Tata Consultancy Services Limited

Devraj Goulikar

Avik Ghose

Sanjay Madhukar Kimbahune

Swarnava Dey

Ashish Omprakash Indani

Saurabh Das

Divya Vasudevan

Kamlesh Mhashilkar

Abhay Sodani

Aparna Joshi

Vishal Ramanlal Jain

Sai Tanishq Nannapaneni

Vimal Chaubey

Rohit Kadam

Prashant Chaturvedi

Ashutosh Pachisia

Prita Venkateswaran

Biswamohan Routray

Vamsee Krishna Kodali

Rajdeep Sarkar

Anupam Srivastava

Sanjaykumar Madhukar Patil

Charusheela Shashikant Thakur

Niraj Vyas

This disclosure relates generally to detection of pathogens from a gaseous mixture associated with secretions. Conventional methods typically involve invasive or biohazardous techniques, the requirement of quantity limits utility of several natural secretions, there is a dependency on immunological reactions to develop in a subject being monitored resulting in long time taken for detecting pathogens, which increases risk to health and environment. There is also reduced specificity and sensitivity considering the dependency on signature identification or training of machine learning models. Again, prior art focusses on designing antibodies for a particular type of sensor which is challenging when dealing with natural immunoglobulin. The present disclosure addresses these challenges by enabling identification of a most viable sensor for the natural immunoglobin, the viability being based on mathematical representations of the relationship between a sensor and the immunoglobulin using an ontology of domain knowledge associated with pathogens, technology, processing and detection.

Detection of pathogens from a gaseous mixture associated with secretions

A method and system for automated continuous validation for regulatory compliance of CS with dynamic component. On identification of learning in the CS, a User Acceptance Testing (UAT) is performed using automated test cases of varying types in accordance with what-if scenarios and synthetic data generated using a unique approach. Thereafter, a base validation testing of the CS is performed with clean data (positive scenarios of outcome of the CS) and dirty data (negative scenarios) by conducting repeatability, stability (consistency) and reliability checks. The base validation testing is then followed by learning saturation testing on only if the dynamic component is validated, is rolled out in production environment else is rolled back to the earlier version.

Automated continuous validation for regulatory compliance of a computer system (CS) comprising a dynamic component

This disclosure relates to a system and method for custom made medical devices life cycle management, the life cycle management of the medical devices include tracking of material and procedure to be used in additive manufacturing of the medical device and analyzing design risk as per the raw material and the design specification. Further, it includes to identify potential hazards based upon the materials, their interactions and effect on process to be followed. An artificial intelligence (AI) enabled model is trained with one or more sample datasets pertaining to a plurality of materials to be used in the additive manufacturing to audit design, to determine materials to be used, and to generate a unique code to be used to label the manufactured medical device. A smart contract of blockchain is configured to record received input and update with new points for regulatory compliance.

System and method for custom-made medical devices life cycle management

This disclosure relates generally to clinical trial management, and more particularly to method of performing a data driven cognitive clinical trial feasibility analysis. In one embodiment, the method comprising (a) receiving, a plurality of protocol requirements to initiate a clinical trial site feasibility; (b) identifying, a plurality of meta-data for at least one protocol requirement from the plurality of protocol requirements; (c) obtaining, an exhaustive list of historic clinical trial site data for the identified meta-data from a site data repository; (d) obtaining, an exhaustive list of third party clinical trial site data for identified meta-data from a third party data repository; and (e) assessing, the exhaustive list of clinical trial site data and the exhaustive list of third party clinical trial site data to obtain a list of identified clinical trial site feasibility.

Method and system for data driven cognitive clinical trial feasibility program

With the scale of information available today along with the existing diverse channels of communication, manual processing of information is becoming a challenge and companies across industries are under tremendous pressure to lower transactional costs. Artificial Intelligence based automation of business transactions has seen regulatory hurdles due to probabilistic nature of the outcome. The main challenge lies in processing of transactions with unstructured information. Systems and methods of the present disclosure uses deterministic as well as probabilistic approaches to maximize accuracy. The larger use of deterministic approach with configurable components and ontologies helps to improvise accuracy, precision and reduce recall. The probabilistic approach is used when there is absence of quality information or less information for learning. Also, confidence indicators are provided at attribute level of data being processed and at each decision level.

Automated cognitive processing of source agnostic data

Growing community of inventors

Mumbai, India

Devraj Goulikar