Tata Consultancy Services Patente

This disclosure relates generally to processing of gas using packed bed column, and more particularly, a split bed packed column for processing gas. In current packed bed column configurations high pressure drop in packed column leads to increased energy consumption and puts a restriction on using high feed velocities, resulting in high cycle time. This results in increased requirement of adsorbent for processing of gas which thereby increases the operational expenses. The disclosed configuration includes multiple beds inside the same column differentiated by partitions instead of a single long bed inside the column. This configuration enables decreasing the pressure drop along the length, resulting in decreased energy consumption for the same cycle time, without compromising on other process key performance indicators. The disclosed configuration is used for gas separation via adsorption, thermal energy storage applications and so on.

This disclosure relates generally to a method and system for optimal power allocation in large-scale electric vehicle (EV) charging network having cluster topology. Currently, methods based on mathematical and constraint programming provide solutions for optimal power allocation but are computationally slow on large problem sizes. Reinforcement learning (RL) based learning-to-optimize (L2O) are also used for solving, but the solutions these methods provide impact the solution quality. The present disclosure provides mathematical programming-based method for optimizing power allocation decisions. The power allocation problem for the EV charging network is modelled with constraints on energy demand, power supply capacity, available network capacity, customer types and their priority level. Further the optimization problem is formulated to maximize the power flow to prime customers in the EV charging network. The problem is formulated using a mixed-integer linear program.

Log data recorded during digital assessments has hardly been ingested for analyzing log information to derive behavioral patterns that enable malpractice detection. A method and system for candidate response analysis during timed interval of digital assessment using statistical approach. The method provides a two-stage approach wherein the first stage analyzes raw log data of candidates to extract a set of attributes capturing activities of candidates in response to the questions appearing in the digital assessment. Unique statistical analysis is performed on the extracted attributes to determine outlier candidates that reflect anomalous behavior. At the second stage the set of outlier candidates are analyzed and filtered based on a plurality of checks against events that contribute towards possible malpractice or unfair means involved before and during exam duration. Thus, the outlier candidates that map to any one of the checks are identified to be attempting exam with Unfair means (UFM).

Most of the existing testing tools automate the process of test case execution, however, these depend largely on an expert to manually write the test cases and pass parameters of the software to the test cases. Embodiments of present disclosure provide a method and system for automated parameterized software testing. The method involves- test case design, test case execution and report generation. The test case design involves identifying a set of parameters from a test automation suite for testing a web-based software application. Then, relevant test cases are identified, and a second set of parameters are obtained from the web-based software application. Next, test case execution is done through one or more of: i) a code development tool, ii) a command with arguments, iii) a build tool, and iv) an application server. Finally, a report is generated based on test case execution indicating the status of the test cases.

Existing model deployment approaches have the disadvantage that they do not consider feasibility of cloud instances for hosting a given LLM model. Embodiments disclosed herein provide a method and system for deployment of LLMs in a plurality of cloud instances. The system checks feasibility of the plurality of cloud instances for hosting an LLM, based on size of the LLM and storage space in each of the cloud instances. Further, a latency value for a plurality of batch sizes is determined for a plurality of LLM-accelerator pairs, in each of the plurality of cloud instances identified as feasible based on the feasibility check, using a performance model. Furthermore, a recommendation of one of the plurality of cloud instances identified as feasible is generated, based on the determined latency, a measured cost of deployment, a user workload, an application type, a plurality of latency constraints, and an evaluated performance.

The embodiments of the present disclosure herein address unresolved problems of cyclic dependency between cost of power procurement and routing of one or more electric vehicles (EVs) of a logistic enterprise. Embodiments herein provide a method and system for an optimization framework to overcome a cyclic dependency between cost of power procurement and routing of electric vehicles (EVs) of a logistic enterprise. The optimization framework comprises two independent routines, coupled through an exchange of parameter values. First routine optimizes routing cost of the EVs to satisfy delivery constraints by assuming that the average EV charging cost is known. This is given as input to a second routine that optimizes electricity procurement cost from different energy sources by assuming that the EVs routes are fixed. The output from the second routine is then given back as input to first routine, and the procedure iterates till a stopping criteria is reached.

Existing approaches for templatization and retrieval of domain knowledge for SQL queries for the enterprise databases have the disadvantages that they do not understand the context of natural language query and retrieve the most suitable templatized domain statement for SQL query generation. Embodiments disclosed herein provide a method and system for templatization and retrieval of domain knowledge for enterprise text-to-SQL semantic parsing. In this approach, the system generates the templatized domain statements for the domain database, the templatized domain statements assist in understanding the natural language query and guides the model for generating the SQL query and retrieve the most suitable templatized domain statement for any natural language query to guide the trained model to generate the associated SQL query.

Development of a new drug molecule on average requires an expenditure of US$2-3 billion and a duration of at least 13-15 years. Subsequently, repurposing the existing drugs outside the scope of the original medical indications, has attracted serious consideration. Present disclosure provides a system and method for recommending drug candidates for a target disease by generating drug-refined Medical Subject Headings (MeSH) terms frequency matrix using MeSH terms assigned to Pubmed articles. Further, topic modelling is performed on the drug-refined MeSH terms frequency matrix to generate drug-topic conditional probabilities, and drug-drug distance is computed using Kullback-Leibler divergence. The drugs are then clustered and unknown drugs of a target disease are shortlisted from a cluster with known drugs based on predicted targets and indications of unknown and known drugs. A subset of shortlisted unknown drugs is recommended as a new candidate for target disease based on supporting literature evidence.

Brain monitoring is performed using an Ear-electroencephalography (Ear-EEG) which records synchronized electrical behaviour of neurons in brain at high temporal resolution using a wearable ear device. For proper functioning of the wearable ear device, sensor electrodes need to be within the ear to remain in constant contact with the skin. However, body movements tend to disrupt extent of contact, leading to noisy signals being captured, which are often difficult to distinguish from a valid EEG signal. Thus, identification of channels that are capturing EEG is necessary. The present disclosure provides a method and system for channel identification using manifolds of an Ear-EEG signal. In the present disclosure, a plurality of manifold features are extracted from a signal received from the wearable ear device. The plurality of manifold features are further classified as EEG and non-EEG channels using an unsupervised clustering model across real-world stimuli in the wearable ear device.

This disclosure relates generally to a method and system for aesthetically placing and subconsciously nudging a plurality of advertisement products within a display screen of a mobile device. State-of-the-art methods demonstrate traditional way of marketing a single product being promoted by an influencer in a short video on social media platforms. The influencer captures a main frame of the display screen while advertising the product. However, most of the space on the display screen as well as the budget associated with an advertisement remain under-utilized. The present disclosure addresses these problems through a method of creating an overlay of a grid having a plurality of aesthetic spaces within the display screen. The aesthetic spaces are supplemented with an aural and an haptic experience via embedded audio files. The grid dynamically aligns the plurality of aesthetic spaces to eye-gaze hotspots to provide an individualistic personalization of the grid.

Multiple Instance Learning (MIL) is a useful method for extracting information from Gigapixel images using weakly supervised learning. Traditionally, it was approached with the hypothesis of considering each instance as an independent and identically distributed entity. Recently, it has been suggested that correlations between different instances would yield better results for classification. The present disclosure is designed to improve classification performance significantly. The self-selective MIL of the present disclosure facilitates binary classification with enhanced insights into more aggressive tumor regions.

Existing multi-agent system are employed collaboratively, where each agent specialized in extracting entities, relations, or events from documents which is a more generalized collaborative framework without specific customizations. The disclosed method presents a collaborative multi-agent knowledge graph Retrieval-Augmented Generation (RAG) framework using a plurality of collaborative multi-agent LLMs for extracting information from a plurality of multimodal documents and constructing the unified knowledge graph to facilitate accurate and efficient query-based retrieval. A query generator agent generates a plurality of queries that aim to uncover all possible information present in the plurality of multimodal documents. A domain model is generated by a domain model generator agent based on the plurality of queries. The domain model populator agent populates the domain model with data extracted from the plurality of multimodal documents. A unified knowledge graph is constructed from ta plurality of populated domain models and organizes the extracted information for accurate retrieval.

This disclosure relates generally to a method of separating one or more target gases from a flue gas using fixed bed reactors. The target gas separation involves swing-adsorption mechanisms. State-of-the-art methods utilizing fixed beds suffers relatively high-pressure drops. To overcome the pressure-drop, coarser particles are used but this further lowers the adsorption capacity of the target gas. These drawbacks also affect the size of the fixed bed reactor, limiting its applicability for gas separation in mobile sources. The present invention provides the target gas separation from the fixed beds that involves simultaneous and sequential treatment of the flue gas in various stages of the swing-adsorption mechanisms. An integral heat recovery unit facilitates a plurality of transfers during target gas separation from the flue gas and manages entire heat requirement of the target gas separation process.

The disclosure relates generally to methods and systems for dynamic drug design of a pharmacological target. Conventional techniques in the drug design that use in-silico, in-vitro, and in-vivo approaches are not explicitly mentioned workflow related details for identifying novel molecules. The methods and systems of the present disclosure make the drug design dynamically by integrating the in-silico, in-vitro and in-vivo approaches through the dynamic generative artificial intelligence (GenAI) and artificial intelligence (AI) technologies. The integration of in-silico (molecular modeling and AI), in-vitro and in-vivo approaches helps in designing the novel optimized lead molecules. Optimization and prediction of ADMET based on QM based descriptors help in filtering the molecules.

Proliferation of edge devices has significantly advanced technologies in sectors such as autonomous driving and surveillance. However, deploying machine learning models on these resource-constrained devices presents challenges including scalability and managing unpredictable workloads thereby affecting real-time performance in edge-only environments. The present disclosure discloses a method and system for workload deployment in a content guided and service level agreement (SLA) aware edge-cloud architecture. In the present disclosure, a camera feed content-guided load balancing technique is provided that dynamically manages workloads between edge and cloud. Features are extracted from an incoming camera feed to perform load-balancing process efficiently. The load balancer determines a maximum number of concurrent feeds for processing at the edge, with the remaining feeds handled by the cloud based on content of the incoming camera feed. Additionally, a cost model to estimate expenses of deploying the edge-cloud architecture in real-world scenarios with dynamic workloads is provided.

Conventional models extract chemical data through querying tables by decomposing complex user queries. This disclosure relates generally to a method and system for extraction of chemical molecules and associated target properties from text and complex tables using Large Language Models (LLMs). The disclosed method extracts a plurality of molecular property values associated with the chemical molecules, from a plurality data sources, via generating a chemical composition schema utilizing LLMs. The chemical composition schema acts as a lens to view tabular information and a text. Relevant tables are identified and tabular information comprising chemical composition instances are extracted from a plurality of documents utilizing the chemical composition schema, along with LLMs and prompting techniques. The chemical composition instances are curated and reconciled into a unified knowledge graph, which is then used for querying. The disclosed method ensures precision in tabular information extraction without the need for extensive model training.

Current approaches for cross pollination contamination detection mainly rely on field surveys that are generally costly and consumes lot of time. The genetic purity of seed estimation techniques needs specialized equipment which again are associated with high cost and longer time. Present disclosure provides method and system for genetic purity estimation and cross-pollination detection in agricultural fields. The system receives remote sensing, weather, historical and soil health data of region of interest. The system then uses data to calculate contamination score representing contamination due to cross pollination. Thereafter, system uses data to determine field suitability and stress score. Further, system detects deviations in crop characteristics indicative of off-types in and around region of interest based on data. Finally, system estimates genetic purity of seeds coming from crop plotted in region of interest based on contamination, field suitability and field stress score, and off-types to determine genetic purity of seed score.

Method and system disclosed uses LLM with multi lingual support to helps identify phishing emails. The method creates sufficient visual indication for the user to notice it as an unusual email message. The method illustrates the email using relevant illustrations and / or emoticons in the notification bar of the phone or the desktop or web email client. Changes the font type face with kerning models as per the device form factor. By doing this disguised domain names or misspelled words can easily be noticed by the user. A sentiment analysis is utilized to identify if the email message is creating a sense of urgency for purported negative or positive event for the user. The result of sentiment analysis is then used to also recommend 2 factor authentication. Thus allow user to establish credibility of the email. accordingly the email header and contents are updated appropriately.

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Existing techniques fail to propose a method for improving model parameters optimization and seamless integration of clinical data with computational model required for personalized cardiac hemodynamic model development. This disclosure relates to a system and method, which receives one or more model parameters comprising height, active pressure components and passive pressure components specific to left ventricle from cardiac hemodynamic model. The first set of values corresponding to one or more input parameters comprising end systolic diameter and end diastole diameter are received from subject specific Echo data. The second set of values corresponding to one or more model parameters are estimated using one or more input parameters. The estimated second set of values are optimized using a Particle swarm optimization to obtain one or more optimized values. Select at least a subset of optimized one or more values corresponding to one or more model parameters for personalization.

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Accurate onset detection helps in fine-tuning training regimens. However, the chaotic nature of raw EMG signals, contaminated with noise and interference from various sources, often complicates the task of accurate onset/offset detection. For the same reason, existing signal processing systems struggle to perform the onset and offset detection effectively, which in turn affects end applications. Embodiments disclosed herein provide a method and system for signal elevation based muscle activity detection. The system performs the signal elevation to highlight and detect onset and offset regions in a signal being processed. Further, based on the determined onset and offset regions, a muscle potential activity of the subject is determined.

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Technical challenge in unusual human activity detection task is to rightly identify only unexpected or unusual movements from constant regular movements present in a scene, with most techniques built on understanding that camera is static. However, ego view camera of mobile surveillance robot is in motion as robot navigates. Embodiments herein provide a method and system for anomalous activity detection for mobile surveillance robots by mimicking 'Konio-Parvocellular-Magno' cells of the human brain into a NN model, which are responsible for detecting slow, normal, and swift changes in perceived scenes. To detect anomalous activity, the static or normal movements of scene captured by ego view camera are identified as redundant information and only RoI is forwarded for further processing using the Optical flow and SSIM techniques. The NN model mimicking KPM is trained only on the RoI to detect normal or anomalous activity.

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Precise estimation of duration of cover crop is a challenge considering multiple factors contributing to the same and complexity involved in capturing them in the estimation process. A method and system for estimation of cover crop duration and generating Integrated Cover Crop Index (ICCI) is disclosed. Firstly, the maincrop is identified and associated time series data is eliminated to avoid false positives. Detection of type of cover crop and its exact duration is derived by integrated use of satellite remote sensing data, sensor data, field observations and phenology based indicators. Duration of cover crop is estimated considering the impact of snow cover, dormant period etc., by integrated use of remote sensing and sensor data along with local domain crop knowledge of the region. The ICCI provides quantitative measure for cover crop effort and can be used for incentivizing farmers following sustainable cropping practices.

Existing approaches provide limitations to scale Hybrid DFT calculations to large systems and there is no such scalable technique to correct errors during Hybrid DFT computation with tensors. This disclosure relates generally to implementation of Hybrid Density Functional Theory on Quantum Processors with Moller-Plesset Perturbation (MP2) corrected simulation. The system receives a plurality of inputs from a classical computer component to transmit to a quantum computer component. Initially, a density matrix is computed from a core Hamilton. Further, the quantum processor computes a direct (J) matrix, a Hybrid matrix, and a correlation exchange potentials to compute a Fock matrix. Further, the density matrix is iteratively performed for a criteria where difference between the density matrix and an updated density matrix converges to a value below a threshold convergence error. MP2 energy corrections are performed for extracting the plurality of properties associated with the chemical compound.

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