The disclosure relates generally to methods and systems for predicting water quality of a river having a varying river ecosystem. Due to multiple and diverse factors, understanding and estimating the water quality of the river stream (river itself) is extremely and technically challenging. The present disclosure discloses a development of a river digital twin model utilizing a multi-modeling approach to comprehensively model the river and its varying ecosystems. The agents encompass entities that directly or indirectly introduce effluents or withdraw water from the river. Agents and their interactions are defined using a combination of behavior rules, correlations, and physics principles, creating the digital twin model that closely mimics the real river system. Physics-based equations are also employed in the present disclosure to capture the dynamics of the river, while relationships between different agents are established.
Multi-object tracking (MOT) in video sequences plays a critical role in various computer vision applications. The primary objective of MOT is to accurately localize and track objects across consecutive frames. However, existing MOT approaches often suffer from computational limitations and low frame rates in commodity machines, which hinders real-time performance. Present disclosure provides method and system for performing content aware multi-object tracking. The system first classifies video into slow and fast moving object content videos depending on features of objects to be tracked in frames. Then, system applies a computationally intensive deep sort algorithm to perform tracking of objects by selectively skipping frames. Thereafter, the system applies linear approximate Kalman prediction for slow object content videos and quadratic interpolation for fast object content videos as low computation tracking techniques for tracking objects present in skipped frames, thus significantly improving execution speed while reducing computational load on the system.
G06T 5/20 - Image enhancement or restoration using local operators
G06V 10/25 - Determination of region of interest [ROI] or a volume of interest [VOI]
G06V 10/764 - Arrangements for image or video recognition or understanding using pattern recognition or machine learning using classification, e.g. of video objects
3.
METHOD AND SYSTEM FOR OPTIMAL SCHEDULING OF POWER SOURCES FOR MEETING ELECTRICITY DEMAND OF ENTERPRISE
The uncertainty of availability and risk associated with the cost of power procurement hinders the consumer from using new sources of supply/storage devices or taking part in the competitive power markets or procuring power from green energy resources. Present disclosure provides a method and a system for optimal scheduling of power sources for meeting electricity demand of enterprise. In particular, the system performs portfolio optimization formulation for cost, risk and carbon emission minimization for the enterprise. The portfolio optimization formulation simulates market scenario and guides in providing effective power purchase strategy for enterprise along with real-time adjustments. In particular, the level of risk to be considered along with cost of power procurement and carbon footprint determine portfolio allocation for each time block in portfolio optimization formulation. Thus, enterprise may choose most appropriate terms of contract, best installation size of renewable resources or batteries, and supply sources.
State of art techniques, need a decoder following the encoder to encode EEG signals, whose morphology is undefined. Embodiments herein disclose a method and system for a Spiking Neural Network (SNN) based low power cognitive load analysis using electroencephalogram (EEG) signal. The method receives a raw EEG signal from multichannel EEG set up, wherein each of the raw EEG signal is re-referenced and encoded into a spike train using a Light-Weight-Lossless-Decoder less-Peak-based (LWDLP) encoding. Further, the spike trains are processed by the SNN architecture using backpropagation based supervised approach, wherein the spatial information and the temporal information are learnt by the SNN in form of neuronal activity and synaptic weights. Post learning the SNN architecture applies an activation function on the neuronal activity for classifying a cognitive load level experienced by a subject from among a plurality of predefined cognitive load levels using a SNN classifier.
This disclosure relates generally to a method and system for three-dimensional (3D) merchandising and space planning. State-of-the-art methods providing the merchandising and space planning for store management in three-dimensional visualization is limited to a specific fixture or to a specific area of the store. However, planning the merchandising and the space for an entire store in three-dimensional hyper-realistic visualization is not yet done. The disclosed method provides three-dimensional hyper-realistic visualization for planning the merchandize and space for an entire store by integrating data analytics capturing real-time information required during planning, and the in-house libraries facilitating quicker and easier design of the layouts for the store. The method includes importing the products from the product library, receiving real-time performance analytics for the products, and creating layout by placing the products on the fixtures imported from the fixture library.
Robotic Process Automation (RPA) systems face challenges in handling complex processes and diverse screen layouts that require advanced human-like decision-making capabilities. These systems typically rely on pixel-level encoding through drag-and-drop or automation frameworks such as Selenium to create navigation workflows, rather than visual understanding of screen elements. Present disclosure provides systems and methods that implement large language models (LLMs) coupled with deep learning based image understanding which adapt to new scenarios, including changes in user interface and variations in input data, without the need for human intervention. System of the present disclosure uses computer vision and natural language processing to perceive visible elements on graphical user interface (GUI) and convert them into a textual representation. This information is then utilized by LLMs to generate one or more navigation workflows that include a sequence of actions that are executed by a scripting engine/code to complete an assigned task from a task-request.
Computational Fluid Dynamic, Finite Element Methods (FEM), and other engineering modelling and simulation requires meshing (numerical grid generation) of the geometry. The meshing is done by meshing software and the CFD expert need to provide meshing parameters. Existing approaches involve a lot of human intervention which leads to additional computational time and may be prone to errors. Present disclosure provides system and method that construct meshed geometry based a first mesh generated using meshing parameters wherein the meshed geometry is compared with original geometry of an object under consideration to estimate the surface size. Further, the first mesh is simulated in an iterative manner to obtain parameters such as domain size, refinement zone, and layer parameters of the object under consideration until these parameters reach an associated threshold and a mesh independent grid is obtained for the object based on the above-mentioned parameters.
Conventional techniques implemented various explicit rules and engineer systems to deliver a solution. Stores and Distribution Centers (DC's) are faced with problems related to excess inventory/lack of inventory which vary with several, and the biggest challenge lies in matching supply with demand. Present disclosure provides systems and methods that perform node ranging wherein range of items are stocked in stores and DCs for fulfilling demand. The system identifies stores and DCs and further recommend store specific items based on a request received by the system and then implements a supervised machine learning model and a reinforcement learning models determine a range of items to be sized from the recommended store specific items which are based on most frequent line items being identified from a set of inputs received therein.
In Microwave radar imaging, obtaining high resolution microwave image from materials remains a challenge due to comparatively longer wavelength. Embodiments of the present disclosure provide a system for microwave imaging by Multiple-Input and Multiple-Output along with Synthetic Aperture Radar. A back-scattered signal is received from object at a target as an input. The back-scattered signal is rearranged to generate sub-array elements. A Fourier transform of the sub-array elements is computed by deploying two dimensional Fast Fourier transform to obtain two dimensional Fast Fourier transform of the sub-array elements. The 2D FFT of the sub-array elements is vectorized to obtain vectorized stacked sub-array matrix. The stacked sub band of the 2D FFT of the entire aperture array is reordered to obtain a two dimensional Fast Fourier transform of the entire aperture array. Three-dimensional reflectivity function of the target is estimated from the 2D FFT of the entire aperture array.
G01S 13/90 - Radar or analogous systems, specially adapted for specific applications for mapping or imaging using synthetic aperture techniques
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
14.
METHODS AND SYSTEMS FOR GENERATING BEHAVIOR EMBEDDED ENTITY SPECIFIC CRYPTOCURRENCY TRANSACTIONS
The huge size of ever-increasing cryptocurrency data makes investigating transactions for identifying fraudulent activities challenging. Conventional Artificial Intelligence (AI) models trained on a sample cryptocurrency transaction dataset are not scalable or efficient and getting required labelled data for training the AI models is a challenge due to the pseudo-anonymity of entities in cryptocurrency transactions. The present disclosure enables generating of patterned transactions pertaining to different entities using an input specification in the form of a transaction schema that describes one or more parameters including one or more entities, a quantity of cryptocurrency transactions, time frame; and a pattern describing a typology for the cryptocurrency transactions to be generated. The input specification is processed to simulate customizable and scalable training data characterized by the behavior or nature of entities seen in the real world and associated with different patterns including the money laundering patterns described in the input specification.
G06Q 20/40 - Authorisation, e.g. identification of payer or payee, verification of customer or shop credentialsReview and approval of payers, e.g. check of credit lines or negative lists
G06Q 20/06 - Private payment circuits, e.g. involving electronic currency used only among participants of a common payment scheme
15.
INTEGRATED PLATFORM ENABLING RAPID AUTOMATED GENERATION OF OPTIMIZED DNNs FOR EDGE DEVICES
Building such optimized DNN models model for resource constrained devices require huge amount of workflow setup, engineering skills and research skills. There is a need to automate the process for generation of optimized DNN models for Green analytics. A method and system providing integrated platform for rapid automated generation of Tiny ML models for edge devices by integrating multiple optimization techniques and recommending the mist appropriate technique based on available input parameters is provided. Further one of the optimization technique the Fast-NAS can be generalized across multiple applications and consume 95% less computational power (GPU hours). The enhancement is achieved using a performance evaluation technique for generated models using new metric, a new reward function with adaptive parameters, an early-exit strategy to further expedite the optimization process, and a new NAS flow enhanced with AutoML (Hyper-Parameter Optimization) to minimize human intervention.
The disclosure relates generally to methods and systems for optimizing performance of enterprise operations using maturity assessment. Existing enterprise operations maturity assessment frameworks are inefficient and lack comprehensive and integrated coverage of all three layers of the enterprise operations. The present disclosure computes or assesses a maturity score of the enterprise operation on a predefined maturity scale. The maturity score of the enterprise operation is determined based on responses to a set of evaluators which are described as the key performance indicators for a given enterprise operation. The enterprise operation maturity assessment outcomes comprise of current overall enterprise operation maturity score, benchmarks for key performance indicators, and a subset of recommended solutions to achieve improved maturity levels. Further, the methods and systems of the present disclosure allow to simulate the improvement in individual metric performance values based the recommended solutions selected for each of the key performance indicators.
G06Q 10/04 - Forecasting or optimisation specially adapted for administrative or management purposes, e.g. linear programming or "cutting stock problem"
G06Q 10/0639 - Performance analysis of employeesPerformance analysis of enterprise or organisation operations
17.
METHOD AND SYSTEM FOR DAMAGE LOCALIZATION USING LOW POWER GUIDED WAVE
Use of ultrasonic guided waves for damage identification and localization is not new in Non-Destructive Testing/Evaluation. However, most of the time it is performed with high voltage pulse excitations that use several hundreds of volts in the form of a short burst, thus making it unsafe and unsustainable for defect localization in large structures. Present disclosure provides a method and a system for damage localization using low power ultrasonic guided waves. The system of the present disclosure uses a Vector network analyzer (VNA) sweep of a defined frequency range of low signal amplitude on a structure to form guided wave resonance spectra. Then, the system performs an Inverse Fast Fourier transform (IFFT) on the guided wave resonance spectra to obtain a time domain pulse propagation picture. Thereafter, the system uses a pulse echo based analysis technique based on time domain pulse propagation picture to locate damage position in the structure.
This disclosure relates to system and method to reconstruct human motion for mobile robot teleoperation using shared control. The method of the present disclosure acquire an input feed of a human operator to perform a task with assistance in a remote environment using shared control. The mobile robot reconstructs to follow a trajectory of the human operator towards the intended goal in the remote environment. The mobile robot determines at least one goal intended by the human operator based on a previously occurred state, a current kinematic state and a future trajectory of the human operator and a known position of the plurality of goals. The model predictive control generates at least one instruction to control the movement of the mobile robot to perform at least one of following the trajectory of the human operator and reaching the operator intended goal based on a joint angle position and a velocity.
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
G06F 3/01 - Input arrangements or combined input and output arrangements for interaction between user and computer
19.
METHOD AND SYSTEM FOR DETERMINING OPTIMAL PRICE PROMOTION PARAMETERS
The disclosure generally relates to determining optimal price promotion parameters for Consumer-packaged goods (CPG). The CPGs includes items that are frequently purchased by consumers and requires routine replacement or replenishment such as food, beverages, clothes, tobacco, makeup, and household products, etc., The techniques to determine the optimal pricing strategy is subjective and is dependent on goods/products being sold. The existing techniques mostly determine the optimal pricing strategy of CPG based on non-behavioral factors or on historic price/sales trends, wherein there is no explicit focus on consumers' behavior patterns and are not efficient. The disclosure proposes to determine the optimal price promotion parameters in several steps including—estimating a plurality of behavioral elements, generating a plurality of synthetic consumer data, mapping the plurality of synthetic consumer data and the plurality of behavioral elements and finally using simulation-based optimization to determine the optimal price promotion parameters.
Reactive systems commonly feature an outer infinite loop that captures environmental input and according to the inputs decides the system's response. The outer infinite loop implies that almost every reactive system contains nested loops. Existing verification techniques, such as model checking and loop abstraction methods, often struggle in terms of accuracy and efficiency in the presence of nested loops. Present disclosure provides a method and a system for performing outer loop abstraction for verification of reactive systems. The system first checks whether code in outer loop can be executed before the outer loop. Then, the system performs optimization of the outer loop. Thereafter, the system abstracts outer loop which infinitely read and process environmental input. Further, the system transforms the input code to obtain outer loop abstracted code which is then passed on to an industrial verifier for verification of the reactive system.
09 - Scientific and electric apparatus and instruments
35 - Advertising and business services
42 - Scientific, technological and industrial services, research and design
Goods & Services
Computer software for use in the retail business industry to enable retailers to rationalize assortment planning based on performance, fixture size, inventory, customer choice sets, loyalty, space elasticity, demand transfer, and markdown considerations; computer software enabling retailers to make key merchandising processes intelligent and autonomous with advanced AI models and systems and including price, assortment, and macro space optimization as well as competitor intelligence; computer software for use in the entire retail supply chain, namely, for planning, allocation, assortment, replenishment, revenue management, merchandise management, customer financial analysis, business data management, verifying prices, ordering stock, operating point-of-sale functions, custom order processing, target customer marketing, data mining, reporting, telemarketing, and call-centre management, and to build cognitive supply chains by transforming core supply chain dimensions such as cross banner network design and node ranging, optimization of omnichannel operations, and end-to-end supply chain visibility and control, dynamic disruption management, inventory optimization, capacity utilization, and hyper automation; computer software used to access and transmit retailer and supplier product data via a global computer network; computer software platforms; computer software platforms for use in retail and supply chain industry; artificial intelligence (AI) powered enterprise personalisation software platform to deliver unified, orchestrated and real time experience for customers across channels; computer software for use in customer relationship management (CRM); enterprise software in the nature of a database for non-transactional data and search engine for database content; downloadable computer software for use in business data integration business data processing business data analysis, and customer service; downloadable computer software for use in omnichannel marketing; downloadable computer software for enterprise automation; computer e-commerce software to allow users to perform electronic business transactions via global computer network. Business management assistance and consultancy, business organization consultancy; business information; cost price analysis; commercial information and advice for consumers [consumer advice shop]; commercial or industrial management assistance, compilation and systemization of information into computer databases; compilation of statistics; professional business consultancy; computerized file management; advisory services for business management; database management marketing and marketing research; personnel management consultancy; computer aided business management services; business monitoring and consulting services, namely, tracking web sites and applications of others to provide strategy, insight, marketing, sales, operation, product design, particularly specializing in the use of analytic and statistic models for the understanding and prediction of consumers, businesses, market trends and actions. Computer software consultation for retail merchandise management, design, installation, configuration, implementation and maintenance of computer software in the retail supply chain field, technical support services, namely, troubleshooting of computer software problems in the retail supply chain field; software as a service (SAAS); design and development of computer hardware and software; platform as a service (PASS) and software as a service (SAAS) featuring computer software platforms for enterprise personalization to deliver unified, orchestrated and real time experience for customers across channels, providing online non-downloadable software for use in customer relationship management; software consultancy; providing customized computer searching services, namely, searching and retrieving information at the customer's specific request via global computer networks, providing an online network environment featuring technology that enables users to share data in the field of AI customer intelligence; design and development of computer software; AI powered software platform for use in retail industries; supply chain solutions; unified and composable commerce platform to deliver seamless and hyper personalized omnichannel experiences with a range of modern commerce capabilities such as click-and-collect, scan and go, save the cart, and self-checkout, along with dynamic promotion management; store optimization suite for use in retail industry to transform traditional stores into lean, intelligent, and automated stores of the future; capabilities including smart task management, targeted shelf replenishment, predictive queue management, and intelligent workforce management; sustainability solutions for use in the retail industry that help retailers to track ESG compliance, meet net zero goals, and drive energy efficiency, among others; accelerators for use in the retail industry that enable retailers to monetize customer data, set up and optimize retail media networks, participate in market places, launch new formats and services, and build customer life time value through loyalty.
22.
SYSTEM AND METHOD FOR DETERMINING A PERSONALIZED PROBIOTIC THERAPEUTIC REGIMEN
Existing techniques fail to provide a method to cumulate effects of interactions between groups of gut-associated microbes to predict efficiency of a probiotic organism in an individual. The present disclosure collects a test biological sample from the subject requiring personalization and extracts DNA from test biological sample and information specific to dietary preferences of the subject. Organisms from probiotic organisms dataset are obtained and a plurality of genome scale metabolic models are created for microbes comprised in gut microbiota of subject and obtained probiotic organisms. Metabolic simulations are performed to ascertain monoculture and co-culture growth of every pair of organisms comprised in gut microbiota of subject and obtained probiotic organisms. Sustainability is computed for evaluating capability of each organism to proliferate within gut. Net-effect is computed by quantifying an overall influence of each probiotic organism. An efficacious probiotic organism is selected based on at least one of net-effect and sustainability.
G16H 20/60 - ICT specially adapted for therapies or health-improving plans, e.g. for handling prescriptions, for steering therapy or for monitoring patient compliance relating to nutrition control, e.g. diets
G16B 10/00 - ICT specially adapted for evolutionary bioinformatics, e.g. phylogenetic tree construction or analysis
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
23.
METHOD AND SYSTEM FOR MANAGING A BIDIRECTIONAL CHARGING AT AN ELECTRIC VEHICLE (EV) CHARGING STATION
This disclosure relates generally to a bidirectional charging at an electric vehicle (EV) charging station by an energy model that uses electricity bought from the day-ahead market for charging the fleet of electric vehicles (EVs) and uses the intra-day market for arbitrage. The competitive pricing of wholesale electricity markets and distributed energy resource capability of EV fleets (in addition) provide a revenue channel through energy arbitrage. To effectively handle electricity price variations and the energy demand of the EV fleet, the present disclosure utilizes a graph representation-based learning agent (LA3_D) with two-stage encoding for day-ahead charge planning; and a priority order based greedy heuristic (GH_I) for intra-day arbitrage planning. Because the agent learns the planning policy of mapping EVs to charging operations over several problem instances, it is able to solve a given instance with limited sub-optimality when put to test at different levels of scale.
09 - Scientific and electric apparatus and instruments
42 - Scientific, technological and industrial services, research and design
Goods & Services
Business management assistance and consultancy, business organization consultancy, business information, cost price analysis, commercial information and advice for consumers [consumer advice shop], commercial or industrial management assistance, compilation and systemization of information into computer databases, compilation of statistics, professional business consultancy, computerized file management, advisory services for business management, database management marketing and marketing research, personnel management consultancy, computer aided business management services, business monitoring and consulting services, namely, tracking web sites and applications of others to provide strategy, insight, marketing, sales, operation, product design, particularly specializing in the use of analytic and statistic models for the understanding and prediction of consumers, businesses, market trends and actions Computer software for use in the retail business industry to enable retailers to rationalize assortment planning based on performance, fixture size, inventory, customer choice sets, loyalty, space elasticity, demand transfer, and markdown considerations; computer software enables retailers to make key merchandising processes intelligent and autonomous with advanced AI models and systems. It includes price, assortment, and macro space optimization as well as competitor intelligence; computer software for use in the entire retail supply chain, namely, for planning, allocation, assortment, replenishment, revenue management, merchandise management, customer financial analysis, business data management, verifying prices, ordering stock, operating point-of-sale functions, custom order processing, target customer marketing, data mining, reporting, telemarketing, and call-centre management, and to build cognitive supply chains by transforming core supply chain dimensions such as cross banner network design and node ranging, optimization of omnichannel operations, and end-to-end supply chain visibility and control, dynamic disruption management, inventory optimization, capacity utilization, and hyper automation; computer software used to access and transmit retailer and supplier product data via a global computer network, computer software platforms, computer software platforms for use in retail and supply chain industry, artificial intelligence (AI) powered enterprise personalisation software platform to deliver unified, orchestrated and real time experience for customers across channels, computer software for use in customer relationship management (CRM), enterprise software in the nature of a database for non-transactional data and search engine for database content, downloadable computer software for use in business data integration, business data processing, business data analysis, and customer service, downloadable computer software for use in omnichannel marketing,; downloadable computer software for enterprise automation, computer e-commerce software to allow users to perform electronic business transactions via global computer network Computer software consultation for retail merchandise management, design, installation, configuration, implementation and maintenance of computer software in the retail supply chain field, technical support services, namely, troubleshooting of computer software problems in the retail supply chain field, software as a service (SAAS), design and development of computer hardware and software, platform as a service (PASS) and software as a service (SAAS) featuring computer software platforms for enterprise personalization to deliver unified, orchestrated and real time experience for customers across channels, providing online non-downloadable software for use in customer relationship management, software consultancy, providing customized computer searching services, namely, searching and retrieving information at the customer's specific request via global computer networks, providing an online network environment featuring technology that enables users to share data in the field of AI customer intelligence. Design and development of computer software, AI powered software platform for use in retail industries, supply chain solutions, unified and composable commerce platform to deliver seamless and hyper personalized omnichannel experiences with a range of modern commerce capabilities such as click-and-collect, scan and go, save the cart, and self-checkout, along with dynamic promotion management, store optimization suite for use in retail industry to transform traditional stores into lean, intelligent, and automated stores of the future. Capabilities include smart task management, targeted shelf replenishment, predictive queue management, and intelligent workforce management,; sustainability solutions for use in the retail industry that helps retailers to track ESG compliance, meet net zero goals, and drive energy efficiency, among others, accelerators for use in the retail industry that enables retailers to monetize customer data, set up and optimize retail media networks, participate in market places, launch new formats and services, and build customer life time value through loyalty
25.
GENERATIVE ARTIFICIAL INTELLIGENCE BASED SYNTHETIC DATA GENERATION FOR VISION-BASED SYSTEMS
Realistic data is an important aspect for training vision based systems. Conventional approaches need complex prerequisites to generate such data and are quite expensive as well. The present disclosure provides one stop solution for different artificial intelligence rendering, scenario generation and future video prediction. The present disclosure provides a provision for fusion of Generative Artificial Intelligence (GenAI), deep learning and image processing techniques and explores the possibilities of generating data from simulated, real, or fused environments. Further, the present disclosure utilizes a panoptic Segmentation approach to create semantic labels and a flow map-based methodology taking cues from a sequence of frames helps to address long time temporal coherence which is a key issue in generated data. This goes as an input to the Generative AI network which generates synthetic dataset based on the aspects of dynamic objects, scene changing and environment variations.
G06V 20/70 - Labelling scene content, e.g. deriving syntactic or semantic representations
G06T 11/60 - Editing figures and textCombining figures or text
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 10/74 - Image or video pattern matchingProximity measures in feature spaces
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
G06V 10/82 - Arrangements for image or video recognition or understanding using pattern recognition or machine learning using neural networks
26.
METHOD AND SYSTEM FOR ENSURING DUAL BAR CODE AUTHENTICATION OF DOCUMENTS
The embodiments of present disclosure herein address unresolved problems of a file path encryption, a manual quality check and a dual bar-code verification while scanning and transferring of the answer sheets to a server via a communication network. Embodiments herein provide a system and method for ensuring a dual bar code-based authentication of answer sheets. The system and method provide a multilevel security that is achieved by a programmatic scanning and validation. A metadata tagging, and manual quality check can be carried out by an operator. The system and method provide a file transfer over a Hypertext Transfer Protocol (HTTP) network and with the help of a blow-fish algorithm, media files path is stored in an encrypted manner. The system and method restrict any third-party entity to get access to the confidential data and ensure a single user authorization from scanning, monitoring to package creation and an operator management.
H04N 1/00 - Scanning, transmission or reproduction of documents or the like, e.g. facsimile transmissionDetails thereof
G06K 7/14 - Methods or arrangements for sensing record carriers by electromagnetic radiation, e.g. optical sensingMethods or arrangements for sensing record carriers by corpuscular radiation using light without selection of wavelength, e.g. sensing reflected white light
G06V 30/12 - Detection or correction of errors, e.g. by rescanning the pattern
27.
METHOD AND SYSTEM FOR REAL-TIME CALIBRATION OF EAR-EEG DEVICE
The embodiments of the present disclosure herein address unresolved problems of quality of signals in real time for wearables to provide optimal signals which can be used for brain signal based applications. Further, conventional techniques fail to provide real-time calibration of wearable devices, to understand the quality of the signals from the wearable device. Embodiments herein provide a method and system for a real-time calibration of one or more Electroencephalography (EEG) signals received from a wearable Ear-EEG device. The system is leveraging quality of signals in real time for wearables to provide optimal signals which can be used for early detection of neurodegenerative disease and brain-computer interface (BCI) applications. Further, the system is able to detect electrodes in the wearable device where the EEG signals have not been collected because the contact was not established.
Indoor localization, which estimates the location of a wireless device using Wi-Fi/Zigbee/Bluetooth, is increasingly important for Industry 4.0 applications, such as tracking of robots and large-scale inventory management. Existing approaches have certain practical issues related to enterprise hardware as specific data required by them is not always available due to security and other reasons. Present disclosure provides system and method that implement indoor Location Aggregator Model, which combines multiple estimation location models to provide localization to wireless/user devices while being compliant to enterprise environments. More specifically, channel state information is used for estimating position and identification of candidate sub-region within a region. Further, at least one Location Estimation Model is identified based on the identified candidate sub-region by all LEMS and an accuracy map. The identified LEM is then used for determining an actual location of a user device.
H04W 64/00 - Locating users or terminals for network management purposes, e.g. mobility management
H04L 41/16 - Arrangements for maintenance, administration or management of data switching networks, e.g. of packet switching networks using machine learning or artificial intelligence
29.
SYSTEM AND METHOD FOR COGNITIVE SURVEILLANCE ROBOT FOR SECURING INDOOR SPACES
Existing works on surveillance robots in indoor scenarios focus only on specific event detections and fail to detect most indoor objects due to lack of proper training. The present disclosure receives and pre-processes stream of input images specific to plurality of scenes related to indoor space from robot mounted camera. Pre-processed stream of input images is passed to a first trained model to obtain plurality of bounding boxes and masks pertaining to objects. An object property detector algorithm is run on masks to detect properties of objects. Classify signages comprised in masks as text signage by passing through optical character recognition or symbol by passing through second trained model. Convert output of optical character recognition and second trained model into facts and infer new facts using steam reasoning. Matching patterns comprised in form of an adaptable business logic with inferred new facts and trigger alerts, if pattern is matched.
Periodic double auction (PDA) setting is where buyers of the auction have multiple (but finite) opportunities to procure multiple but fixed units of commodity. The goal of each buyer participating in such auctions is to reduce their cost of procurement by planning purchase across multiple rounds of the PDA. Formulating such optimal bidding strategies in multi-agent periodic double auction setting is a challenging problem as such strategies involve planning across current and future auctions. The method and system disclosed herein addresses such setup wherein the composite supply curve is known to all buyers. Specifically, for the complete information setting, the method models the PDA as Markov game and derives Markov perfect Nash equilibrium (MPNE) solution to devise an optimal bidding strategy for the case when each buyer is allowed to make one bid per round of the PDA. The efficacy of the Nash policies obtained is demonstrated with numerical experiments.
High-performance deployment of DNN recommendation models heavily rely on embedding tables, and their performance bottleneck lies in the latency of embedding access. To optimize the deployment of RMs, the method and system is disclosed, which leverages heterogeneous memory types on FPGAs to improve the overall performance by maximizing the availability of frequently accessed data in faster memory. The system, using a optimizer dynamically allocates table partitions of the embedding tables based on history of input access history. A pre-optimizer block disclosed determines whether smaller tables should be partitioned or placed entirely in smaller memories, improving overall efficiency. The performance of RM is improved with improvement in average embedding fetch latency and effectively inference latency via modified Round Trip computation.
Existing electricity consumption prediction approaches depend largely on data-based models, which may be statistical techniques or more precisely time-series predictions that predict based on the auto-regressive nature of the load curve with a few external variables at best, such as calendar events and ambient temperature. While these models are effective to a certain degree for the overall grid level requirements, they may not be able to predict disruptive changes that may happen over longer periods of time, such as the demographic shifts, etc. Method and system disclosed herein predict the electricity consumption by taking into consideration various parameters associated with such disruptive changes, and then predict the electricity consumption in a target area by aggregating the electricity consumption predicted at the agent level.
Though Density functional theory (DFT) based approaches such as Kohn-Sham DFT (KS-DFT) are useful for calculating energetics and other physical properties of physical/chemical systems, computational time complexity bottleneck of the DFT approach has remained a cubic function of the number of electronic orbitals, hence adversely affects efficiency of calculation of the energy estimation and other parameter calculations. Method and system disclosed herein computes a computational complexity for an electron density value as received as input, by performing a Kohn-Sham Hamilton simulation of the input. Further, one or more eigen states of the input are determined, via the one or more hardware processors. Further, the one or more eigen states of the input are mapped to a recursive sequence of nonlinear least squares problem solved by executing a Quantum linear system algorithm at every step, wherein the mapping causes reduction in the computational complexity of the input.
G16C 10/00 - Computational theoretical chemistry, i.e. ICT specially adapted for theoretical aspects of quantum chemistry, molecular mechanics, molecular dynamics or the like
The embodiments of present disclosure herein address unresolved problems in existing initiatives to optimize costs and streamline the return decisions which are based on legacy infrastructure and explicit rules such as a SQL database. Embodiments herein provide a method and system for streamlining return decision in a supply chain network and optimizing costs. The system is configured to create a returns decision environment using an OpenAI gym base class. Created classes lend extensibility for Reinforcement Learning (RL) applications through a supply chain management environment base class and more specific returns decision environment class. These encapsulate all of the environment functions including exploration of contextual information in the dataset.
This disclosure relates generally to method and system of generating optimal portfolio for seller bidding strategy in decoupled multi-energy markets. Energy sellers generate multiple forms of energy and can trade in specific energy markets by maximizing returns and minimizing risk of price fluctuations. Portfolio optimization across multi-energy markets lacks proper consideration of inter energy conversion efficiencies, market price risks, and asset constraints. The present disclosure provides to multi-energy markets prior to bidding day at least one energy type available with a seller, a bidding price of corresponding energy type, and at least one energy type required by a market participant to generate an optimal portfolio. The seller determines a risk factor for energy type in the multi-energy markets and then computes a total risk for each energy type. The optimal portfolio splits the generated volume of each energy type to bid at every timeslot of the multi-energy markets.
Retailers need information about their competitor's pricing and promotions and marketing collaterals are one of the most vital sources of this information. Conventional approaches for extracting product names from marketing collaterals depends on large volume of data repositories and complex machine learning based approaches. The present disclosure extracts product name blocks from marketing collaterals using image processing techniques. The inputs to the present disclosure are seed words and the marketing collateral. A plurality of word level text regions from the image and text value are obtained. Further, a plurality of text characteristics corresponding to each of the plurality of word level text regions are extracted and matching seed word regions are obtained. Further a plurality of meaningful text blocks and a plurality of seed blocks are obtained. Finally, a plurality of product names are extracted using a matrix based product name detection technique and updated in the product dictionary.
This disclosure relates generally to a method and system for sensing best-connected future path for a mobile telerobot based on radio signal strength (RSS) prediction algorithm through in-situ radio-sensing. State-of-the-art methods predict the future path from the plurality of possible paths based on a radio-source in the environment. However, prediction of the suitable future path in the absence of the radio-source or in no signal zone is not yet achieved. The proposed in-situ algorithm is based on Log-Normal Shadowing Model (LNSM) and found efficient for prediction error minimization. The method enables the mobile telerobot to predict the future path on a trajectory of the telerobot even without prior knowledge of a radio-source location. The mobile telerobot can predict the most suitable path from a plurality of possible paths for a move based on virtual location estimation.
This disclosure relates generally to a method and system for predicting safe time of operation for a rotary kiln. Over the period of time, the rotary kiln develops a ring within the inner walls of the kiln and suffers sudden shut down due to choking of the kiln. State-of-the-art methods provide the various methods of predicting safe time of operation, but the prediction is based on limited features and hence suffers accuracy. The disclosed method predicts safe time of operation for a rotary kiln based on mathematical model that estimates size of ring by estimating plurality of derived parameters based on operational parameters and design parameters. The derived parameters are the estimations provided by a solid bed height variation model, a gas stream model, a solid stream, a melt model, an agglomeration model, a volatile model, and a ring formation model.
The disclosure relates generally to methods and systems for distributed task scheduling in a satellite cluster for earth observation data analytics. Conventional techniques proposed different scheduling techniques to gather earth observation data and suitable routing algorithms to route the data gathered to the ground. However, utilizing on-board computing facility are limited. The present disclosure discloses a distributed task scheduling algorithm for effective utilization of idle computing resources, which works in two phases namely an offline or a static phase and an online or a dynamic phase. In the offline phase, a scheduler takes a first subtask of each task for execution, the soft real-time constraints, and the resource capabilities to schedule the tasks. In the dynamic phase, the scheduler takes a changing inter-satellite and satellite-to-ground station connectivity, resource status and the changing task status to schedule the tasks, through a task partitioning, a task distribution, and a task handover.
This disclosure provides a system and method for flexibility based profit allocation for aggregator with distributed energy resources (DERs). The method of the present disclosure considers an aggregation model that allows an aggregator to orchestrate a set of heterogeneous DERs while enabling energy exchange among subscribed DERs and to participate in a day-ahead market. Further, a flexibility index is used to quantify the flexibility offered by a DER and a novel profit allocation model is proposed based on the flexibility index. Efficacy of the proposed models is demonstrated by evaluating their performance on a group of heterogeneous DERs using data traces from real-world electricity market.
A method and system for dynamic value determination and performance based royalty calculation of non-fungible tokens (NFTs) created for agricultural assets is disclosed. The method enables reliable and authentic ownership tracking and the removal of frauds/biases affecting the valuation of the said asset(s), as all the transactions are recorded and visible to everybody on the platform. Various biases, such as location (or spatial), temporal or human judgment-based biases in the existing valuation systems are removed by having dynamic and performance-based valuation approach proposed in this invention. NFT valuations of farm diary and farm art have hardly been proposed in earlier works. Royalty or reward computations from agricultural NFTs has not been attempted. Thus, the method provides performance or creation-based (creation of IP, Knowledge base, Geographical Indication IP) royalties/rewards for the NFT owner or entities involved in the process.
Unaddressed technical problem is how to make the window manager select and update the appropriate visual design given the Just-In-Time constraint to optimize Profit/Economic Goal for a Basic Emergent User and not have a locking/permanent binding on a particular Socio Economic Cohort (SEC) or Contract Management System Companies (CMSCs). A method and system is disclosed that provides interface layout to discover and optimize economics of information exchange of digitally virtualized physical space for a Basic Emergent User (BEU). A correlation score is determined based on correlation between i) the expected profit range provided by the BEU indicating the tolerance around the SP(selling price) and ii) the buying price(BP) and the tolerance specified by the SEC or the CMSC to identify a best match for the BEU based on goal provided by the BEU in terms of expected profit range and corresponding time range within which it is to be achieved.
G06Q 40/06 - Asset managementFinancial planning or analysis
G06F 3/04815 - Interaction with a metaphor-based environment or interaction object displayed as three-dimensional, e.g. changing the user viewpoint with respect to the environment or object
43.
SYMBIOTIC WARNING METHODS AND SYSTEMS TO WARN NEARBY TARGET ACTORS WHILE DRIVING FOR COLLISION AVOIDANCE
The disclosure relates generally to symbiotic warning methods and systems to warn nearby target actors while driving for collision avoidance. Current ADAS systems are limited in assisting only the driver of the host vehicle in the potential dangers. The present disclosure identifies various driving scenarios and gives necessary warning to the target vehicles, pedestrians, and animals around the host vehicle. A symbiotic warning method involves receiving one or more road contextual parameters to create a 360-degree scene perception of road surroundings of a host vehicle, with one or more actors. Then, estimating one or more 3-dimensional (3-D) scene semantics of each of the one or more actors and detecting one or more priority actors those lead to probable collisions. Further, deciding to generate a symbiotic warning signal, to one or more priority actors, and generating the symbiotic warning signal to one or more priority actors those lead to probable collisions.
B60W 30/095 - Predicting travel path or likelihood of collision
B60W 10/30 - Conjoint control of vehicle sub-units of different type or different function including control of auxiliary equipment, e.g. air-conditioning compressors or oil pumps
G06V 20/56 - Context or environment of the image exterior to a vehicle by using sensors mounted on the vehicle
G06V 20/70 - Labelling scene content, e.g. deriving syntactic or semantic representations
H04W 4/46 - Services specially adapted for particular environments, situations or purposes for vehicles, e.g. vehicle-to-pedestrians [V2P] for vehicle-to-vehicle communication [V2V]
44.
SYSTEMS AND METHODS FOR VISION-BASED MEASUREMENT OF LIQUID LEVEL IN CONTAINERS HAVING LINEAR SCALES
Conventionally, mechanical methods of measuring linear position over a scale required physical coupling with system(s), such as potentiometer-based feedback etc. Such kinds of mechanical feedback mechanisms need geared coupling. And along with a mechanical system comes problems of wear and tear of parts which can lead to increased inaccuracy of the setup over time. With increased capabilities of computer vision-based techniques, a non-contact image-based measurement of linear position has become of prime importance. Embodiments of the present disclosure provide system and method that implement direct visual measurement of liquid level inside a linear measurement setup (syringe here) by employing various techniques such as computer vision, machine learning techniques, and the like, wherein various features are extracted that in turn allow to have a measurement of a liquid level identified or indicator (plug/meniscus) position in a syringe/container.
G01F 23/00 - Indicating or measuring liquid level or level of fluent solid material, e.g. indicating in terms of volume or indicating by means of an alarm
45.
LEVERAGING BLOCKCHAIN AND CONDITIONAL HOMOMORPHIC PROXY RE-ENCRYPTION FOR ENHANCED TRANSACTION PRIVACY AND AUDITING
Privacy enabled inter-bank settlements and auditing of fund transfer has been addressed using private and shared databases in the blockchain. These are resource inefficient and incur additional overhead on banks to maintain the extra databases. These also pose some security concerns. A method and system disclosed leverages blockchain and Conditional Homomorphic Proxy Re-encryption (CH-PRE) to preserve the privacy of the customer information (balances and transfer amounts) and enables faster auditable settlement between the banks in the presence of governing body. Perform computations on encrypted data (customer balances and transfer amounts) to audit and validate the transaction data without revealing customer information such as balances. As customer balances and transfer amounts are stored in encrypted format, an attacker who is able to gain access to this encrypted data, will not be able to read the customer balances and transfer amounts, as the attacker does not possess bank's private key.
G06Q 20/40 - Authorisation, e.g. identification of payer or payee, verification of customer or shop credentialsReview and approval of payers, e.g. check of credit lines or negative lists
Existing Convolutional Dictionary Learning (CDL) based machine fault classification do not utilize label information while learning the dictionary, hence the representation learned are not class-discriminative. Method and system disclosed herein provide a label-consistent convolutional dictionary learning approach for machine fault classification. The approach involves generating a training data for a classifier, wherein coefficients forming a plurality of class-discriminative features form the training data. The training data is then used to train a classifier, which is then used to perform machine fault classification for a given test data.
This disclosure relates generally to a method and system for damage localization on surfaces made of composites and metals. State-of-the-art methods for ultrasonic guided wave-based damage localization provide a reasonable accuracy. However, accuracy of prediction based on minimum number of observations is not yet achieved. The disclosed method provides damage localization by capturing response to the ultrasonic tone burst transmitted by a plurality of active piezoelectric sensors. The disclosed method provides a modified RAPID algorithm that considers an attenuation of the ultrasonic guided waves and factors energy of transmitted and received signals while predicting damage location. The method provides iterative grid search reduction mechanism to predict damage on the surfaces made of composites and metals.
Developability of a drug candidate is decided based on the Pharmacokinetic (PK) and Pharmacodynamic (PD) parameters of the drug candidate under investigation. Present disclosure provides systems and methods that are implemented using universal PK parameters' bounds and optimization technique(s) to produce robust and optimized set of PK parameters. More specifically, the system and method for estimating optimized set of PK parameters by a) creating universal parameter bounds, b) performing logical operations on universal PK parameters' bounds to create multiple bound combinations c) computing a performance threshold for residual sum of squares (RSS) d) performing global optimization to estimate globally optimized set of PK parameters act as initial PK parameters and e) performing local optimization of initial PK parameters to estimate locally optimized set of PK parameters, the best PK parameters that can used for assessing the developability of drug candidates within Pharmaceutical industry.
The disclosure relates generally to methods and systems for real-time voltage stabilization of electrical distribution networks with non-linear power flows. Existing real-time voltage control techniques are neither performance nor stability guarantees. The present disclosure proposes an online robust control algorithm, which operates without knowing an exact information of the line-parameters and resolves the voltage stability problem. In the proposed method a load data, a distributed energy resources (DER) data, and a network data of an electrical distribution network is obtained, to obtain a voltage profile at each time-step of the electrical distribution network. Next, line-parameters of the electrical distribution network are predicted using an on-line convex optimization technique and a Gauss-Seidel technique. Then, a stable control signal for each bus that stabilizes a voltage of the electrical distribution network is determined to utilize the stable voltage for the voltage stabilizing of the electrical distribution network in real-time.
As discussed earlier, labelling techniques that are available for labelling of unlabelled tabular data use some semi supervised models for identification purposes. However, they require sample labeled data for training purposes. Further, the same labelling model/technique cannot be used for all data types. Present disclosure provides method and system for identifying labels of unlabeled column data. The system uses a hybrid approach i.e., it uses language models, regular expressions and known dictionaries for labelling of unlabelled tabular data. For performing labelling, system first classifies received unlabelled tabular data into one or more data buckets. The system then uses appropriate techniques, based on data types, for identification of labels of unlabeled data present in data buckets. Thereafter, system uses feedback mechanism which will impart maturity to system over time. Finally, once system is matured, system can identify labels for all types of data.
This disclosure provides a system and method for transform based subspace interpolation for unsupervised domain adaptation for machine inspection. Embodiments of the present disclosure present a deep transform-based subspace interpolation method to cater to challenging unsupervised adaptation scenario for machine inspection of different but related machines. In the present disclosure, source and target domain data are modeled as low-dimensional subspace using deep transforms. The intermediate domains connecting the two domains are then learned to generate domain invariant features for cross-domain classification. The requisite formulation employing deep transform learning and the closed-form updates for the transforms and their corresponding coefficients are presented. The method of the present disclosure demonstrates potential in learning reliable data representations, particularly in limited data scenario and real-life industrial applications requiring adaptation between different machines.
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
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
64.
METHOD AND SYSTEM FOR DOMAIN AWARE SEMI-SUPERVISED LEARNING
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
65.
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.
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
68.
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
69.
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
70.
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
71.
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
72.
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
75.
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
76.
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
77.
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
78.
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
81.
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
82.
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
93.
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.
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
95.
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
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
99.
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
