DETAILED ACTION
1. The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Status of Claims
2. Claims 1-11 are currently pending. Claims 1-8 have been amended. Claims 9-11 have been added as new claims. Claims 1-11 have been rejected.
Status of the Application
3. Claims 1-11 are currently pending and have been examined in this application. This communication is the first action on the merits.
Response to Amendments
4. Applicant’s amendment filed on 05/14/2026 necessitated new grounds of rejection in this office action.
Foreign Priority
5. The Examiner has noted the Applicants claiming Priority from Foreign Application IL284896 filed on 07/15/2021. Receipt is acknowledged of papers submitted under 35 U.S.C. § 119(a)-(d), which papers have been placed of record in the file. Therefore, the earliest effective filing date examined for this case is 07/15/2021.
Continued Examination under 37 CFR 1.114
6. 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and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114.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 05/14/2026 has been entered.
Response to Arguments
7. Due to Applicant’s proposed claim amendments, Examiner adds Claim Objections to Claims 1-11. See Claim Objections Section shown below.
8. Due to Applicant’s proposed claim amendments, Examiner adds 35 U.S.C. § 112 (b) Claim Rejections to Claims 1-11. See 35 U.S.C. § 112 (b) Claim Rejections Section shown below.
9. Applicant’s arguments, see pages 11-13 filed on 05/14/2026, with respect to the 35 U.S.C. § 101 Claim Rejections for Claims 1-11 have been fully considered and are found to be persuasive.
Therefore, the 35 U.S.C. § 101 Claim Rejections for Claims 1-11 have been withdrawn. See the 35 U.S.C. § 101 Subject Matter Eligibility Analysis Section below explaining why Claims 1-11 are deemed patent eligible over 35 U.S.C. § 101.
10. Applicant’s arguments, see pages 13-16 filed on 05/14/2026, with respect to the 35 U.S.C. § 102 (a) (1) Claim Rejections for Claims 1-8 have been fully considered and are found to be persuasive. Therefore, the 35 U.S.C. § 102 (a) (1) Claim Rejections for Claims 1-11 have been withdrawn. See Examining Claim with Respect to Prior Art Section shown below.
Claim Objections
11. Claims 1-11 are objected to because of the following informalities:
(A). The preambles for Claims 2-11 which each recite the following: “The system for executing a multi-agent mission according to claim 1,…” Each of these preambles for Claims 2-11 have a minor claim informality regarding “a multi-agent mission” and should be amended as “the multi-agent mission” since they are referring back to parent Independent Claim 1 as the 2nd instance. Therefore, for the purposes of examination, Examiner suggests to Applicant to amend the preamble of Dependent Claims 2-11 to recite the following: “The system for executing [[ the multi-agent mission according to claim 1,….”.
(B). The 3rd claim limitation of Independent Claim 1 recites the following: “at least one Multi-Agent Planner (MAP), executed by the one or more computer processors, configured to receive said set of MAL missions and said world-sate input, compute a k-degree polynomial based on said world-state input and the set of MAL missions, and generate an allocation vector mapping said MAL missions to said plurality of n autonomous platforms by maximizing said k-degree polynomial or computing an allocation that provides a constant-fraction approximation of a maximum value of said k-degree polynomial.” There appears to be a minor claim informality regarding “said MAL missions” which should be amended as “said set of MAL missions” or “the MAL mission”. It is unclear whether this instance should be a “singular” context or a “plural” context (e.g., more than one). For the purposes of examination, Examiner suggests to Applicant to amend the 3rd claim limitation of Independent Claim 1 to recite: “at least one Multi-Agent Planner (MAP), executed by the one or more computer processors, configured to receive said set of MAL missions and said world-sate input, compute a k-degree polynomial based on said world-state input and the set of MAL missions, and generate an allocation vector mapping said set of MAL missions to said plurality of n autonomous platforms by maximizing said k-degree polynomial or computing an allocation that provides a constant-fraction approximation of a maximum value of said k-degree polynomial.”
(C). The 2nd claim limitation of Dependent Claim 2 recites the following: “wherein the Tactical Group Planners of said set of allocated autonomous platforms are configured to compute actuator-level commands according to said GD identification.” There appears to be a minor claim informality regarding “the Tactical Group Planners” when referring back to Independent Claim 1 which as “Tactical Group Planner (TGP)” as a singular context. Examiner suggests to Applicant to amend the 2nd claim limitation of Dependent Claim 2 as: “wherein [[ Tactical Group Planners (TGPs) of said set of allocated autonomous platforms are configured to compute actuator-level commands according to said GD identification.”
(D). The claim limitation of Dependent Claim 4 recites the following: “wherein the Multi-Agent Planner (MAP) and the Tactical Group Planners (TGPs) are executed as asynchronous processes.” There appears to be a minor claim informality regarding “the Tactical Group Planners” when referring back to Independent Claim 1 which as “Tactical Group Planner (TGP)” as a singular context. For the purposes of examination, Examiner suggests to Applicant to amend the claim limitation of Dependent Claim 4 to read as follows: “wherein the at least one Multi-Agent Planner (MAP) and [[ Tactical Group Planners (TGPs) are executed as asynchronous processes.”
(E). The claim limitation of Dependent Claim 10 recites the following: “wherein k remains constant as n varies.” There appears to be a minor claim informality regarding missing “the” since “k” is referred to as the 2nd instance or 2nd time when referring back to Independent Claim 1. For the purposes of examination, Examiner suggests to Applicant to amend the claim limitation of Dependent Claim 10 to read as follows: “wherein the k remains constant as n varies.” Appropriate corrections are required.
Claim Rejections - 35 USC § 112
12. The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
13. Claims 1-11 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
(A). The 1st claim limitation of Independent Claim 1 recites the following: “a mission generator (MG), executed by one or more computer processors, configured to compute, based on world-state input, said world-state input being a collection of data that corresponds to targets and autonomous platforms, a set of Mission Agent Language (MAL) missions, wherein said MAL is an abstract language defining a set of single-agent combat primitives.” There is a lack of antecedent basis with respect to the word or phrase of “said MAL” when referring back to the previous steps shown in Independent Claim 1, which renders this vague and indefinite. It is unclear whether “the Mission Agent Language (MAL)” is being referred to previously as “a set of Mission Agent Language (MAL) missions” or just initially introducing the term “Mission Agent Language (MAL)” which in that case should be amended as “a Mission Agent Language (MAL)”.
For the purposes of examination, Examiner suggests to Applicant to amend the 1st claim limitation of Independent Claim 1 to recite the following: “a mission generator (MG), executed by one or more computer processors, configured to compute, based on world-state input, said world-state input being a collection of data that corresponds to targets and autonomous platforms, a set of Mission Agent Language (MAL) missions, wherein [[ a Mission Agent Language (MAL) [[ is an abstract language defining a set of single-agent combat primitives.”
(B). The 2nd claim limitation of Independent Claim 1 recites the following: “a plurality of n autonomous platforms, each autonomous platform comprising one or more actuators and one or more computer processors executing a Tactical Group Planner (TGP) configured to translate a MAL mission mapped to the respective autonomous platform into actuator-level commands for controlling said one or more actuators.” There is a lack of antecedent basis with respect to the word or phrase of “the respective autonomous platform” when referring back to the previous steps shown in Independent Claim 1, which renders this vague and indefinite. For the purposes of examination, Examiner suggests to Applicant to amend the 2nd claim limitation of Independent Claim 1 to recite the following: “a plurality of n autonomous platforms, each autonomous platform comprising one or more actuators and one or more computer processors executing a Tactical Group Planner (TGP) configured to translate a MAL mission mapped to [[ a respective autonomous platform into actuator-level commands for controlling said one or more actuators.”
(C). The 4th claim limitation of Independent Claim 1 recites the following: “wherein k is a correlation order corresponding to a maximum number of autonomous platforms in a tactical group considered by the MAP per MAL mission, and k<n.” There is a lack of antecedent basis with respect to the word or phrase of “the MAP per MAL mission” when referring back to the previous steps shown in Independent Claim 1, which renders this vague and indefinite. For the purposes of examination, Examiner suggests to Applicant to amend the 4th claim limitation of Independent Claim 1 to recite the following: “wherein k is a correlation order corresponding to a maximum number of autonomous platforms in a tactical group considered by [[ a Multi-Agent Planner (MAP) per Mission Agent Language (MAL) mission [[, and k<n.”
Furthermore, Dependent Claims 2-11 depend from Independent Claim 1 and therefore inherit the 35 U.S.C. § 112 (b) deficiencies of Independent Claim 1 discussed above. Appropriate corrections are required.
35 U.S.C. § 101 Subject Matter Eligibility Analysis
14. 35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
15. Step 1: Claims 1-11 are focused to a statutory category namely, a “system” or an “apparatus” (Claims 1-11).
Step 2A Prong 1: Does Claims 1-11 Recite an Abstract Idea, Law of Nature, or Natural Phenomenon?
Examiner performs the following 35 U.S.C. § 101 analysis of Independent Claim 1 under step 2a prong 1.
The first claim limitation step of “a mission generator (MG)… configured to compute, based on world-state input… a set of Mission Agent Language (MAL) missions…” This claim limitation step does not clearly recite a mental process, because the mission generation is based on machine world-state inputs for autonomous systems nor does it recite a method of organizing human activity. This limitation is tied to autonomous platforms, combat primitives, machine missions and real-world operational control, which renders this not directed to an abstract idea.
The second claim limitation step of “translating a MAL mission… into actuator-level commands…” This is not directed to an abstract idea. Here, this step directly controls physical actuators. It transforms mission abstractions into machine-control signals and it improves autonomous machine operation. This resembles robotics control, industrial automation and autonomous vehicle control. Federal Circuit cases routinely treat actuator-level control as technological rather than abstract.
The third claim limitation step of “compute a k-degree polynomial… generate an allocation vector… maximizing said polynomial or computing a constant-fraction approximation…” This expressly recites computation of a polynomial, optimization, approximation algorithms and mathematical maximization. This squarely implicates mathematical concepts, mathematical relationships and optimization algorithms.
The fourth claim limitation step of “wherein k is a correlation order corresponding to a maximum number of autonomous platforms in a tactical group considered by the MAP per MAL mission, and k<n” is a mathematical parametrization of the optimization framework. This is likely considered part of the mathematical concept.
Here, the last claim limitation step of “compute… actuator-level commands…. Based on allocation vector and mission assignment information for other autonomous platforms….”. Examiner notes that this step pertains to machine coordination/control. The focus of this step pertains to distributed autonomous coordination, tactical group behavior and machine execution. This step is not a mental process and humans cannot realistically perform distributed real-time autonomous coordination, polynomial optimization across tactical groups, actuator command generation. This step is technological in nature, and therefore not directed to an abstract idea / judicial exception.
Conclusion for Step 2A Prong 1: Examiner notes that Claims 1-11 are determined to be patent eligible under 35 U.S.C. § 101 step 2a prong 1 as not being directed to a judicial exception.
Here, the 35 U.S.C. 101 analysis concludes under step 2a prong 1.
Step 2A Prong 2: Does Claims 1-11 Recite Additional Elements that Integrate the Judicial Exception into a Practical Application under 35 U.S.C. § 101 step 2a prong 2?
Examiner performs the following 35 U.S.C. § 101 analysis of Independent Claim 1 under step 2a prong 2.
Alternatively, even assuming arguendo that Independent Claim 1 was determined to recite a judicial exception, which it is not, Examiner notes that Claims 1-11 recite additional elements that integrate the judicial exception into a practical application under 35 U.S.C. § 101 step 2a prong 2.
Here, the mathematical optimization is practically applied by mapping the computed allocation vector to control physical hardware—specifically, "one or more actuators" on physical autonomous platforms. Independent Claim 1 recites real autonomous platforms, actuators, tactical groups and mission assignments. This anchors the invention in machine control. Transformation into Actuator-Level Commands occurs whereby the optimization is not merely displayed or stored. It is used to generate actuator-level commands, control physical systems, and coordinate autonomous behavior. This is a strong practical application indicator under MPEP § 2106.05 (b), machine control cases and robotics precedents. Distributed Tactical Coordination in Independent Claim 1 improves multi-agent coordination, distributed planning and autonomous tactical execution. Technological Constraint k< n. Independent Claim 1 does not merely optimize generally, as it introduces bounded tactical correlation structures, scalable approximation allocation and distributed coordination architecture. This suggests computational scalability improvements, operational efficiency improvements and technical architecture constraints. Independent Claim 1 applies mathematics in a technological environment, improves autonomous platform coordination, controls real-world machinery and achieves tactical distributed execution.
Therefore, because Independent Claim 1 applies the abstract concept to a specific technological field (autonomous platform control) in a meaningful way, it passes Step 2A. Therefore, Examiner notes that the additional elements of Independent Claim 1 such as (e.g., “Mission Generator (MG)”, “one or more computer processors”, “a plurality of autonomous platforms”, “one or more actuators”, “Tactical Group Planner (TGP)”, “actuator-level commands”, “Mission Agent Language (MAL)” & “MAL mission”) when considered in view of the claim limitations both individually and as an ordered combination (as a whole), these additional elements integrate the judicial exception into a practical application due to (1) improvements to the functioning of a computer, or to any other technology or technical field (see MPEP § 2106.05 (a)) or (2) use of a particular machine (MPEP § 2106.05 (b)) or (3) applying or using the judicial exception in some other meaningful way beyond generally linking the use of the judicial exception to a particular technological environment, such that the claim as a whole is more than a drafting effort designed to monopolize the exception (see MPEP § 2106.05 (e)).
Conclusion for Step 2A Prong 2: Examiner notes that Claims 1-11 are determined to be patent eligible under 35 U.S.C. § 101 step 2a prong 2 as reciting additional elements that integrate the judicial exception into a practical application.
In Summary: Claims 1-11 are patent eligible under the 35 U.S.C. § 101 analysis.
Examining Claims with Respect to Prior Art
16. Applicant’s arguments, see pages 13-16 filed on 05/14/2026, with respect to the 35 U.S.C. § 102 (a) (1) Claim Rejections for Claims 1-11 have been fully considered and are found to be persuasive. Therefore, Claims 1-11 overcome the prior art rejections. Please note that the following issues still remain: (1) Claim Objections for Claims 1-11 and (2) 35 U.S.C. § 112 (b) Claim Rejections for Claims 1-11.
Regarding Independent Claim 1, there is no disclosure in the existing prior art or any new art that either teaches and/or discloses the sequence operation of features either individually or in combination relating to:
- at least one Multi-Agent Planner (MAP), executed by one or more computer processors, configured to receive said set of MAL missions from said world-state input, compute a k-degree polynomial based on said world-state input and the set of MAL missions, and generate an allocation vector mapping said MAL missions to said plurality of n autonomous platforms by maximizing said k-degree polynomial or computing an allocation that provides a constant-fraction approximation of a maximum value of said k-degree polynomial;
- wherein k is a correlation order corresponding to a maximum number of autonomous platforms in a tactical group considered by the MAP per MAL mission, and k < n;
- wherein each Tactical Group Planner (TGP) is further configured to computer, for the respective autonomous platform, actuator level commands for performing the MAL mission mapped to the respective autonomous platform, based on the allocation vector and on MAL mission assignment information for one or more other autonomous platforms that are part of a common tactical group for said MAL mission.onf
The closest prior arts are as follows:
#1) US PG Pub (US 2020/0134491 A1) – “Swarm System Including an Operator Control Section…”, hereinafter Cruise.
#2) US PG Pub (US 2022/0027798 A1) – “Autonomous Behaviors in a Multiagent Adversarial Scene”, hereinafter Liebman, et. al.
#3) US PG Pub (US 2020/0410399 A1) – “Method and System for Determining Policies, Rules, and Agent Characteristics, for Automating Agents, and Protection, hereinafter Lang, et. al.
Regarding Independent Claim 1, Cruise system for multi-agent mission planning teaches the following:
- a mission generator (MG) (see at least Cruise: ¶ [0063] & Fig. 4.) configured to compute a set of Mission Agent Language (MAL) missions (see at least Cruise: ¶ [0057] & ¶ [0094-0095] & ¶ [0117-0118]. Cruise teaches that OA Agents 61 are another information fusion agent. A single Level 1 OA agent is provided on a given platform. An exemplary force/tactical group can be defined as a set N={1, 2, . . . , n} of UxSs that each host a single OA Agent 61. A swarm includes of a set N={1, 2, . . . , n} of force/tactical groups of which a given UxS is one, each hosting a single SA Agent 71. A group's SA Agent 71 composes situation vectors from patterns of OSVs within a group-distributed OSV database 65. The n SA Agents 71 in an entire swarm of force tactical groups interact with each other to update the swarm-distributed situation vectors (SV) database 75, which is a distributed database accessible to the swarm's SA Agents 71. See also Cruise at ¶ [0117-0118]. Cruise teaches that Integrated engagement control agents take mission actions as input, undertake action decomposition or diffusion, and output patterns (aka plans) of integrated engagement sensing, fires, and platform control actions. The cohort of integrated engagement control agents aboard the entire command-guided swarm, each integrated engagement control agent hosted by one force tactical group of UxSs, participate in repeated play of a game to determine which integrated engagement control agent decomposes which mission action stored in the swarm-distributed mission plans database.)
- a group of agents (TGPs) each capable of performing at least one MAL mission (see at least Cruise: ¶ [0057] & ¶ [0060-0061]. Cruise teaches that operator infusion agents 31 enable and operate with a human-in-the-loop for interpreting/assessing processed information/data, establishing mission objectives or making engagement decisions, and interacting with CGS SoS for purposes of enabling machine learning and fusion/diffusion augmentation/refinement. See also Cruise at ¶ [0060]: Engagement Control Agents' 73 knowledge, and for mission planning with the Mission Control Agents' 91 knowledge. Operator Infusion Agent 31 has a variety of interactions. For example, multiple Operator Infusion Agents 31 share among themselves access to the human operator guiding their force/tactical group 34 such that the human operator is not cognitively overloaded; e.g., all Operator Infusion Agents 31, each aboard a different force/tactical group 34, must divide up or allocate among themselves channels of their user interface to an assigned human operator by playing a game to decide which Operator Infusion Agent 31 will access a given user interface channel for purposes of direct communication with the human operator at a given time or under a set of circumstances. See also Cruise at ¶ [0061]: Mission control (MC) SIMD agents 91 (A Sub-Class of the Control Diffusion Class 29).)
- at least one Multi-Agent Planner (MAP) adapted to receive said set of MAL missions from said MG to build an allocation vector between said MAL missions and said agents in said group (see at least Cruise: ¶ [0077] & ¶ [0085-0086] & ¶ [0101]. Cruise notes that IEC Agents 73 can request operator action decomposition preferences for formulating force/tactical group integrated engagement plan from swarm mission plan actions. MC agents 91 queries can seek human operator action decomposition preferences for formulating swarm mission plans from high level mission objectives. See also Cruise at ¶ [0077]: An IF SRL model seeks, first, to recognize a complex pattern formed among multiple feature vectors within an input feature space, and second, to output a new higher-level feature vector characterizing these patterns. The output feature vector essentially labels the pattern formed by the multiple input feature vectors. The completed output vector itself becomes a feature vector in the next higher-level feature space. See also Cruise at ¶ [0082]: The ith MC agent's utility depends upon a subset of threat vectors contained in the swarm-distributed threat vectors (TV) database, with subset members denoted Vi. This subset of threat vectors is directly relevant to the ith force/tactical group, where relevancy has been determined by the ith threat assessment (TA) agent. See also Cruise at ¶ [0085-0086]: ith IEC agent's 73 utility is higher when the MP action ai is relevant to the ith force/tactical group, and when the set a−i includes MP actions less relevant to the ith force/tactical group, considering the relevant current situation vector(s) Vi. On the other hand, if the set a−i contains MP actions more relevant to the ith force/tactical group than the MP action ai, considering the relevant current situation vector(s) Vi, then the ith IEC agent's 73 utility is lower.)
- wherein each agent (TGP) (see at least Cruise: ¶ [0150]. Cruise teaches that each IEC agent 73 in the cohort now knows which MP action to decompose into the lower-level actions of an integrated engagement plan (IEP). An integrated engagement plan describes how a single mission plan action is implemented by a specified force/tactical group of autonomous systems within the CGS. A tactical group of autonomous systems including integrated sensing, fires, and platform resources.) is further capable of performing its assigned MAL mission per said allocation vector (see at least Cruise: ¶ [0059] & ¶ [0077] & ¶ [0082]. Cruise teaches that control diffusion agents include diffusion or pattern generation of multiple action vectors (or simply actions); e.g., generate device control signals (a device signaling action set or plan is generated or decomposed from a UxS action), UxS actions (a UxS action set or plan is generated or decomposed from a force/tactical group integrated engagement action), integrated engagement actions (an integrated engagement action set or plan is generated or decomposed from a mission action). See also Cruise at ¶ [0018] & ¶ [0058]: “Task allocations followed by additional agent gaming and task allocation/interactions. Allocation of tasking out to specific engagement capabilities is performed by the control diffusion agents 29.” See also Cruise at ¶ [0077]: An IF SRL model seeks, first, to recognize a complex pattern formed among multiple feature vectors within an input feature space, and second, to output a new higher-level feature vector characterizing these patterns. The output feature vector essentially labels the pattern formed by the multiple input feature vectors. The completed output vector itself becomes a feature vector in the next higher-level feature space. See also Cruise at ¶ [0082]: The ith MC agent's utility depends upon a subset of threat vectors contained in the swarm-distributed threat vectors (TV) database, with subset members denoted Vi. This subset of threat vectors is directly relevant to the ith force/tactical group, where relevancy has been determined by the ith threat assessment (TA) agent. See also Cruise at ¶ [0085-0086]: ith IEC agent's 73 utility is higher when the MP action ai is relevant to the ith force/tactical group, and when the set a−i includes MP actions less relevant to the ith force/tactical group, considering the relevant current situation vector(s) Vi. On the other hand, if the set a−i contains MP actions more relevant to the ith force/tactical group than the MP action ai, considering the relevant current situation vector(s) Vi, then the ith IEC agent's 73 utility is lower. See also Cruise at ¶ [0140]: The force/tactical group's global objective is that the most important planned group integrated engagement actions in the group-distributed IEP database are optimally decomposed for sensing and allocated to the group's UxSs, in light of the most critical battlespace objects identified in the group-distributed OSV database.) while considering all other agents in its corresponding tactical group (see at least Cruise: ¶ [0055] & ¶ [0059-0060] & Fig. 3B. Cruise teaches that from a physical perspective, embodiments of a CGS can be divided into force or tactical groups of UxSs. These force/tactical groups are subject to integrated engagement plans that address integrated fires, integrated maneuver, and integrated posturing/positioning among the UxSs within the force/tactical group. These groups may be considered as “swarms within swarms”. See also Cruise at ¶ [0059]: UxS actions (a UxS action set or plan is generated or decomposed from a force/tactical group integrated engagement action), integrated engagement actions (an integrated engagement action set or plan is generated or decomposed from a mission action). See also Cruise at ¶ [0060] & Fig. 3B: “multiple Operator Infusion Agents 31 share among themselves access to the human operator guiding their force/tactical group 34 such that the human operator is not cognitively overloaded; e.g., all Operator Infusion Agents 31, each aboard a different force/tactical group 34, must divide up or allocate among themselves channels of their user interface to an assigned human operator by playing a game to decide which Operator Infusion Agent 31 will access a given user interface channel for purposes of direct communication with the human operator at a given time or under a set of circumstances.”).
However, neither Cruise and the other prior art of record do not reach or render obvious the sequence of limitations directed to:
- at least one Multi-Agent Planner (MAP), executed by one or more computer processors, configured to receive said set of MAL missions from said world-state input, compute a k-degree polynomial based on said world-state input and the set of MAL missions, and generate an allocation vector mapping said MAL missions to said plurality of n autonomous platforms by maximizing said k-degree polynomial or computing an allocation that provides a constant-fraction approximation of a maximum value of said k-degree polynomial;
- wherein k is a correlation order corresponding to a maximum number of autonomous platforms in a tactical group considered by the MAP per MAL mission, and k < n;
- wherein each Tactical Group Planner (TGP) is further configured to computer, for the respective autonomous platform, actuator level commands for performing the MAL mission mapped to the respective autonomous platform, based on the allocation vector and on MAL mission assignment information for one or more other autonomous platforms that are part of a common tactical group for said MAL mission.onfi
Regarding the Liebman reference, Liebman teaches or suggests that for two agents, the set becomes {da1, AAa1, AOa1, da2, AAa2, AOa2}, with additional EAa1, EAa2 for three dimensionality. {d, AA, AO} provides a local description suited to sub-scenes and in a reference frame centered on the target. It is not affected by translations and rotations in the global space. It is understood that this is merely one example for a workable state representation, and that other state representations exist. For example, an image representation of the scene, a matrix representation, or a vector representation can be used. Multiagent DDPG (MA-DDPG) (a deterministic, multiagent, joint learning algorithm that is an extension of DDPG, and requires a continuous action space). In this example scenario, the selected algorithm is PPO with a continuous action space, with actions defined as a vector of changes in velocity, rotation, and heading with limits of 20° on how fast the agent can turn per step. See at Liebman at ¶ [0060] that Once the cost matrix is calculated, assignment is optimally derived using an extension to the Hungarian method, a combinatorial optimization algorithm which solves the assignment problem and runs efficiently in time polynomial in the number of tasks and agents (in this case, the total number of agents). For cases in which agents remain unassigned, they are given defensive roles. At this point a full assignment is generated.
However, neither Liebman and the other prior art of record do not reach or render obvious the sequence of limitations directed to:
- at least one Multi-Agent Planner (MAP), executed by one or more computer processors, configured to receive said set of MAL missions from said world-state input, compute a k-degree polynomial based on said world-state input and the set of MAL missions, and generate an allocation vector mapping said MAL missions to said plurality of n autonomous platforms by maximizing said k-degree polynomial or computing an allocation that provides a constant-fraction approximation of a maximum value of said k-degree polynomial;
- wherein k is a correlation order corresponding to a maximum number of autonomous platforms in a tactical group considered by the MAP per MAL mission, and k < n;
- wherein each Tactical Group Planner (TGP) is further configured to computer, for the respective autonomous platform, actuator level commands for performing the MAL mission mapped to the respective autonomous platform, based on the allocation vector and on MAL mission assignment information for one or more other autonomous platforms that are part of a common tactical group for said MAL mission.onfi
Regarding the Lang reference, Lang teaches or suggests that unstructured data (e.g. unstructured documents) is read into the metadata extraction entity (step 405). From this data, the metadata extraction entity extracts a characteristics vector (step 410), which involves determining characteristic metadata vectors of unstructured info using machine learning techniques known to those skilled in the art (e.g. the abovementioned). An environment can pertain to Information Technology (IT) environments, for example assets (systems, applications, networks, information buses etc.), as well as behaviors (such a network traffic, state changes, sensor/actuator changes etc.) In IT environments, the agent's actions can include for example inputs, data processing, and outputs related to an IT environment, for example configuring/re-configuring, generating, reading/ingesting, writing, transmitting, configuring etc. A user GUI 716, which provides mission information, login credentials into the defender playing field machine(s), pre/post briefing, hints/cheats, tutorials etc. A neural network learning engine 720 includes two main parts: firstly, it trains one or more neural networks using the abovementioned simulation; secondly, the trained neural network(s) are used to predict/select nodes through the attacker graph/tree, which is executed by the attacker graph execution engine 730.
However, neither Lang and the other prior art of record do not reach or render obvious the sequence of limitations directed to:
- at least one Multi-Agent Planner (MAP), executed by one or more computer processors, configured to receive said set of MAL missions from said world-state input, compute a k-degree polynomial based on said world-state input and the set of MAL missions, and generate an allocation vector mapping said MAL missions to said plurality of n autonomous platforms by maximizing said k-degree polynomial or computing an allocation that provides a constant-fraction approximation of a maximum value of said k-degree polynomial;
- wherein k is a correlation order corresponding to a maximum number of autonomous platforms in a tactical group considered by the MAP per MAL mission, and k < n;
- wherein each Tactical Group Planner (TGP) is further configured to computer, for the respective autonomous platform, actuator level commands for performing the MAL mission mapped to the respective autonomous platform, based on the allocation vector and on MAL mission assignment information for one or more other autonomous platforms that are part of a common tactical group for said MAL mission.onfi
Therefore, when taken as a whole, the claims are not rendered obvious as the available prior art does not suggest or otherwise render obvious the noted features nor do the available art suggest or otherwise render obvious further modification of the evidence at hand. Such modification would require substantial reconstruction relying solely on improper hindsight bias, and thus would not be obvious.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to DERICK HOLZMACHER whose telephone number is (571) 270-7853. The examiner can normally be reached on Monday-Friday 9:00 AM – 6:30 PM EST.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Brian Epstein can be reached on 571-270-5389. The fax phone number for the organization where this application or proceeding is assigned is 571-270-8853.
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/DERICK J HOLZMACHER/Patent Examiner, Art Unit 3625A
/BRIAN M EPSTEIN/Supervisory Patent Examiner, Art Unit 3625
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