Systems and methods to measure performance
Inventors
Tolland, Michael • Zuzack, Zachary
Assignees
Publication Number
US-11676500-B1
Publication Date
2023-06-13
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 and 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 characterize each relevant performance data so that it can be parsed into separate processing streams to increase system performance, and the key attributes 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.
In the field of performance measuring systems, measuring and assessing the performance of individuals and teams in real-time using multiple data sources is difficult because the multiple data sources have different data structures and are read at variable frequencies within source and different frequencies across sources, making it difficult to combine and compute measures from these heterogeneous data. Traditional attempts have focused on only the real-time measurement challenge or the multi-modal measurement challenge in isolation, and existing solutions often address real-time measurement by focusing on a single data source or incorporate additional multi-modal sources only in a post-hoc manner which requires manual evaluation and integration.
The disclosed solution provides a plugin architecture and a common data model format that allows processing and merging of different types of data, including merging or fusing the data in real-time, in near real-time or in a synchronized time. Using payload processors, processors, data models, time processors and model listeners, the system separates and unifies processing of multiple types of data into the common data model to maximize efficiency, determines when data has changed to limit computation necessary for performance measurement, and uses model listeners to notify measures when relevant changes occur.
Claims Coverage
Independent claims identified: 1 and 9. The summary extracts 9 main inventive features from these independent claims.
User environment configured to communicate training content to a team
Providing a user environment configured to communicate a training content to a team and receiving a plurality of performance data inputs from at least one user interface of the user environment.
Translating performance data into common data model components
Translating the plurality of performance data inputs into a plurality of components according to a common data model format, the plurality of components comprising component properties defining states and events.
Separation and processing by payload processors
Separating the plurality of components for processing by one or more payload processors and processing the plurality of components by the one or more payload processors.
Merging with synchronized time as a complex component
After processing by payload processors, merging the plurality of components with a synchronized time value as a complex component that represents a state of the team and storing the complex component into a common data model store according to the common data model format.
Determining and communicating performance measure values
Determining a performance measure value for one of the plurality of events and communicating the performance measure value as the measure of the team performance.
Connectors transform inputs and assign unique keys
At least one connector configured to transform each performance data input into at least one component property of at least one component according to the common data model, identify a unique key for each component, and communicate the component with the unique key.
Payload processor merges components with synchronized time and stores them
The payload processor configured to receive the component and the unique key, merge the component with a synchronized time value as a complex component, and store the complex component into a common data model store according to the common data model format.
Processor determines performance measure value for complex component
The at least one processor configured to determine a performance measure value for the complex component and communicate the performance measure value as the performance measure of a team.
Use of physiological interfaces as performance data inputs
Including embodiments where the at least one user interface comprises a physiological interface such as electrocardiogram (ECG) electrodes for recording electrical impulses as the performance data inputs, as recited in the claims.
The independent claims cover a method and a system that translate heterogeneous performance data into a common data model, separate processing via payload processors, merge data with a synchronized time value as complex components representing team state, use connectors that assign unique keys, and determine and communicate performance measure values based on monitored changes.
Stated Advantages
Efficiently merge and monitor different types of performance data using a common data model and connector-based plugin architecture.
Parse performance data into separate processing streams using unique attributes to increase system performance.
Use model listeners to identify changes to components so calculations are reduced by focusing on changed data and reusing past state data when unchanged.
Merge different types of performance data in real-time, near real-time, or with synchronized time to enable real-time multi-modal assessment.
Enable triggering of measures across different multi-modal data sources to provide comprehensive characterization of individual and team performance.
Provide high processing capability as stated: capable of 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.
Measuring and accessing the performance of individuals and teams in real-time using one or more data sources from the training and simulation environment.
Multi-modal training simulators, including vehicle training simulators.
Communications training involving synthetic entities, simulation datasets, and interaction management as described in the communications training example.
User environments comprising a computer game console and user interfaces such as a computer game controller.
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