公开(公告)号：US11449413B2

公开(公告)日：2022-09-20

申请号：US17345166

申请日：2021-06-11

Applicant: Tata Consultancy Services Limited

Inventor： Rekha Singhal ,  Gautam Shroff ,  Dheeraj Chahal ,  Mayank Mishra ,  Shruti Kunde ,  Manoj Nambiar

IPC: G06F11/36 ,  G06N3/02 ,  G06K9/62 ,  G06F9/48 ,  G06F40/20

Abstract: This disclosure relates generally to accelerating development and deployment of enterprise applications where the applications involve both data driven and task driven components in data driven enterprise information technology (IT) systems. The disclosed system is capable of determining components of the application that may be task-driven and/or those components which may be data-driven using inputs such as business use case, data sources and requirements specifications. The system is capable of determining the components that may be developed using task-driven and data-drive paradigms and enables migration of components from the task driven paradigm to the data driven paradigm. Also, the system trains a reinforcement learning (RL) model for facilitating migration of the identified components from the task driven paradigm to the data driven paradigm. The system is further capable of integrating the migrated and existing components to accelerate development and deployment an integrated IT application.

公开(公告)号：US11098455B2

公开(公告)日：2021-08-24

申请号：US16432448

申请日：2019-06-05

Applicant: Tata Consultancy Services Limited

Inventor： Sunil Dattatraya Joshi ,  Mayank Mishra ,  Vaibhav Vyawahare ,  Shripad Salsingikar ,  Jayavardhana Rama Gubbi Lakshminarasimha ,  Srinivas Kotamraju ,  Sreehari Kumar Bhogineni ,  Rishin Raj ,  Vishnu Hariharan Anand ,  Vishal Bajpai ,  Jegan Mohan Ponraj ,  Mahesh Rangarajan ,  Balamuralidhar Purushothaman ,  Gopi Kandaswamy

IPC: E01B35/06 ,  G06N20/00 ,  B64C39/02 ,  G06T7/00 ,  G06T7/11

Abstract: This disclosure relates generally to systems and methods for data acquisition and asset inspection in presence of magnetic interference. Data acquisition and assets inspection systems in many infrastructures such as railway, power line, and bridges provide inaccurate results in presence of magnetic interference. The proposed system and method proposes UAV based navigation through a dynamic correction path to inspect one or more assets in one or more infrastructures. A plurality of sensors are integrated with the UAV to acquire images of the one or more assets in presence of magnetic field. The acquired images are further processed to segment and detect anomalies in one or more parts of the one or more assets. The detected anomalies are further classified as potential anomalies and non-potential anomalies. The proposed method provides accurate results with reduced processing time.

公开(公告)号：US12050563B2

公开(公告)日：2024-07-30

申请号：US18049363

申请日：2022-10-25

Applicant: Tata Consultancy Services Limited

Inventor： Mayank Mishra ,  Archisman Bhowmick ,  Rekha Singhal

IPC: G06F17/00 ,  G06F7/00 ,  G06F16/21

CPC classification number: G06F16/211

Abstract: The present disclosure provides a scalable acceleration of data processing in Machine Learning pipeline which is unavailable in conventional methods. Initially, the system receives a dataset and a data processing code. A plurality of sample datasets are obtained based on the received dataset using a sampling technique. A plurality of performance parameters corresponding to each of the plurality of sample datasets are obtained based on the data processing code using a profiling technique. A plurality of scalable performance parameters corresponding to each of a plurality of larger datasets are predicted based on the plurality of performance parameters and the data processing code using a curve fitting technique. Simultaneously, a plurality of anti-patterns are located in the data processing code using a pattern matching technique. Finally, an accelerated code is recommended based on the plurality of anti-patterns and the predicted plurality of scalable performance parameters using an accelerated code recommendation technique.

公开(公告)号：US20240220245A1

公开(公告)日：2024-07-04

申请号：US18544697

申请日：2023-12-19

Applicant: Tata Consultancy Services Limited

Inventor： Mayank Mishra ,  Rekha Singhal

IPC: G06F9/30 ,  G06F9/38

CPC classification number: G06F9/30007 ,  G06F9/3877

Abstract: Data processing code in machine learning pipelines is primarily done using data frame APIs provided by Pandas and similar libraries. Though, these libraries are easy to use, their temporal performance is worse than similar code written using NumPy or other high-performance libraries. Embodiments herein provide a system and method for acceleration of slower data processing code in machine learning pipelines by automatically generating an accelerated data processing code. Initially, a code is received and pre-processed based on a predefined format to get a standardized code. Further, system identifies code statements having operations that to be performed on a data frame, and an ordered list of data frame columns to generate a filtered dictionary code. Further, a data processing representation is generated using filtering dictionary code and ordered list of data frame columns. Finally, an accelerated data processing code is recommended based on the data processing representation.

公开(公告)号：US11775264B2

公开(公告)日：2023-10-03

申请号：US17446726

申请日：2021-09-02

Applicant: Tata Consultancy Services Limited

Inventor： Rekha Singhal ,  Mayank Mishra ,  Dheeraj Chahal ,  Shruti Kunde ,  Manju Ramesh

IPC: G06F9/44 ,  G06F8/35 ,  G06N20/00 ,  G06F9/455 ,  G06F16/901 ,  G06F8/20 ,  G06F16/245

CPC classification number: G06F8/35 ,  G06F8/20 ,  G06F9/455 ,  G06F16/245 ,  G06F16/9024 ,  G06N20/00

Abstract: This disclosure relates generally to configuring/building of applications. Typically, a deep learning (DL) application having multiple models composed and interspersed with corresponding transformation functions has no mechanism of efficient deployment on underlying system resources. The disclosed system accelerates the development of application to compose multiple models where each model could be a primitive model or a composite model itself. In an embodiment, the disclosed system optimally deploys a composable model application and transformation functions on underlying resources using performance prediction models, thereby accelerating the development and deployment of the application.

公开(公告)号：US20220092354A1

公开(公告)日：2022-03-24

申请号：US17471564

申请日：2021-09-10

Applicant: Tata Consultancy Services Limited

Inventor： Shruti Kunde ,  Mayank Mishra ,  Rekha Singhal ,  Amey Pandit ,  Manoj Nambiar ,  Gautam Shroff

IPC: G06K9/62 ,  G06N20/00

Abstract: This disclosure relates generally to a method and system for generating labelled dataset using a training data recommender technique. Recommender systems face major challenges in handling dynamic data on machine learning paradigms thereby rendering inaccurate unlabeled dataset. The method of the present disclosure is based on a training data recommender technique suitably constructed with a newly defined parameter such as the labelled data prediction threshold to determine the adequate amount of labelled training data required for training the one or more machine learning models. The method processes the received unlabeled dataset for labelling the unlabeled dataset based on a labelled data prediction threshold which is determined using a trained training data recommender technique. This labelling data threshold leads to a significant reduction in training time while performing the one or more machine learning models and thus recommender systems to quickly adapt disruptions thereby decreasing the reduction factor.