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Assignees
Member
Aptima, Inc.Aptima, Inc. is a leader in optimizing and improving human performance in mission-critical, technology-intensive environments. Since 1995, Aptima has combined social science principles with advanced computational methods to deliver innovative, human-centered engineering solutions for defense, intelligence, aviation, law enforcement, and healthcare sectors. The company is recognized for its multidisciplinary approach, integrating expertise from engineering, data science, psychology, and cognitive science to address complex challenges. Aptima is committed to advancing research and development in artificial intelligence, machine learning, data analytics, and simulation, with a strong focus on enhancing human-machine teaming, learning, and readiness. Their mission is to ensure synergy between humans, technology, and organizations, accelerating learning and productivity in the age of AI. Aptima's culture is rooted in innovation, impact, and empathy, fostering an inclusive and supportive environment for its diverse team of experts.
Aptima, Inc. is a leader in optimizing and improving human performance in mission-critical, technology-intensive environments. Since 1995, Aptima has combined social science principles with advanced computational methods to deliver innovative, human-centered engineering solutions for defense, intelligence, aviation, law enforcement, and healthcare sectors. The company is recognized for its multidisciplinary approach, integrating expertise from engineering, data science, psychology, and cognitive science to address complex challenges. Aptima is committed to advancing research and development in artificial intelligence, machine learning, data analytics, and simulation, with a strong focus on enhancing human-machine teaming, learning, and readiness. Their mission is to ensure synergy between humans, technology, and organizations, accelerating learning and productivity in the age of AI. Aptima's culture is rooted in innovation, impact, and empathy, fostering an inclusive and supportive environment for its diverse team of experts.
Publication Number
US-10431106-B1
Publication Date
2019-10-01
Expiration Date
Abstract
A performance measurement system is provided utilizing an architecture configured to efficiently merge and monitor different types of performance data. Connectors are provided to receive and translate different types of performance data from different sources. The performance data is translated into and stored in a common data model format. In some embodiments, key attributes are defined for each of the performance data sources that uniquely characterizes each relevant performance data so that is can be parsed into separate processing streams to increase system performance. The key attributes also act as cues to organize the performance data as it is being merged so that it can be accessed without requiring a specific source data linkage. Using model listeners, determinations can be quickly made regarding when performance data is changed to reduce calculations necessary to determine measure values. Some embodiments merge different types of performance data in real-time.
Core Innovation
A performance measurement system is provided utilizing an architecture configured to efficiently merge and monitor different types of performance data. Connectors are provided to receive and translate different types of performance data from different sources. The performance data is translated into and stored in a common data model format. Key attributes are defined for each of the performance data sources that uniquely characterizes each relevant performance data so that it can be parsed into separate processing streams to increase system performance.
Using model listeners, determinations can be quickly made regarding when performance data is changed to reduce calculations necessary to determine measure values, and some embodiments merge different types of performance data in real-time. The connector-based, plugin architecture and a common data model format allow the processing and merging of different types of data, and unique keys act as cues to organize the performance data as it is being merged so that it can be accessed without requiring a specific source data linkage.
The system separates and unifies the processing of multiple types of data into the common data model to maximize efficiency of updating the common data model store, synchronizes components and component properties into the common data model with a time processor, and monitors the common data model store so that changes can be communicated to measures which calculate measure values that may be merged with existing component properties.
Claims Coverage
Independent claims identified: 1, 10, and 16. Eleven inventive features extracted describing system components, data transformations, distribution, merging, and change detection.
User environment detects user input and transforms to performance data
A user environment configured to detect at least one user input received at at least one user interface and transform the user input to at least one performance data; the user environment comprises a multi-modal training simulator configured to communicate the performance data to a performance measurement subsystem.
Connector transforms performance data into component properties and identifies unique key
The connector configured to transform each of the at least one performance data into at least one component property of at least one component according to a format of a common data model, identify a unique key for each of the at least one component, and communicate the at least one component with the unique key.
Payload processor distributes components to processors according to unique key
The payload processor configured to receive the at least one component and the unique key and distribute the at least one component and the at least one component property to one of the at least one processors according to the unique key.
Processor merges component properties with existing component properties in common data model store
The processor configured to merge the at least one component property of the at least component with the at least one existing component property of the existing component in the common data model store.
Model listener identifies changes and communicates to a measure
The at least one model listener configured to identify a change to one of the at least one component property or the at least one component whereby the change can be communicated to a measure.
User environment comprising a computer game console and game controller
Wherein the user environment comprises a computer game console and the user interface comprises a computer game controller.
Performance measurement subsystem comprising connector, payload processor, processor, common data model store and model listener
The performance measurement subsystem comprising at least one connector, at least one payload processor, at least one processor, at least one common data model store and at least one model listener, wherein the connector is configured to transform each of the at least one performance data into at least one component property of at least one component according to a format of a common data model, identify a unique key for each of the at least one component, and communicate the at least one component with the unique key; the payload processor configured to receive the at least one component and the unique key and distribute the at least one component and the at least one component property to one of the at least one processors according to the unique key; the processor configured to merge the at least one component property of the component with the at least one existing component property of the existing component in the common data model store; and the at least one model listener configured to identify a change to one of the at least one component property or the at least one component whereby the change can be communicated to a measure.
Multi-modal simulator with physiological interface and joystick
A multi-modal simulator configured to detect at least one user input received at a first and a second user interface and transform the user input to a first and a second performance data; the first user interface comprises a physiological interface and the second user interface comprises a joystick.
Connector identifies first and second unique keys and transforms and communicates first and second components
The at least one connector configured to identify a first unique key from the first performance data, transform the first performance data into at least one first component property of a first component according to a format of a common data model, communicate the first component with the first unique key, identify a second unique key from the second performance data, transform the second performance data into at least one second component property of a second component according to the format of the common data model, and communicate the second component with the second unique key.
Payload processor distributes first and second components to corresponding processors according to unique keys
The payload processor configured to receive the first component and the first unique key and distribute the first component to a first processor according to the first unique key, and receive the second component and the second unique key and distribute the second component to a second processor according to the second unique key.
Merge physiological state and calculate physiological measure triggered by model listener
The second processor configured to merge the physiological state with the at least one existing physiological state in the common data model store; the at least one model listener configured to identify a change to the at least one existing physiological state and communicates the change to a measure; and the measure configured to calculate a physiological measure of the user given the simulation event.
The independent claims disclose a system for translating diverse performance data into a common data model using connectors and unique keys, distributing and merging components via payload processors and processors, and detecting changes via model listeners to trigger measures; independent claim variants specify embodiments including a computer game console with a game controller and a multi-modal simulator with physiological and joystick interfaces.
Stated Advantages
Efficiently merge and monitor different types of performance data using a common data model.
Increase system performance by parsing data into separate processing streams using key attributes and unique keys.
Organize merged performance data so it can be accessed without requiring a specific source data linkage.
Reduce calculations by using model listeners to identify when performance data changes and focus processing on changed data.
Enable real-time or near-real-time multi-modal measurement across data sources and trigger measures across modalities.
Faster processing than existing models and ability to combine multi-modal data quickly for instructors and scientists.
High-throughput capability described: receiving 25000 Hz data messages and performing 5000+ independent calculations per second.
Documented Applications
Training and simulation systems and methods of measuring the performance of one or more users as they use the systems.
Measuring and accessing the performance of individuals and teams in real-time using one or more data sources from the training and simulation environment.
Vehicle training simulators.
Communications training systems (systems for communications training integrating performance measurement engine and synthetic entities).
User environments comprising computer game consoles where the user interface comprises a computer game controller.
Multi-modal training simulators using physiological interfaces such as electrocardiogram (ECG) or electroencephalogram (EEG) electrodes for recording electrical impulses as performance data.
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