| Patent application number | Description | Published |
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| 20090290025 | METHOD, DEVICE AND SYSTEM FOR DETERMINING THE PRESENCE OF VOLATILE ORGANIC COMPOUNDS (VOC) IN VIDEO - A video based method to detect volatile organic compounds (VOC) leaking out of components used in chemical processes in petrochemical refineries. Leaking VOC plume from a damaged component causes edges present in image frames to loose their sharpness, leading to a decrease in the high frequency content of the image. Analysis of image sequence frequency data from visible and infrared cameras enable detection of VOC plumes in real-time. Analysis techniques using adaptive background subtraction, sub-band analysis, threshold adaptation, and Markov modeling are described. | 11-26-2009 |
| 20120038776 | Automatically Expanding the Zoom Capability of a Wide-Angle Video Camera - A system for automatically expanding the zoom capability of a wide-angle video camera using images from multiple camera locations. One preferred embodiment achieves this using images from the wide-angle video camera that are analyzed to identify regions of interest (RoI). Pan-Tilt-Zoom (PTZ) controls are then sent to aim slave cameras toward the RoI. Processing circuitry is then used to replace the RoI from the wide-angle images with the higher-resolution images from one of the slave cameras. In addition, motion-detecting software can be utilized to automatically detect, track, and/or zoom in on moving objects. | 02-16-2012 |
| 20130050466 | METHOD, DEVICE AND SYSTEM FOR DETERMINING THE PRESENCE OF VOLATILE ORGANIC AND HAZARDOUS VAPORS USING AN INFRARED LIGHT SOURCE AND INFRARED VIDEO IMAGING - An enhanced infrared (IR) imaging based method for detecting volatile organic and hazardous vapors using the infrared spectral absorption properties of these vapors, and using a tunable infrared light source and a plurality of cameras tuned to particular frequency ranges to detect spectral absorption properties corresponding to the respective vapors. Illumination by an IR light source is used to enhance the visibility of vapor plumes in LWIR and MWIR cameras because ambient light may not have enough power in the specific absorption band of the VOC vapor. Plume regions are automatically determined by image and video processing methods by the system. Specific vapors can be detected by using tunable IR light sources because leaking plumes from a damaged component causes dark regions in images of LWIR and/or MWIR cameras depending on the absorption wavelength of the plume. | 02-28-2013 |
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| 20100250335 | SYSTEM AND METHOD USING TEXT FEATURES FOR CLICK PREDICTION OF SPONSORED SEARCH ADVERTISEMENTS - An improved system and method using text features for click prediction of sponsored search advertising is provided. A maximum entropy click prediction model that predicts the click probability of query-advertisement pairs may be generated from click feedback features and word pair features of a query and an advertisement. The maximum entropy click prediction model may be used to obtain click probabilities for query-advertisement pairs to determine and serve a ranked list of advertisements for display with query results in an online keyword search auction. A search query may be received and word features from the search query may be input into the maximum entropy click prediction model to obtain click probabilities for query-advertisement pairs. A list of advertisements may be ranked using click probabilities for query-advertisement pairs, the list of ranked advertisements may be priced in an online search keyword auction and served for display with search query results. | 09-30-2010 |
| 20110225042 | MAXIMUM LIKELIHOOD ESTIMATION UNDER A COVARIANCE CONSTRAINT FOR PREDICTIVE MODELING - Embodiments employ a maximum likelihood estimation (MLE) under a covariance matrix floor constraint to predict missing data from observed data. An MLE solution is obtained for approximately Gaussian distributions under the constraint that the covariance matrix is greater than or equal to a positive-definite matrix. In one embodiment, an offline model estimation is performed using an expectation-maximization (EM) approach to estimate various statistical parameters based on observed data. Then, in an online approach, parameters for various missing CTR data may be predicted based on the offline estimated statistical parameters. A non-limiting, non-exhaustive example using the constrained MLE approach is described for predicting missing click-through rate data useable in selecting an advertisement to display with a search query result. | 09-15-2011 |
| 20110246286 | CLICK PROBABILITY WITH MISSING FEATURES IN SPONSORED SEARCH - Sponsored search advertising utilizes a click probability as one factor in selecting and ranking advertisements that are displayed with search results. The probability of click may also be referred to as a predicted click-through rate (“CTR”) that may be multiplied by an advertiser's bid for a particular advertisement to rank the display of advertisements. An accurate prediction of the click probability improves the potential revenue that is generated by advertisements in a pay per click system. Other advertising systems may benefit from an accurate and reliable estimate for an advertisement's probability of click in different environments and scenarios. | 10-06-2011 |
| 20120022952 | Using Linear and Log-Linear Model Combinations for Estimating Probabilities of Events - A method for combining multiple probability of click models in an online advertising system into a combined predictive model, the method commencing by receiving a feature set slice (e.g. corresponding to demographics or taxonomies or clusters), and using the sliced data for training multiple slice-wise predictive models. The trained slice-wise predictive models are combined by overlaying a weighted distribution model over the trained slice-wise predictive models. The combined predictive model then is used in predicting the probability of a click given a query-advertisement pair in online advertising. The method can flexibly receive slice specifications, and can overlay any one or more of a variety of distribution models, such as a linear combination or a log-linear combination. Using an appropriate weighted distribution model, the combined predictive model reliably yields predictive estimates of occurrence of click events that are at least as good as the best predictive model in the slice-wise predictive model set. | 01-26-2012 |
| 20120023043 | Estimating Probabilities of Events in Sponsored Search Using Adaptive Models - A machine-learning method for estimating probability of a click event in online advertising systems by computing and comparing an aggregated predictive model (a global model) and one or more data-wise sliced predictive models (local models). The method comprises receiving training data having a plurality of features stored in a feature set and constructing a global predictive model that estimates the probability of a click event for the processed feature set. Then, partitioning the global predictive model into one or more data-wise sliced training sets for training a local model from each of the data-wise slices, and then determining whether a particular local model estimates probability of click event for the feature set better than the global model. A given feature set may be collected from historical data, and may comprise a feature vector for a plurality of query-advertisement pairs and a corresponding indicator that represents a click on the advertisement. | 01-26-2012 |
