by the one or more processing circuits, (1) a portion of the at least one cyber resilience operation to perform corresponding to at least one of the one or more functions or one or more frameworks of the dynamic data structure and (2) a portion of the at least one cyber resilience operation to transmit to the DNCNDS for performance by a second agent; and providing, by the one or more processing circuits, usage statistics and metrics of the dynamic data structure to the DNCNDS for the DNCNDS, the second agent or an entity to track activity levels and resource consumption.
18. The method of claim 10, wherein receiving or identifying cyber resilience data from at least one computing system external to the DNCNDS comprises receiving or identifying cyber resilience data from an entity computing system, third-party computing system, or data source and receiving or identifying cyber resilience data from at least one computing system internal to the DNCNDS comprises receiving or identifying cyber resilience data from an agent network, second autonomous agent, or orchestration computing system.
19. A non-transitory computer readable medium (CRM) comprising one or more instructions stored thereon and executable by one or more processors to:
receive or identify a dynamic data structure comprising one or more functions or one or more frameworks for performing a plurality of cyber resilience operations;
register an autonomous agent with a decentralized network, centralized network, or data source (DNCNDS) to cause the one or more processors to be discoverable on the DNCNDS; receive or identify, from at least one computing system external or internal to the DNCNDS, cyber resilience data; perform, in real-time, at least one cyber resilience operation of the plurality of cyber resilience operations based at least on:
selecting at least one function of the one or more functions of the dynamic data structure based at least on the cyber resilience data, wherein the at least one function corresponds to (i) updating a computing environment, (ii) generating and providing an assessment to a plurality of computing systems, (iii) updating a dashboard; or generating a new function based at least on the one or more frameworks of the dynamic data structure, wherein the new function corresponds to generating at least one new cyber resilience operation based on to the cyber resilience data; update the dynamic data structure based on the at least one cyber resilience operation performed; wherein performing the at least one cyber resilience operation comprises generating one or more tokens comprising at least one proof of the at least one cyber resilience operation, wherein the at least one proof corresponds with a verification; and wherein registering with the DNCNDS allows the autonomous agent to be discoverable by at least one entity computing system via the DNCNDS, wherein registering with the DNCNDS comprises causing one or more of the one or more functions or one or more of the one or more frameworks received by the dynamic data structure to be discoverable on the DNCNDS.
20. The CRM of claim 19, wherein performing the at least one cyber resilience operation comprises: generating a data package corresponding to the new function or at least one function of the one or more functions, wherein the data package comprises one or more deployable sub-functions of the new function or at least one function of the one or more functions; and
providing, via DNCNDS, the data package configured to deploy on at least one entity computing system.
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.
Claim(s) 1 – 6, 8 – 15, 17 – 20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Lembersky et al (US 20260133869), Lem and Thompson; Jonathan (US 20230388324), Tho.
Claim 1: Lem teaches an autonomous agent, comprising: one or more processing circuits configured to (Fig. 1, 4): receive or identify, from at least one computing system external or internal to a [decentralized network], centralized network, or [data] source (DNCNDS), cyber resilience data, ([08] determining, by a processor, a causal graph based on key performance indicators, behaviors, and metrics. Also determining, by the processor, one or more agents of a plurality of agents that have a decrease in performance based on key performance indicators).
wherein the DNCNDS is registered with a plurality of autonomous agents to allow the plurality of autonomous agents to be [discoverable by at least one entity] computing system via the DNCNDS; ([013] The processor is configured to determine one or more agents of a plurality of agents. [064] A root cause analysis service (as implemented on a server) can transmit a first request to get data to a data storage. The get data request can include an identifier for an agent (agentId i.e., agent discovery), KPI and a current time period).
perform at least one cyber resilience operation of a plurality of cyber resilience operations based at least on: selecting at least one function of a dynamic data structure based at least on the cyber resilience data; or generating a new function based at least on one or more frameworks of the dynamic data structure; ([064] The values for the particular KPI for the particular agentId over the current time period can be returned from the data storage to the root cause analysis service. [015] the one or more metrics, one or more behaviors or a combination thereof, that the causal graph is based on are received from a database, input through a graphical user interface, or any combination thereof. [052] involve determining, a causal graph based on key performance indicators, behaviors and metrics. The input to the causal graph can be historical values of metrics, key performance indicators (KPIs) and/or behaviors. KPIs can be a measure of performance, metrics can be factors that contribute to the KPIs, and behaviors can also contribute to the KPIs).
and update the dynamic data structure based on the at least one cyber resilience operation performed. ([065-66] The values for the particular KPI for the particular agentId over the previous time period can be returned from the data storage to the root cause analysis service. The root cause analysis service can determine an average of the values. The root cause analysis service can determine a difference between the average values for the current period and the average values for the previous period, and if the difference is above a threshold, then the agent that corresponds to agentId is transmitted to a candidate list database. The candidate list database can include a list of all agents of the plurality of agents that have had a decrease in performance).
Lem is silent on decentralized network or data source and agents or service discoverable by at least one entity and generate one or more tokens comprising at least one proof of the at least one cyber resilience operation;
But analogous art Tho teaches decentralized network or data source and agents or service discoverable by at least one entity and generate one or more tokens comprising at least one proof of the at least one cyber resilience operation; ([073, 117] device connectivity data includes data obtained from a search or discovery engine for Internet-connected devices which includes a third-party product (e.g., Shodan), a proprietary product, or a combination thereof; [104] the analysis circuit utilizes a distributed ledger to tokenize and broadcast the security posture, ensuring transparency and immutability. a “security posture” refers to the current state and overall cybersecurity profile of an organization or vendor. [238, 241, 260] the decentralized nature of a distributed ledger, is maintained by multiple nodes or participants in the network. Broadcasting the token to the distributed ledger ensures that the token(s), representing the security posture, is stored in a decentralized, immutable, and transparent manner).
Therefore, it is prima facie obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to modify the invention of Lem to include the idea of decentralized network and tokens as proof of cyber resilience as taught by Tho so that time and effort savings for users, a reduction in errors due to manual data entry, and an increased likelihood of consistent and accurate information across multiple forms [178].
Claim 2: the combination of Lem and Tho teaches the autonomous agent of claim 1, wherein performing the at least one cyber resilience operation comprises: generating a data package corresponding to the new function or at least one function of the dynamic data structure, wherein the data package comprises one or more deployable sub-functions of the new function or at least one function; and providing, via the DNCNDS, the data package configured to deploy on at least one entity computing system. (Lem: [0114] The list of metrics can be shared through the cloud, e.g., stream (e.g., the AWS Kinesis stream), messaging queue, and/or S3, where they can be used by the coaching manager service to automatically create a coaching package and/or by a second performance application to automatically select relevant interactions for evaluation (i.e., package for deployment)).
Claim 3: the combination of Lem and Tho teaches the autonomous agent of claim 1, wherein generating the new function comprises: storing the new function in the dynamic data structure; and providing the new function and corresponding performance data to at least one second autonomous agent of the DNCNDS, wherein the new function and the corresponding performance data causes the at least one second autonomous agent of the DNCNDS to update a corresponding dynamic data structure. (Tho [291]: Once the data has been identified and categorized, the processing circuits package the information and seamlessly provide it to an application programming interface (API). This API serves as a conduit for transmitting the wrapped cybersecurity data, along with the underwriting requirements, to the underwriting system; [097] the responder dashboard and architecture provided by the response system enable the vendor and/or organization to effectively prepare for, track, and update incidents and readiness).
Therefore, it is prima facie obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to modify the invention of Lem to include the idea of providing new package and updating agent as taught by Tho so that time and effort savings for users, a reduction in errors due to manual data entry, and an increased likelihood of consistent and accurate information across multiple forms [178].
Claim 4: the combination of Lem and Tho teaches the autonomous agent of claim 1, wherein the one or more processing circuits are further configured to: apply the at least one cyber resilience operation as input to an artificial intelligence (Al) model to cause the Al model to generate a performance metric corresponding with selecting the at least one function or generating the new function; and update the dynamic data structure based at least on the performance metric. (Lem [014, 69]: to determine the one or more agents that have a decrease in performance the processor is further configured to determine a difference between the key performance indicators for a first time period and a second time period for each of the plurality of agents and to create the first casual graph and the second causal graph the processor is further configured to train a linear regression based on nodes of the respective causal graph and edges of the respective causal graph).
Claim 5: the combination of Lem and Tho teaches the autonomous agent of claim 1, wherein performing the at least one cyber resilience operation comprises: responsive to determining the cyber resilience data corresponds with a verification request, transmitting, by the one or more processing circuits, at least one of the one or more tokens to the DNCNDS for verification of the at least one cyber resilience operation or cyber resilience data. (Tho [0104, 145]: the analysis circuit utilizes a distributed ledger to tokenize and broadcast the security posture… This transparent and immutable record-keeping enhances trust among stakeholders and provides a verifiable proof of the entity's security posture).
Therefore, it is prima facie obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to modify the invention of Lem to include the idea of providing token for verification as taught by Tho so that time and effort savings for users, a reduction in errors due to manual data entry, and an increased likelihood of consistent and accurate information across multiple forms [178].
Claim 6: the combination of Lem and Tho teaches the autonomous agent of claim 1, wherein the one or more processing circuits are further configured to: re-register with the DNCNDS to cause the one or more processing circuits to be discoverable on the DNCNDS with the updated dynamic data structure. (Tho [073, 117] device connectivity data includes data obtained from a search or discovery engine for Internet-connected devices which includes a third-party product (e.g., Shodan), a proprietary product, or a combination thereof; [104] the analysis circuit utilizes a distributed ledger to tokenize and broadcast the security posture, ensuring transparency and immutability. a “security posture” refers to the current state and overall cybersecurity profile of an organization or vendor. [083] a notification may be transmitted to the client device indicating the user, vendor, or client device is registered and can utilize the dashboards to perform actions associated with one or more applications; [238, 241, 260] the decentralized nature of a distributed ledger, is maintained by multiple nodes or participants in the network. Broadcasting the token to the distributed ledger ensures that the token(s), representing the security posture, is stored in a decentralized, immutable, and transparent manner).
Therefore, it is prima facie obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to modify the invention of Lem to include the idea of decentralized network and tokens as proof of cyber resilience as taught by Tho so that time and effort savings for users, a reduction in errors due to manual data entry, and an increased likelihood of consistent and accurate information across multiple forms [178].
Claim 8: the combination of Lem and Tho teaches the autonomous agent of claim 1, wherein the one or more processing circuits are configured to: identify (1) a portion of the at least one cyber resilience operation to perform corresponding to at least one of the at least one functions or one or more frameworks of the dynamic data structure and (2) a portion of the at least one cyber resilience operation to transmit to the DNCNDS for performance by a second agent; and provide usage statistics and metrics of the dynamic data structure to the DNCNDS for the DNCNDS, the second agent or an entity to track activity levels and resource consumption. (Lem [072-073] A root cause analysis service for each node in the initial causal graph, transmits a first request to get data to a data storage. The get data request includes for each node, an agentId, node identification, and a current time period. The values for each node for the particular agentId over the current time period can be returned from the data storage to the root cause analysis service. The root cause analysis service, for each node in the initial causal graph, transmits a second request to get data to the data storage. The get data request includes for each node, an agentId, node identification, and a past time period. The values for each node for the particular agentId over the past time period is returned from the data storage to the root cause analysis service; [069] if there is a statistically significant change in the current node's metric's value between the current time period and the past time period, for the first causal graph (e.g., the current causal graph) use values from the current time period to train a linear regression model in the form: x.sub.i=f(parents(x.sub.i)+ϵ) where x.sub.i is the current node, and ϵ is a self-contribution factor, and set the trained linear regression model as a causal function of a given node in the current causal graph, and for the second causal graph (e.g., the past causal graph) use the data from the past period to train a linear regression model in the form: x.sub.i=f(parents(x.sub.i)+ϵ) where x.sub.i is the current node, and ϵ is a self-contribution factor, and set the trained linear regression model as a causal function of the given node in the past causal graph).
Claim 9: the combination of Lem and Tho teaches the autonomous agent of claim 1, wherein receiving or identifying cyber resilience data from at least one computing system external to the DNCNDS comprises receiving or identifying cyber resilience data from an entity computing system, third-party computing system, or data source and receiving or identifying cyber resilience data from at least one computing system internal to the DNCNDS comprises receiving or identifying cyber resilience data from an agent network, second autonomous agent, or orchestration computing system. (Lem [010-12] the one or more metrics, one or more behaviors or a combination thereof, that the causal graph is based on are received from a database, input through a graphical user interface, or any combination thereof. determining the root cause further comprises identifying which node has a biggest effect on the value and whether a change in value has a negative or positive effect on performance based on a type of the one or more metrics or the one or more behaviors of the node. Determining one or more agents that have decreased in performance further comprising receiving, by the processor, the key performance indicators from another computing device, a graphical user interface, or any combination thereof).
Claim 10: Lem teaches a method for executing an autonomous agent, comprising: receiving or identifying, by one or more processing circuits, cyber resilience data, wherein the autonomous agent is [discoverable by at least one entity] computing system via a [decentralized] network, centralized network, or [data] source (DNCNDS); performing, by the one or more processing circuits, at least one cyber resilience operation based at least on: selecting at least one function of a dynamic data structure based at least on the cyber resilience data; or generating a new function based at least on one or more frameworks of the dynamic data structure; generating, by the one or more processing circuits, one or more tokens comprising at least one proof of the at least one cyber resilience operation; and updating, by the one or more processing circuits, the dynamic data structure based on the at least one cyber resilience operation performed. ([08] determining, by a processor, a causal graph based on key performance indicators, behaviors, and metrics. Also determining, by the processor, one or more agents of a plurality of agents that have a decrease in performance based on key performance indicators; [013] The processor is configured to determine one or more agents of a plurality of agents. [064] A root cause analysis service (as implemented on a server) can transmit a first request to get data to a data storage. The get data request can include an identifier for an agent (agentId), KPI and a current time period; [064] The values for the particular KPI for the particular agentId over the current time period can be returned from the data storage to the root cause analysis service. [015] the one or more metrics, one or more behaviors or a combination thereof, that the causal graph is based on are received from a database, input through a graphical user interface, or any combination thereof. [052] involve determining, a causal graph based on key performance indicators, behaviors and metrics. The input to the causal graph can be historical values of metrics, key performance indicators (KPIs) and/or behaviors. KPIs can be a measure of performance, metrics can be factors that contribute to the KPIs, and behaviors can also contribute to the KPIs; [065-66] The values for the particular KPI for the particular agentId over the previous time period can be returned from the data storage to the root cause analysis service. The root cause analysis service can determine an average of the values. The root cause analysis service can determine a difference between the average values for the current period and the average values for the previous period, and if the difference is above a threshold, then the agent that corresponds to agentId is transmitted to a candidate list database. The candidate list database can include a list of all agents of the plurality of agents that have had a decrease in performance).
Lem is silent on decentralized network or data source and agents or service discoverable by at least one entity and generate one or more tokens comprising at least one proof of the at least one cyber resilience operation;
But analogous art Tho teaches decentralized network or data source and agents or service discoverable by at least one entity and generate one or more tokens comprising at least one proof of the at least one cyber resilience operation; ([073, 117] device connectivity data includes data obtained from a search or discovery engine for Internet-connected devices which includes a third-party product (e.g., Shodan), a proprietary product, or a combination thereof; [104] the analysis circuit utilizes a distributed ledger to tokenize and broadcast the security posture, ensuring transparency and immutability. a “security posture” refers to the current state and overall cybersecurity profile of an organization or vendor. [238, 241, 260] the decentralized nature of a distributed ledger, is maintained by multiple nodes or participants in the network. Broadcasting the token to the distributed ledger ensures that the token(s), representing the security posture, is stored in a decentralized, immutable, and transparent manner).
Therefore, it is prima facie obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to modify the invention of Lem to include the idea of decentralized network and tokens as proof of cyber resilience as taught by Tho so that time and effort savings for users, a reduction in errors due to manual data entry, and an increased likelihood of consistent and accurate information across multiple forms [178].
Claim 11: the combination of Lem and Tho teaches the method of claim 10, wherein performing the at least one cyber resilience operation comprises: generating a data package corresponding to the new function or at least one function of the dynamic data structure, wherein the data package comprises one or more deployable sub-functions of the new function or at least one function; and providing, via the DNCNDS, the data package configured to deploy on at least one entity computing system. (Lem: [0114] The list of metrics can be shared through the cloud, e.g., stream (e.g., the AWS Kinesis stream), messaging queue, and/or S3, where they can be used by the coaching manager service to automatically create a coaching package and/or by a second performance application to automatically select relevant interactions for evaluation (i.e., package for deployment)).
Claim 12: the combination of Lem and Tho teaches the method of claim 10, wherein generating the new function comprises: storing the new function in the dynamic data structure; and providing the new function and corresponding performance data to at least one second autonomous agent of the DNCNDS, wherein the new function and the corresponding performance data causes the at least one second autonomous agent of the DNCNDS to update a corresponding dynamic data structure. (Tho [291]: Once the data has been identified and categorized, the processing circuits package the information and seamlessly provide it to an application programming interface (API). This API serves as a conduit for transmitting the wrapped cybersecurity data, along with the underwriting requirements, to the underwriting system; [097] the responder dashboard and architecture provided by the response system enable the vendor and/or organization to effectively prepare for, track, and update incidents and readiness).
Therefore, it is prima facie obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to modify the invention of Lem to include the idea of providing new package and updating agent as taught by Tho so that time and effort savings for users, a reduction in errors due to manual data entry, and an increased likelihood of consistent and accurate information across multiple forms [178].
Claim 13: the combination of Lem and Tho teaches the method of claim 10, further comprising: applying, by the one or more processing circuits, the at least one cyber resilience operation as input to an artificial intelligence (Al) model to cause the Al model to generate a performance metric corresponding with selecting the at least one function or generating the new function; and updating, by the one or more processing circuits, the dynamic data structure based at least on the performance metric.. (Lem [014, 69]: to determine the one or more agents that have a decrease in performance the processor is further configured to determine a difference between the key performance indicators for a first time period and a second time period for each of the plurality of agents and to create the first casual graph and the second causal graph the processor is further configured to train a linear regression based on nodes of the respective causal graph and edges of the respective causal graph).
Claim 14: the combination of Lem and Tho teaches the method of claim 10, wherein performing the at least one cyber resilience operation comprises: responsive to determining the cyber resilience data corresponds with a verification request, transmitting, by the one or more processing circuits, at least one of the one or more tokens to the DNCNDS for verification of the at least one cyber resilience operation or cyber resilience data. (Tho [0104, 145]: the analysis circuit utilizes a distributed ledger to tokenize and broadcast the security posture… This transparent and immutable record-keeping enhances trust among stakeholders and provides a verifiable proof of the entity's security posture).
Therefore, it is prima facie obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to modify the invention of Lem to include the idea of providing token for verification as taught by Tho so that time and effort savings for users, a reduction in errors due to manual data entry, and an increased likelihood of consistent and accurate information across multiple forms [178].
Claim 15: the combination of Lem and Tho teaches the method of claim 10, further comprising: re-registering, by the one or more processing circuits, with the DNCNDS to cause the one or more processing circuits to be discoverable on the DNCNDS with the updated dynamic data structure. (Tho [073, 117] device connectivity data includes data obtained from a search or discovery engine for Internet-connected devices which includes a third-party product (e.g., Shodan), a proprietary product, or a combination thereof; [104] the analysis circuit utilizes a distributed ledger to tokenize and broadcast the security posture, ensuring transparency and immutability. a “security posture” refers to the current state and overall cybersecurity profile of an organization or vendor. [083] a notification may be transmitted to the client device indicating the user, vendor, or client device is registered and can utilize the dashboards to perform actions associated with one or more applications; [238, 241, 260] the decentralized nature of a distributed ledger, is maintained by multiple nodes or participants in the network. Broadcasting the token to the distributed ledger ensures that the token(s), representing the security posture, is stored in a decentralized, immutable, and transparent manner).
Therefore, it is prima facie obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to modify the invention of Lem to include the idea of decentralized network and tokens as proof of cyber resilience as taught by Tho so that time and effort savings for users, a reduction in errors due to manual data entry, and an increased likelihood of consistent and accurate information across multiple forms [178].
Claim 17: the combination of Lem and Tho teaches the method of claim 10, further comprising: identifying, by the one or more processing circuits, (1) a portion of the at least one cyber resilience operation to perform corresponding to at least one of the at least one functions or one or more frameworks of the dynamic data structure and (2) a portion of the at least one cyber resilience operation to transmit to the DNCNDS for performance by a second agent; and providing, by the one or more processing circuits, usage statistics and metrics of the dynamic data structure to the DNCNDS for the DNCNDS, the second agent or an entity to track activity levels and resource consumption. (Lem [072-073] A root cause analysis service for each node in the initial causal graph, transmits a first request to get data to a data storage. The get data request includes for each node, an agentId, node identification, and a current time period. The values for each node for the particular agentId over the current time period can be returned from the data storage to the root cause analysis service. The root cause analysis service, for each node in the initial causal graph, transmits a second request to get data to the data storage. The get data request includes for each node, an agentId, node identification, and a past time period. The values for each node for the particular agentId over the past time period is returned from the data storage to the root cause analysis service; [069] if there is a statistically significant change in the current node's metric's value between the current time period and the past time period, for the first causal graph (e.g., the current causal graph) use values from the current time period to train a linear regression model in the form: x.sub.i=f(parents(x.sub.i)+ϵ) where x.sub.i is the current node, and ϵ is a self-contribution factor, and set the trained linear regression model as a causal function of a given node in the current causal graph, and for the second causal graph (e.g., the past causal graph) use the data from the past period to train a linear regression model in the form: x.sub.i=f(parents(x.sub.i)+ϵ) where x.sub.i is the current node, and ϵ is a self-contribution factor, and set the trained linear regression model as a causal function of the given node in the past causal graph).
Claim 18: the combination of Lem and Tho teaches the method of claim 10, wherein receiving or identifying cyber resilience data from at least one computing system external to the DNCNDS comprises receiving or identifying cyber resilience data from an entity computing system, third-party computing system, or data source and receiving or identifying cyber resilience data from at least one computing system internal to the DNCNDS comprises receiving or identifying cyber resilience data from an agent network, second autonomous agent, or orchestration computing system. (Lem [010-12] the one or more metrics, one or more behaviors or a combination thereof, that the causal graph is based on are received from a database, input through a graphical user interface, or any combination thereof. determining the root cause further comprises identifying which node has a biggest effect on the value and whether a change in value has a negative or positive effect on performance based on a type of the one or more metrics or the one or more behaviors of the node. Determining one or more agents that have decreased in performance further comprising receiving, by the processor, the key performance indicators from another computing device, a graphical user interface, or any combination thereof).
Claim 19: Lem teaches a non-transitory computer readable medium (CRM) comprising one or more instructions stored thereon and executable by one or more processors to (Fig. 1, [018]): receive or identify, from at least one computing system external or internal to a [decentralized] network, centralized network, or [data] source (DNCNDS), cyber resilience data, wherein the DNCNDS is registered with a plurality of autonomous agents to allow the plurality of autonomous agents to be [discoverable by at least one entity] computing system via the DNCNDS; perform at least one cyber resilience operation of a plurality of cyber resilience operations based at least on: selecting at least one function of a dynamic data structure based at least on the cyber resilience data; or generating a new function based at least on one or more frameworks of the dynamic data structure; generate one or more tokens comprising at least one proof of the at least one cyber resilience operation; and update the dynamic data structure based on the at least one cyber resilience operation performed. ([08] determining, by a processor, a causal graph based on key performance indicators, behaviors, and metrics. Also determining, by the processor, one or more agents of a plurality of agents that have a decrease in performance based on key performance indicators; [013] The processor is configured to determine one or more agents of a plurality of agents. [064] A root cause analysis service (as implemented on a server) can transmit a first request to get data to a data storage. The get data request can include an identifier for an agent (agentId), KPI and a current time period; [064] The values for the particular KPI for the particular agentId over the current time period can be returned from the data storage to the root cause analysis service. [015] the one or more metrics, one or more behaviors or a combination thereof, that the causal graph is based on are received from a database, input through a graphical user interface, or any combination thereof. [052] involve determining, a causal graph based on key performance indicators, behaviors and metrics. The input to the causal graph can be historical values of metrics, key performance indicators (KPIs) and/or behaviors. KPIs can be a measure of performance, metrics can be factors that contribute to the KPIs, and behaviors can also contribute to the KPIs; [065-66] The values for the particular KPI for the particular agentId over the previous time period can be returned from the data storage to the root cause analysis service. The root cause analysis service can determine an average of the values. The root cause analysis service can determine a difference between the average values for the current period and the average values for the previous period, and if the difference is above a threshold, then the agent that corresponds to agentId is transmitted to a candidate list database. The candidate list database can include a list of all agents of the plurality of agents that have had a decrease in performance).
Lem is silent on decentralized network or data source and agents or service discoverable by at least one entity and generate one or more tokens comprising at least one proof of the at least one cyber resilience operation;
But analogous art Tho teaches decentralized network or data source and agents or service discoverable by at least one entity and generate one or more tokens comprising at least one proof of the at least one cyber resilience operation; ([073, 117] device connectivity data includes data obtained from a search or discovery engine for Internet-connected devices which includes a third-party product (e.g., Shodan), a proprietary product, or a combination thereof; [104] the analysis circuit utilizes a distributed ledger to tokenize and broadcast the security posture, ensuring transparency and immutability. a “security posture” refers to the current state and overall cybersecurity profile of an organization or vendor. [238, 241, 260] the decentralized nature of a distributed ledger, is maintained by multiple nodes or participants in the network. Broadcasting the token to the distributed ledger ensures that the token(s), representing the security posture, is stored in a decentralized, immutable, and transparent manner).
Therefore, it is prima facie obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to modify the invention of Lem to include the idea of decentralized network and tokens as proof of cyber resilience as taught by Tho so that time and effort savings for users, a reduction in errors due to manual data entry, and an increased likelihood of consistent and accurate information across multiple forms [178].
Claim 20: the combination of Lem and Tho teaches the CRM of claim 19, wherein performing the at least one cyber resilience operation comprises: generating a data package corresponding to the new function or at least one function of the dynamic data structure, wherein the data package comprises one or more deployable sub-functions of the new function or at least one function; and providing, via the DNCNDS, the data package configured to deploy on at least one entity computing system. (Lem: [0114] The list of metrics can be shared through the cloud, e.g., stream (e.g., the AWS Kinesis stream), messaging queue, and/or S3, where they can be used by the coaching manager service to automatically create a coaching package and/or by a second performance application to automatically select relevant interactions for evaluation (i.e., package for deployment)).
Allowable Subject Matter
Claims 7 and 16 are objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. See PTO-892.
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/BADRINARAYANAN /Primary Examiner, Art Unit 2494.
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