Architecture Technology Corporation

Paul C Davis

Douglas Sweet

Eric R Chartier

William Colligan

Andrew Murphy

Ian J McLinden

Ryan L Hagelstrom

Erin Gambucci

Richard Chandler

Evan Eaves

Ashley B Novak

Mark Peters

Doug Sweet

Mir F Ali

Hiren M Mandalia

An automatic, autonomous predictive aircraft surface state event track (ASSET) system, includes a mobile device onboard an aircraft and a remote service in communication with the mobile device. The mobile device includes a processor, and an application that in turn includes machine instructions encoded on a non-transitory computer-readable storage medium. The processor executes the machine instructions to receive sensor data from aircraft onboard sensors, the sensor data indicating an operational state of the aircraft; and transmit the sensor data. The remote service receives the sensor data and includes a remote processor that executes machine instructions to compute an operational state of the aircraft; identify an aircraft event associated the aircraft; and using the aircraft operational data, the sensor data, and the event, predict that the aircraft will meet a next scheduled aircraft event within a specified time window.

Autonomous and automatic, predictive aircraft surface state event track system and corresponding methods

A method for minimizing aircraft collisions, includes detecting a flight of an unmanned aerial system (UAS) in a restricted area and determining a location of a radio frequency (RF) emitter in communication with the UAS. The method includes, at each of a plurality of RF sensors of a network of wireless RF sensors, receiving RF emissions within an RF band pertaining to UAS control, processing the received RF emissions, and transmitting data derived from the processed RF emissions. The method further includes at a designated one of the plurality of RF sensors, receiving the transmitted data from the RF sensors, a processor computing, using the transmitted data received from the RF sensors, a location estimate for the RF emitter and to predict the UAS is flying, and based on the prediction, the processor generating an alert.

Mobile emergency perimeter system and method

A real-time recommendation system includes a non-transitory, computer-readable storage medium having encoded thereon instructions that a processor executes to receive data from data sources that includes one or more data records. The processor then identifies features in data of a data record, extracts and buffers the features, and applies the features to a feature detect mechanism. The feature detect mechanism includes feature detect algorithm and more recommender algorithms that are encapsulated in a feature detection object. The feature detection object may be a Strategy/Composite feature detection object that includes Strategy design patterns. The processor further executes to reject a feature in response to failures of the feature to match at least one of the Strategy design patterns, and in real time, select and install features that match one or more of the Strategy design patterns into one or more recommender algorithms.

Systems and methods for generating real-time recommendations

A software migration system includes a processor; a user interface that receives and displays components of a legacy software and a new software system; and a migration program as machine instructions stored on a non-transitory, computer-readable storage medium. The processor executes to identify reference models, standards, and requirements of a system common architecture that together define a process for migrating the legacy software to the new software system; load the legacy software onto a virtual machine instantiated on a dedicated hardware platform; refactor the legacy software; subdivide the refactored legacy software into legacy software subsets; move a first legacy software subset to a second virtual machine instantiated on the hardware platform; for a second legacy software subset, adapt the second legacy software subset to the system common architecture; and combine the adapted second legacy software subset and the first legacy software subset to produce the new software system.

System and method for migrating legacy software to a system common architecture

A method, executed by a processor, includes the processor receiving signals information from a device located on a departing airplane; verifying an identification of the airplane and identifying an expected departure sequence of aircraft surface states; monitoring and identifying additional signals information received from the mobile device, including comparing the additional signals information to known data; logging the additional signals information, and processing the additional signals information, and determining the logged data corresponds to events indicative of an aircraft surface state; sending an aircraft surface state reached message to Local and Center flight management; and executing a statistical routine and providing statistical data from the execution relating to an occurrence of upcoming aircraft surface state event and sending the statistical data with the aircraft surface state message.

Aircraft surface state event track system and method

A system and a corresponding computer-implemented method identifies and classifies community-sourced documents as true documents. The community-sourced documents include one or more data objects such as data items, including text, strings, phrases, and words; image items, including still image items, video image items, and icons; and drawing items. The system and corresponding method then report the analysis results.

Systems and methods for identifying and classifying community-sourced documents as true documents

A computer-implemented method for establishing and controlling a mobile perimeter and for determining a geographic location of an RF emitting source at or within the mobile perimeter includes receiving from RF sensors in a network, processed RF emissions from the source collected at RF sensors. The RF emissions follow a wireless protocol and include frames encoding RF emitting source identification information. The method further includes extracting RF emitting source identification information from the frames, processing the source identification information to identify the RF emitting source, and classifying the RF emitting source by one or more of UAS type, UAS capabilities, and UAS model. The method also includes receiving from the RF sensors, a geographic location of each RF sensor and a time of arrival (TOA) of the RF emissions at the RF sensor; and executing a multilateration process to estimate a geographic location of the RF emitting source.

Systems and methods for identifying, classifying, locating, and tracking radio-frequency emitting objects in a temporary flight restriction area

A computer-implemented method for migrating a monolithic legacy software system to a well-defined modular target software architecture includes selecting a method, based on predefined patterns, for transforming the software legacy software system; creating an abstract syntax tree from the legacy software system's source code; from the abstract syntax tree, determining a flow of the source code and any coding violations and coding smells in the legacy software system's source code; using the flow and the coding violations, identifying architecture issues in the legacy software system; scheduling tasks for transforming the legacy software system into the target software architecture; automatically generating new source code according to the target software architecture; and automatically and autonomously refactoring the new source code.

A method for minimizing aircraft collisions, comprising includes detecting a flight of an unmanned aerial system (UAS) in a restricted area and determining a location of a radio frequency (RF) emitter in communication with the UAS. The method includes, at each of a plurality of RF sensors of a network of wireless RF sensors, receiving RF emissions within an RF band pertaining to UAS control, processing the received RF emissions, and transmitting data derived from the processed RF emissions. The method further includes at a designated one of the plurality of RF sensors, receiving the transmitted data from the RF sensors, a processor computing, using the transmitted data received from the RF sensors, a location estimate for the RF emitter and to predict the UAS is flying, and based on the prediction, the processor generating an aircraft collision alert.

A computer-implemented method for training a neural network system to classify data items as true mentions of relevant data objects includes, based on a domain of interest, a processor controlling an input component and a data store to receive and store a document related to the domain of interest, control a natural language processor to extract data items related to the domain of interest from the document, create a first training data set of selected ones of the data items, using the first training data set, train the neural network to identify true mentions of data objects relevant to the domain of interest, apply tweaks to one or more of the data items to create a list of tweaked data items, creating a second training data set including the tweaked data items, and using the first and second training data sets, further train the neural network to identify true mentions of relevant data objects.

Community-based reporting and analysis system and method

A computer-implemented method for establishing and controlling a mobile perimeter and for determining a geographic location of an RF emitting source at or within the mobile perimeter includes receiving from RF sensors in a network, processed RF emissions from the source collected at RF sensors. The RF emissions follow a wireless protocol and include frames encoding RF emitting source identification information. The method further includes extracting RF emitting source identification information from one or more of the frames, processing the source identification information to identify the RF emitting source, and classifying the RF emitting source by one or more of UAS type, UAS capabilities, and UAS model. The method also includes receiving from the RF sensors, a geographic location of each RF sensor and a time of arrival (TOA) of the RF emissions at the RF sensor; and executing a multilateration process to estimate a geographic location of the RF emitting source.

A real-time recommender method includes receiving streaming data records; applying tokenizer objects to a received data record to identify, extract and buffer data elements. The method applies the buffered data elements to a feature detect tree that includes a root node and one or more subsequent branch nodes, a set of the subsequent branch nodes defining a path culminating in a leaf node, by applying a data element to the root node and testing the data element to reject or select the data element, applying a prior-selected data element to subsequent branch nodes, and conducting at each of the subsequent branch nodes, a test to reject or select the prior-selected data element, and stopping, for a selected path, the applying and testing after the prior-selected data element reaches a path's leaf node. The method still further includes installing each data element reaching the path's leaf node, as a feature, into one or more recommender algorithm models.

Growing community of inventors

Saratoga, CA, United States of America

Paul C Davis