EDAA™ features

This section provides detailed information about the different features, capabilities, and components of EDAA™.

Features	Description
Motivation tracking	Ability to determine and manage user motivation during free and imposed motivation
Anomaly detection	Ability to detect mismatches in end user behavior with respect to the expected motivation
Mood identification	Ability to categorize mood states and emotional responses to interpret the emotional context of user interactions, which, in turn, facilitates responding appropriately to different emotional states
Depth profiling	Includes the following: Psychological profile generation: Ability to establish users' psychological profiles Persona profiling: Ability to establish user personas through diagnostic questions
Diagnostics and calibration	Diagnostics: Ability to establish users' preliminary psychological profiles by posing diagnostic question. Calibration: Product calibration: Ability to ensure that the solution's end goal and experience's ideal outcome are aligned by understanding contextual reality. User calibration: Ability to validate and adjust previously-established user psychological profiles considering current psychological states
Multimodal data processing	Ability to support, process, and analyze physiological, behavioral, and environmental data
Action prediction	Ability to personalize actions, content delivery, and triggered actions for end-users
Interaction Automation System (IAS)	Ability to predict and automate effective actions and interactions for end-users
Action and attribute feeding	Ability to do the following: Create, verify, and anchor actions Auto-feed actions through connections or by uploading a document
Feature engineering	Ability to generate and select features through attributes
Logic blueprinting	Ability to customize how personalization is delivered by configuring the following components using a no-code blueprinting tool: Activators Conditions Actions
Simulation	Ability to simulate an experience (to implement the concept of Virtual Twins) in the following ways: Exact scenario simulation (using clones) Bulk simulation The emotionally-driven Virtual Humans (VHs) that participate in the simulated experience are powered by EDAA™ and have psychological profiles based on those of real humans.
Feedback system	Ability to evaluates the effectiveness of actions selected by the system.
Ethical success metrics	Solution-agnostic metrics to measure solution success: Engagement: Level of the user motivation reached Efficiency: Efficiency of a specific logic strategy in delivering personalization Micro-moment: The most appropriate moment for action delivery