Patent application number | Description | Published |
20120309949 | METHOD FOR PREPARATION OF THE TETRASACCHARIDE LACTO-N-NEOTETRAOSE (LNNT) CONTAINING N-ACETYLLACTOSAMINE - The present invention relates to a method for preparation of the tetrasaccharide lacto-N-neotetraose (LNnt, formula (I)) especially in large scale, as well as intermediates in the synthesis, a new crystal form (polymorph) of LNnt, and the use thereof in pharmaceutical or nutritional compositions. | 12-06-2012 |
20130035481 | PRODUCTION OF 6'-O-SIALYLLACTOSE AND INTERMEDIATES - The present invention relates to a method for preparation of the trisaccharide 6′-0-sialyllactose (formula (I)) or salts thereof as well as intermediates in the synthesis and for the use of 6′-0-sialyllactose salts in pharmaceutical or nutritional compositions. | 02-07-2013 |
20130072675 | METHOD FOR CYRSTALLIZATION OF FUCOSE - The present application discloses a method for the crystallization of fucose, characterized in that the crystallization is carried out from a mixture comprising fucose and at least one 6-deoxy sugar selected from 6-deoxy-talose and 6-deoxy-gulose. In one embodiment, the mixture comprises fucose and 6-deoxy-talose. | 03-21-2013 |
20130165406 | POLYMORPHS OF 2'-O-FUCOSYLLACTOSE AND PRODUCING THEREOF - The present invention relates to novel polymorphs of the trisaccharide 2′-O-fucosyllactose (2-FL) of formula (1), methods for producing said polymorphs and their use in pharmaceutical or nutritional compositions. | 06-27-2013 |
20130171696 | SYNTHESIS OF NEW SIALOOLIGOSACCHARIDE DERIVATIVES - The invention relates to a method for the synthesis of compounds of general formula (1A) and salts thereof wherein one of the R groups is an α-sialyl moiety and the other is H, X | 07-04-2013 |
20130172548 | DERIVATIZATION OF OLIGOSACCHARIDES - A method for purifying, separating and/or isolating an oligosaccharide or a salt thereof is presented. An embodiment of the invention is based upon the formation of anomeric O-benzyl/substituted O-benzyl derivatives in a selective anomeric alkylation reaction. | 07-04-2013 |
20130245250 | METHOD FOR PRODUCING L-FUCOSE - Method for producing L-fucose includes in a first aspect, a method for the preparation of L-fucose, wherein L-fucose precursors are produced from pectin and L-fucose is produced from the L-fucose precursors; in a second aspect, a method for the preparation of L-fucose from D-galacturonic acid or a salt thereof, wherein L-fucose precursors are produced from D-galacturonic acid of a salt thereof, and L-fucose is produced from the L-fucose precursors; and an L-fucose precursor as shown in Formula A, wherein R is a linear or branched chain saturated hydrocarbon group with 1-6 carbon atoms, such as methyl, ethyl, n-propyl, i-propyl, n-butyl, i-butyl, s-butyl, t-butyl, n-hexyl, etc., preferably a methyl group. | 09-19-2013 |
20140046044 | NOVEL GLYCOSYL PHOSPHITES - The present invention relates to providing compounds of general formula 1 | 02-13-2014 |
20140303363 | Preparation and Use of Isolactosamine and Intermediates therefor - The invention relates to providing isolactosamine (Galβ1-3GlcNH | 10-09-2014 |
20150133647 | Method for Producing Oligosaccharides and Oligosaccharide Glycosides by Fermentation - The application discloses a method for producing anomerically protected glycosidic oligosaccharide derivatives comprising the step of culturing, in a culture medium containing an anomerically protected lactose acceptor, a genetically modified cell having a recombinant gene that encodes a glycosyl transferase that can transfer a glycosyl residue of an activated sugar nucleotide to said lactose acceptor. The application further discloses a method for producing an oligosaccharide comprising the steps of: (a) culturing, in a culture medium containing an anomerically protected lactose acceptor, a genetically modified cell having a recombinant gene that encodes a glycosyl transferase that can transfer a glycosyl residue of an activated sugar nucleotide to said lactose acceptor to produce an anomerically protected glycosidic oligosaccharide derivative, then (b) removing/deprotecting the anomeric protective group. | 05-14-2015 |
Patent application number | Description | Published |
20160125245 | FOREGROUND DETECTOR FOR VIDEO ANALYTICS SYSTEM - Techniques are disclosed for creating a background model of a scene using both a pixel based approach and a context based approach. The combined approach provides an effective technique for segmenting scene foreground from background in frames of a video stream. Further, this approach can scale to process large numbers of camera feeds simultaneously, e.g., using parallel processing architectures, while still generating an accurate background model. Further, using both a pixel based approach and context based approach ensures that the video analytics system can effectively and efficiently respond to changes in a scene, without overly increasing computational complexity. In addition, techniques are disclosed for updating the background model, from frame-to-frame, by absorbing foreground pixels into the background model via an absorption window, and dynamically updating background/foreground thresholds. | 05-05-2016 |
20160125255 | DYNAMIC ABSORPTION WINDOW FOR FOREGROUND BACKGROUND DETECTOR - Techniques are disclosed for creating a background model of a scene using both a pixel based approach and a context based approach. The combined approach provides an effective technique for segmenting scene foreground from background in frames of a video stream. Further, this approach can scale to process large numbers of camera feeds simultaneously, e.g., using parallel processing architectures, while still generating an accurate background model. Further, using both a pixel based approach and context based approach ensures that the video analytics system can effectively and efficiently respond to changes in a scene, without overly increasing computational complexity. In addition, techniques are disclosed for updating the background model, from frame-to-frame, by absorbing foreground pixels into the background model via an absorption window, and dynamically updating background/foreground thresholds. | 05-05-2016 |
20160125621 | INCREMENTAL UPDATE FOR BACKGROUND MODEL THRESHOLDS - Techniques are disclosed for creating a background model of a scene using both a pixel based approach and a context based approach. The combined approach provides an effective technique for segmenting scene foreground from background in frames of a video stream. Further, this approach can scale to process large numbers of camera feeds simultaneously, e.g., using parallel processing architectures, while still generating an accurate background model. Further, using both a pixel based approach and context based approach ensures that the video analytics system can effectively and efficiently respond to changes in a scene, without overly increasing computational complexity. In addition, techniques are disclosed for updating the background model, from frame-to-frame, by absorbing foreground pixels into the background model via an absorption window, and dynamically updating background/foreground thresholds. | 05-05-2016 |
Patent application number | Description | Published |
20090016599 | SEMANTIC REPRESENTATION MODULE OF A MACHINE-LEARNING ENGINE IN A VIDEO ANALYSIS SYSTEM - A machine-learning engine is disclosed that is configured to recognize and learn behaviors, as well as to identify and distinguish between normal and abnormal behavior within a scene, by analyzing movements and/or activities (or absence of such) over time. The machine-learning engine may be configured to evaluate a sequence of primitive events and associated kinematic data generated for an object depicted in a sequence of video frames and a related vector representation. The vector representation is generated from a primitive event symbol stream and a phase space symbol stream, and the streams describe actions of the objects depicted in the sequence of video frames. | 01-15-2009 |
20090016600 | COGNITIVE MODEL FOR A MACHINE-LEARNING ENGINE IN A VIDEO ANALYSIS SYSTEM - A machine-learning engine is disclosed that is configured to recognize and learn behaviors, as well as to identify and distinguish between normal and abnormal behavior within a scene, by analyzing movements and/or activities (or absence of such) over time. The machine-learning engine may be configured to evaluate a sequence of primitive events and associated kinematic data generated for an object depicted in a sequence of video frames and a related vector representation. The vector representation is generated from a primitive event symbol stream and a phase space symbol stream, and the streams describe actions of the objects depicted in the sequence of video frames. | 01-15-2009 |
20140072206 | SEMANTIC REPRESENTATION MODULE OF A MACHINE LEARNING ENGINE IN A VIDEO ANALYSIS SYSTEM - A machine-learning engine is disclosed that is configured to recognize and learn behaviors, as well as to identify and distinguish between normal and abnormal behavior within a scene, by analyzing movements and/or activities (or absence of such) over time. The machine-learning engine may be configured to evaluate a sequence of primitive events and associated kinematic data generated for an object depicted in a sequence of video frames and a related vector representation. The vector representation is generated from a primitive event symbol stream and a phase space symbol stream, and the streams describe actions of the objects depicted in the sequence of video frames. | 03-13-2014 |
20150046155 | COGNITIVE NEURO-LINGUISTIC BEHAVIOR RECOGNITION SYSTEM FOR MULTI-SENSOR DATA FUSION - Embodiments presented herein describe techniques for generating a linguistic model of input data obtained from a data source (e.g., a video camera). According to one embodiment of the present disclosure, a sequence of symbols is generated based on an ordered stream of normalized vectors generated from the input data. A dictionary of words is generated from combinations of the ordered sequence of symbols based on a frequency at which combinations of symbols appear in the ordered sequence of symbols. A plurality of phrases is generated based an ordered sequence of words from the dictionary observed in the ordered sequence of symbols based on a frequency by which combinations of words in ordered sequence of words appear relative to one another. | 02-12-2015 |
20150110388 | SEMANTIC REPRESENTATION MODULE OF A MACHINE-LEARNING ENGINE IN A VIDEO ANALYSIS SYSTEM - A machine-learning engine is disclosed that is configured to recognize and learn behaviors, as well as to identify and distinguish between normal and abnormal behavior within a scene, by analyzing movements and/or activities (or absence of such) over time. The machine-learning engine may be configured to evaluate a sequence of primitive events and associated kinematic data generated for an object depicted in a sequence of video frames and a related vector representation. The vector representation is generated from a primitive event symbol stream and a phase space symbol stream, and the streams describe actions of the objects depicted in the sequence of video frames. | 04-23-2015 |
20160125233 | SEMANTIC REPRESENTATION MODULE OF A MACHINE-LEARNING ENGINE IN A VIDEO ANALYSIS SYSTEM - A machine-learning engine is disclosed that is configured to recognize and learn behaviors, as well as to identify and distinguish between normal and abnormal behavior within a scene, by analyzing movements and/or activities (or absence of such) over time. The machine-learning engine may be configured to evaluate a sequence of primitive events and associated kinematic data generated for an object depicted in a sequence of video frames and a related vector representation. The vector representation is generated from a primitive event symbol stream and a phase space symbol stream, and the streams describe actions of the objects depicted in the sequence of video frames. | 05-05-2016 |
Patent application number | Description | Published |
20110043689 | FIELD-OF-VIEW CHANGE DETECTION - Techniques are disclosed for detecting a field-of-view change for a video feed. These techniques differentiate between a new or changed scene and a temporary variation in the scene to accurately detect field-of-view changes for the video feed. A field-of-view change is detected when the position of a camera providing the video feed changes, the video feed is switched to a different camera, the video feed is disconnected, or the camera providing the video feed is obscured. A false-positive field-of-view change is not detected when the scene changes due to a sudden variation in illumination, obstruction of a portion of the camera providing the video feed, blurred images due to an out-of-focus camera, or a transition between bright and dark light when the video feed transitions between color and near infrared capture modes. | 02-24-2011 |
20110044536 | PIXEL-LEVEL BASED MICRO-FEATURE EXTRACTION - Techniques are disclosed for extracting micro-features at a pixel-level based on characteristics of one or more images. Importantly, the extraction is unsupervised, i.e., performed independent of any training data that defines particular objects, allowing a behavior-recognition system to forgo a training phase and for object classification to proceed without being constrained by specific object definitions. A micro-feature extractor that does not require training data is adaptive and self-trains while performing the extraction. The extracted micro-features are represented as a micro-feature vector that may be input to a micro-classifier which groups objects into object type clusters based on the micro-feature vectors. | 02-24-2011 |
20120163670 | BEHAVIORAL RECOGNITION SYSTEM - Embodiments of the present invention provide a method and a system for analyzing and learning behavior based on an acquired stream of video frames. Objects depicted in the stream are determined based on an analysis of the video frames. Each object may have a corresponding search model used to track an object's motion frame-to-frame. Classes of the objects are determined and semantic representations of the objects are generated. The semantic representations are used to determine objects' behaviors and to learn about behaviors occurring in an environment depicted by the acquired video streams. This way, the system learns rapidly and in real-time normal and abnormal behaviors for any environment by analyzing movements or activities or absence of such in the environment and identifies and predicts abnormal and suspicious behavior based on what has been learned. | 06-28-2012 |