Tata Consultancy Services Patente

The embodiments of the present disclosure herein address unresolved problems of communication from the semantic aspects. Embodiments herein provide a method and system for a semantic communication with networked AI in sixth-generation technology for wireless communications (6G). Here, rather than encoding the input and transmitting the encoded symbols, the semantically relevant information is inferred from the input as per the context of the application and that information is encoded through conventional source encoding and transmitted. Conversion of the actual input to semantic information allows a huge reduction in the bandwidth requirement for the end-to-end channel. On the receiver side, these symbols are first decoded to extract semantic information. The semantic information is further used to predict the semantically relevant output to carry out the intended meaning at the receiver side.

The Range Migration Algorithm (RMA) is widely used for SAR image reconstruction from backscattered signals. Traditional RMA relies on the Fast Fourier Transform (FFT), which is unsuitable for irregular scanning trajectories. An interpolator based reconstruction method, referred as Local Interpolation Function-based Range Migration Algorithm (LIF-RMA), is provided for SAR imaging. The LIF-RMA efficiently addresses challenges associated with irregularly sampled trajectories. The LIF-RMA utilizes a complex-valued encoder-decoder network to transform irregularly sampled, complex-valued backscattered data into uniformly sampled complex raw data. The complex-valued encoder maps the distorted raw data into a feature space, while the complex-valued decoder interpolates and predicts the interpolated complex raw data at missing spatial coordinates. This data is then processed through a 2D FFT block before being fed to RMA for final SAR image reconstruction. In this process, as the LIF is applied prior to RMA, it aids in preserving phase integrity.

In traditional ensemble modeling multiple individual models are trained on the same set of input variables without considering variations in spatial and temporal resolutions. A method and system for dynamic forest fire prediction and source prediction is proposed using dynamic weighting-based ensemble modeling and probabilistic cause determination. The adaptive ensemble model dynamically re-weights multiple base models, each optimized for different data scales, to provide accurate predictions based on the spatial and temporal granularity of incoming data. The method enables adjusting fire predictions probabilities due to shifts in the feature space caused by phenological and environmental changes. Key environmental variables are continuously monitored for recalibrating model predictions using shift detection algorithms, ensuring long-term accuracy without frequent retraining. Probabilistic determination is applied for likely causes of forest fires, which integrates data from various sources to provide a nuanced, data-driven understanding of the factors contributing to fire outbreaks.

Usage of generative artificial intelligence (GenAI) models require appropriate prompts that describe a task to be completed. Conventionally complexities of large sizes and monolithic nature of input code are difficult for handling. The present disclosure resolves problems of conventional approaches by providing a system and method for generation of prompts for GenAI model using contextual information from legacy source code. The method of the present disclosure extracts relevant information from the legacy source code as context in natural language form which is provided as input to create the prompts required to enable successful usage of the GenAI model in multiple tasks of code analysis. In the present disclosure, information from legacy application code about business domain is extracted. Input legacy source code is parsed, and technical explanation is provided by handling the syntax and semantics of language. The legacy source code is split logically into blocks of manageable units.

The disclosure relates generally to methods and systems for generating design component knowledge graph and design component retrieval from the same. Conventional techniques that utilize customizable knowledge graphs for retrieval of the design components are very limited in the digital design domain. The methods and systems of the present disclosure generate the design component knowledge graph using filtered captions generated for the design component objects. The design component objects are of the design components and are selected using an instruction triplet generated based on a user requirement query of the digital design that the user is interested in. Then, a plurality of captions is generated and filtered using a semantic filtering to obtain the filtered captions. Then the design component knowledge graph is queried with the user input to retrieve the design components that best suit the design requirement of the user.

This disclosure relates generally to a method and system for predicting cardiologist level clinical explanations for electrocardiogram signal classification using deep learning. Conventional methods for explainability of artificial intelligence models are influenced by data distribution and do not provide clinical level explanations in cardiac healthcare. The method disclosed provides clinical level explanations for rhythm diagnosis from ECG signals. The method validates that clinical concepts in ECG signals can be extracted by generating a set of synthetic ECG signals. The set of synthetic ECG signals are generated using trained generative adversarial networks and statistical parameterization techniques. Further the set of synthetic ECG signals are validated by classifying and comparing them with a set of real ECG signals. Then a clinical concept identifier model is trained for predicting the clinical concepts present in an ECG signal classified into a set of signal classes. These clinical concepts predicted are cardiologist level explanations.

Embodiments herein provide a method and system for splitting an artificial neural network (ANN) between edge devices (edge) and a user equipment (UE) under supervision of an application cloud (AC) through a unified splitting AI framework (SAF). In AI operation splitting, a few layers of the artificial neural network (ANN) are computed at the endpoint itself. The intermediate result is transferred to the Edge/ Cloud for further computation. Besides distributing the computation load, this has another important advantage. If the first few layers of the ANN are computed at the endpoint itself, then that saves the resources required for transmission of the entire input signal because the intermediate result needs much less bandwidth than the actual input. Also, the ANN could start in the offloaded node only after receiving the entire input. So, splitting the ANN computation is beneficial in terms of the total computation latency as well.

The disclosure relates generally to method and system to generate decision-fused classification map using different modalities in remote sensing applications. In remote sensing applications, the problem of missing data from satellite images occurs due to various environmental factors and spatial resolution provides inconsistent class labels during segmentation. The method receives from each satellite among a plurality of satellites a plurality of remote sensing images capturing a land use and land cover (LULC) geographical area on earth. Each pretrained classifier obtains the plurality of remote sensing images to generate a segmentation map. Further, Kolmogorov Arnold networks (KAN) decision fusion combines two or more closest segmentation maps using an ontological knowledge tree to determine a minimal common parent class based on at least one satisfying criteria. Finally, a fused classification map is generated to obtain finer grained LULC classes for each remote sensing image using the ontological knowledge tree.

Conventional traffic classification methods mainly depend on pre-defined criteria from known data, highlighting demand for advanced methods that can handle new types of traffic. The present disclosure receives an unlabeled data set comprising a plurality of data points from one or more Downlink Control Information messages and pre-processes the received unlabeled data set. A set of relevant features is selected from plurality of data points using a correlation matrix. One or more hyper-parameters of Gaussian Mixture Model (GMM) are evaluated for the unlabeled dataset with selected set of relevant features using Component-wise space Expectation Maximization technique (CEM) method. An optimal number of clusters is estimated using the evaluated one or more hyper-parameters of GMM. Each of the plurality of data points are labeled using the estimated optimal number of clusters to obtain a labelled data set. A classifier model is created to perform traffic classification of the obtained labelled dataset.

This disclosure relates generally to a method and system for measuring social awareness of crowd based on ignorance index. Conventional methods for measuring social awareness through social experimentation are conducted by collecting data manually. This manual approach takes more time and incurs more money. The disclosed method counts the number of people using object detection techniques whenever preferred action towards sustainability is performed by people. The disclosure uses camera-based computer vision techniques to identify the objects in the scene and detect sustainable activity. Further the number of subjects is counted who care about sustainability and who do not perform sustainable actions. Then an ignorance index is calculated from the number of subjects who gazed at the device but have not performed any sustainable action. The disclosed method is used for social awareness measuring through social experimentation to understand the human behavior in the social gathering.

Conventional risk estimation techniques perform dynamic analysis of application or use models which require training data. Present disclosure provides method and system to estimate risk for a software application due to quantum threat by static analysis. A set of records pertaining to the application is received and parsed to obtain application, crypto and platform parameters. In addition, list of quantum vulnerable algorithms, number of Qubits required to break a cryptographic algorithm used by the application and a current Qubit number are also received. Then, value of Quantum Day is determined based on the current Qubit number and the number of Qubits required to break the cryptographic algorithm used by the application. Further, SOD (Severity, Occurrence, Detection) scores are calculated for each parameter, and they are multiplied to determine Risk Priority Number (RPN) for each parameter. Finally, RPNs of all parameters are summed up to estimate overall risk of the application.

This disclosure relates generally to a method and system for enhancing fine-motor skills based on adaptive thresholding. The disclosure relates to an assistive system to improve the fine motor skills of specially abled children through a gamified format. The conventional methods are repetitive and disengaging, which use rule-based threshold changes which do not help in developing fine-motor skills. The disclosed system uses rotational and translational movements detected via accelerometer and gyroscope sensors comprised in a wearable device and applies adaptive thresholding to perform various actions in a first-person controller (FPC) game environment. The disclosed method uses dynamic thresholding concept for adjusting maximum and minimum sensor readings. The method computes threshold values which are computed based on previous readings and predicted user improvement values from a regression model. The disclosed method is used for improving the fine-motor skills of specially abled children who struggle with daily activities and personal independence.

This disclosure relates generally to a method and system for the automated farm management by utilizing connected graphs to navigate through unexpected future events affecting the farm. State-of-art methods for prediction of the unexpected future events is based a vast asynchronous data distributed across various complex data analytics tools. And it becomes difficult to choose most suitable actions to be taken to navigate the future event based on the asynchronous data. The present disclosure addresses these problems through a method that utilizes a graphical form of a plurality of sensor data collected from the farm to derive refined multi-contextual intelligent queries. The refined multi-contextual intelligent query is provided to a remotely located LLM model to generate a set of recommendations. The most suitable recommendation is transmitted to an associated autonomous farm machinery to take a suitable action. The method utilizes a feedback mechanism to improve future query generation.

An affordable compact and ergonomic bulk container handling apparatus is disclosed. A machine support structure supports a plurality of dual pair of guide rods which form the guide profile through the plurality of support brackets, a dual drive arm assembly, that further holds container holders. Dual drive arm assembly holds each container holder at a first end and a second end of each container holder via a pair of guide follower driver roller system at each drive arm of the dual drive arm assembly. Each of guide follower driver roller system couples each drive arm to a plurality of dual pair of guide rods which form the guide profile, through the plurality of support brackets at the first end and the second end of each container holder. A drive motor is attached on side of the dual drive arm assembly to power the dual drive arm assembly along guide profile.

This disclosure relates generally to method and system for predicting pregnancy complications using a ranked incremental cumulative risk (RICR) scoring. During pregnancy, undetected medical or obstetric events may result in complications that pose risks to both the mother and the neonate. The method displays a questionnaire comprising a plurality of predefined questions on a clinical device to be answered by a subject. In response from the subject a plurality of answers and one or more unanswered questions for the questionnaire are received to compute a plurality of risk score. Then, a ranked incremental cumulative risk score is computed for the plurality of risk scores using a ranked incremental cumulative risk (RICR) scoring technique to generate a rank index. Finally, the risk index, an action plan, a risk tier, and an action plan is displayed on the clinical device to notify a clinician with a machine-generated alert.

One of the primary challenges in the current data analysis landscape is the ability to efficiently capture and simplify complex datasets. A one-size-fits-all approach to data transformation is often ineffective, as it fails to accommodate the diverse needs and styles of different users. Embodiments disclosed herein provide a method and system for cognitive transformation of data into human-perceptions. In this approach, during data processing, the system reasons through impact of removing one or more data points in the summarized information by simulating removal of each of the one or more data points evaluating changes in remaining data's semantic integrity. Further, the summarized information is modified by fitting a new set of requirements to adapt the summarized information for one or more applications, while taking into consideration semantic integrity of the data.

The ability to accurately estimate the Lines of Codes (LoC) needed to complete migration of application has been a challenge over the past decades. Embodiments disclosed herein provide a method and system for estimation of efforts in migrating applications to post quantum cryptography (PQC) state. The system, based on a determined cyclomatic complexity for one or more components present in a refined slicing output and a refined coupling output is estimated. Further, an effort of migration is estimated using the cyclomatic complexity and number of LoC impacted by the one or more cryptographic APIs.

Embodiments herein provide a method and system for an information technology (IT) service management using AIOps. The system integrates diverse data sources, such as events, logs, metrics, health checks, change requests, and traces into a centralized platform. This enables the system to create detailed blueprints of IT entities, using dependency data to improve issue analysis. The system employs advanced algorithms, including deep learning and predictive models, to analyze the collected data and identify a wide range of anomalies, that include univariate, multivariate, and complex anomalies. Additionally, the system includes a Human-in-the-Loop (HITL) mechanism that allows users to provide feedback and contribute to problem resolution, ensuring that the system continuously learns and adapts. This proactive approach not only predicts and prevents potential issues but also empowers users with automated resolution tools, reducing dependency on service desks. Overall, the system transforms IT operations by making them more proactive, integrated, and user centric.

Enterprises are exploring the option of multi-cloud setup to avoid the vendor lock in and cloud concentration index. This process of migration to multi cloud setup for the applications throws up various challenges in terms of identity management. The Cloud Service Providers (CSP) do not provide portability and endorse their vendor specific identity credentials. As a result, there is a need for common Identity Access Management (IAM) capable of mapping this heterogenous identities and giving the enterprise a common view of identities among the CSPs. This also solves the identity silos and helps consolidating identities in various permission and access evaluation metrics to check for access sprawl. Here, a plurality of attributes is merged based on a privacy-based weight. Further, a weighted sum-based confidence score is computed for the plurality of merged identity tuples and optimal merged identity tuples are identified based on the associated weighted sum-based confidence score.

The disclosure relates generally to methods and systems for bundle purchase probability estimation and revenue-maximized bundles recommendation to customer segment. Conventional techniques mostly focused on customer-item, customer-bundle, or customer-item-bundle interactions, but do not model interactions among customer segments, items, and prices to form the bundles. The present disclosure discloses an approach for ancillary bundle recommendation using an item-level purchase data for a given customer segment. Firstly, the item-level sales data is converted into the bundle-level sales data having a plurality of ancillary bundles. Then, a nested MLP network is trained which can predict the probability of purchase of a given ancillary bundle at a given price for a specific customer segment. Further, the ancillary bundles are priced for given customer segment by maximizing the revenue. Finally, the recommendations are provided for given customer segment by ranking ancillary bundles based on their estimated revenue by selecting the top-N bundles.

Volume solids (VS) is considered to be a very important and critical property of paint in paint related industries. Conventional methods for VS measurement are usually cumbersome, time-consuming and laborious. The present disclosure resolves the problems of the conventional approaches by providing a system and method for predicting volume solids measurement of paints based on non-invasive photoacoustic sensing. The present disclosure provides a non-invasive and compact photoacoustic (PA) sensing method with artificial intelligence (AI) model to predict volume solid measurements of paint samples. In the method of the present disclosure, a time domain photoacoustic (PA) signal obtained from the paint is used to predict the volume solids measurements. A temporal shift of the time domain PA signal is used as a feature and a regression based prediction model is trained to predict volume solids measurements of the paint samples.

Textual data from social platforms captures various aspects of mental health through discussions around and across issues, while users reach out for help and others sympathize and offer support. Present disclosure leverages Natural Language Processing (NLP) and Generative AI techniques to identify and assess mental health disorders, detect their severity, and create recommendations for behavior change and therapeutic interventions based on input data associated with users. To classify the disorders, the system leverages Large Language Models (LLMs) to filter relevant data from input data, the relevant data pertains to health disorder of users. Further, from the relevant data, various features are extracted to create user profiles and different summaries. The summaries are then aggregated to obtain a final summary for generation of actionable health disorder specific recommendations.

In automotive ecosystem, modem vehicles are being exposed to broad range of cyberattacks through software components connected in network environment leading to unwanted events. Embodiments of the present disclosure provide method and system for dynamically managing intrusion with secure context aware over the air (OTA) update. A data associated with event detected at vehicles connected in network environment is received and classified either as normal event or qualified security event (QSEV). An adaptive intrusion prevention unit (IPU) sanitized data is generated by populating a knowledge graph for training contextual analysis model based on input data through which appropriate context aware OTA update is identified. The context aware OTA update corresponding to vulnerability associated with the QSEV is determined based on the adaptive IPU sanitized data. Information associated with appropriate context aware OTA update corresponding to the vulnerability is fetched from patch database and triggered for mitigation of the corresponding QSEV.

Existing works perform subsurface imaging without paying attention to the presence of surface undulation, which can cause irregular travel paths for waves emitted by a Radar and may produce defocusing effects in the reconstructed image. The present disclosure estimates noise level of a LiDAR (216) in a stationary mode by considering the standard deviation of a LiDAR sensor data. Successive LiDAR values at every position are tracked for a pre-defined time intervals and if it satisfies a LiDAR stabilization criterion, the LiDAR is considered stable and the LiDAR value obtained is logged as a LiDAR elevation data. A two-layer velocity model is estimated from the LiDAR elevation data and a phase shift operator of a Phase shift migration (PSM) is modified by incorporating the two-layer velocity model. Surface normal information is extracted from the LiDAR elevation data and an amplitude correction factor is formulated using the extracted surface normal information.

Traditional algorithms for computing/estimating project completion time often fail to provide accurate project duration estimates. Pre-existing methods rely on project size and team size to estimate project duration and overlook the complex interdependencies between tasks and the variability in task execution times. System and method of proposed approach fetches a requirement vector of a target project as input. Further, a plurality of instance precedence graphs of the target project are generated by processing the requirement vector along with a merged probability graph. The merged probability graph represents a transition likelihood between different types of tasks based on a historical project data. Further, a plurality of projects that are similar to the target project are determined. Further, a completion time of the target project represented by the requirement vector is computed based on completion time of the plurality of projects that are similar to the target project.

Applying large language models (LLMs), even with very large token-sized prompts, does not achieve the task planning performance required for a domain-specific industrial use case (DSU). The method and system disclosed overcome the obstacles of a robotic task planner for DSUs by introducing a task planning framework. Central to the framework is a robotic system ontology that organizes the components of the robotic system in a coherent and systematic manner. This ontology empowers the planning framework to efficiently capture a contextual representation of a DSU using the LLM. Additionally, the research introduces a LLM-tuning regimen referred as chain of hierarchical thought (CoHT), specifically crafted to complement the robotic system ontology. Integrating these two components enables enhancing accuracy, robustness, and throughput of the robot in a cost-effective manner. Furthermore, provided is an empirical methodology to generate LLM-tuning datasets size for a guaranteed performance, leveraging a heuristics-based method.

The embodiments of the present disclosure herein address unresolved problems of predicting future fund rate in the next meeting of a financial regulatory body responsible for regulation of interest rate based on current economic conditions and data of last meeting happened. Embodiments herein provide a method and system for predicting change in a future fund rate by a financial regulatory body responsible for regulation of interest rates. Herein, textual data as well as numerical data are collected to extract useful textual summary of forward-looking statements from large corpus of text data using a pre-trained Large Language Model (LLM) which will contribute to predicting future fund rate. A domain insight matrix is used as a comprehensive framework for guiding the pre-trained LLM on how to approach a task and validate the outputs based on predefined categories and parameters set by domain experts. With prompt optimization efforts, a good quality summary of forward-looking statements is achieved.

This disclosure relates generally to a method and system for identification of mobility trends of group of users in an indoor environment. State-of-the-art methods provide capture of localization data of users. However, analysis of collective user data, specifically understanding the mobility of group that reveals macroscopic trends is not yet achieved. The present disclosure addresses these problems through a method of identifying mobility trends among multiple groups of users in real-time using location data of each user within the group. The method involves creating pairs within the group of users based on current location of each user, and a pre-defined threshold value of distance between the current location of paired users. Mobility similarity metric is calculated for each pair of the one or more users. Based on the mobility similarity metric, users are segregated into one or more clusters, each cluster representing a similar mobility trend.

Every product in the market is associated with a lifecycle stage. It is crucial to identify the current life cycle stage of a product from a business perspective. Conventional approaches fails to identifying the lifecycle stages of the product. The primary reason being the unavailability of the data. The present disclosure looks at continuous capture of supply chain data of a product from procurement to manufacturing to distributor to end customer using smart sensors and various other electronic modes. The data as available for the product is stored in the data warehouse and subsequently taken through data classification and preprocessing steps to apply necessary data science models that involve use of segmentation/clustering algorithms, classification models, demand forecasting and prediction algorithms, as well as other required Deep Learning models that are relevant to the product under preview to design and develop association rules to provide the predictions on life cycle stages.

Phase gradient metasurface surface plays a crucial role in wireless and satellite communication, radars and remote sensing applications. However, conventional approaches for obtaining high gain of an incident wave suffer a low phase variation. The present disclosure provides a ring loaded alford-loop based phase gradient metasurface lens for x-band applications to achieve 0-360° transmission phase variation. The phase gradient metasurface lens of present disclosure includes a twodimensional periodic array of a plurality of unit cells arranged as a M*N matrix along x-axis and y-axis. Each of the plurality of unit cells is a four layered slot typed structure with a periodicity. Each of a plurality of unit cell layers in the four layered slot typed structure comprises a modified alford-loop structure with four L-shaped arcs and it is enclosed by an outer square ring. The four layered slot typed structures are identical and separated by an air gap.

The disclosure relates generally to methods and systems for automated personalized de-stressor recommendation based on stressor estimation. Conventional techniques for detecting the stress and the stressors are mostly manual and employ only some of the influencing parameters such as physiological parameters, behavioral parameters, and so on, thus leading to inaccurate detection of stressors. The present disclosure solves the technical problems in the art by detecting the stress using multi-model evaluation design including physiological changes, psychological changes, behavioral changes, environmental changes, and a bio-chemical indicator. Next, the stressors related to stress detection are identified using a large language model (LLM), which correlates with stress patterns to create a personalized model. Then, appropriate de-stressors are recommended using a recommendation lookup table.

This disclosure relates generally to a method and system for generating feed recommendations based on vocalization patterns of an animal. State-of-the-art methods based on analysis of the vocalization patterns are limited to livestock monitoring, individual identification, physiological states determination, and health diagnosis. However, precise identification of hunger state not yet achieved. The disclosed method involves a deep learning model to identify hunger state and one or more hunger state level of the animal from audio chunks. A state machine maps a plurality of emotions and behaviors associated with the hunger state levels. Further, one or more emotions and behaviors associated with the hunger state level are detected. The large language model (LLM) utilizes the identified state, level, emotions and behaviors to make feed recommendations to the animal.

Hallucinations in LLMs pose significant challenges and it is hard to accurately identify hallucinated predictions of LLMs in the absence of references. The present disclosure utilizes sample responses from LLMs and classifies them using a model trained on Natural Language Inference (NLI) scores. The plurality of NLI scores include an entailment, a neutrality and a plurality of contradiction scores. Post computing NLI scores, an average NLI score is computed based on the plurality of NLI scores. Further, a plurality of individual response predictions are obtained by classifying the plurality of responses based on the NLI scores using a hallucination classifier. Simultaneously, overall response predictions are obtained and a confidence score is computed for the overall prediction of hallucination classifier Finally, an optimal response is predicted based on the confidence score associated with the overall prediction of the hallucination classifier and the plurality of NLI scores.

With increasing focus on observability and analytics, organizations are flooded with analytics insights and often struggle to sift through these insights to identify the relevant ones. When it comes to defining the relevance of an insight, there is no single answer. Different users may have different preferences, and these preferences may change with time. Furthermore, when it comes to data-driven insights, user journey does not stop at one single insight but often leads to a chain of insights to create a story. Present disclosure addresses these technical problems in the context of insights derived for IT operations. More specifically, insights are recommended based on user preferences, wherein the system adapts the insight search based on user's likes and dislikes, and chain insights together to form data stories to serve different use cases.

AI systems, while have excelled in environments defined by clear rules and singular tasks, have been found to be struggling to handle Knowledge works which encompass tasks requiring judgment, interpretation, and creative problem-solving. Method and system disclosed herein provide an Artificial Intelligence (AI) agent training approach. In this approach, the system 100 achieves collaboration between different AI agents that are part of a network, for handling each task. During the training, randomly initialized weights associated with a plurality of edges are used, based on an action that is dynamically decided, based on one or more activation levels of one or more of the plurality of AI agents identified as active based on the decided action.

The disclosure relates generally to methods and systems for predicting explainable drug dosage of a subject. Conventional techniques for advising drug dosage are mostly manual processes and the drug dosage advised by physician may not be effective as such dosages are not validated. The present disclosure solves the technical problems in the art with the methods and systems for predicting explainable drug dosage of the subject. According to the present disclosure, current clinical data and historical data are received from a patient repository. Drug dosage possibilities are generated based on the historical information available in the art. The possibilities and the scenarios are generated and evaluated using a possibility evaluation engine. The possibility score of each drug dosage and a weightage for each treatment decision scenario is computed. An explainability for each of the ranked possibilities is then generated to predict the explainable drug dosage of the subject.

This disclosure relates generally to a method and system for modeling a three-dimensional (3D) wearable device assisting nasal administration of drugs. State-of-the-art methods include leaflets or manuals with a set of instructions for administering the drug. However, this may result in improper administration due to variations in the nasal geometry and manual operations. The disclosed method involves the 3D wearable device that aids in achieving maximum concentration of the drug at the target site. The modeling facilitate an introduction of a collared geometry that provides customed passage to the drug within the nasal cavity based on a plurality of nasal spray specifications. Further, the 3D wearable device model is transformed to a scale based on the CT scan data of the nose to obtain a 3D printable wearable device for the nose.

This disclosure relates generally to a method and system for performing multi-fold operations on complex data. Conventional methods do not have plug and play reusable components for processing high volume complex data concurrently. Also, these do not consider distributed or interactive data processing using optimal resources. The disclosed method performs operations on complex data with optimal execution time, memory and computation resource. The input data fed into the disclosed system are packaged into data chunks and distributed across available resources for preprocessing. The data chunks are pre-processed and converted into machine readable binary format. The data is again traversed to generate operation data chunks. The operation is performed for each operation data chunk, using the converted machine-readable binary format to generate the result of the operation. The disclosed method is used for operations such as search, comparison and so on in image processing.

Current networks provide extremely low latency and high data rate, but it is not clear how they are to be orchestrated and managed efficiently with optimal utilization of the available resources. There is a need for orchestration method which can adapt to prevailing conditions, implement pre-defined policies, and respond to dynamic changes in the network. Hence, embodiments of present disclosure provide method and system for cognitive resource aware orchestration of services in communication networks which works on top of the underlying SDN and NFV orchestration solutions for enabling better operational efficiency in communication networks.

Preparing for live music performances such as curating and sequencing list of tracks and remixing them with musical loops can be challenging. The present disclosure predicts a first set of features relevant to an event. One or more music tracks are generated using at least a subset of the predicted first set of features. The one or more music tracks are split into one or more individual arrangement tracks. One or more segments are generated by identifying one or more repetitive patterns for each of the one or more individual arrangement tracks. A second set of features are extracted from each of the one or more segments. One or more loops are generated based on at least a subset of extracted second set of features. The one or more loops are provided for one or more loop sequencers for usage in the event.