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公开(公告)号：US11880345B2
公开(公告)日：2024-01-23
申请号：US17463591
申请日：2021-09-01
Applicant: Tata Consultancy Services Limited
Inventor： Atreya Bandyopadhyay , Indrajit Bhattacharya , Rajdip Chowdhury , Debayan Mukherjee
IPC: G06F16/21
CPC classification number: G06F16/211
Abstract: This disclosure relates generally to generating annotations and field-names for a relational schema. Typically, most domains have relational database (RDB) system built for them instead of domain ontologies and usually linguistic information of the schema is not used to recover the domain terms. The disclosed method and system facilitate generating annotations and field-names for a relational schema, while considering the linguistic information of a schema by using a trained model, trained through a proposed training technique. The trained model comprises of at least one knowledge graph and a set of associated parameters. The trained model is further used to perform a plurality of tasks, wherein the plurality of tasks include generating a plurality of new fieldnames for a relational schema through a stochastic generative process and for generating a new annotation for a fieldname of a relational schema through a probabilistic inference technique.
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公开(公告)号：US20180075861A1
公开(公告)日：2018-03-15
申请号：US15456172
申请日：2017-03-10
Applicant: Tata Consultancy Services Limited
Inventor： Arijit Ukil , Soma Bandyopadhyay , Chetanya Puri , Arpan Pal , Rituraj Singh , Ayan Mukherjee , Debayan Mukherjee
IPC: G10L21/0232 , G10L15/06 , G10L15/02
CPC classification number: G10L21/0232 , A61B5/7203 , A61B5/7221 , A61B5/7267 , A61B7/00 , G06K9/00496 , G10L15/02 , G10L15/063 , G10L25/84 , G10L2025/786 , G10L2025/935
Abstract: Traditionally known classification methods of non-stationary physiological audio signals as noisy and clean involve human intervention, may involve dependency on particular type of classifier and further analyses is carried out on classified clean signals. However, in non-stationary audio signals a major portion may end up being classified as noisy and hence may get rejected which may cause missing of intelligence which could have been derived from lightly noisy audio signals that may be critical. The present disclosure enables automation of classification based on auto-thresholding and statistical isolation wherein noisy signals are further classified as highly noisy and lightly noisy through continuous dynamic learning.
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公开(公告)号：US11589760B2
公开(公告)日：2023-02-28
申请号：US15828540
申请日：2017-12-01
Applicant: Tata Consultancy Services Limited
Inventor： Arijit Ukil , Soma Bandyopadhyay , Chetanya Puri , Rituraj Singh , Arpan Pal , Debayan Mukherjee
Abstract: This disclosure relates generally to physiological monitoring, and more particularly to feature set optimization for classification of physiological signal. In one embodiment, a method for physiological monitoring includes identifying clean physiological signal training set from an input physiological signal based on a Dynamic Time Warping (DTW) of segments associated with the physiological signal. An optimal features set is extracted from a clean physiological signal training set based on a Maximum Consistency and Maximum Dominance (MCMD) property associated with the optimal feature set that strictly optimizes on the objective function, the conditional likelihood maximization over different selection criteria such that diverse properties of different selection parameters are captured and achieves Pareto-optimality. The input physiological signal is classified into normal signal components and abnormal signal components using the optimal features set.
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公开(公告)号：US09978392B2
公开(公告)日：2018-05-22
申请号：US15456172
申请日：2017-03-10
Applicant: Tata Consultancy Services Limited
Inventor： Arijit Ukil , Soma Bandyopadhyay , Chetanya Puri , Arpan Pal , Rituraj Singh , Ayan Mukherjee , Debayan Mukherjee
CPC classification number: G10L21/0232 , A61B5/7203 , A61B5/7221 , A61B5/7267 , A61B7/00 , G06K9/00496 , G10L15/02 , G10L15/063 , G10L25/84 , G10L2025/786 , G10L2025/935
Abstract: Traditionally known classification methods of non-stationary physiological audio signals as noisy and clean involve human intervention, may involve dependency on particular type of classifier and further analyses is carried out on classified clean signals. However, in non-stationary audio signals a major portion may end up being classified as noisy and hence may get rejected which may cause missing of intelligence which could have been derived from lightly noisy audio signals that may be critical. The present disclosure enables automation of classification based on auto-thresholding and statistical isolation wherein noisy signals are further classified as highly noisy and lightly noisy through continuous dynamic learning.
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