International Business Machines Corporation

Terrence Theodore Nixa

Zachary Adam Garbow

Kevin Glynn Paterson

Bryan Mark Logan

John Edward Petri

Michelle A Schlicht

Anthony W Eichenlaub

Cynthia M Murch

Jan M Nordland

A first question may be received. A first tag may be identified. The first tag may correspond to a first demographic trait. The first tag may be for use in providing a context for generating a first answer estimate to the first question. The first answer estimate may be generated using natural language processing and based on the first tag.

Demographic-based learning in a question answering system

Automatically completing a GUI task. Exemplary embodiments a method including providing a user configurable auto-completion application operating in the background of a computer operating system and executing on a computing device, the auto-completion application monitoring and archiving user activities, wherein certain actions by the user are parsed and indexed, analyzing the parsed and indexed actions of the user to determine at least one of relationships, user interactions, user statistics, various states of the user's actions over time, and images or video of the user's interaction to create an index of the user's typical interactions with the system and providing a fast-forward functionality based on the index such that a tree structure is presented to the user on a display device of the computing device, the tree structure indicating the user's current state in the system and one or more future states the user typically takes based on a past act.

Systems, methods and computer products to automatically complete a GUI task

A method that emphasizes at least one drop destination for a selected entity based upon monitored drop destinations. Drop destinations may be emphasized by generating a plurality of path vectors between the selected entity and at least a portion of the plurality of drop destinations. Generally, by emphasizing drop destinations, the potential drop destinations for the selected entity may be anticipated and emphasized to the user, which may increase the likelihood that the user will drop onto the desired drop destination. Moreover, a user may choose one of the drop destinations by dropping the selected entity along the path vector associated with the desired drop destination. As such, drag and drop operations may be successfully completed with fewer mouse movements and/or less precision, often resulting in fewer accidental drops.

Emphasizing drop destinations for a selected entity based upon prior drop destinations

Growing community of inventors

Rochester, MN, United States of America

Terrence Theodore Nixa