Hyperactive Technologies, Inc.

Carnegie Mellon University

Other

Kerien W Fitzpatrick

Anthony Joseph Stentz

William L Whittaker

Mark Ollis

Ralph Gross

Robert Craig Coulter

Henning M Pangels

R Craig Coulter

Michael Happold

Thomas E Pilarski

Abhishek A Sharma

Regis Hoffman

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&#x2212;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.

Real-time demand prediction in a fast service restaurant environment

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.

Automated service measurement, monitoring and management

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.

Vision-based measurement of bulk and discrete food products

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.

Real-time prediction and management of food product demand

An agricultural harvesting machine (harvester) is automatically controlled and steered so as to completely harvest all crop in a field. A Global Positioning System (GPS) and an Inertial Navigation System provide data regarding the position and orientation of the harvester in a field. A Field Coverage Planner constructs a field coverage plan which defines a path for the harvester to follow in cutting crop from a field. Video images of the field in front of the harvester are derived alternately from two video cameras mounted on opposite sides of the harvester. The video images are processed in a Video Processing Computer to detect the crop line between cut and uncut crop and generate steering signals for steering the harvester along the crop line. Video images are also processed to detect the end of a row at the end of a field and supply a measure of the distance to the end of the row. When the end of a row is reached, the harvester executes a 180&deg; spin turn and traverses the field in the opposite direction.

Agricultural harvester with robotic control

A self-propelled harvester is provided with apparatus for tracking a crop line and steering the harvester along the crop line without operator assistance. The apparatus includes a video camera for viewing the landscape in front of the harvester and forming an image which may be electronically scanned pixel-by-pixel and line by line to produce a video signal containing information regarding the spectral characteristics of the image at each pixel. A microprocessor is programmed to act as a discriminator and a segmentor. The discriminator produces discriminant signals D(i,j) where i is the magnitude of a discriminant function and j is the relative position of a pixel on a scan line. The segmentor determines a step function which best fits the magnitudes of the discriminant signals for one scan line. The pixel position j.sub.n corresponding to the step of this step function is taken as a vote on the location of the crop line and thus a vote on the direction the harvester should be steered. The votes are summed for each pixel position j over one full scan of image. The summed votes are then normalized, time-weighted, and averaged with the normalized and time-weighted votes derived from previously scanned images to derive a steering command.

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Sewickley, PA, United States of America

Kerien W Fitzpatrick