Abstract: 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.

Abstract: 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.

Abstract: State of art techniques hardly provide solutions for validation testing of Computer Systems (CS) with dynamic components that undergo frequent changes or learnings. Embodiments of the present disclosure provide 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.

Abstract: 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.

Abstract: While facing polluted environments one does not need extreme breath filtering protection all the time. Methods of the art hardly focus on breathability aspect while working on smart masks. Embodiments of the present disclosure provide a system and method for context aware networked mask with dynamic air impedance for optimum breathability. The system provides a smart mask that regulates the air inlet (by modulating the position/pore size of filtering membrane or by using resizable gel) as per the prevalent risk at that instance. The context aware mask with a mesh network, disclosed herein, comprises sensors and processing unit, which use contextual information and Artificial Intelligence to develop models for generating alerts along with mechanism of dynamically regulating the pore size to ensure breathability by adjusting air impedance.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.