Abstract: The disclosure relates to a method and system of generating training data for fine-tuning of a Machine Learning (ML) model. The method includes generating one or more natural language interpretations of a dataset corresponding to one or more parameters associated with configuration of the dataset, and collating the one or more natural language interpretations of the dataset corresponding to one or more parameters, to generate a combined natural language interpretation of the dataset. The method further include generating a conceptual explanation of the dataset, based on the combined natural language interpretation of the dataset, and assigning one or more labels to each sub-dataset of the dataset, based on the conceptual explanation of the dataset, to generate training data for fine-tuning of the ML model, wherein the dataset comprises a plurality of sub-datasets.

Abstract: The disclosure relates to method and system for securing delivery of a package. The method may include receiving, from a token generating server, a security token corresponding to security data associated with the delivery of the package. The token generating server may be configured to generate the security token corresponding to the security data upon receiving the security data from a sender device. A security code representative of the security token may be attached to the package. The method may further include capturing the security code from the package at the time of delivery of the package to a receiver, correlating the security code with the security token, to identify a match between the security code and the security token, and upon successful matching of the security code with the security token, allowing access of the package to the receiver.

Abstract: A method and system for recording, organizing, and tracing events is disclosed. In some embodiments, the method includes obtaining real-time video data captured using a video capturing equipment. The real-time video data comprises a plurality of sequential image frames and audio data associated with each of a plurality of events. The method further includes extracting first timestamp data and first text data from each of the plurality of sequential image frames, extracting second timestamp data and second text data from the audio data, generating text data associated with each of the plurality of events based on the first timestamp data, the first text data, the second timestamp data, and the second text data, storing the generated text data for subsequent audit, and discarding the real-time video data associated with each of the plurality of events.

Abstract: The invention relates to method and system for enhancing computer network security. The method includes receiving a plurality of requests from client devices to avail a plurality of responses from services running on servers; determining a URL pattern for each of the plurality of requests based on URL associated with that request; determining a request data signature for each of the plurality of requests or a response data signature for each of the plurality of responses based on a set of request parameters associated with that request or based on a set of response parameters associated with that response, respectively, using a first machine learning model; and determining an authenticity of each of the plurality of requests based on the URL pattern and the data signature associated with that request, or an authenticity of each of the plurality of responses based on the data signature associated with that response.

Abstract: The disclosure relates to method and system for securing delivery of a package. The method may include receiving, from a token generating server, a security token corresponding to security data associated with the delivery of the package. The token generating server may be configured to generate the security token corresponding to the security data upon receiving the security data from a sender device. A security code representative of the security token may be attached to the package. The method may further include capturing the security code from the package at the time of delivery of the package to a receiver, correlating the security code with the security token, to identify a match between the security code and the security token, and upon successful matching of the security code with the security token, allowing access of the package to the receiver.

Abstract: A method and system dynamically predicting driving range of vehicles is disclosed. In some embodiments, the method includes dynamically determining real-time values corresponding to in-transit parameters associated with vehicle; and determining, via trained Machine Learning (ML) model, variance in in-transit parameters when compared with pre-defined parameters. Determining the variance includes identifying overlapping subset of parameters between in-transit parameters and pre-defined parameters; identifying non-overlapping subset of parameters between in-transit parameters and pre-defined parameters; determining difference in each of the real-time values determined for overlapping subset of parameters with corresponding optimal value; and computing variance based on non-overlapping subset of parameters and difference in real-time values determined for the overlapping subset of parameters.

Abstract: This disclosure relates to method and system for identifying Personally Identifiable Information (PII) through secret patterns. The method includes receiving user data from at least one data source through a plurality of communication channels. The user data includes PII and non-PII. The user data is associated with a user. The PII includes a plurality of personal identifiers. The method further includes identifying the PII in user data through a predictive model. The method further includes generating a secret pattern based on the PII identified through the predictive model. The secret pattern is an identifiable label. The method further includes adding the secret pattern to each of the plurality of personal identifiers in PII. The method further includes identifying each of the plurality of personal identifiers through the secret pattern in real-time, when user data is transmitted from the at least one data source to at least one data destination.

Abstract: The present disclosure relates to a system and method for controlling data interception in a communication network. One or more requests from a user for accessing one or more microservices are received through an Application Programming Interface (API). Information associated with one or more requests is the detected and requests are classified as secured microservice request and non-secured microservice request. The information is detected through predefined rules. Authentication token is then issued for secured microservice based on the detecting. The authentication token stores information detected by the detector in a geo storage system. The one or more requests are then routed according to the authentication token towards one or more corresponding microservices of the one or more microservices.

Abstract: A method and system for recording, organizing, and tracing events is disclosed. In some embodiments, the method includes obtaining real-time video data captured using a video capturing equipment. The real-time video data comprises a plurality of sequential image frames and audio data associated with each of a plurality of events. The method further includes extracting first timestamp data and first text data from each of the plurality of sequential image frames, extracting second timestamp data and second text data from the audio data, generating text data associated with each of the plurality of events based on the first timestamp data, the first text data, the second timestamp data, and the second text data, storing the generated text data for subsequent audit, and discarding the real-time video data associated with each of the plurality of events.

Abstract: The invention relates to method and system for enhancing computer network security. The method includes receiving a plurality of requests from client devices to avail a plurality of responses from services running on servers; determining a URL pattern for each of the plurality of requests based on URL associated with that request; determining a request data signature for each of the plurality of requests or a response data signature for each of the plurality of responses based on a set of request parameters associated with that request or based on a set of response parameters associated with that response, respectively, using a first machine learning model; and determining an authenticity of each of the plurality of requests based on the URL pattern and the data signature associated with that request, or an authenticity of each of the plurality of responses based on the data signature associated with that response.

Abstract: This disclosure relates to method and system for identifying Personally Identifiable Information (PII) through secret patterns. The method includes receiving user data from at least one data source through a plurality of communication channels. The user data includes PII and non-PII. The user data is associated with a user. The PII includes a plurality of personal identifiers. The method further includes identifying the PII in user data through a predictive model. The method further includes generating a secret pattern based on the PII identified through the predictive model. The secret pattern is an identifiable label. The method further includes adding the secret pattern to each of the plurality of personal identifiers in PII. The method further includes identifying each of the plurality of personal identifiers through the secret pattern in real-time, when user data is transmitted from the at least one data source to at least one data destination.

Abstract: The present disclosure relates to a system and method for controlling data interception in a communication network. One or more requests from a user for accessing one or more microservices are received through an Application Programming Interface (API). Information associated with one or more requests is the detected and requests are classified as secured microservice request and non-secured microservice request. The information is detected through predefined rules. Authentication token is then issued for secured microservice based on the detecting. The authentication token stores information detected by the detector in a geo storage system. The one or more requests are then routed according to the authentication token towards one or more corresponding microservices of the one or more microservices.

Abstract: Disclosed is a system for delegating access of sensitive information by a user device to a requestor device through a central server. A receiving module receives a first token Identification (ID) generated by the user device in an offline mode and a request, comprising a second token ID, from the requestor device. A validation module validates the request by comparing the first token ID and the second token ID. An identification module identifies a subset of the sensitive information based on a profile of the requestor, when the first token ID is matched with the second token ID. A watermarking module watermarks the subset of the sensitive information with a set of variables to generate watermarked sensitive information. Upon generating the watermarked sensitive information, the access delegation module delegates the access of the watermarked sensitive information to the requestor device.

Abstract: Disclosed is a system for delegating access of sensitive information by a user device to a requestor device through a central server. A receiving module receives a first token Identification (ID) generated by the user device in an offline mode and a request, comprising a second token ID, from the requestor device. A validation module validates the request by comparing the first token ID and the second token ID. An identification module identifies a subset of the sensitive information based on a profile of the requestor, when the first token ID is matched with the second token ID. A watermarking module watermarks the subset of the sensitive information with a set of variables to generate watermarked sensitive information. Upon generating the watermarked sensitive information, the access delegation module delegates the access of the watermarked sensitive information to the requestor device.