Tata Consultancy Services Patente

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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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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.

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.

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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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Metacognitive confidence is defined as the confidence generated from the observation and critical analysis of one's own the decision making process. There are various studies indicative of the importance of measurement of confidence level of the person while doing a task. The existing confidence level measurement methods provide various limitations such invasive and complex experimental setup, noise and artefacts in the signal. A system and method for determining confidence level of a person using electroencephalogram has been provided. The system is configured to build a metric to determine the amount of metacognitive confidence, in presence of different cognitive load condition, directly from brain activity using electroencephalogram signals. The brain activity acquired from the frontal and temporal part of the brain at different frequency bands and combined with suitable weights to form the confidence metric.

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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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A method and system of a low-latency FPGA framework based on reliable UDP and TCP re-assembly middleware is disclosed. The need for low-latency communication in digital systems has increased drastically. The disclosed FPGA framework enables low-latency communication as a hybrid framework that supports both UDP & TCP communication. As known in art, TCP provides error checking support hence making TCP more reliable as compared to UDP, while UDP is faster but not reliable. Hence the disclosed low-latency FPGA framework latency utilizes the advantage of both UDP and TCP by utilizing UDP for its speed, while switching to TCP in case of a missing sequence in UDP. Further, the disclosed system proposes a TCP re-assembly middleware architecture for processing TCP with a lower-latency, wherein the TCP re-assembly middleware is an independent middleware that is a modular and a plug-play independent middleware.

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This disclosure relates generally to visio-linguistic understanding. Conventional methods use contextual visio-linguistic reasoner for visio-linguistic understanding which requires more compute power and large amount of pre-training data. Embodiments of the present disclosure provide a method for visio-linguistic understanding using contextual language model reasoner. The method converts the visual information of an input image into a format that the contextual language model reasoner understands and accepts for a downstream task. The method utilizes the image captions and confidence score associated with the image captions along with a knowledge graph to obtain a combined input in a format compatible with the contextual language model reasoner. Contextual embeddings corresponding to the downstream task is obtained using the combined input. The disclosed method is used to solve several downstream tasks such as scene understanding, visual question answering, visual common-sense reasoning and so on.

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Accurate detection of anomaly in sensor signals is critical and can have an immense impact in the health care domain. Accordingly, identifying outliers or anomalies with reduced error and reduced resource usage is a challenge addressed by the present disclosure. Self-learning of normal signature of an input sensor signal is used to derive primary features based on valley and peak points of the sensor signals. A pattern is recognized by using discrete nature and strictly rising and falling edges of the input sensor signal. One or more defining features are identified from the derived features based on statistical properties and time and frequency domain properties of the input sensor signal. Based on the values of the defining features, clusters of varying density are identified for the input sensor signal and based on the density of the clusters, anomalous and non-anomalous portions of the input sensor signals are classified.

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Product recommendation is a very important aspect of e-commerce applications. Traditional product recommendation systems recommend products similar to a query image provided by a user and allows minimum or no personalization. It is challenging to incorporate personalization due to the presence of overlapping fine-grained attributes, variations in attribute style and visual appearance, small inter-class variation and class imbalance in the images of products. Embodiments of present disclosure address these challenges by a method of personalized substitute product recommendation using Personalized Attribute Search Networks (PAtSNets) comprising neural network layers interleaved with Attentive Style Embedding (ASE) modules to generate attribute-aware feature representation vector of a query image provided by the user and conforming to the personalization instructions specified by the user. This feature representation vector is then used to recommend substitute products to the user. Thus, embodiments of present disclosure enable accurate substitute product recommendation suiting user requirements.

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Conventional fork type autonomous mobile robots (AMRs) are suitable to handle pallets and are typically designed with two forks. Such AMRs are very bulky and designed for a cart handling application, and usually have large openings and less suitable for lifting roller carts. Present disclosure provides chassis with integrated single fork assembly for AMRs/Autonomous Guided Vehicles (AGVs) for transporting roller cages/carts within warehouses. Chassis with integrated single fork assembly enables performing tasks given by end users. Chassis carries steer and drive wheel and swivel wheels to increase stability of the AMR wherein a fork mechanism is provided which includes fork wheels and lifting mechanism. Such design and mechanism of the chassis with integrated single fork assembly overcomes the limitations of smaller widths between wheels of roller cage/carts for placement/movement of payload within warehouses and logistics environments and counter imbalance and deflection of payload and fork assembly caused therebetween.