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    Anomaly detection by self-learning of sensor signals
    22.
    发明授权
    Anomaly detection by self-learning of sensor signals 有权

    公开(公告)号：US10743821B2

    公开(公告)日：2020-08-18

    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 Pal ,  C A Murthy

    IPC: G06N3/08 ,  G06F11/22 ,  G06N3/02 ,  A61B5/00 ,  A61B5/021 ,  G06N20/00 ,  G06F17/18 ,  G06K9/00 ,  A61B5/0468 ,  A61B5/1455

    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.

    Method and system of detecting arrhythmia using photoplethysmogram signal
    23.
    发明授权
    Method and system of detecting arrhythmia using photoplethysmogram signal 有权

    公开(公告)号：US10206593B2

    公开(公告)日：2019-02-19

    申请号：US15453479

    申请日：2017-03-08

    Applicant: Tata Consultancy Services Limited

    Inventor： Arijit Ukil ,  Soma Bandyopadhyay ,  Chetanya Puri ,  Arpan Pal ,  Kayapanda Mandana

    IPC: A61B5/024 ,  A61B5/00

    Abstract: A method and system of detecting arrhythmia using photoplethysmogram (PPG) signal is provided. The method is performed by extracting photoplethysmogram (PPG) signals from a patient, extracting cardiac parameter from the extracted photoplethysmogram (PPG) signals, identifying presence of cardiac abnormalities as reinforcement filtering of detecting premature ventricular contraction and ventricular flutter from the extracted cardiac parameters, analysing the extracted cardiac parameters to investigate statistical trend and to perform statistical closeness approximation of the extracted photoplethysmogram (PPG) signals and predicting and subsequently classifying type of arrhythmia.

    Generic Device Attributes for Sensing Devices
    26.
    发明申请
    Generic Device Attributes for Sensing Devices 审中-公开
    传感设备的通用设备属性

    公开(公告)号：US20140379878A1

    公开(公告)日：2014-12-25

    申请号：US14365352

    申请日：2012-12-10

    Applicant: Tata Consultancy Services Limited

    Inventor： Soma Bandyopadhyay ,  Abhijan Bhattacharyya ,  Munmun Sengupta

    IPC: H04L12/24

    CPC classification number: H04L41/0823 ,  H04L12/2807 ,  H04L41/12 ,  H04L67/12 ,  H04L67/16 ,  H04L67/303 ,  H04L69/24

    Abstract: A method and system is provided for optimizing time and complexity during an interoperation of at least two smart sensing device's operating in a heterogeneous environment, each device is configured to predetermined characteristics for a heterogeneous environment with a dynamic degree of prioritization in interoperation. The said method and system is adapted for creation of generic device attributes for smart sensing devices by an edge gateway system during the device discovery phase and at the same time performing semantic analysis on the content of the attributes to optimize the device interoperation mechanism in any smart environment.

    Abstract translation: 提供了一种用于在异构环境中操作的至少两个智能感测装置的互操作期间优化时间和复杂性的方法和系统，每个设备被配置为具有在互操作中具有动态优先级的异构环境的预定特性。 所述方法和系统适于在设备发现阶段期间由边缘网关系统创建用于智能感测设备的通用设备属性，并且同时对属性内容执行语义分析以优化任何智能中的设备互操作机制 环境。

    SYSTEM AND METHOD FOR LABEL GENERATION FOR TIMESERIES CLASSIFICATION
    28.
    发明申请
    SYSTEM AND METHOD FOR LABEL GENERATION FOR TIMESERIES CLASSIFICATION 有权

    公开(公告)号：US20220138503A1

    公开(公告)日：2022-05-05

    申请号：US17477771

    申请日：2021-09-17

    Applicant: Tata Consultancy Services Limited

    Inventor： Soma Bandyopadhyay ,  Anish Datta ,  Arpan PAL

    IPC: G06K9/62 ,  G06N20/20

    Abstract: This disclosure relates generally to method and system for time series classification. Conventional methods for time-series classification requires substantial amount of annotated data for classification and label generation. The disclosed method and system are capable of generating accurate labels for time-series data by utilizing a small amount of representative data for each class. In an embodiment, the disclosed method generates a time-series data synthetically and associated labels by using a portion of the representative time-series data in each iteration, and self-correcting the generated labels based on a determination of quality of the generated labels using label quality checker models.

    ANNOTATION OF TIME SERIES DATA AND VALIDATION FOR GENERATING MACHINE LEARNING MODELS
    29.
    发明申请
    ANNOTATION OF TIME SERIES DATA AND VALIDATION FOR GENERATING MACHINE LEARNING MODELS 有权

    公开(公告)号：US20220092474A1

    公开(公告)日：2022-03-24

    申请号：US17366810

    申请日：2021-07-02

    Applicant: Tata Consultancy Services Limited

    Inventor： Tanushyam Chattopadhyay ,  Arijit Ukil ,  Avijit Sur ,  Prateep Misra ,  Arpan Pal ,  Soma Bandyopadhyay

    IPC: G06N20/00 ,  G06F16/28

    Abstract: Conventionally, applying analytics on dataset is the scarcity of labelled data. With increase of data there is cost fact effecting nature of servicing required for data (e.g., cost in terms of resource and time and effort is high for data annotation). Though data is analysed, it may be prone to error. Present disclosure provides systems/methods for reducing volume of data to be annotated for time series data thereby reducing time and effort of resources, thus resulting in effective utilization of system's resources (e.g., memory, processor, etc.). More specifically, the method of the present disclosure adaptively modifies the volume of the data to be annotated based on the performance of the unsupervised learning method applied in the system. Moreover, in the absence of an annotation mechanism for clusters of time series data, meta data associated with the time series data is utilized for annotation and validation of dataset.

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