User effort detection
A variety of systems and methods can include evaluation of human user effort data. Various embodiments apply techniques to identify anomalous effort data for the purpose of detecting the efforts of a single person, as well as to segment and isolate multiple persons from a single collection of data. Additional embodiments describe the methods for using real-time anomaly detection systems that provide indicators for scoring effort data in synthesized risk analysis. Other embodiments include approaches to distinguish anomalous effort data when the abnormalities are known to be produced by a single entity, as might be applied to medical research and enhance sentiment analysis, as well as detecting the presence of a single person's effort data among multiple collections, as might be applied to fraud analysis and insider threat investigations. Embodiments include techniques for analyzing the effects of adding and removing detected anomalies from a given collection on subsequent analysis.
1. A system comprising:
an input to receive multiple sensed efforts;
a memory device to store the received multiple sensed efforts and to store instructions; and
a processor coupled to the memory device to execute the instructions to perform operations comprising:
evaluating each sensed effort with respect to other sensed efforts by iteratively removing and adding each sensed effort from and to the multiple sensed efforts and observing an evaluation effect for each sensed effort;
dynamically setting a threshold as a function of the evaluation effects; and
classifying each sensed effort as a valid user effort, an irregular effort by the user, or an impostor effort as a function of the threshold, wherein the input is structured to receive multiple sensed efforts from multiple users and the operations include:
evaluating each sensed effort of the multiple sensed efforts from multiple users with respect to other sensed efforts by iteratively removing and adding each sensed effort from and to the multiple sensed efforts and observing an evaluation effect for each sensed effort;
dynamically setting a threshold for each of the multiple sensed efforts from multiple users as a function of the evaluation effect; and
classifying each sensed effort as a valid user effort or an irregular user effort as a function of the thresholds, wherein valid user efforts of each user of the multiple users include clusters of regularity.