Tata Consultancy Services

The present disclosure leverages a graph-based digital twin network and generative artificial intelligence for solving problems of generalized and static risk assessments to create a dynamic risk profile by combining intrinsic farm risks with propagated risks from neighboring farms. This ensures that risk assessments reflect both local and networked influences. The dynamic risk profile is continuously adapted based on real-time data and interconnected farm relationships. Each farm is modeled as a node in the graph-based digital twin network, where edges represent risk propagation pathways influenced by factors such as pest migration, shared resources, and weather dependencies. Further, a real-time, dynamic premium recalibration is implemented for addressing inefficiencies of static premium model, based on the dynamic risk profile, compliance of farm users with risk mitigation advisory recommendations, and unforeseen risk factors. Furthermore, claims validation is performed by integrating remote sensing, IoT sensor data, and crowd-sourced inputs to resolve inaccuracies and fraud.

The embodiments of the present disclosure herein address unresolved problems of occurring hallucination when an LLM generates a response to a query despite not having encountered sufficient or relevant training data to accurately address that specific query of Embodiments herein provide a method and system for detecting and mitigating hallucination in a retrieval augmented generation (RAG). Herein, a pipeline is proposed to make a fine-grained analysis of various components in the RAG pipeline where hallucination can occur and how to mitigate it. By extending the standard RAG pipeline, four major components including a relevance check, an attribution check, an LLM reasoning check, a metric check are introduced to perform the fine-grained hallucination detection and mitigation in RAG-based approach.

The present invention relates to the field of model driven engineering. Creation of the purposive machine-processable models is an intellect-intensive activity requiring business domain as well as modeling expertise, which is in short supply. Hence, embodiments of present disclosure provide a method and system for generating purposive models using a Large Language Model (LLM). The method includes receiving a purposive metamodel and associated pattern trees. Each pattern tree has pattern elements including pattern nodes, with associated pattern properties, connected via pattern edges. The pattern elements may be associated with pattern mapping statements which are executed by a pattern interpretation technique and corresponding objects are created in the purposive model. The pattern mapping statements include prompt generation functions which are executed by the pattern interpretation technique to generate prompt and obtain response from the LLM which is used to populate the purposive model.

Existing approaches used for HIF detection utilize non-algebraic operations which add to computational complexity, consume more time, use complex algorithms and require a very high-sampling frequency (10-20 kHz). Embodiments disclosed herein provide a method and system for high-impedance fault (HIF) detection in a network. In this method, by shifting a domain digital data one cycle time at a time, and processing associated data, a plurality of Harmonics-based Algebraic Indexes (HAI) that include a half-cycle time domain digital data of a one-cycle time domain digital data, and a magnitude strength of a plurality of non-harmonic frequencies of each of the plurality of digital current signals is determined, which captures information on associated magnitude strength information. Further, a time domain aggregated harmonics-based algebraic index (AHAI) indicating presence or absence of High-Impedance Fault (HIF) in a network being monitored is generated, and accordingly the presence of absence of HIF is detected.

The present disclosure provides a system and method for performance monitoring of photovoltaic power generation using a reference power prediction model. In the present disclosure, a plurality of environmental data and a plurality of operational data of a PV power generation unit is received and preprocessed. Further, a clean reference PV power data and a reference irradiance value is obtained in absence of a GTI data in the plurality of the environmental data. However, in presence of the GTI data in the plurality of the environmental data, the clean reference PV power data is predicted using a power prediction model. Furthermore, a uniform value of a performance index for the PV generation unit is computed using a performance ratio for a predefined duration using an optimization framework. Performance monitoring of the PV power generation unit is performed by identifying a rising and a dropping pattern of the performance index.

With growing digitization, lifestyle has become sedentary for many citizens. This sedentary behaviour has proven to be dangerous to humans, leading to obesity, cancer, psychological disorders, etc. Hence, organizations and government entities/institutions are promoting an active life cycle. However, it is not easy. Use of mobile applications could be an easy approach to start with, but mobile devices worsen the sedentary behaviour. Gamification is a technique by which intended objectives or tasks performed by users are met through games. Present disclosure provides a system and method that implement sensor boards at different locations in an environment for enabling tasks to be performed by users based on calibrated objects in the environment. Calories burnt by the users are computed based on tasks performed and are compared with the target calories to be burnt. Based on the comparison, the speed of the calibrated objects and an associated difficulty level are adjusted.

This disclosure relates generally to a sequence based neural network method for predicting change in stability of proteins upon single point mutations (SPM). The Stateof-the-art methods are unable to capture change in residue environment using protein sequences information alone. The method of the present disclosure considers novel representation of a wild-type protein sequence and a mutant-type protein sequence by considering residue environment in addition to the residue at the position of mutation. Highly attended residues with respect to the position of mutation form residue environment and are identified by leveraging attention mechanism of the Protein Language Model (PLM) used in this study. Thus, the method captures change in the residue neighbour on mutation without using structural information. The method leads to a more accurate prediction of change in free energy (ΔΔG) at the site of mutation. as compared to existing state of the art methods.

A method and system for intelligent robotic controller with task failure detection and handling via a multi-level failure handler is disclosed. A validated task plan received from a task planning framework is analyzed to extract set of skills for execution of the task plan. Each skill comprising atomic skills or compound skills. For the set of skills, set of provisioned skill specific behavior tree (BT) nodes are loaded from a set of skill ledgers from a skill library defining execution of the extracted set of skills. BT train is created for the set of skills based on the set of skill ledgers and the set of provisioned skill specific BT nodes. Failure event is detected and handled by multi-level failure handler and replanning of task is routed to robotic controller or task planning framework based on detected failure mapping to the set of preconfigured failures and a failure handling capability.

The present invention relates to the field of document processing. Existing method of knowledge extraction from documents involve multi-step process to classify bounding boxes into plain text, special symbol, mathematical expression or table and then extract its contents. Identifying proper bounding boxes is also a challenge. Thus, embodiments of present disclosure provide a method and system for extracting contents from documents into predefined format. The method initially divides each page of an input document into multiple bounding boxes. Then, each bounding box is cropped and processed via an OCR reader. Completeness of the bounding box is checked, completeness of equation within the bounding box is checked and text within the bounding box is extracted. Same process is repeated for all bounding boxes in all the pages of the input document. Finally, text extracted from all the pages is consolidated into a predefined format.

The disclosure relates generally to methods and systems for cross-domain based change detection of region due to an event. Conventional techniques on cross-domain change detection (CDCD) rely on transformation-based approaches involving two tasks: image translation from one modal to another modal (SAR to optical) and then performing the change detection (CD) in one of the translated modals (for example, in SAR or optical). The methods and system of the present disclosure propose a deep-learning (DL) architecture called ReFUjetNet leverages generic embeddings from the extensively pre-trained domain adaptive foundation model alongside locally trained embeddings. The ReFUjetNet efficiently fuses these embeddings and employs a Kolmogorov-Arnold Network (KAN)-based convolutional neural network (CNN) classifier to generate a binary change matrix, which is then joined with another binary change matrix derived from the Gabor jet-based dissimilarity checker, resulting in the final binary change map.