Tata Consultancy Services Patente

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Human-understandable explanations of Artificial Intelligence (AI) based models are crucial to building transparency and trust in AI based solutions. More importantly, these explanations need to be contextual, applicable to the domain the model is used in and relevant to the concerned stakeholder. Conventionally, there is a lack of communicating these explanations to various stakeholders in a language that they can understand and relate to. The present disclosure facilitates the conversational agents (chat bots) with intelligence and actions that would help them communicate the right information to the right stakeholder in the right way. In the present disclosure, contextual explanation for user queries is generated based on the output from AI models. Here, the impacting features are obtained from the explainer model associated with the prediction model and the contextual information is generated. Further, the contextual information is converted to the contextual explanation to the user.

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The disclosure relates generally to methods and systems for generating an optimized planting schedule of a crop to overcome storage capabilities. Conventional techniques in the art are limited in dealing with actual planting schedule of the crops in accordance with the storage capacities, thus leading to suboptimal harvest cycles that fail to meet the optimal storage requirements. In accordance with the present disclosure, the optimization model makes use of the cumulative maturity value (CMV) data for each day of the planting period and the harvest period of the crop, and optimizes the planning associated with planting of crops for a set of farms so that interval between harvest period is minimized and the end-of-harvest produce volumes meet certain thresholds to facilitate storage of all procurement without wastage and as per the market demand.

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The disclosure herein relates to methods and systems for enabling human-robot interaction (HRI) to resolve task ambiguity. Conventional techniques that initiates continuous dialogue with the human to ask a suitable question based on the observed scene until resolving the ambiguity are limited. The present disclosure use the concept of Talk-to-Resolve (TTR) which initiates a continuous dialogue with the user based on visual uncertainty analysis and by asking a suitable question that convey the veracity of the problem to the user and seek guidance until all the ambiguities are resolved. The suitable question is formulated based on the scene understanding and the argument spans present in the natural language instruction. The present disclosure asks questions in a natural way that not only ensures that the user can understand the type of confusion, the robot is facing; but also ensures minimal and relevant questioning to resolve the ambiguities.

This disclosure relates to a method and system for real time prediction of tire remaining useful life using a tire digital twin. Herein, the digital twin of the tire monitors a plurality of tires while accounting for runtime as well as parking operation of tires. During runtime, tire goes through multiple operational changes, therefore, the system is configured to estimate the operational changes. The estimation is done in the form of index, which is compared with the manufacturers' recommended value. Similarly, while vehicle is parked, tire still goes through changes which play an important role in its overall health, like, long pending wheel rotation can lead to flat spot. Tire inflation can change owing to cool down effect as well as uncontrolled natural leakages. Further, these parameter indexes are clubbed together to calculate the health index of the tire, which is used to estimate the tire's current remaining useful life.

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In retail industry, retailers need accurate price elasticity (PE) values to offer optimal prices for products to business constraints. Existing approaches provide approximate PE values leading to suboptimal price recommendations. This disclosure relates to method of computing PE values for optimal pricing based on sequential price elasticity computation is provided. Transaction data and attribute data of products are processed to obtain preprocessed data. One or more selected models are determined based on the preprocessed data. Priors at one or more levels are computed and likelihoods are computed based on one or more monetary objectives from historical transaction data of the products. One or more parameters of one or more price elasticity distributions are iteratively determined through one or more component approaches based on the priors and the likelihoods. The PE values are derived based on ensemble techniques applied to one or more parameters of the one or more PE distributions.

Conventional systems and methods of classifying heart signals include segmenting them which can fail due to the presence of noise, artifacts and other sounds including third heart sound 'S3', fourth heart sound 'S4', and murmur. Heart sounds are inherently prone to interfering noise (ambient, speech, etc.) and motion artifact, which can overlap time location and frequency spectra of murmur in heart sound. Embodiments of the present disclosure provide parallel implementation of Deep Neural Networks (DNN) for classifying heart sound signals (HSS) wherein spatial (presence of different frequencies component) filters from Spectrogram feature(s) of the HSS are learnt by a first DNN while time-varying component of the signals from MFCC features of the HSS are learnt by a second DNN for classifying the heart sound signal as one of normal sound signal or murmur sound signal.

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A device and method is provided for the detection of diabetes in a person using pulse palpation signals. The pulse palpation signal is captured from the radial artery of the person using a photo-plethysmograph (PPG) sensor. The PPG signal is then preprocessed by a processor. The preprocessed PPG signal is then analyzed by the processor to detect the peak in the PPG signal. The detected peaks are used to extract a first set of feature parameters. The first of feature parameters are compared with a second set of feature parameters, wherein the second set of feature parameters are extracted from the control group of individuals. Based on the comparison it is detected that the person is one of in normal condition, pre-diabetic condition or a diabetic condition. According to another embodiment, the invention also provides a method to determine the severity index and progression risk of diabetes in the person.

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Advanced analytics refers to theories, technologies, tools, and processes that enable an in-depth understanding and discovery of actionable insights in big data, wherein conventional systems and methods may be prone to errors leading to inaccuracies. Embodiments of the present disclosure provide systems and methods for performing data analytics in an IoT platform wherein input data pertaining to problem type, solution associated thereof, sensory information corresponding to sensors deployed in the platform, knowledge of domain expert(s) specific to input data are depicted in graphical knowledge representations, wherein relationships between (i) sensors and associated attributes and (ii) the domain knowledge across the plurality of graphical knowledge representations are determined wherein Machine Learning (ML) models are then applied to the determined relationship for optimizing ML models wherein problem statement, root cause, from one knowledge to another are translated using translation technique(s) to determine root cause analysis and interpretable root cause information.

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The disclosure generally relates to determining a breathing rate and a heart rate from cardiopulmonary signal. Conventional systems use additional hardware to improve signal quality or different signal processing techniques to calculate the heart rate from the cardiopulmonary signal. However accurately determining the heart rate is always a continuous area of an improvement. The present methods and systems solve the problem of determining the heart rate accurately, from the cardiopulmonary signals, by determining the signal quality of the cardiopulmonary signal and the signal associated with the heart rate, comprised in the cardiopulmonary signal. A signal processing technique that best performs, out of a set of signal processing techniques is identified based on the signal quality to determine the heart rate. A long-term and an effective health monitoring of healthy as well as patient and infant subjects is achieved by the present disclosure.

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A system and method for real time estimation of heart rate (HR) from one or more face videos acquired in non-invasive manner. The system receives face videos and obtains several blocks as ROI (Region of Interest) consisting of facial skin areas. Subsequently, the temporal fragments are extracted from the blocks and filtered to minimize the noise. In the next stage, several temporal fragments are extracted from the video. The several temporal fragments, corrupted by noise are determined using an image processing range filter and pruned for further processing. The HR of each temporal fragment, referred as local HR is estimated along with its quality. Eventually, a quality based fusion is applied to estimate a global HR corresponding to the received face videos. In addition, the disclosure herein is also applicable for frontal, profile and multiple faces and performs in real-time.

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