Abstract: A system and method for monitoring pH of an intraoral device involves receiving the oral pH data from at least one pH sensor embedded within the intraoral device. The oral pH data is then correlated with one or more user related data to classify the oral pH data into one or more pre-defined categories indicative of a health condition. Further, the machine learning module generates feedback and/or recommendations for the user based on the classification of the oral pH data into one or more pre-defined categories. The oral pH data and/or feedback and/or recommendations are then communicated to the user and/or external device.

Abstract: An intraoral monitoring device includes a flexible substrate, a plurality of sensors, a rechargeable battery, a device module and a communication module. The plurality of sensors senses a plurality of health parameters from an associated user, in real time, to generate a plurality of sensed signals. The device module includes a memory, a controller and a segregation module. The memory stores various health parameters and their respective ranges, various health reports of the user, and basic details of all users. A controller is configured to generate sensed data and the segregation module is configured to segregate the data corresponding to the plurality of health parameters associated with the users. A communication module is configured to enable data communication to and from the intraoral device.

Abstract: An intraoral monitoring device includes a plurality of sensors integrated on a flexible substrate. The plurality of sensors includes first and second photoplethysmography (PPG) sensors. The first PPG sensor is coupled along an anterior portion of the flexible substrate and adapted to be located intraorally along a first anatomical location defined along a mid-line of a maxilla, facing the labial mucosa, directly at a septal branch of a superior labial artery in a front lip to consistently provide a plurality of first PPG signal, relating to a plurality of cardiorespiratory parameters. Further, the second PPG sensor is coupled to a rear portion of the flexible substrate, adapted to be located intraorally along a second anatomical location defined along the interior part of an oral cavity facing outward toward the labial mucosa to consistently provide a plurality of second PPG signals, relating to a plurality of muscular parameters.

Abstract: Analyzing patterns in a volume of data and taking an action based on the analysis involves receiving data and training the data to create training examples, and then selecting features that are predictive of different classes of patterns in the data stream, using the training examples. The process further involves training in parallel a set of artificial neural networks (“ANNs”), using the data, based on the selected features, and extracting only active nodes that are representative of a class of patterns in the data stream from the set of ANNs. The process continues with adding class labels to each extracted active node, classifying patterns in the data based on the class-labeled active nodes, and taking an action based on the classifying patterns in the data.

Abstract: A physical system receives and records measurements from a plurality of sensors for the physical system over a period of time and creates therefrom a statistical model of normal behavior of the physical system. The statistical model is applied to monitor the physical system for events or significant changes. The method detects events or significant changes in the operation or behavior of the physical system responsive to the monitoring of the physical system for events or significant changes. Events or significant changes in the operation or behavior of the physical system may cause a notification or alert, which can be sent a supervisory system.

Abstract: Analyzing patterns in a volume of data and taking an action based on the analysis involves receiving data and training the data to create training examples, and then selecting features that are predictive of different classes of patterns in the data stream, using the training examples. The process further involves training in parallel a set of ANNs, using the data, based on the selected features, and extracting only active nodes that are representative of a class of patterns in the data stream from the set of ANNs. The process continues with adding class labels to each extracted active node, classifying patterns in the data based on the class-labeled active nodes, and taking an action based on the classifying patterns in the data.

Abstract: Systems and related methods of user-guided searching using preference feedback from user searching to arrive at user-preferred ordered results from a large collection of objects.

Abstract: Systems and related methods of user-guided searching using preference feedback from user searching to arrive at user-preferred ordered results from a large collection of objects.

Abstract: A method and apparatus (system) is provided for the separation into and the identification of classes of events wherein each of the events is represented by a signal vector comprising the signals x1, x2, . . . , xj, . . . , xn. The system in principle is different from neural networks and statistical classifiers. The system comprises a plurality of assemblies. The training or adaptation module stores a set of training examples and has a set of procedures (linear programming, clustering and others) that operate on the training examples and determine a set of transfer function and threshold values. These transfer functions and threshold values are installed on a recognized module for use in the identification phase. The training module is extremely fast and can deal with very difficult pattern classification problems that arise in speech and image recognition, robotics, medical diagnosis, warfare systems and others. The systems also exploits parallelism in both the learning and recognition phases.