Abstract: A smart-card based system and methods to control access to a plurality of attractions within a geographical area. The system may include one or more reward terminals that are located at attractions and are configured to read smart cards presented to them and, assuming the card is valid for that location, allow the card holder to access the attraction. Each smart card may be programmed with a product code that defines the attractions at which the card may be used. Product codes may be stored in a central database along with a list of the attractions associated with the each product code. The list of attractions may be updated as desired, thereby updating and changing the attractions at which any given card may be used.

Abstract: An aggregate User Defined Function (UDF) processing used for multi-regression is provided. The aggregate UDF initializes storage space for multiple nodes of a database environment. Data is then extracted from a relational database and populated according to groupings on each of the nodes. Multiple rows or records are then processed to create a merge and multi-regression processed.

Abstract: A method, based on autocorrelation techniques, for measuring the relative significance of the systematic versus random components of product sales data. The results of this determination can be used to improve product demand forecast and product seasonal profile determinations. When a product's sales variation is primarily due to systematic patterns, the accuracy of demand predictions and forecasts can be improved by understanding and modeling the underlying pattern. On the other hand, when variations in sales are merely random, these variations can be discounted when determining demand forecasts or product seasonal profiles.

Abstract: A partitioning system that provides a fast, simple and flexible method for partitioning a dataset. The process, executed within a computer system, retrieves product and sales data from a data store. Data items are selected and sorted by a data attribute of interest to a user and a distribution curve is determined for the selected data and data attribute. The total length of the distribution curve is calculated, and then the curve is divided into k equal pieces, where k is the number of the partitions. The selected data is thereafter partitioned into k groups corresponding to the curve divisions.

Abstract: A smart-card based system and methods to control access to a plurality of attractions within a geographical area. The system may include one or more reward terminals that are located at attractions and are configured to read smart cards presented to them and, assuming the card is valid for that location, allow the card holder to access the attraction. Each smart card may be programmed with a product code that defines the attractions at which the card may be used. Product codes may be stored in a central database along with a list of the attractions associated with the each product code. The list of attractions may be updated as desired, thereby updating and changing the attractions at which any given card may be used.

Abstract: An improved method for forecasting and modeling product demand for a product. The forecasting methodology employs a causal methodology, based on multiple regression techniques, to model the effects of various factors on product demand, and hence better forecast future patterns and trends, improving the efficiency and reliability of the inventory management systems. The improved method identifies linear dependent causal factors and removes redundant causal factors from the regression analysis. A product demand forecast is generated by blending forecast or expected values of the non-redundant causal factors together with corresponding regression coefficients determined through the analysis of historical product demand and factor information.

Abstract: An improved method for forecasting and modeling product demand for a product. The forecasting methodology employs a causal methodology, based on multiple regression techniques, to model the effects of various factors on product demand, and hence better forecast future patterns and trends, improving the efficiency and reliability of the inventory management systems. A product demand forecast is generated by blending forecast or expected values of the non-redundant causal factors together with corresponding regression coefficients determined through the analysis of historical product demand and factor information. The improved method provides for the saving and updating of previously calculated intermediate regression analysis results and regression coefficients, significantly reducing data transfer time and computational efforts required for additional regression analysis and coefficient determination.

Abstract: A forecast response factor (RF) determines how quickly product demand forecasts should react to recent changes in demand. When a product sales pattern changes (e.g., a sudden increase in product demand), RF is adjusted accordingly to adjust the forecast responsiveness. The present subject matter provides automatic calculation of the RF, based at least in part on the nature of the product sales (autocorrelation) and the status of recent forecasts (bias).

Abstract: A repeatability score is described for determining the quality and reliability of product sales data for generating seasonal demand forecasts. The repeatability scores are calculated from seasonal sales data stored in a data warehouse. Products are sorted based on their reliability scores such that those products that are highly seasonal and have a reliable year-to-year demand pattern are used to form initial or unique demand models. Products that are determined to be less reliable based on their repeatability score are added to the unique demand models through an iterative matching process or left out of the unique demand models.

Abstract: A product demand forecasting technique is presented which employs multivariable regression analysis to identify demand associated with annual events and shift demand associated with those events when the events occur in different weeks of different years. Historical weekly product demand data is acquired for one or more years. An event influencing demand for products which occurs at in different weeks in a prior year than in the forecast year is identified. Mulitvariable regression techniques are used to analyze the historical weekly product demand data to determine demand components associated with the event. These demand components can then be removed from the historical weekly demand data and re-applied to weeks in the prior year corresponding to the week the event occurs in the forecast year to create a shifted historical weekly demand for said product.

Abstract: A product demand forecasting methodology is presented that applies daily weight values to a weekly forecast to determine daily forecasts for a product or service. The method determines daily weight values for use in forecasting current product sales by blending daily weight values calculated from historical demand data for both recent weeks and year-prior weeks. Recent weeks are used to account for recent correlations and alternation effects, and year-prior weeks are used to account for seasonality effects. The method automatically calculates a measure of significance for the daily weights calculated from the recent weeks and year-prior weeks. The significance of each week is applied as a weighting factor during the blending of recent weeks and year-prior daily weight values.

Abstract: A method to select causal factors to be used within a causal product demand forecasting framework. The methodology determines the set of factors that have statistically significant effects on historical product demand, and hence are believed to be of greatest relevance in determining product demand changes in the future. The effects of all factors are determined simultaneously and the net effect of each variable is calculated. When several factors are operative at the same time, the net influence of each factor is calculated. Lesser and redundant factors in the causal forecasting model can be eliminated to improve the stability, scalability and efficiency of the model. The method is employed to optimize causal models to achieve maximum forecast accuracy.

Abstract: A voice command acquisition method and system for motor vehicles is improved in that noise source information is obtained directly from the vehicle system bus. Upon receiving an input signal with a voice command, the system bus is queried for one or more possible sources of a noise component in the input signal. In addition to vehicle-internal information (e.g., window status, fan blower speed, vehicle speed), the system may acquire external information (e.g., weather status) in order to better classify the noise component in the input signal. If the noise source is found to be a window, for example, the driver may be prompted to close the window. In addition, if the fan blower is at a high speed level, it may be slowed down automatically.

Abstract: A system, according to one embodiment, includes a hybrid account provided by an issuer to a user, the hybrid account having a balance, the balance being either positive, zero, or negative; and a financial instrument including a hybrid card, the hybrid card providing access to the hybrid account, in which transactions using the hybrid card provide interchange income to the issuer, the issuer charges interest to the hybrid account when the balance is negative, and the issuer pays interest to the hybrid account when the balance is positive.

Abstract: An improved method for forecasting and modeling product demand. The forecasting methodology employs a multivariable regression model to model the causal relationship between product demand and the attributes of past promotional activities. This improved forecasting methodology enhances the applicability of regression models when dealing with logistic variables. It provides a novel technique to transform such variables into numerical values, resulting in more accurate and more efficient regression models. Furthermore, the reduction in the number of variables improves the stability and predictive power of the regression models.

Abstract: The invention relates to a speech recognition assembly for acoustically controlling a function of a motor vehicle, wherein the speech recognition assembly comprises a microphone disposed in the motor vehicle for inputting a voice command, a data base disposed in the motor vehicle in which respectively at least one meaning is allocated to phonetic representations of voice commands and an on-board-speech-recognition-system disposed in the motor vehicle for determining a meaning of the voice command by use of a meaning of a phonetic representation of a voice command stored in the data base, and wherein the speech recognition assembly further comprises an off-board-speech-recognition-system disposed spatially separated from the motor vehicle for determining a meaning of the voice command.

Abstract: A navigation configuration includes a microphone, disposed in a motor vehicle, for acoustically inputting a location designation. A server, disposed outside the motor vehicle, performs a speech recognition of an acoustically input location designation and converts the acoustically input location designation into a location designation of a database. A communication system transmits the acoustically input location designation from the motor vehicle to the server and transmits the location designation of the database from the server to the motor vehicle. A navigation system, disposed in the motor vehicle, determines a route from a location of the motor vehicle to a destination as a function of the location designation of the database. A motor vehicle having a navigation system for determining a route and a method for determining a route from a location of the motor vehicle to a destination are also provided.

Abstract: A needle system comprising a lancet or syringe having multiple thin needles for accessing bodily fluids so as to inflict less pain and promote faster healing.

Abstract: Techniques for casual demand forecasting are provided. Information is extracted from a database and is preprocessed to produce adjusted input regression variables. The adjusted input regression variables are fed to a regression service to produce regression coefficients. The regression coefficients are then post processed to produce uplifts and adjustments to the uplifts for the regression coefficients.

Abstract: An aggregate User Defined Function (UDF) processing used for multi-regression is provided. The aggregate UDF initializes storage space for multiple nodes of a database environment. Data is then extracted from a relational database and populated according to groupings on each of the nodes. Multiple rows or records are then processed to create a merge and multi-regression processed.