DETAILED ACTION
This communication is a Final Office Action rejection on the merits. Claims 1-5, 7-13, and 15-22 are currently pending and have been addressed below.
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 (IDS)
The information disclosure statement(s) filed on 09/03/2026 comply with the provisions 37 CFR 1.97, 1.98, and MPEP 609 and is considered by the Examiner.
Response to Arguments
Applicant's arguments filed 09/03/2026 (related to the 103 Rejection) have been fully considered but are moot in view of new grounds of rejection. Applicant's amendments necessitated the new ground(s) of rejection presented in this Office action. Rejection based on a newly cited reference(s) follows.
Applicant's arguments filed on 09/03/2026 (related to the 101 Rejection) have been fully considered but they are not persuasive.
Applicant states, on pages 13-15, that human commanders have planned missions for centuries without needing to normalize threat-effect pair algorithm outputs or aggregate probabilistic LOS values hierarchically across domains with per-level confidence values. These are computational challenges that arise because the invention implements multi-domain mission analysis using domain-specific tools that produce incompatible outputs. The claimed normalization and hierarchical aggregation are solutions to problems created by the computer implementation itself, a strong indicator of patent eligibility under McRO and Enfish.
Also, the amended claims recite operations that are categorically not performable in the human mind: per-pixel or per-object ML-based classification of geographical ISR imagery with confidence value output; normalization of disparate threat-effect pair algorithm outputs from domain-specific units into equivalent units; hierarchical aggregation of probabilistic LOS values across activities, echelons, and domains with per-level confidence values; and dependency-based parallel/series scheduling of simulation model execution. These are not mental processes or social activities under any recognized category of the 2019 PEG.
Examiner respectfully disagrees with Applicant. Claim 1 is considered to be an abstract idea the claim limitations are directed to “certain methods of organizing human activity” which include “managing personal behavior.” In this case, providing information to support planning and execution across all echelons and domains is a social activity. Also, normalizing data and calculating confidence values recite mathematical calculations (see MPEP 2106.04(a)(2)). If a claim limitation, under its broadest reasonable interpretation, covers managing personal behavior or mathematical calculations, then it falls within the “certain methods of organizing human activity” and “mathematical concepts” grouping of abstract ideas. Accordingly, the claim recites an abstract idea.
The main functions of the additional elements recited in claim 1 are merely used to: collect data (e.g. COA data & intelligence data), analyze the data (e.g. classify intelligence data as threat or no threat & determine a likelihood of success based on the classification), and display certain results of the collection and analysis (e.g. provide a dynamic view of the likelihood of success as the COA is implemented). Those are functions that the courts have described as merely indicating a field of use or technological environment in which to apply a judicial exception (see MPEP 2106.05(h)). The trained machine learning model is merely used to receive an image and classify objects in the images as either a threat or not a threat. The classification can be made on a per-pixel or per-object basis (see Paragraph 0034). The trained machine learning model is recited at a high level of generality such that it amounts no more than mere instructions to apply the exception using a generic computer element (MPEP 2106.05f). Although the claim recites inputs (e.g., images) and outputs (e.g., threat or not threat classification) of the machine learning model, the claim does not include any specific details about how the machine learning model classifies the images. Thus, the machine learning model is a black box, which is merely claiming the idea of a solution or outcome (MPEP 2106.05(a)). The additional elements of ISR and multiple applications are considered an “insignificant extra-solution activity” since they’re merely used for “necessary data gathering” for an analysis (MPEP 2106.05g). Lastly, the steps of “providing a dynamic location of the threat and threat mitigation activities” and “providing a dynamic view of the LOS” are considered well-understood, routine, and conventional functions since they’re just “receiving or transmitting data over a network” and “performing repetitive calculations” (MPEP 2106.05(d)).
The claim fails to recite any improvements to another technology or technical field, improvements to the functioning of the computer itself, use of a particular machine, effecting a transformation or reduction of a particular article to a different state or thing, adding unconventional steps that confine the claim to a particular useful application, and/or meaningful limitations beyond generally linking the use of an abstract idea to a particular environment. See 84 Fed. Reg. 55. Viewed individually or as a whole, these additional claim element(s) do not provide meaningful limitation(s) to transform the abstract idea into a patent eligible application of the abstract idea such that the claim(s) amounts to significantly more than the abstract idea itself.
Independent claims 9 and 16 recite similar features and therefore are rejected for the same reasons as independent claim 1. Claims 2-5, 7-8, 10-13, 15, and 17-22 are rejected for having the same deficiencies as those set forth with respect to the claims that they depend from, independent claims 1, 9, and 16.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-5, 7-13, and 15-22 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e., an abstract idea) without reciting significantly more.
Independent Claim 1
Step One - First, pursuant to step 1 in the January 2019 Revised Patent Subject Matter Eligibility Guidance (“2019 PEG”) on 84 Fed. Reg. 53, the claim 1 is directed to a method which is a statutory category.
Step 2A, Prong One - Claim 1 recites: A method for mission planning and execution, the method comprising: receiving, by a commander, course of action (COA) data regarding multiple COAs, the COA data including activities, timing of the activities, entities to perform the activities, and threat data, the activities including intelligence gathering and threat mitigation activities, the entities including multiple different domains; coordinating simulation of performing of the activities by the entities, the simulation including gathering the intelligence data based on visibility and location of intelligence, surveillance, and reconnaissance (ISR), wherein gathering the intelligence data comprises: receiving images of a geographical region; classifying objects within the images as a threat or not a threat on a per-pixel or per-object basis; and outputting, based on the classifying, a classification and a corresponding confidence value; determining, based on the classification and the corresponding confidence value, a likelihood of success (LOS) of the COAs, wherein determining the LOS comprises computing a hierarchical LOS across the multiple different domains in which activity-level LOS values are combined to produce echelon-level LOS values, echelon-level LOS values are combined to produce domain-level LOS values, and domain-level LOS values are combined to produce a mission-level LOS, each level including a corresponding confidence value reflecting the statistical reliability of the LOS at that level the analysis engine determines the LOS to normalized threat effect pair, wherein normalization converts results of disparate threat-effect pair models from domain-specific units into equivalent units enabling mathematical combination of results across domains; and executing models of the entities performing the activities; generating a view of the simulation of the COAs including scores associated with each COA; implementing a COA of the COAs selected by the commander; receiving information regarding a state of executing the COA; and providing a view of the state of executing the COA including an overall map of a geographical region in which the COA is implemented, the view including a dynamic location of the threat and threat mitigation activities, and a dynamic view of the LOS updated as the COA is implemented. These claim elements are considered to be abstract ideas because they are directed to “certain methods of organizing human activity” which include “managing personal behavior.” In this case, providing information to support planning and execution across all echelons and domains is a social activity. Also, normalizing data and calculating confidence values recite mathematical calculations (see MPEP 2106.04(a)(2)). If a claim limitation, under its broadest reasonable interpretation, covers managing personal behavior or mathematical calculations, then it falls within the “certain methods of organizing human activity” and “mathematical concepts” grouping of abstract ideas. Accordingly, the claim recites an abstract idea.
Step 2A Prong 2 - The judicial exception is not integrated into a practical application. Claim 1 includes additional elements: a user interface (UI); an orchestrator service including an ISR device; a patterns of life service comprising a trained machine learning model; an analysis engine; a stochastic math modeling; a command and control engine; and multiple applications including an intelligence management service, a non-kinetic fires management service, a video sensor management service, a kinetic fires management service, and a sustainment management service that concurrently operate across the multiple domains.
The UI engine is merely used to: receive data regarding multiple COAs, the COA data including activities, timing of the activities, entities to perform the activities, and threat data, the activities including intelligence gathering and threat mitigation activities, the entities including multiple different domains; and provide a graphical view of the state of executing the COA including an overall map of a geographical region in which the COA is implemented, the graphical view including a dynamic location of the threat and threat mitigation activities, and a dynamic view of the LOS updated as the COA is implemented (Paragraph 0068). The orchestrator service is merely used to: coordinate simulation of performing of the activities by the entities, the simulation including gathering the intelligence data based on visibility and location of intelligence, surveillance, and reconnaissance (ISR) device; generate a graphical view of the simulation of the COAs including scores associated with each COA; and receive information regarding a state of executing the COA (Paragraph 0068). The trained machine learning model is merely used to receive an image and classify objects in the images as either a threat or not a threat. The classification can be made on a per-pixel or per-object basis (see Paragraph 0034). The analysis engine is merely used to determine a likelihood of success (LOS) of the COAs. Also, the analysis engine operates based on threat-effect pairs in which each effect associated with a threat is intended to at least partially neutralize the threat. More details regarding threat-effect pairs are detailed in United States Patent Application Number 17/721,896 titled "Normalized Techniques for Threat Effect Pairing" and filed on April 15, 2022, which is incorporated herein by reference in its entirety. (Paragraph 0068). The stochastic math modeling is merely used to indicate the LOS, the cost, the amount of collateral damage, the amount of attribution for the activity, or a variability thereof (Paragraph 0038). The command and control engine is merely used to execute models of the entities performing the activities (Paragraph 0068). The multiple applications are merely used to receive information regarding a state of executing the COA (Paragraph 0068). Merely stating that the step is performed by a computer component results in “apply it” on a computer (MPEP 2106.05f). These elements of “UI,” “orchestrator service,” “analysis engine,” “command and control engine,” and “applications” are recited at a high level of generality such that it amounts no more than mere instructions to apply the exception using a generic computer element. The UI and the multiple applications are considered “field of use” since they are just used to receive information for a simulation analysis, but the technology is not improved (MPEP 2106.05h). Accordingly, alone and in combination, these additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. Therefore, the claim is directed to an abstract idea.
Step 2B - The claim does not include additional elements that are sufficient to amount significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the claims describe how to generally “apply” the concept of planning a mission based on the results of the simulation. The specification shows that the UI engine is merely used to: receive data regarding multiple COAs, the COA data including activities, timing of the activities, entities to perform the activities, and threat data, the activities including intelligence gathering and threat mitigation activities, the entities including multiple different domains; and provide a graphical view of the state of executing the COA including an overall map of a geographical region in which the COA is implemented, the graphical view including a dynamic location of the threat and threat mitigation activities, and a dynamic view of the LOS updated as the COA is implemented (Paragraph 0068). The orchestrator service is merely used to: coordinate simulation of performing of the activities by the entities, the simulation including gathering the intelligence data based on visibility and location of intelligence, surveillance, and reconnaissance (ISR) device; generate a graphical view of the simulation of the COAs including scores associated with each COA; and receive information regarding a state of executing the COA (Paragraph 0068). The trained machine learning model is merely used to receive an image and classify objects in the images as either a threat or not a threat. The classification can be made on a per-pixel or per-object basis (see Paragraph 0034). The analysis engine is merely used to determine a likelihood of success (LOS) of the COAs. (Paragraph 0068). The stochastic math modeling is merely used to indicate the LOS, the cost, the amount of collateral damage, the amount of attribution for the activity, or a variability thereof (Paragraph 0038). The command and control engine is merely used to execute models of the entities performing the activities (Paragraph 0068). The multiple applications are merely used to receive information regarding a state of executing the COA (Paragraph 0068). Examiner notes that although the claim recites inputs (e.g., images) and outputs (e.g., threat or not threat classification) of the machine learning model, the claim does not include any specific details about how the machine learning model classifies the images. Thus, the machine learning model is a black box, which is merely claiming the idea of a solution or outcome (MPEP 2106.05(a)). Also, the steps of “providing a dynamic location of the threat and threat mitigation activities” and “providing a dynamic view of the LOS” are considered a well-understood, routine, and conventional function since it's just “performing repetitive calculations” and “receiving or transmitting data over a network” (MPEP 2106.05(d)). Further, claim 1 does not provide any specific details of how the selected COA is implemented (MPEP 2106.05(f)). Lastly, the UI is merely used to arrange information in a manner that assists users in processing information more quickly, which is not sufficient to show an improvement in computer functionality (see MPEP 2106.05a). Thus, nothing in the claim adds significantly more to the abstract idea. The claim is ineligible.
Independent claim 9 is directed to an article of manufacture at step 1, which is a statutory category. Claim 9 recites similar limitations as claim 1 and is rejected for the same reasons at step 2a, prong one; step 2a, prong 2; and step 2b. Claim 9 further recites: a non-transitory machine-readable medium; and a machine – which are treated as just an explicit “processor/computer” for executing the operations and are treated under MPEP 2106.05f in the same manner as claim 1. Thus, nothing in the claim adds significantly more to the abstract idea. The claim is ineligible.
Independent claim 16 is directed to an apparatus at step 1, which is a statutory category. Claim 16 recites similar limitations as claim 1 and is rejected for the same reasons at step 2a, prong one; step 2a, prong 2; and step 2b. Claim 16 further recites: a processing circuitry; a display; and a memory – which are treated as just an explicit “processor/computer” for executing the operations and are treated under MPEP 2106.05f in the same manner as claim 1. Thus, nothing in the claim adds significantly more to the abstract idea. The claim is ineligible.
Dependent claims 2, 10, and 17 are directed to an additional element such as: a feasibility service. The feasibility service is merely used to narrow down a list of all possible friendly assets that can perform the actions to only those assets that can satisfy one or more of the feasibility criterion: (i) perform a required activity of the mission, (ii) are or can be in range of the target in time, (iii) have enough fuel to reach the target, (iv) are not adversely impacted by the weather, (v) are not impacted by readiness (training), (vi) are not adversely impacted by the terrain (uses data from modified combined obstacle overlays (MCOOs)), (vii) have sufficient field of view (if a satellite), or a combination thereof (Paragraph 0040). Merely stating that the step is performed by a computer component results in “apply it” on a computer (MPEP 2106.05f) being applicable at both Step 2A, Prong 2 and Step 2B. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept. Thus, nothing in the claim adds significantly more to the abstract idea. The claim is not patent eligible.
Dependent claims 3, 11, and 18 are directed to an additional element such as: a user data service. The user data service is merely used to receive possible actions that can be performed by each feasible entity selected for each activity (Paragraph 0070). Merely stating that the step is performed by a computer component results in “apply it” on a computer (MPEP 2106.05f) being applicable at both Step 2A, Prong 2 and Step 2B. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept. Thus, nothing in the claim adds significantly more to the abstract idea. The claim is not patent eligible.
Dependent claims 4-5, 7, 12-13, 15, and 19-20 are directed to additional elements such as: machine learning tools. The machine learning tools include: an asset optimization service that determines, for each feasible entity of the feasible entities, a time that the entity is to take action to perform a corresponding activity of the activities; a patterns of life service that monitors the geographical region for a new threat; and receive a change in location of the threat (Paragraphs 0070-0071). Merely stating that the step is performed by a computer component results in “apply it” on a computer (MPEP 2106.05f) being applicable at both Step 2A, Prong 2 and Step 2B. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept. In this case, the claims do not provide any specific details about how the machine learning operates (e.g., how the machine learning is trained to identify the best time to attack the enemy and/or identify a threat). See 2024 AI Guidance, example 47, claim 2. Further, the step of “providing updated information regarding a new state of executing the COA” is considered a well-understood, routing, and conventional function since it's just “performing repetitive calculations” and “receiving or transmitting data over a network” (MPEP 2106.05(d)). Thus, nothing in the claim adds significantly more to the abstract idea. The claim is ineligible.
Dependent claims 8 and 21-22 are directed to an additional element such as: a single pane of glass. The single pane of glass is merely used to provide improved course of action support in an integrated common operational picture across the echelons (Paragraph 0001). Merely stating that the step is performed by a computer component results in “apply it” on a computer (MPEP 2106.05f) being applicable at both Step 2A, Prong 2 and Step 2B. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept. Further, the single pane of glass is merely used to arrange information in a manner that assists users in processing information more quickly, which is not sufficient to show an improvement in computer functionality (see MPEP 2106.05a). Thus, nothing in the claim adds significantly more to the abstract idea. The claim is ineligible.
Claim Rejections - 35 USC § 103
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 set forth in Graham v. John Deere Co., 383 U.S. 1, 148 USPQ 459 (1966), that are applied 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.
Claims 1-4, 7, 9-12, and 15-20 are rejected under 35 U.S.C. 103 as being unpatentable over Yuksek (Yuksek, B., Guner, G., Karali, H., Candan, B. and Inalhan, G., 2023. Intelligent wargaming approach to increase course of action effectiveness in military operations. In AIA A SCITECH 2023 Forum (p. 2531)), in view of Bar-Nahum et al. (US 2020/0162489 A1), in further view of Canty et al. (US 2024/0027157 A1).
Regarding claim 1 (Currently Amended), Yuksek discloses a method for mission planning and execution, the method comprising: receiving, by a commander through a user interface (UI), course of action (COA) data regarding multiple COAs, the COA data including activities, timing of the activities, entities to perform the activities, and threat data, the activities including intelligence gathering and threat mitigation activities, the entities including multiple different domains (see Figure 1, Summary of military decision making process, Steps 1-3; Page 1 Introduction, Wargaming is an important part of the MDMP, which is the armies’ doctrinal approach to create operation plans, to predict counter-actions of the opposite forces and to evaluate the effectiveness of the proposed operation plan, since it offers a safe and vicarious reflection of some of the situational and decision-making dynamics associated with armed conflict. Although, there is no single definition for the term ‘war-gaming’, the commonly accepted one dated back to the beginning of 19𝑡ℎ century, which defines it as a simulation of a military operation, by whatever means, using specific rules, data, methods and procedures [4]. Therefore, it is important to give a clear statement about wargaming before to proceed with the definition and importance of MDMP. The MDMP starts with the receipt of a mission from higher headquarters. Then, mission analysis is performed by utilizing intelligence from other sources. In the next step, it processes commander’s intents, operational requirements and available resources to develop course of actions (COAs) which include task organization plans. After development of the COA, COA analysis, which is focused on action, reaction, counteraction, and the adjudication process, is performed through wargaming to refine the COAs and potential decision points);
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coordinating, by an orchestrator service, simulation of performing of the activities by the entities, the simulation including gathering the intelligence data based on visibility and location of intelligence, surveillance, and reconnaissance (ISR) device, wherein gathering the intelligence data comprises (see Figure 1, Summary of military decision making process, Step 4 Wargame; Pages 1-2, Introduction, After development of the COA, COA analysis, which is focused on action, reaction, counteraction, and the adjudication process, is performed through wargaming to refine the COAs and potential decision points. In this study, it is proposed to develop intelligence, surveillance, and reconnaissance (ISR) and suppression of enemy air defense (SEAD) operation plans that are supported by upper AI and assistive, decentralized decision-making strategies within a war-game to evaluate generated COAs in terms of probability of success, survivability of the assets and operational efficiency):
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receiving, …, images of a geographical region captured by the ISR device; classifying, … objects within the images as a threat or not a threat on a per-pixel or per-object basis; and outputting, by the patterns of life service and based on the classifying, a classification … (Page 2, Introduction, In this study, it is proposed to develop intelligence, surveillance, and reconnaissance (ISR) and suppression of enemy air defense (SEAD) operation plans that are supported by upper AI and assistive, decentralized decision-making strategies within a war-game to evaluate generated COAs in terms of probability of success, survivability of the assets and operational efficiency; Page 6, B. Lethality Heatmap Generation, Example heatmap generation results for air, ground and sea layers are given in Figure 5. Here, lethality heatmap of the red team units are given for a) ground layer, b) Naval layer, and c) Air layer. On the ground layer, both UAV, UGV, USV and CAD units are effective threats against the blue forces in given letality areas. On the naval layer, USV is the main threat but UAV, UGV and CAD are also effective. On the air layer, in first look, there seems no threat for blue team because the UAV, UGV and USV of the red team are not effective against the air force of the blue team. However, if CAD asset presence exists in the area, it would be crucial threat against the blue and results would be fatal);
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determining, based on the classification …, a likelihood of success (LOS) of the COAs by an analysis engine, wherein determining the LOS comprises computing a hierarchical LOS across the multiple different domains in which activity-level LOS values are combined to produce echelon-level LOS values, echelon-level LOS values are combined to produce domain-level LOS values, and domain-level LOS values are combined to produce a mission-level LOS, each level including a corresponding confidence value reflecting the statistical reliability of the LOS at that level the analysis engine determines the LOS by applying stochastic math modeling (SMM) to normalized threat effect pair algorithms, wherein normalization converts results of disparate threat-effect pair models from domain-specific units into equivalent units enabling mathematical combination of results across domains (see Figure 1, Summary of military decision making process, Step 5 COA Comparison; Pages 1-2, Introduction, After development of the COA, COA analysis, which is focused on action, reaction, counteraction, and the adjudication process, is performed through wargaming to refine the COAs and potential decision points. In this study, it is proposed to develop intelligence, surveillance, and reconnaissance (ISR) and suppression of enemy air defense (SEAD) operation plans that are supported by upper AI and assistive, decentralized decision-making strategies within a war-game to evaluate generated COAs in terms of probability of success, survivability of the assets and operational efficiency; Page 6, B. Lethality Heatmap Generation, Example heatmap generation results for air, ground and sea layers are given in Figure 5. Here, lethality heatmap of the red team units are given for a) ground layer, b) Naval layer, and c) Air layer. On the ground layer, both UAV, UGV, USV and CAD units are effective threats against the blue forces in given letality areas. On the naval layer, USV is the main threat but UAV, UGV and CAD are also effective. On the air layer, in first look, there seems no threat for blue team because the UAV, UGV and USV of the red team are not effective against the air force of the blue team. However, if CAD asset presence exists in the area, it would be crucial threat against the blue and results would be fatal; Page 8, C. Combat Model, Combat modeling abstracts and simplifies the combat entities, their behaviors, activities, and interrelations to answer defense-related research questions. There is no general model that answers all questions, and even if such a model could be constructed it would become more complex than the reality, as it not only includes real systems but also imagined ones. Combat models can be either stochastic or deterministic. Intuitively, a stochastic model assumes uncertain or probabilistic inputs regarding a situation and makes an indefinite prediction of the results. A deterministic model states exactly what will happen, as if there were no uncertainty. More formally, a stochastic model requires the terminology of the theory of probability for its description, whereas a deterministic model does not. Where F, Hp, L, Ph, Pd, Pt, Pw, El are total firepower, firepower health, lethality, hit probability, detection probability, targeting system reliability, weapon reliability and layer effectiveness, respectively. Here, it is also important to model damage matrix of the military units which deőnes their effectiveness against the enemy forces in an engagement; Examiner notes that a LOS is calculated by taking into consideration different parameters from different domains such as ground, sea, and air);
and executing models of the entities performing the activities by a command and control engine (see Figure 1, Summary of military decision making process, Step 5 COA Comparison; Pages 1-2, Introduction, After development of the COA, COA analysis, which is focused on action, reaction, counteraction, and the adjudication process, is performed through wargaming to refine the COAs and potential decision points. In this study, it is proposed to develop intelligence, surveillance, and reconnaissance (ISR) and suppression of enemy air defense (SEAD) operation plans that are supported by upper AI and assistive, decentralized decision-making strategies within a war-game to evaluate generated COAs in terms of probability of success, survivability of the assets and operational efficiency);
generating, by the orchestrator service, a graphical view of the simulation of the COAs including scores associated with each COA; implementing, by the orchestrator service, a COA of the COAs selected by the commander (see Figure 1, Summary of military decision making process, Step 5 COA Comparison, Recommended COAs, Commander approved COAs and modifications; Pages 1-2, Introduction, After development of the COA, COA analysis, which is focused on action, reaction, counteraction, and the adjudication process, is performed through wargaming to refine the COAs and potential decision points. In this study, it is proposed to develop intelligence, surveillance, and reconnaissance (ISR) and suppression of enemy air defense (SEAD) operation plans that are supported by upper AI and assistive, decentralized decision-making strategies within a war-game to evaluate generated COAs in terms of probability of success, survivability of the assets and operational efficiency);
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receiving, by the orchestrator service and from multiple applications including an intelligence management service, …, a video sensor management service, a kinetic fires management service, and a sustainment management service that concurrently operate across the multiple domains, information regarding a state of executing the COA (Page 2, Introduction, In this study, it is proposed to develop intelligence, surveillance, and reconnaissance (ISR) and suppression of enemy air defense (SEAD) operation plans that are supported by upper AI and assistive, decentralized decision-making strategies within a war-game to evaluate generated COAs in terms of probability of success, survivability of the assets and operational efficiency. This process is developed on the classical MDMP scheme for ease of implementation into the real-world applications and it is able to provide fast evaluation and objective comparison of the COAs before or during the operation; Page 6, B. Lethality Heatmap Generation, Example heatmap generation results for air, ground and sea layers are given in Figure 5. Here, lethality heatmap of the red team units are given for a) ground layer, b) Naval layer, and c) Air layer; As stated in Paragraph 0032 of Applicant’s specification, a kinetic threat may include airplanes or the like. Therefore, based on broadest reasonable interpretation in light of the specification, Yuksek discloses a kinetic fires management system since it can receive real-time intelligence information of the unmanned vehicles. Also, Examiner notes that Yuksek discloses: an intelligence management system since it can receive real-time intelligence information; and a sustainment management service since it can operate across multiple domains such as air support and naval support);
and providing, by the UI, a graphical view of the state of executing the COA including an overall map of a geographical region in which the COA is implemented, …, and a dynamic view of the LOS updated as the COA is implemented (Page 2, Introduction, In this study, it is proposed to develop intelligence, surveillance, and reconnaissance (ISR) and suppression of enemy air defense (SEAD) operation plans that are supported by upper AI and assistive, decentralized decision-making strategies within a war-game to evaluate generated COAs in terms of probability of success, survivability of the assets and operational efficiency. This process is developed on the classical MDMP scheme for ease of implementation into the real-world applications and it is able to provide fast evaluation and objective comparison of the COAs before or during the operation; Page 3, II. Related Works, On the other hand, due to the structure of the centralized task allocation, a persistent communication should be maintained between the agents and the operation base to provide cooperation which requires sending/receiving operational updates).
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Although Yuksek discloses classifying objects within the images as a threat or not a threat based on images of a geographical region captured by the ISR device (e.g., identifying unmanned vehicles that are a threat to the blue team), Yuksek does not specifically disclose wherein the images are classified by a patterns of life service comprising a trained machine learning model.
However, Bar-Nahum et al. discloses receiving, by a patterns of life service comprising a trained machine learning model, images of a geographical region captured by the ISR device; classifying, by the trained machine learning model, objects within the images as a threat or not a threat on a per-pixel or per-object basis; and outputting, by the patterns of life service and based on the classifying, a classification …; determining, based on the classification … (Paragraph 0018, In view of this risk posed by malicious UAVs, it may be necessary to have a system to detect, monitor, track, classify, and/or assess a UAV that has entered a restricted area, such as a protected airspace. For example, a user interface may provide a visual indication of current and past locations of detected UAVs as well their trajectories and corresponding threat level classification ranking (e.g., determined using a machine learning trained model). The user interface may also provide a visual indication of a location of an operator of a UAV (e.g., by detecting and locating source of RF control signal sent by the UAV operator); Paragraph 0020, Examples of sensors included in the second layer of sensors include, but are not limited to, cameras, image sensors, unmanned aerial vehicles (UAVs) equipped with detection sensors, directional sensor, RF sensor, infrared sensor, etc.; Paragraph 0023, The additional information may include whether the detected security event is stationary or moving. The additional information may include object identification information associated with a detected security event, such as make and model of a vehicle, type of UAV, identification of one or more individuals associated with the detected security event. For example, the image sensor may be coupled to an image classifier that is trained to classify detected objects associated with the detected security event; Paragraph 0024, The security event detection system may use the additional information detected by at least one of the selected sensors to determine a risk level assessment associated with the detected security event. The security event detection system may include a machine learning model. A feature vector may be applied to the machine learning model. The feature vector may be comprised of a plurality of elements. Each element may correspond to a feature associated with a security event. A feature value may be based directly or indirectly on the additional information obtained by the one or more selected sensors. In response to the feature vector being applied to the machine learning model, the machine learning model may be configured to output a value that indicates a threat level associated with the detected security event); …
receiving, by the orchestrator service and from multiple applications including an intelligence management service, a non-kinetic fires management service, a video sensor management service, a kinetic fires management service, … (Paragraph 0020, Examples of sensors included in the second layer of sensors include, but are not limited to, cameras, image sensors, unmanned aerial vehicles (UAVs) equipped with detection sensors, directional sensor, RF sensor, infrared sensor, etc.; Paragraph 0023, The additional information may include whether the detected security event is stationary or moving; Paragraph 0058, The sensor data may be associated with a region (e.g., airspace, geographical region, etc.) being monitored. Examples of the sensor data include 2D/3D radar signal data, UAV location tracking data, UAV operator location tracking data, LIDAR data, image/video data, data about detected communication between UAV and operator, and any other signal or information associated with an area being monitored. The sensor data may be received from multiple sources (e.g., 2D/3D radar, RF antenna, camera, LIDAR sensor, etc.). Conventional radar is used to quickly detect and track points in space);
and providing, by the UI, …, the graphical view including a dynamic location of the threat and threat mitigation activities, … (Paragraph 0068, At 210, a user interface is displayed. For example, the user interface provides a visual indication of current and past locations of detected security events, such as UAVs as well their trajectories and corresponding threat level classification ranking (e.g., determined using a machine learning trained model). The user interface may also provide a visual indication of a current, past and/or movement trajectory locations of an operator of a detected UAV (e.g., by detecting and locating source of RF control signal sent by the UAV operator). A threat level score is automatically represented to the user through the user interface. The user interface displays a color trajectory of the UAV along with the threat level score; Paragraph 0070, In various embodiments, for every threat level associated with a detected aerial object/UAV, there is a set of preconfigured actions associated with that threat level. Automatic actions associated with threat levels may be triggered when certain threat levels are assessed. Examples of actions that can be initiated based on threat level assessments include contacting law enforcement, generating a notification/message to send to specified people or organizations, triggering an alarm, sending an interceptor UAV to capture/interdict the detected UAV considered a threat, sending personnel to a where the operator of the UAV is located, initiating a lockdown of a sensitive/restricted area, and starting other procedures).
It would have been obvious to one ordinary skill in the art before the effective filing date to modify the method for mission planning and execution, wherein a likelihood of success is determined for the different COAs based on real-time intelligence data (e.g., detected threats) of the invention of Yuksek to further incorporate wherein the detected threat is identified using a trained machine learning model of the invention of Bar-Nahum et al. because doing so would allow the method to monitor, track, classify, and/or assess a UAV that has entered a restricted area, such as a protected airspace. For example, a user interface may provide a visual indication of current and past locations of detected UAVs as well their trajectories and corresponding threat level classification ranking, wherein the threat level was determined using a machine learning trained model (see Bar-Nahum et al., Paragraph 0018). Further, the claimed invention is merely a combination of old elements, and in combination each element would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable.
Although the combination of Yuksek and Bar-Nahum et al. discloses using a trained machine learning model for classifying objects within the images as a threat or not a threat based on images of a geographical region captured by the ISR device (e.g., threat level score of a detected aerial object/UAV), the combination of Yuksek and Bar-Nahum et al. does not specifically disclose a corresponding confidence value of the classification.
However, Canty et al. discloses and outputting, by the patterns of life service and based on the classifying, a classification and a corresponding confidence value; … based on the classification and the corresponding confidence value, … (Paragraph 0101, Threat detection and analysis module 402 uses sensor measurements, existing knowledge, and/or trained machine learning models related to users, firearms, other assets, and/or conditions within a deployment location to detect whether a threat exists within the deployment location and to analyze the severity of detected threats. The sensor measurements, existing knowledge, and/or trained machine learning models are taken as inputs to threat detection and analysis module 402. Threat detection and analysis module 402 outputs data indicating one or more of a detected threat, a threat severity, or no threat detected; Paragraph 0107, In embodiments, system 100 can prompt a user of a firearm for input verifying that a threat exists. For example, where threat detection and analysis module 402 detects a potential threat with low confidence, a signal may be transmitted to a personal computing device of a user within the deployment location to verify whether a threat is present in the deployment location).
It would have been obvious to one ordinary skill in the art before the effective filing date to modify the method for mission planning and execution, wherein a likelihood of success is determined for the different COAs based on real-time intelligence data (e.g., classify objects as threat or not threat using a trained machine learning model) of the invention of Yuksek and Bar-Nahum et al. to further incorporate wherein the classification includes a corresponding confidence value of the invention of Canty et al. because doing so would allow the method to user a trained machine learning model to detect a potential threat and the confidence (see Canty et al., Paragraphs 0101 & 107). Further, the claimed invention is merely a combination of old elements, and in combination each element would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable.
Regarding claim 9 (Currently Amended), Yuksek discloses a [method] for mission planning and execution, the operations comprising: receiving, by a commander through a user interface (UI), course of action (COA) data regarding multiple COAs, the COA data including activities, timing of the activities, entities to perform the activities, and threat data, the activities including intelligence gathering and threat mitigation activities, the entities including multiple different domains (see Figure 1, Summary of military decision making process, Steps 1-3; Page 1 Introduction, Wargaming is an important part of the MDMP, which is the armies’ doctrinal approach to create operation plans, to predict counter-actions of the opposite forces and to evaluate the effectiveness of the proposed operation plan, since it offers a safe and vicarious reflection of some of the situational and decision-making dynamics associated with armed conflict. Although, there is no single definition for the term ‘war-gaming’, the commonly accepted one dated back to the beginning of 19𝑡ℎ century, which defines it as a simulation of a military operation, by whatever means, using specific rules, data, methods and procedures [4]. Therefore, it is important to give a clear statement about wargaming before to proceed with the definition and importance of MDMP. The MDMP starts with the receipt of a mission from higher headquarters. Then, mission analysis is performed by utilizing intelligence from other sources. In the next step, it processes commander’s intents, operational requirements and available resources to develop course of actions (COAs) which include task organization plans. After development of the COA, COA analysis, which is focused on action, reaction, counteraction, and the adjudication process, is performed through wargaming to refine the COAs and potential decision points);
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coordinating, by an orchestrator service, simulation of performing of the activities by the entities, the simulation including gathering the intelligence data based on visibility and location of intelligence, surveillance, and reconnaissance (ISR) device, wherein gathering the intelligence data comprises (see Figure 1, Summary of military decision making process, Step 4 Wargame; Pages 1-2, Introduction, After development of the COA, COA analysis, which is focused on action, reaction, counteraction, and the adjudication process, is performed through wargaming to refine the COAs and potential decision points. In this study, it is proposed to develop intelligence, surveillance, and reconnaissance (ISR) and suppression of enemy air defense (SEAD) operation plans that are supported by upper AI and assistive, decentralized decision-making strategies within a war-game to evaluate generated COAs in terms of probability of success, survivability of the assets and operational efficiency):
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receiving, …, images of a geographical region captured by the ISR device; classifying, … objects within the images as a threat or not a threat on a per-pixel or per-object basis; and outputting, by the patterns of life service and based on the classifying, a classification … (Page 2, Introduction, In this study, it is proposed to develop intelligence, surveillance, and reconnaissance (ISR) and suppression of enemy air defense (SEAD) operation plans that are supported by upper AI and assistive, decentralized decision-making strategies within a war-game to evaluate generated COAs in terms of probability of success, survivability of the assets and operational efficiency; Page 6, B. Lethality Heatmap Generation, Example heatmap generation results for air, ground and sea layers are given in Figure 5. Here, lethality heatmap of the red team units are given for a) ground layer, b) Naval layer, and c) Air layer. On the ground layer, both UAV, UGV, USV and CAD units are effective threats against the blue forces in given letality areas. On the naval layer, USV is the main threat but UAV, UGV and CAD are also effective. On the air layer, in first look, there seems no threat for blue team because the UAV, UGV and USV of the red team are not effective against the air force of the blue team. However, if CAD asset presence exists in the area, it would be crucial threat against the blue and results would be fatal);
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determining, based on the classification …, a likelihood of success (LOS) of the COAs by an analysis engine, …, wherein determining the LOS comprises computing a hierarchical LOS across the multiple different domains in which activity-level LOS values are combined to produce echelon-level LOS values, echelon-level LOS values are combined to produce domain-level LOS values, and domain-level LOS values are combined to produce a mission-level LOS, each level including a corresponding confidence value reflecting the statistical reliability of the LOS at that level the analysis engine determines the LOS by applying stochastic math modeling (SMM) to normalized threat effect pair algorithms, wherein normalization converts results of disparate threat-effect pair models from domain-specific units into equivalent units enabling mathematical combination of results across domains (see Figure 1, Summary of military decision making process, Step 5 COA Comparison; Pages 1-2, Introduction, After development of the COA, COA analysis, which is focused on action, reaction, counteraction, and the adjudication process, is performed through wargaming to refine the COAs and potential decision points. In this study, it is proposed to develop intelligence, surveillance, and reconnaissance (ISR) and suppression of enemy air defense (SEAD) operation plans that are supported by upper AI and assistive, decentralized decision-making strategies within a war-game to evaluate generated COAs in terms of probability of success, survivability of the assets and operational efficiency; Page 6, B. Lethality Heatmap Generation, Example heatmap generation results for air, ground and sea layers are given in Figure 5. Here, lethality heatmap of the red team units are given for a) ground layer, b) Naval layer, and c) Air layer. On the ground layer, both UAV, UGV, USV and CAD units are effective threats against the blue forces in given letality areas. On the naval layer, USV is the main threat but UAV, UGV and CAD are also effective. On the air layer, in first look, there seems no threat for blue team because the UAV, UGV and USV of the red team are not effective against the air force of the blue team. However, if CAD asset presence exists in the area, it would be crucial threat against the blue and results would be fatal; Page 8, C. Combat Model, Combat modeling abstracts and simplifies the combat entities, their behaviors, activities, and interrelations to answer defense-related research questions. There is no general model that answers all questions, and even if such a model could be constructed it would become more complex than the reality, as it not only includes real systems but also imagined ones. Combat models can be either stochastic or deterministic. Intuitively, a stochastic model assumes uncertain or probabilistic inputs regarding a situation and makes an indefinite prediction of the results. A deterministic model states exactly what will happen, as if there were no uncertainty. More formally, a stochastic model requires the terminology of the theory of probability for its description, whereas a deterministic model does not. Where F, Hp, L, Ph, Pd, Pt, Pw, El are total firepower, firepower health, lethality, hit probability, detection probability, targeting system reliability, weapon reliability and layer effectiveness, respectively. Here, it is also important to model damage matrix of the military units which deőnes their effectiveness against the enemy forces in an engagement; Examiner notes that a LOS is calculated by taking into consideration different parameters from different domains such as ground, sea, and air);
and executing models of the entities performing the activities by a command and control engine (see Figure 1, Summary of military decision making process, Step 5 COA Comparison; Pages 1-2, Introduction, After development of the COA, COA analysis, which is focused on action, reaction, counteraction, and the adjudication process, is performed through wargaming to refine the COAs and potential decision points. In this study, it is proposed to develop intelligence, surveillance, and reconnaissance (ISR) and suppression of enemy air defense (SEAD) operation plans that are supported by upper AI and assistive, decentralized decision-making strategies within a war-game to evaluate generated COAs in terms of probability of success, survivability of the assets and operational efficiency);
generating, by the orchestrator service, a graphical view of the simulation of the COAs including scores associated with each COA; implementing, by the orchestrator service, a COA of the COAs selected by the commander (see Figure 1, Summary of military decision making process, Step 5 COA Comparison, Recommended COAs, Commander approved COAs and modifications; Pages 1-2, Introduction, After development of the COA, COA analysis, which is focused on action, reaction, counteraction, and the adjudication process, is performed through wargaming to refine the COAs and potential decision points. In this study, it is proposed to develop intelligence, surveillance, and reconnaissance (ISR) and suppression of enemy air defense (SEAD) operation plans that are supported by upper AI and assistive, decentralized decision-making strategies within a war-game to evaluate generated COAs in terms of probability of success, survivability of the assets and operational efficiency);
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receiving, by the orchestrator service and from multiple applications including an intelligence management service, …, a video sensor management service, a kinetic fires management service, and a sustainment management service that concurrently operate across the multiple domains, information regarding a state of executing the COA (Page 2, Introduction, In this study, it is proposed to develop intelligence, surveillance, and reconnaissance (ISR) and suppression of enemy air defense (SEAD) operation plans that are supported by upper AI and assistive, decentralized decision-making strategies within a war-game to evaluate generated COAs in terms of probability of success, survivability of the assets and operational efficiency. This process is developed on the classical MDMP scheme for ease of implementation into the real-world applications and it is able to provide fast evaluation and objective comparison of the COAs before or during the operation; Page 6, B. Lethality Heatmap Generation, Example heatmap generation results for air, ground and sea layers are given in Figure 5. Here, lethality heatmap of the red team units are given for a) ground layer, b) Naval layer, and c) Air layer; As stated in Paragraph 0032 of Applicant’s specification, a kinetic threat may include airplanes or the like. Therefore, based on broadest reasonable interpretation in light of the specification, Yuksek discloses a kinetic fires management system since it can receive real-time intelligence information of the unmanned vehicles. Also, Examiner notes that Yuksek discloses: an intelligence management system since it can receive real-time intelligence information; and a sustainment management service since it can operate across multiple domains such as air support and naval support);
and providing, by the UI, a graphical view of the state of executing the COA including an overall map of a geographical region in which the COA is implemented, …, and a dynamic view of the LOS updated as the COA is implemented (Page 2, Introduction, In this study, it is proposed to develop intelligence, surveillance, and reconnaissance (ISR) and suppression of enemy air defense (SEAD) operation plans that are supported by upper AI and assistive, decentralized decision-making strategies within a war-game to evaluate generated COAs in terms of probability of success, survivability of the assets and operational efficiency. This process is developed on the classical MDMP scheme for ease of implementation into the real-world applications and it is able to provide fast evaluation and objective comparison of the COAs before or during the operation; Page 3, II. Related Works, On the other hand, due to the structure of the centralized task allocation, a persistent communication should be maintained between the agents and the operation base to provide cooperation which requires sending/receiving operational updates).
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Although Yuksek discloses classifying objects within the images as a threat or not a threat based on images of a geographical region captured by the ISR device (e.g., identifying unmanned vehicles that are a threat to the blue team), Yuksek does not specifically disclose wherein the images are classified by a patterns of life service comprising a trained machine learning model.
However, Bar-Nahum et al. discloses a non-transitory machine-readable medium including instructions that, when executed by a machine, cause the machine to perform operations for mission planning and execution, the operations comprising (Paragraph 0015, The invention can be implemented in numerous ways, including as a process; an apparatus; a system; a composition of matter; a computer program product embodied on a computer readable storage medium; and/or a processor, such as a processor configured to execute instructions stored on and/or provided by a memory coupled to the processor. In this specification, these implementations, or any other form that the invention may take, may be referred to as techniques; Paragraph 0055, Risk assessment module 105 may provide its determined threat level to response module 106. Response module 106 may perform or cause to perform a response based on the determined threat level): …
receiving, by a patterns of life service comprising a trained machine learning model, images of a geographical region captured by the ISR device; classifying, by the trained machine learning model, objects within the images as a threat or not a threat on a per-pixel or per-object basis; and outputting, by the patterns of life service and based on the classifying, a classification …; determining, based on the classification …, …, wherein gathering the intelligence data comprises: receiving, by a patterns of life service comprising a trained machine learning model, images of a geographical region captured by the ISR device; classifying, by the trained machine learning model, objects within the images as a threat or not a threat on a per-pixel or per-object basis; and outputting, by the patterns of life service and based on the classifying, a classification … (Paragraph 0018, In view of this risk posed by malicious UAVs, it may be necessary to have a system to detect, monitor, track, classify, and/or assess a UAV that has entered a restricted area, such as a protected airspace. For example, a user interface may provide a visual indication of current and past locations of detected UAVs as well their trajectories and corresponding threat level classification ranking (e.g., determined using a machine learning trained model). The user interface may also provide a visual indication of a location of an operator of a UAV (e.g., by detecting and locating source of RF control signal sent by the UAV operator); Paragraph 0020, Examples of sensors included in the second layer of sensors include, but are not limited to, cameras, image sensors, unmanned aerial vehicles (UAVs) equipped with detection sensors, directional sensor, RF sensor, infrared sensor, etc.; Paragraph 0023, The additional information may include whether the detected security event is stationary or moving. The additional information may include object identification information associated with a detected security event, such as make and model of a vehicle, type of UAV, identification of one or more individuals associated with the detected security event. For example, the image sensor may be coupled to an image classifier that is trained to classify detected objects associated with the detected security event; Paragraph 0024, The security event detection system may use the additional information detected by at least one of the selected sensors to determine a risk level assessment associated with the detected security event. The security event detection system may include a machine learning model. A feature vector may be applied to the machine learning model. The feature vector may be comprised of a plurality of elements. Each element may correspond to a feature associated with a security event. A feature value may be based directly or indirectly on the additional information obtained by the one or more selected sensors. In response to the feature vector being applied to the machine learning model, the machine learning model may be configured to output a value that indicates a threat level associated with the detected security event); …
receiving, by the orchestrator service and from multiple applications including an intelligence management service, a non-kinetic fires management service, a video sensor management service, a kinetic fires management service, … (Paragraph 0020, Examples of sensors included in the second layer of sensors include, but are not limited to, cameras, image sensors, unmanned aerial vehicles (UAVs) equipped with detection sensors, directional sensor, RF sensor, infrared sensor, etc.; Paragraph 0023, The additional information may include whether the detected security event is stationary or moving; Paragraph 0058, The sensor data may be associated with a region (e.g., airspace, geographical region, etc.) being monitored. Examples of the sensor data include 2D/3D radar signal data, UAV location tracking data, UAV operator location tracking data, LIDAR data, image/video data, data about detected communication between UAV and operator, and any other signal or information associated with an area being monitored. The sensor data may be received from multiple sources (e.g., 2D/3D radar, RF antenna, camera, LIDAR sensor, etc.). Conventional radar is used to quickly detect and track points in space);
and providing, by the UI, …, the graphical view including a dynamic location of the threat and threat mitigation activities, … (Paragraph 0068, At 210, a user interface is displayed. For example, the user interface provides a visual indication of current and past locations of detected security events, such as UAVs as well their trajectories and corresponding threat level classification ranking (e.g., determined using a machine learning trained model). The user interface may also provide a visual indication of a current, past and/or movement trajectory locations of an operator of a detected UAV (e.g., by detecting and locating source of RF control signal sent by the UAV operator). A threat level score is automatically represented to the user through the user interface. The user interface displays a color trajectory of the UAV along with the threat level score; Paragraph 0070, In various embodiments, for every threat level associated with a detected aerial object/UAV, there is a set of preconfigured actions associated with that threat level. Automatic actions associated with threat levels may be triggered when certain threat levels are assessed. Examples of actions that can be initiated based on threat level assessments include contacting law enforcement, generating a notification/message to send to specified people or organizations, triggering an alarm, sending an interceptor UAV to capture/interdict the detected UAV considered a threat, sending personnel to a where the operator of the UAV is located, initiating a lockdown of a sensitive/restricted area, and starting other procedures).
It would have been obvious to one ordinary skill in the art before the effective filing date to modify the method for mission planning and execution, wherein a likelihood of success is determined for the different COAs based on real-time intelligence data (e.g., detected threats) of the invention of Yuksek to further incorporate wherein the detected threat is identified using a trained machine learning model of the invention of Bar-Nahum et al. because doing so would allow the method to monitor, track, classify, and/or assess a UAV that has entered a restricted area, such as a protected airspace. For example, a user interface may provide a visual indication of current and past locations of detected UAVs as well their trajectories and corresponding threat level classification ranking, wherein the threat level was determined using a machine learning trained model (see Bar-Nahum et al., Paragraph 0018). Further, the claimed invention is merely a combination of old elements, and in combination each element would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable.
Although the combination of Yuksek and Bar-Nahum et al. discloses using a trained machine learning model for classifying objects within the images as a threat or not a threat based on images of a geographical region captured by the ISR device (e.g., threat level score of a detected aerial object/UAV), the combination of Yuksek and Bar-Nahum et al. does not specifically disclose a corresponding confidence value of the classification.
However, Canty et al. discloses and outputting, by the patterns of life service and based on the classifying, a classification and a corresponding confidence value; … based on the classification and the corresponding confidence value, …, and a corresponding confidence value (Paragraph 0101, Threat detection and analysis module 402 uses sensor measurements, existing knowledge, and/or trained machine learning models related to users, firearms, other assets, and/or conditions within a deployment location to detect whether a threat exists within the deployment location and to analyze the severity of detected threats. The sensor measurements, existing knowledge, and/or trained machine learning models are taken as inputs to threat detection and analysis module 402. Threat detection and analysis module 402 outputs data indicating one or more of a detected threat, a threat severity, or no threat detected; Paragraph 0107, In embodiments, system 100 can prompt a user of a firearm for input verifying that a threat exists. For example, where threat detection and analysis module 402 detects a potential threat with low confidence, a signal may be transmitted to a personal computing device of a user within the deployment location to verify whether a threat is present in the deployment location).
It would have been obvious to one ordinary skill in the art before the effective filing date to modify the method for mission planning and execution, wherein a likelihood of success is determined for the different COAs based on real-time intelligence data (e.g., classify objects as threat or not threat using a trained machine learning model) of the invention of Yuksek and Bar-Nahum et al. to further incorporate wherein the classification includes a corresponding confidence value of the invention of Canty et al. because doing so would allow the method to user a trained machine learning model to detect a potential threat and the confidence (see Canty et al., Paragraphs 0101 & 107). Further, the claimed invention is merely a combination of old elements, and in combination each element would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable.
Regarding claim 16 (Currently Amended), Yuksek discloses a [method] for mission planning and execution, the operations comprising: receiving, by a commander through a user interface (UI) of the display, course of action (COA) data regarding multiple COAs, the COA data including activities, timing of the activities, entities to perform the activities, and threat data, the activities including intelligence gathering and threat mitigation activities, the entities including multiple different domains (see Figure 1, Summary of military decision making process, Steps 1-3; Page 1 Introduction, Wargaming is an important part of the MDMP, which is the armies’ doctrinal approach to create operation plans, to predict counter-actions of the opposite forces and to evaluate the effectiveness of the proposed operation plan, since it offers a safe and vicarious reflection of some of the situational and decision-making dynamics associated with armed conflict. Although, there is no single definition for the term ‘war-gaming’, the commonly accepted one dated back to the beginning of 19𝑡ℎ century, which defines it as a simulation of a military operation, by whatever means, using specific rules, data, methods and procedures [4]. Therefore, it is important to give a clear statement about wargaming before to proceed with the definition and importance of MDMP. The MDMP starts with the receipt of a mission from higher headquarters. Then, mission analysis is performed by utilizing intelligence from other sources. In the next step, it processes commander’s intents, operational requirements and available resources to develop course of actions (COAs) which include task organization plans. After development of the COA, COA analysis, which is focused on action, reaction, counteraction, and the adjudication process, is performed through wargaming to refine the COAs and potential decision points);
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coordinating, by an orchestrator service, simulation of performing of the activities by the entities, the simulation including gathering the intelligence data based on visibility and location of intelligence, surveillance, and reconnaissance (ISR) device, wherein gathering the intelligence data comprises (see Figure 1, Summary of military decision making process, Step 4 Wargame; Pages 1-2, Introduction, After development of the COA, COA analysis, which is focused on action, reaction, counteraction, and the adjudication process, is performed through wargaming to refine the COAs and potential decision points. In this study, it is proposed to develop intelligence, surveillance, and reconnaissance (ISR) and suppression of enemy air defense (SEAD) operation plans that are supported by upper AI and assistive, decentralized decision-making strategies within a war-game to evaluate generated COAs in terms of probability of success, survivability of the assets and operational efficiency):
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receiving, …, images of a geographical region captured by the ISR device; classifying, … objects within the images as a threat or not a threat on a per-pixel or per-object basis; and outputting, by the patterns of life service and based on the classifying, a classification … (Page 2, Introduction, In this study, it is proposed to develop intelligence, surveillance, and reconnaissance (ISR) and suppression of enemy air defense (SEAD) operation plans that are supported by upper AI and assistive, decentralized decision-making strategies within a war-game to evaluate generated COAs in terms of probability of success, survivability of the assets and operational efficiency; Page 6, B. Lethality Heatmap Generation, Example heatmap generation results for air, ground and sea layers are given in Figure 5. Here, lethality heatmap of the red team units are given for a) ground layer, b) Naval layer, and c) Air layer. On the ground layer, both UAV, UGV, USV and CAD units are effective threats against the blue forces in given letality areas. On the naval layer, USV is the main threat but UAV, UGV and CAD are also effective. On the air layer, in first look, there seems no threat for blue team because the UAV, UGV and USV of the red team are not effective against the air force of the blue team. However, if CAD asset presence exists in the area, it would be crucial threat against the blue and results would be fatal);
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determining, based on the classification …, a likelihood of success (LOS) of the COAs by an analysis engine, wherein determining the LOS comprises computing a hierarchical LOS across the multiple different domains in which activity-level LOS values are combined to produce echelon-level LOS values, echelon-level LOS values are combined to produce domain-level LOS values, and domain-level LOS values are combined to produce a mission-level LOS, each level including a corresponding confidence value reflecting the statistical reliability of the LOS at that level the analysis engine determines the LOS by applying stochastic math modeling (SMM) to normalized threat effect pair algorithms, wherein normalization converts results of disparate threat-effect pair models from domain-specific units into equivalent units enabling mathematical combination of results across domains (see Figure 1, Summary of military decision making process, Step 5 COA Comparison; Pages 1-2, Introduction, After development of the COA, COA analysis, which is focused on action, reaction, counteraction, and the adjudication process, is performed through wargaming to refine the COAs and potential decision points. In this study, it is proposed to develop intelligence, surveillance, and reconnaissance (ISR) and suppression of enemy air defense (SEAD) operation plans that are supported by upper AI and assistive, decentralized decision-making strategies within a war-game to evaluate generated COAs in terms of probability of success, survivability of the assets and operational efficiency; Page 6, B. Lethality Heatmap Generation, Example heatmap generation results for air, ground and sea layers are given in Figure 5. Here, lethality heatmap of the red team units are given for a) ground layer, b) Naval layer, and c) Air layer. On the ground layer, both UAV, UGV, USV and CAD units are effective threats against the blue forces in given letality areas. On the naval layer, USV is the main threat but UAV, UGV and CAD are also effective. On the air layer, in first look, there seems no threat for blue team because the UAV, UGV and USV of the red team are not effective against the air force of the blue team. However, if CAD asset presence exists in the area, it would be crucial threat against the blue and results would be fatal; Page 8, C. Combat Model, Combat modeling abstracts and simplifies the combat entities, their behaviors, activities, and interrelations to answer defense-related research questions. There is no general model that answers all questions, and even if such a model could be constructed it would become more complex than the reality, as it not only includes real systems but also imagined ones. Combat models can be either stochastic or deterministic. Intuitively, a stochastic model assumes uncertain or probabilistic inputs regarding a situation and makes an indefinite prediction of the results. A deterministic model states exactly what will happen, as if there were no uncertainty. More formally, a stochastic model requires the terminology of the theory of probability for its description, whereas a deterministic model does not. Where F, Hp, L, Ph, Pd, Pt, Pw, El are total firepower, firepower health, lethality, hit probability, detection probability, targeting system reliability, weapon reliability and layer effectiveness, respectively. Here, it is also important to model damage matrix of the military units which defines their effectiveness against the enemy forces in an engagement; Examiner notes that a LOS is calculated by taking into consideration different parameters from different domains such as ground, sea, and air);
and executing models of the entities performing the activities by a command and control engine (see Figure 1, Summary of military decision making process, Step 5 COA Comparison; Pages 1-2, Introduction, After development of the COA, COA analysis, which is focused on action, reaction, counteraction, and the adjudication process, is performed through wargaming to refine the COAs and potential decision points. In this study, it is proposed to develop intelligence, surveillance, and reconnaissance (ISR) and suppression of enemy air defense (SEAD) operation plans that are supported by upper AI and assistive, decentralized decision-making strategies within a war-game to evaluate generated COAs in terms of probability of success, survivability of the assets and operational efficiency);
generating, by the orchestrator service, …, a graphical view of the simulation of the COAs including scores associated with each COA; implementing, by the orchestrator service, a COA of the COAs selected by the commander (see Figure 1, Summary of military decision making process, Step 5 COA Comparison, Recommended COAs, Commander approved COAs and modifications; Pages 1-2, Introduction, After development of the COA, COA analysis, which is focused on action, reaction, counteraction, and the adjudication process, is performed through wargaming to refine the COAs and potential decision points. In this study, it is proposed to develop intelligence, surveillance, and reconnaissance (ISR) and suppression of enemy air defense (SEAD) operation plans that are supported by upper AI and assistive, decentralized decision-making strategies within a war-game to evaluate generated COAs in terms of probability of success, survivability of the assets and operational efficiency);
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receiving, by the orchestrator service and from multiple applications including an intelligence management service, …, a video sensor management service, a kinetic fires management service, and a sustainment management service that concurrently operate across the multiple domains, information regarding a state of executing the COA (Page 2, Introduction, In this study, it is proposed to develop intelligence, surveillance, and reconnaissance (ISR) and suppression of enemy air defense (SEAD) operation plans that are supported by upper AI and assistive, decentralized decision-making strategies within a war-game to evaluate generated COAs in terms of probability of success, survivability of the assets and operational efficiency. This process is developed on the classical MDMP scheme for ease of implementation into the real-world applications and it is able to provide fast evaluation and objective comparison of the COAs before or during the operation; Page 6, B. Lethality Heatmap Generation, Example heatmap generation results for air, ground and sea layers are given in Figure 5. Here, lethality heatmap of the red team units are given for a) ground layer, b) Naval layer, and c) Air layer; As stated in Paragraph 0032 of Applicant’s specification, a kinetic threat may include airplanes or the like. Therefore, based on broadest reasonable interpretation in light of the specification, Yuksek discloses a kinetic fires management system since it can receive real-time intelligence information of the unmanned vehicles. Also, Examiner notes that Yuksek discloses: an intelligence management system since it can receive real-time intelligence information; and a sustainment management service since it can operate across multiple domains such as air support and naval support);
and providing, by the UI, a graphical view of the state of executing the COA including an overall map of a geographical region in which the COA is implemented, …, and a dynamic view of the LOS updated as the COA is implemented (Page 2, Introduction, In this study, it is proposed to develop intelligence, surveillance, and reconnaissance (ISR) and suppression of enemy air defense (SEAD) operation plans that are supported by upper AI and assistive, decentralized decision-making strategies within a war-game to evaluate generated COAs in terms of probability of success, survivability of the assets and operational efficiency. This process is developed on the classical MDMP scheme for ease of implementation into the real-world applications and it is able to provide fast evaluation and objective comparison of the COAs before or during the operation; Page 3, II. Related Works, On the other hand, due to the structure of the centralized task allocation, a persistent communication should be maintained between the agents and the operation base to provide cooperation which requires sending/receiving operational updates).
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Although Yuksek discloses classifying objects within the images as a threat or not a threat based on images of a geographical region captured by the ISR device (e.g., identifying unmanned vehicles that are a threat to the blue team), Yuksek does not specifically disclose wherein the images are classified by a patterns of life service comprising a trained machine learning model.
However, Bar-Nahum et al. discloses a system comprising: processing circuitry; a display; a memory coupled to the processing circuitry and the display, the memory including instructions that, when executed by the processing circuitry, cause the processing circuitry to perform operations for mission planning and execution, the operations comprising (Paragraph 0015, The invention can be implemented in numerous ways, including as a process; an apparatus; a system; a composition of matter; a computer program product embodied on a computer readable storage medium; and/or a processor, such as a processor configured to execute instructions stored on and/or provided by a memory coupled to the processor. In this specification, these implementations, or any other form that the invention may take, may be referred to as techniques; Paragraph 0055, Risk assessment module 105 may provide its determined threat level to response module 106. Response module 106 may perform or cause to perform a response based on the determined threat level; Paragraph 0068, The user interface displays a color trajectory of the UAV along with the threat level score): …
receiving, by a patterns of life service comprising a trained machine learning model, images of a geographical region captured by the ISR device; classifying, by the trained machine learning model, objects within the images as a threat or not a threat on a per-pixel or per-object basis; and outputting, by the patterns of life service and based on the classifying, a classification …; determining, based on the classification … (Paragraph 0018, In view of this risk posed by malicious UAVs, it may be necessary to have a system to detect, monitor, track, classify, and/or assess a UAV that has entered a restricted area, such as a protected airspace. For example, a user interface may provide a visual indication of current and past locations of detected UAVs as well their trajectories and corresponding threat level classification ranking (e.g., determined using a machine learning trained model). The user interface may also provide a visual indication of a location of an operator of a UAV (e.g., by detecting and locating source of RF control signal sent by the UAV operator); Paragraph 0020, Examples of sensors included in the second layer of sensors include, but are not limited to, cameras, image sensors, unmanned aerial vehicles (UAVs) equipped with detection sensors, directional sensor, RF sensor, infrared sensor, etc.; Paragraph 0023, The additional information may include whether the detected security event is stationary or moving. The additional information may include object identification information associated with a detected security event, such as make and model of a vehicle, type of UAV, identification of one or more individuals associated with the detected security event. For example, the image sensor may be coupled to an image classifier that is trained to classify detected objects associated with the detected security event; Paragraph 0024, The security event detection system may use the additional information detected by at least one of the selected sensors to determine a risk level assessment associated with the detected security event. The security event detection system may include a machine learning model. A feature vector may be applied to the machine learning model. The feature vector may be comprised of a plurality of elements. Each element may correspond to a feature associated with a security event. A feature value may be based directly or indirectly on the additional information obtained by the one or more selected sensors. In response to the feature vector being applied to the machine learning model, the machine learning model may be configured to output a value that indicates a threat level associated with the detected security event); …
receiving, by the orchestrator service and from multiple applications including an intelligence management service, a non-kinetic fires management service, a video sensor management service, a kinetic fires management service, … (Paragraph 0020, Examples of sensors included in the second layer of sensors include, but are not limited to, cameras, image sensors, unmanned aerial vehicles (UAVs) equipped with detection sensors, directional sensor, RF sensor, infrared sensor, etc.; Paragraph 0023, The additional information may include whether the detected security event is stationary or moving; Paragraph 0058, The sensor data may be associated with a region (e.g., airspace, geographical region, etc.) being monitored. Examples of the sensor data include 2D/3D radar signal data, UAV location tracking data, UAV operator location tracking data, LIDAR data, image/video data, data about detected communication between UAV and operator, and any other signal or information associated with an area being monitored. The sensor data may be received from multiple sources (e.g., 2D/3D radar, RF antenna, camera, LIDAR sensor, etc.). Conventional radar is used to quickly detect and track points in space);
and providing, by the display, …; and providing, by the UI, …, the graphical view including a dynamic location of the threat and threat mitigation activities, … (Paragraph 0068, At 210, a user interface is displayed. For example, the user interface provides a visual indication of current and past locations of detected security events, such as UAVs as well their trajectories and corresponding threat level classification ranking (e.g., determined using a machine learning trained model). The user interface may also provide a visual indication of a current, past and/or movement trajectory locations of an operator of a detected UAV (e.g., by detecting and locating source of RF control signal sent by the UAV operator). A threat level score is automatically represented to the user through the user interface. The user interface displays a color trajectory of the UAV along with the threat level score; Paragraph 0070, In various embodiments, for every threat level associated with a detected aerial object/UAV, there is a set of preconfigured actions associated with that threat level. Automatic actions associated with threat levels may be triggered when certain threat levels are assessed. Examples of actions that can be initiated based on threat level assessments include contacting law enforcement, generating a notification/message to send to specified people or organizations, triggering an alarm, sending an interceptor UAV to capture/interdict the detected UAV considered a threat, sending personnel to a where the operator of the UAV is located, initiating a lockdown of a sensitive/restricted area, and starting other procedures).
It would have been obvious to one ordinary skill in the art before the effective filing date to modify the method for mission planning and execution, wherein a likelihood of success is determined for the different COAs based on real-time intelligence data (e.g., detected threats) of the invention of Yuksek to further incorporate wherein the detected threat is identified using a trained machine learning model of the invention of Bar-Nahum et al. because doing so would allow the method to monitor, track, classify, and/or assess a UAV that has entered a restricted area, such as a protected airspace. For example, a user interface may provide a visual indication of current and past locations of detected UAVs as well their trajectories and corresponding threat level classification ranking, wherein the threat level was determined using a machine learning trained model (see Bar-Nahum et al., Paragraph 0018). Further, the claimed invention is merely a combination of old elements, and in combination each element would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable.
Although the combination of Yuksek and Bar-Nahum et al. discloses using a trained machine learning model for classifying objects within the images as a threat or not a threat based on images of a geographical region captured by the ISR device (e.g., threat level score of a detected aerial object/UAV), the combination of Yuksek and Bar-Nahum et al. does not specifically disclose a corresponding confidence value of the classification.
However, Canty et al. discloses and outputting, by the patterns of life service and based on the classifying, a classification and a corresponding confidence value; … based on the classification and the corresponding confidence value, … (Paragraph 0101, Threat detection and analysis module 402 uses sensor measurements, existing knowledge, and/or trained machine learning models related to users, firearms, other assets, and/or conditions within a deployment location to detect whether a threat exists within the deployment location and to analyze the severity of detected threats. The sensor measurements, existing knowledge, and/or trained machine learning models are taken as inputs to threat detection and analysis module 402. Threat detection and analysis module 402 outputs data indicating one or more of a detected threat, a threat severity, or no threat detected; Paragraph 0107, In embodiments, system 100 can prompt a user of a firearm for input verifying that a threat exists. For example, where threat detection and analysis module 402 detects a potential threat with low confidence, a signal may be transmitted to a personal computing device of a user within the deployment location to verify whether a threat is present in the deployment location).
It would have been obvious to one ordinary skill in the art before the effective filing date to modify the method for mission planning and execution, wherein a likelihood of success is determined for the different COAs based on real-time intelligence data (e.g., classify objects as threat or not threat using a trained machine learning model) of the invention of Yuksek and Bar-Nahum et al. to further incorporate wherein the classification includes a corresponding confidence value of the invention of Canty et al. because doing so would allow the method to user a trained machine learning model to detect a potential threat and the confidence (see Canty et al., Paragraphs 0101 & 107). Further, the claimed invention is merely a combination of old elements, and in combination each element would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable.
Regarding claims 2, 10, and 17 (Original), which are dependent of claims 1, 9, and 16, the combination of Yuksek, Bar-Nahum et al., and Canty et al. discloses all the limitations in claims 1, 9, and 16. Yuksek further discloses determining, by a feasibility service communicatively coupled to the orchestration service, entities that are capable of performing each activity, and for each entity that is capable, are in range to perform the activity, …, and can operate in terrain of the geographical region resulting in feasible entities (Page 9, VI Simulation Studies; In simulation studies, asset types of the fleet are assumed to be heterogeneous and include unmanned aerial vehicles (UAVs), unmanned ground vehicles (UGVs) and unmanned surface vessels (USVs). As an unmanned aerial vehicle, a system with medium-high altitude long endurance capability, which is required for ISR missions, is modeled. For the unmanned ground vehicle, the performance of an average armored personnel carrier vehicle is modeled. It is assumed that this vehicle has the ability to move in any terrain. On the other hand, unmanned surfaces vessels for use in coastal areas are modeled similar to the lightweight high-performance rigid-hull inflatable boat (RHIB). The performance parameters of these agents are given in Table 2. Since the fuel and fuel consumption data of the vehicles in the task assignment algorithm are of primary importance in the distribution of tasks, these parameters are especially included in the table);
providing, to the commander and by the UI, for each activity of the activities that has multiple feasible entities, a software control through which the commander selects a feasible entity of the multiple feasible entities; and receiving, by the commander and through the UI, a selection of the feasible entity of the feasible entities for each activity of the activities that has multiple feasible entities (see Figure 1, Summary of military decision making process, Step 5 COA Comparison, Recommended COAs, Commander approved COAs and modifications; Pages 1-2, Introduction, After development of the COA, COA analysis, which is focused on action, reaction, counteraction, and the adjudication process, is performed through wargaming to refine the COAs and potential decision points. In this study, it is proposed to develop intelligence, surveillance, and reconnaissance (ISR) and suppression of enemy air defense (SEAD) operation plans that are supported by upper AI and assistive, decentralized decision-making strategies within a war-game to evaluate generated COAs in terms of probability of success, survivability of the assets and operational efficiency).
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Although Yuksek discloses determining entities that are capable of performing each activity (e.g., unmanned aerial vehicles (UAVs), unmanned ground vehicles (UGVs) and unmanned surface vessels (USVs)), Yuksek does not specifically disclose wherein the determination is based on weather conditions of a geographic region corresponding to the activity and terrain of the geographical region.
However, Canty et al. further discloses determining, by a feasibility service communicatively coupled to the orchestration service, entities that are capable of performing each activity, and for each entity that is capable, are in range to perform the activity, can operate in weather conditions of a geographic region corresponding to the activity, and can operate in terrain of the geographical region resulting in feasible entities (Paragraph 0057, For example, the threat may be or relate to a terrain element which presents a risk of bodily harm or obstructs a traveling path of the users of firearms 104, wearable devices 106, and/or stationary devices 108; Paragraph 0350, Analyzing the data by the firearm usage monitoring system 2800 may produce a set of candidate intents 3080 of the human firearm user or of another individual in proximity to the firearm user (such as where camera information, voice information, and the like is available). The candidate intents 3080 may, in embodiments, be combined with physical and operation machine state information to select one or more action plans 3082. The machine teamed with the human user of the firearm 2020 may then execute and adjust the selected action plan 3082 based on updated intents, machine states, and environmental factors; Paragraph 0351, Environmental factors 3084 may include weather factors, location data factors, altitude factors, topography factors, video factors and the like. Weather factors may include temperature, humidity, wind speed, wind direction and precipitation factors, among others. Location data factors may include streaming data, as well as data acquired from global positioning systems (GPS) and beacons, access points or the like, as well as through cellular triangulation. Topography factors may include data and observations, while video factors may include both live and archived video feeds).
It would have been obvious to one ordinary skill in the art before the effective filing date to modify the method for mission planning and execution, wherein the planning includes to determine entities that are capable of performing each activity of the invention of Yuksek to further incorporate wherein the determination is based on weather conditions of a geographic region corresponding to the activity and terrain of the geographical region of the invention of Canty et al. because doing so would allow the method to adjust the selected action plan 3082 based on updated intents, machine states, and environmental factors (see Canty et al., Paragraph 0350). Further, the claimed invention is merely a combination of old elements, and in combination each element would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable.
Regarding claims 3, 11, and 18 (Original), which are dependent of claims 2, 10, and 17, the combination of Yuksek, Bar-Nahum et al., and Canty et al. discloses all the limitations in claims 2, 10, and 17. Yuksek further discloses receiving, by a user data service communicatively coupled to the orchestration service, possible actions that can be performed by each feasible entity selected for each activity (Page 10, A. Results, Since the remaining enemy presence consists aerial threat, the fleet configuration is updated in a way that to increase the number of UAV assets. Therefore, the second scenario is executed with 3 UAVs in the blue team and 2 UAVs, 1 UGV in enemy red team as previous scenario).
Regarding claims 4, 12, and 19 (Original), which are dependent of claims 2, 10, and 17, the combination of Yuksek, Bar-Nahum et al., and Canty et al. discloses all the limitations in claims 2, 10, and 17. Yuksek further discloses: receiving, from the commander and through the UI, a selection of machine learning (ML) tools; and coordinating, by the orchestration service, operation of the ML tools in the simulation (see Figure 1, Summary of military decision making process, Steps 1-3, Commander’s initial guidance; Page 2, Introduction, In this study, it is proposed to develop intelligence, surveillance, and reconnaissance (ISR) and suppression of enemy air defense (SEAD) operation plans that are supported by upper AI and assistive, decentralized decision-making strategies within a war-game to evaluate generated COAs in terms of probability of success, survivability of the assets and operational efficiency. This process is developed on the classical MDMP scheme for ease of implementation into the real-world applications and it is able to provide fast evaluation and objective comparison of the COAs before or during the operation).
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Regarding claims 7, 15, and 20 (Original), which are dependent of claims 4, 12, and 19, the combination of Yuksek, Bar-Nahum et al., and Canty et al. discloses all the limitations in claims 4, 12, and 19. Yuksek further comprising: receiving, by the orchestration service and from an alert service communicatively coupled to the orchestration service, an alert indicating an updated location that is a change in location of the threat; providing, by the orchestration service, the updated location to the ML tools, the COA analysis engine, …, the kinetic fires management service, and the sustainment management service; and receiving, by the orchestrator service, based on the updated location, and from the multiple applications, updated information regarding a new state of executing the COA (Page 2, Introduction, In this study, it is proposed to develop intelligence, surveillance, and reconnaissance (ISR) and suppression of enemy air defense (SEAD) operation plans that are supported by upper AI and assistive, decentralized decision-making strategies within a war-game to evaluate generated COAs in terms of probability of success, survivability of the assets and operational efficiency. This process is developed on the classical MDMP scheme for ease of implementation into the real-world applications and it is able to provide fast evaluation and objective comparison of the COAs before or during the operation; Page 6, B. Lethality Heatmap Generation, In the simulation environment, lethality heatmaps are generated on air, ground and sea layers to model the firepower of the opposite forces in a given area. These heatmaps are generated based on kernel density estimation (KDE) algorithm which is used to estimate danger level (i.e. effectiveness level of an opposite force unit) of a given point on the map with respect to the opposite force location; As stated in Paragraph 0032 of Applicant’s specification, a kinetic threat may include airplanes or the like. Therefore, based on broadest reasonable interpretation in light of the specification, Yuksek discloses a kinetic fires management system since it can receive real-time intelligence information of the unmanned vehicles. Also, Examiner notes that Yuksek discloses a sustainment management service since it can operate across multiple domains such as air support and naval support).
Although Yuksek discloses evaluation of COAs during operation (e.g., based on updated location of the threat), Yuksek does not specifically disclose wherein the updated location is provided to a video sensor management service.
However, Bar-Nahum et al. discloses receiving, by the orchestration service and from an alert service communicatively coupled to the orchestration service, an alert indicating an updated location that is a change in location of the threat; providing, by the orchestration service, the updated location to the ML tools, the COA analysis engine, the video sensor management service, the kinetic fires management service, and the sustainment management service; and receiving, by the orchestrator service, based on the updated location, and from the multiple applications, updated information regarding a new state of executing the COA (Paragraph 0018, In view of this risk posed by malicious UAVs, it may be necessary to have a system to detect, monitor, track, classify, and/or assess a UAV that has entered a restricted area, such as a protected airspace. For example, a user interface may provide a visual indication of current and past locations of detected UAVs as well their trajectories and corresponding threat level classification ranking (e.g., determined using a machine learning trained model). The user interface may also provide a visual indication of a location of an operator of a UAV (e.g., by detecting and locating source of RF control signal sent by the UAV operator); Paragraph 0020, Examples of sensors included in the second layer of sensors include, but are not limited to, cameras, image sensors, unmanned aerial vehicles (UAVs) equipped with detection sensors, directional sensor, RF sensor, infrared sensor, etc.; Paragraph 0023, The additional information may include whether the detected security event is stationary or moving; Paragraph 0058, The sensor data may be associated with a region (e.g., airspace, geographical region, etc.) being monitored. Examples of the sensor data include 2D/3D radar signal data, UAV location tracking data, UAV operator location tracking data, LIDAR data, image/video data, data about detected communication between UAV and operator, and any other signal or information associated with an area being monitored. The sensor data may be received from multiple sources (e.g., 2D/3D radar, RF antenna, camera, LIDAR sensor, etc.). Conventional radar is used to quickly detect and track points in space)).
It would have been obvious to one ordinary skill in the art before the effective filing date to modify the method for mission planning and execution, wherein a likelihood of success is determined for the different COAs based on real-time intelligence data (e.g., detected threats based on updated location data) of the invention of Yuksek to further incorporate wherein the detected threat is identified using a trained machine learning model of the invention of Bar-Nahum et al. because doing so would allow the method to monitor, track, classify, and/or assess a UAV that has entered a restricted area, such as a protected airspace. For example, a user interface may provide a visual indication of current and past locations of detected UAVs as well their trajectories and corresponding threat level classification ranking, wherein the threat level was determined using a machine learning trained model (see Bar-Nahum et al., Paragraph 0018). Further, the claimed invention is merely a combination of old elements, and in combination each element would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable.
Claims 5 and 13 are rejected under 35 U.S.C. 103 as being unpatentable over Yuksek (Yuksek, B., Guner, G., Karali, H., Candan, B. and Inalhan, G., 2023. Intelligent wargaming approach to increase course of action effectiveness in military operations. In AIA A SCITECH 2023 Forum (p. 2531)), in view of Bar-Nahum et al. (US 2020/0162489 A1), in further view of Canty et al. (US 2024/0027157 A1) and Boury-Brisset (Boury-Brisset, A.C. and Berger, J., 2020. Benefits and Challenges of AI/ML in Support of Intelligence and Targeting in Hybrid Military Operations. NATO SI Organ).
Regarding claims 5 and 13 (Original), which are dependent of claims 4 and 12, the combination of Yuksek, Bar-Nahum et al., and Canty et al. discloses all the limitations in claims 4 and 12. Although the combination of Yuksek and Bar-Nahum et al. discloses the use of different machine learning tools to assist in the creation of multiple COAs (e.g., machine learning model to identify threat level), the combination of Yuksek and Bar-Nahum et al. does not specifically disclose wherein the ML tools include an asset optimization service that determines, for each feasible entity of the feasible entities, a time that the entity is to take action to perform a corresponding activity of the activities.
However, Boury-Brisset discloses wherein the ML tools include an asset optimization service that determines, for each feasible entity of the feasible entities, a time that the entity is to take action to perform a corresponding activity of the activities (Page 1, 1.0 Introduction, Artificial intelligence (AI) and machine learning (ML) explosion in the last decade is mainly due to the combination of deep learning algorithms, the availability of large datasets used for training, as well as an increase in computational power and advance of hardware devices. AI/ML has demonstrated significant results, expedited by the use of Deep learning, in two major areas, namely pattern recognition from data sources such as images/videos, text and acoustics, as well as decision making in search of optimal solutions, in particular: Machine learning approaches for pattern recognition (e.g. object, event/activity detection/recognition) from imagery or multisensory data, anomaly detection, patterns of life, etc., and natural language processing for information extraction, text analysis, from open source/social media; Search for optimal solutions in support of decision making (e.g. resource allocation, path planning) using reinforcement learning and other AI approaches (e.g. evolutionary algorithms)).
It would have been obvious to one ordinary skill in the art before the effective filing date to modify the method for mission planning and execution, wherein a likelihood of success is determined for the different COAs based on real-time intelligence data (e.g., detected threats using machine learning tools) of the invention of Yuksek and Bar-Nahum et al. to further incorporate wherein the ML tools include an asset optimization service of the invention of Boury-Brisset because doing so would allow the method to use machine learning tools to search for optimal solutions in support of decision making such as resource allocation (see Boury-Brisset, Page 1, 1.0 Introduction). Further, the claimed invention is merely a combination of old elements, and in combination each element would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable.
Claims 8 and 21-22 are rejected under 35 U.S.C. 103 as being unpatentable over Yuksek (Yuksek, B., Guner, G., Karali, H., Candan, B. and Inalhan, G., 2023. Intelligent wargaming approach to increase course of action effectiveness in military operations. In AIA A SCITECH 2023 Forum (p. 2531)), in view of Bar-Nahum et al. (US 2020/0162489 A1), in further view of Canty et al. (US 2024/0027157 A1) and Manochio et al. (US 2015/0353204 A1).
Regarding claims 8 and 21-22 (8 Original & 21-22 New), which are dependent of claims 1, 9, and 16, the combination of Yuksek, Bar-Nahum et al., and Canty et al. discloses all the limitations in claims 1, 9, and 16. Although the combination of Yuksek and Bar-Nahum et al. discloses a graphical view of the simulation of the COAs (see Yuksek, Figure 1, Summary of military decision making process, Step 5 COA Comparison, Recommended COAs, Commander approved COAs and modifications) and a graphical view of the state of executing the COA (see Bar-Nahum et al., Paragraph 0068, At 210, a user interface is displayed. For example, the user interface provides a visual indication of current and past locations of detected security events, such as UAVs as well their trajectories and corresponding threat level classification ranking), the combination of Yuksek and Bar-Nahum et al. does not specifically disclose wherein the graphical views are provided on a single pane of glass (SPOG).
However, Manochio et al. discloses wherein the graphical view of … are provided on a single pane of glass (SPOG) (Paragraph 0028, In FIG. 1, display screen 20 comprises a single pane of glass or other suitable material that is configured as a rear projection screen and that is compatible for use with one or more rear projectors. In other embodiments, display screen 20 may comprise a conventional monitor or display screen that is configured for use with a conventional display system and that is further configured to extend along substantially the entire lateral length of instrument panel 16 without any discontinuity of display capability. As used herein, the phrase “without any discontinuity of display capability” refers to a display screen's ability to display images at any and all points along its surface without obstruction).
It would have been obvious to one ordinary skill in the art before the effective filing date to modify the method for mission planning and execution, wherein different COAs and the state of executing the COA are displayed in a different graphical view/display of the invention of Yuksek and Bar-Nahum et al. to further incorporate wherein the multiple pieces of information are displayed on a SPOG of the invention of Manochio et al. because doing so would allow the display to comprise a conventional monitor or display screen that is configured for use with a conventional display system and that is further configured to extend along substantially the entire lateral length of instrument panel 16 without any discontinuity of display capability (see Manochio et al., Paragraph 0008). Further, the claimed invention is merely a combination of old elements, and in combination each element would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant’s disclosure.
Herman (US 2006/0271245 A1) – discloses sensing conditions in the engagement scenario. This may entail sensing conditions, such as threat type and range, via the sensors 70 (see FIG. 3). The process 300 also includes the step 304 of comparing the sensed engagement scenario conditions with data derived from simulation. This may entail looking-up corresponding conditions in the tables of FIGS. 6A-6D. The process 300 further includes the step 306 of determining values for suggesting a course of action for the attack vehicle. This may entail selecting one or more values associated with the corresponding conditions in the table(s) (see at least Paragraph 0052, Figure 4, and Figures 7-8).
Mathew (US 2019/0353492 A1) – discloses analyzing and identifying the kinds of combat variables, intelligence sources as well as relevancy to the mission is the critical component that facilitates the commander's course of action (COA). Compilation of combat variables are sourced from subject matter experts (SME)s, DoD institutions, combat instructors, combat operators, commanders with combat experience, and/or historical data. Vast amounts of combat variables, empirical data, historical data, and miscellaneous factors, actionable intelligence and available and known courses of action (COA) are formulated to a hybrid or ensemble formula to produce a logical and mission success oriented “recommended” course of action (see at least Paragraph 0065).
Hanneman et al. (US 2007/0130098 A1) – discloses non-kinetic model 211 provides non-kinetic input. Examples of such input are dropping leaflets, implementing a computer malfunction, deceptively re-routing supplies, or electronic warfare operations such as jamming a specific frequency. For example, a non-kinetic support tool provides a decision to jam a radio tower disabling communication. Microworld 205 evaluates the effect of the radio jamming beyond frequency detection that confirms that a radio tower has been jammed. For example, by jamming a radio tower, the local population does not get traffic related information and causes traffic jams delaying emergency operation. Microworld 205 will evaluate and consider these plural possibilities (see at least Paragraph 0048).
Hanson et al. (US 2019/0310639 A1) – discloses a Mission Simulation and Training module 2020 may enable mission plans to be “run virtually” with various scenarios including variations in weather, sea state, and external system encounters. Operators may interject simulated manual control of unmanned vehicles. The Mission Simulation and Training module 2020 may provide a valuable estimate as to the likely success of the planned mission under various scenarios (see at least Paragraph 0198).
Manouchehri et al. (US 11,960,283 B1) – discloses the AI measures used by the drones may include convolutional neural networks (CNNs), which are a type of deep learning algorithm commonly used for image and video analysis. Drones equipped with high-resolution cameras and sensors can use CNNs to identify and classify objects in their environment, such as third party vehicles or personnel. This can help the drone to detect and track potential threats, and provide valuable intelligence to alliance entities nearby. In the context of countering cyber or Offensive drone attacks, CNNs can be used to identify and track incoming drones in real-time. This can help personnel to quickly respond to potential threats and deploy defensive measures to neutralize the attack (see at least Column 21, lines 40-59).
Christensen (Christensen, C. and Salmon, J., 2022. Principles for small-unit sUAS tactical deployment from a combat-simulating agent-based model analysis. Expert Systems with Applications, 190, p.116156) – discloses recent increases in the operational and information gathering capabilities of small unmanned aircraft systems (sUAS) have made their deployment with small units of infantry attractive. Determining the potential impacts of sUAS deployment on infantry combat effectiveness and effectively disseminating that information to decision making warfighters is imperative. This work presents an analysis of data generated by a Monte-Carlo simulation of 137,000 unique simulations of a static-defense scenario wherein defending units deploy sUAS in an information, surveillance, and reconnaissance role to aid in detecting, tracking, and targeting attacking units with indirect fires. The relationships between the number of UAVs deployed, the patrol method for those UAVs, and 40 combat effectiveness metrics are explored using a correlation matrix and other graphical methods. A set of eight principles for effective sUAs deployment in the aforementioned scenario are distilled from the simulation data and presented for further testing and validation (see at least Abstract).
Grossman (CN 108140057 B) – discloses this virtual network participant both real and simulated or "army" avatar makes network defense to the scene simulation and virtual reality environment for participation and cooperation. by simulating multiple course of action (COA), influence of defensive strategy, action and incorporated into the information sharing operation understanding and decision-making process in the virtual environment for enhanced visualization support more completely and quickly to enable such a real-time collaboration (see at least Description).
Mittal (Mittal, V. and Davidson, A., 2020. Combining wargaming with modeling and simulation to project future military technology requirements. IEEE Transactions on Engineering Management, 68(4), pp.1195-1207)) - Wargaming is typically done as part of the Military decision making process, which generates, analyzes, and compares different courses of action before finalizing a plan [24]. During the wargame, the staff officers break up each course of action into phases. After each phase, the intelligence officer, role-playing as the enemy, discusses what the enemy would do to react to that phase. This reaction must then be accounted for every subsequent phase, until the mission reaches an end-state. This process is repeated for each course of action to identify the one with the highest likelihood of success (see at least Page 1198, IV. Military Wargaming).
Rosolio et al. (US 2021/0295732 A1) – discloses the simulation image may also depict weapon firings, such as a firing of a surface to air missile (SAM), with indications of successful or unsuccessful target hits. Further displayed information may include flight instructions, navigational parameters, weather and climate data (e.g., instrument meteorological conditions (IMC)), and other important information relating to the simulated environment (and/or the real environment) of the aircraft (see at least Paragraph 0037).
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to MARJORIE PUJOLS-CRUZ whose telephone number is (571)272-4668. The examiner can normally be reached Mon-Thru 7:30 AM - 5:00 PM.
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/M.P./Examiner, Art Unit 3624 /PATRICIA H MUNSON/Supervisory Patent Examiner, Art Unit 3624
