Rare event prediction
AssigneeHewlett Packard Enterprise Development LP
InventorsAjeet Subramanian, Debashree Ghosh, Swati Gupta
Life of this patent
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Abstract
An example method for building a model to predict rare events is disclosed. The example disclosed herein comprises receiving a plurality of historical input logs wherein each log includes at least one key variable and unstructured data. The example further comprises applying text mining techniques to the unstructured data to obtain at least one predictor based on the unstructured data. The example further comprises creating a rare events prediction model based on the at least one key variable and the at least one predictor.
Claims18
A method comprising:
- receiving, by a processor, a plurality of historical input logs, wherein each log includes at least one key variable and unstructured data;
- applying, by the processor, text mining techniques to the unstructured data to obtain at least one predictor based on the unstructured data;
- selecting, from the plurality of historical input logs, a first sample of historical key variables and corresponding predictors;
- oversampling the selected first sample into a plurality of simulated samples;
- calculating escalation prediction model regression coefficients based on the plurality of simulated samples;
- and
- creating, by the processor, an escalation prediction model based on the escalation prediction model regression coefficients, the at least one key variable, and the at least one predictor.
A system for predicting escalations in a customer service center the system comprising:
- a physical processor;
- a non-transitory storage medium storing machine readable instructions to cause the processor to:
- receive a plurality of historical customer case logs, each log including a plurality of fields;
- obtain a plurality of predictors from data included in the plurality of fields;
- select a first sample of the customer case logs;
- oversample the selected first sample into a plurality of simulated samples;
- calculate escalation prediction model regression coefficients for the plurality of predictors based on the plurality of simulated samples;
- and
- build an escalation prediction model based on the escalation prediction model regression coefficients.
A non-transitory machine-readable medium storing machine-readable instructions executable by a physical processor that cause the processor to:
- receive a plurality of historical customer case logs, wherein each log includes at least one key variable and unstructured data;
- parse the unstructured data into a plurality of parsed words;
- remove stop words from the plurality of parsed words, wherein stop words are frequently used words that do not provide predictive value;
- stem the plurality of parsed words into a plurality of predictors;
- create an escalation prediction model based on the at least one key variable and the plurality of predictors;
- receive a new customer case log;
- identify at least one key variable and unstructured data from the new customer case log;
- apply text mining techniques to the unstructured data from the new customer case log to obtain a corresponding plurality of predictors from the new customer case log;
- and
- forecast an escalation to result from the new customer case log based on the escalation prediction model, the key variables from the new customer case log, and the plurality of predictors from the new customer case log.
Dependencies are inferred from the claim text.