Patent ID: 11948083
Assignee: UMNAI LIMITED
Field: Computer technology (Electrical engineering)
Classification: CPC G | IPC G

Claim 15:
16. The method of claim 1, wherein an AutoXAI system is configured to adapt the explainable model to one or more specific application domains or tasks, and
wherein the AutoXAI system is adapted to provide a practical solution to the incorporation of meta-learning systems within the explainable model, and
wherein the explainable model is further configured to be trained to learn one or more suggested actions for a given user with a specific context which lead to a change in outcome while minimizing a total cost of actions, wherein the suggested actions comprise a sequence that leads to a desired goal, and wherein the explainable model is configured to utilize a nearest-neighbor method for providing a plurality of actual examples of applications that have had the desired goal or outcome or a hypothetical average of the applications, wherein the nearest-neighbor methods utilize one or more partition hierarchy structures within the explainable model for generating examples from a same partition, or from a nearby partition or from a partition that is further away from a current partition, and wherein the explainable model is further configured to rank and prioritize possible changes in variables and present to a user or to an automated process in the form of an Identify-Assess-Recommend-Resolve framework, and wherein the explainable model is further configured to utilize one or more optimization methods for generating scenario-based explanations, wherein the optimization methods comprise Multiple Objective Optimization (MOO), Pareto Front Methods, Particle Swarm Optimization, Genetic Algorithms, Bayesian Optimization, Evolutionary Strategies, Gradient Descent techniques and/or Monte Carlo Simulation, and wherein encoding-decoding information for the explainable model are fully or partially linked to attribution values and data stored in an Explanation Structure Model.