Cisco Technology, Inc.

Splunk Inc.

Yanpei Chen

Archana Sulochana Ganapathi

This document discloses methods and systems for cohort identification. The methods and systems include improved calculations to perform cohort identification and practical applications of the improved calculations. Specifically, the systems and methods described herein may utilize key components that include enhancements of existing cohort clustering techniques with regard to selecting a number of cohort input dimensions, normalizing input data using a logarithm kernel-function, treatment of categorical data with mutually exclusive and not-mutually exclusive values, methods and visualization tool to determine appropriate number of cohorts, methods and visualization tool to compare cohorts extracted from different input dimensions, and methods to quantify the difference in cohorts. Beyond improvements to the cohort clustering techniques, also disclosed are ancillary tools to prepare input data by joining CRM and product usage data and facilitate subsequent automated action via an API to retrieve cohort results.

Monitoring and visualization of model-based clustering definition performance

Product usage may be modeled based on large volume, machine generated product usage data to optimize product pricing and operations. Specifically, the systems and methods described herein may utilize methods with key components to select the maximum number of dimensions that can be modeled based on the number of data points, use a logarithm kernel function to normalize machine data with long-tailed statistical distributions on different numerical scales, compare a large number of candidate models with different candidate dimensions and different structures, and quantify the amount of change and drift in models over time.

Model driven configuration of software installation operating states

Disclosed are embodiments of a installed software program that receive a model from a product management system. The model is trained to select one of a plurality of predefined states based on operational parameter values of the installation of the software program. Each of the plurality of predefined states define configuration values of the installation of the software program. The defined configuration values indicate, in some embodiments, updates to operational parameter values of the installation of the software program.

Configuring an installation of a software program using a model trained based on information from a customer relationship management system

This document discloses methods and systems for modeling product usage. In one practical application, the systems and methods may be utilized to model product usage based on large volume, machine generated product usage data to optimize product pricing and operations. Specifically, the systems and methods described herein may utilize methods with key components to select the maximum number of dimensions that can be modeled based on the number of data points, use a logarithm kernel function to normalize machine data with long-tailed statistical distributions on different numerical scales, compare a large number of candidate models with different candidate dimensions and different structures, and quantify the amount of change and drift in models over time.

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Sunnyvale, CA, United States of America

Yanpei Chen