Tata Consultancy Services Patente

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This disclosure relates generally to system and method for assessing insider influence on enterprise assets. Existing work focuses on the detection of insider threat and does not consider the influence of an insider on their peers and subordinates. The present disclosure aggregates and preprocesses the enterprise data specific to the individuals received from various sources, and further creates an enterprise graph between entities. Weights of every edge connected between any two entities in the enterprise graph is then calculated. Community of the individuals are detected wherein, relevant insider(s) are identified, and susceptibility of the individuals for probable influence by relevant insider(s) based on the analysis scenarios(s) is calculated. Paths taken by the relevant insider(s) is calculated for estimating probability of data loss. The present disclosure identifies the assets which are under possible threat from the relevant insider(s), obtains cumulative risk associated with the enterprise and generates an analysis report accordingly.

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The embodiments of present disclosure herein address unresolved problem of key capabilities that an intelligent robot must possess to perform complex and long-horizon object manipulation tasks without human intervention. The present disclosure overcomes above mentioned challenges by decoupling imitation learning task into two parts using a decouple neural network architecture comprising two modules. A high-level module that segments out sequence of primitive actions from video of human demonstration of a complex task and a low-level module that executes those primitives sequentially on robot. The high-level module, Task Sequencing Network (TSNet), is a hybrid neural network composed of Convolutional Neural Network (CNN), Recurrent Neural Network (RNN) with Connectionist Temporal Classification (CTC) loss, while low-level controller is built using dynamic movement primitives (DMP). The network is trained in a supervised learning setting. The CNN, RNN with CTC combination helps in efficiently segmenting task primitives in sequence for given input video demonstrations.

Embodiments herein provide a system for electrically heated gas mixture mediated industrial plant processes providing a fossil fuel free approach. The system utilizes a gas mixture as heating medium that directly transfers heat to a raw material required for processing the raw material into an end product. The gas mixture used as a heating medium can be a byproduct generated during processing or a combination of stable gases that are reutilized and recirculated to carry heat and dissipate the heat at various stages of processing in accordance with a heating medium design loop. The heating medium design loop disclosed is designed such that it eliminates the need of heat management equipment like a Thermal Energy Storage (TES) system used by existing approaches. Entire heating of the plant is carried out using electric gas heaters heated using renewable electric sources to provide the fossil fuel free design.

The use of mobile telephones have come into widespread use in such a way that day to day life has become very much dependent on them. This results in unintentional or intentional use of mobile phones in the prohibited areas such as examination centers. A method for detecting active radiofrequency wireless communication signal in a region has been provided. The method involves two stages, calibration stage and mobile phone detection stage. In the calibration stage, the frequency on which the mobile towers in that region are operating are identified. The identified frequency is then used to detect the active wireless RF communication using a plurality of software defined radios (SDR) and the existing client systems present in the region. The method further configured to determine the exact location of the mobile phone from the RF communication signal is generated.

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Most techniques to estimate the service life of coatings are experimental in nature. Experimental testing of coatings is expensive (destructive in nature) and time consuming. Present disclosure provides system and method that predict change in chemical composition of the coating using reaction mechanism of the coating components in presence of weathering causing environmental agents. The system partial differential equations wherein chemical model is combined with the physical model. The changes in chemical composition enable determining the change in surface roughness and thickness as the weathering of the coating happens which can be correlated with other parameters such as gloss loss, fracture toughness, and the like. Rate of initiation reaction which leads to micro-randomness in concentration profile is randomized to mimic inherently stochastic (random) nature of coating degradation. Service life of coating is further estimated based on values of physical properties of coating beyond which the coating is characterized as unserviceable.

This disclosure relates generally to method and system of electric vehicle route planning for multi-service delivery and on-route energy replenishment. Last mile delivery is a critical component of supply chains that impacts both customer experience and delivery cost. The method disclosed processes a received user request comprising a current location of the user, one or more required services, and a time window rendered between each of the required services, and generates a graph for the user request. Further, a trained learning agent generates a route map indicating a plurality of waypoint locations for the electric vehicle to visit each node in accordance with minimized trip cost of fleet and time duration where each node has a state action pair for the electric vehicle. The learning agent learns continuously during the interaction with the delivery environment and obtains feedback for every associated action.

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In conventional robot navigation techniques learning and planning algorithms act independently without guiding each other simultaneously. A method and system for robotic navigation with simultaneous local path planning and learning is disclosed. The method discloses an approach to learn and plan simultaneously by assisting each other and improve the overall system performance. The planner acts as an actuator and helps to balance exploration and exploitation in the learning algorithm. The synergy between dynamic window approach (DWA) as a planning algorithm and a disclosed Next best Q-learning (NBQ) as a learning algorithm offers an efficient local planning algorithm. Unlike the traditional Q-learning, dimension of Q-tree in the NBQ is dynamic and does not require to define a priori.

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The disclosure generally related to a learning-based modelling of an emergent behavior of a complex system. Existing decision-making at complex systems primarily relies on qualitative approaches, which often results in inaccurate outputs. The disclosed system includes a digital twin of the complex system and a digital twin of an environment of said complex system and captures an interaction and dynamic behavior of agents of the digital twins. The agents of the digital twins are simulated and modelled using learning-based models such as RL and genetic algorithms that learns the behavior (i.e. actions and their outcomes) over a period of time. Hence, the agents (or actors) of the digital twins are dynamic in nature. The actor-based bottom up simulation approach is capable of producing sufficient insight for effective decision making prior to implementation.

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This disclosure relates generally to the field of source code processing, and, more particularly to a method and system for identification of redundant function-level slicing calls. The method disclosed generates program dependence graphs (PDGs) based on a slicing criteria and a function corresponding to the function-level slicing call. Further the method classifies the function-level slicing call into redundant or non-redundant by traversing the PDGs and checking if a predefined condition is satisfied or not. The function-level slicing calls are classified as redundant if the check is not satisfied and are classified as non-redundant if the check is satisfied. The disclosed method can be used in identifying redundant function-level slicing calls in applications such as automated false positive elimination (AFPE), automated test case generation and so on.

Currently available live media distribution systems have to continuously maintain multiple P2P connections which further increases the computational load of robot. Present disclosure provides a method and a system for managing multimedia exchange in tele-robotics using dew computing. The system first identifies a dew signaling server wherein a tele-presence robot device present in enterprise network offer to act as dew signaling server. The system then establishes first peer-to-peer (P2P) connection between dew interface and dew signaling server. Thereafter, system establishes second P2P connection between dew signaling server and cloud media manager present in public cloud server. Further, system identifies computing device present in enterprise network as dew media manager. The system then establishes third P2P connection between dew media manager and the cloud media manager via the cloud signaling server. The dew media manager then perform multimedia exchange within one or more enterprise user devices connected to enterprise network.

The disclosure generally relates to methods and systems for generating synthetic microstructure images of a material with desired features. Conventional techniques that make use of unsupervised deep generative models has no control on the generated microstructure images with specific, desired set of features. The present disclosure generates the synthetic microstructure images of the material with desired feature, by using a variational autoencoder defined with a style loss function. In the first step, the variational autoencoder is trained to learn latent representation of microstructure image of the material. In the second step, some of the dimensions of learned latent representation is interpreted as physically significant features. In the third and last step, the latent representation required for getting the desired features is appropriately sampled based on the interpretation to generate the synthetic microstructure images of the material with desired features.

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This disclosure relates generally to method and system for determining label value for labels in unstructured documents. Typical systems have challenge in understanding variations in layout of unstructured documents and extract information therefrom. The disclosed method and system facilitate systematically identifying sections and bounding boxes in the page images, taking image portion of the bounding boxes and extracting labels and label values therefrom. In case the label values are not present in the same bounding box having the label, the neighboring labels are examined for the matching label values. The system also obtains label-label value pairs from the document by utilizing a trained deep learning model, and compares the output with the label-label value pairs extracted earlier. An aggregated confidence score is assigned to the text in the bounding box.

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Conventionally, most equipment manipulating object have been huge with rotatable pivot platforms, multiple roller conveyors, telescopic boom conveyors, hydraulic cranes, etc. These systems may have higher lifecycle cost due to their size and bill of materials (BoM) count and have limited themselves to handling either loading or unloading and lack to perform both the operations. Present disclosure provides apparatus for organized loading and unloading of objects. The apparatus addresses the issue of single equipment for both loading, unloading, and further automatically organizes the objects in bulk thus ensuring safety, without damages. The objects are placed optimally while being unloaded onto pallets or other carriers. While loading into a container, the objects are loaded to a greater fill volume. These operations of multiple objects handling in one go are enabled through various belt conveyors, manipulators, vacuum generators, suction panels, object gripping and conveying platforms, and object gripping systems.

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This disclosure relates to system and method to reconstruct human motion for mobile robot teleoperation using shared control. The method of the present disclosure acquire an input feed of a human operator to perform a task with assistance in a remote environment using shared control. The mobile robot reconstructs to follow a trajectory of the human operator towards the intended goal in the remote environment. The mobile robot determines at least one goal intended by the human operator based on a previously occurred state, a current kinematic state and a future trajectory of the human operator and a known position of the plurality of goals. The model predictive control generates at least one instruction to control the movement of the mobile robot to perform at least one of following the trajectory of the human operator and reaching the operator intended goal based on a joint angle position and a velocity.

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