Abstract: An item velocity monitoring system is provided which interfaces with a consumer retail store that has several cash registers that are tied into a “point of sale” store controller. The item velocity monitoring system is capable of detecting when sales (or other movement activities) of an item are occurring too quickly, or too slowly. The item velocity monitoring system is first “trained” in a learning mode of operations, during which item patterns and group patterns are evaluated and placed into a pattern database. The system then compares the observed item velocity to its model probability velocity, and if the observed item velocity deviates beyond the statistical model, a “velocity event” is generated, declaring one of the above selling “too quick” or “too slow” conditions. Once a velocity event is detected, an event handling routine displays the event, and can transmit the event information over a network (including the INTERNET) to a remote computer for additional analysis or record keeping.

Abstract: Methods and apparatus are described for effecting a computer transaction using speech as a primary input. The speech is captured using a speech recognition program. A context associated with the captured speech is determined. Where the context has been determined, the computer transaction is built based on the context and at least a portion of the captured speech. A representation of the computer transaction is presented to a human operator for verification. The computer transaction is effected upon verification by the human operator.

Abstract: An item velocity monitoring system is provided which interfaces with a consumer retail store that has several cash registers that are tied into a “point of sale” store controller. The item velocity monitoring system is capable of detecting when sales (or other movement activities) of an item are occurring too quickly, or too slowly. The item velocity monitoring system is first “trained” in a learning mode of operations, during which item patterns and group patterns are evaluated and placed into a pattern database. The system then compares the observed item velocity to its model probability velocity, and if the observed item velocity deviates beyond the statistical model, a “velocity event” is generated, declaring one of the above selling “too quick” or “too slow” conditions.