Salesforce Com, Inc.

Salesforce, Inc.

Elizabeth Balsam

Zachary Alexander

Samuel William Bailey

James Bock Wunderlich

Scott Thurston Rickard, Jr

Leo Duy Tran

Thomas Pollinger

Miguel Canizares

Tracy Morgan Backes

An online system stores objects representing potential transactions of an enterprise. The online system uses predictor models to determine an aggregate score based on values of the objects associated with a time interval, for example, a month. Each object is configured to take one of a plurality of states. The online system stores historical data describing activities associated with potential transaction objects and uses the stored data for generating the predictor models. The online system categorizes the objects into bins based on states of the objects. The online system may generate different predictions for each category. The online system may use machine learning based models as predictor models. The online system extracts features describing potential transaction objects and provides these as input to the predictor model.

Predicting aggregate value of objects representing potential transactions based on potential transactions expected to be created

A request associated with a functions that are provided by a server may be received from a client system. A subset of the functions that are provided by the server and that are available based on the request may be identified. Information based on the identified subset of functions may be generated. Furthermore, the information that is based on the identified subset of functions may be transmitted to the client system.

Discovery of server functions

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San Francisco, CA, United States of America

Elizabeth Balsam