Tata Consultancy Services Patente

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Industries deploy a plethora of sensors that are attached to a system or human being, respectively. Under multi-sensor environment scenarios, there is a need to detect which sensors are behaving similarly within a time span. Sensor values often vary in range of values yet depict similar time series characteristic and sometimes have a phase difference in operation, thus making it impossible to detect such sensor similarity in a large system where the number of input parameters/ sensor observations. Systems and methods of the present disclosure determine similar behavioral pattern between time series data obtained from multiple sensors and cluster the sensors. The system implements a pattern recognition-based approach to find the similarity and then applies a Dynamic Programming-based approach to detect similarity in at least two time series data and cluster the sensors and corresponding time series data into specific cluster(s).

This disclosure relates generally to a method and system for classification of cognitive load (CL) using data obtained from wearable sensors. The disclosed method uses a multi-modal based approach using wrist-worn sensors for real time monitoring of CL in real world scenarios and improves the accuracy of detection of CL. A set of distinguishing features are selected from physiological signals received from the wrist-worn sensors. These features are used for training a classification model for classifying the CL of a patient into a no load or a high load. The set of distinguishing features are selected from domain specific features and signal property based generic features of the physiological signals. The disclosed method is used for classification of CL in scenarios such as to check how the cognitive load of a candidate varies during interviews, to assess the participants workload during online meetings and so on.

Embodiments provide a method and system for audio generation from contextual text input is provided. The disclosure gives due importance to the granularity of the content. The system allows the user to specify the properties of the audio to be generated. Here, context is used to identify the importance of a particular sound over the others and thus automatic adjustments of the audio output to give a more realistic feel. The system generates dataset for training audio models. The user can give input query in natural language and the audio requested will be generated for training and developing the necessary classification or other necessary audio models. The system provides a feature of automated fine-tuning of the model parameters to suit the new automatically collected training data. Furthermore, the system provides a pre-trained inbuilt model repository with audio models belonging to the main categories of noises.

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This disclosure provides a simulation platform to study and perform predictive analysis on valvular heart disease, Mitral stenosis (MS) and provides a control approach to correct hemodynamic imbalances during MS conditions. Conventional approaches of valve repair or replacement are often associated with risk of thromboembolism, need for anticoagulation, prosthetic endocarditis, and impaired left ventricle function. The cardiovascular hemodynamics model of the present disclosure helps to create 'what if conditions to study variations in different hemodynamic parameters like blood flow, aortic and ventricular pressure, etc. during normal and pathological conditions. An adaptive control system in conjunction with the hemodynamic cardiovascular system (CVS) is provided to handle hemodynamic disbalance during moderate to severe MS conditions. The adaptive controller is hypothesized in line with the neuromodulation approach and modulates left ventricular contractility and vagal tone to counter the symptoms associated with MS.

State of art techniques performing image labeling of remotely sensed data are computation intensive, consume time and resources. A method and system for efficient retrieval of a target in an image in a collection of remotely sensed data is disclosed. Image scanning is performed efficiently, wherein only a small percentage of pixels from the entire image are scanned to identify the target. One or more samples are intelligently identified based on sample selection criteria and are scanned for detecting presence of the target based on cumulative evidence score Plurality of sampling approaches comprising active sampling, distributed sampling and hybrid sampling are disclosed that either detect and localize the target or perform image labeling indicating only presence of the target.

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This relates generally to a method and a system for spatio-temporal polarization video analysis. The spatio-temporal polarization data is analyzed for a computer vision application such as object detection, image classification, image captioning, image reconstruction or image inpainting, face recognition and action recognition. Numerous classical and deep learning methods have been applied on polarimetric data for polarimetric imaging analysis, however, the available pre-trained models may not be directly suitable on polarization data, as polarimetric data is more complex. Further compared to analysis of the polarimetric images, a significant number of actions can be detected by polarimetric videos, hence analyzing polarimetric videos is more efficient. The disclosure is a spatio-temporal analysis of polarization video. The disclosed techniques include configuring a set of parameters from the polarization video to train a spatio-temporal deep network architecture for analyzing polarimetric videos for computer vision applications.

This disclosure relates generally to a method and system for identifying candidate bioreceptors suitable for designing biosensors to identify an analyte of interest. State-of-art methods mainly focus on interaction of the bioreactor and the analyte. However, interaction of the bioreceptor with a substrate before binding to the analyte as well as a biofluid surrounding the bioreceptor and the analyte greatly influences such interactions. The present method utilizes combined approach of molecular dynamics (MD) and artificial neural network (ANN) based screening to systematically identify candidate bioreceptor with favorable energy profile and feasible interactions with the analyte of interest. The method involves computing RMSD plots to study individual interactions among a bioreceptor-substrate, a bioreceptor-analyte and an analyte-bioreceptor-substate complex. Further, potential of mean force (PMF) is computed for the analyte-bioreceptor-substate complex. The RMSD plots and PMF features are fed to an ANN model that predicts the suitable candidate bioreceptors through feature engineering.

As the withering process of tea leaves takes a long time to reach a desired moisture level, estimating when it is time to move to the next step to reach the target tea leaf moisture is difficult and inefficient. Method and system disclosed herein provide an approach for withering schedule prediction of tea leaves. The system, by performing a spectral data analysis on an image of a plurality of tea leaves, estimates the moisture percentage in the plurality of tea leaves, for a selected time stamp. Based on the predicted moisture level, a current temperature value, a current relative humidity value, and a current time stamp, the system generates a withering schedule for the plurality of tea leaves. The generated withering schedule is fine-tuned based on a course correction of an impact of deviation in one or more ambient parameters on the prediction of the withering schedule.

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Warehouse management is an important aspect in agricultural sector due to dynamism and vulnerability to multiple risks for crop yield storage and processing. Conventional centralized warehouse management possess limited accessibility to stakeholders. The present disclosure addresses the unresolved problem of the conventional approaches by providing a system and method for creating non-fungible token (NFT) based dynamic decentralized network for warehouse commodity management. NFTs are generated for warehouses where commodities are stored with a warehouse receipt (WHR) as security collateral. Generation of NFTs for warehouses helps to create a dynamic decentralized network for warehouse commodity management wherein various stakeholders can participate while satisfying relevant quality and regulatory compliance. Further, a dynamic collateral value is determined for the generated NFT of the warehouses and WHR based on sustainable cultivation, transport and storage. Furthermore, an optimal commodity selling schedule is generated based on commodity quality, risk and market demand in real time.

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The present disclosure paves way for transferring of application from classical cryptographic algorithms to post quantum cryptographic algorithms code, due to increased vulnerability to risks in classical cryptographic algorithm. Since Post Quantum Cryptographic (PQC) algorithms are more secure, they result in more execution time and resource requirements. Hence, it becomes necessary to select those PQC algorithms that satisfy enterprise constraints on time, computation cost and memory. Due to presence of various PQC algorithms and security levels with different key size and operations there is a requirement for recommender that can ensure optimal conversion from classical cryptographic schemes to PQC schemes at appropriate security levels. This lowers the risk and the selected PQC algorithms also satisfies the enterprise security requirements.

Unlike rule based offer generation in state of the art, a method and system for personalized promotion offer generation by understanding customer buying intent to maximize return on investment is disclosed. Product and associated customer details are obtained from product catalog and transaction data. RFM score is computed based on transaction data to create customer segments capturing demographics behavioral data for customer. A tree based ensemble classifier predicts promotion offer redemption probability for each customer segment. The linear optimization model assigns an optimal number of promotion offers, to attain an increasing profit margin, thereby generating offers as per relevance. Budget constraint is applied so that maximum revenue can be attained. The final per customer allocation of the offer is based on different rules of personalization as required from the industry. The eligibility of getting gift card is computed by setting threshold of what customer should spend to get gift card.

In state of art techniques, it is challenging to predict how a specific Ankle Foot Orthosis (AFO) will impact muscle action and reduce an energy cost of walking for individual subjects. The disclosed method focusses on personalized and optimal selection of an AFO controller using an AFO torque, and a plurality joint ankle angles of each of a plurality of AFO controllers integrated with a musculoskeletal human lower limb model (MHLLM). The plurality of muscle forces is computed using the MHLLM for each of the plurality of AFO controllers. Further the method computes a plurality of muscle response metrics, from the plurality of muscle forces and an additional joint torque for each of the AFO controllers. Further the method combines the plurality of muscle response metrics which enables the selection of a personalized optimal AFO controller among the plurality of AFO controllers of a (cerebral palsy) CP subject.

The present disclosure is related to methods and systems for predicting a category of mammographic breast density (MBD) for a subject using a microbial profile obtained from biological sample of the subject. The state-of-art diagnostic/ screening strategies for breast cancer are limited by one or more of factors like technical shortcomings, radiation exposure, and physical discomfort. In the present disclosure, a biological sample is collected from a subject. Then a quantitative abundance of each of a plurality of predetermined microbes associated with the biological sample is determined using a set of probes through a multiplex quantitative Polymerase Chain Reaction (qPCR) technique. Further the quantitative abundance is collated to obtain a microbial abundance matrix. Next a model score is determined based on the microbial abundance matrix, using a pre-determined machine learning (ML) model. Lastly the risk category of breast cancer of the subject is assessed based on the model score.

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This disclosure relates generally to system and method to track performance of an AI application during development and production lifecycle. Libraries and tools that are used to develop an enterprise AI application are rapidly expanding across vendors and open-source community. These libraries are coming up with new features and breaking changes with new versions making it difficult to application developer and enterprise runtime executor. The method of the present disclosure receives an enterprise artificial intelligence (AI) application and corresponding software environment as input generate a logger plug-in using one or more fine-tuned large language models (LLMs) based on the plurality of instructions provided by the one or more instructors. Additionally, the logger plug-in is utilized to validate correctness of errors and then the logger plug-in is executed with the enterprise AI application to log and track performance of the enterprise AI application.

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There is a need to address the problem of compressed scanning in SAR acquisitions for microwave imaging. Embodiments of the present disclosure provide microwave imaging with multiple-input and multiple-output synthetic aperture radar using compressed multi-coset range migration technique. The present disclosure reduces the SAR acquisition time by compressed scanning, where a compressed scanning acquisition setup captures radar measurements to obtain a microwave image of a target scene by intermittently skipping blocks during SAR acquisition. Further, reconstruction of a microwave image of a target scene is performed using 2D Fourier Transform (FT) of the radar measurements based on a multi-coset range-migration framework. The multi-coset range migration framework makes use of intermittent scanning and formulates a compressed sensing-based architecture by leveraging block-sparsity constraints. Subsequently, a denoising convolutional neural network (DnCNN) is used to enhance and denoise the reconstructed microwave image to obtain a high-resolution image of the target scene.

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