Systems and methods to measure performance
Inventors
Tolland, Michael • Zuzack, Zachary
Assignees
Publication Number
US-10997868-B1
Publication Date
2021-05-04
Expiration Date
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Abstract
Methods of and systems to provide performance measurement are 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
Methods of and systems to provide performance measurement are 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.
Claims Coverage
This coverage identifies three independent claims and extracts their main inventive features.
Receiving a plurality of performance data inputs from at least one user interface of a training simulator
Receiving a plurality of performance data inputs from at least one user interface of a training simulator; the plurality of performance data inputs represents a plurality of types of performance data; each of the plurality of types of performance data having a unique attribute.
Translating performance data into components according to a common data model format
Translating the plurality of types performance data into a plurality of components according to a common data model format; the plurality of components comprising a plurality of component properties defining a plurality of states and a plurality of events.
Separating components for processing by payload processors
Separating the plurality of components for processing by one or more payload processors.
Merging components with a synchronized time into a common data model store
Merging the plurality of components with a synchronized time value into a common data model store according to the common data model format.
Monitoring merged components and identifying changes
Monitoring the merging of the components in the common data model store; identifying a change to the component or the component property.
Communicating changes to a measure and outputting a measure value
Communicating the change to a measure to calculate a measure value; and communicating the measure value as a performance measurement wherein the performance measurement comprises one of the plurality of states of the user.
Connector-based parsing and unique key assignment with processor matching
Receiving a performance data from a user environment at a connector; the performance data identifying a unique key; parsing the performance data into one or more component property of a common data model format for a data model; assigning the unique key as the identification for the performance data and the one or more component property; communicating the one or more component property to a payload processor; determining, with the payload processor, whether one of one or more processors matches the identification of the one or more component property.
Processor behavior on match and non-match including model listener notifications
If one of the one or more processors matches the identification: appending the one or more component property to a queue of the one of the one or more processors for execution, merging the one or more component property into an existing state or an existing event, detecting any changes or additions, notifying one or more model listeners of the change, and notifying a measurement of the change as the performance measurement data; if none matches: creating a new processor, subscribing the new processor to a model listener, selecting a component listener for the new processor, populating the component listener with the one or more component property, and adding the one or more component property to the data model.
Vehicle training simulator specification
The training simulator can be a vehicle training simulator and the claimed method recites the same steps with the training simulator specified as a vehicle training simulator.
The independent claims focus on receiving heterogeneous performance data with unique attributes, translating to a common data model of states and events, routing components to payload processors, merging with synchronized time into a common data model store, monitoring for changes via model listeners, and computing and communicating measure values; a connector/unique key/processor matching workflow and a vehicle training simulator embodiment are also claimed.
Stated Advantages
Efficiently merge and monitor different types of performance data by translating performance data into and storing in a common data model format.
Use of key attributes and unique keys to parse performance data into separate processing streams to increase system performance and organize data without requiring specific source data linkage.
Model listeners enable quick determination of when performance data has changed to reduce calculations necessary to determine measure values and focus processing on changing data.
Ability in some embodiments to merge different types of performance data in real-time or near real-time to achieve multi-modal real-time assessment and triggering of measures across data sources.
Improved processing speed and scalability; in some embodiments capable of receiving 25000 hz data messages and performing 5000+ independent calculations per second.
Documented Applications
Training and simulation systems for measuring the performance of one or more users and teams in real-time using one or more data sources from the training and simulation environment.
Vehicle training simulators.
Multi-modal training simulators including physiological interfaces such as electrocardiogram (ECG) or electroencephalogram (EEG) electrodes.
Computer game consoles and game controllers as user environments and user interfaces for providing performance data.
Communications training systems integrating synthetic entities, simulation datasets, and performance measurement engines.
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