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公开(公告)号：US20150269744A1
公开(公告)日：2015-09-24
申请号：US14665345
申请日：2015-03-23
Applicant: TATA CONSULTANCY SERVICES LIMITED
Inventor： Dipti Prasad MUKHERJEE , Tamal BATABYAL , Tanushyam CHATTOPADHYAY
CPC classification number: G06K9/44 , G06F3/011 , G06K9/00342 , G06K9/4685
Abstract: A processor implemented system and method for identification of an activity performed by a subject based on sensor data analysis is described herein. In an implementation, the method includes capturing movements of the subject in real-time using a sensing device. At least one action associated with the subject is ascertained from a predefined set of actions. From the predefined set of actions, a plurality of actions can collectively form at least one activity. The ascertaining is based on captured movements of the subject and at least one predefined action rule. The at least one action rule is based on context-free grammar (CFG) and is indicative of a sequence of actions for occurrence of the at least one activity. Further, a current activity performed by the subject is dynamically determined, based on the at least one action and an immediately preceding activity, using a non-deterministic push-down automata (NPDA) state machine.
Abstract translation: 本文描述了一种用于基于传感器数据分析来识别被摄体执行的活动的处理器实现的系统和方法。 在实现中，该方法包括使用感测装置实时地捕获被摄体的移动。 根据预定义的一组动作来确定与主题相关联的至少一个动作。 从预定义的一组动作，多个动作可以共同形成至少一个活动。 确定是基于被摄体的捕获的移动和至少一个预定义的动作规则。 所述至少一个动作规则基于上下文无关语法（CFG），并且指示用于发生所述至少一个活动的一系列动作。 此外，使用非确定性下推自动机（NPDA）状态机，基于所述至少一个动作和紧接在前的活动来动态地确定由所述对象执行的当前活动。
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公开(公告)号：US20220092432A1
公开(公告)日：2022-03-24
申请号：US17361974
申请日：2021-06-29
Applicant: Tata Consultancy Services Limited
Inventor： Tanushyam CHATTOPADHYAY , Suvra DUTTA , Abhisek DAS , Shubhrangshu GHOSH , Prateep KISRA
Abstract: Conventionally, detecting time when a device is going to fail in real time has been a real challenge given the associated constraints and requirements. Due to absence in any supporting information or annotated data, traditional approaches have failed to detection abnormality in devices. Present disclosure provide systems and methods for detecting abnormal behaviour of a device from associated unlabeled sensor observations wherein KPIs are computed based on unlabeled sensor observations of at least two sensor parameters and windowing technique is applied on modified dataset to obtain windowed dataset based on which hyper-parameters of deep learning-based auto-encoder are optimized to obtain set of embeddings. Dimensionality reduction technique is applied on the embeddings to obtain embeddings with reduced dimension. Density based clustering technique with hyper-parameters is applied on embeddings with reduced dimension and cluster(s) for unlabeled sensor observations are obtained. Cardinality is assigned to cluster(s) to predict abnormal behaviour of the device.
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公开(公告)号：US20150227784A1
公开(公告)日：2015-08-13
申请号：US14614891
申请日：2015-02-05
Applicant: Tata Consultancy Services Limited
Inventor： Sangheeta ROY , Tanushyam CHATTOPADHYAY , Dipti Prasad MUKHERJEE
CPC classification number: G06K9/00369 , G06K9/00201 , G06K9/00342 , G06K9/00771 , G06K9/40 , G06K9/4638 , G06K2009/485 , G06T2207/10024 , G06T2207/10028 , G06T2207/20021 , G06T2207/20072 , G06T2207/30196 , G06T2207/30232
Abstract: Disclosed is a system and method for detecting a human in an image, and a corresponding activity. The image is captured, wherein the image comprises a plurality of pixels having gray scale information and a depth information. The image is segmented into a plurality of segments based upon the depth information. A connected component analysis is performed on a segment in order to segregate the one or more objects into noisy objects and candidate objects, the noisy objects are eliminated from the segment. A plurality of features are extracted from the candidate objects, and are evaluated using a Hidden Markov Model (HMM) model in order to determine the candidate objects as one of the human or non-human. The corresponding activity associated with the human is detected based on a depth value associated with each pixel corresponding to the candidate object in the image.
Abstract translation: 公开了一种用于检测图像中的人的系统和方法以及相应的活动。 拍摄图像，其中图像包括具有灰度信息和深度信息的多个像素。 基于深度信息将图像分割成多个片段。 在段上执行连接分量分析，以便将一个或多个对象分离成噪声对象和候选对象，从该段中消除嘈杂对象。 从候选对象中提取多个特征，并且使用隐马尔可夫模型（HMM）模型来评估，以便将候选对象确定为人或非人之一。 基于与图像中的候选对象对应的每个像素相关联的深度值来检测与人相关联的相应活动。
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4.发明申请公开(公告)号：US20220269689A1
公开(公告)日：2022-08-25
申请号：US17359818
申请日：2021-06-28
Applicant: Tata Consultancy Services Limited
Inventor： Tanushyam CHATTOPADHYAY , Arindam HALDER , Sangram Dasharath GAIKWAD , Tania GHOSH , Abhisek DAS , Shubhrangshu GHOSH , Suvra DUTTA , Prateep MISRA
IPC: G06F16/25 , G06F16/2455
Abstract: Sensor data (or IoT) analytics plays a critical role in taking business decisions for various entities (e.g., organizations, project owners, and the like). However, scaling of such analytical solutions beyond certain point requires adopting to various computing environments which seems to be challenging with the constrained resources available. Embodiments of the present disclosure provide system and method for analysing and executing sensor observational data in computing environments, wherein extract, transform, load (ETL) workflow pipeline created by users in the cloud, can be seamlessly deployed to job execution service available in cloud/edge without any changes in the code/config by end user. The configuration changes are internally handled by the system based on the selected computing environment and queries are executed either in distributed or non-distributed environments to output data frames. The data frames are further pre-processed in a desired computing environment and thereafter visualized accordingly.
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5.发明申请公开(公告)号：US20190205778A1
公开(公告)日：2019-07-04
申请号：US16179771
申请日：2018-11-02
Applicant: Tata Consultancy Services Limited
Inventor： Ishan SAHU , Snehasis BANERJEE , Tanushyam CHATTOPADHYAY , Arpan PAL , Utpal GARAIN
CPC classification number: G06F17/148
Abstract: Systems and methods for obtaining optimal mother wavelets for facilitating machine learning tasks. The traditional systems and methods provide for selecting a mother wavelet and signal classification using some traditional techniques and methods but none them provide for selecting an optimal mother wavelet to facilitate machine learning tasks. Embodiments of the present disclosure provide for obtaining an optimal mother wavelet to facilitate machine learning tasks by computing values of energy and entropy based upon labelled datasets and a probable set of mother wavelets, computing values of centroids and standard deviations based upon the values of energy and entropy, computing a set of distance values and normalizing the set of distance values and obtaining the optimal mother wavelet based upon the set of distance values for performing a wavelet transform and further facilitating machine learning tasks by classifying or regressing, a new set of signal classes, corresponding to a new set of signals.
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Patent Agency Ranking
6.发明申请公开(公告)号：US20190138806A1
公开(公告)日：2019-05-09
申请号：US15900987
申请日：2018-02-21
Applicant: Tata Consultancy Services Limited
Inventor： Snehasis BANERJEE , Tanushyam CHATTOPADHYAY , Ayan MUKHERJEE
CPC classification number: G06K9/00523 , G06F16/24568 , G06K9/0051 , G06K9/6232 , G06N3/10
Abstract: Development of sensor data based descriptive and prescriptive system involves machine learning tasks like classification and regression. Any such system development requires the involvement of different stake-holders for obtaining features. Such features typically obtained are not interpretable for 1-D sensor signals. Embodiments of the present disclosure provide systems and methods that perform signal analysis for features extraction and interpretation thereof wherein input is raw signal data where origin of a feature is traced to signal data, and mapped to domain/application knowledge. Feature(s) are extracted using deep learning network(s) and machine learning (ML) model(s) are implemented for sensor data analysis to perform causality analysis for prognostics. Layer(s) (say last layer) of Deep Network(s) contains the automatically derived features that can be used for ML tasks. Parameter(s) tuning is performed based on the set of features that were recommended by the system to determined performance of systems (or applications) under consideration.
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公开(公告)号：US20200151153A1
公开(公告)日：2020-05-14
申请号：US16596986
申请日：2019-10-09
Applicant: Tata Consultancy Services Limited
Inventor： Satanik PANDA , Dibyendu BISWAS , Hemanta DUTTA , Tanushyam CHATTOPADHYAY , Prateep MISRA
Abstract: Data cataloging has become a necessity for empowering organizations with analytical ability. Conventional cataloging systems may fail to provide proper visualization of data to the different stakeholders of an organization. The present disclosure provides a hierarchical dynamic cataloging system so that visualization of data at different levels would be possible for different stake holders. In the present disclosure, a hierarchical structure of algorithms and multiple stake holders along with relevant metadata is generated. Further, a catalog is generated by performing a mapping across components comprised in the hierarchical structure and identifying relationship across the components based on mapping. The catalog gets dynamically updated and provides a dynamic view of the algorithms and associated metadata to the multiple stakeholders of an organization. Further, the disclosure supports reuse of already developed algorithms across multiple applications and domains resulting in optimization of resources and time.
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公开(公告)号：US20180146047A1
公开(公告)日：2018-05-24
申请号：US15817720
申请日：2017-11-20
Applicant: Tata Consultancy Services Limited
Inventor： Swagata BISWAS , Tanushyam CHATTOPADHYAY , Arpan Pal , Snehasis Banerjee , Utpal GARIN
CPC classification number: H04L67/125 , G06F8/30 , G06F8/36 , G06F17/141 , G06F17/148 , G06K9/6231 , G06K9/6245 , G06K9/6269 , G06N7/00 , H04W4/38
Abstract: A method and system has been provided for recommending features for developing an IoT analytics application. The method follows a deep like architecture. It comprises of three distinct layers. First layer is for input signal processing and other two layers are for feature reduction. The time domain, frequency domain and time-frequency domain features are extracted from the input signal. The invention uses multiple feature selection methods so that the union of the recommended features by these feature selection methods is significantly lesser than the initial set of features. The best feature combination is recommended using an exhaustive search.
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公开(公告)号：US20160035124A1
公开(公告)日：2016-02-04
申请号：US14767161
申请日：2014-01-22
Applicant: TATA CONSULTANCY SERVICES LIMITED
Inventor： Aniruddha SINHA , Tanushyam CHATTOPADHYAY , Sangheeta ROY , Apurbaa MALLIK
CPC classification number: G06T15/08 , G06T7/11 , G06T7/187 , G06T7/20 , G06T7/50 , G06T2207/10016 , G06T2207/10024 , G06T2207/10028 , G06T2207/30196 , G06T2207/30232
Abstract: Disclosed is a method for segmenting a plurality of objects from a two-dimensional (2D) video captured through a depth camera and an RGB/G camera. The method comprises detecting camera motion in each 2D frame of the plurality of 2D frames from the 2D video and generate a first set of 2D frames without any camera motion. The method further comprises generating a plurality of cloud points for the first set of 2D frames corresponding to each pixel associated a 2D frames in the first set of 2D frames. The method further comprises generating a 3D grid comprising a plurality of voxels. The method further comprises determining valid voxels and an invalid voxels in the 3D grid. Further, a 3D connected component labeling technique is applied on to the set of valid voxels to segment the plurality of objects in the 2D video.
Abstract translation: 公开了一种用于通过深度相机和RGB / G相机捕获的二维（2D）视频来分割多个对象的方法。 该方法包括从2D视频检测多个2D帧的每个2D帧中的相机运动，并且生成第一组2D帧而没有任何相机运动。 该方法还包括为与第一组2D帧中的2D帧相关联的每个像素对应的第一组2D帧生成多个浊点。 该方法还包括生成包括多个体素的3D网格。 该方法还包括确定3D网格中的有效体素和无效体素。 此外，3D连接的部件标注技术被应用于一组有效的体素以分割2D视频中的多个对象。
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10.发明申请公开(公告)号：US20220221847A1
公开(公告)日：2022-07-14
申请号：US17366777
申请日：2021-07-02
Applicant: Tata Consultancy Services Limited
Inventor： Tanushyam CHATTOPADHYAY , Abhisek DAS , Suvra DUTTA , Shubhrangshu GHOSH , Prateep MISRA
Abstract: State-of-the-art approaches have concentrated on building solution(s) to match the amplitude of a time series with a user given one. However, these have failed to implement solution(s) which enables searching for pattern(s) that can depict human vision psychology. Embodiments of the present disclosure determine occurrence of pattern of interest in time series data for anomaly detection, wherein time series data is obtained, and first order derivative is computed. Further an angle of change in direction is derived based on a gradient of change in value of the time series data. This angle is further converted to a measurement unit. The time series data is quantized into bins and a weighted finite state transducers diagram (WFSTD) is obtained based on domain knowledge which is then converted to specific pattern. The specific pattern is searched in the bins to determine occurrence/count of the specific pattern for anomaly detection.
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