Systems and methods for tactical encounters

Abstract

Provided herein are systems and methods that support preferred outcomes of tactical encounters. More particularly, the invention relates to artificial intelligence and machine learning systems and methods to select preferred ballistics solutions and tactical courses of action.

Description

[0001] HVRT-42620.601 SYSTEMS AND METHODS FOR TACTICAL ENCOUNTERS CROSS-REFERENCE TO RELATED APPLICATIONS

[0002] The present application claims the priority benefit of U.S. Provisional Patent Application No. 63 / 663,289, filed June 24, 2024, which is incorporated by reference in its entirety.

[0003] FIELD

[0004] Provided herein are systems and methods that support preferred outcomes of tactical encounters. More particularly, the invention relates to artificial intelligence and machine learning systems and methods to select preferred ballistics solutions and tactical courses of action.

[0005] BACKGROUND

[0006] Selection of a preferred tactical course of action arises from a range of factors including, for example, user firearm ballistics, target and / or rival firearm ballistics, target and / or rival locations, environmental conditions, and user physiologic variables. Historically, tactical courses of action were evaluated by human minds. More recently, computers have assisted tactical decision making. For example, ballistic calculators and computing systems have allowed individuals or groups of people to more accurately select optimal tactical courses of action. Further, additional sources of information, collected by satellites, drones, and other imaging systems have enhanced tactical decision-making. However, further improvements are needed.

[0007] SUMMARY

[0008] Provided herein are systems and methods that support preferred outcomes of tactical encounters. More particularly, the invention relates to artificial intelligence and machine learning systems and methods to select preferred ballistics solutions and tactical courses of action.

[0009] In some embodiments, the present invention provides a method for selecting a ballistics solution and / or tactical course of action, comprising: inputting ballistic and / or tactical data into a computer system; processing the data using an artificial intelligence system to generate a HVRT-42620.601

[0010] ballistics solution and / or tactical course of action, wherein the artificial intelligence system was trained on historical ballistic and / or tactical data; and displaying the ballistics solution and / or tactical course of action to a user, wherein the ballistics solution and / or tactical course of action comprises one or more of: a safe zone, a safe navigation course of action, user firearm ballistics recommendation, and target location. In some embodiments, the user is a human user. In some embodiments, the user is a firearm shooter. In some embodiments, the user is a spotter. In some embodiments, the user is a machine user. In some embodiments, the machine user is a non-autonomous machine user. In some embodiments, the machine user is a semi-autonomous machine user. In some embodiments, the machine user is an autonomous machine user.

[0011] In some embodiments, the historical ballistic and / or tactical data comprises one or more of firearm ballistics data, target location, adversary firearm ballistics data, environmental conditions data, user performance data, user physiologic data, map data, threat location, and vehicle location and performance data. In some embodiments, the safe zone or the navigation course of action comprises one or more of: a location to engage a target, a safe route for a user to avoid adversary firearm ballistics, adversary positions, protected non-target entities or events, evacuation zones, or moving adversary vehicles, speed and direction of travel of adversary firearm ballistics, environmental and man-made barriers, locations and capacities of other users and spotters, and drone and autonomous vehicle location. In some embodiments, the firearm ballistics recommendation comprises a projectile accuracy probability model based on real-time firearm ballistic and environmental factors. In some embodiments, the ballistic and / or tactical data comprises one or more of: road map data, threat location, threat ballistics data, environmental conditions data, and vehicle performance data. In some embodiments, the ballistic and / or tactical data comprises information obtained from bullet impact images and data, a highspeed camera image, a thermal bullet flight sensor, day-view optic and / or sensor data, a GPS sensor, a satellite, a drone, a 3-dimensional map, LIDAR, and photogrammetry. In some embodiments, the ballistic and / or tactical data comprises user biometric data. In some embodiments, the user biometric data comprises user heart rate, endurance performance, carrying weight capacity, reaction time, and user speed of travel.

[0012] In some embodiments, the displaying comprises providing data to one or more of: a headset (for example, a visual augmentation (IVAS) headset), a mobile device (for example, a HVRT-42620.601

[0013] type allocation code (TAC) mobile device), a digital target acquisition device, and or a digital overlay in a day-view optic device. In some embodiments, the ballistic and / or tactical data comprises one or more of: environmental information (e.g., wind information), firearm information (e.g., rate and direction of barrel twist), projectile being used, user biometric information, locations of users, spotters, or targets, range to target, slope information, Coriolis effect, and movement of users or targets. In some embodiments, the historical ballistic and / or tactical data comprises one or more of: environmental information (e.g., wind information), firearm information, projectile being used, user biometric information, locations of users, spotters, or targets, range to target, slope information, Coriolis effect, and movement of users or targets.

[0014] In some embodiments, the present invention provides a system, comprising: one or more computers configured to receive ballistic and / or tactical data, to process the data using an artificial intelligence system to generate a ballistics solution and / or tactical course of action, and to display information comprising said ballistics solution and / or tactical course of action to a user.

[0015] DESCRIPTION OF THE FIGURES FIG. 1 shows a block diagram of an embodiment of a system described herein.

[0016] FIG. 2 shows a block diagram of an embodiment of a system described herein.

[0017] FIG. 3 shows a block diagram of an embodiment of a system described herein.

[0018] FIG. 4 shows a block diagram of an embodiment of a system described herein.

[0019] DEFINITIONS

[0020] Throughout the specification and claims, the following terms take the meanings explicitly associated herein, unless the context clearly dictates otherwise. Various embodiments of the invention may be readily combined, without departing from the scope or spirit of the invention. To facilitate an understanding of the present disclosure a number of terms and phrases are defined below:

[0021] As used herein, a “system” refers to a plurality of components operating together for a common purpose. In some embodiments, a “system” is an integrated assemblage of hardware HVRT-42620.601

[0022] and / or software components. In some embodiments, each component of the system interacts with one or more other components and / or is related to one or more other components. In some embodiments, a system refers to a combination of components and software for controlling and directing methods. For example, a “system” or “subsystem” may comprise one or more of, or any combination of, the following: mechanical devices, hardware, components of hardware, circuits, circuitry, logic design, logical components, software, software modules, components of software or software modules, software procedures, software instructions, software routines, software objects, software functions, software classes, software programs, files containing software, etc., to perform a function of the system or subsystem. Thus, the systems or methods provided herein, or certain aspects or portions thereof, may take the form of program code (e.g., instructions) embodied in tangible media, such as floppy diskettes, CD-ROMs, hard drives, flash memory, or any other machine-readable storage medium wherein, when the program code is loaded into and executed by a machine, such as a computer, the machine becomes an apparatus for practicing the embodiments. In the case of program code execution on programmable computers, the computing device generally includes a processor, a storage medium readable by the processor (e.g., volatile and non-volatile memory and / or storage elements), at least one input device, and at least one output device. One or more programs may implement or utilize the processes described in connection with the embodiments, e.g., through use of an application programming interface (API), reusable controls, or the like. Such programs are preferably implemented in a high-level procedural or object-oriented programming language to communicate with a computer system. However, the program(s) can be implemented in assembly or machine language, if desired. In any case, the language may be a compiled or interpreted language, and combined with hardware implementations.

[0023] As used herein, the term “machine learning algorithm” refers to a method that produces a machine learning model (e.g., by receiving data as an input and performing the algorithm on the data). In some embodiments, a machine learning algorithm comprises recognizing patterns in data to determine or “learn” from the data how to generate output or make a prediction based on input data. In some embodiments, a machine learning algorithm is described using a mathematical equation, pseudocode, or using code in a specific programming language (e.g., BASIC, Java, C, C++, C#, Objective-C, MATLAB, Mathematica, Python, R, PHP, Ruby, Perl, HVRT-42620.601

[0024] Object Pascal, Swift, Scala, Common Lisp, or Smalltalk, etc.). In some embodiments, computer science techniques may be used to evaluate the efficiency of a machine learning algorithm. In some embodiments, a machine learning algorithm comprises an optimization method that minimizes an error calculated from data and / or a prediction algorithm for a training dataset.

[0025] As used herein, the term “machine learning model” or “model” refers to the output of a machine learning algorithm. In some embodiments, a machine learning model is a machine learning algorithm that has been optimized (e.g., having optimized parameters) using training data to identify certain patterns in data or produce certain outputs. In some embodiments, a machine learning model comprises a saved set of rules, parameterized algorithms, numbers, methods, and / or data structures that are produced by the machine learning algorithm using training data and that may be used to make predictions or produce output using new data as input. That is, a machine learning model is a program created by performing the machine learning algorithm on data to produce a trained model that is used for prediction or output when provided with new data.

[0026] As used herein, the term “model training” or “training a model” and the like refers to a method comprising inputting a dataset (called training data) to a machine learning algorithm and optimizing the algorithm to identify certain patterns or produce certain outputs. The resulting rules, parameterized algorithms, numbers, methods, and / or data structures are termed collectively the trained machine learning model. Accordingly, a machine learning algorithm may be trained to produce a machine learning model and, because a machine learning model is an optimized machine learning algorithm, a machine learning model may be trained (and retrained) by inputting a dataset into the machine learning model.

[0027] As used herein, the term “machine learning network” refers to a machine learning algorithm or machine learning model having a defined organization of algorithms, functions, methods, weights, parameters, data flows between algorithms or methods, and / or data formats used for input and output. In some embodiments, a machine learning network comprises weights applied to data communicated within the network; and / or weights applied to parameters used in algorithms, functions, or methods. In some embodiments, the organization is described as a hierarchy of layers between which inputs and outputs are communicated. HVRT-42620.601

[0028] As used herein, the term “machine learning architecture” refers to a specific organizational structure of a machine learning network. A machine learning architecture may be described in terms of a map or topology (e.g., comprising nodes, connections between nodes, weights of nodes, directions of flow between nodes).

[0029] As used herein, a machine learning “node” refers to a computational unit that has one or more weighted input connections, a function (e.g., comprising an algorithm, method, function) that transforms the inputs, and an output connection. In some embodiments, nodes are organized into layers to comprise a machine learning network comprising a particular machine learning architecture.

[0030] As used herein, the term “element”, when referring to a graphical user interface of an application, refers to a component of the graphical user interface upon which a user performs an action. Exemplary elements include but are not limited to a window, a menu, a menu item, a drop down menu, a combo box, a spin button, a tool bar, a widget, an image, a tab strip, a tab, a thumbnail, a checkbox, a button, a radio button, a drop down list, a list box, a list item, a dropdown button, a hyperlink, a toggle, a text box, a text area, a text field, a visual button, a search field, a scroll bar, a dial, and a slider. An element may have a unique identifier that is a string, such as a name, number, or symbol. Accordingly, the element may be referenced and / or retrieved using the identifier. Further, if a particular element is the first child element of a parent element, then the particular element may be referenced and / or retrieved using a pointer to the parent element and then retrieving a pointer to the first child element. An application may provide one or more Application Programming Interfaces (“APIs”) for referencing and / or retrieving elements. Thus, in some embodiments, the term “element” refers to a component of a software application with which a user (e.g., a person, another application, an application programming interface, etc.) interacts. In some embodiments, interacting with an element causes the application to perform a function. In some embodiments, an element is a web page or screen. In some embodiments, an element comprises other elements, e.g., a web page comprising one or more buttons, text fields, etc. In some embodiments, source code corresponding to an element or associated with an element is mappable to a visible element presented on a screen of a client HVRT-42620.601

[0031] device for viewing by a user. An element has one or more attributes and / or attribute values, e.g., that can be provided by analyzing the visual render, text, code, and / or context of the element.

[0032] As used herein, the term “target element” is an element on which an action (e.g., an action of a step of a test case) is to be performed (e.g., by the step of the test case). For example, if a step of a test case is “click on the login button”, the element that is the login button is the target element of the test case step.

[0033] As used herein, the term “attribute” refers to data that identify and / or describe the appearance, behavior, and / or content of an element. An element may have any number of attributes, e.g., element type; location on a screen, window or page; color; text; size; border; typeface; and code associated with the element. In some embodiments, attributes have “attribute values” - for example, the location attribute may have an attribute value comprising x, y coordinates describing a screen location. Attribute values may be integral, continuous, and / or discontinuous; numbers; classes; types; categories; etc.

[0034] As used herein, the term “action”, when referring to a graphical user interface of an application, refers to an action performed by a user on an element of a graphical user interface, e.g., by manipulating an input device and thereby controlling a cursor, entering text, or otherwise providing input to the application through the graphical user interface. Exemplary actions include a left click, a right click, a drag, a drag and drop, a hover, a double click, keyboard input, a mouseover, a mousedown, a mouseup. In some embodiments, an action may be performed by a computer simulating the actions of a human user (e.g., by executing a test script or by a runtime agent requesting and executing steps from a fine-tuned model).

[0035] As used herein, the term “selector” refers to a pattern used to identify and / or locate elements on a graphical user interface.

[0036] As used herein, the term “test case” refers to a defined set of actions and / or inputs performed on a software application that generates a defined set of outputs. Generally, a test case includes instructions specifying actions and / or inputs, predicted results, and a set of execution conditions. The test case can be viewed as a predetermined collection of one or more actions involving one or more elements of a software application. In some embodiments, a test case comprises a series of actions and / or inputs executed in a predetermined order specified in a test case script to simulate use of a software application or system by a user. Each input and / or action HVRT-42620.601

[0037] executed may be represented by individual test cases that can be joined together to represent a more complex sequence of actions within a larger test case. In some embodiments, a test case is executed to identify errors needing repair in a software application or in components of an interrelated system.

[0038] As used herein, the term “script” or “test script” refers to an implementation of a test case in a particular script language. In some embodiments, a script is a written description of the set of inputs and / or actions to be executed in a test case and a list of expected results for comparison to the actual results. A script is typically associated with each test case. The instructions for inputs and / or actions to execute in a script may be written in descriptive terms to tell a human operator what transactions to execute or it may comprise or access computer instructions to execute the transactions automatically without human user interaction or with minimal or reduced human user interaction. In some embodiments, a script may comprise a combination of computer-executed and human-executed instructions.

[0039] As used herein, the term “interaction”, e.g., when referring to a user interaction with a graphical user interface of an application, refers to a sequence of actions performed by a user or simulated user on the graphical user interface of the application.

[0040] As used herein, the term “device” or “user device” refers to a laptop computer, a desktop computer, a tablet computer, a smart phone, or other computing device. The term “device” or “user device” also refers to virtual machines emulating a physical device (e.g., a virtual machine emulating a laptop computer, a desktop computer, a tablet computer, a smart phone, or other computing device). A “computer program”, “computer executable code”, and the like refers to a program or executable code that may be executed by a processor of a device and is not limited to a program or executable code that is executable only by a computer, though it may be. As used herein, the terms "processor" and "central processing unit" or " CPU" are used interchangeably and refer to a device that is able to read a program from a computer memory (e.g., ROM) or other computer memory) and perform a set of steps according to the program.

[0041] As used herein, the term “application” or “software application” is a computer program designed to carry out one or more task(s) on a user device and includes, but is not limited to, web-based (e.g., browser-based) applications, mobile applications, operating systems, and utilities. In some contexts of this description, the term “application” refers to a patent application HVRT-42620.601

[0042] and is distinguishable by one of ordinary skill in the art based on its use and context from references to the term “application” referring to a computer program.

[0043] As used herein, the term “user” refers to a person (e.g., real or virtual) that interacts with an application (e.g., with an element of an application). In some embodiments, a user is a person (e.g., that interacts with an application through a graphical user interface). In some embodiments, a user is another application (e.g., a script) or software component (e.g., a runtime agent) that interacts with an application. In some embodiments, the systems and methods find use for all types of firearm users and firearm scenarios, including, but not limited to hunting, target shooting, recreational shooting, and combat and military uses.

[0044] As used herein, the term “or” is an inclusive “or” operator and is equivalent to the term “and / or” unless the context clearly dictates otherwise. The term “based on” is not exclusive and allows for being based on additional factors not described, unless the context clearly dictates otherwise. In addition, throughout the specification, the meaning of “a”, “an”, and “the” include plural references. The meaning of “in” includes “in” and “on.”

[0045] As used herein, the terms “about”, “approximately”, “substantially”, and “significantly” are understood by persons of ordinary skill in the art and will vary to some extent on the context in which they are used. If there are uses of these terms that are not clear to persons of ordinary skill in the art given the context in which they are used, “about” and “approximately” mean plus or minus less than or equal to 10% of the particular term, and “substantially” and “significantly” mean plus or minus greater than 10% of the particular term.

[0046] As used herein, disclosure of ranges includes disclosure of all values and further divided ranges within the entire range, including endpoints and sub-ranges given for the ranges. As used herein, the disclosure of numeric ranges includes the endpoints and each intervening number therebetween with the same degree of precision. For example, for the range of 6-9, the numbers 7 and 8 are contemplated in addition to 6 and 9, and for the range 6.0-7.0, the numbers 6.0, 6.1, 6.2, 6.3, 6.4, 6.5, 6.6, 6.7, 6.8, 6.9, and 7.0 are explicitly contemplated.

[0047] As used herein, the suffix “-free” refers to an embodiment of the technology that omits the feature of the base root of the word to which “-free” is appended. That is, the term “X-free” as used herein means “without X”, where X is a feature of the technology omitted in the “X-free” HVRT-42620.601

[0048] technology. For example, a “calcium-free” composition does not comprise calcium, a “mixing-free” method does not comprise a mixing step, etc.

[0049] Although the terms “first”, “second”, “third”, etc. may be used herein to describe various steps, elements, compositions, components, regions, layers, and / or sections, these steps, elements, compositions, components, regions, layers, and / or sections should not be limited by these terms, unless otherwise indicated. These terms are used to distinguish one step, element, composition, component, region, layer, and / or section from another step, element, composition, component, region, layer, and / or section. Terms such as “first”, “second”, and other numerical terms when used herein do not imply a sequence or order unless clearly indicated by the context. Thus, a first step, element, composition, component, region, layer, or section discussed herein could be termed a second step, element, composition, component, region, layer, or section without departing from technology.

[0050] As used herein, the word “presence” or “absence” (or, alternatively, “present” or “absent”) is used in a relative sense to describe the amount or level of a particular entity (e.g., component, action, element). For example, when an entity is said to be “present”, it means the level or amount of this entity is above a pre-determined threshold; conversely, when an entity is said to be “absent”, it means the level or amount of this entity is below a pre-determined threshold. The pre-determined threshold may be the threshold for detectability associated with the particular test used to detect the entity or any other threshold. When an entity is “detected” it is “present”; when an entity is “not detected” it is “absent”.

[0051] As used herein, an “increase” or a “decrease” refers to a detectable e.g., measured) positive or negative change, respectively, in the value of a variable relative to a previously measured value of the variable, relative to a pre-established value, and / or relative to a value of a standard control. An increase is a positive change preferably at least 10%, more preferably 50%, still more preferably 2-fold, even more preferably at least 5-fold, and most preferably at least 10-fold relative to the previously measured value of the variable, the pre-established value, and / or the value of a standard control. Similarly, a decrease is a negative change preferably at least 10%, more preferably 50%, still more preferably at least 80%, and most preferably at least 90% of the previously measured value of the variable, the pre-established value, and / or the value of a HVRT-42620.601

[0052] standard control. Other terms indicating quantitative changes or differences, such as “more” or “less,” are used herein in the same fashion as described above.

[0053] DETAILED DESCRIPTION

[0054] Provided herein are systems and methods that support preferred outcomes of tactical encounters. More particularly, the invention relates to artificial intelligence and machine learning systems and methods to select preferred ballistics solutions and tactical courses of action.

[0055] A multiplicity of variables must be ascertained and accounted for in providing a user with one or more ballistics solutions and user courses of action for safely and effectively aiming one or more projectiles at one or more targets and / or adversaries. Many factors co-vary, for example, the position of one or more users relative to one or more targets, and / or the direction and velocity of target movement and the direction and velocity of air movement between a user and a target. Other variables with diverse impacts on ballistics solutions and courses of action change or are modifiable, but do not co-vary with one another. Many variables fluctuate over diverse intervals including intervals imperceptible to a user. At present it is not possible for a user to integrate and coordinate diverse and fluctuating variables into optimal ballistics solutions and courses of action to safely and effectively aim a projectile at a target or adversary. Accordingly, in some embodiments, the present invention provides an improvement in the technical fields of tactical ballistics solutions and tactical courses of action comprising a computer implemented system that utilizes machine learning software models to provide an improvement to the technical fields in support of preferred outcomes of tactical encounters.

[0056] In some embodiments of the present invention, a user is one or more firearm shooters, and / or one or more spotters. In some embodiments, a user is a human user. In some embodiments, a user is a machine user. In some embodiments, a machine user is a non-autonomous machine user, a semi-autonomous machine user or an autonomous machine user. In some embodiments, a projectile is a bullet. In some embodiments, a projectile is projected focused energy. In some embodiments, a target or adversary is a human target or adversary. In some embodiments, a human target is a combatant. In some embodiments, a target is support HVRT-42620.601

[0057] infrastructure for a non-autonomous machine target, a semi-autonomous machine target or an autonomous machine target.

[0058] As shown in FIG. 1, in some embodiments systems and methods of the present invention provide one or more preferred safe zone and safe navigation courses of action to a user from an artificial intelligence (Al) and / or machine learning (ML) algorithm configured to coordinate user firearm ballistics data, adversary location and adversary firearm ballistics data, environmental conditions data, user performance data, and user physiologic data. In some embodiments, the algorithm infers a preferred location to engage an adversary. In some embodiments, the algorithm infers a safe route for a user to avoid adversary firearm ballistics including speed and direction of travel, environmental and man-made barriers, and locations and capacities of other users and spotters. In some embodiments, the algorithm provides a projectile accuracy probability model based on real-time firearm ballistic and environmental factors.

[0059] As shown in FIG. 2, in some embodiments systems and methods of the present invention provides one or more safe navigation routes and preferred security locations to a user from an artificial intelligence and / or machine learning algorithm configured to coordinate security force ballistics data, potential threat ballistics and locations data, environmental conditions data, user performance data, and user physiologic data. In some embodiments, the algorithm infers terminal effects of a projectile. In some embodiments, the algorithm further coordinates drone and autonomous vehicle location data. In some embodiments, the algorithm infers the direction and speed of travel of self-driving vehicles to assure a preferred safe route in the presence of adversary firearm ballistics.

[0060] As shown in FIG. 3, in some embodiments systems and methods of the present invention provide one or more safe navigation routes to a user from an artificial intelligence and / or machine learning algorithm configured to coordinate road map data, threat location, threat ballistics data, environmental conditions data, and vehicle performance data. In some embodiments, the algorithm infers a safest route for an event, for example, a race, a parade and / or a concert event. In some embodiments, the algorithm infers a safest transit and / or evacuation route from, for example, a building. In some embodiments, the algorithm infers target location based on target ballistics and the configuration and location of target projectile impacts. In some embodiments, the algorithm comprises computer vision to capture one or more HVRT-42620.601

[0061] bullet impact images and data, and to infer a source and location of the source thereby. In some embodiments, a computer vision camera comprises image acquisition in visible wavelengths. In some embodiments, a computer vision comprises image acquisition in non-visible wavelengths including, for example, Near Infrared Wavelength (NIR), Short Wavelength

[0062] Infrared (SWIR), Medium Wavelength Infrared (MWIR), and Long Wavelength

[0063] Infrared (LWIR). In some embodiments, the algorithm comprises input from a day-view optic and / or sensor to provide a video image of bullet impact. In some embodiments, the algorithm comprises data from a GPS sensor on a user, on one or more additional users, on a spotter, and / or within a target acquisition device or firearm telescopic sight.

[0064] As shown in FIG. 4, in some embodiments systems and methods of the present invention provide one or more threat prediction locations from an artificial intelligence and / or machine learning algorithm configured to coordinate computer vision of bullet impact data, predicted threat or adversary ballistics data, environmental conditions data, and map data. In some embodiments, the algorithm infers one or more autonomous vehicle navigation routes aligned with predicted ballistics capacities of an adversary and / or target. In some embodiments, the algorithm comprises data from a 3-dimensional map, LIDAR and / or photogrammetry. In some embodiments, the algorithm generates one or more mission rehearsal simulations of use, for example, in user preparation for mission conduct and maintenance of skills. In some embodiments, the algorithm comprises data from user biometrics including, for example, user heart rate, endurance performance, carrying weight capacity, reaction time, and user speed of travel. In some embodiments, systems and methods of the present invention provide one or more data displays comprising, for example, a headset (for example, a visual augmentation (IVAS) headset), a mobile device (for example, a type allocation code (TAC) mobile device), a digital target acquisition device, and or a digital overlay in a day-view optic device. In some embodiments, the algorithm generates tactical instructions from user and target ballistics to a non-autonomous, semi-autonomous or autonomous drone or robot and / or unmanned water vehicle to navigate safely, and to effectively engage a target. In some embodiments, the algorithm tracks a target and / or adversary. In some embodiments, the algorithm infers a target location and ballistics capacities from images and configurations of target and / or adversary projectile impacts. HVRT-42620.601

[0065] Data Acquisition

[0066] In some embodiments, systems and methods of the present invention comprise a data acquisition component or step. In some embodiments, the data comprise wind information. The wind information may be selected or input by a user from a database. In some embodiments, the wind information comprises wind speed (e.g., in miles per hour, meters per second, kilometers per hour, or knots per hour). In some embodiments, the wind information comprises wind direction. In certain embodiments, the system projects wind arrows comprising wind velocity, acceleration, flow (e.g., laminar, turbulent or a combination of flow), and direction in 1, 2 or 3 axes.

[0067] In some embodiments, the data comprise information regarding the rate and direction of barrel twist (that is, right or left), barrel length, internal barrel diameter, internal barrel caliber and spin drift. Spin drift is a force exerted on a spinning body traveling through the air due to uneven air pressure at the surface of the object due to its spinning. This effect causes a baseball to curve when a pitcher imparts a spin to the baseball as he hurls it toward a batter. In some embodiments, the data comprise information regarding, type of reticle, power of magnification, first, second or fixed plane of function, distance between the target acquisition device and the barrel, the positional relation between the target acquisition device and the barrel, the range at which the telescopic gunsight was zeroed using a specific firearm and cartridge.

[0068] In some embodiments, the data comprise information regarding the type of projectile being used. In some embodiments, the data comprise information regarding the weight of the projectile (e.g., in grains). The weight of the projectile may be stored in memory and automatically retrieved by the program when the user selects a standard, defined cartridge. In some embodiments, the data comprise information regarding the ballistic coefficient and muzzle velocity of the projectile. Muzzle velocity (MV) is a function of the projectile's characteristics (e.g., projectile weight, shape, composition, construction, design, etc.), the kind, quality and amount propellant used in the cartridge case, and the primer. Muzzle velocity is also a function of the barrel length of the firearm, such that the longer the barrel length, the greater the muzzle velocity. In some embodiments, the data comprise information regarding projectile configuration, propellant type, propellant amount, propellant potential force, primer and drag HVRT-42620.601

[0069] model being used.

[0070] In some embodiments, systems and methos of the present invention request or measure a user’s eyesight acuity and idiosyncrasies, heart rate and rhythm (as measured by the electrocardiogram (EKG)), respiratory rate (as measured by a spirometer, capnometer or impedance pneumography), blood oxygen saturation, muscle activity (as measured by the electromyogram (EMG)), and brain wave activity (as measured by the electroencephalogram (EEG)), prior marksmanship, fitness, weight carrying capacity, endurance training, cognitive status, reaction time and speed of aiming, and / or other physiologic and / or cognitive variable. In some embodiments, the system provides training exercises to assist a shooter in improved shooting that take into account a user’s biological characteristics.

[0071] In some embodiments, the data comprise information regarding the number and positional coordinates of one or more spotters. In some embodiments the system automatically queries other units to determine the number, location and type of spotters and devices. In some embodiments, the user and spotters use identical target acquisition device reticles. The target acquisition devices and reticles used by users and spotters may be fixed or variable power. In some embodiments, the spotting information data and aiming points are projected on reticles shared by one or more users and spotters. In some embodiments multiple shooters and spotters share optical or electronically linked target acquisition devices and reticles.

[0072] In some embodiments, the data comprise information regarding the range or distance from a user to a target. For example, a user may enter a distance estimated by reference to a rangefinder on the reticle. In some embodiments, the distance from a user to a target is provided by a peripheral device, for example a laser rangefinder. In some embodiments, the distance from a user to a target is provided by spotters assisting a user by the use of a topographic map, or by triangulation. In some embodiments, a virtual reality application, for example an augmented virtual reality application and / or consensual virtual reality application, of the present invention comprises images and data derived from real world landscapes obtained from, for example, Google Earth, autonomous and non-autonomous drone and / or vehicle images and tracking, satellite images, 2-dimensional maps and 3-dimensional maps, and the like that disclose to a user conditions and circumstances to be encountered at a remote site. In some embodiments, the data comprise photogrammetry comprising aerial photogrammetry and / or terrestrial photogrammetry. HVRT-42620.601

[0073] In some embodiments, the data comprise data from autonomous vehicles and drones that track a target, and input target location in real time.

[0074] In some embodiments, the data comprise slope information, that is, the angle from 0 to 90 degrees up or down between a user and a target or adversary, that is, the vertical angle when the user is shooting uphill or downhill. This information is used to adjust the downrange aiming point based on the projectile's flight through space from the point of firing to target. As can be appreciated, the distance to a target at a sloped angle is somewhat longer than the horizontal distance to a target the same distance from the shooter at the same level, and typically requires the shooter to raise or lower the barrel of the firearm relative to an axis perpendicular to the force of gravity. A shooter aiming downhill lowers the barrel relative to the perpendicular axis forming an angle which is the "downhill" angle. As will be understood, when the shooter raises the barrel above the perpendicular axis (for example, when shooting at a target located above the shooter), the angle formed between the perpendicular axis and the barrel will be an "uphill" angle. In some embodiments, the data comprise cant information.

[0075] In some embodiments, for long range shooting (e.g., from 1000 to 3000 yards or more), the data comprise information for the Coriolis effect and spin drift. The Coriolis effect is caused by the rotation of the earth. The Coriolis effect is an inertial force described by the 19th-century French engineer-mathematician Gustave-Gaspard Coriolis in 1835. Coriolis showed that, if the ordinary Newtonian laws of motion of bodies are used in a rotating frame of reference, an inertial force-acting to the right of the direction of body motion for counterclockwise rotation of the reference frame or to the left for clockwise rotation must be included in the equations of motion. The effect of the Coriolis force is an apparent deflection of the path of an object that moves within a rotating coordinate system. The object does not actually deviate from its path, but it appears to do so because of the motion of the coordinate system. While the effect of the earth's movement while a bullet is in flight is negligible for short and medium range shots, for longer range shots the Coriolis effect may cause a shooter to miss.

[0076] In some embodiments, the data comprise target movement information with movement relative to a user or, in some embodiments, movement of a user (e.g., shooting from a moving vehicle at a stationary or moving target, or running from one shooting site to another). In certain embodiments, both a target and / or rival and a user are in motion. In some embodiments, the data HVRT-42620.601

[0077] comprise target movements in response to projectile strikes. In some embodiments, the data comprise computer images of projectile impact bullet impact comprising, for example, day view or night view optic sensors.

[0078] In some embodiments, a projectile trajectory is projected before the trigger pull, after the trigger pull, or both before and after the trigger pull. In some embodiments, a projected trajectory is modified to display the influence of individual variables alone and / or in combination on the projectile trajectory. In some embodiments, the projected trajectory may be viewed from any perspective including, for example, from a user’s perspective, a target’s perspective, a spotter’s perspective, a bystander’s perspective, or an aerial or satellite perspective. In further embodiments, two or more projected trajectories may be overlaid upon one another and may be visually and mathematically compared.

[0079] In some embodiments, systems and methods of the present invention comprise data from devices, databases and external sources. In some embodiments, the data comprise information from sensors and control systems that interact with the physical, real-world environment. In some embodiments, the data comprise information from ballistics calculators, range finders, global positioning satellite (GPS) systems (for example, GPS sensors on a user, a target acquisition device and / or one or more reference points), weather meters, altimeters, thermometers, barometers, cant monitors, slope monitors, user physiologic monitors, the world wide web, the cloud (z.e., a distributed collection of servers that host software and infrastructure, and it is accessed over the Internet), and other ballistics equipment or accessories. In some embodiments, a ballistics application communicates with a database to retrieve relevant data and to generate images according to selected criteria including, for example, the delay time between the shot and the impact, and diverse factors that influence projectile trajectory. In some embodiments, the data comprise information regarding external conditions in a database and / or entered by a user in response, for example, to a query.

[0080] In some embodiments, data is entered into the system using any conventional input device linked to the system, such as a keyboard, mouse, touchscreen and the like. In some embodiments, preset conditions are selected from a database. In some embodiments, a speech recognition system using a microphone and appropriate software for converting the spoken words to data is used to input data. In some embodiments, cabled or wireless components from HVRT-42620.601

[0081] other measuring devices and sources is used to input data, for example Bluetooth components. In some embodiments, instruments for data input, for example, a Kestrel handheld device or similar handheld, weather station, laptop or desktop device, handheld global positioning system (GPS), a wearable device, or similar device, Leica Vector 4 rangefinder or similar device, and the like, are integrated with the computing device in such a way as to allow input data items to be made available to a ballistic program. In some embodiments, a direct connection is made between the external instruments and systems of the present invention. In some embodiments, the data comprise information from LIDAR, radar, Doppler radar, satellite and other weather forecast data. In some embodiments, data input is updated at fixed or variable intervals and / or in real time.

[0082] In some embodiments, systems and methods of the present invention comprise data that are downloaded to a local network. In some embodiments, systems and methods of the present invention comprise data that are uploaded to a cloud computer. In some embodiments, systems and methods of the present invention comprise data on one or more mobile devices and applications. In some embodiments, systems and methods of the present invention comprise data that are uploaded and downloaded automatically. In some embodiments, systems and methods of the present invention comprise data that are controlled by a computer.

[0083] In some embodiments, data detection and input is triggered by one or more events including, for example, opponent and / or target movement, a predetermined change in a physical real-world environment., or acquisition of a new data point, for example, the trace of an incoming projectile by machine vision. In some embodiments, data detection and input comprises real-time streaming. In some embodiments, a preferred data sampling rate is determined by Al. In some embodiments, a user predetermines a data sampling rate, a range of data detection and a range of data resolution.

[0084] Data preprocessing

[0085] In some embodiments, systems and methods of the present invention comprise data preprocessing. In some embodiments, a user selects a preferred data structure. In some embodiments, raw data is preprocessed to remove noise and incomplete observations, and is reorganized and / or reformulated according to a predetermined preference. In some embodiments, HVRT-42620.601

[0086] missing observations are imputed according to a predetermined algorithm. In some embodiments, data and / or image input is assigned merit and plausibility according to predetermined thresholds. In some embodiments, data and image precision, accuracy, completeness, formatting, transformation, removal of outliers, and normalization of data and / or images are accomplished and confirmed according to predetermined thresholds and algorithms. In some embodiments, an Al deletes implausible readings, lowers values of lower figures of merit, and provides an overall figure of merit to rank and / or to score outputs. In some embodiments, data and images are pre-processed for input entry to an Al system. For example, in some embodiments, images are modified for features of rotation, brightness, color, erosion, dilation, filtering, segmentation, and or resolution. In some embodiments, methods and systems of the present invention are configured to detect and account for multiple co-variates between data and images from diverse sources. In some embodiments, system capacities for storage and processing speed are predetermined by a user according to a specific preference or a specific application. In some embodiments, diverse sources of data and images are correlated with global positioning data (GPS), with gyroscopic data, and / or with predetermined thresholds and / or range scales.

[0087] Artificial intelligence

[0088] In some embodiments, systems and methods of the present invention utilize artificial intelligence. In some embodiments, the Al is a reactive machine Al. In some embodiments, the Al is a limited memory Al. In some embodiments, the Al is a theory of mind Al. In some embodiments, methods and systems of the present invention comprise an Al configured to provide optimal ballistics solutions and optimal user courses of actions in settings with multiple dynamic variables, conditions and environments that impact ballistic and user performance. In some embodiments, an Al of the present invention comprises an algorithm the supports software that learns automatically from patterns and or features in input data and / or images. In some embodiments, an Al comprises one or more of machine learning (ML), a neural network, deep learning, natural language processing, voice recogntion and / or computer vision. In some embodiments, an Al of the present invention identifies objects, interprets speech, generates natural language, and makes ballistics and user safety predictions by rapid processing of massive HVRT-42620.601

[0089] amounts of input data to identify patterns that support optimal decision making in ballistics and user course of action applications. In some embodiments, an Al’s capacity to identify patterns and to provide predictions and instructions is improved by learning as a response to one or more outcomes by adaptation to new inputs without explicit programming to do so.

[0090] In some embodiments, an Al of the present invention comprises a convolutional neural network (CNN) comprising a feed-forward neural network that learns feature engineering automatically by filter and / or kernel optimization. In some embodiments, a neural network comprises a set of nodes organized in layers, a set of weights that connect layers, a set of biases one for each node, and an activation function that transforms the output of each node. In some embodiments, a CNN comprises an input layer, an output layer, and one or more hidden layers arranged in three dimensions (i.e., width, height, and depth dimensions) configured to transform an input volume in 3 dimensions to an output volume. In some embodiments, the layers are a combination of convolution layers, pooling layers, normalization layers, and fully connected layers. CNNs use multiple convolutional layers in some embodiments to filter input volumes to greater levels of abstraction. In some embodiments, a CNN of the present invention comprises back propagation. In some embodiments, methods and systems of the present invention monitor conditions and the environment in real time, and select and predict actions based on rewards and penalties, and determine preferred strategies to maximize selected rewards over time. In some embodiments, methods and systems of the present invention analyze real time data and provide predictive analytics and decision support systems.

[0091] In some embodiments, an Al of the present invention provides one or more probability models for striking a target and / or an adversary. In some embodiments, a probability estimate is followed by one or more reassessments to test inferred actions and to learn. In some embodiments, an Al algorithm of the present invention comprises a Markov decision process / transition model and / or a cost / reward model. In some embodiments, an Al algorithm of the present invention models game theory vs. combatant agents, and adversarial search and reconnaissance protocols. In some embodiments, an Al of the present invention is integrated with machine perception including, for example, perception of camera, microphone, wireless, LIDAR, sonar, radar, tactile, auditory, somatosensory and haptic inputs. In some embodiments, systems and methods of the present invention comprise image classification and / or object HVRT-42620.601

[0092] recognition. In some embodiment, image classification identifies weapons, types of targets (for example, human targets, vehicle targets, and drone targets), and distance to objects based on geometric ranging. In some embodiments, Al algorithms comprise scene interpretation and facial recognition of use, for example, in identifying a target. In some embodiments, a decision support system comprises a probabilistic model, a Bayesian network model, and / or a decision network model. In some embodiments, an Al of the present invention scans large databases for actionable inferences comprising, for example, one or more ballistics solutions and / or user courses of action. In some embodiments, methods and systems of the present invention support detection of language and / or conversational content from visual data.

[0093] Machine learning

[0094] In some embodiments, systems and methods of the present invention utilize machine learning (ML). In some embodiments, ML is performed by a processor. In some embodiments, a processor is on site proximal to a user. In some embodiments, a processor is remote from a user, or from one or more linked users. In some embodiments, a processor is a cloud run processor or a cloud server. In some embodiments, a processor is attached to, or is a component of, a vehicle including, for example, an aerial, terrestrial and / or aquatic drone. In some embodiments, a processor comprises an edge computing device comprising, for

[0095] example, physical hardware connected to an edge computing platform that serves to collect and transmit data including, for example, an Advanced Technology Airborne Computer (ATAC) and / or an Integrated Visual Augmentation System (IVAS). In some embodiments, a user may interact with one or more processors In some embodiments, two or more users are linked in a network. In some embodiments, a network is a cloud network. In some embodiments, data acquisition sensors and detectors are linked to and cooperate with one or more networks.

[0096] In some embodiments, one or more hardware and / or software components of the methods and systems of the present invention comprise one or more hardware and or software solutions that assure cyber security that protect the methods ands systems from, for example, un¬ authorized use, hacking, cyber attack, malware and the like. In some embodiments, a processor of the present invention comprises one or more of a Central Processing Unit (CPU), a Graphics Processing Unit (GPU) a Multi-Core Processor, a Microprocessor, a Quantum Processor, a HVRT-42620.601

[0097] Digital Signal Processor (DSP), and / or an Application-Specific Integrated Circuit (ASIC). In some embodiments, a processor is a dedicated processor. In some embodiments, a processor is a multi-functional processor including, for example, a hand-held computing device, a helmet computing device, a virtual reality device, and / or a cell phone. In some embodiments, methods and systems of the present invention comprise more than one processor. In some embodiments, two or more processors are redundant processors.

[0098] In some embodiments, methods and systems of the present invention comprise an algorithm In some embodiments, an algorithm comprises a ballistics calculator. In some embodiments, a ballistics calculator algorithm is in wired or wireless linkage with an. Al and / or ML algorithm. In some embodiments, the algorithm comprises one or more high level architectural elements. In some embodiments, a. Run-time Infrastructure (RTI) provides one or more of a standardized set of services through different programming languages comprising information exchange, synchronization and federation management, one or more Federates that are individual simulation systems using RTI services, and / or A Federation Object Model (FOM) that specifies the Object Classes and Interaction Classes used to exchange data able to describe information for any domain.

[0099] In some embodiments, the ML algorithm comprises one or more layers that serve as a

[0100]

[0101] and then passes these values as output to a next layer. In some embodiments, methods and systems of the present invention comprise a multi-layered deep neural network (DNN) with 3 or more layers trained on large amounts of data to identify and classify phenomena, recognize patterns and relationships, evaluate possibilities, and make predictions and decisions. In some embodiments, a single-layer neural network provides useful, approximate ballistic predictions and decisions. In some embodiments additional layers in a DNN refine and optimize ballistic outcomes for greater accuracy, in some embodiments, the algorithm comprises linear activation

[0102]

[0103] hardware elements linked through a network with synchronization between blocks and flow between blocks. In some embodiments, the blocks are structurally and functionally interlinked HVRT-42620.601

[0104] comprising for example, a feature extraction block linked to a mathematical optimization block. In some embodiments, implementation of an algorithm comprises one or more field-programmable array gates (FPGAs) integrated into a circuit hardware. In some embodiments, implementation takes place on a GPU and / or a conventional microprocessor.

[0105] In some embodiments, an ML model of the present invention is a functional model comprising, for example, a neural network trained for ballistics applications. In some embodiments, an ML model of the present invention comprises a high-level training paradigm comprising, for example, supervised learning, unsupervised learning, and / or reinforcement learning. In some embodiments, systems and methods of the present invention comprise unsupervised ML comprising acquisition and coordination of large amounts of data with no previously recognized correlations. In some embodiments, an ML model of the present invention comprises loss functions of supervised learning, policy models of a recurrent neural network (RNN), layers, nodes, and one or more activation features. In some embodiments, an ML model of the present invention comprises a training phase comprising ballistics inputs and / or an execution phase comprising ballistics solutions and / or instructions. In some embodiments, hardware and software of the present invention identify patterns and make predictions aligned to predetermined features and objectives including, for example, identity of a user, conditions at inception of an implementation, conditions at intervals and at completion of an implementation, aims of one or more specific missions, and correlation and correction for anticipated and unanticipated events during implementation. In some embodiments, patterns and predictions are provided to a user in diverse post-processing outputs singly and in combination comprising one or more image outputs, real world visual outputs, virtual reality visual outputs, graphic outputs, auditory outputs, somatosensory outputs and / or haptic outputs.

[0106] In some embodiments, methods and systems of the present invention comprise supervised ML wherein the systems and methods comprise acquisition and coordination of data with human neural network training that comprises human classification and prediction of preferred ballistics solutions and courses of action. In some embodiments, an algorithm identifies relationships and encodes relationships between data and image inputs into one or more ballistics solutions and / or preferred courses of action models. In some embodiments, supervised ML supports mission rehearsal, skill and maintenance training events, and real-world mission data collection. In some HVRT-42620.601

[0107] embodiments, training data is mapped onto features comprising, for example feature engineering, gradient-descent training, genetic algorithm training, and multi-batch options for organizing training data. In some embodiments, methods and systems of the present invention comprise one or more algorithms configured to detect and to correct loss functions including, for example, differences between network output and “ground truth” followed by parameters updates. In some embodiments, algorithms of the present invention comprise one or more ballistic optimization algorithms. In some embodiments, methods and systems of the present invention comprise one or more hyperparameters that impact training including, for example, the number of training batches and / or the learning rate. In some embodiments, hyperparameters are parameters whose values control the learning process and determine the values of model parameter that a learning algorithm adopts comprising top-level parameters that control the learning process and the model parameters that arise. In some embodiments, different regions of the network are fixed, trained separately, and / or trained jointly. In some embodiments, input data used by the trained model changes over time as conditions change and as results of output predictions and / or instructions are observed and / or inferred. In some embodiments, the trained model is updated as the result of one or more updated inferences supported by the ML architecture. In some embodiments, modification of a single factor data point is made in accord with a target hit or miss in increments over a predetermined range. In some embodiments, two or more factor data points are modified sequentially, simultaneously or randomly. In some embodiments, DNN supports computer vision, speech recognition and image classification.

[0108] Post-processing artificial intelligence (Al) and machine learning (ML) outputs

[0109] Ins some embodiment, methods and systems of the present invention comprise postprocessing of Al and ML output. In some embodiments, post-processing comprises data framing, data targeting, image enhancement, image filtering, image subtraction, image addition, and data and image superimposition. In some embodiments, output post-processing comprises projection of one more real or simulated projectile pathways on one or more virtual or real world landscapes. In some embodiments, post-processing data and images are displayed on any type of screen in numerical fashion including, for example, cardinal directions, aiming sites, distance to travel, features for concealment and the like. In some embodiments, output post-processing HVRT-42620.601

[0110] comprises overlay of data, images and or simulations on 2 dimensional and / or 3 dimensional displays, and / or a geo-reference overlay on a virtual reality, augmented reality, mixed reality, and consensual reality headset display. HVRT-42620.601

[0111] Algorithm quality assurance

[0112] In some embodiments, methods and systems of the present invention comprise hardware and software components configured to detect and correct data inconsistencies, biases and errors. In some embodiments, components are configured to assure algorithmic accuracy and precision, generalizability, reproducibility and replicability. In some embodiments, methods and systems of the present invention comprise data calibrants, image calibrants, data positive controls, data negative controls, image positive controls and image negative controls. In some embodiments, controls provide dummy data and / or random data. In some embodiments, methods and systems comprise hardware and software to detect, identify and display updated prediction sets amended for sensitivity, specificity, accuracy, precision, false positive data and false negative data. In some embodiments, methods and systems comprise schedules, hardware and software configured for system maintenance and repair. In some embodiments, schedules, hardware and software are directed to sensor calibration, wired and wireless linked integrity, and system-wide test communications. In some embodiments, methods and systems of the present invention comprise hardware and software configured for the detection and impairment of rival Al and ML algorithms, hardware and software. In some embodiments, methods and systems are configured to detect and to amend undisclosed hyperparameters. In some embodiments, methods and systems of the present invention provide one or more users with real time measures and warnings of computational demand thresholds that are being approached.

[0113] Machine learning and artificial intelligence outputs

[0114] In some embodiments, systems and methods of the present invention comprise inference of preferred outcomes of tactical encounters and preferred courses of action. In some embodiments, the systems and methods infer an optimal location for target engagement. In some embodiments, the systems and method infer a safe route of navigation accounting for target and / or adversary firearm ballistics. In some embodiments, the systems and methods infer a projectile impact probability model based on user ballistics and environmental conditions. In some embodiments, the systems and methods infer terminal effects of a projectile.

[0115] In some embodiments the present invention provides preferred ballistic solutions and courses of action to support advantageous outcomes of tactical encounters to a user in visual, HVRT-42620.601

[0116] auditory and haptic media. In some embodiments, the visual media comprise a viewer. In some embodiments, the viewer projects an image to a user e.g., a landscape comprising one or more targets. In some embodiments, the viewer is a display screen, goggles, a graphic user interface, a helmet or a headset, for example, a visual augmentation (IVAS) headset), or a mobile device (for example, a type allocation code (TAC) mobile device). In some embodiments the viewer comprises a numeric display, an algebraic display, a trigonometric display, a mathematical display, 2-dimensional data image display, a 3 -dimensional data image display, a predictive evolving 3-dimensional image display, a holographic image display, a 3-dimensional augmented visual reality image display, and / or a 4-dimensional predictive evolving image display. In some embodiments, the viewer comprises one or more aiming points, and / or one or more tracking points to track a target with system correction for ballistic variables. In some embodiments, the viewer comprises aiming points in random and / or sequential order. In some embodiments, the auditory media comprises non-verbal communication and / or a non-verbal communication.

[0117] In some embodiments, the viewer is a virtual reality viewer. In some embodiments, the viewer comprises a headset comprising one or more of a processor, a power source connected to the processor, memory connected to the processor, a communication interface connected to processor, a display unit connected to the processor, and sensors connected to processor. In certain embodiments, the viewer is a virtual reality unit, for example, an Oculus Rift headset available from Oculus VR, LLC. In some embodiments, the virtual reality unit is the HTC Vive headset available from HTC Corporation. Any suitable virtual reality unit known in the art may be employed. Other exemplary embodiments include hardware comprising an Intel Core i5-4590 or AMD FX 8350 processor equivalent or better, a NVIDIA GeForce GTX 1060 or AMD Radeon Rx 480 graphics card or better, 4 GB of RAM or better, a 1X HDMI 1.4 port or DiplayPort 1.2 or better, USB IxUSB 2.0 port or better, and a Windows 7 SP1, Windows 8.1, Windows 10 or better operating system.

[0118] In some embodiments, the viewer is a display device that may be removably attached to a target acquisition device that displays data and images that are superimposed over real world images. In some embodiments, the viewer is physically or electronically integrated with a target acquisition device. In some embodiments, the viewer superimposes a computer-generated image on a user's view of the real world as seen, for example, through a target acquisition device HVRT-42620.601

[0119] thereby providing a composite view of the real world augmented by computer-generated data and / or one or more images. In some embodiments, the composite view of the real world is augmented by computer-generated data and / or one for more images is further augmented by additional computer-generated perceptual information including visual, auditory, haptic, somatosensory, and / or olfactory information. In some embodiments, the computer-generated perceptual information comprises information from and to multiple sensory modalities. In some embodiments, the systems and methods of the present invention are used for user training and user skill maintenance.

[0120] Implementation

[0121] In some embodiments, systems and methods of the present invention comprise one or more systems and / or devices for implementation of the technology. In some embodiments, the systems and / or devices comprise a processor, a network interface connected to the processor, and memory connected to the processor. In some embodiments, the systems and / or devices comprise an application stored in memory and executed by processor. In some embodiments, the systems and / or devices provide a ballistic solution application and / or a tactical course of action application. In some embodiments, the systems and / or devices generate and project a ballistic solution projectile trajectory. In some embodiments, the systems and / or devices comprise a virtual reality sub-system, for example, an augmented reality system and / or a consensual virtual reality system. In some embodiments the systems and / or devices generate one or more aiming points comprising a preferred ballistics solution. In some embodiments, the systems and / or devices provide one or more safe and effective courses of action comprising a map, a landscape field of view, a user course of action trajectory, a pathway, auditory instructions, written instructions, symbolic instructions, and / or go-no go instructions. In some embodiments, a systems and / or devices comprise one or more virtual assistants.

[0122] All publications and patents mentioned in the above specification are herein incorporated by reference. Various modifications and variations of the described compositions and methods of the invention will be apparent to those skilled in the art without departing from the scope and spirit of the invention. One skilled in the art will recognize at once that it would be possible to construct the present invention from a variety of materials and in a variety of different ways. HVRT-42620.601

[0123] Although the invention has been described in connection with specific further embodiments, it should be understood that the invention should not be unduly limited to such specific embodiments. While the further embodiments have been described in detail, and shown in the accompanying drawings, it will be evident that various further modification are possible without departing from the scope of the invention as set forth in the appended claims. Indeed, various modifications of the described modes for carrying out the invention which are obvious to those skilled in marksmanship, computers or related fields are intended to be within the scope of the following claims.

Claims

HVRT-42620.601CLAIMS1. A method for selecting a ballistics solution and / or tactical course of action, comprising:a) inputting ballistic and / or tactical data into a computer system;b) processing said data using an artificial intelligence system to generate a ballistics solution and / or tactical course of action, wherein said artificial intelligence system was trained on historical ballistic and / or tactical data; andc) displaying said ballistics solution and / or tactical course of action to a user, wherein said ballistics solution and / or tactical course of action comprises one or more of: a safe zone, a safe navigation course of action, user firearm ballistics recommendation, and target location.

2. The method of claim 1, wherein the user is a human user.

3. The method of claim 1, wherein the user is a firearm shooter.

4. The method of claim 1, wherein the user is a spotter.

5. The method of claim 1, wherein the user is a machine user.

6. The method of claim 5, wherein the machine user is a non-autonomous machine user.

7. The method of claim 5, wherein the machine user is a semi-autonomous machine user.

8. The method of claim 5, wherein the machine user is an autonomous machine user.HVRT-42620.6019. The method of claim 1, wherein said historical ballistic and / or tactical data comprises one or more of firearm ballistics data, target location, adversary firearm ballistics data, environmental conditions data, user performance data, user physiologic data, map data, threat location, and vehicle location and performance data.

10. The method of claim 1, wherein said safe zone or said safe navigation course of action comprises one or more of: a location to engage a target, a safe route for a user to avoid adversary firearm ballistics, adversary positions, protected non-target entities or events, evacuation zones, or moving adversary vehicles, speed and direction of travel of adversary firearm ballistics, environmental and man-made barriers, locations and capacities of other users and spotters, and drone and autonomous vehicle location.

11. The method of claim 1, wherein user said firearm ballistics recommendation comprises a projectile accuracy probability model based on real-time firearm ballistic and environmental factors.

12. The method of claim 1, wherein said ballistic and / or tactical data comprises one or more of: road map data, threat location, threat ballistics data, environmental conditions data, and vehicle performance data.

13. The method of claim 1, wherein said ballistic and / or tactical data comprises information obtained from bullet impact images and data, a high-speed camera image, a thermal bullet flight sensor, day-view optic and / or sensor data, a GPS sensor, a satellite, a drone, a 3-dimensional map, LIDAR, and photogrammetry.

14. The method of claim 1, wherein said ballistic and / or tactical data comprises user biometric data.

15. The method of claim 14, wherein the user biometric data comprises user heart rate, endurance performance, carrying weight capacity, reaction time, and user speed of travel.HVRT-42620.60116. The method of claim 1, wherein said displaying comprises providing data to one or more of: a headset (for example, a visual augmentation (IVAS) headset), a mobile device (for example, a type allocation code (TAC) mobile device), a digital target acquisition device, and or a digital overlay in a day-view optic device.

17. The method of claim 1, wherein said ballistic and / or tactical data comprises one or more of: environmental information (e.g., wind information), firearm information (e.g., rate and direction of barrel twist), projectile being used, user biometric information, locations of users, spotters, or targets, range to target, slope information, Coriolis effect, and movement of users or targets.

18. The method of claim 1, wherein said historical ballistic and / or tactical data comprises one or more of: environmental information (e.g., wind information), firearm information, projectile being used, user biometric information, locations of users, spotters, or targets, range to target, slope information, Coriolis effect, and movement of users or targets.

19. A system configured to carry out the method of any of claims 1 to 18, comprising: one or more computers configured to receive ballistic and / or tactical data, to process said data using an artificial intelligence system to generate a ballistics solution and / or tactical course of action, and to display information comprising said ballistics solution and / or tactical course of action to a user.

Images