公开(公告)号：US20180110471A1

公开(公告)日：2018-04-26

申请号：US15456199

申请日：2017-03-10

Applicant: Tata Consultancy Services Limited

Inventor： Soma Bandyopadhyay ,  Arijit Ukil ,  Rituraj Singh ,  Chetanya Puri ,  Arpan Pai ,  C. A. Murthy

IPC: A61B5/00 ,  A61B5/021 ,  A61B5/1455 ,  G06N99/00

CPC classification number: A61B5/7267 ,  A61B5/021 ,  A61B5/0468 ,  A61B5/14551 ,  G06F17/18 ,  G06K9/0053 ,  G06N20/00

Abstract: Accurate detection of anomaly in sensor signals is critical and can have an immense impact in the health care domain. Accordingly, identifying outliers or anomalies with reduced error and reduced resource usage is a challenge addressed by the present disclosure. Self-learning of normal signature of an input sensor signal is used to derive primary features based on valley and peak points of the sensor signals. A pattern is recognized by using discrete nature and strictly rising and falling edges of the input sensor signal. One or more defining features are identified from the derived features based on statistical properties and time and frequency domain properties of the input sensor signal. Based on the values of the defining features, clusters of varying density are identified for the input sensor signal and based on the density of the clusters, anomalous and non-anomalous portions of the input sensor signals are classified.

公开(公告)号：US20160371228A1

公开(公告)日：2016-12-22

申请号：US15184494

申请日：2016-06-16

Applicant: Tata Consultancy Services Limited

Inventor： Arijit UKIL ,  Soma Bandyopadhyay ,  Arpan Pal

IPC: G06F17/18

Abstract: Disclosed is a method and system for detecting outliers in real-time for a univariate time-series signal. The system may receive the univariate time-series signal, comprising a plurality of datasets, from a data source. The system may compute a standard deviation of a dataset of the plurality of datasets. Subsequently, the system may compute the optimal sample block size and the critical sample size of the dataset. Further, the system may determine the optimal operational block size of the dataset. The system may segment the plurality of datasets into blocks based upon the optimal operational block size. The system may detect the outliers by performing an outlier detection technique on the blocks, thereby ensuring improved execution time while minimally affecting precision and accuracy of the outcome of the outlier detection method.

Abstract translation: 公开了一种用于单变量时间序列信号实时检测异常值的方法和系统。 系统可以从数据源接收包含多个数据集的单变量时间序列信号。 系统可以计算多个数据集的数据集的标准偏差。 随后，系统可以计算最佳样本块大小和数据集的关键样本大小。 此外，系统可以确定数据集的最佳操作块大小。 系统可以基于最佳操作块大小将多个数据集分段成块。 该系统可以通过在块上执行异常值检测技术来检测异常值，从而确保改进的执行时间，同时最小化影响异常值检测方法的结果的精度和精度。

公开(公告)号：US11531830B2

公开(公告)日：2022-12-20

申请号：US16102069

申请日：2018-08-13

Applicant: Tata Consultancy Services Limited

Inventor： Arijit Ukil ,  Soma Bandyopadhyay ,  Chetanya Puri ,  Rituraj Singh ,  Arpan Pal

IPC: G06K9/62 ,  A61B5/1455 ,  A61B5/0205 ,  G06N7/08 ,  G06N20/00 ,  G16H50/70 ,  G16C20/70

Abstract: In many real-life applications, ample amount of examples from one class are present while examples from other classes are rare for training and learning purposes leading to class imbalance problem and misclassification. Methods and systems of the present disclosure facilitate generation of an extended synthetic rare class super dataset that is further pruned to obtain a synthetic rare class dataset by maximizing similarity and diversity in the synthetic rare class dataset while preserving morphological identity with labeled rare class training dataset. Oversampling methods used in the art result in cloning of datasets and do not provide the needed diversity. The methods of the present disclosure can be applied to classification of noisy phonocardiogram (PCG) signals among other applications.

公开(公告)号：US20180075861A1

公开(公告)日：2018-03-15

申请号：US15456172

申请日：2017-03-10

Applicant: Tata Consultancy Services Limited

Inventor： Arijit Ukil ,  Soma Bandyopadhyay ,  Chetanya Puri ,  Arpan Pal ,  Rituraj Singh ,  Ayan Mukherjee ,  Debayan Mukherjee

IPC: G10L21/0232 ,  G10L15/06 ,  G10L15/02

CPC classification number: G10L21/0232 ,  A61B5/7203 ,  A61B5/7221 ,  A61B5/7267 ,  A61B7/00 ,  G06K9/00496 ,  G10L15/02 ,  G10L15/063 ,  G10L25/84 ,  G10L2025/786 ,  G10L2025/935

Abstract: Traditionally known classification methods of non-stationary physiological audio signals as noisy and clean involve human intervention, may involve dependency on particular type of classifier and further analyses is carried out on classified clean signals. However, in non-stationary audio signals a major portion may end up being classified as noisy and hence may get rejected which may cause missing of intelligence which could have been derived from lightly noisy audio signals that may be critical. The present disclosure enables automation of classification based on auto-thresholding and statistical isolation wherein noisy signals are further classified as highly noisy and lightly noisy through continuous dynamic learning.

公开(公告)号：US09547768B2

公开(公告)日：2017-01-17

申请号：US14627185

申请日：2015-02-20

Applicant: Tata Consultancy Services Limited

Inventor： Arijit Ukil ,  Soma Bandyopadhyay ,  Arpan Pal

IPC: G06F21/00 ,  G06F21/57 ,  G06F21/62 ,  G06F11/30 ,  G06F11/34 ,  G06F11/00 ,  G06F12/14 ,  G06F12/16 ,  G08B23/00

CPC classification number: G06F21/577 ,  G06F11/3024 ,  G06F11/3409 ,  G06F21/6254 ,  G06F2221/034

Abstract: System(s) and method(s) to provide privacy measurement and privacy quantification of sensor data are disclosed. The sensor data is received from a sensor. The private content associated with the sensor data is used to calculate a privacy measuring factor by using entropy based information theoretic model. A compensation value with respect to distribution dissimilarity is determined. The compensation value compensates a statistical deviation in the privacy measuring factor. The compensation value and the privacy measuring factor are used to determine a privacy quantification factor. The privacy quantification factor is scaled with respect to a predefined finite scale to obtain at least one scaled privacy quantification factor to provide quantification of privacy of the sensor data.

Abstract translation: 公开了提供传感器数据的隐私测量和隐私定量的系统和方法。 从传感器接收传感器数据。 与传感器数据相关联的私有内容用于通过使用基于熵的信息理论模型来计算隐私测量因子。 确定相对于分布不相似度的补偿值。 补偿值补偿隐私测量因子中的统计偏差。 补偿值和隐私测量因子用于确定隐私量化因子。 隐私量化因子相对于预定义的有限比例被缩放以获得至少一个缩放的隐私量化因子，以提供传感器数据的隐私的量化。

公开(公告)号：US20140351334A1

公开(公告)日：2014-11-27

申请号：US14344464

申请日：2012-09-04

Applicant: TATA CONSULTANCY SERVICES LIMITED

Inventor： Soma Bandyopadhyay ,  Souvik Maiti ,  Munmun Sengupta ,  Subhajit Dutta

IPC: H04L29/08

CPC classification number: H04L67/10 ,  H04L67/12 ,  H04L67/125 ,  H04L67/16 ,  H04W4/38

Abstract: The present disclosure provides a system and method to provide a mechanism to perform faster collaboration among the services by retrieving the context information from the central device using service identifier as key element. The system is adapted to create a unique device identifier by associating device MAC address, context information and operation/service identifier while performing the device registration.

Abstract translation: 本公开提供了一种系统和方法，用于提供通过使用服务标识符作为关键元素从中央设备检索上下文信息来执行服务之间的更快协作的机制。 该系统适于通过在执行设备注册时关联设备MAC地址，上下文信息和操作/服务标识符来创建唯一的设备标识符。

公开(公告)号：US20230281429A1

公开(公告)日：2023-09-07

申请号：US18093594

申请日：2023-01-05

Applicant: Tata Consultancy Services Limited

Inventor： Soma Bandyopadhyay ,  Sridhar Balakrishnan ,  Shruti Sachan ,  Yasasvy Tadepalli ,  Arpan Pal ,  Anish Datta ,  Karthik Leburi ,  Srinivas Raghu Raman Gadepally

IPC: G06N3/0455 ,  G06F18/21

CPC classification number: G06N3/0455 ,  G06F18/217

Abstract: Existing machine learning systems require historical data to perform analytics to detect faults in a machine and are unable to detect new types of faults/changes occurring in real time. These systems further fail to identify operation changes due to sensor drift and forget past events that have occurred. Present application provides systems and methods for identifying and classifying sensor drifts and diverse varying operational conditions from continually received sensor data using continual training of variational autoencoders (VAE) following drift specific characteristics, wherein sensor drift is compensated based on identified changes in sensors and degradation in machine(s). Rehearsal technique is performed by either VAE based generative models trained in previous iterations that are configured to generate a dataset corresponding to a current iteration, or discriminative instances of original dataset in previous iterations that are configured to generate a dataset corresponding to a current iteration, thus preventing from catastrophic forgetting.

公开(公告)号：US11699522B2

公开(公告)日：2023-07-11

申请号：US16396276

申请日：2019-04-26

Applicant: Tata Consultancy Services Limited

Inventor： Avik Ghose ,  Arijit Chowdhury ,  Sakyajit Bhattacharya ,  Vivek Chandel ,  Arpan Pal ,  Soma Bandyopadhyay

IPC: G06N5/02 ,  G06N5/04 ,  G06K9/62 ,  G06F16/9035 ,  A61B5/00 ,  G16H40/67 ,  G06F16/908 ,  G06N5/022 ,  G06F18/25

CPC classification number: G16H40/67 ,  A61B5/7275 ,  G06F16/908 ,  G06F16/9035 ,  G06F18/251 ,  G06N5/022 ,  G06N5/04

Abstract: This disclosure relates generally to a unified platform for domain adaptable human behaviour inference. The platform provides a unified, low level inference and high level inference of domain adaptable human behaviour inference. The low level inferences include cross-sectional analysis techniques to infer location, activity, physiology. Further the high inference that provide useful and actionable for longitudinal tracking, prediction and anomaly detection is performed based on several longitudinal analysis techniques that include welch analysis, cross-spectrum analysis, Feature of interest (FOI) identification and time-series clustering, autocorrelation-based distance estimation and exponential smoothing, seasonal and non-seasonal models identification, ARIMA modelling, Hidden Markov models, Long short term memory (LSTM) along with low level inference, human meta-data and application domain knowledge. Further the unified human behaviour inference can be obtained across multiple domains that include health, retail and transportation.

公开(公告)号：US11432753B2

公开(公告)日：2022-09-06

申请号：US16534955

申请日：2019-08-07

Applicant: Tata Consultancy Services Limited

Inventor： Shahnawaz Alam ,  Rohan Banerjee ,  Soma Bandyopadhyay

IPC: G06K9/62 ,  A61B5/316 ,  A61B5/00 ,  G06N3/04 ,  A61B5/349

Abstract: Conventional systems and methods of classifying heart signals include segmenting them which can fail due to the presence of noise, artifacts and other sounds including third heart sound ‘S3’, fourth heart sound ‘S4’, and murmur. Heart sounds are inherently prone to interfering noise (ambient, speech, etc.) and motion artifact, which can overlap time location and frequency spectra of murmur in heart sound. Embodiments of the present disclosure provide parallel implementation of Deep Neural Networks (DNN) for classifying heart sound signals (HSS) wherein spatial (presence of different frequencies component) filters from Spectrogram feature(s) of the HSS are learnt by a first DNN while time-varying component of the signals from MFCC features of the HSS are learnt by a second DNN for classifying the heart sound signal as one of normal sound signal or murmur sound signal.

公开(公告)号：US11304663B2

公开(公告)日：2022-04-19

申请号：US16230053

申请日：2018-12-21

Applicant: Tata Consultancy Services Limited

Inventor： Soma Bandyopadhyay ,  Arijit Ukil ,  Chetanya Puri ,  Rituraj Singh ,  Arpan Pal ,  C A Murthy

IPC: A61B5/00 ,  G06K9/00 ,  G16H50/20 ,  G16H50/30 ,  A61B5/318 ,  G16H50/70 ,  A61B5/024

Abstract: Systems and methods for detecting an anomaly in a cardiovascular signal using hierarchical extremas and repetitions. The traditional systems and methods provide for some anomaly detection in the cardiovascular signal but do not consider the discrete nature and strict rising and falling patterns of the cardiovascular signal and frequency in terms of hierarchical maxima points and minima points. Embodiments of the present disclosure provide for detecting the anomaly in the cardiovascular signal using hierarchical extremas and repetitions by smoothening the cardiovascular signal, deriving sets of hierarchical extremas using window detection, identifying signal patterns based upon the sets of hierarchical extremas, identifying repetitions in the signal patterns based upon occurrences and randomness of occurrences of the signal patterns and classifying the cardiovascular signal as anomalous and non-anomalous for detecting the anomaly in the cardiovascular signal.