Granular binarization for extended reality

A system, comprising:

• a processor, the processor configured to:
• receive events derived from historical sensor data, the events respectively associated with user tokens, the historical sensor data generated by a computer-augmented environment;
• generate repeat event records based on the events, each of the repeat event records associated with at least one of the user tokens, the repeat event records comprising occurrence frequency metrics, each of the occurrence frequency metrics indicative of a quantity of occurrences of repeated physical actions captured by a sensor in the computer-augmented environment;
• cluster, based on a clustering framework, the repeat event records into repeat event clusters, the repeat event clusters comprising respective groups of repeat event records;
• determine filter ranges for the repeat event clusters, the filter ranges comprises separate numerical ranges for each of the repeat event clusters;
• generate a binarization filter comprising instructions to determine an input occurrence frequency metric is included in at least one of the filter ranges and generate a binary sequence indicative of the at least one of the filter ranges;
• execute the binarization filter to generate a plurality of binary sequences based on the occurrence frequency metrics of the repeat event records;
• train a prediction model based on the binary sequences, the prediction model configured to identify a subsequent event based on at least one of the binary sequences;
• and
• deploy the binarization filter and the prediction model to a real-time platform in communication with the computer-augmented environment.

A system comprising a processor, the processor configured to:

• receive deployment parameters comprising a binarization filter and a prediction model, the binarization filter comprising a plurality of filter ranges and the prediction model trained based on associations between binary sequences and subsequent events;
• receive real-time sensor data generated by a computer-augmented environment;
• determine a repeat event record based on the real-time sensor data, the repeat event record comprising an occurrence frequency metric, the occurrence frequency metric representative of a number of times an event derived from the real-time sensor data is repeated;
• generate, based on the binarization filter and the occurrence frequency metric, a binary sequence indicative of the occurrence frequency metric being included in at least one of the filter ranges;
• determine, based on the prediction model and the binary sequence, at least one of the subsequent events is associated with the binary sequence;
• identify an action message associated with the at least one of the subsequent events;
• and
• cause the computer-augmented environment to display the action message before the at least one of the subsequent events occurs.