Systems and methods for determining a user specific mission operational performance metric, using machine-learning processes
View Patent ↗Aspects relate to system and methods for determining a user specific mission operational performance, using machine-learning processes. An exemplary system includes a computing device configured to perform operations including receiving user-input structured data from at least a user device, receiving observed structured data related to the user and a mission performance metric, inputting the user-input structured data and the observed structured data to a machine-learning model, generating a user performance metric as a function of the machine-learning model, receiving a deterministic mission operational performance metric, disaggregating a deterministic user performance metric as a function of the deterministic mission operation performance metric and the mission performance metric, inputting training data to a machine-learning algorithm, where the training data includes the user-input structured data and the observed structured data correlated to the deterministic user performance metric, and training the machine-learning model as a function of the machine-learning algorithm and the training data.
1. A method of determining a user specific mission operational performance metric, using machine-learning processes, comprising:
receiving, using a computing device, user-input structured data from at least a user device;
receiving, using the computing device, observed structured data related to a user and a mission performance metric associated with a mission of the user;
inputting, using the computing device, the user-input structured data and the observed structured data to a machine-learning model comprising an unsupervised machine learning process configured to continuously discover correlations among the user-input structured data and the observed structured data to a performance metric, wherein the machine learning model is selected based on a user identifier;
generating, using the computing device and the machine-learning model, a user performance metric as a function of the machine-learning model;
inputting, using the computing device, training data into a machine-learning algorithm, wherein the training data includes the user-input structured data and the observed structured data correlated to the user performance metric; and
training, using the computing device and the machine-learning algorithm, the machine-learning model as a function of the machine-learning algorithm and the training data.
2. The method of claim 1 , further comprising:
receiving, using the computer device, a deterministic mission operational performance metric.
3. The method of claim 2 , further comprising:
disaggregating, using the computing device, a deterministic user performance metric as a function of the deterministic mission operation performance metric and the mission performance metric.
4. The method of claim 1 , wherein the mission performance metric comprises objective data associated with the mission and a plurality of environmental risk factors.
5. The method of claim 1 , wherein the user performance metric comprises a change in a probability for success of the mission.
6. The method of claim 1 , wherein the user performance metric comprises a risk factor.
7. The method of claim 1 , further comprising:
generating, using the computing device, at least a likelihood metric as a function of the machine learning model.
8. The method of claim 1 , further comprising:
classifying, using the computing device, the user identifier to a user class, wherein classifying the user identifier further comprises:
inputting the user identifier to a classifier; and
classifying the user identifier to the user class, as a function of the classifier.
9. The method of claim 1 , wherein inputting training data into the machine-learning algorithm comprises:
correlating the user-input structured data and the observed structured data to the deterministic user performance metric.
10. The method of claim 1 , further comprising:
receiving, using the computing device, second user-input structured data from the at least a user device;
receiving, using the computing device, second observed structured data related to the user and a second mission performance metric;
inputting, using the computing device, the second user-input structured data and the second observed structured data to the machine-learning model; and
generating, using the computing device and the machine-learning model, a second user performance metric as a function of the machine-learning model.
11. A system for determining a user specific mission operational performance, using machine-learning processes, comprising a computing device configured to perform operations comprising:
receiving, using a computing device, user-input structured data from at least a user device;
receiving, using the computing device, observed structured data related to a user and a mission performance metric associated with a mission of the user;
inputting, using the computing device, the user-input structured data and the observed structured data to a machine-learning model comprising an unsupervised machine learning process configured to continuously discover correlations among the user-input structured data and the observed structured data to a performance metric, wherein the machine learning model is selected based on a user identifier;
generating, using the computing device and the machine-learning model, a user performance metric as a function of the machine-learning model;
inputting, using the computing device, training data into a machine-learning algorithm, wherein the training data includes the user-input structured data and the observed structured data correlated to the user performance metric; and
training, using the computing device and the machine-learning algorithm, the machine-learning model as a function of the machine-learning algorithm and the training data.
12. The system of claim 11 , wherein the computing device is further configured to:
receiving, using the computer device, a deterministic mission operational performance metric.
13. The system of claim 12 , wherein the computing device is further configured to:
disaggregating, using the computing device, a deterministic user performance metric as a function of the deterministic mission operation performance metric and the mission performance metric.
14. The system of claim 11 , wherein the mission performance metric comprises objective data associated with the mission and a plurality of environmental risk factors.
15. The system of claim 11 , wherein the user performance metric comprises a change in a probability for success of the mission.
16. The system of claim 11 , wherein the user performance metric comprises a risk factor.
17. The system of claim 11 , wherein the computing device is further configured to:
generating, using the computing device, at least a likelihood metric as a function of the machine learning model.
18. The system of claim 11 , wherein the computing device is further configured to:
classifying, using the computing device, the user identifier to a user class, wherein classifying the user identifier further comprises:
inputting the user identifier to a classifier; and
classifying the user identifier to the user class, as a function of the classifier.
19. The system of claim 11 , wherein inputting training data into the machine-learning algorithm comprises:
correlating the user-input structured data and the observed structured data to the deterministic user performance metric.
20. The system of claim 11 , the computing device is further configured to:
receiving, using the computing device, second user-input structured data from the at least a user device;
receiving, using the computing device, second observed structured data related to the user and a second mission performance metric;
inputting, using the computing device, the second user-input structured data and the second observed structured data to the machine-learning model; and
generating, using the computing device and the machine-learning model, a second user performance metric as a function of the machine-learning model.