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公开(公告)号：US20250099014A1
公开(公告)日：2025-03-27
申请号：US18822583
申请日：2024-09-03
Applicant: Tata Consultancy Services Limited
Inventor： Jayavardhana Rama GUBBI LAKSHMINARASIMHA , Tanuja JAYAS , Kartik MURALIDHARAN , Adarsh ANAND , Ramesh Kumar RAMAKRISHNAN , Arpan PAL
Abstract: The embodiments of the present disclosure herein address unresolved problems of quality of signals in real time for wearables to provide optimal signals which can be used for brain signal based applications. Further, conventional techniques fail to provide real-time calibration of wearable devices, to understand the quality of the signals from the wearable device. Embodiments herein provide a method and system for a real-time calibration of one or more Electroencephalography (EEG) signals received from a wearable Ear-EEG device. The system is leveraging quality of signals in real time for wearables to provide optimal signals which can be used for early detection of neurodegenerative disease and brain-computer interface (BCI) applications. Further, the system is able to detect electrodes in the wearable device where the EEG signals have not been collected because the contact was not established.
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42.发明公开公开(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.
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43.发明公开公开(公告)号：US20240053221A1
公开(公告)日：2024-02-15
申请号：US18218815
申请日：2023-07-06
Applicant: Tata Consultancy Services Limited
Inventor： Raj RAKSHIT , Arijit SINHARAY , Supriya GAIN , Arpan PAL , Chirabrata BHAUMIK , Tapas CHAKRAVARTY
CPC classification number: G01M3/243 , G01M3/2815
Abstract: One of the biggest challenges faced by oil and gas companies is to monitor such long pipelines for leak events and generate false leak event alarms during routine pipe maintenance. A data associated with a first sensing unit is processed to obtain an instant timing information (T0) of a leak event in a conduit at a test environment. A data associated with a second sensing unit is processed to obtain a transient signal associated with the leak event at a specific band. An accelerometer data is filtered to obtain a band passed filtered accelerometer signal (Accelbpf). The Accelbpf is truncated in a time domain from the T0 to a duration Td of the leak event to obtain a temporal template signal (Acceltemplate). A leak event of a real-time conduit is dynamically detected at a physical environment based on Acceltemplate when a cross-correlation value is greater than a threshold value (∝).
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公开(公告)号：US20240020962A1
公开(公告)日：2024-01-18
申请号：US18216119
申请日：2023-06-29
Applicant: Tata Consultancy Services Limited
Inventor： Jayavardhana Rama GUBBI LAKSHMINARASIMHA , Vivek Bangalore SAMPATHKUMAR , Rajan Mindigal Alasingara BHATTACHAR , Balamuralidhar PURUSHOTHAMAN , Arpan PAL
Abstract: The disclosure generally relates to scene graph generation. Scene graph captures rich semantic information of an image by representing objects and their relationships as nodes and edges of a graph and has several applications including image retrieval, action recognition, visual question answering, autonomous driving, robotics. However, to leverage scene graphs, computationally efficient scene graph generation methods are required, which is very challenging to generate due presence of a quadratic number of potential edges and computationally intensive/non-scalable techniques for detecting the relationship between each object pair using the traditional approach. The disclosure proposes a combination of edge proposal neural network and the Graph neural network with spatial message passing (GNN-SMP) along with several techniques including a feature extraction technique, object detection technique, un-labelled graph generation technique and a scene graph generation technique to generate scene graphs.
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公开(公告)号：US20230373096A1
公开(公告)日：2023-11-23
申请号：US18198661
申请日：2023-05-17
Applicant: Tata Consultancy Services Limited
Inventor： Jayavardhana Rama GUBBI LAKSHMINARASIMHA , Vartika SENGAR , Vighnesh VATSAL , Balamuralidhar PURUSHOTHAMAN , Arpan PAL , Nijil GEORGE , Aditya KAPOOR
IPC: B25J9/16
CPC classification number: B25J9/1697 , B25J9/161 , B25J9/163 , B25J9/1653
Abstract: Conventional task planners assume that the task-plans provided are executable, hence these are not task-aware. Present disclosure alleviates the downward refinability assumption, that is, planning can be decomposed separate symbolic and continuous planning steps by introducing bi-level planning, a plan which is a series of actions that the robot needs to take to achieve the goal task is curated. Firstly, abstract symbolic actions are converted to continuous vectors and used therein to enable interaction with an environment. Images of objects placed in the environment are captured and concepts are learnt from the captured images and attributes of objects are detected. A hierarchical scene graph is generated from the concepts and attributes wherein the graph includes interpretable sub-symbolic representations and from these interpretable symbolic representations are obtained for identifying goal task. Anomalies are detected from the scene graph and robotic actions are generated to correct the detected anomalies.
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Patent Agency Ranking
46.发明申请公开(公告)号：US20210290091A1
公开(公告)日：2021-09-23
申请号：US17204090
申请日：2021-03-17
Applicant: Tata Consultancy Services Limited
Inventor： Ramesh Kumar RAMAKRISHNAN , Rahul Dasharath GAVAS , Venkata Subramanian VIRARAGHAVAN , Arpan PAL , Balamuralidhar PURUSHOTHAMAN , Lalit Kumar HISSARIA
Abstract: Wellness estimation allows tracking of various health parameters and estimating health condition(s) of the user being monitored. Photophlethysmogram (PPG) based health monitoring systems exist. This disclosure relates generally to PPG based wellness monitoring, and more specifically to a pulse harmonics based wellness estimation. The system collects PPG signals from a user being monitored, as input. The system calculates 12 pulse harmonics from a fundamental frequency of the PPG signal. From the pulse harmonics, further a plurality of key features are extracted, and in turn a wellness metric and a wellness index are calculated, which represents health of the user.
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公开(公告)号：US20210012863A1
公开(公告)日：2021-01-14
申请号：US16923877
申请日：2020-07-08
Applicant: Tata Consultancy Services Limited
Inventor： Rajat Kumar DAS , Arijit SINHARAY , Arijit CHOWDHURY , Tapas CHAKRAVARTY , Arpan PAL
Abstract: Temperature measurement is an important part of many potential applications in process industry. Conventional temperature measurement methods require manual intervention for process monitoring and fail to provide accurate and precise measurement of temperature of an enclosed mixed fluid chamber. The present disclosure provides artificial intelligence based temperature measurement in mixed fluid chamber. A plurality of inputs pertaining to the mixed fluid chamber are received to build a fluid based model. The fluid based model is used to generate one or more fluid parameters. The one or more fluid parameters are used along with a ground truth temperature data and the received plurality of inputs for training an artificial intelligence (AI) based model. However, the AI based model is trained with and without knowledge of fluid flow. The trained AI based model is further used to accurately estimate temperature of the mixed fluid chamber for a plurality of test input data.
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公开(公告)号：US20200007624A1
公开(公告)日：2020-01-02
申请号：US16353108
申请日：2019-03-14
Applicant: Tata Consultancy Services Limited
Inventor： Swagata BISWAS , Swarnava DEY , Arijit MUKHERJEE , Arpan PAL
IPC: H04L29/08 , H04L12/911
Abstract: Cloud robotics infrastructures generally support heterogeneous services that are offered by heterogeneous resources whose reliability or availability also varies widely with varying lifetime. For such systems, defining a static redundancy configuration for all services is difficult and often biased. Also, it is not feasible to define a redundancy configuration separately for each unique service. Therefore, in the present disclosure a trade-off between the two is ensured by providing At-most M-Modular Flexible Redundancy Model wherein an exact degree of redundancy is defined and is given to each service in a heterogeneous service environment and monitoring each task and subtask status to ensure that each subtask gets accomplished thereby enabling the tuning of the tradeoff between redundancy and cost and determining efficiency of the system by estimating number of resources utilized to complete specific subtask and comparing the resources utilization with the exact degree of redundancy defined.
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49.发明申请公开(公告)号：US20190313920A1
公开(公告)日：2019-10-17
申请号：US16285519
申请日：2019-02-26
Applicant: Tata Consultancy Services Limited
Inventor： Rohan BANERJEE , Sakyajit BHATTACHARYA , Soma BANDYOPADHYAY , Arpan PAL , Kayapanda Muthana MANDANA
Abstract: Non-invasive methods for accurately classifying Coronary Artery Disease (CAD) is a challenging task. In the present disclosure, a two stage classification is performed. In the first stage of classification, a metadata based rule engine is utilized to classify a subject into one of a confirmed CAD subject, a CAD subject and a non-CAD subject. Here, a set of optimal parameters are selected from a set of metadata associated with the subject based on a difference in frequency of occurrence of the CAD among a disease population and a non-disease population. Further, an optimal threshold associated with each optimal parameter is calculated based on an inflexion based correlation analysis. Further, the CAD subject, classified by the metadata based rule engine is further reclassified in a second stage by utilizing a set of cardiovascular signal into one of the CAD subject and the non-CAD subject.
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50.发明申请公开(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
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.
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