Abstract: The disclosure relates generally to methods and systems for predicting water quality of a river having a varying river ecosystem. Due to multiple and diverse factors, understanding and estimating the water quality of the river stream (river itself) is extremely and technically challenging. The present disclosure discloses a development of a river digital twin model utilizing a multi-modeling approach to comprehensively model the river and its varying ecosystems. The agents encompass entities that directly or indirectly introduce effluents or withdraw water from the river. Agents and their interactions are defined using a combination of behavior rules, correlations, and physics principles, creating the digital twin model that closely mimics the real river system. Physics-based equations are also employed in the present disclosure to capture the dynamics of the river, while relationships between different agents are established.

Abstract: This disclosure relates generally to the field of retrieval and extraction for designing formulated products by constructed ingredient-functionality lists for formulated products using search engine result excerpts in an automated manner. Many ingredients used in formulated products are multi-functional. Conventionally, in a given category of formulated product, only a single functionality or subset of functionalities of an ingredient is used ignoring other functionalities of which the ingredient is representative. However, there is a need to put together representative ingredients before proceeding further with process steps of making and testing the formulated product. The present disclosure addresses unresolved problems of the conventional methods by obtaining information regarding the functionalities of ingredients used in formulated products using search engines based on a loop processing involving query formation, and construction of a list of ingredients and functionalities till exhaustion.

Abstract: Existing electricity consumption prediction approaches depend largely on data-based models, which may be statistical techniques or more precisely time-series predictions that predict based on the auto-regressive nature of the load curve with a few external variables at best, such as calendar events and ambient temperature. While these models are effective to a certain degree for the overall grid level requirements, they may not be able to predict disruptive changes that may happen over longer periods of time, such as the demographic shifts, etc. Method and system disclosed herein predict the electricity consumption by taking into consideration various parameters associated with such disruptive changes, and then predict the electricity consumption in a target area by aggregating the electricity consumption predicted at the agent level.

Abstract: This disclosure relates to the field of project related document analysis. Conventionally, process of retrieving right information related to a stakeholder with relevant project initiation concerns involves manual intervention resulting in more time consumption. The method of the present disclosure describes a system and method for automated extraction and classification of project initiation related information from request for proposal response documents The RFP response document is parsed using document structure based parsing technique to identify questions and answers. The questions from the RFP response document are classified into different classes of interest and important information from the answers are extracted and mapped to an identified class. The method of the present disclosure demonstrates significant improvement in terms of time consumption by reducing volume of information and providing a quick access to class-specific relevant information from the RFP response document.

Abstract: The present disclosure provides a dynamic functional adaptation of software systems based on real time need, which is unaddressed by the conventional methods. Initially, the system receives an output generated by a software system under execution corresponding to a given input. Further, a deviation in the generated output is identified based on a comparison between the generated output and a corresponding expected output. Further, an optimal policy is identified based on the generated output and the identified deviation using a reward based Reinforcement Learning (RL) model. Further, an adaptation specification is generated based on the identified optimal policy using an Adaptation Specification Generation (ASG) technique, Further, a software code is generated dynamically for the identified optimal policy using an ASG based code generation technique.

Abstract: Dependency on limited availability of subject matter experts (SMEs) who are well versed in Model-Driven Engineering (MDE) technology is a significant barrier to MDE utilized for generating conceptual models. In the present disclosure, MDE and generative Artificial Intelligence (AI) operate in a symbiotic relationship complimenting respective strengths and overcoming limitations. The generative AI techniques lower the knowledge barrier and enable domain SMEs to construct purposive models by operating at natural language level instead of at MDE technology level, thereby simplifying the method of generating conceptual models that are purposive. When operating at natural language level, the method and system of the present disclosure ensures that the generative AI receives focused and well directed prompts to optimize the number of interactions and reduce computing power utilized.

Abstract: This disclosure relates generally to impact analysis and, more particularly, to generation of impact analysis specification document for a change request. The existing state-of-art techniques for impact analysis for a change request are mostly manual, and further most of the research on impact analysis is based on source code analysis and does not address the impact of the CR at multi-granular levels. The disclosed techniques perform a fine-grained impact analysis of the CR at multi-granular levels. The fine-grained impact analysis at multi-granular includes identifying a set of impacted specification elements, a set of impacted processes, and a set of impacted features based on several steps including generating a contextual specification model and extracting a plurality of key-phrases.

Abstract: A method is provided for decoding an encoded video stream on a processor having a plurality of processing cores includes receiving and examining a video stream to identify any macroscopic constructs present therein that support parallel processing. Decoding of the video stream is divided into a plurality of decoding functions. The plurality of decoding functions is scheduled for decoding the video stream in a dynamic manner based on availability of any macroscopic constructs that have been identified and then based on a number of bytes used to encode each block into which each picture of the video stream is partitioned. Each of the decoding functions is dispatched to the plurality of processing cores in accordance with the scheduling.

Abstract: This disclosure relates generally to the field of retrieval and extraction for designing formulated products by constructed ingredient-functionality lists for formulated products using search engine result excerpts in an automated manner. Many ingredients used in formulated products are multi-functional. Conventionally, in a given category of formulated product, only a single functionality or subset of functionalities of an ingredient is used ignoring other functionalities of which the ingredient is representative. However, there is a need to put together representative ingredients before proceeding further with process steps of making and testing the formulated product. The present disclosure addresses unresolved problems of the conventional methods by obtaining information regarding the functionalities of ingredients used in formulated products using search engines based on a loop processing involving query formation, and construction of a list of ingredients and functionalities till exhaustion.

Abstract: The present disclosure is of a method for automated authoring of purposive models from Natural Language (NL) documents. Conventional model extractors to automatically extract models from NL documents are specific to a metamodel, do not consider document structure and are not configurable. Initially, the system receives a plurality of Natural Language (NL) documents, a metamodel, a plurality of pattern trees corresponding to the metamodel, and a configurable domain dictionary. Each of the plurality of pattern trees includes a plurality of pattern elements. Further, a document information is generated from each of the plurality of NL documents using a document information reading technique. Finally, a plurality of purposive models are generated for each of the plurality of NL documents by interpreting a corresponding document information based on the plurality of pattern trees and the metamodel using a pattern interpretation technique.

Abstract: Enterprises need assurance that their internal documents like policies, procedures, controls, standard operating procedures (SOPS) are adherent to the regulatory obligations. In conventional practice this requires a manual effort where legal, business and IT experts collaborate for assuring completeness and consistency in enterprise documents with respect to regulatory obligations, thereby establishing regulatory hygiene. Governance risk and compliance (GRC) frameworks help experts with collaboration but do not provide automation aids necessary to reduce the analysis and synthesis burden. Present disclosure provides system and method for determining hygiene in the plurality of enterprise documents with respect to the plurality of regulatory obligations by extracting concept ontology models from multiple enterprise documents and multiple regulations and enabling navigation across multiple documents via the ontology.

Abstract: Organizations are struggling to ensure business continuity without compromising on delivery excellence in the face of pandemic related uncertainties which exists along multiple dimensions effected by authorities. This uncertainty plays out in a non-uniform manner thus leading to highly heterogeneous evolution of pandemic. Present disclosure provides digital twin based systems and methods for business continuity plan and safe return to workplace wherein a simulation-based data-driven evidence-backed approach is implemented that captures details pertaining to virus, individualistic characteristics of employees and their dependents, offices, locations of the employees and offices, and various pandemic control measures that are in effect and need to be explored using a hybrid modelling and simulation approach that combines fine-grained actor/agent model and coarse-grained stock-and-flow model.

Abstract: This disclosure relates to the field of project related document analysis. Conventionally, process of retrieving right information related to a stakeholder with relevant project initiation concerns involves manual intervention resulting in more time consumption. The method of the present disclosure describes a system and method for automated extraction and classification of project initiation related information from request for proposal response documents The RFP response document is parsed using document structure based parsing technique to identify questions and answers. The questions from the RFP response document are classified into different classes of interest and important information from the answers are extracted and mapped to an identified class. The method of the present disclosure demonstrates significant improvement in terms of time consumption by reducing volume of information and providing a quick access to class-specific relevant information from the RFP response document.

Abstract: The present disclosure provides a dynamic functional adaptation of software systems based on real time need, which is unaddressed by the conventional methods. Initially, the system receives an output generated by a software system under execution corresponding to a given input. Further, a deviation in the generated output is identified based on a comparison between the generated output and a corresponding expected output. Further, an optimal policy is identified based on the generated output and the identified deviation using a reward based Reinforcement Learning (RL) model. Further, an adaptation specification is generated based on the identified optimal policy using an Adaptation Specification Generation (ASG) technique, Further, a software code is generated dynamically for the identified optimal policy using an ASG based code generation technique.

Abstract: The present disclosure provides a holistic model for feature specification and dependency representation for requirement specification documents where the conventional models fail to provide. The present disclosure receives a plurality of requirement specification documents and a related data. A product feature model is generated based on the plurality of requirement specification documents and the related data using a feature model generation technique. The product feature model includes a plurality of product feature elements. The plurality of product feature elements includes a feature area, a major feature and a plurality of features. A specification model is generated further for each of the plurality of features using a specification extraction technique. Post generating the specification model, a plurality of dependency associations are generated for each of a plurality of specification elements of the specification model using a dependency extraction technique.

Abstract: The present disclosure extracts contextual product feature model from requirement specification documents where the conventional methods fail to perform. Initially, the system receives a plurality of requirement specification documents pertaining to a product, a domain dictionary, a plurality of configuration parameters, and a plurality of extraction patterns. A product feature model is generated using a NLP based feature extraction technique. The product feature model includes a plurality of product feature elements comprising a feature area, a major feature and a plurality of features arranged hierarchically and classified into feature types. Further, a plurality of ContextType associations like core, client, geography and market are extracted for each of the plurality of features using a ContextType extraction technique. Finally, the plurality of ContextType associations is updated in the product feature model to obtain a contextual product feature model.

Abstract: This disclosure relates generally to the method and system for dynamically optimizing the operations of logistics management system. The existing methods for optimizing the operational processes in the sorting terminals are not dynamic as the sorting terminal operations are largely manual and experience driven. The proposed method and system describe an actor-based representation of simulatable digital twin of the sorting terminal that enables in-silico quantitative exploration of design space to help human experts arrive at the right decisions related to the logistics management system. The proposed method and system initially construct a high-fidelity simulatable digital twin of the sorting terminal, validate it, set it up with real data, and simulate various adaptation and design alternatives to understand their impacts on the key performance indicator values.

Abstract: A method is provided for decoding an encoded video stream on a processor having a plurality of processing cores includes receiving and examining a video stream to identify any macroscopic constructs present therein that support parallel processing. Decoding of the video stream is divided into a plurality of decoding functions. The plurality of decoding functions is scheduled for decoding the video stream in a dynamic manner based on availability of any macroscopic constructs that have been identified and then based on a number of bytes used to encode each block into which each picture of the video stream is partitioned. Each of the decoding functions is dispatched to the plurality of processing cores in accordance with the scheduling.

Abstract: 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.

Abstract: This disclosure relates to method of extracting an information associated with design of formulated products and representing as a graph. A graph domain model of a plurality of vertices, and at least one formulation text as text file are received as an input. The information extraction is applied to identify at least one sentence and extract at least one subject-verb-object triple from every sentence of the at least one formulation text. A sentence including an ingredient listing and associated weights indicated by presence of weight numerals, and a sentence including at least one verb from the at least one subject-verb-object based on the graph domain model are classified. A representation of the recipe text is generated in terms of at least one action, ingredients on which the at least one action is performed, and condition. An insert query string is generated and executed to store the formulations as the graph.