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Patent Agency Ranking
1.发明授权公开(公告)号：US11625538B2
公开(公告)日：2023-04-11
申请号：US17036254
申请日：2020-09-29
Applicant: Tata Consultancy Services Limited
Inventor： Deepa Adiga , Maitry Bhavsar , Mayuri Duggirala , Sachin Patel
IPC: G06F40/30 , G06N20/00 , G06F40/253 , G06F40/284 , G06F40/268 , G06F18/10 , G06F18/25
Abstract: This disclosure relates generally to methods and systems for automatic extraction of self-reported activities of an individual from a freestyle narrative text. Manual extraction of such self-reported activities of the individual from the freestyle narrative text over the period of time is a complex task and consume a significant amount of time. The present systems and methods utilize a predefined grammar pattern and a natural language processing technique to generate one or more candidate activity phrases, from the pre-processed input text posted by the individual. A deep learning based supervised classification model is utilized to automatically extract the one or more self-reported activities of the individual, from the one or more candidate activity phrases. Manual intervention and efforts of analyzing the freestyle narrative text to extract the self-reported activities are avoided. Longitudinal assessment of the self-reported activities may reveal routines and behavior of the individual.
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公开(公告)号：US12002291B2
公开(公告)日：2024-06-04
申请号：US17453634
申请日：2021-11-04
Applicant: Tata Consultancy Services Limited
Inventor： Sushovan Chanda , Gauri Deshpande , Sachin Patel
CPC classification number: G06V40/20 , G06F18/214 , G06N3/08 , G06T7/73 , G06V10/95 , G06V20/46 , G06V40/171 , G06T2207/10016 , G06T2207/20081 , G06T2207/20084 , G06T2207/30201
Abstract: State of art techniques attempt in extracting insights from eye features, specifically pupil with focus on behavioral analysis than on confidence level detection. Embodiments of the present disclosure provide a method and system for confidence level detection from eye features using ML based approach. The method enables generating overall confidence level label based on the subject's performance during an interaction, wherein the interaction that is analyzed is captured as a video sequence focusing on face of the subject. For each frame facial features comprising an Eye-Aspect ratio, a mouth movement, Horizontal displacements, Vertical displacements, Horizontal Squeezes and Vertical Peaks, are computed, wherein HDs, VDs, HSs and VPs are features that are derived from points on eyebrow with reference to nose tip of the detected face. This is repeated for all frames in the window. A Bi-LSTM model is trained using the facial features to derive confidence level of the subject.
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公开(公告)号：US11996118B2
公开(公告)日：2024-05-28
申请号：US17504556
申请日：2021-10-19
Applicant: Tata Consultancy Services Limited
Inventor： Ramesh Kumar Ramakrishnan , Venkata Subramanian Viraraghavan , Rahul Dasharath Gavas , Sachin Patel , Gauri Deshpande
Abstract: An important task in several wellness applications is detection of emotional valence from speech. Two types of features of speech signals are used to detect valence: acoustic features and text features. Acoustic features are derived from short frames of speech, while text features are derived from the text transcription. Present disclosure provides systems and methods that determine the effect of text on acoustic features. Acoustic features of speech segments carrying emotion words are to be treated differently from other segments that do not carry such words. Only specific speech segments of the input speech signal are considered based on a dictionary specific to a language to assess emotional valence. A model trained (or trained classifier) for specific language either by including the acoustic features of the emotion related words or by omitting it is used by the system for determining emotional valence in an input speech signal.
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4.发明授权公开(公告)号：US11227624B2
公开(公告)日：2022-01-18
申请号：US16812757
申请日：2020-03-09
Applicant: Tata Consultancy Services Limited
Inventor： Gauri Ashutosh Deshpande , Sachin Patel , Mayuri Duggirala , Venkata Subramanian Viraraghavan
Abstract: This disclosure relates generally to speech signal processing, and more particularly to a method and system for processing speech signal for emotion identification. The system processes a speech signal collected as input, during which a plurality of differential features corresponding to a plurality of frames of the speech signal are extracted. Further, the differential features are compared with an emotion recognition model to identify at least one emotion matching the speech signal, and then the at least one emotion is associated with the speech signal.
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公开(公告)号：US20230018693A1
公开(公告)日：2023-01-19
申请号：US17453634
申请日：2021-11-04
Applicant: Tata Consultancy Services Limited
Inventor： Sushovan Chanda , Gauri Deshpande , Sachin Patel
Abstract: State of art techniques attempt in extracting insights from eye features, specifically pupil with focus on behavioral analysis than on confidence level detection. Embodiments of the present disclosure provide a method and system for confidence level detection from eye features using ML based approach. The method enables generating overall confidence level label based on the subject's performance during an interaction, wherein the interaction that is analyzed is captured as a video sequence focusing on face of the subject. For each frame facial features comprising an Eye-Aspect ratio, a mouth movement, Horizontal displacements, Vertical displacements, Horizontal Squeezes and Vertical Peaks, are computed, wherein HDs, VDs, HSs and VPs are features that are derived from points on eyebrow with reference to nose tip of the detected face. This is repeated for all frames in the window. A Bi-LSTM model is trained using the facial features to derive confidence level of the subject.
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