A method and system for task planning for visual room rearrangement under partial observability is disclosed. The system or the robotic agent utilizes a visual input to efficiently plan a sequence of actions for simultaneous object search and rearrangement in an untidy room, to achieve a desired tidy state. Unlike search networks in the art that follow ad hoc approach, the method discloses a search network utilizing commonsense knowledge from large language models to find unseen objects. A Deep RL network used for task planning is trained with proxy reward, along with unique graph-based state representation to produce a scalable and effective planner that interleaves object search and rearrangement to minimize the number of steps taken and overall traversal of the agent, and to resolve blocked goal and swap cases. Sample efficient cluster-biased sampling is utilized for simultaneous training of the proxy reward network along with the Deep RL network.
G05B 19/4155 - Numerical control [NC], i.e. automatically operating machines, in particular machine tools, e.g. in a manufacturing environment, so as to execute positioning, movement or co-ordinated operations by means of programme data in numerical form characterised by programme execution, i.e. part programme or machine function execution, e.g. selection of a programme
The disclosure relates generally to methods and systems for transforming qualitative survey into quantitative survey. Current approaches depend on manual analysis of these user responses which is so troublesome task. The present disclosure transforms the qualitative survey questionnaire into the quantitative survey questionnaire using a domain knowledge and a natural language processing. The method first receives responses to each question present in qualitative survey questionnaire, from multiple batches. Then valid responses out of all the responses are determined for each question, pertaining to each batch, using domain taxonomy and natural language knowledge graph. Further, semantic relation-based technique is employed to determine the questions that are transformable batch wise. Then, the response options are created for each transformable question. The non-transformable questions are considered for the next batch and the responses pertaining to the next batch are processed and so on until all the questions becomes transformable.
Existing techniques for automated generation of test data for testing web applications need detailed requirement documents. The present disclosure receives a plurality of textual documents to extract context. Rephrasing the extracted context by implementing a plurality of rules and passing extracted context along with a first set of prompts to Large Language Model (LLM). Generating program, validator and first set of constraints for extracted context and generating test data by running the generated program. Assigning ranking to test data and selecting the test data with highest ranking. Statically refining the generated program by calling a mathematical library function on the highest ranked test data to generate structural information and modifying language of the second set of prompts passed to the LLM. Dynamically refining the generated program by passing feedback generated by executing the highest ranked test data on a web application and refining the response obtained.
The present disclosure discloses a method and system for generating recommendations for cloud instances for high performance computing (HPC) applications. The present disclosure provides an intelligent Cloud instance Recommender framework comprising a suitability matcher, a performance analyzer, and a decision making enabler. The method of the present disclosure ensures that the HPC application is assessed for its suitability for the cloud since there is no need of recommending cloud services if the HPC application cannot be migrated to the cloud. This assessment is performed using a machine learning (ML) predictor engine which is trained upon some parameters of the HPC application. The ML predictor engine predicts execution time of the HPC application on cloud instances, and then a cost of execution is estimated by a mathematical model based on the predicted execution time. Also, a weightage to user's input is provided using a recommender engine to generate final recommendations.
This disclosure relates generally to a method and system for re-encryption of an encrypted data. State-of-the-art methods provide the re-encryption scheme for a specific Fully Homomorphic Encryption (FHE) encrypted data. However, a generic scheme that converts any given FHE scheme to an HPRE scheme is not yet achieved. The disclosed method provides re-encryption of the encrypted data transferred between a first user and a second user by a re-encryption key. The re-encryption key is obtained by splitting a private key of the first user into a primary private key and a secondary private key. The primary private key generates a public re-key component using probabilistic encryption algorithm; and the secondary private key generates a private re-key component using probabilistic switch key generation algorithm. Both the private re-key and the public re-key are consolidated further to generate the re-encryption key.
Deep learning-based generative models have improved the exploration of chemical space in small molecule drug discovery. Although thousands of novel small molecules can be generated with such models, synthesizing them still remains a challenging task. In literature, several methods have been proposed to predict the synthetic route of a target molecule by working backwards to find the most suitable starting reactants (retrosynthesis). While retrosynthesis is shown to be successful, for novel molecules it is often difficult to find the synthesis path. System and method of the present disclosure generate molecules along with its synthesis route and also provide an insight into the interactions in the active site of target protein, using graph convolution networks (GCNs) and Monte Carlo tree search (MCTS). A target-specific bioactivity prediction model is used as the scoring function to navigate the MCTS search space efficiently.
This disclosure relates generally to shelf life of produce and, more particularly, for predicting and enhancing shelf life of produce in storage facility. A significant quantity of produce such as fresh fruits and vegetable are lost before reaching the consumer, during its long-term storage in a warehouse or a storage facility. Many techniques have been employed to preserve-enhance the shelf life. However, the existing techniques do not explicitly consider factors such as air circulation, stacking of container, and respiration of the produce during shelf-life prediction. The disclosed techniques predict and enhance the shelf life of produce in storage facilities in several steps including determining a set of modelling parameters, determining a plurality of shelf-life parameters, predicting a shelf life of the produce based on generating a shelf-life prediction model, predicting a quality index and finally, enhancing the shelf-life of the produce based on an optimization technique.
This disclosure relates generally to method and system for monitoring human parameters using hierarchical human activity sensing. The method is based on sensing as service (SEAS) model which processes continuous mobility data from multiple sensors on the client edge-device by optimizing the on-device processing pipelines. The method requests a subject to select a human parameter of the human body to be monitored using a master device and capture the plurality of signals by recognizing sensors corresponding to the health parameter. The master device transmits to the server the subject selected human parameter of the human body to be monitored and requesting the server to recommend a hierarchical classifier structure. Further, the human body is monitored based on the on-device hierarchical sensing pipeline by executing a plurality of algorithms. In addition, the system is suitable for remote monitoring and flexible edge cloud arbitration, optimizing costs, infrastructure, and energy.
This disclosure relates generally to a method and system for generative Al based unified virtual assistant. Conventional virtual assistant for enterprise systems needs to be configured for a specific industry or stakeholder and does not provide support for all stakeholders in the enterprise. Also, conventional rule-based virtual assistant or machine learning based virtual assistant need a large database for proper functioning. The disclosed method and system provide a unified virtual assistant for all processes in the enterprise. The unified virtual assistant provides support for all stakeholders in the enterprise and can answer all kinds of queries related to any process of the enterprise according to a role of a user logged into the system. The unified virtual assistant interprets user's query and generates effective prompts depending on the user's query which can be specific to customer, employee, executive or support desk users of the enterprise.
Existing approaches for identifying a prosthesis model involve rigorous examinations and visual inspection comparison of X-ray images which is difficult for both radiologists and orthopedic surgeons. This can be a meticulous task that is tedious, dependent on the surgeon's experience, time-consuming and an erroneous recognition can have certain consequences. Method and system disclosed herein provide an approach which involves use of a 3-block classifier for extracting finer features of implant from an X-ray image being processed, and then comparison of the extracted features with manufacturer specifications for identifying manufacturer and type.
G06V 10/75 - Organisation of the matching processes, e.g. simultaneous or sequential comparisons of image or video featuresCoarse-fine approaches, e.g. multi-scale approachesImage or video pattern matchingProximity measures in feature spaces using context analysisSelection of dictionaries
G06V 10/44 - Local feature extraction by analysis of parts of the pattern, e.g. by detecting edges, contours, loops, corners, strokes or intersectionsConnectivity analysis, e.g. of connected components
G06V 10/764 - Arrangements for image or video recognition or understanding using pattern recognition or machine learning using classification, e.g. of video objects
G06V 10/77 - Processing image or video features in feature spacesArrangements for image or video recognition or understanding using pattern recognition or machine learning using data integration or data reduction, e.g. principal component analysis [PCA] or independent component analysis [ICA] or self-organising maps [SOM]Blind source separation
There is a need for design and implementation of interfaces for providing user-friendly feedback while creating and updating compliant passwords. This disclosure relates to a method of creating compliant password by in-place feedback to a password composition policy (PCP). A policy-enabled-virtual keyboard (PKBD) receives input from a user and is processed based on parameters associated with the PCP to identify accessible and inaccessible keys on the PKBD with in-place feedback. The in-place feedback is provided to highlight accessible keys of the PKBD for the user, if class associated with character, or mandated number of characters by the PCP are received are covered. Alternatively, the keys of the PKBD are disabled for access to the user by validating if class associated with character received are not covered, and a deviation of the parameters. A compliant password is created based on the PCP by providing in-place feedback for a resultant validation.
G06F 21/46 - Structures or tools for the administration of authentication by designing passwords or checking the strength of passwords
G06F 3/04886 - Interaction techniques based on graphical user interfaces [GUI] using specific features provided by the input device, e.g. functions controlled by the rotation of a mouse with dual sensing arrangements, or of the nature of the input device, e.g. tap gestures based on pressure sensed by a digitiser using a touch-screen or digitiser, e.g. input of commands through traced gestures by partitioning the display area of the touch-screen or the surface of the digitising tablet into independently controllable areas, e.g. virtual keyboards or menus
Classification of images is inherently a semi-supervised classification problem. Often, the labeled pixels and the unlabeled pixels in the image may have different distribution. Hence classification accuracy of such images is affected. The present disclosure proposes an umbrella framework for semi-supervised learning that considers the domains shifts in labeled and unlabeled pixels. The method proposed a two way optimization solution using deep learning models based on spectral features, spatial features, and fused spectral-spatial features. The model is trained in such a way that it is not only trained on the correct class of pixel but also on the source category of the pixel, for example, labeled pixel or unlabeled pixel. The error in the pixel class is minimized, whereas the error in the source category is encouraged simultaneously.
G06V 10/82 - Arrangements for image or video recognition or understanding using pattern recognition or machine learning using neural networks
G06V 10/58 - Extraction of image or video features relating to hyperspectral data
G06V 10/77 - Processing image or video features in feature spacesArrangements for image or video recognition or understanding using pattern recognition or machine learning using data integration or data reduction, e.g. principal component analysis [PCA] or independent component analysis [ICA] or self-organising maps [SOM]Blind source separation
13.
METHOD AND A SYSTEM FOR OPTIMIZING E-COMMERCE MARKDOWN PRICE BASED ON CONTEXTUAL BANDIT TECHNIQUE
This disclosure relates generally to optimizing markdown price and, more particularly, to a method and a system for optimizing E-commerce markdown price based on contextual bandit technique. E-commerce and retail industries employ several strategies to boost business, of which markdown pricing is popular. The online markdown pricing problem is particularly challenging due to the high variability in demand. The existing state-of-art CB based techniques to optimize the markdown price, are designed to either clear off maximum inventory or as a revenue maximization problem and do not explicitly consider contextual features. The disclosed techniques optimize E-commerce markdown price based on contextual bandit technique focusing on both margin optimization and inventory reduction, while considering contextual features by employing a suite of Contextual Bandit (CB) algorithms, including LinUCB, Mini-monster in Vowpal Wabbit (VW), Contextual Thompson Sampling (CTS), and Bayes Upper Confidence Bounds (UCB), which tackle the dynamic nature of e-commerce.
State of the art telescope designs require increasing number of sub-apertures for optimum performance, however, with the increasing number of sub-apertures, number, and amplitudes of the sidelobes increase along with that of the primary maxima, resulting in a trade-off of the imaging quality. Disclosed herein are three configurations using a central sub-aperture and a plurality of peripheral sub-apertures, encompassing the central sub-aperture. Size of the central sub-aperture and the plurality of peripheral sub-apertures is in a proportionate relationship. Further, the plurality of the peripheral sub-apertures forms at least two concentric zones, wherein each concentric zone has equal number peripheral sub-apertures from among the plurality of peripheral sub-apertures, and the sizes of the peripheral sub-apertures in each two adjacent concentric zones have a proportionate relationship. This way there is significant side lobe suppression compensating for the imaging performance loss due to reduced aperture area.
G02B 23/12 - Telescopes, e.g. binocularsPeriscopesInstruments for viewing the inside of hollow bodiesViewfindersOptical aiming or sighting devices with means for image conversion or intensification
G02B 23/06 - Telescopes, e.g. binocularsPeriscopesInstruments for viewing the inside of hollow bodiesViewfindersOptical aiming or sighting devices involving prisms or mirrors having a focusing action, e.g. parabolic mirror
15.
METHODS AND SYSTEMS FOR GRAPH ASSISTED UNSUPERVISED DOMAIN ADAPTATION FOR MACHINE FAULT DIAGNOSIS
The disclosure generally relates to methods and systems for graph assisted unsupervised domain adaptation for machine fault diagnosis. The present disclosure solves the technical problems in the art using a Graph Assisted Unsupervised Domain Adaptation (GA-UDA) technique for the machine fault diagnosis. The GA-UDA technique carries out the domain adaptation in two stages. In the first stage, a Class-wise maximum mean discrepancy (CMMD) loss is minimized to transform the data from both source and target domains to a shared feature space. In the second stage, the augmented transformed (projected) data from both the source and the target domains are utilized to construct a joint graph. Subsequently, the labels of target domain data are estimated through label propagation over the joint graph. The GA-UDA technique of the present disclosure helps in addressing significant distribution shift between the two domains.
Dependency on limited availability of subject matter experts (SMEs) who are well versed in Model-Driven Engineering (MDE) technology is a significant barrier to MDE utilized for generating conceptual models. In the present disclosure, MDE and generative Artificial Intelligence (AI) operate in a symbiotic relationship complimenting respective strengths and overcoming limitations. The generative AI techniques lower the knowledge barrier and enable domain SMEs to construct purposive models by operating at natural language level instead of at MDE technology level, thereby simplifying the method of generating conceptual models that are purposive. When operating at natural language level, the method and system of the present disclosure ensures that the generative AI receives focused and well directed prompts to optimize the number of interactions and reduce computing power utilized. The method and system of the present disclosure also address limitations of generative AI platforms such as attention fading, non-deterministic behavior and hallucination.
G06F 30/27 - Design optimisation, verification or simulation using machine learning, e.g. artificial intelligence, neural networks, support vector machines [SVM] or training a model
17.
LONG DURATION ALARM SEQUENCE PREDICTIONS USING Bi-LSTM AND OPERATOR SEQUENCE RECOMMENDATIONS THEREOF
Techniques used by the state of the art alarm prediction systems rely mostly on LSTM, which have technical limitation in predicting a sequence with long durations. Embodiments of the present disclosure provide a method and system for long duration alarm sequence predictions from past alarm sequence using a Bi-LSTM and operator sequence recommendations thereof. The BiLSTM with an encoder decoder technique uses true output sequence as input to decoder at each time step during training. This allows the BiLSTM to learn dependencies among input and output sequence effectively. The operator sequence recommended is identified based on closeness of the predicted future output alarm sequence with one among the unique alarm sequences in a mapping table. The alarm sequence closeness is computed using a Matching Sequence Score (MSS) disclosed by the method, since known sequence evaluation metrics such as Blue Score has limitations to be directly applied in alarm sequence evaluation.
G08B 31/00 - Predictive alarm systems characterised by extrapolation or other computation using updated historic data
G06N 3/0442 - Recurrent networks, e.g. Hopfield networks characterised by memory or gating, e.g. long short-term memory [LSTM] or gated recurrent units [GRU]
G08B 29/18 - Prevention or correction of operating errors
18.
METHODS AND SYSTEMS FOR CROP DAMAGE ASSESSMENT USING SEMANTIC REASONING
The disclosure generally relates to methods and systems for crop damage assessment using semantic reasoning. Conventional techniques using only specific data either individually or in a combination may result in bias and may not accurately estimate the crop damage, due to diversity in each of the natural calamities. The present disclosure solves the technical problems in the art using domain ontologies and a semantic reasoning over the spatio-temporal data for the automatic assessment of the crop damage due to the natural calamities. The present disclosure establishes automated crop loss assessment using trigger-based analysis of plurality of sources like satellite-based earth observations, weather observations, social media posts and news articles, for obtaining a spatio-temporal data. Then the spatio-temporal data is reasoned over the domain knowledge graph, using the semantic reasoning technique, for the crop damage assessment.
G06V 10/764 - Arrangements for image or video recognition or understanding using pattern recognition or machine learning using classification, e.g. of video objects
19.
NON-CONTACT METHOD AND SYSTEM FOR INSPECTION AND DETECTION OF WATER SATURATED REGIONS IN ROCKMASS
Current approaches for detecting water saturated regions in a rockmass uses geophysical methods, such as electrical resistivity tomography (ERT), self-potential (SP), and seismic imaging to spatially detect and map the rock water content in underground mines. However, all these approaches are contact based. Present disclosure provides a non-contact method and system for inspecting and detecting water saturated regions in a rockmass. The system uses coherent radar generated range-compressed data collected from a plurality of rock specimens for generating a generalized calibration function using a range doppler algorithm and a phase tracking algorithm. The system then uses the generated generalized calibration function for estimating water saturation of a target rockmass along with the use of the RDA and the phase tracking algorithm.
G01S 7/295 - Means for transforming co-ordinates or for evaluating data, e.g. using computers
G01S 7/41 - Details of systems according to groups , , of systems according to group using analysis of echo signal for target characterisationTarget signatureTarget cross-section
G01S 13/88 - Radar or analogous systems, specially adapted for specific applications
G01S 13/90 - Radar or analogous systems, specially adapted for specific applications for mapping or imaging using synthetic aperture techniques
20.
SYSTEM FOR SECURING COMMUNICATION BETWEEN CENTRAL CONTROLLER AND SIGNALING DEVICES IN TRAFFIC SIGNALING NETWORKS
The present disclosure provides a system for securing communication between Central Controller and signaling devices including actuator devices and signaling sensors used in railway and road traffic signaling networks. Conventional signaling systems use unsecured metal cables to communicate between the Central Controller and the signaling devices, making them vulnerable to unauthorized intrusion and mischief. In the present disclosure, two uniquely addressable communication modules, one (SCM1) securely housed with the Central Controller and another (SCM2) securely housed in an assembly also containing the signaling device and the related power switches are used to establish a transparent but standard secure digital communication protocol between them to authenticate and validate mutual communication, making them secure and safe from intrusion and undesirable manipulation.
H04L 67/12 - Protocols specially adapted for proprietary or special-purpose networking environments, e.g. medical networks, sensor networks, networks in vehicles or remote metering networks
H04L 69/40 - Network arrangements, protocols or services independent of the application payload and not provided for in the other groups of this subclass for recovering from a failure of a protocol instance or entity, e.g. service redundancy protocols, protocol state redundancy or protocol service redirection
21.
SYSTEMS AND METHODS FOR TRUSTED SELF-CHECKOUT AT RETAIL STORES
Post pandemic, retailers are adopting more contactless services for shopper's checkout such as self-checkout, hybrid checkout and mobile checkout, and these touchpoints have become the potential areas for fraudulent activity during check out process. For detecting fraud carried out by a customer at the time of self-checkout, existing approaches require respective customer identity and his purchase history. Embodiments of the present disclosure do not require customer identity and information about his historical shopping carts and provide a method and system for approving a user shopping cart for self-checkout from the items picked by the customers in real time.
This disclosure provides methods and systems for enabling a hybrid architecture in an enterprise application. The present disclosure addresses problems of conventional approaches which falls back on a common representation or pluggable framework to handle challenges of composing multiple libraries. Conventional approaches work for the problem of maintaining performance while handling diversity of libraries which cannot be reused either in as-is form or with a slight modification. The present disclosure provide a method and system that enable hybrid architecture in an enterprise application to mitigate quantum attacks. The method of the present disclosure changes codebase of the enterprise application to enable hybrid architecture. New paths are created within the enterprise application to support execution of a new library code that enables double encryption of data being processed by the application. Code that transforms program variables is injected to address the compatibility challenges between existing and new libraries.
This disclosure provides a Kalman filter based predictive jitter buffer adaptation for smooth live video streaming. In the present disclosure, at receiver of a live video steaming system, reassembly of received data packets is performed to reconstruct different types of encoded frames transmitted by a transmitter. The different types of encoded frames are Full encoded frames in basic state and Delta encoded frames. To tackle data packet loss, the receiver is also equipped with a frugal yet efficient loss handling mechanism for both basic and delta frames. To achieve smooth rendering of the live video, the receiver employs a Kalman Filter based Jitter Buffer Adaptation mechanism. The Kalman Filter based Jitter Buffer Adaptation mechanism observes variability in arrival time of the open-loop best-effort traffic and adapts a jitter-buffer based on future end-to-end delay estimates. Thus, smoothness of streaming is preserved at the receiving end augmented with robust loss-resilience.
H04N 21/44 - Processing of video elementary streams, e.g. splicing a video clip retrieved from local storage with an incoming video stream or rendering scenes according to encoded video stream scene graphs
H04N 21/434 - Disassembling of a multiplex stream, e.g. demultiplexing audio and video streams or extraction of additional data from a video streamRemultiplexing of multiplex streamsExtraction or processing of SIDisassembling of packetised elementary stream
H04N 21/435 - Processing of additional data, e.g. decrypting of additional data or reconstructing software from modules extracted from the transport stream
H04N 21/462 - Content or additional data management e.g. creating a master electronic program guide from data received from the Internet and a Head-end or controlling the complexity of a video stream by scaling the resolution or bit-rate based on the client capabilities
24.
METHOD AND SYSTEM FOR RECOMMENDING ALTERNATIVES TO BIOLOGICS
High cost of biotherapy drug makes it unaffordable for patients to seek treatments. Further, access to information related to new low-cost alternatives like Biosimilars and Interchangeable may not be available with the physician at the time of consultation. Most of the conventional approaches aims to select an alternative biosimilar for a reference drug without considering patient's information. The present disclosure recommends a list of low-cost alternatives to high-cost reference drugs thereby enabling the physician to get timely and updated information on development of Biosimilars. The solution leverages Natural Language Processing (NLP) technology to extract known adverse events for a reference drug and a relative scoring based technique to identify and optimum alternative to prescribed biologics. The capability of the solution is further extended to identify secondary adverse events due to multiple drugs, thereby providing a clinical decision support system to help physicians take an informed decision.
G16H 20/10 - ICT specially adapted for therapies or health-improving plans, e.g. for handling prescriptions, for steering therapy or for monitoring patient compliance relating to drugs or medications, e.g. for ensuring correct administration to patients
G16H 10/60 - ICT specially adapted for the handling or processing of patient-related medical or healthcare data for patient-specific data, e.g. for electronic patient records
G16H 50/70 - ICT specially adapted for medical diagnosis, medical simulation or medical data miningICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for mining of medical data, e.g. analysing previous cases of other patients
G16H 70/40 - ICT specially adapted for the handling or processing of medical references relating to drugs, e.g. their side effects or intended usage
25.
DISTRIBUTED ARCHITECTURE FOR FUSION-TRANSFORMER TRAINING ACCELERATION
The disclosure addresses problems associated with a systematic integration of multi-modal data for effective training, and handling of large volume of data because of high resolution of the multiple modalities. Embodiments herein provide a method and a system for a distributed training of a multi-modal data fusion transformer. Herein, a distributed training approach called a Distributed Architecture for Fusion-Transformer Training Acceleration (DAFTA) is proposed for processing large multimodal remote sensing data. DAFTA is enabled to handle any combination of remote sensing modalities. Additionally, similarity of feature space is leveraged to optimize the training process and to achieve the training with reduced data set which is equivalent to a complete data set. The proposed approach provides a systematic and efficient method for managing large sensing data and enables accurate and timely insights for various applications.
G06V 10/26 - Segmentation of patterns in the image fieldCutting or merging of image elements to establish the pattern region, e.g. clustering-based techniquesDetection of occlusion
G06V 10/762 - Arrangements for image or video recognition or understanding using pattern recognition or machine learning using clustering, e.g. of similar faces in social networks
G06V 10/764 - Arrangements for image or video recognition or understanding using pattern recognition or machine learning using classification, e.g. of video objects
G06V 10/766 - Arrangements for image or video recognition or understanding using pattern recognition or machine learning using regression, e.g. by projecting features on hyperplanes
G06V 10/77 - Processing image or video features in feature spacesArrangements for image or video recognition or understanding using pattern recognition or machine learning using data integration or data reduction, e.g. principal component analysis [PCA] or independent component analysis [ICA] or self-organising maps [SOM]Blind source separation
26.
SYSTEMS AND METHODS FOR IN BODY MICROWAVE IMAGING OF A SUBJECT
Detecting cancer early can significantly reduce mortality rate, but this still remains a challenge owing to shortcomings in early screening and detection with existing modalities. Cancer detection is done using known screening methods such as X-ray mammography, Magnetic Resonance Imaging (MRI) and Ultrasound imaging (US). But these conventional methods have their own limitations such as compression discomfort, inherent health risks, expensive, and consume more time and effort. Present disclosure provides system and method for enhanced microwave imaging (MWI) for efficient breast tumor detection by scanning subject's specific body portion to optimize the scan duration. The MWI is framed as an inverse problem by building forward model using a Point Spread Function (PSF) and is solved by imposing sparsity prior since tumor is concentrated to limited regions. The entire scanning duration is optimized by viewing the problem as a sequential decision making process for a Deep Reinforcement Learning (DRL) agent.
A61B 5/00 - Measuring for diagnostic purposes Identification of persons
A61B 5/0507 - Detecting, measuring or recording for diagnosis by means of electric currents or magnetic fieldsMeasuring using microwaves or radio waves using microwaves or terahertz waves
G16H 50/20 - ICT specially adapted for medical diagnosis, medical simulation or medical data miningICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for computer-aided diagnosis, e.g. based on medical expert systems
27.
METHOD AND SYSTEM FOR PERSONALIZED OUTFIT COMPATIBILITY PREDICTION
Unlike visual similarity, visual compatibility is a complex concept. Existing approaches for outfit compatibility prediction does not focus on methods with personalization. The present disclosure proposes a novel approach to model the user's preference for different styles. The outfit compatibility prediction module is a critical component of an outfit recommendation system. An outfit is said to be compatible if all the items are visually compatible and match the user's preferences. The present disclosure represents the outfit as a graph and uses Graph Neural Network (GNN) with attention mechanism to capture the inter-relationship between the items. A graph read-out layer generates the final outfit embedding. The proposed approach efficiently models the preferences of the users for different styles. Finally, the outfit compatibility score is generated by computing the similarity between the outfit embedding and the user embedding.
Current approaches for identifying statute facets consider facet type similar to rhetorical roles defined for statute text. However, the nature and content of statutes are quite different from court judgements and established set of rhetorical roles for court judgements are either not applicable for statutes or not sufficient to cover all the key aspects in statutes. Present disclosure provides method and system for extraction and classification of statute facets from legal statutes. The system first takes text of a statute as input. The system then automatically extracts candidate statute facets from statute text using dependency structure and then computes statute specificity for candidate statute facets. Thereafter, the system classifies candidate statute facets into facet types using weak supervision for validation purpose. Further, system selects statute facets from candidate statute facets based on statute specificity of candidate statute facet and statute facet type of candidate statute facet using customized filtering technique.
State of the art techniques have challenges for recoloring a product, which includes non-realistic images, incorrect color mapping, structural distortion, color spilling into background, and in handling multi-color, multi-apparel and multi-product scenario images. Embodiments of the present disclosure provide a method and system for recoloring a product using a dual attention (DA) U-Net based on a generative adversarial network (GAN) framework to generate a recolored product with a target color from an input image. The disclosed DAU-Net enables recoloring (i) a single-color in a single-product scenario, (ii) a plurality of colors in a single-product scenario, and (iii) multi-product scenario with a human model. The DAU net uses (i) a product components aware feature (PCAF) extraction to generate feature representations comprising information of the target color with finer details, and (b) a critical feature selection (CFS) mechanism applied on the feature representation, to generate enhanced feature representations.
G06T 7/90 - Determination of colour characteristics
G06V 10/44 - Local feature extraction by analysis of parts of the pattern, e.g. by detecting edges, contours, loops, corners, strokes or intersectionsConnectivity analysis, e.g. of connected components
G06V 10/56 - Extraction of image or video features relating to colour
G06V 10/762 - Arrangements for image or video recognition or understanding using pattern recognition or machine learning using clustering, e.g. of similar faces in social networks
30.
METHOD AND SYSTEM FOR A LOW-POWER LOSSLESS IMAGE COMPRESSION USING A SPIKING NEURAL NETWORK
This disclosure relates generally to reducing earth-bound image volume with an efficient lossless compression technique. The embodiment thus provides a method and system for reducing earth-bound image volume based on a Spiking Neural Network (SNN) model. Moreover, the embodiments herein further provide a complete lossless compression framework comprises of a SNN-based Density Estimator (DE) followed by a classical Arithmetic Encoder (AE). The SNN model is used to obtain residual errors which are compressed by AE and thereafter transmitted to the receiving station. While reducing the power consumption during transmission by similar percentages, the system also saves in-situ computation power as it uses SNN based DE compared to its Deep Neural Network (DNN) counterpart. The SNN model has a lower memory footprint compared to a corresponding Arithmetic Neural Network (ANN) model and lower latency, which exactly fit the requirement for on-board computation in small satellite.
H04N 19/42 - Methods or arrangements for coding, decoding, compressing or decompressing digital video signals characterised by implementation details or hardware specially adapted for video compression or decompression, e.g. dedicated software implementation
H04N 19/156 - Availability of hardware or computational resources, e.g. encoding based on power-saving criteria
H04N 19/17 - Methods or arrangements for coding, decoding, compressing or decompressing digital video signals using adaptive coding characterised by the coding unit, i.e. the structural portion or semantic portion of the video signal being the object or the subject of the adaptive coding the unit being an image region, e.g. an object
H04N 19/46 - Embedding additional information in the video signal during the compression process
H04N 19/91 - Entropy coding, e.g. variable length coding [VLC] or arithmetic coding
31.
METHOD AND SYSTEM FOR CONTEXT-BASED RETRANSMISSION OF LOST PACKETS
State of the art data transmission approaches require all the lost data packets to be retransmitted, which may not be required in a variety of scenarios. Existing protocols lack the option to change packet semantics on the fly for individual fragments on the fly for the same stream. Also, parameters including maximum number of retransmissions permitted are set beforehand, one for entire stream. Method and system in embodiments disclosed herein provide an approach for context-based retransmission of lost packets. Method and system disclosed in the embodiments herein identifies one or more packets as lost packets based on reception status of acknowledgement (ACK) within a dynamically adaptive timeout period, and then based on value of a NRTx header field associated with the lost packet, determines whether or not to retransmit the lost packet. Value of the dynamically adaptive timeout period is recalculated dynamically, based on a determined instantaneous channel condition.
Providing the right mechanism to apply blockchain technology with flexible or dynamic credit disbursement while also addressing valuation of the agricultural assets that are dynamic in nature is required. A method and a system for estimating flexible credit eligibility and disbursement schedule using NFTs of agricultural assets is disclosed. A detailed blockchain based approach is provided for evaluating credit collateral of the NFTs representing the agricultural assets, also interchangeably referred to as physical farm assets or agri-assets. Valuation of the credit eligibility is based on flexibility provided to the farmer on deciding the farm assets to be used, current status of the farmer selected agricultural assets and credit equivalent value of the each of NFTed asset selected by the farmer for the season/year. The dynamic disbursement schedule is based on the personalized or customized parameters associated with the current status of farm and farm assets linked to the credit collateral.
Comprehensive and high-quality disease dictionaries are invaluable resources for tasks such as building ontologies, automated relation extraction, text summarization, question answering etc. Such curated resources are useful to clinicians, researchers, and various Biomedical Natural Language Processing tasks. However, these are manually curated and are labor and time intensive, and additionally suffer from lower recall and coverage is also less. Present disclosure provides systems and methods for augmenting rare disease dictionaries, wherein the system retrieves (new) rare diseases terms from medical literature that are related to the given dictionary terms (seed terms) and recommends new terms (or NPs) in a ranked order. This method is useful for rare diseases dictionary augmentation as a significant fraction of the top recommendations are new synonym candidates for dictionary augmentation. The method uses syntactic and semantic similarity measures in combination with efficient nearest neighbor search for efficient retrieval.
State of the art model fairness approaches do not address the degree of local fairness of a ML model. A method and system for determining local fairness of a classification Machine Learning (ML) model with degree of fairness is disclosed. The method creates multiple perturb instances using multilevel GMM clustering approach and a constrained perturbation technique to ensure feature distribution of perturbed data, generated from a tabular base data is within the feature distribution of the tabular base data of the ML model. Further, the class of a protected attribute is flipped, black box model prediction probabilities and the cosine similarity constraint and multiplication factor on the probabilities is used to provide a degree of fairness for the local instance. Thus, provides magnitude of fairness or unfairness to the local instance.
This disclosure relates generally to a method and system for task feasibility analysis with explanation for robotic task execution. Conventional methods for task feasibility analysis does not utilize an ontology for task capability understanding. The present disclosure uses an explainable semantic approach for checking task feasibility in a real world. The method creates scene graphs which is further used for generating a global knowledge graph and a semantic map. These are used for task feasibility analysis for an input task instruction received from a user. When the user provides the task instruction the method checks whether it is feasible or not. This helps in avoiding dead end tasks and provides the user to alter the task instruction towards feasible task. The disclosed method is used for robotic task execution in an environment.
G05D 101/15 - Details of software or hardware architectures used for the control of position using artificial intelligence [AI] techniques using machine learning, e.g. neural networks
State of the art approaches for 3D garment simulation approaches have the disadvantages that they 1) work on fixed garment type, 2) work on fixed body shapes, and 3) assume fixed garment topology. As a result, they do not offer a generic solution for garment simulation. Method and system disclosed herein use a combination of a body motion aware ARAP garment deformation and a Physics Enforcing Network (PEN), so as to generate garment simulations irrespective of garment type, body shapes, and garment topology, thus offering a generic solution.
The disclosure generally relates to methods and systems for complex natural language task understanding for embodied robots or agents. Conventional works on relation extraction generally find relevant triplets in a natural language phrase, but neither ground the task nor ground the arguments. The present disclosure implements a Grounded Argument and Task Extraction (GATE) technique that extracts a set of tasks and relevant arguments from the complex natural language instruction. The GATE uses an encoder-decoder neural network with nested decoding technique. The extracted tasks are mapped (grounded) to the known skill set of the robot and arguments are mapped (grounded) to objects within the environment, classifies the tokens as many times as possible which existing sequence labeling cannot do. The encoder-decoder neural network of the present disclosure extracts grounded task-argument pairs from a natural language instruction in a generative mechanism, and grounds the arguments based on object detector vocabulary.
This disclosure relates generally to a micro-activity identification associated with a task. Industrial operations involving complex processes are difficult to monitor due to multifaceted number of micro-activities within it. Surveillance of such complex processes is important as in the real environments there is no control over the working style of workers executing the task and the sequence of assembly process. To effectively monitor the task comprising plurality of micro-activity, the artificial intelligence (AI) based model is presented, which effectively monitors the micro-activity within and generates the quality score for the task under surveillance. The quality score is derived by assigning individual scores to the micro-activity performed correctly and by assigning penalty upon wrong performance of the micro-activity.
Millimeter (mm) waves, in comparison to microwaves, have short wavelengths and can penetrate to few centimeters inside the body. The embodiments herein provide a method and system for millimeter (mm) wave synthetic aperture radar (SAR) imaging for superficial implant monitoring. The mmWave SAR and consecutive an autofocusing SAR imaging are suitable for a superficial tissue and subsequent continuous implant monitoring due to their smaller form-factor and faster processing coupled with focused dielectric lens. Additionally, a limb topography is approximated for localization of implant region on interest (ROI) in the SAR amplitude image. Further, the method and system provide a bone implant monitoring in order to assess any unwanted mobility or dislocation of the implant, and thus bone health is a critical issue.
G01S 13/90 - Radar or analogous systems, specially adapted for specific applications for mapping or imaging using synthetic aperture techniques
A61B 5/0507 - Detecting, measuring or recording for diagnosis by means of electric currents or magnetic fieldsMeasuring using microwaves or radio waves using microwaves or terahertz waves
Current approaches for minimizing energy requirement of buildings are not designed to handle multi-input multi-output systems, such as electric vehicle-heating, ventilation, and air conditioning (EV-HVAC) system. Further, scalability of the solutions is another challenge. Present disclosure provides method and system for jointly controlling EV-HVAC system of a building. The system utilizes the potential of electric vehicle (EVs) in building energy management by treating EVs as buffers with random availability. The system performs EV-HVAC joint control that scales seamlessly with increasing EVs while respecting both thermal constraints of HVAC and state of charge (SoC) constraints of EV users.
Coordinated Multipoint (CoMP) transmission is a potential candidate to optimize the performance of a network with added flexibility to serve a UE from multiple Base Stations (BSs). However, the performance gain in CoMP is as good as the dynamic clustering. The existing approaches are applicable for a fixed cluster size, which does not capture time-varying channel conditions and the cost of transmission. Embodiments herein provide a method and system for a learning based dynamic clustering of BSs for a CoMP transmission in communication networks. Herein, a framework for the CoMP transmission in 5th Generation (5G) and beyond networks is disclosed. Further, an optimal user-centric dynamic clustering technique is disclosed for the CoMP with the aim of maximizing the throughput subject to the constraint on the cost of transmission from the CoMP cluster i.e., coordinating set of BSs.
H04B 7/024 - Co-operative use of antennas at several sites, e.g. in co-ordinated multipoint or co-operative multiple-input multiple-output [MIMO] systems
H04W 72/0446 - Resources in time domain, e.g. slots or frames
H04W 72/21 - Control channels or signalling for resource management in the uplink direction of a wireless link, i.e. towards the network
42.
METHOD AND SYSTEM FOR GENERATION OF IMPACT ANALYSIS SPECIFICATION DOCUMENT FOR A CHANGE REQUEST
This disclosure relates generally to impact analysis and, more particularly, to generation of impact analysis specification document for a change request. The existing state-of-art techniques for impact analysis for a change request are mostly manual, and further most of the research on impact analysis is based on source code analysis and does not address the impact of the CR at multi-granular levels. The disclosed techniques perform a fine-grained impact analysis of the CR at multi-granular levels. The fine-grained impact analysis at multi-granular includes identifying a set of impacted specification elements, a set of impacted processes, and a set of impacted features based on several steps including generating a contextual specification model and extracting a plurality of key-phrases.
G06Q 10/0637 - Strategic management or analysis, e.g. setting a goal or target of an organisationPlanning actions based on goalsAnalysis or evaluation of effectiveness of goals
G06F 40/103 - Formatting, i.e. changing of presentation of documents
G06F 40/289 - Phrasal analysis, e.g. finite state techniques or chunking
43.
METHOD AND SYSTEM FOR OPTIMIZING OPERATION AND PRICE OF AN ENERGY STORAGE AS A SERVICE (ESaaS)
The embodiments of present disclosure address a need of a framework to holistically utilize storage capacity of an Energy Storage System (ESS) to serve forecast errors of several Renewable Energy Generators (REGens) participating in a day-ahead market. Embodiments herein provide a method and system for optimizing the operation and price of an Energy Storage as a Service (ESaaS) framework. In anticipation of the forecast errors from REGens, the ESS operator takes suitable countermeasures such as charging/discharging of storage system through market transactions. This is done in a way to reduce imbalance in the market commitments made by individual REGens without reserving any storage volume for each REGen. Further, the system is configured to schedule the storage, determine the settlement volumes, and decide the service prices. The disclosed ESaaS framework is beneficial for all entities such as REGens (revenue outflow decreases), system operator (imbalance volume reduces), and ESS (revenue earned increases).
This disclosure relates generally to method and system for predicting distance of gazed objects using IR camera. Eye tracking technology is widely used to study human behavior and patterns in eye movements. Existing gaze trackers focus on predicting gaze point and hardly analyzes distance of the gazed object from the gazer or directly classify region of focus. The method of the present disclosure predicts gazed objects distance using a pair of IR cameras placed on either side of a smart glass. The gaze predictor ML model predicts distance at least one gazed object positioned from eye of each subject during systematic execution of a set of tasks. From each pupillary information of each pupil a set of features are extracted which are utilized to classify the gazed object of the subject based on the distance into at least one of a near class, an intermediate class, and a far class.
G06V 10/32 - Normalisation of the pattern dimensions
G06V 10/764 - Arrangements for image or video recognition or understanding using pattern recognition or machine learning using classification, e.g. of video objects
G06V 10/774 - Generating sets of training patternsBootstrap methods, e.g. bagging or boosting
G06V 40/18 - Eye characteristics, e.g. of the iris
45.
SYSTEM AND METHOD FOR ASSESSMENT OF INFORMATION TECHNOLOGY (IT) OPERATIONAL ENDURANCE OF AN ENTITY
Current business environments are dynamic, highly competitive, and are facing challenges in dynamically addressing changing needs of end users with legacy IT technologies and operational downtimes. Current available methods do not provide mechanisms to quantitatively estimate the current state of alignment of IT operations with the business' operation model based on the significance of the operation model to the business. Present disclosure provides system and method that determine the IT service alignment, operational endurance and IT operations trust for each of the operating models based on the business priority and current state of IT operations. More specifically, technology services for the IT operations are dynamically assessed and a maturity level of operational endurance is computed to meet the business's priority and needs for its operation model.
41 - Education, entertainment, sporting and cultural services
42 - Scientific, technological and industrial services, research and design
Goods & Services
Business data analysis; compiling and analyzing statistics, data and other sources of information for business purposes; systematization of data in computer databases; business consultation services, namely, business process improvement; business consulting services relating to the integration of the areas of business process technology, organizational learning, change management, and operational sustainability; business consultation in the field of education leadership development; assistance, advisory services and consultancy with regard to business planning, business analysis, business management, and business organization; business management and enterprise organization consultancy; conducting business productivity analyses; marketing research services; compilation and systemization of information into computer databases; business management and consultation in the field of data conversions and business intelligence; business collaboration services, specifically, providing online business networking services for businesses and educational institutions to collaborate with consumers to help improve goods and services; business development services for others Education; education services in the nature of courses at the university level; providing of training in the field of education and business; entertainment; sporting and cultural activities; arranging and conducting of colloquiums, conferences, congresses, seminars and workshops; online publication of electronic books and journals; providing information in the field of education; research in the field of education; teaching, educational and instruction services; transfer of business knowledge and know-how Computer software development; research and development of new products and services; research and development of computer software; research and development of new products for others; research and development of advanced learning technologies and teaching methods; research and development and consultation related thereto in the field of business, finance, insurance and education; innovation consulting services, namely, advising others in the areas of product development; business technology software consultation services; consultancy in the design and development of computer hardware and software for business and educational purposes; artificial intelligence consultancy; conducting technical project studies; Industrial design; providing scientific information, advice and consultancy relating to net zero emissions; providing scientific information, advice and consultancy relating to carbon offsetting; quantum computing; research in the field of artificial intelligence technology; scientific research; technological consultancy services for digital transformation
41 - Education, entertainment, sporting and cultural services
42 - Scientific, technological and industrial services, research and design
Goods & Services
Business data analysis; compiling and analyzing statistics, data and other sources of information for business purposes; systematization of data in computer databases; business consultation services, namely, business process improvement; business consulting services relating to the integration of the areas of business process technology, organizational learning, change management, and operational sustainability; business consultation in the field of education leadership development; assistance, advisory services and consultancy with regard to business planning, business analysis, business management, and business organization; business management and enterprise organization consultancy; conducting business productivity analyses; marketing research services; compilation and systemization of information into computer databases; business management and consultation in the field of data conversions and business intelligence; business collaboration services, specifically, providing online business networking services for businesses and educational institutions to collaborate with consumers to help improve goods and services; business development services for others Education; education services in the nature of courses at the university level; providing of training in the field of education and business; entertainment; sporting and cultural activities; arranging and conducting of colloquiums, conferences, congresses, seminars and workshops; online publication of electronic books and journals; providing information in the field of education; research in the field of education; teaching, educational and instruction services; transfer of business knowledge and know-how Computer software development; research and development of new products and services; research and development of computer software; research and development of new products for others; research and development of advanced learning technologies and teaching methods; research and development and consultation related thereto in the field of business, finance, insurance and education; innovation consulting services, namely, advising others in the areas of product development; business technology software consultation services; consultancy in the design and development of computer hardware and software for business and educational purposes; artificial intelligence consultancy; conducting technical project studies; Industrial design; providing scientific information, advice and consultancy relating to net zero emissions; providing scientific information, advice and consultancy relating to carbon offsetting; quantum computing; research in the field of artificial intelligence technology; scientific research; technological consultancy services for digital transformation
48.
SYSTEMS AND METHODS FOR PERFORMING AN AUTONOMOUS AIRCRAFT VISUAL INSPECTION TASK
This disclosure provides system and method for performing an autonomous aircraft visual inspection task using an unmanned aerial vehicle (UAV). The UAV is equipped with a front-facing RGB-D camera, one Velodyne three dimensional Light Detection and Ranging with 64 channels, and one Inertial Measurement Unit. In the method of the present disclosure, the UAV takeoff from any nearby location of the aircraft and face the RGB-D camera towards the aircraft. The UAV find the nearest landmark using a template matching approach and register with the aircraft coordinate system. The UAV navigate using LiDAR and IMU measurements, whereas the inspection process uses measurements from the RGB-D camera. The UAV navigate using a proposed safe navigation around the aircraft by avoiding obstacles. The system identifies the objects of interest using a deep-learning based object detection tool and then performs the inspection. A simple measuring algorithm for simulated objects of interest is implemented.
This disclosure relates more particularly to risk assessment of autism spectrum disorder (ASD) present in the subject and designing a personalized recommendation for the same. Current diagnostic tools and procedures, though abundant in numbers, are all based on psychiatric or behavioral evaluations, checklists and associated statistical inferences, which highlight the inherent limitation in making a reliable and early diagnosis. The present disclosure makes use of oral microbial samples of both saliva and dental plaque. The present disclosure involves a paired extraction and quantification of site-specific unique microbial sequences pertaining to the oral microbial samples of an ASD subject and subsequent classification of the subject under the ASD risk category using a metric based on a predefined ensemble of mathematical formulas. Further, a guided development of personalized microbial cocktail(s) is then designed based on the most relevant formula-set for the subject.
C12Q 1/6883 - Nucleic acid products used in the analysis of nucleic acids, e.g. primers or probes for diseases caused by alterations of genetic material
C12Q 1/689 - Nucleic acid products used in the analysis of nucleic acids, e.g. primers or probes for detection or identification of organisms for bacteria
G16B 40/00 - ICT specially adapted for biostatisticsICT specially adapted for bioinformatics-related machine learning or data mining, e.g. knowledge discovery or pattern finding
50.
METHOD AND SYSTEM FOR RISK ASSESSMENT OF POLYCYSTIC OVARIAN SYNDROME (PCOS)
This disclosure relates generally to and, more particularly, to assessment of PCOS. Polycystic ovarian syndrome (PCOS) is a hormonal disorder common among women of reproductive age that causes infertility and affects overall health of the woman. As PCOS is common and curable cause of infertility, an efficient early screening to assess a potential risk of PCOS can ensure early treatment. The current state-of-the-art techniques include diagnostic, screening solutions, imaging techniques which are invasive, complex, expensive. The disclosure is a supervised machine learning algorithm on the samples of individuals to arrive at a panel of biological features/indicators/markers/signatures that can accurately stratify/classify/group individuals into ‘PCOS’ and ‘healthy’ based upon the differences in the composition of the gut/oral microbial communities.
G16H 50/30 - ICT specially adapted for medical diagnosis, medical simulation or medical data miningICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for calculating health indicesICT specially adapted for medical diagnosis, medical simulation or medical data miningICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for individual health risk assessment
G16H 10/40 - ICT specially adapted for the handling or processing of patient-related medical or healthcare data for data related to laboratory analysis, e.g. patient specimen analysis
51.
METHOD AND SYSTEM FOR DETERMINING POST-EXERCISE RECOVERY SCORE USING PERSONALIZED CARDIAC MODEL
It is important to monitor the cardiac condition of an individual outside the clinic, using wearable physiological sensors. However, existing methods for calculating the cardiac risk score of an individual are primarily based on static information like individual's metadata, lifestyle, family history, clinical assessment, etc. but do not consider the cardiac state in a daily living scenario using wearable-based measurements. Embodiments herein provide a method and a system for determining post-exercise cardiac score in a recovery period using personalized cardiac model. A clinical decision support system (CDSS) is disclosed to predict cardiac recovery score of a subject in post-exercise conditions. The system employs a hybrid approach using a computational cardiac model and wearable data. Further, several personalized cardiac parameters are simulated using a cardiovascular simulation (CVS) platform. These parameters are used along with the wearable ECG data and meta-data information to derive the post-exercise recovery score.
G16H 40/67 - ICT specially adapted for the management or administration of healthcare resources or facilitiesICT specially adapted for the management or operation of medical equipment or devices for the operation of medical equipment or devices for remote operation
G16H 50/30 - ICT specially adapted for medical diagnosis, medical simulation or medical data miningICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for calculating health indicesICT specially adapted for medical diagnosis, medical simulation or medical data miningICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for individual health risk assessment
G16H 50/50 - ICT specially adapted for medical diagnosis, medical simulation or medical data miningICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for simulation or modelling of medical disorders
52.
ELECTRONIC BEAM-STEERING REFLECTARRAY ANTENNA SYSTEM WITH VARACTOR DIODE EMBEDDED COMB-SHAPED UNIT CELL
This disclosure relates generally to electronic beam-steering reflectarray antenna system with varactor diode embedded comb-shaped unit cell. The present disclosure optimizes design of a plurality of comb-shaped unit cells arranged over a reflectarray metasurface. The plurality of comb-shaped unit cells designed as one of (i) a first unit cell structure and (ii) a second unit cell structure helps in tilting reflected beam over a desired direction. Moreover, a standard half-wavelength dipole antenna is integrated with the proposed reflectarray metasurface to produce electronically steerable antenna. The reflectarray metasurface is positioned at a predetermined height below the standard half-wavelength dipole antenna. Each of the plurality of comb-shaped unit cells is embedded with a commercially available varactor diode. These varactor diodes, when driven by appropriate direct current (DC) biasing voltages offer different capacitance values.
This disclosure relates more particularly to risk assessment of preterm delivery (PTD) in the subject and designing a personalized recommendation for the same. Conventional techniques for PTD risk assessment are either invasive or minimally invasive and leaves little time for subjects to take precautionary or corrective medical advice or procedures to reduce or obviate the risk. The present disclosure provides the risk assessment of the PTD, by quantifying a microbial abundance in oral or gut microbiome for a pregnant woman, identifying a certain combination of microbial biomarkers using an ensemble of models for accurate risk assessment of the PTD and subsequently suggesting a personalized recommendation for at risk subject. The present assessment technique is completely non-invasive and further helps in characterizing the risk of the PTD.
C12Q 1/6883 - Nucleic acid products used in the analysis of nucleic acids, e.g. primers or probes for diseases caused by alterations of genetic material
This disclosure relates generally to method and system for multi-object tracking and navigation without pre-sequencing. Multi-object navigation is an embodied Al task where object navigation only searches for an instance of at least one target object where a robot localizes an instance to locate target objects associated with an environment. The method of the present disclosure employs a deep reinforcement learning (DRL) based framework for sequence agnostic multi-object navigation. The robot receives from an actor critic network a deterministic local policy to compute a low-level navigational action to navigate along a shortest path calculated from a current location of the robot to the long-term goal to reach the target object. Here, a deep reinforcement learning network is trained to assign the robot with a computed reward function when the navigational action is performed by the robot to reach an instance of the plurality of target objects.
G06V 20/58 - Recognition of moving objects or obstacles, e.g. vehicles or pedestriansRecognition of traffic objects, e.g. traffic signs, traffic lights or roads
G05D 1/246 - Arrangements for determining position or orientation using environment maps, e.g. simultaneous localisation and mapping [SLAM]
G05D 1/644 - Optimisation of travel parameters, e.g. of energy consumption, journey time or distance
G05D 101/00 - Details of software or hardware architectures used for the control of position
G05D 101/15 - Details of software or hardware architectures used for the control of position using artificial intelligence [AI] techniques using machine learning, e.g. neural networks
G06T 7/246 - Analysis of motion using feature-based methods, e.g. the tracking of corners or segments
G06V 10/82 - Arrangements for image or video recognition or understanding using pattern recognition or machine learning using neural networks
56.
TOPOLOGICALLY MODULATED REFLECTING INTELLIGENT SURFACES AND METHOD TO ENABLE SECTORAL AREA COVERAGE UNDER NETWORK APPLICATIONS
The advent of fifth generation technology systems leads to seamless connectivity requirements for loT-based applications. High frequencies of operation lead to inherent problem of path loss, and to mitigate designing of passive reflecting intelligent surfaces for practical operations are challenging. This disclosure relates a method to enable sectoral area coverage under network applications by topologically modulated reflecting intelligent surfaces. A Minkowski-shaped fractal unit cell is received as an input. The Minkowski-shaped fractal unit cell is characterized to obtain unit cell dimension with independent reflection-phase characteristics. The independent reflection-phase characteristics are utilized to identify distinct unit cell elements. Surface layouts are generated by the distinct unit cell elements. The surface layouts are characterized with respect to an incoming source radiation pattern to identify reflected radiation pattern parameters. The real-time sectoral signal coverage is designed based on the reflected radiation pattern parameters of the surface layouts and position of Ku-band horn antennas.
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.
G16H 50/20 - ICT specially adapted for medical diagnosis, medical simulation or medical data miningICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for computer-aided diagnosis, e.g. based on medical expert systems
A61B 10/00 - Instruments for taking body samples for diagnostic purposesOther methods or instruments for diagnosis, e.g. for vaccination diagnosis, sex determination or ovulation-period determinationThroat striking implements
C12Q 1/689 - Nucleic acid products used in the analysis of nucleic acids, e.g. primers or probes for detection or identification of organisms for bacteria
G16B 40/00 - ICT specially adapted for biostatisticsICT specially adapted for bioinformatics-related machine learning or data mining, e.g. knowledge discovery or pattern finding
This disclosure relates generally to method and system for source code verification using machine learning based strategy prediction. The method receives a source code comprising a plurality of function properties to be verified. Further, the source code is sliced using at least one sequence slicer among a plurality of sequence slicers and a feature vector generator extracts a plurality of feature vectors from each slice. Further, a neural network generates a plurality likelihood of success values by applying a plurality of verification techniques over each feature vector among the plurality of feature vectors to predict a verification strategy to be applied over each slice to be verified. Furthermore, a verification result is displayed as one of a verification successful (S), a verification failure (F) and an unknown (U) when each slice is not verified for the verification strategy run out of time or memory.
The present disclosure is related to a method and system for stratification of subjects as one of responders or non-responders to a therapy. It is imperative to critically evaluate the baseline/initial microbiome structure and composition of individuals and stratifying them before prescribing any microbiome-based drug/dietary interventions. The method identifies a panel of biological features/indicators/markers/signatures that can accurately stratify/classify/group individuals into responders and non-responders (for a given microbiome-based drug/therapy) based upon the differences in the metabolic functions of the gut microbial communities between the baseline gut microbiome profile (i.e. before the administration of an intervention) and after treatment gut microbiome profile (i.e. after the administration of the intervention). Individuals with samples showing an improvement in gut-health status after the administration of the pre-biotic intervention were tagged as responders and the rest were tagged as non-responders.
G16H 20/10 - ICT specially adapted for therapies or health-improving plans, e.g. for handling prescriptions, for steering therapy or for monitoring patient compliance relating to drugs or medications, e.g. for ensuring correct administration to patients
G16H 10/40 - ICT specially adapted for the handling or processing of patient-related medical or healthcare data for data related to laboratory analysis, e.g. patient specimen analysis
G16H 10/60 - ICT specially adapted for the handling or processing of patient-related medical or healthcare data for patient-specific data, e.g. for electronic patient records
G16H 50/30 - ICT specially adapted for medical diagnosis, medical simulation or medical data miningICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for calculating health indicesICT specially adapted for medical diagnosis, medical simulation or medical data miningICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for individual health risk assessment
G16H 50/70 - ICT specially adapted for medical diagnosis, medical simulation or medical data miningICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for mining of medical data, e.g. analysing previous cases of other patients
62.
METHOD AND SYSTEM FOR IDENTIFYING AND UTILIZING FRUGAL MARKERS FOR CLASSIFICATION OF BIOLOGICAL SAMPLE
The present disclosure is related to method and system for identifying and utilizing frugal markers for classification of biological sample. Discovering an optimal and/or frugal set of features/biomarkers form a large set of features measured through high-throughput screening techniques, which can characterize a disease/anomaly with sufficient accuracy, still remains a challenge. According to the present disclosure, given a set of measurements of multiple features characterizing biological samples obtained from disease cases and healthy controls, a classification model combining the measured values of a small subset of the features is computed. The classification model is then used for classifying between disease cases and healthy controls.
Retail stores have limited visibility of on shelf inventory. Conventional approaches for targeted replenishment are reactive in nature and are also infrastructure and labor heavy. Present disclosure provides systems and methods for identification and replenishment of targeted items on shelves of stores wherein input data pertaining to sales of items is pre-processed and stock keeping unit (SKU) wise optimal bucket size is determined for predicting sales events for individual SKU based on historical events. Top-up requests are generated for each SKU for the planning bucket sizes and further a pick-up list using smart batching of the top-up requests is created based on SKU priorities. The pick-up list and top-up requests are executed to ensure items are topped up at the right time. Further, rate of sales or forecast the rate of sales are continually monitored throughout the day to ensure items are identified for targeted replenishment in retail stores.
This disclosure relates generally to systems and methods for monitoring and controlling a manufacturing process using contextual hybrid digital twin. Data pertaining to the manufacturing process is obtained from a plurality of data generation sources which is further inputted to one or more physics based models and train machine learning models such that simulated data and real time tata is obtained. Further, a gap between the simulated data and the real time data is determined and learnt. The learnt gap is further minimized and an augmented set of models are obtained. The augmented set of models along with a set of soft-sensing data is used to create the contextual hybrid digital twin for the manufacturing process. The performance of the manufacturing process is monitored and controlled using a performance analytics and decision making enablers of the contextual hybrid digital twin respectively in real time.
G05B 19/418 - Total factory control, i.e. centrally controlling a plurality of machines, e.g. direct or distributed numerical control [DNC], flexible manufacturing systems [FMS], integrated manufacturing systems [IMS] or computer integrated manufacturing [CIM]
66.
METHOD AND SYSTEM FOR ESTIMATING TEMPORALLY CONSISTENT 3D HUMAN SHAPE AND MOTION FROM MONOCULAR VIDEO
Estimating temporally consistent 3D human body shape, pose, and motion from a monocular video is a challenging task due to occlusions, poor lightning conditions, complex articulated body poses, depth ambiguity, and limited availability of annotated data. Embodiments of present disclosure provide a method for temporally consistent motion estimation from monocular video. A monocular video of person(s) is captured by a weak perspective camera and spatial features of body of the persons are extracted from each frame of the video. Then, initial estimates of body shape, body pose, and features of the weak perspective camera are obtained. The spatial features and initial estimates are then aggregated to obtain spatio-temporal features by a combination of self-similarity matrices between the spatial features, pose and the camera and self-attention maps of the camera features and the spatial features. The spatio-temporal aggregated features are then used to predict shape and pose parameters of the person(s).
G06T 7/246 - Analysis of motion using feature-based methods, e.g. the tracking of corners or segments
G06T 7/73 - Determining position or orientation of objects or cameras using feature-based methods
G06V 10/44 - Local feature extraction by analysis of parts of the pattern, e.g. by detecting edges, contours, loops, corners, strokes or intersectionsConnectivity analysis, e.g. of connected components
67.
SYSTEM AND METHOD FOR EXTRACTION OF SMALL MOLECULE FRAGMENTS AND THEIR EXPLANATION FOR DRUG-LIKE PROPERTIES
The embodiments of present disclosure herein address the inability of existing techniques to fragment both small molecules and substituents of a core scaffold. It addresses generation of lesser number of unique fragments which hinders application of graph propagation approaches to predict properties from molecular datasets. The method and system for extraction of small molecule fragments and their explanation for drug-like properties. A molecular graph representation is used to train graph convolution network (GCN) models for prediction of various absorption, distribution, metabolism, excretion, and toxicity (ADMET) properties. The models developed are compared with an existing atom-level graph model trained using a similar architecture. Further, the explanations obtained from the predictive models are validated based on their relevance to the existing knowledgebase of substructure contributions using matched molecular pairs (MMP) analysis.
Financial audits establish trust in the governance and processes in an organization, but they are time-consuming and knowledge intensive. To increase the effectiveness of financial audit, present disclosure provides system and method that address the task of generating audit recommendations that can help auditors to focus their investigations. Adverse remarks, financial variables mentioned in each sentence are extracted/identified from audit reports and category tag is assigned accordingly, thus creating a knowledge base for generating audit recommendations using a trained sentence classifier. In absence of labeled data, the system applies linguistic rule(s) to identify adverse remark sentences, and automatically create labeled training data for training the sentence classifier. For a given financial statement and financial variables in the audit report that contribute to suspiciousness, the system compares these with the extracted knowledge base and identify aligned adverse remarks that help auditor(s) in focusing on specific directions for further investigations.
State of the art techniques rely on Neural Network based approaches for tabular synthetic data generation are computationally intensive require data preprocessing. A method and system for generating tabular synthetic data falling within data distribution of base data is disclosed that utilizes statistical and unsupervised techniques directly on the raw base data providing computationally less intensive solution without need for data preprocessing. Constrained perturbation is applied on multi-dimensional tabular base data and dimensionality reduction is applied on both the base data and the perturbed data to generate 2D data. The 2D base data is used to train GMMs to obtain optimum number of clusters, using first local maxima of Silhouette score technique. Using median cluster distance approach between the 2D perturbed data and cluster centers of the 2D base data, the outlier in the perturbed data are discarded to obtain final synthetic data samples lying within the base data distribution.
State of the art approaches for training chained neural network models for delay prediction train the data models using only real data and not predicted data. Such models when used in a chained way leads to worse results as they are not exposed to predicted data during training. This leads to the model prediction errors showing sharp increase as the models tries to predict for subsequent stations past the immediate station. Embodiments disclosed herein provide a method and system for training of chained neural networks for delay prediction in transit networks. In this approach, a chained neural network model used by the system is trained such that data containing a mix of real data and predicted data is used for training each data model in a sequence of data models in the chained neural network model.
G06Q 10/04 - Forecasting or optimisation specially adapted for administrative or management purposes, e.g. linear programming or "cutting stock problem"
This disclosure relates generally to method and system of constructing refactoring path for on-premises application migration to cloud. Migrating an on-premises application to a new cloud environment without altering the application code is time consuming and complex. The method disclosed discovers a set of attributes of on-premises application received from a user for cloud migration to create a target cloud environment. The on-premises application is parsed to the target cloud environment through a set of pre-constructed refactoring stages. Further, a migration analysis is performed through one or more impacts of the set of pre-constructed refactoring stages over a set of considerations. Each impact is analyzed to recommend the on-premises application that qualifies for at least one of a migration, a transformation and a rebuild. The on-premises application is categorically mapped to the target cloud environment based on one or more variations identified across each technology domain with corresponding sub-technology domain.
In recent years, researchers have been focusing on the capabilities of radar-based microwave imaging in detection of concealed objects in sealed packages, through-the-wall imaging approach which faces challenge in suppress unwanted return signals. This disclosure relates a method to detect objects in sealed packages. One or more parameters associated with a conveyor are received to obtain a scan time of a radar. A sequence of scanning is determined between one or more antenna pairs based on corresponding position. A sealed package is scanned in the predetermined sequence to obtain range-time datasets with identifiers. The range-time datasets are processed by virtual antenna-pattern-weighted delay-multiply-and-sum technique based on one or more positions of each virtual antenna to determine one or more object signature images. A trained classification model based on extracted features associated with one or more object signature images.
G01S 13/88 - Radar or analogous systems, specially adapted for specific applications
G01S 7/41 - Details of systems according to groups , , of systems according to group using analysis of echo signal for target characterisationTarget signatureTarget cross-section
G01S 13/86 - Combinations of radar systems with non-radar systems, e.g. sonar, direction finder
G06V 10/56 - Extraction of image or video features relating to colour
G06V 10/764 - Arrangements for image or video recognition or understanding using pattern recognition or machine learning using classification, e.g. of video objects
The disclosure relates generally to methods and systems for precise estimation of carbon emission due to tillage operations. Conventional techniques that estimate the carbon emission due to the tillage operation are not efficient and effective as only the tillage operation is considered. The present disclosure combines the type of implement used for tillage and the depth of tillage for precise estimation of carbon emission. In the present method, the geo-tagged fields where the tillage operation is performed are identified based on a satellite image data. Next, an implement type used for the tillage operation is detected. Further a spatial tillage depth having tillage depths are estimated. Lastly precise estimation of carbon released due to the tillage operation is calculated based on the soil organic carbon released due to tillage operation and the carbon emission due to fuel consumed by the type of implement used and the spatial tillage depth.
This disclosure relates generally to method and system to classify news snippets into categories using an ensemble of machine learning models. The ensemble is between a bidirectional long short memory (BILSTM) based text classification network and a pretrained language model (PLM) based natural language inference (NLI) which is robust and accurate for such categorization. The method trains a first machine learning model using a training dataset to learn text representations. Further, the training dataset is used to finetune a second machine learning model to classify at least one unlabeled news snippet of unknown category based on a premise-hypothesis pair. Further, an ensemble of machine learning models is generated by using the first machine learning model and the second machine learning model to classify a set of test news snippets received as input request to corresponding category.
Current approaches create web based simulated lab type of setups for students/learners in which lab environment is created according to course of student. However, these conventional approaches require student systems to have some basic setup for working on virtual labs. Few available techniques also provide cloud-based virtual labs in which private cloud is created but they use open-source tool for creation. Further, available virtual lab techniques do not provide seamless integration with LMS. Present disclosure provides methods and systems for provisioning virtual lab with learning management system. The system provides virtual desktop that is created on an infrastructure-as-a-service platform. The learner desktop is accessed by a user/learner from respective existing computer system via LMS account. The virtual desktop includes hardware configuration and software packages required to solve one or more problems or perform one or more experiments related to course for which learners have enrolled in LMS.
Existing methods of fabric design are restrictive and incomplete allowing focus on parts of the fabric instead of the whole. The representations do not allow full expression of all possible states of the entire loom setup, fabric layout, design requirements, weaving steps and methods and the resulting fabric output as a whole. Existing technologies work in a way where the entire loom mechanism is reduced to only some key essential parts and configurations. The disclosure herein generally relates to fabric design, and, more particularly, to a method and system for loom design for fabric design. This method and system permits simulation of fabric design by enabling configuration of different parameters and associated values as per requirements.
The present disclosure provides weight based slotted synchronous blockchain client network. Conventional blockchain consensus protocols heavily rely on synchrony assumptions of the underlying network. However, little attention has so far been given to similar assumptions on blockchain client networks. Such assumptions would be useful in deriving bounds on confirmation times of transactions through the blockchain protocol. To overcome the challenges of the conventional approaches, the present disclosure provides a weight based slotted synchronous blockchain client network. The blockchain client network of the present disclosure is slotted, wherein at most one transaction is submitted in each slot by one of the client nodes. Further, there exists a notion of synchrony in the peering between the client nodes and the blockchain nodes. In an embodiment, for each submitted transaction, there is an upper-bound on the number of slots on the reception of the said transaction at an arbitrary blockchain node.
H04L 9/32 - Arrangements for secret or secure communicationsNetwork security protocols including means for verifying the identity or authority of a user of the system
H04L 9/00 - Arrangements for secret or secure communicationsNetwork security protocols
78.
METHOD AND SYSTEM FOR EVALUATING NON-FICTION NARRATIVE TEXT DOCUMENTS
Existing approaches for processing and evaluation of documents containing non-fiction narrative texts have the disadvantage that they are comparatively less studied in linguistics, and hence do not provide sufficient data required for evaluations. Method and system are for evaluating non-fiction narrative text documents are provided. The system processes a plurality of non-fiction narrative text documents and computes a plurality of corpus statistics. The plurality of corpus statistics is then used for evaluation of any non-fiction narrative text document that may or may not be collected as real-time input.
Cleaning systems proposed in the art have technical construct limitations in the cleaning mechanisms used, which leads to a lower ratio of power consumed to area cleaned, directly affecting the cleaning efficiency. Thus, an Unmanned Aerial Vehicle (UAV) Propelled Autonomous Multiplane Cleaning System (UPAMCS) is disclosed. An UAV and Mopping Interface Mechanism (UAV-MIM) connects a UAV to one or more mopping systems comprising an epicyclic gear driven moppers with no additional power devices used. A maneuvering mechanism disclosed enables the UAV to propel the mopping systems to reach any geometric shape or inclination. The UPAMCS provides cost, time, and power efficient surface cleaning. The UPAMCS is also equipped with vision cameras and LiDAR for guidance during landing and crawling over surfaces along with additional surface defect detection by processing the captured images.
Precise estimation of duration of cover crop is a challenge considering multiple factors contributing to the same and complexity involved in capturing them in the estimation process. A method and system for estimation of cover crop duration and generating Integrated Cover Crop Index (ICCI) is disclosed. Firstly, the maincrop is identified and associated time series data is eliminated to avoid false positives. Detection of type of cover crop and its exact duration is derived by integrated use of satellite remote sensing data, sensor data, field observations and phenology based indicators. Duration of cover crop is estimated considering the impact of snow cover, dormant period etc., by integrated use of remote sensing and sensor data along with local domain crop knowledge of the region. The ICCI provides quantitative measure for cover crop effort and can be used for incentivizing farmers following sustainable cropping practices.
Improvement in the accuracy of disease diagnosis associated with cardiac abnormalities is an open research area. Appropriate feature selection to capture the underlying signs of a disease is critical in Machine Learning (ML) based approaches. A method and system for, determining cardiac abnormalities using chaos-based classification model from multi-lead ECG signals, is disclosed. The method combines the commonly used chaos parameter with other set of chaos-related statistical parameters like non-linearity, self-similarity, Chebyshev distance and spectral flatness for a holistic approach to the study of cardiac abnormalities. The method disclosed thus attempts to use above ML based measures for disease classification. The set of chaos-related features used herein contribute to improving the accuracy of detection of various cardiac diseases arising due to cardiac abnormalities such as Atrial Fibrillation (AF) and the like. The improved accuracy in the detection of AF effectively improves the accuracy in percentage of AF burden.
G16H 50/20 - ICT specially adapted for medical diagnosis, medical simulation or medical data miningICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for computer-aided diagnosis, e.g. based on medical expert systems
G06F 18/2415 - Classification techniques relating to the classification model, e.g. parametric or non-parametric approaches based on parametric or probabilistic models, e.g. based on likelihood ratio or false acceptance rate versus a false rejection rate
82.
ROBOTIC NAVIGATION WITH SIMULTANEOUS LOCAL PATH PLANNING AND LEARNING
In conventional robot navigation techniques learning and planning algorithms act independently without guiding each other simultaneously. A method and system for robotic navigation with simultaneous local path planning and learning is disclosed. The method discloses an approach to learn and plan simultaneously by assisting each other and improve the overall system performance. The planner acts as an actuator and helps to balance exploration and exploitation in the learning algorithm. The synergy between dynamic window approach (DWA) as a planning algorithm and a disclosed Next best Q-learning (NBQ) as a learning algorithm offers an efficient local planning algorithm. Unlike the traditional Q-learning, dimension of Q-tree in the NBQ is dynamic and does not require to define a priori.
Conventional approaches lack in traceability of various factors that led to changes in farms states. Further, conventional approaches for managing farms, and associated data obtained from various sources do not fully allow for employing farm's practices to other similar conditions as they may have adverse effects over other identical/similar crops during cultivations. Present disclosure present systems and methods that codify/pre-process and store farm parameters, associated versions and farm interactions in a systematic manner, along with associated knowledge, inferences, that bring changes to a farm's state, which can be referred as farm versions. The inferences, and relationships between farm configurations, versions and the like are used by the system to generate farm portability score for target farm and identify farm state portability. Once the current farm state is identified, the system further creates an optimized routing mechanism for the target farm to attend an optimal state.
G06Q 10/0637 - Strategic management or analysis, e.g. setting a goal or target of an organisationPlanning actions based on goalsAnalysis or evaluation of effectiveness of goals
41 - Education, entertainment, sporting and cultural services
42 - Scientific, technological and industrial services, research and design
Goods & Services
Business data analysis; compiling and analyzing statistics, data and other sources of information for business purposes; systematization of data in computer databases; business consultation services, namely, business process improvement; business consulting services relating to the integration of the areas of business process technology, organizational learning, change management, and operational sustainability; business consultation in the field of education leadership development; assistance, advisory services and consultancy with regard to business planning, business analysis, business management, and business organization; business management and enterprise organization consultancy; conducting business productivity analyses; marketing research services; compilation and systemization of information into computer databases; business management and consultation in the field of data conversions and business intelligence; business collaboration services, specifically, providing online business networking services for businesses and educational institutions to collaborate with consumers to help improve goods and services; business development services for others. Education; education services in the nature of courses at the university level; providing of training in the field of education and business; entertainment; sporting and cultural activities; arranging and conducting of colloquiums, conferences, congresses, seminars and workshops; online publication of electronic books and journals; providing information in the field of education; research in the field of education; teaching, educational and instruction services; transfer of business knowledge and know-how. Computer software development; research and development of new products and services; research and development of computer software; research and development of new products for others; research and development of advanced learning technologies and teaching methods; research and development and consultation related thereto in the field of business, finance, insurance and education; innovation consulting services, namely, advising others in the areas of product development; business technology software consultation services; consultancy in the design and development of computer hardware and software for business and educational purposes; artificial intelligence consultancy; conducting technical project studies; Industrial design; providing scientific information, advice and consultancy relating to net zero emissions; providing scientific information, advice and consultancy relating to carbon offsetting; quantum computing; research in the field of artificial intelligence technology; scientific research; technological consultancy services for digital transformation.
41 - Education, entertainment, sporting and cultural services
42 - Scientific, technological and industrial services, research and design
Goods & Services
Business data analysis; compiling and analyzing statistics, data and other sources of information for business purposes; systematization of data in computer databases; business consultation services, namely, business process improvement; business consulting services relating to the integration of the areas of business process technology, organizational learning, change management, and operational sustainability; business consultation in the field of education leadership development; assistance, advisory services and consultancy with regard to business planning, business analysis, business management, and business organization; business management and enterprise organization consultancy; conducting business productivity analyses; marketing research services; compilation and systemization of information into computer databases; business management and consultation in the field of data conversions and business intelligence; business collaboration services, specifically, providing online business networking services for businesses and educational institutions to collaborate with consumers to help improve goods and services; business development services for others. Education; education services in the nature of courses at the university level; providing of training in the field of education and business; entertainment; sporting and cultural activities; arranging and conducting of colloquiums, conferences, congresses, seminars and workshops; online publication of electronic books and journals; providing information in the field of education; research in the field of education; teaching, educational and instruction services; transfer of business knowledge and know-how. Computer software development; research and development of new products and services; research and development of computer software; research and development of new products for others; research and development of advanced learning technologies and teaching methods; research and development and consultation related thereto in the field of business, finance, insurance and education; innovation consulting services, namely, advising others in the areas of product development; business technology software consultation services; consultancy in the design and development of computer hardware and software for business and educational purposes; artificial intelligence consultancy; conducting technical project studies; Industrial design; providing scientific information, advice and consultancy relating to net zero emissions; providing scientific information, advice and consultancy relating to carbon offsetting; quantum computing; research in the field of artificial intelligence technology; scientific research; technological consultancy services for digital transformation.
86.
SYSTEMS AND METHODS FOR IDENTIFYING AND ANALYZING RISK EVENTS FROM DATA SOURCES
Conventional methods of analyzing social media content involves performing sentimental analysis to understand related sentiment and effects of events on communities. However, such analysis may not be completely accurate and are prone to errors. Present disclosure provides system and method that identify and analyze risk events from data collected from various sources. Key phrases obtained from sources is received, pre-processed, and clustered accordingly. The clustering is performed based on frequency of incoming words. The clustered dataset obtained is classified into one or more categories based on a polarity score. Dataset of specific category (e.g., negative category dataset) is analysed to identify events and topics which are then grouped using an associated label to obtain grouped entities. Each entity is then ranked and assigned a risk score for identifying high-risk events which are then analyzed using simulation and optimization technique(s) and an explainability text for the analyzed risk events is generated.
State of art techniques supply chain management system tend to mostly focus on end to end supply chain management and lose focus on the port of dispatch to retailer segment of supply chain. A method and system for demand driven, constraint-based and cost optimized inbound container prioritization for resilient supply chain is disclosed. Current demand, multiple constraints across destination port, transport carriers, distribution centers, external factors, looks at multiple supply chain cost components like free days, demurrage, transportation, manpower is considered and a ‘n’ week rolling dynamically prioritized list of inbound containers, is generated, which needs to be picked up from the destination port for addressing the volatile customer demand. The dynamic prioritization uses an intelligent ranking algorithm that calculates an urgency score for each container based on crucial parameters before creating a pickup list and helps in maximizing profit while reducing logistic overheads for the retailer.
In apparel search context, process of finding a similar item out of thousands of other items is a cumbersome and computationally heavy process. In order to build a deep learning model that can perform the similarity search, hundreds of training images per Stock Keeping Unit (SKU) are required. Due to shortage of training data, this approach fails to generate a deep learning model that can perform the similarity search in intended manner. The existing approaches may also require domain experts to perform classification of apparels, so as to generate the training data. The method and system disclosed herein provide an approach in which positive images and negative images are generated from each query image, which in turn are used for generating a training data. The training data is then used to generate a deep learning model, which is used to perform the similarity search.
The embodiments of present disclosure herein address the need of minimizing web services relocation and user centers reallocation to comply with data residency regulations and change in latency threshold for web services based on user demands. The method and system provide a framework that assists enterprises in migrating web services and user center allocation to different data centers with lower additional operational cost from its current configurations and minimal changes (migrations). In case of change in data regulations and invocation frequency from users demand, non-compliant users are allocated to compliant data centers with minimal changes in the original configuration. Though the key decision is to serve the customers effectively, web services need to be deployed across a finite number of servers, there are multiple sub-problems such as minimizing latency and reduction in operational cost that needs to be addressed.
H04L 41/5041 - Network service management, e.g. ensuring proper service fulfilment according to agreements characterised by the time relationship between creation and deployment of a service
H04L 41/5025 - Ensuring fulfilment of SLA by proactively reacting to service quality change, e.g. by reconfiguration after service quality degradation or upgrade
Current solutions in literature use Zero Knowledge Proofs (ZKP) to ensure cryptographic guarantees for account balances. These are highly complex and require large memory. Hence there is a need for a computationally efficient mechanism that can preserve the privacy of customer financial information and provides verifiable cryptographic guarantees for debits and credits of a transaction without revealing customer information. Embodiments of the present disclosure provide a method and system for an inter-bank transaction with privacy enabled auditing and a privacy enabled inter-bank settlements in blockchain network. The method disclosed leverages cryptographic primitives such as Boneh-Lynn-Shacham (BLS) to preserve the privacy of the customer's financial information and to enable faster auditable settlement between the banks in the presence of a governing body.
G06Q 20/10 - Payment architectures specially adapted for electronic funds transfer [EFT] systemsPayment architectures specially adapted for home banking systems
G06Q 20/02 - Payment architectures, schemes or protocols involving a neutral third party, e.g. certification authority, notary or trusted third party [TTP]
G06Q 20/06 - Private payment circuits, e.g. involving electronic currency used only among participants of a common payment scheme
Existing approaches for building digital twins specific to industrial plants require industry domain experts, process modeling engineers, data scientists, and solution developers to spend considerable time and effort to build the right solution. This is not an easily reproducible process. For each type of industry and for each specific plant, the design, and development process must start all over, more or less from scratch and the effort needs to be reinvested. Hence this is not a scalable proposition. Method and system disclosed herein provide a knowledge-based plant monitoring and optimization approach. In this approach, for a given high-level problem statement, a detailed problem definition is derived, a plant view of interest is identified using the knowledge based approach, and in turn plant data of interest is identified. Further, a digital twin is generated using the plant data of interest, which is then used for the plant monitoring and optimization.
G05B 13/02 - Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric
92.
SYSTEMS AND METHODS FOR OPTIMIZING A BIOREACTOR FOR CONTROLLING GROWTH OF HUMAN STEM CELLS
Conventionally control and optimization of bioreactor has been challenging due to the unpredictable behaviour of living cells and non-linear process dynamics. Present disclosure provides systems and methods that implement a physics-based model that performs simulation on pre-processed sensor values obtained from various soft sensors placed on a bioreactor to obtain simulated variables. Using the set of simulated variables, real-time operating parameters pertaining to the bioreactor are optimized. The optimized real-time operating variables are then compared with the actual real-time operating parameters to obtain a set of correction factors. One or more recommended parameter values are identified based on the correction factors and the real-time operating parameters are updated accordingly.
Perturbed data generation for explainable Artificial Intelligence (AI) is still an evolving field and attempts are made towards to addressing the technical challenge of correlation of features that degrades generated explanations for block box models in Machine Learning (ML) or AI domain. A method and system for local explanation of black box model based on constrained perturbation and ensemble-based surrogate model is disclosed. The method disclosed averts data correlation problem by performing data perturbation around the local instance in accordance with distribution of test data set and primarily ensures the values of input features associated with the local instance stay within the feature space and does not form out of distribution scenarios (add adversary cheating). The method autogenerates labels for the perturbed data to fit or train an ensemble based surrogate model that eliminates data bias and improves accuracy of generated explanations.
There has been a surge in usage of biotherapeutic products in multiple industries. The biotherapeutic products are a mixture of their charge variants which are separated by a continuous chromatography process. This disclosure provides a method and an apparatus for control and optimization of the continuous chromatography process. The present disclosure helps controlling composition of charge variants in biotherapeutic products by developing an apparatus that has unique architecture including advanced Distributed Control System (DCS), programmable logic controllers (PLCs), Local area network (LAN) setup and Python layer with user interface. This allows an operator to monitor charge variant concentrations and obtain an optimal schedule to implement such that a target product composition is achieved. The present disclosure comprises a data-pre-processing step followed by prediction of process parameters using soft sensor and prediction models. The chromatography process is optimized to achieve targeted purity and yield by recommending optimal values of manipulated variables.
B01D 15/36 - Selective adsorption, e.g. chromatography characterised by the separation mechanism involving ionic interaction, e.g. ion-exchange, ion-pair, ion-suppression or ion-exclusion
B01D 15/38 - Selective adsorption, e.g. chromatography characterised by the separation mechanism involving specific interaction not covered by one or more of groups , e.g. affinity, ligand exchange or chiral chromatography
The disclosure relates generally to methods and systems for obtaining an end-to-end classification model using a mixture density network (MDN). Conventional MDNs have been mostly used in regression tasks due to their direct applicability, but not for classification tasks. The MDN model of the present disclosure captures the intrinsic multi-modality in the data and learns the distribution parameters. According to the present disclosure, input training samples are passed through the mixture density network to learn the distribution parameters and to model the intrinsic multi-modality present in the data. The learned parameters are then used to evaluate the cumulative distribution function (CDF) value with respect to each of the original input feature or learnt latent feature by passing inputs through a simple feed-forward layer. The evaluated CDF values are then fed to a softmax layer with LASSO penalty on its weights to predict the classification scores.
This disclosure relates generally to a field of industrial machine inspection, and, more particularly, to method and system for acoustic based industrial machine inspection using Delay-and-Sum beamforming (DAS-BF) and dictionary learning (DL). The disclosed method presents a two-stage approach for anomaly detection using a multi-channel acoustic mixed signal. In the first stage, separation of a plurality of acoustic signals corresponding to the spatially distributed acoustic sources is performed at a coarser level by using the DAS-BF. Subsequently, dictionaries pre-trained using the plurality of acoustic signals of the individual source machines are utilized for generating a plurality of separated acoustic source signals. The generated plurality of separated acoustic source signals are analyzed for the anomaly detection by comparing them with a corresponding normal machine sound template.
State of the art code matching approaches have the disadvantage that they rely on the line numbers provided by source code and binary parsers to establish mapping and do not work since changes introduced by compiler may be much more complex. Some approaches do not work as the source code of application and its binary version may not share the vocabulary. Machine learning based techniques have the disadvantage that they require significant amount of training data, which may not be available in abundance. Method and system disclosed herein provide a mechanism matching score for each of a plurality of code fragments in the intermediate representation of the source code file for each of a plurality of binary fragments in the intermediate representation of the binary file.
Conventional ESPRIT (Estimation of Signal Parameters via Rational Invariance Techniques) cannot be directly applied to SFCW MIMO radar for localization of targets as the performance would be restricted by geometry of spatial MIMO. Thus, the present disclosure provides a method and system for localization of targets using SFCW MIMO radar. In this method, the channel response of the virtual uniform rectangular array (vURA) obtained by scanning at uniformly spaced frequency points is combined to form a larger array referred as Space-Frequency (SF) array. The 3D localization of targets is done by estimating azimuth angle, elevation angle and range using this SF array. The localization capability of the disclosed method largely depends upon the number of frequency scanning points and enables localizing far more targets than the dimension of the vURA. In addition, the inter-element spacing requirement of vURA is also greatly relaxed.
G01S 13/00 - Systems using the reflection or reradiation of radio waves, e.g. radar systemsAnalogous systems using reflection or reradiation of waves whose nature or wavelength is irrelevant or unspecified
