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51. 发明申请

US20150006656A1 Method and System for Creating an Intelligent Social Network Between a Plurality of Devices 有权
标题翻译：在多种设备之间创建智能社交网络的方法和系统
公开(公告)号：US20150006656A1
公开(公告)日：2015-01-01
申请号：US14369263
申请日：2012-12-27
申请人： Tata Consultancy Services Limited
发明人： Priyanka Sinha , Chirabrata Bhaumik , Avik Ghose
IPC分类号： H04L29/08 , H04L12/58
CPC分类号： H04L67/22 , H04L51/32 , H04L67/02 , H04L67/10 , H04L67/12 , H04L67/306
摘要： A method and system is provided for creating an intelligent social network between a plurality of devices participating in a social network over internet. Particularly, the disclosure provides a method and system for creating an intelligent social network of a plurality of devices, wherein an intent of at least one user of a first device is detected by one or more of subsequent devices out of the plurality of devices based on one or more pre-defined parameters; a match between one or more common intent of the user of the first device and the one or more users of the one or more subsequent devices is detected and information pertaining to the same is transmitted for enabling communication and formation of intelligent social network between the first device and the one or more of subsequent devices.
摘要翻译：提供了一种方法和系统，用于通过互联网在参与社交网络的多个设备之间创建智能社交网络。具体地，本公开提供了一种用于创建多个设备的智能社交网络的方法和系统，其中基于多个设备中的一个或多个后续设备检测到第一设备的至少一个用户的意图，基于一个或多个预定义参数; 检测第一设备的用户的一个或多个共同意图与一个或多个后续设备的一个或多个用户之间的匹配，并且发送与其相关的信息，以便能够在第一设备之间进行通信和形成智能社交网络设备和一个或多个后续设备。

52. 发明申请

US20140351337A1 SYSTEM AND METHOD FOR IDENTIFYING AND ANALYZING PERSONAL CONTEXT OF A USER 有权
标题翻译：用户识别和分析个人情境的系统和方法
公开(公告)号：US20140351337A1
公开(公告)日：2014-11-27
申请号：US14376536
申请日：2013-01-22
申请人： Tata Consultancy Services Limited
发明人： Arpan Pal , Balamuralidhar Purushothaman , Prateep Misra , Sunil Kumar Kopparapu , Aniruddha Sinha , Chirabrata Bhaumik , Priyanka Sinha , Avik Ghose
IPC分类号： H04L29/06
CPC分类号： H04L65/403 , G01S5/0257 , G06Q30/02 , G09B19/06 , H04L67/306 , H04M1/72569 , H04M1/72572 , H04W4/02 , H04W4/08
摘要： A method and system for identifying personal context of a user having a portable mobile communication device at a particular location for deriving social interaction information of the user, wherein the user within a predefined range is identified using personal context of the user at the particular location and the identified personal context of the user is assigned with the confidence value. Further the current location information of the user within the particular location is obtained by fusing assigned confidence value. Further the proximity of the user in the current location is estimated by finding the accurate straight line distance between users. Further the two users having similar current location information at the particular location are grouped together with the predefined density criteria. Finally the social interaction information of the user is derived by multimodal sensor data fusion at the fusion engine and represented using a human network graph.
摘要翻译：一种用于识别具有在特定位置处的便携式移动通信设备的用户的个人情境的方法和系统，用于导出用户的社交交互信息，其中使用在特定位置处的用户的个人情境来识别预定范围内的用户，以及所识别的用户的个人上下文被分配有置信度值。此外，通过融合分配的置信度值来获得特定位置内用户的当前位置信息。此外，通过找到用户之间的准确的直线距离来估计用户在当前位置的接近度。此外，在特定位置处具有相似的当前位置信息的两个用户与预定义的密度标准一起被分组在一起。最后，用户的社交交互信息是通过融合引擎的多模态传感器数据融合得出的，并使用人类网络图表示。

53. 发明申请

US20140288766A1 Real-Time Monitoring of Vehicle 有权
标题翻译：车辆实时监控
公开(公告)号：US20140288766A1
公开(公告)日：2014-09-25
申请号：US14182833
申请日：2014-02-18
申请人： Tata Consultancy Services Limited
发明人： Tapas Chakravarty , Avik Ghose , Balamuralidhar Purushothaman , Arpan Pal , Arijit Chowdhary
IPC分类号： G07C5/00
CPC分类号： G07C5/00 , G05B23/0235
摘要： A monitoring unit for vehicle monitoring comprising a receiving module configured to receive data from an OBD, wherein the data is associated with a plurality of jerks detected by a 3-axis accelerometer. The monitoring unit comprises an analytics module configured to compare an intensity of each jerk of the plurality of jerks to a predefined jerk threshold and capture high intensity jerks from the plurality of jerks. The high intensity jerks have intensity equal to or more than the predefined jerk threshold. The method further comprises determining an elapsed time for each of the high intensity jerks. The elapsed time for each of the high intensity jerks is compared to a predefined time threshold. Further it is determined whether an analysis on the high intensity jerks is to be performed at the vehicle or at a server located remotely.
摘要翻译：一种用于车辆监视的监控单元，包括被配置为从OBD接收数据的接收模块，其中所述数据与由三轴加速度计检测到的多个跳线相关联。监测单元包括分析模块，其被配置为将多个抖动中的每个冲击的强度与预定的加加速度阈值进行比较，并捕获来自多个跳动的高强度跳动。高强度强度具有等于或大于预定的加加速度阈值的强度。该方法还包括确定每个高强度跳跃的经过时间。将每个高强度抖动的经过时间与预定义的时间阈值进行比较。此外，确定是否在车辆或位于远程的服务器上执行对高强度刺激的分析。

54. 发明申请

US20140134948A1 Method and System for Accurate Straight Line Distance Estimation Between Two Communication Devices 有权
标题翻译：两种通信设备之间精确直线距离估计的方法和系统
公开(公告)号：US20140134948A1
公开(公告)日：2014-05-15
申请号：US14051733
申请日：2013-10-11
申请人： Tata Consultancy Services Limited
发明人： Avik Ghose , Abhinav Jha , Tapas Chakravarty , Chirabrata Bhaumik
IPC分类号： H04W4/00
CPC分类号： H04W4/80 , G01S5/021 , G01S5/0215 , G01S5/0252 , G01S5/0273 , G01S5/0284 , H04W4/023
摘要： A method and system is provided for estimating proximity and accurately calculating the straight line distance between the communicating Bluetooth enabled portable communication devices. Particularly, the invention provides a method and system for capturing the received signal strength indicator (RSSI) values form at least one target communication device (204) by the reference communication device (202); calculating the constant values of properties of communication environment of the devices by utilizing captured received signal strength indicator (RSSI) values; and deriving accurate straight line distance between the reference communication device (202) and the target communication device (204) by utilizing calculated constant values of properties of communication environment of the devices.
摘要翻译：提供了一种用于估计接近度并准确计算通信蓝牙功能的便携式通信设备之间的直线距离的方法和系统。特别地，本发明提供了一种用于通过参考通信设备（202）从至少一个目标通信设备（204）捕获接收到的信号强度指示符（RSSI）值的方法和系统; 通过利用捕获的接收信号强度指标（RSSI）值来计算设备的通信环境的属性的常数值; 以及通过利用计算出的设备的通信环境的特性的常数值，得出参考通信设备（202）和目标通信设备（204）之间的准确的直线距离。

55. 发明授权

US12106196B2 Method and system for generating synthetic time domain signals to build a classifier 有权
公开(公告)号：US12106196B2
公开(公告)日：2024-10-01
申请号：US17196406
申请日：2021-03-09
申请人： Tata Consultancy Services Limited
发明人： Sakyajit Bhattacharya , Oishee Muzumder , Aniruddha Sinha , Dibyendu Roy , Avik Ghose
IPC分类号： G06N20/10 , G06F18/2134 , G06F18/214 , G06F18/2321 , G06F18/243 , G16H50/20 , G16H50/70
CPC分类号： G06N20/10 , G06F18/2134 , G06F18/2148 , G06F18/2321 , G06F18/24317 , G16H50/20 , G16H50/70 , G06F2218/14
摘要： State of the art systems and methods attempting to generate synthetic biosignals such as PPG generate patient specific PPG signatures and do not correlate with pathophysiological changes. Embodiments herein provide a method and system for generating synthetic time domain signals to build a classifier. The synthetic signals are generated using statistical explosion. Initially, a parent dataset of actual sample data of class and non-class subjects is identified, and statistical features are extracted. Kernel density estimate (KDE) is used to vary the feature distribution and create multiple data template from a single parent signal. PPG signal is again reconstructed from the distribution pattern using non-parametric techniques. The generated synthetic data set is used to build the two stage cascaded classifier to classify CAD and Non CAD, wherein the classifier design enables reducing bias towards any class.

56. 发明授权

US12016705B2 Methods and systems for identifying presence of abnormal heart sounds of a subject 有权
公开(公告)号：US12016705B2
公开(公告)日：2024-06-25
申请号：US17060009
申请日：2020-09-30
申请人： Tata Consultancy Services Limited
发明人： Rohan Banerjee , Avik Ghose
IPC分类号： A61B5/00 , A61B5/02 , A61B7/04 , G06N3/00 , G06N3/02 , G06N3/045 , G06N3/08 , G06N3/088 , G16H40/67 , G16H50/20 , G16H50/30
CPC分类号： A61B5/7282 , A61B5/02028 , A61B5/725 , A61B5/7257 , A61B5/7267 , A61B5/7278 , A61B7/04 , G06N3/00 , G06N3/02 , G06N3/045 , G06N3/08 , G06N3/088 , G16H40/67 , G16H50/20 , G16H50/30
摘要： The disclosure generally relates to methods and systems for identifying presence of abnormal heart sounds from heart sound signals of a subject being monitored. Conventional Artificial intelligence (AI) based abnormal heart sounds detection models with supervised learning requires a substantial amount of accurate training datasets covering all heart disease types for the training, which is quiet challenging. The present methods and systems solve the problem solves the problem of identifying presence of the abnormal heart sounds using an efficient semi-supervised learning model. The semi-supervised learning model is generated based on probability distribution of spectrographic properties obtained from heart sound signals of healthy subjects. A Kullback-Leibler (KL) divergence between a predefined Gaussian distribution and an encoded probability distribution of the semi-supervised learning model is determined as an anomaly score for identifying the abnormal heart sounds.

57. 发明授权

US11915524B2 Method and system for handwritten signature verification 有权
公开(公告)号：US11915524B2
公开(公告)日：2024-02-27
申请号：US17044299
申请日：2019-02-18
申请人： Tata Consultancy Services Limited
发明人： Dibyendu Roy , Arijit Chowdhury , Arijit Sinharay , Avik Ghose
IPC分类号： G06F21/31 , G06F21/32 , G06V40/20
CPC分类号： G06V40/28 , G06F21/316 , G06F21/32
摘要： This disclosure relates generally to a method and system for online handwritten signature verification providing a simpler low cost system. The method comprises extracting signature data for the subject from a sensor array for the predefined time window at regular predefined time instants. Further, differentiating the matrix row wise and column wise to generate a row difference matrix and a column difference matrix. Further, determining an idle signature time fraction for the extracted signature data of the subject being monitored from the column difference matrix. Further, determining a plurality of signature parameters based on the row difference matrix and the column difference matrix. Further, analyzing the idle signature time fraction and the plurality of signature parameters of the subject being monitored based on a Support Vector Machine (SVM) classifier, wherein the SVM classifier performs online classification of the extracted signature data into one of a matching signature class and a non-matching signature class.

58. 发明授权

US11887730B2 Systems and methods for unobtrusive digital health assessment 有权
公开(公告)号：US11887730B2
公开(公告)日：2024-01-30
申请号：US16526340
申请日：2019-07-30
申请人： Tata Consultancy Services Limited
发明人： Avik Ghose , Arpan Pal , Sundeep Khandelwal , Rohan Banerjee , Sakyajit Bhattacharya , Soma Bandyopadhyay , Arijit Ukil , Dhaval Satish Jani
IPC分类号： G16H50/20 , G16H50/70 , G06N3/043 , G06N20/00
CPC分类号： G16H50/20 , G06N3/043 , G16H50/70 , G06N20/00
摘要： 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.

59. 发明授权

US11696736B2 Anatomical landmark detection and identification from digital radiography images containing severe skeletal deformations 有权
公开(公告)号：US11696736B2
公开(公告)日：2023-07-11
申请号：US17060016
申请日：2020-09-30
申请人： Tata Consultancy Services Limited
发明人： Aparna Kanakatte Gurumurthy , Pavan Kumar Reddy Kancham , Jayavardhana Rama Gubbi Lakshminarasimha , Avik Ghose , Murali Poduval , Balamuralidhar Purushothaman
IPC分类号： A61B6/00 , G06N3/08 , G06N3/047 , G06T7/00 , G06V10/764 , G06V10/82 , G06V10/44
CPC分类号： A61B6/5258 , A61B6/468 , A61B6/527 , A61B6/5211 , A61B6/5217 , A61B6/5264 , G06N3/047 , G06N3/08 , G06T7/0012 , G06V10/454 , G06V10/764 , G06V10/82
摘要： Conventionally, systems and methods have been provided for manual annotation of anatomical landmarks in digital radiography (DR) images. Embodiments of the present disclosure provides system and method for anatomical landmark detection and identification from DR images containing severe skeletal deformations. More specifically, motion artefacts and exposure are filtered from an input DR image to obtain a pre-processed DR image and probable/candidate anatomical landmarks comprised therein are identified. These probable candidate anatomical landmarks are assigned a score. A subset of the candidate anatomical landmarks (CALs) is selected as accurate anatomical landmarks based on comparison of the score with a pre-defined threshold performed by a trained classifier. Position of remaining CALs may be fine-tuned for classification thereof as accurate anatomical landmarks or missing anatomical landmarks. The CALs may be further fed to the system for checking misalignment of any of the CALs and correcting the misaligned CALs.

60. 发明授权

US11631247B2 System and method to capture spatio-temporal representation for video reconstruction and analysis 有权
公开(公告)号：US11631247B2
公开(公告)日：2023-04-18
申请号：US17197316
申请日：2021-03-10
申请人： Tata Consultancy Services Limited
发明人： Jayavardhana Rama Gubbi Lakshminarasimha , Akshaya Ramaswamy , Balamuralidhar Purushothaman , Aparna Kanakatte Gurumurthy , Avik Ghose
IPC分类号： G06V20/40 , G06K9/62 , G06N3/08
摘要： State of the art techniques in the domain of video analysis have limitations in terms of capability to capture spatio-temporal representation. This limitation in turn affects interpretation of video data. The disclosure herein generally relates to video analysis, and, more particularly, to a method and system for video analysis to capture spatio-temporal representation for video reconstruction and analysis. The method presents different architecture variations using three main deep network components: 2D convolution units, 3D convolution units and long short-term memory (LSTM) units for video reconstruction and analysis. These variations are trained for learning the spatio-temporal representation of the videos in order to generate a pre-trained video analysis module. By understanding the advantages and disadvantages of different architectural configurations, a novel architecture is designed for video reconstruction. Using transfer learning, the video reconstruction pre-trained model is extended to other video applications such as video object segmentation and surgical video tool segmentation.

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