Abstract: A machine learning travel management system is operable to provide end-to-end travel solutions and bookings based on machine-learning-assisted choice and selection of travel destinations. Travel solutions may include travel destination choices, which in many instances is one of the most difficult decisions made by travelers. Additionally, the system may interact with active bands, which comprise wearable RFID devices. The wearable RFID devices may provide for identification and location determination to provide location-based services. Also, the system may include a mobile application that provides mapping of travel routes and waypoints, which can interact with location information determined based on the active bands.

Abstract: A method and system for quality control of a digital facility based on machine learning. The system connects a plurality of elements associated with a plurality of regions of the digital facility. The system allocates a unique identity to the plurality of elements. The system receives a set of data associated with the plurality of regions. The system collects a set of data associated with a plurality of micro descriptors. The system processes the second set of data to discover a plurality of patterns. The system predicts issues associated with the plurality of elements. The system assigns high severity issue to the one or more severe issues. The system stores information associated with the digital facility. The system updates the patterns associated with the plurality of elements. The system recommends characteristic parameters to the plurality of elements. The system notifies manpower associated with the digital facility.

Abstract: A system for quality control of a facility based on feedback from multiple sources. The system receives data of the facility from plurality of data sources. The system creates one or more digital replica of the facility and classifies each region of the plurality of regions into plurality of checkpoint levels. The system assigns a plurality of micro-descriptors to the last level of the plurality of checkpoint levels and collects a feedback for the plurality of micro-descriptors from plurality of sources. The system collects data associated with the facility including facility-specific matrix and manpower specific matrix. The system maps the facility specific matrix with pre-defined quality specific matrix and evaluates deviation of the mapped data to identify one or more issues associated with the facility. The system assigns degree of severity to the identified issue and alerts if degree of severity is high.

Abstract: A method and system for quality control of a facility based on Internet of things. The system obtains data of the facility from plurality of architectural sources. The system creates a digital replica of the facility and defines plurality of control point levels for each region of the plurality of regions. The system allocates plurality of smart devices based on plurality of micro-descriptors. The system collects data associated with the facility and deciphers the data into one or more matrix. The system analyzes data with the pre-defined standard quality matrix in real time. The system assigns a degree of severity of the identified one or more issues. The system apprises one or more workforces associated with the facility to resolve the one or more issues including high degree of severity and the plurality of smart devices to take action to resolve the one or more issues.

Abstract: A method and system to conduct an audit for control and maintain the quality of a digital facility. The system executes instructions to causes one or more processors to perform a method. The method includes a first step of collecting a first set of data and a second step of creating one or more tasks. The method includes a third step of receiving a second set of data. The method includes a fourth step of analyzing the first set of data and the second set of data and a fifth step of assigning the one or more tasks. The method includes a sixth step of obtaining the third set of data. The method includes a seventh step of determining the deviation in the first set of data and the third set of data and an eighth step of rating a plurality of auditors.

Abstract: Implementations are directed to a field services management (FSM) platform by providing a services layer including a plurality of micro-services, each micro-service executing one or more FSM tasks associated with an asset, providing at least one presentation layer including a plurality of channels, through which a user communicates with the FSP platform, receiving, by a virtual agent, and through a channel of the one or more channels, input data from one of the user and the asset, transmitting, by the virtual agent, at least a portion of the input data to an artificial intelligence (AI) system, receiving, by the virtual agent, response data from the AI system, the response data representing an intent, initiating at least one micro-service to perform at least one FSM task based on the intent, the at least one micro-service being performed based on interactions between the user and the virtual agent to progress through a work-flow of the at least one micro-service, and storing interaction data and asset data in

Abstract: A machine learning travel management system is operable to provide end-to-end travel solutions and bookings based on machine-learning-assisted choice and selection of travel destinations. Travel solutions may include travel destination choices, which in many instances is one of the most difficult decisions made by travelers. Additionally, the system may interact with active bands, which comprise wearable RFID devices. The wearable RFID devices may provide for identification and location determination to provide location-based services. Also, the system may include a mobile application that provides mapping of travel routes and waypoints, which can interact with location information determined based on the active bands.