International Business Machines Corporation

Tata Consultancy Services Limited

Other

Nuance Communications, Inc.

Prasanta Kumar Ghosh

Ashish Verma

Sachindra Joshi

Shantanu Ravindra Godbole

Srujana Merugu

Arpan Pal

Anirban Dutta Choudhury

Tanuka Bhattacharjee

Deepan Das

Rama Mukherjee

Anand C Burman

Rohan Banerjee

Sanjeeb Kumar Sahoo

Kenneth W Church

Dhiraj Khattar

Jeffrey N Marcus

Shreyasi Datta

Shajith I Mohamed

Mukesh Kumar

Shahnawaz Alam

Amarnath Maitra

Achuth Rao Melavarige Venkatagiri

Ayush Ranjan Lohani

Soumendu Paul

Anurban Dutta Choudhury

Monitoring the quality of sleep of an individual is essential for ensuring one's overall well-being. The existing methods for non-apnea sleep arousal detection are manual. A system and method for the non-apnea sleep arousal detection has been provided. The method uses a feature engineering based binary classification approach for distinguishing non-apnea arousal and non-arousal. A training data set is prepared using a plurality of physiological signals. A plurality of features are derived from the training data set. Out of those only a set of features are selected for training a plurality of random forest classifier models. A test sample is then provided to the plurality of random forest classifier models in the instances of fixed duration. This results in generation of prediction probabilities for each instances. The prediction probabilities are then used to predict the probabilities of non-apnea sleep arousal in the test sample.

System and method for non-apnea sleep arousal detection

Traditionally arousal classification has been broadly done in multiple classes but have been insufficient to provide information about how arousal level of user changes over time. Present disclosure propose a continuous and unsupervised approach of monitoring the arousal trend of individual from his/her heart rate by obtaining instantaneous HR for time windows from a resampled time series of RR intervals obtained from ECG signal. A measured average heart rate (a measured) is computed from instantaneous HR specific to user for each time window thereby estimating apriori state based on a last instance of an aposteriori state initialized and observation of a state space model of Kalman Filter is determined for computing error and normalizing thereof which gets compared with a threshold for continuous monitoring of arousal trend of the user. The aposterior state is further updated using Kalman gain computed based on measurement noise determined for state space model.

Heart rate driven unsupervised techniques for continuous monitoring of arousal trend of users

A computer generates labels for machine learning algorithms by retrieving, from a data storage circuit, multiple label sets that contain labels that each classify data points in a corpus of data. A graph is generated that includes a plurality of edges, each edge between two respective labels from different label sets of the multiple label sets. Weights are determined for the plurality of edges based upon a consistency between data points classified by two labels connected by the edges. An algorithm is applied that groups labels from the multiple label sets based upon the weights for the plurality of edges. Data points are identified from the corpus of data that represent conflicts within the grouped labels. An electronic message is transmitted in order to present the identified data points to entities for further classification. A new label set is generated using the further classification received from the entities.

Labeling of data for machine learning

Identification of an intent of a conversation can be useful for real-time or post-processing purposes. According to example embodiments, a method, and corresponding apparatus of identifying at least one intent-bearing utterance in a conversation, comprises determining at least one feature for each utterance among a subset of utterances of the conversation; classifying each utterance among the subset of utterances, using a classifier, as an intent classification or a non-intent classification based at least in part on a subset of the at least one determined feature; and selecting at least one utterance, with intent classification, as an intent-bearing utterance based at least in part on classification results by the classifier. Through identification of an intent bearing utterance, a call center for example, can provide improved service for callers through, for example, more effective directing of a call to a live agent.

System and method for identification of intent segment(s) in caller-agent conversations

This invention relates to pharmaceutical formulations of paclitaxel, its derivatives or analogs entrapped into nanoparticles of co-polymeric micelles, a process for preparing the same and the use thereof.

Formulations of paclitaxel, its derivatives or its analogs entrapped into nanoparticles of polymeric micelles, process for preparing same and the use thereof

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Bengaluru, India

Prasanta Kumar Ghosh