Methods and systems for optimized selection of data features for a neuro-linguistic cognitive artifical intelligence system

A non-transitory computer-readable storage medium storing instructions, that when executed by a computer system, perform operations to generate an adaptive linguistic model, the instructions comprising instructions to:

• identify a sensor based on a sample vector of input data, the sample vector of input data obtained from the sensor and indicating a type of the at least one sensor;
• generate a plurality of feature symbols by organizing the sample vector of input data into probabilistic clusters;
• determine, after the identification, optimization parameters based on the sensor;
• generate a plurality of betas based at least in part on at least one of combinations of the plurality of feature symbols or combinations of the optimization parameters;
• and
• generate the adaptive linguistic model based at least in part on combinations of the plurality of betas, the adaptive linguistic model configured for use in a cognitive artificial intelligence system to distinguish between normal and abnormal activity in received input data.

A system, comprising:

• a processor;
• and a memory, including an application program configured to perform operation for processing data, the operations comprising:
• identifying a sensor based on a sample vector of input data, the sample vector of input data obtained from the sensor and indicating a type of the at least one sensor;
• generating a plurality of feature symbols by organizing the sample vector of input data into probabilistic clusters;
• determining, after the identifying, optimization parameters based on the sensor;
• generating a plurality of betas based at least in part on at least one of combinations of the plurality of feature symbols or combinations of the optimization parameters;
• generating the adaptive linguistic model based at least in part on combinations of the plurality of betas, the adaptive linguistic model configured for distinguishing between normal and abnormal activity in received input data.