| CPC G06Q 10/0637 (2013.01) [G06F 9/451 (2018.02); G06Q 10/0635 (2013.01); G06Q 10/087 (2013.01); G06Q 30/0202 (2013.01)] | 18 Claims |
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1. A decision support system for an enterprise, comprising:
server processing circuitry configured with
a data consolidation module to collect data from a plurality of internal data sources and at least one external data source that is external to the enterprise;
a plurality of domain-specific machine learning models to generate respective domain-specific outputs using the collected data;
an enterprise decision intelligence model that dynamically incorporates real-time trends and outputs from the plurality of domain-specific machine learning models, and leveraging the outputs from at least two of the domain-specific machine learning models to generate real-time, context-aware recommendations,
wherein the enterprise decision intelligence model utilizes a decision graph to establish causal relationships between decision variables of the domain-specific machine learning models, enabling the domain-specific machine learning models to operate as an interconnected network guided by the decision graph,
wherein the domain-specific machine learning models generate the respective domain-specific decisions based on effects of the decision variables from other domain-specific machine learning models;
a computing device having a user interface layer that facilitates interactive decision-making through the enterprise decision intelligence model and enables visualization of the recommendations, wherein the user interface layer includes an interface for entering overrides for operation of the enterprise decision intelligence model to override a decision by a selected one of the plurality of domain-specific machine learning models; and
a network connecting the server processing circuitry and the computing device so that the interactive-decision making is performed based on the enterprise decision intelligence model of the server processing circuitry,
wherein the enterprise decision intelligence model is configured to perform a utility function that defines criteria that an outcome of a decision is optimized by a decision intelligence module,
wherein the enterprise decision intelligence model is configured to map out decision paths by a decision tree, guided by utility scores determined by the utility function, for generation of optimal recommendations,
wherein the enterprise decision intelligence model includes a self-learning system having a feedback loop that captures outcomes of decisions by the domain-specific machine learning models,
wherein the enterprise decision intelligence model is configured to
track the captured outcomes to determine whether the decisions led to the captured outcomes,
compare the tracked captured outcomes with an expected outcome,
when the enterprise decision intelligence model determines that a captured outcome deviates from the expected outcome, then the enterprise decision intelligence model refines parameters of the corresponding domain-specific machine learning model, and
generate a refined decision using the refined domain-specific machine learning model to make an adjustment to the outcome based on the captured outcome deviated from the expected outcome.
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