Abstract: System (100) for optimization of industrial operation, based on simulation data thereof, system comprising processing unit (102) configured to generate simulation data related to industrial operation; generate knowledge graph ontology; generate knowledge graph instance representing simulation data; receive user request to analyze quality of industrial operation, from user device (104) of user; analyze quality of industrial operation; generate response to user request, based on analyzed quality of industrial operation; send response to user device; receive first user input from user device, wherein first user input is indicative of user approval on industrial operation; and when user approval on industrial operation is positive, employ simulation data for implementation of industrial operation; or when user approval on industrial operation is negative, generate updated simulation data, based on analysis of quality of industrial operation, and repeat aforementioned steps for updated simulation data.

Abstract: A system (100) for fine-tuning a code translation model is disclosed. The system comprises a processing arrangement (102) and a user device (104). The processing arrangement is configured to receive at least one seed sample in a source code language, a code translation base model, and a set of system specific requirements, from a user device. The processing arrangement is configured to generate synthetic dataset comprising training dataset and evaluation dataset; determine at least one parameter to finetune the code translation model; finetune the code translation model based on at least one parameter; evaluate the code translation model to generate a evaluation score; determine a training loss and an evaluation loss; based on the evaluation score, the training loss and the evaluation loss, finetune the code translation model or approve the code translation model. A method for fine-tuning a code translation model is also disclosed.

Abstract: Disclosed is method for automated code retrieval and code generation, comprising: receiving code repository (302); extracting coding components (CC); generating knowledge graph (KG) comprising nodes representing extracted CC, and edges representing relationships between the extracted CC; identifying node(s) in generated KG for indexing; creating first index (FI) (406, 512) and second index (SI) (408, 514) of generated KG; receiving user request (502) for generating code; searching FI and SI based on entity(s) (510) extracted from user query (UQ) and embeddings (506) of UQ, respectively, for identifying predefined number of nodes (PNN) (516, 520A) in KG; retrieving identified PNN from KG; retrieving neighbouring nodes (NN) (520B-C); generating subgraph of retrieved NN and relationships between retrieved NN; filtering generated subgraph (GS); retrieving code snippets (404C) of retrieved NN; generating contextualized subgraph; and prompting Large Language Model (608) with generated contextualized subgraph, prede

Abstract: Disclosed is method for distributed decision-making in multi-role large language models (LLMs) architecture (mLLMa). Method comprises: receiving user query for initiating conversation between user and mLLMa; using graphical representation (GR) for identifying role(s) associated with user query, wherein role(s) is based on context thereof; assigning relevance score to each role; dynamically passing role(s), to mLLMa; generating role-specific prompt for role assumed by LLM; generating, by each LLM, role-specific response (RR) corresponding to role-specific prompt for each role and context; presenting RR from each LLM to peer LLMs; conducting polling process among LLMs for ranking RR therefrom; aggregating rankings from polling process to determine final ranking of RR for each LLM; selecting RR having highest final ranking as an action-inducing response (AR), and transmitting AR to user for providing user action; and updating GR based on AR and user action.

Abstract: Disclosed is method for generating a target molecule (412, 514, 802), comprising receiving first user input (806) indicative of properties associated with target molecule, and identifying properties (808A-C) associated with targeted molecule and corresponding objectives (810A-B); generating property scores (832A-B) for properties using property predictor algorithm (812); receiving second user input indicative of molecular structure (202, 402, 502, 602, 702, 814) of input molecule (204); generating corresponding target molecules (CTMs) (200, 406, 508, 600, 700, 816); generating embeddings (824) of CTMs; determining aggregate similarity score (828); determining aggregate property score; determining fitness scores (410, 512, 834) of CTMs; determining whether given target molecule amongst CTMs fulfill termination criteria (TC); when it is determined that TC is fulfilled by given target molecule, deeming given target molecule as target molecule to be generated; when it is determined that TC is not fulfilled, updat

Abstract: A validation system enables concurrent visual validation of multiple electronic documents. A processor generates a custom user interface (UI) framework comprising two sections. The first section displays document previews, while the second holds extracted data, including identifiers for document and page IDs, along with entity positions and associated values across the documents. Upon user input on a specific document entity in the second section, the validation system concurrently loads document previews in the first section, displaying corresponding data values from the multiple documents. The updated visualization allows for validation and review operations across the documents, utilizing the received user input and loaded information.

Abstract: A system for recognizing vertically oriented alphanumeric text in images, the system including a processor configured to receive one or more images comprising vertically oriented alphanumeric text and detect one or more regions-of-interest in each image via a trained text detector. The processor is configured to execute a cropping of the detected one or more regions-of-interest encompassing vertically oriented alphanumeric text from each image to obtain one or more text crop portions and rotate the one or more text crop portions to obtain one or more orthogonally rotated text crop portions. The processor is configured to execute a trained ensemble of two different text recognition models on each of the obtained one or more text crop portions and the one or more orthogonally rotated text crop portions and generate a set of candidate recognized text strings based on the executed trained ensemble and determine a final recognized text string.

Abstract: A system for generation of a sub-skill is disclosed. The system includes a processor that is configured to: receive an input comprising goal information to achieve a first sub-skill, and requirement information to achieve the goal for the first sub-skill; generate a machine readable meta-plan based on the input. The processor is further configured to generate a first executable logic based on the generated machine readable meta-plan; and iteratively refine the generated first executable logic to obtain a refined executable logic based on a validation operation. In the validation operation, when the first executable logic is executed, an output dataset is generated and compared with an outcome specified by the goal and the requirement information such that in each iteration of the refinement of the generated first executable logic, one or more errors or inconsistencies in the first executable logic is removed.

Abstract: A method for automatic visual workflow model generation and management is disclosed that utilizes multimodal inputs and user feedback. The method further comprises receiving, through a processor, descriptions using an advanced AI model, generating elaborate plans that visually organize sequential tasks. User feedback via natural language on these plans refines them, establishing connections between detailed plans and numerous sub-skills. The processor constructs a directed acyclic graph (DAG) visualizing sub-skill execution order based on the established mapping, culminating in an executable workflow model. The method further comprises seamlessly translating user descriptions into detailed plans, refine them iteratively, and generate an executable workflow model, all driven by user interactions and advanced AI techniques supporting natural language understanding and generation.

Abstract: A validation system enables concurrent visual validation of multiple electronic documents. A processor generates a custom user interface (UI) framework comprising two sections. The first section displays document previews, while the second holds extracted data, including identifiers for document and page IDs, along with entity positions and associated values across the documents. Upon user input on a specific document entity in the second section, the validation system concurrently loads document previews in the first section, displaying corresponding data values from the multiple documents. The updated visualization allows for validation and review operations across the documents, utilizing the received user input and loaded information.

Abstract: Disclosed is a method comprising receiving user query (UQ) indicative of task (202) associated with retrieval of data (204) from database (206) comprising structured and unstructured data sources (SAUDS) (208) and generating relevant response (RR) (210) for UQ; generating task plan (220) for determining embedding (224) for task, performing retrieval task (226) for identifying relevant SAUDS (228), generating relevance scores (230), and when relevance scores indicate unstructured pipeline (232): triggering unstructured data query engine (UDQE) (234, 306) for retrieving data, and generating RR, or when relevance scores indicate structured pipeline (236): triggering structured data query engine (SDQE) (238, 308) for retrieving data, and generating RR, or when relevance scores indicate hybrid pipeline (240): triggering SDQE for retrieving first portion of data (FPD) (242), transforming received UQ and FPD, triggering UDQE for retrieving second portion of data (SPD) (246), and combining FPD and SPD for generating

Abstract: The present disclosure provides a method for recommendation of suitable assets using Large Language Model (LLM), method comprising receiving descriptors for each asset from amongst plurality of assets; generating embeddings for each asset from amongst plurality of assets, by employing LLM; creating database of assets by storing generated embeddings for each asset from amongst plurality of assets in database; receiving user query pertaining to request for recommendation to solve enterprise problem; generating cosine similarity scores, wherein each cosine similarity score is generated between user query and corresponding generated embedding for given asset stored in database of assets; identifying predefined number of similar assets from amongst database of assets, based on highest cosine similarity scores; constructing optimal prompt based on identified predefined number of similar assets and user query; and prompting LLM, using constructed optimal prompt for generating response as recommendation of identified

Abstract: A method for multi-stage processing of user queries for enhanced information retrieval. The method includes generating self-complete derived queries from a search query. The method includes extracting query entities from each derived query and mapping the query entities with a plurality of electronic documents to identify a set of relevant electronic documents. The method includes sorting the derived queries based on the number of relevant electronic documents related to each derived query to obtain a sorted sequence of derived queries and searching a result for each derived query sequentially from the set of relevant electronic documents according to the sorted sequence of derived queries, and appending the result retrieved for one derived query with a consequent derived query to obtain a final search result. The method involves breakdown of a search query into derived queries and resolve each derived query separately and sequentially to reduce complexity and computation cost.

Abstract: A method for processing one or more electronic documents for enhanced search includes defining a bounding box around each key and value of key-value pairs in a first schema file, tagging coordinates of a key corresponding to a first bounding box and coordinates of a value corresponding to a second bounding box in the first schema file. Furthermore, obtaining a first inference file, detecting coordinates of a key corresponding to a third bounding box, and determining coordinates of a fourth bounding box and the value of the first inference file that are determined by applying a normalization operation. Thereafter, extracting value encompassed by the fourth bounding box of the first inference file and automatically creating a searchable index of the first inference file with searchable key-value pairs. The method achieves an efficient and accurate clustering of data items with an accurate, meaningful, and formal objective function.

Abstract: A method and system for optimizing performance of Genetic Algorithm (GA) in solving scheduling problem is disclosed. The method includes receiving input constraints associated with supply and demand sides, for scheduling problem. The method include initializing set of schedules using initializer that sets initial set of solutions for GA to start optimization. The method may include generating parent population for GA. The method may include creating child population via evolution using current probabilistic parameters including crossover and mutation operators. The method may include utilizing a Multi-Level Hierarchical Grouping (MLHG) to de-duplicate child population. The method includes determining a new population from a total population including the parent population and the child population, using custom multi-objective sorting technique. The method may further include updating probabilistic parameters of the GA during runtime using runtime adapter, when pre-determined iterations unattained.

Abstract: A method and system for updating prediction model for curing process design is disclosed. The method includes updating a prediction model based on the input through a semi-supervised learning technique. The method may include receiving an input corresponding to a curing process. The method may further include generating a second set of experiments using the updated prediction model and an optimization component associated with the prediction model. The method may further include obtaining a second set of data upon performing a second set of experiments on a physical set-up. The method may further include determining an error between the predicted set of data and the second set of data. The method may further include updating the prediction model based on the second set of data when the error is out of a predefined threshold.

Abstract: Disclosed is a method for identifying multi-level hierarchical relationships between data elements of a document, the method comprising receiving a plurality of sample documents each having a plurality of data elements arranged in a multi-level hierarchical data structure; classifying each of the plurality of data elements into a key entity field or a key field value based on a hierarchical relationship therebetween; identifying key entity fields, from among the classified key entity field of the plurality of data elements, having the hierarchical relationship therebetween; pairing the key entity field, with a corresponding key field value or an identified key entity field, to form a training dataset; and employing the training dataset on a neural network framework, having at least one of a textual modality or a visual modality, to identify the multi-level hierarchical relationships between the data elements of the document.

Abstract: Disclosed is a system and a method for training a neural network to detect one or more anomalies in an event data. The system comprises a processing arrangement, communicably coupled to a database configured to store the event data. Herein, the processing arrangement is configured to receive event data associated with a plurality of log events for a given time period, pre-process the received event data to generate refined event data, process the refined event data using an encoder architecture of the processing arrangement, to generate one or more event embeddings based on a first transformation model, and wherein the second encoder is configured to generate one or more contextual embeddings based on a second transformation model for each log event, and process the one or more contextual embeddings via at least one statistical technique to generate an embedding matrix to detect the one or more anomalies.

Abstract: A method and system of predicting molecular properties is provided herein. The method includes representing a molecule as a graph and a string. The method further includes encoding the graph into a first feature representation and the string into a second feature representation, using a graph neural network and a transformer-based network, respectively. The method further includes concatenating the first feature representation obtained from the graph neural network and the second feature representation obtained from the transformer-based network to create a combined feature representation. The method further includes fusing the combined feature representation using a linear layer to obtain a synergistic combined feature representation for the molecule. The method further includes predicting one or more molecular properties for the molecule using the synergistic combined feature representation and a predictor network.

Abstract: A method and system for optimal scheduling of nursing services in a hospital is provided herein. The method and system comprises forecasting demand for the nursing services for a time interval based on historical data and external data. The method and system further comprises extracting nurses' availability data for the time interval. The method and system comprises aggregating constraints applicable to the nursing services. The method and system also comprises computing a schedule of nurses based on the constraints using at least an optimization model.