Oracle International Corporation

Tibco Software Inc.

Najeeb Andrabi

Tuck Chang

Tim Diekmann

Anna Igorevna Bokhan-Dilawari

Techniques for transforming plug-in application recipe (PIAR) variables are disclosed. A PIAR definition identifies a trigger and an action. Trigger variable values, exposed by a first plug-in application, are necessary to evaluate the trigger. Evaluating the trigger involves determining whether a condition is satisfied, based on values of trigger variables. A second plug-in application exposes an interface for carrying out an action. Evaluating the action involves carrying out the action based on input variable values. A user selects, via a graphical user interface of a PIAR management application, a variable for a trigger or action operation and a transformation operation to be applied to the variable. The PIAR management application generates a PIAR definition object defining the trigger, the action, and the transformation operation, and stores the PIAR definition object for evaluation on an ongoing basis.

Transforming plug-in application recipe variables

Techniques for managing a plug-in application recipe ('PIAR') are disclosed. A mapping between (a) a user-selected field, selected via a PIAR creation interface, and (b) a third-party application field, exposed by an application programming interface ('API') of a third-party application, is stored. The third-party application field is associated with a trigger or action of a PIAR. Managing the PIAR in an active state involves periodically receiving and checking data from the third-party application field against a PIAR condition. During or after managing the PIAR in the active state, information is stored to update the mapping to a different mapping. The different mapping maps the user-selected field to a different third-party application field. Without modifying the PIAR, managing the PIAR in the active state then involves periodically receiving and checking data from the different third-party application field against the PIAR condition.

Managing a plug-in application recipe via an interface

Predictive systems for designing enterprise applications include memory structures that output predictions to a user. The predictive system may include an HTM structure that comprises a tree-shaped hierarchy of memory nodes, wherein each memory node has a learning and memory function, and is hierarchical in space and time that allows them to efficiently model the structure of the world. The memory nodes learn causes, predicts with probability values, and form beliefs based on the input data, where the learning algorithm stores likely sequence of patterns in the nodes. By combining memory of likely sequences with current input data, the nodes may predict the next event. The predictive system may employ an HHMM structure comprising states, wherein each state is itself an HHMM. The states of the HHMM generate sequences of observation symbols for making predictions.

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Cupertino, CA, United States of America

Najeeb Andrabi