公开(公告)号：US20240079140A1

公开(公告)日：2024-03-07

申请号：US18227480

申请日：2023-07-28

Applicant: Tata Consultancy Services Limited

Inventor： Arijit UKIL ,  Jayavardhana Rama Gubbi Lakshminarasimha ,  Arpan Pal ,  Trisrota Deb ,  Sai Chander Racha ,  Ishan Sahu ,  Sundeep Khandelwal

IPC: G16H50/20

CPC classification number: G16H50/20 ,  G16H40/67

Abstract: Portable ECG monitors available in market have the disadvantage that the ECG data they provide as input aren't directly interpretable and requires medical knowledge for the users. The disclosure herein generally relates to Electrocardiogram (ECG), and, more particularly, to a method and system for generating 2d representation of electrocardiogram (ECG) signals. The system provides a mechanism for determining variability between a plurality of segments of an ECG data measured, and uses the information on the determined variability to generate the 2D representation corresponding to the ECG signal. The system further provides means to generate a data model that can be further used for processing real-time ECG data for generating corresponding interpretations. This allows a user to obtain the interpretations as output.

公开(公告)号：US20150261959A1

公开(公告)日：2015-09-17

申请号：US14627185

申请日：2015-02-20

Applicant: Tata Consultancy Services Limited

Inventor： Arijit UKIL ,  Soma BANDYOPADHYAY ,  Arpan PAL

IPC: G06F21/57 ,  G06F11/30 ,  G06F11/34

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：US20240143630A1

公开(公告)日：2024-05-02

申请号：US18383930

申请日：2023-10-26

Applicant: Tata Consultancy Services Limited

Inventor： Arijit UKIL ,  Arpan PAL ,  Soumadeep SAHA ,  Utpal GARAIN

IPC: G06F16/28

CPC classification number: G06F16/285

Abstract: This disclosure relates generally to multi-class multi-label classification and more particularly to contradiction avoided learning for multi-class multi-label classification. Conventional classification methods do not consider contradictory outcomes in multi-label classification tasks wherein contradictory outcomes have significant negative impact in the classification problem solution. The present disclosure provides a contradiction avoided learning multi-class multi-label classification. The disclosed method utilizes a binary contradiction matrix constructed using domain knowledge. Based on the binary contradiction matrix the training dataset is divided into two parts, one comprising contradictions and the second without contradictions. The classification model is trained using the divided datasets using a contradiction loss and a binary cross entropy loss to avoid contradictions during learning of the classification model. The disclosed method is used for electrocardiogram classification, shape classification and so on.

公开(公告)号：US20190200935A1

公开(公告)日：2019-07-04

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

CPC classification number: G16H50/30 ,  G16H50/20 ,  G16H50/70

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.

公开(公告)号：US20190050690A1

公开(公告)日：2019-02-14

申请号：US15922435

申请日：2018-03-15

Applicant: Tata Consultancy Services Limited

Inventor： Arijit UKIL ,  Soma BANDYOPADHYAY ,  Chetanya PURI ,  Rituraj SINGH ,  Arpan PAL

IPC: G06K9/62 ,  G06F15/18 ,  G06F17/16 ,  G06F17/18

Abstract: Absence of well-represented training datasets cause a class imbalance problem in one-class support vector machines (OC-SVMs). The present disclosure addresses this challenge by computing optimal hyperparameters of the OC-SVM based on imbalanced training sets wherein one of the class examples outnumbers the other class examples. The hyperparameters kernel co-efficient γ and rejection rate hyperparameter ν of the OC-SVM are optimized to trade-off the maximization of classification performance while maintaining stability thereby ensuring that the optimized hyperparameters are not transient and provide a smooth non-linear decision boundary to reduce misclassification as known in the art. This finds application particularly in clinical decision making such as detecting cardiac abnormality condition under practical conditions of contaminated inputs and scarcity of well-represented training datasets.

公开(公告)号：US20220284293A1

公开(公告)日：2022-09-08

申请号：US17447625

申请日：2021-09-14

Applicant: Tata Consultancy Services Limited

Inventor： Swarnava DEY ,  Arpan PAL ,  Gitesh KULKARNI ,  Chirabrata BHAUMIK ,  Arijit UKIL ,  Jayeeta MONDAL ,  Ishan SAHU ,  Aakash TYAGI ,  Amit SWAIN ,  Arijit MUKHERJEE

IPC: G06N3/08 ,  G06N3/063 ,  G06F1/3206 ,  G06K9/62

Abstract: Small and compact Deep Learning models are required for embedded Al in several domains. In many industrial use-cases, there are requirements to transform already trained models to ensemble embedded systems or re-train those for a given deployment scenario, with limited data for transfer learning. Moreover, the hardware platforms used in embedded application include FPGAs, AI hardware accelerators, System-on-Chips and on-premises computing elements (Fog/Network Edge). These are interconnected through heterogenous bus/network with different capacities. Method of the present disclosure finds how to automatically partition a given DNN into ensemble devices, considering the effect of accuracy—latency power—tradeoff, due to intermediate compression and effect of quantization due to conversion to AI accelerator SDKs. Method of the present disclosure is an iterative approach to obtain a set of partitions by repeatedly refining the partitions and generating a cascaded model for inference and training on ensemble hardware.

公开(公告)号：US20220027711A1

公开(公告)日：2022-01-27

申请号：US17357614

申请日：2021-06-24

Applicant: Tata Consultancy Services Limited

Inventor： Arijit UKIL ,  Soma BANDYOPADHYAY ,  Arpan PAL

IPC: G06N3/04 ,  G06N3/08 ,  G06N3/063

Abstract: This disclosure relates generally to a system and a method for mitigating generalization loss in deep neural network for time series classification. In an embodiment, the disclosed method includes compute an entropy of a timeseries training dataset, and a mean and a variance of the entropy and a regularization factor is computed. A plurality of iterations are performed to dynamically adjust the learning rate of the deep Neural Network (DNN) using a Mod-Adam optimization, and obtain a network parameter, and based on the network parameter, the regularization factor is updated to obtain an updated regularized factor. The learning rate is adjusted in the plurality of iterations by repeatedly updating the network parameter based on a variation of a generalization loss during the plurality of iterations. The updated regularized factor of the current iteration is used for adjusting the learning rate in a subsequent iteration of the plurality of iterations.

公开(公告)号：US20210326765A1

公开(公告)日：2021-10-21

申请号：US17156821

申请日：2021-01-25

Applicant: Tata Consultancy Services Limited

Inventor： Arijit UKIL ,  Arpan PAL ,  Soma BANDYOPADHYAY ,  Ishan SAHU ,  Trisrota DEB

IPC: G06N20/10 ,  G06F17/16 ,  G06K9/62

Abstract: This disclosure relates generally to method and system for an adaptive filter based learning model for time series sensor signal classification on edge devices. The adaptive filter based learning model for time series sensor signal classification enables automated-computationally lightweight learning (significant reduction in computational resources) and inferring/classification in real-time or near-real-time on CPU/memory/battery life constrained edge devices. The disclosed techniques for time series sensor signal classification on edge devices characterizes the intrinsic signal processing properties of the input time series sensor signals using linear adaptive filtering and derivative spectrum to efficiently construct the adaptive filter based learning model based on standard classification algorithms for time series sensor signal classification.

公开(公告)号：US20200012941A1

公开(公告)日：2020-01-09

申请号：US16506828

申请日：2019-07-09

Applicant: Tata Consultancy Services Limited

Inventor： Arijit UKIL ,  Soma BANDYOPADHYAY ,  Pankaj MALHOTRA ,  Arpan PAL ,  Lovekesh VIG ,  Gautam SHROFF ,  Tulika BOSE ,  Ishan SAHU ,  Ayan MUKHERJEE

IPC: G06N3/08 ,  G06N3/04

Abstract: The disclosure herein describes a method and a system for generating hybrid learning techniques. The hybrid learning technique refers to learning techniques that are a combination a plurality of techniques that include of deep learning, machine learning and signal processing to enable a rich feature space representation and classifier construction. The generation of the hybrid learning techniques also considers influence/impact of domain constraints that include business requirements and computational constraints, while generating hybrid learning techniques. Further from the plurality hybrid learning techniques a single hybrid learning technique is chosen based on performance matrix based on optimization techniques.

公开(公告)号：US20200034690A1

公开(公告)日：2020-01-30

申请号：US16526340

申请日：2019-07-30

Applicant: Tata Consultancy Services Limited

Inventor： Avik GHOSE ,  Arpan PAL ,  Sundeep KHANDELWAL ,  Rohan BANERJEE ,  Sakyajit BHATTACHARYA ,  Soma BANDYOPADHYAY ,  Arijit UKIL ,  Dhaval Satish JANI

IPC: G06N3/04 ,  A61B5/11 ,  A61B5/00

Abstract: This disclosure relates generally to methods and systems for unobtrusive digital health assessment of high risk subjects, wherein bio-markers pertaining to a disease are identified automatically using physical activity and physiology monitoring on a continuous basis. Identification of bio-markers in the medical domain is conventionally dependent on insights derived from medical tests which are obtrusive in nature. Systems and methods of the present disclosure integrate physical characteristics, lifestyle habits and prevailing medical conditions with monitored physical activities and physiological measurements to assess health of high risk subjects. Systems and methods of the present disclosure also enable automatic generation of control class and treatment class that may be effectively used for health assessment.