Patent application number | Description | Published |
20120150489 | MULTI-STEP TIME SERIES PREDICTION IN COMPLEX INSTRUMENTED DOMAINS - A system, method and computer program product for improving a manufacturing or production environment. The system receives two or more time series data having values that represent current conditions of the manufacturing or production environment as inputs. The system determines one or more different regimes in the received two or more time series data. The system predicts future or unmeasured values of the received two or more time series data in the determined different regimes. The future or unmeasured values represent future conditions of the manufacturing or production environment. | 06-14-2012 |
20130018830 | PREDICTIONS USING AGGREGATE INFORMATIONAANM Dhurandhar; AmitAACI Yorktown HeightsAAST NYAACO USAAGP Dhurandhar; Amit Yorktown Heights NY US - Predictions of a given predictive model may be improved using aggregate information. A plurality of targets to predict in a given domain may be identified, and may be predicted based on raw data set. Aggregate information associated with the plurality of targets is received, the aggregate information including estimated or actual values at a coarser level of the plurality of targets, and based on the aggregate information, the predicted target values may be improved in prediction accuracy. | 01-17-2013 |
20130080125 | CONTINUOUS PREDICTION OF EXPECTED CHIP PERFORMANCE THROUHOUT THE PRODUCTION LIFECYCLE - A system, method and computer program product for predicting at least one feature of at least one product being manufactured. The system receives, from at least one sensor installed in equipment performing one or more manufacturing process steps, at least one measurement of the feature of the product being manufactured. The system selects one or more of the received measurement of the feature of the product. The system estimates additional measurements of the feature of the product at a current manufacturing process step. The system creates a computational model for predicting future measurements of the feature of the product, based on the selected measurement and the estimated additional measurements. The system predicts the future measurements of the feature of the product based on the created computational model. The system outputs the predicted future measurements of the feature of the product. | 03-28-2013 |
20130173493 | OPTIMIZING PROCUREMENT SPEND COMPLIANCE - Managing spend compliance may include receiving a set of spend transaction records containing one or more spend attributes, one or more compliance business rules and one or more investment scenarios that increase spend compliance. The compliance business rules may be applied to the transaction records and segments of transactions with predetermined high savings opportunities may be determined. A prioritized investment plan over one or more time periods that yield optimized return on investment may be generated based on applying the segments of transactions and the investment scenarios. | 07-04-2013 |
20140258196 | SYSTEM AND METHOD FOR USING GRAPH TRANSDUCTION TECHNIQUES TO MAKE RELATIONAL CLASSIFICATIONS ON A SINGLE CONNECTED NETWORK - A system and method for extending partially labeled data graphs to unlabeled nodes in a single network classification by weighting the data with a weight matrix that uses a modified graph Laplacian based regularization framework and applying graph transduction methods to the weighted data. The technique may be applied to data graphs that are directed or undirected, that may or may not have attributes and that may be homogeneous or heterogeneous. | 09-11-2014 |
20140358838 | DETECTING ELECTRICITY THEFT VIA METER TAMPERING USING STATISTICAL METHODS - A system and method for detecting anomalous energy usage of building or household entities. The method applies a number of successively stringent anomaly detection techniques to isolate households that are highly suspect for having engaged in electricity theft via meter tampering. The system utilizes historical time series data of electricity usage, weather, and household characteristics (e.g., size, age, value) and provides a list of households that are worthy of a formal theft investigation. Generally, raw utility usage data, weather history data, and household characteristics are cleansed, and loaded into an analytics data mart. The data mart feeds four classes of anomaly detection algorithms developed, with each analytic producing a set of households suspected of having engaged in electricity theft. The system allows a user to select households from each list or a set based on the intersection of all individual sets. | 12-04-2014 |
20140358839 | DETECTING ELECTRICITY THEFT VIA METER TAMPERING USING STATISTICAL METHODS - A method for detecting anomalous energy usage of building or household entities. The method applies a number of successively stringent anomaly detection techniques to isolate households that are highly suspect for having engaged in electricity theft via meter tampering. The system utilizes historical time series data of electricity usage, weather, and household characteristics (e.g., size, age, value) and provides a list of households that are worthy of a formal theft investigation. Generally, raw utility usage data, weather history data, and household characteristics are cleansed, and loaded into an analytics data mart. The data mart feeds four classes of anomaly detection algorithms developed, with each analytic producing a set of households suspected of having engaged in electricity theft. The system allows a user to select households from each list or a set based on the intersection of all individual sets. | 12-04-2014 |
20150242856 | System and Method for Identifying Procurement Fraud/Risk - A computer-based system provides identification and determination of possible fraud/risk in procurement. Both transactional data and social media data are analyzed to identify fraud and discover potentially colluding parties. A comprehensive solution incorporates text analytics, business/procurement rules, and social network analysis. Furthermore, both unsupervised and supervised machine learning can provide improved accuracy over time as more data is captured and analyzed and updates repeated. The system can include modular or integrated components, allowing for certain customized or commercially available components to be utilized in accordance with the comprehensive solution. | 08-27-2015 |