Patent application number | Description | Published |
20120158385 | THREE DIMENSIONAL LOAD PACKING - One embodiment is a three dimensional load method for simulating loading of items into at least one container to be transported to at least one destination. The method includes receiving a list of items to be transported, determining at least one container as an optimal number and type of container to be used for transporting the items, and initializing an empty space list to include one space equal to a size of the at least one container. The method also includes initializing a placed item list and an unplaced item list, such that the placed item list includes a list of items already loaded on the at least one container and the unplaced item list includes a list of items to be loaded on the at least one container. The method further includes selecting a subset of items from the unplaced item list for one of the destinations and, while there are more items to be loaded on the at least one container, selecting an item, space in the at least one container, and rotation using an item iterating process, inserting the selected item into the space, the item oriented according to the selected rotation, and updating the empty space list. The method then includes updating the placed item list and the unplaced item list. | 06-21-2012 |
20140180952 | COST AND LATENCY REDUCTIONS THROUGH DYNAMIC UPDATES OF ORDER MOVEMENT THROUGH A TRANSPORTATION NETWORK - A method, system, and computer program product for shipping management. The computer implemented method commences upon identifying a set of orders to be shipped from a source region to a destination region using a transportation network, and determining candidate options for performing stops over possible routes between the source region and the destination region. A clustering analysis process is performed over the candidate options such that the clustering analysis considers many order consolidation possibilities while observing timing constraints. Low-cost options from among the candidate options are considered to identify one or more low-cost options, and a multi-stop route plan is generated to correspond to a selected low-cost option. The orders are shipped in accordance with the multi-stop route plan, and in accordance with the corresponding consolidation of the set of orders. | 06-26-2014 |
20140180954 | CONCURRENT DETERMINATION OF SHIPPING MODE AND SHIP UNIT PACKING DURING TRANSPORTATION PLANNING - A method, system, and computer program product for shipping route selection and transportation planning systems. The computer-implemented method commences upon receiving a set of candidate transportation carrier descriptions, individual ones of the set of candidate transportation carrier descriptions having one or more shipping mode options and having one or more shipping unit options. Then, the method evaluates combinations of using multiple transportation carriers that provide any of the one or more shipping mode options with compatible shipping unit options to determine a combination cost for each of the combinations. Based on the combination costs, the method selects an equipment option that corresponds to the lowest combination cost. Low-cost itineraries can be formed by calculating the cost of a particular itinerary by summing combination costs of constituent legs. | 06-26-2014 |
20140180956 | CARRIER CAPACITY AWARE MULTI-STOP SHIPMENT GENERATOR - A method, system, and computer program product for shipment consolidation in high-performance transportation systems. The method commences upon receiving a plurality of orders to be transported from a source location to a destination location over a transportation lane, then retrieving one or more records that describe transportation carrier resource capacity covering at least a portion of the transportation lane. An enumerator facility of the method generates a plurality of candidate shipments using the transportation carrier resource capacities. Infeasible candidate shipments such as where the transportation carrier resource capacity is exceeded are eliminated, and the remaining feasible shipments are evaluated to determine a respective cost. Then the remaining shipments are ordered based at least in part on the cost, and one of the low cost feasible shipments is selected to be used for transporting the shipment. | 06-26-2014 |
20140180957 | COST AND LATENCY REDUCTIONS THROUGH DYNAMIC UPDATES OF ORDER MOVEMENT THROUGH A TRANSPORTATION NETWORK - A method, system, and computer program product for shipping management. The computer implemented method commences upon receiving a first set of orders to be shipped to a destination region in accordance with a first set of timing constraints, then building a first set of multi-stop shipments, the first set of multi-stop shipments comprising a first multi-stop carrier schedule that satisfies the set of timing constraints. The method waits a calculated duration before receiving a second set of orders to be shipped to the destination region in accordance with a second set of timing constraints. A second set of multi-stop shipments is built, wherein the second set of multi-stop shipments comprises a second multi-stop carrier schedule for at least one stop not included in the first set of multi-stop shipments, and the second multi-stop carrier schedule satisfies both the first set of timing constraints and the second set of timing constraints. | 06-26-2014 |
20140180958 | FINDING MINIMUM COST TRANSPORTATION ROUTES FOR ORDERS THROUGH A TRANSPORTATION NETWORK - A method, system, and computer program product for enterprise software application modules for order consolidation management. The method commences by receiving a set of orders where individual orders have one or more order constraints, then mapping the orders onto one or more transportation legs, where the individual transportation legs have leg constraints. A set of feasible paths through the legs for the orders is generated and ranked based on a total cost through the legs to pick-up an order from a source location and deliver it to a destination location. The method continues by determining a set of shortest paths through a transportation network for the set of orders, wherein the determination of any one of the shortest paths is made subject to honoring respective order constraints while concurrently honoring the leg constraints. The orders are then remapped onto one of the shortest paths. | 06-26-2014 |
Patent application number | Description | Published |
20090150126 | System and method for sparse gaussian process regression using predictive measures - An improved system and method is provided for sparse Gaussian process regression using predictive measures. A Gaussian process regressor model may be construction by interleaving basis vector set selection and hyper-parameter optimization until the chosen predictive measure stabilizes. One of various LOO-CV based predictive measures may be used to find an optimal set of active basis vectors for building a sparse Gaussian process regression model by sequentially adding basis vectors selected using a chosen predictive measure. In a given iteration, a predictive measure is computed for each of the basis vectors in a candidate set of basis vectors and the basis vector with the best predictive measure is selected. The iterative addition of basis vectors may stop when predictive performance of the model degrades or no significant performance improvement is seen. | 06-11-2009 |
20090157578 | SYSTEM AND METHOD FOR GENERATING A CLASSIFIER MODEL - Generally, the present invention provides a method and computerized system for generating a classifier model, wherein the classifier model is operative to classify web content. The method and computerized system includes a first step of defining a plurality of predictive performance measures based on a leave one out (LOO) cross validation in terms of selectable model parameters. Exemplary predictive performance measures includes smoothened predictive measures such as F-measure, weighted error rate measure, area under curve measure, by way of example. The method and computerized system further includes deriving efficient analytical expressions for predictive performance measures to compute the LOO predictive performance and their derivatives. The next step is thereupon selecting a classifier model based on the LOO predictive performance. | 06-18-2009 |
20090274376 | METHOD FOR EFFICIENTLY BUILDING COMPACT MODELS FOR LARGE MULTI-CLASS TEXT CLASSIFICATION - A method of classifying documents includes: specifying multiple documents and classes, wherein each document includes a plurality of features and each document corresponds to one of the classes; determining reduced document vectors for the classes from the documents, wherein the reduced document vectors include features that satisfy threshold conditions corresponding to the classes; determining reduced weight vectors for relating the documents to the classes by comparing combinations of the reduced weight vectors and the reduced document vectors and separating the corresponding classes; and saving one or more values for the reduced weight vectors and the classes. Specific embodiments are directed to formulations for determining the reduced weight vectors including one-versus-rest classifiers, maximum entropy classifiers, and direct multiclass Support Vector Machines. | 11-05-2009 |
20100161527 | EFFICIENTLY BUILDING COMPACT MODELS FOR LARGE TAXONOMY TEXT CLASSIFICATION - A taxonomy model is determined with a reduced number of weights. For example, the taxonomy model is a tangible representation of a hierarchy of nodes that represents a hierarchy of classes that, when labeled with a representation of a combination of weights, is usable to classify documents having known features but unknown class. For each node of the taxonomy, the training example documents are processed to determine the features for which there are a sufficient number of training example documents having a class label corresponding to at least one of the leaf nodes of a subtree having that node as a root node. For each node of the taxonomy, a sparse weight vector is determined for that node, including setting zero weights, for that node, those features determined to not appear at least a minimum number of times in a given set of leaf nodes in the sub-tree with that node as a root node. The sparse weight vectors can be learned by solving an optimization problem using a maximum entropy classifier, or a large margin classifier with a sequential dual method (SDM) with margin or slack resealing. The determined sparse weight vectors are tangibly embodied in a computer-readable medium in association with the tangible representation of the nodes of the taxonomy. | 06-24-2010 |
20100161534 | PREDICTIVE GAUSSIAN PROCESS CLASSIFICATION WITH REDUCED COMPLEXITY - A computer-implemented method of generating a model of a sparse GP classifier includes performing basis vector selection and adding a thus-selected basis vector to a basis vector set, including performing a margin-based method that accounts for predictive mean and variance associated with all the candidate basis vectors at that iteration. Hyperparameter optimization is performed. The basis vector selection step and hyperparameter optimization step are such that the steps are alternately performed until a specified termination criteria is met. The selected basis vectors and optimized hyperparameters are stored in at least one tangible computer readable medium organized in a manner to be usable as the model of the sparse GP classifier. | 06-24-2010 |
20110099131 | PAIRWISE RANKING-BASED CLASSIFIER - The present invention provides methods and systems for binary classification of items. Methods and systems are provided for constructing a machine learning-based and pairwise ranking method-based classification model for binary classification of items as positive or negative with regard to a single class, based on training using a training set of examples including positive examples and unlabelled examples. The model includes only one hyperparameter and only one threshold parameter, which are selected to optimize the model with regard to constraining positive items to be classified as positive while minimizing a number of unlabelled items classified as positive. | 04-28-2011 |
20110264651 | LARGE SCALE ENTITY-SPECIFIC RESOURCE CLASSIFICATION - A system and method is described for large scale entity-specific classification of each entity-specific set of candidates in a collection of candidates for each specific entity in a collection of entities. The collection of entities may comprise a specific category or domain of entities (e.g. schools, restaurants, manufacturers, products, events, people). Candidates may comprise webpages or other resources with resource identifiers. Entity specific sets of candidates may be found by leveraging search engine query results and user interaction therewith for queries based on entity-specific attributes. The relationship(s) or class(es) for which candidate resources are being classified relative to a specific entity may comprise an authoritative, official home page (OHP), or other class (e.g. fan page, review, aggregator) relative to a specific entity. A feature generator generates entity-specific features for candidates. In accordance with its features, one or more classifiers rank each candidate for a specific class for a specific entity. | 10-27-2011 |
20120254191 | METHOD AND SYSTEM FOR CONCEPT SUMARIZATION - A method and a system for summarizing a concept are provided. A query corresponding to a concept is received from a user. A plurality of images and corresponding descriptive information may be collected based on the query. The plurality of images and the descriptive information may be processed to form feature vectors and processed descriptive information respectively. Further, one or more topics may be identified for the plurality of images. Each of the plurality of images may be assigned with one or more topic distribution values corresponding to the one or more topics. The one or more topics correspond to the processed descriptive information. A sparse set of images may be determined based on the feature vectors and the assigned topic distribution values, to summarize the concept. Also, a target summary may be built from the summarized concept, by regularizing one or more distribution constraints. | 10-04-2012 |