Abstract: Predicting future sales of a product includes determining a first, historical sales rate curve of historical sales of the product and determining a second sales rate curve for a time t from sales of the product occurring up to a time t?1. The second sales rate curve is determined for a window of time W that includes time t, the window of time W has a shorter duration than the first sales rate curve, and the first and second sales rate curves are for different periods of time. The second sales rate curve is compared to the first sales rate curve and a section of the first sales rate curve is identified based on predetermined criteria for the comparison. Future sales of the product are predicted as a function of a portion of the first sales rate curve in time after the identified section of the first sales rate curve.

Abstract: In a method and system of service management, a radiative sensor is positioned to observe an area of interest. At least one frame of data of the area of interest is electronically acquired from the radiative sensor. The acquired frame of data is electronically processing to determine the presence or absence of at least one object in the area of interest. Based on the presence or absence of the object in the area of interest, (1) an alert is electronically caused to be generated in response to also electronically detecting another object in another area of interest, and/or (2) a timer is electronically caused to initiate or terminate counting a period of time.

Abstract: In a method and system of service management, a radiative sensor is positioned to observe an area of interest. At least one frame of data of the area of interest is electronically acquired from the radiative sensor. The acquired frame of data is electronically processing to determine the presence or absence of at least one object in the area of interest. Based on the presence or absence of the object in the area of interest, (1) an alert is electronically caused to be generated in response to also electronically detecting another object in another area of interest, and/or (2) a timer is electronically caused to initiate or terminate counting a period of time.

Abstract: In a real time food production management system and method, real time data about consumers inside or in the vicinity of a food outlet and an amount of prepared food available to serve and/or an amount of food being prepared is electronically generated. Based on the real time data, a demand for an amount of food to be ordered by the consumers in a specified interval immediately succeeding the generation of the real time data is electronically predicted. In addition, at least one of the following is electronically determined based on the real time data: a predicted amount of additional food requiring preparation; the time at least one prepared food product has been available; a length of time that a cooked food component has been cooked; and an amount of a food component, a condiment and/or a topping resides in a food product assembly buffer.

Abstract: Predicting future sales of a product includes determining a first, historical sales rate curve of historical sales of the product and determining a second sales rate curve for a time t from sales of the product occurring up to a time t?1. The second sales rate curve is determined for a window of time W that includes time t, the window of time W has a shorter duration than the first sales rate curve, and the first and second sales rate curves are for different periods of time. The second sales rate curve is compared to the first sales rate curve and a section of the first sales rate curve is identified based on predetermined criteria for the comparison. Future sales of the product are predicted as a function of a portion of the first sales rate curve in time after the identified section of the first sales rate curve.

Abstract: A real-time buffer manager system that calculates optimal food buffer levels, for both completed products and product components, based on real-time counts of restaurant patrons throughout a restaurant's property and the estimated time for them to arrive at a food ordering station. The real-time buffer manager employs a computer vision system, running a series of 2D image processing techniques that detect and track vehicles and people in several camera views. Patron counts are fed from the computer vision system into a queuing model that estimates when each patron will arrive at an ordering station. Thus, instead of analyzing historical sales data, the buffer manager according to the present invention electronically performs direct measurement of probable future demand, and electronically predicts, in real-time, what the future food product demand will be in a predetermined time (e.g., 3-5 minutes) immediately following the direct measurement of the demand.

Abstract: A real-time buffer manager system that calculates optimal food buffer levels, for both completed products and product components, based on real-time counts of restaurant patrons throughout a restaurant's property and the estimated time for them to arrive at a food ordering station. The real-time buffer manager employs a computer vision system, running a series of 2D image processing techniques that detect and track vehicles and people in several camera views. Patron counts are fed from the computer vision system into a queuing model that estimates when each patron will arrive at an ordering station. Thus, instead of analyzing historical sales data, the buffer manager according to the present invention electronically performs direct measurement of probable future demand, and electronically predicts, in real-time, what the future food product demand will be in a predetermined time (e.g., 3-5 minutes) immediately following the direct measurement of the demand.