Tata Consultancy Services Patente

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Conventional systems fail to recommend covenants in processes at financial institutions, and such systems rely on an underwriter who decides the covenants based on his experience without considering experience of other underwriters and similar type of customers historical records. Embodiments of the present disclosure provides systems and methods that implement data exploration analysis on received input data for categorizing covenants into various categories and training various machine learning models. The trained machine learning models are applied on test data for predicting covenants along with a probability score (e.g., also referred as optimized, unbiased set of covenants). The predicted covenants of various categories are then sorted to obtain a prioritized list of covenants for recommendation.

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This disclosure relates to a system and method for custom made medical devices life cycle management. the life cycle management of the medical devices include tracking of material and procedure to be used in additive manufacturing of the medical device and analyzing design risk as per the raw material and the design specification. Further, it includes to identify potential hazards based upon the materials, their interactions and effect on process to be followed. An artificial intelligence (AI) enabled model is trained with one or more sample datasets pertaining to a plurality of materials to be used in the additive manufacturing to audit design, to determine materials to be used, and to generate a unique code to be used to label the manufactured medical device. A smart contract of blockchain is configured to record received input and update with new points for regulatory compliance.

State of art techniques for color regeneration are complex and fail to provide color control. Embodiments of the present disclosure provide a method and system for generating color variants for fashion apparels by providing a Fashion Apparel Regeneration - Generative Adversarial Network (FAR-GAN) to generate color variants of the fashion apparels. The FAR-GAN utilizes a two-step encoding process to encapsulate both an input image and an edge-map information along with a target color embedding branch to manipulate the color information present in the fashion apparel present in the input image that is to be changed to a desired target color. Furthermore, the color and structural information is disentangled by controlling them using a color consistency loss. The FAR-GAN can be trained end-to-end without incorporating complex multi-step process.

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A method and a system for capacitive touch panel based bio-signal measurement sense a change in raw capacitance value of a capacitive touch panel. However, unlike existing methods and systems that directly utilize the raw signal for bio-signal measurements, a normalized signal is derived by normalizing a raw signal corresponding to the change in raw capacitance of the capacitive touch panel. The normalization considers interrelationships between a plurality of variables that affect the raw capacitance value such as a set of device specific parameters associated with the capacitive touch panel of the device, a set of ethnographic parameters, and metadata associated with the subject. Thus, higher accuracy in bio-signal measurement using capacitive touch panel based devices by personalizing the bio-signal measurements is provided.

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An important task in several wellness applications is detection of emotional valence from speech. Two types of features of speech signals are used to detect valence: acoustic features and text features. Acoustic features are derived from short frames of speech, while text features are derived from the text transcription. Present disclosure provides systems and methods that determine the effect of text on acoustic features. Acoustic features of speech segments carrying emotion words are to be treated differently from other segments that do not carry such words. Only specific speech segments of the input speech signal are considered based on a dictionary specific to a language to assess emotional valence. A model trained (or trained classifier) for specific language either by including the acoustic features of the emotion related words or by omitting it is used by the system for determining emotional valence in an input speech signal.

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Natural language elements are present in both the executable lines and non-executable lines of the code. Rich information hidden within them are often ignored in code analysis as extraction of meaningful insights from its raw form is not straight forward. A system and method extracting natural language elements from an application source code is provided. The disclosure provides a method for performing detailed analytics on the natural language elements, classify those using deep learning networks and create meaningful insights. The system understands the different type of natural language elements, comment patterns present in the application source code and segregates the authentic comments having valuable insights, version comments, data element level comments from other non-value adding comments. The embedded intelligence finally takes care of mapping the extracted natural language elements with the code blocks, thus making it consumable and opening a range of applications in domain contextualization, code documentation and maintenance.

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An apparatus and a method for continuous solvothermal synthesis of nanoparticles, are provided. The apparatus includes an inlet section, a reactor section, a flexible quenching unit, and an outlet section. The inlet section separately receives reactants including the solvent and a precursor solution that are allowed to flow into the reactor section. The reactor section includes multiple spiral turns such that each of the spiral turns includes a helical channel followed by a counter-helical channel for enabling mixing of the reactants to cause solvothermal reactions between them. The counter-helical channel changes the direction of flow of reactants upon flow of said reactants from the helical channel to the counter-helical channel. The flexible quenching section enclosing a portion of the reactor section quenches a slurry formed as a result of the solvothermal reactions, wherein the slurry includes the nanoparticles of targeted characteristics. The outlet section facilitates withdrawal of the slurry.

This disclosure relates generally to assessment of cognitive workload using breathing pattern of a person, where cognitive workload is the amount of mental effort required while doing a task. The method and system provides assessment of cognitive workload based on breathing pattern extracted from photoplethysmograph (PPG) signal, which is collected from the person using a wearable device. The PPG signal collected using the wearable device are processed in multiple stages that include breathing signal extraction to extract breathing pattern. The extracted breathing pattern is used for assessment of cognitive workload using a generated personalized training model, wherein the personalized training model is generated and dynamically updated for each person based on selection of a sub-set of breathing pattern features using feature selection and classification techniques that include maximal information coefficient (MIC) techniques. Finally based on personalized training model, the extracted breathing pattern is classified as high cognitive workload or low cognitive workload.

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State of the art techniques in the domain of video analysis have limitations in terms of capability to extract the spatial and temporal information. This limitation in turn affects interpretation of the video data. The disclosure herein generally relates to video analysis, and, more particularly, to a method and system for video analysis to extract spatio-temporal information from a video being analyzed. The system uses a neural network architecture which has multiple layers to extract spatial and temporal information from the video being analyzed. The method of training the neural network that extracts a micro-scale information from a latent representation of the video is presented. This is generated using an attention network, which is then used to extract spatio-temporal information corresponding to the collected video, which is then used in multiple video analysis applications such as searching actions in videos, action detection and localization.

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State of art techniques attempt in extracting insights from eye features, specifically pupil with focus on behavioral analysis than on confidence level detection. Embodiments of the present disclosure provide a method and system for confidence level detection from eye features using ML based approach. The method enables generating overall confidence level label based on the subject's performance during an interaction, wherein the interaction that is analyzed is captured as a video sequence focusing on face of the subject. For each frame facial features comprising an Eye-Aspect ratio, a mouth movement, Horizontal displacements, Vertical displacements, Horizontal Squeezes and Vertical Peaks, are computed, wherein HDs, VDs, HSs and VPs are features that are derived from points on eyebrow with reference to nose tip of the detected face. This is repeated for all frames in the window. A Bi-LSTM model is trained using the facial features to derive confidence level of the subject.

Traditionally, counterweight (123) fork type autonomous mobile robots (AMR) have been used for any kind of pallet. But the challenge is it occupies lot more maneuvering space while making turns, which cannot work in narrow operating zones. Hence fork over AMR is preferred. However, these fork over AMR have extended parts always touching the ground surface and thus are not suitable for pallets with a wooden plank at the bottom of the fork opening in the pallet. To overcome the above technical problems, an Adjustable Counterweight-based Fork Type Autonomous Mobile Robot (ACFTAMR) (100) is provided that includes chassis assembly (102) and vertical mast unit (104), a horizontal cross slide mechanism and forks (110A, 11). The chassis assembly (102) is provided counterweight assembly (122) and counterbalance shafts (126A, 128A, 128C) that move forward and backward during pickup and release of payload when the vertical mast unit (104) moves in upward/downward direction, thus providing better stability and counterbalance to the ACFTAMR (100)

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This disclosure relates generally to a Parkinson's disease detection system. Parkinson's disease is a neuro-degenerative disorder affecting motor and cognitive functions of subjects. Since symptom manifestation is limited in Parkinson's disease, identifying Parkinson's disease in the early stage is a challenging task. The present disclosure overcomes the limitations of the conventional methods for detecting Parkinson's disease by utilizing a graph theory approach. Here, each pressure sensor attached to an insole corresponding to a plurality of pressure points associated with a foot of the subject is considered as a node of a connectivity graph. The foot dynamics analysis is performed based on a metric known as mediolateral stability index and the mediolateral stability index is calculated by utilizing a betweenness centrality associated with each node of the connectivity graph. Further, the mediolateral stability index is compared with standard values to detect the intensity of the Parkinson's disease.

An e-commerce business model has witnessed several cases where packages with faulty goods, returned by buyers, without procured object, rather replaced by different device. This disclosure relates a method to detect whether object under test a desired object. A plurality of back-scattered signals is received from the object under test occluded by packaging with continuous motion on conveyer based on first antennaradar combination. The plurality of back-scattered signals is processed by applying four-tap difference filter to obtain motion-filtered data matrix. A low pass filter is applied on the motion-filtered data matrix to obtain enveloped motion-filtered data matrix. A sliding constant false alarm rate is applied on the enveloped motion-filtered data matrix to determine detection threshold value. A check is performed to detect whether the object under test is the desired object based on whether intensity of the plurality back-scattered signals exceeds the detection threshold value.

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