Systems and methods of incident resolution using chunking, vector embedding, and clustering techniques

A system for finding historically similar incidents in a system, the system comprising:

• a memory having processor-readable instructions stored therein;
• and
• at least one processor configured to access the memory and execute the processor-readable instructions to perform operations including:
• receiving a plurality of historical data objects corresponding to a plurality of previous incidents, each of the plurality of historical data objects indicating an occurrence of a previous incident and including a historical resolution text description;
• generating a historical embedding of each of the plurality of historical data objects;
• extracting noun phrases from each of the historical resolution text descriptions;
• applying topic modeling to the extracted noun phrases to assign each of the plurality of historical data objects to a cluster of a plurality of clusters;
• storing the historical embedding and cluster for each of the plurality of historical data objects in an index;
• receiving a current data object indicating an occurrence of a current incident associated with a configurable item, the current data object including an incident description;
• generating a current embedding of the current data object;
• extracting noun phrases from the current embedding;
• applying topic modeling to the extracted noun phrase of the current data object to assign the current data object to a particular cluster of the plurality of clusters;
• and
• identifying a set of historically similar incidents by applying a Euclidean distance formula to the current embedding and at least a subset of the historical embeddings associated with the particular cluster.

A non-transitory computer readable medium configured to store processor-readable instructions which, when executed by at least one processor, cause the at least one processor to perform operations including:

• receiving a plurality of historical data objects corresponding to a plurality of previous incidents, each of the plurality of historical data objects indicating an occurrence of a previous incident and including a historical resolution text description;
• generating a historical embedding of each of the plurality of historical data objects;
• extracting noun phrases from each of the historical resolution text descriptions;
• applying topic modeling to the extracted noun phrases to assign each of the plurality of historical data objects to a cluster of a plurality of clusters;
• storing the historical embedding and cluster for each of the plurality of historical data objects in an index;
• receiving a current data object indicating an occurrence of a current incident associated with a configurable item, the current data object including an incident description;
• generating a current embedding of the current data object;
• extracting noun phrases from the current embedding;
• applying topic modeling to the extracted noun phrase of the current data object to assign the current data object to a particular cluster of the plurality of clusters;
• and
• identifying a set of historically similar incidents by applying a Euclidean distance formula to the current embedding and at least a subset of the historical embeddings associated with the particular cluster.