Tata Consultancy Services Patente

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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.

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.

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.

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 disclosure relates generally to methods and systems for monitoring lubricant oil condition using a photoacoustic modelling. Conventional techniques in the art for checking the condition of the lubricant oil is laboratory based and thus time consuming, error prone and not efficient. The present disclosure discloses a photoacoustic simulation model which is developed utilizing a photonic model such as a Monte Carlo method-based optical simulation integrated with a finite element model such as a k-wave toolbox-based acoustic measurement. The photoacoustic simulation model of the present disclosure is used to obtain a photoacoustic signal of the lubricant oil sample and a set of statistical features are determined from the obtained photoacoustic signal. The determined set of statistical features are then used as a training data to develop a machine learning (ML) model which is used to classify a type of contamination of the test lubricating oil.

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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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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.

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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Agriculture is impacted due to various parameters that results into heavy losses to the crops. Existing methods rely on manual inventories from fields and statistical results are prone to errors thus classification of crops may not be accurate. Present disclosure provides systems and methods for detection and prediction of disguised and potential non-performing assets (NPAs) in agriculture. Disguised NPAs detection involves, obtaining satellite data from sources and natural calamity, detecting of crop for past years, crop growth, estimating yields, market prices for individual years, AC zones, etc. wherein a generate performance score is generated for each field for classifying crop as disguised NPA. Predicting potential NPAs includes obtaining current season satellite data, data received from the fields via sensors, determining crop protocol being followed, forecast weather, estimating cumulative growth, feedback received from fields, projected yield, estimating crop losses, and then performance score is generated for predicting crops as potential NPA.

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Many microbes are capable of synthesizing anti-cancer products, however existing state of art is limited by focus on industrial production of the said products. A method and system for preparing a knowledgebase of microbes and microbial functions to identify good and bad microbes have been provided. The present disclosure therefore further describes methods and compositions for the risk assessment, prevention and management of various forms of cancer by using microbes, microbial products utilizing the knowledgebase of microbes and microbial function. The method is configured to priming the microbes inside the host for boosting the immune response against cancer initiation, progression, recurrence and associated side effects. The use of microbes and microbial products can be provided in the form of probiotics, supplements, and prebiotics etc. along with creation of right sets of nutrition conditions in the host for the proper functioning of the microbes and microbial products.

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