AI-Based Real-Time Asset Allocations Based on Predictive Analytics of Chip Counts Data

A method for AI-based real-time assets' allocations based on predictive analytics of chips counts data acquired from table smart trays, comprising:

• acquiring, by an asset allocation server (AAS) node configured to host a machine learning (ML) module coupled to an Artificial Neural Network (ANN), chip count data from at least one chip count entity connected to a table smart tray;
• parsing out, by the AAS node, the chip count data to derive features defining chip count by type;
• generating, by the AAS node, a set of classifiers based on the derived features;
• querying, by the AAS node, a local database to retrieve local historical asset allocation-related data based on the set of classifiers;
• generating, by the AAS node, a feature vector based on the local historical asset allocation-related data and the set of classifiers;
• providing, by the AAS node, the feature vector to the ML module;
• receiving, by the AAS node, a plurality of asset allocation parameters from an asset allocation predictive model generated by the ML module using outputs of the ANN based on the feature vector;
• and
• generating, by the AAS node, asset allocations based on the plurality of the asset allocation parameters.

A non-transitory computer-readable medium comprising instructions, that when read by a processor, cause the processor to perform:

• acquiring chip count data from at least one chip count entity coupled to a table smart tray;
• parsing out the chip count data to derive features defining chip count by type;
• generating a set of classifiers based on the derived features;
• querying a local database to retrieve local historical asset allocation-related data based on the set of classifiers;
• generating a feature vector based on the local historical asset allocation-related data and the set of classifiers;
• providing the feature vector to a machine learning (ML) module coupled to an Artificial Neural Network (ANN);
• receiving a plurality of asset allocation parameters from an asset allocation predictive model generated by the ML module using outputs of the ANN based on the feature vector;
• and
• generating asset allocations based on the plurality of the asset allocation parameters.