Abstract: A system and method (500) are disclosed for a method and apparatus for manipulating GUIs. A system that incorporates teachings of the present disclosure may include, for example, a computing device (100) having a controller (108) that manages a display (110). The controller can be programmed to alter (518) a graphical user interface (GUI) (200-400) presented on the display according to a behavior model that predicts (508-510) an expectation of an end user from observations of the end user's interactions with the GUI. Additional embodiments are disclosed.

Abstract: A smart call list for a wireless mobile device (12) can include a storage medium (14 or 304) containing a call list having a plurality of contact entries (19) and a processor (302) coupled to the storage medium. The processor can monitor (902) the use of the call list according to a plurality of criteria to intelligently improve the chances of successfully contacting the entries on the call list. The processor can rank (903) the plurality of criteria and assign (906) relative weights to the plurality of criteria to facilitate a selection and order of the contact entries to call (904). The processor can further automatically select (908) a next best number to call if an attempt to connect with a current number is unsuccessful. In one arrangement, the smart call list can use a phone tree (30) in an attempt to contact members of the call list.

Abstract: Methods of training neural networks (100, 600) that include one or more inputs (102-108) and a sequence of processing nodes (110, 112, 114, 116) in which each processing node may be coupled to one or more processing nodes that are closer to an output node are provided. The methods include establishing an objective function that preferably includes a term related to differences between actual and expected output for training data, and a term related to the number of weights of significant magnitude. Training involves optimizing the objective function in terms of weights that characterize directed edges of the neural network. The objective function is optimized using algorithms that employ derivatives of the objective function. Algorithms for accurately and efficiently estimating derivatives of the summed input going into output processing nodes of the neural network with respect to the weights of the neural network are provided.

Abstract: A method and apparatus for writing to solid-state memory is provided herein. In particular, a controller is provided that monitors operating parameters of each die within the system. In order to enable fast, real-time write operations, feedback from each die is analyzed and compared with a stored set of operating parameters. Based on this comparison, a particular die is chosen for write operations such that system performance is optimized.

Abstract: A method and apparatus for writing to solid-state memory is provided herein. In particular, a controller is provided that monitors operating parameters of each die within the system. In order to enable fast, real-time write operations, feedback from each die is analyzed and compared with a stored set of operating parameters. Based on this comparison, a particular die is chosen for write operations such that system performance is optimized.

Abstract: A data management system (100) including a knowledge container creator module (118) operative to create at least a first data descriptor item (112) and at least a second data descriptor item (114) based upon a raw data item (110). The raw data item (110) is capable of containing data representing raw data that is in one of a plurality of different formats. The knowledge container creator module (118) also operative to link the raw data item (110) to at the least a first data descriptor item (112) and to link the raw data item (110) to the at least a second data descriptor item (114).

Abstract: A data management system (100) including a knowledge container creator module (118) operative to create at least a first data descriptor item (112) and at least a second data descriptor item (114) based upon a raw data item (110). The raw data item (110) is capable of containing data representing raw data that is in one of a plurality of different formats. The knowledge container creator module (118) also operative to link the raw data item (110) to at the least a first data descriptor item (112) and to link the raw data item (110) to the at least a second data descriptor item (114).

Abstract: A method of optimization of a product assembly line utilizes a mixed-integer linear programming (MILP) formulation. Line information, such as product data of products to be assembled by the line, parts data of the parts to be used to assemble the products, line data descriptive of the product assembly line and machine data of the machines of the line, is compiled and used to formulate a balancing strategy of the product assembly line (210, 240). The balancing strategy is formulated by the selective manipulation of one or more optimization variables to make the MILP closely representative of the actual manufacturing environment (240, 250, 270). Solving the MILP generates MILP output information from which output reports and metrics may be generated (270, 280).

Abstract: A method of optimization of a product assembly line utilizes a mixed-integer linear programming (MILP) formulation. Line information, such as product data of products to be assembled by the line, parts data of the parts to be used to assemble the products, line data descriptive of the product assembly line and machine data of the machines of the line, is compiled and used to formulate a balancing strategy of the product assembly line (210, 240). The balancing strategy is formulated by the selective manipulation of one or more optimization variables to make the MILP closely representative of the actual manufacturing environment (240, 250, 270). Solving the MILP generates MILP output information from which output reports and metrics may be generated (270, 280).