Tata Consultancy Services Limited

Halliburton Energy Services, Inc.

Other

Sterlite Technologies Limited

Puneet Kumar Agarwal

Arpan Pal

Gautam Shroff

Lovekesh Vig

Soma Bandyopadhyay

Tanushyam Chattopadhyay

Arijit Ukil

Bhargav Gajji

Snehasis Banerjee

Prateep Misra

Rahul Ramchandra Gaikwad

Ashwin Srinivasan

Pankaj Malhotra

Karamjit Singh

Mayur Patidar

Aditeya Pandey

Geetika Sharma

Prerna Khurana

Ehtesham Hassan

Garima Gupta

Gunjan Sehgal

Siddharth Panwar

Shiv Dutt Joshi

Anubha Gupta

Guatam Shroff

Kaushal Ashokbhai Paneri

Sarmimala Saikia

Rohit Kumar

Mrinal Rawat

Narendhar Gugulothu

Satanik Panda

Abhisek Das

Rishabh Gupta

Mohit Yadav

Mahesh Prasad Singh

Rajgopal Vaithiyanathan

Brijendra Singh

Mouli Rastogi

Ashish Chaudhary

Syed Afshan Ali

Vivek Bisht

Rachit Bansal

Prateek Sachan

Vishnu K

Vishnu T V

Vishnu Krishna

Gauta M Shroff

Pankaj Malhot Ra

Lovek Esh Vig

Surya Yadav

Rahul R Gaikwad

Richa Rawat

Indrajit Bhattachyarya

Vishnu Tv

Organizations are constantly flooded with questions, ranging from mundane to the unanswerable. It is therefore respective department that actively looks for automated assistance, especially to alleviate the burden of routine, but time-consuming tasks. The embodiments of the present disclosure provide BiLSTM-Siamese Network based Classifier for identifying target class of queries and providing responses to queries pertaining to the identified target class, which acts as an automated assistant that alleviates burden of answering queries in well-defined domains. Siamese Model (SM) is trained for a epochs, and then the same Base-Network is used to train Classification Model (CM) for b epochs iteratively until best accuracy is observed on validation test, wherein SM ensures it learns which sentences are similar/dissimilar semantically while CM learns to predict target class of every user query. Here a and b are assumed to be hyper parameters and are tuned for best performance on the validation set.

BiLSTM-siamese network based classifier for identifying target class of queries and providing responses thereof

This disclosure relates to a method and system for extracting information from images of one or more templatized documents. A knowledge graph with a fixed schema based on background knowledge is used to capture spatial and semantic relationships of entities present in scanned document and an adaptive lattice-based approach based on formal concepts analysis (FCA) is used to determine a similarity metric that utilizes both spatial and semantic information to determine if the structure of the scanned document image adheres to any of the known document templates. If a known document template whose structure is closely matching the structure of the scanned document is detected, then an inductive rule learning based approach is used to learn symbolic rules to extract information present in scanned document image and if a new document template is detected, then future scanned document images belonging to new document template are automatically processed using the learnt rules.

Methods and systems for extracting information from document images

Users have to assign labels to a ticket to route to right domain expert for resolving issue(s). In practice, labels are large and organized in form of a tree. Lack in clarity in problem description has resulted in inconsistent and incorrect labeling of data, making it hard for one to learn/interpret. Embodiments of the present disclosure provide systems and methods that identify relevant queries to obtain user response, for identification of right category and ticket logging there. This is achieved by implementing attention based sequence to sequence (seq2seq) hierarchical classification model to assign the hierarchical categories to tickets, followed by a slot filling model to enable identifying/deciding right set of queries, if the top-k model predictions are not consistent. Further, training data for slot filling model is automatically generated based on attention weight in the hierarchical classification model.

Systems and methods for conversational based ticket logging

In automated assistant systems, a deep-learning model in form of a long short-term memory (LSTM) classifier is used for mapping questions to classes, with each class having a manually curated answer. A team of experts manually create the training data used to train this classifier. Relying on human curation often results in such linguistic training biases creeping into training data, since every individual has a specific style of writing natural language and uses some words in specific context only. Deep models end up learning these biases, instead of the core concept words of the target classes. In order to correct these biases, meaningful sentences are automatically generated using a generative model, and then used for training a classification model. For example, a variational autoencoder (VAE) is used as the generative model for generating novel sentences and a language model (LM) is utilized for selecting sentences based on likelihood.

Techniques for correcting linguistic training bias in training data

The present disclosure provides a method for dynamically allocating an optical channel. The method includes receiving a user input request from a user. In addition, the method includes receiving an OLS spectrum data of the optical channel. Further, the method includes computing one or more configuration parameters for a predefined pair of transponders of one or more transponders based on the user input request and the OLS spectrum data. Furthermore, the method includes configuring the one or more configuration parameters on the predefined pair of transponders of the one or more transponders. Moreover, the user is associated with the one or more transponders. The user input request is associated with the predefined pair of transponders of the one or more transponders. The user input request includes parameters for the optical channel allocation on the predefined pair of transponders.

Automated system provisioning for programmable photonics flex networks

Conversational systems are required to be capable of handling more sophisticated interactions than providing factual answers only. Such interactions are handled by resolving abstract anaphoric references in conversational systems which includes antecedent fact references and posterior fact references. The present disclosure resolves abstract anaphoric references in conversational systems using hierarchically stacked neural networks. In the present disclosure, a deep hierarchical maxpool network based model is used to obtain a representation of each utterance received from users and a representation of one or more generated sequences of utterances. The obtained representations are further used to identify contextual dependencies with in the one or more generated sequences which helps in resolving abstract anaphoric references in conversational systems. Further, a response for an incoming sequence of utterances is retrieved based on classification of incoming sequence of utterances into one or more pre-created responses. The proposed model takes lesser time to retrain.

Method and system for resolving abstract anaphora using hierarchically-stacked recurrent neural network (RNN)

The present disclosure provides system to configure one or more photonics components using a method. The method includes a first step to initialize a photonic abstraction interface driver at a photonic abstraction system. In addition, the method includes another step to call a node detection function at the photonic abstraction system. Further, the method includes yet another step to call a plurality of application programming interfaces (APIs) at the photonic abstraction system. Furthermore, the method includes yet another step to de-initialize the photonic abstraction interface driver at the photonic abstraction system. Moreover, the photonic abstraction interface driver is initialized by a photonic abstraction interface host. Also, the photonic abstraction interface host and the photonic abstraction interface driver are layers of a photonic abstraction interface (PAI). Also, the photonic abstraction interface driver translates the photonic abstraction interface (PAI) into a plurality of shared libraries.

System and method for configuring photonic components using photonic abstraction interface

The present invention relates a novel system and method to diagnose and predict systemic disorders including brain disorders and mental states in early stage and more accurately. More particularly, this invention relates to a novel method of EEG recording and processing through which multiple output data streams are taken together from a system like brain and the structure of their correlation matrix is studied through its eigenvector, eigendirection and eigenspaces and other signal processing techniques including compression sensing, wavelet transform, fast fourier transform etc.

System and method to diagnose and predict different systemic disorders and mental states

Systems and methods for Deep Learning techniques based multi-purpose conversational agents for processing natural language queries. The traditional systems and methods provide for conversational systems for processing natural language queries but do not employ Deep Learning techniques, and thus are unable to process large number of intents. Embodiments of the present disclosure provide for Deep Learning techniques based multi-purpose conversational agents for processing the natural language queries by defining and logically integrating a plurality of components comprising of multi-purpose conversational agents, identifying an appropriate agent to process one or more natural language queries by a High Level Intent Identification technique, predicting a probable user intent, classifying the query, and generate a set of responses by querying or updating one or more knowledge graphs.

Deep learning techniques based multi-purpose conversational agents for processing natural language queries

This disclosure relates generally to data processing, and more particularly to a system and a method for mapping heterogeneous data sources. For a product being sold globally, there might be one global database listing characteristics of the product, and from various System and method for mapping attributes of entities are disclosed. In an embodiment, the system uses a combination of Supervised Bayesian Model (SBM) and an Unsupervised Textual Similarity (UTS) model for data analysis. A weighted ensemble of the SBM and the UTS is used, wherein the ensemble is weighted based on a confidence measure. The system, by performing data processing, identifies data match between different data sources (a local databases and a corresponding global database) being compared, and based on matching data found, performs mapping between the local databases and the global database.

Method and system for mapping attributes of entities

This disclosure relates generally to health monitoring of systems, and more particularly to monitor health of a system for fault signature identification. The system estimates Health Index (HI) of the system as time series data. By analyzing data corresponding to the estimated HI, the system identifies one or more time windows in which majority of the estimated HI values are low as a low HI window, and one or more time windows in which majority of the estimated HI values are high as a high HI window. Upon identifying a low HI window, which indicates an abnormal behavior of the system being monitored, based on a local Bayesian Network generated for the system being monitored, an Explainability Index (EI) for each sensor is generated, wherein the EI quantifies contribution of the sensor to the low HI. Further, associated component(s) is identified as contributing to abnormal/faulty behavior of the system.

Method and system for health monitoring and fault signature identification

The present subject matter discloses system and method for executing prescriptive analytics. Simulation is performed from an input data (x) and simulation parameters (&#x3bc;) to generate simulating data (D). Further, forecast data may be predicted by processing the simulating data (D) using predictive model (M). Further, prescriptive value (x') may be determined based on the forecast data by using optimization model. The prescriptive value (x&#x2032;) may be determined such that an objective function associated with the optimization model is optimized, whereby the optimization of the objective function indicates business objective being achieved. Further, the steps of simulating, predicting and determining may be iteratively performed until the objective function is not further optimized, satisfying predefined condition. Further, at each iteration, except the first iteration, the input data (x) is the prescriptive value (x&#x2032;) determined at immediate previous iteration, whereby at the first iteration, the input data (x) is a reference data.

System and method for prescriptive analytics

A method and system for interpreting a dataset is described herein. The method include computing a rule set pertaining to the dataset, followed by generating a rule cover pertinent to a subset of the rule set. Further, a plurality of distances between the plurality of rule pairs in the rule cover is calculated and a distance matrix based on the calculated plurality of distances is generated. Consequently, the overlapping rules within the rule cover are clustered using the distance matrix and a representative rule from each cluster is selected. Further, at least one exception for each representative rule is determined and the dataset is interpreted using the representative rules and the at least one exception. Thereby, the method provides succinct results in terms of rules and exceptions along with multiple interpretations of the same set of transactions from the dataset, thereby providing a holistic view about the dataset.

Interpretation of a dataset for co-occurring itemsets using a cover rule and clustering

The present disclosure discloses system and method for providing perceptually efficient visualization of rules and exceptions mined from dataset. Further, parsing is performed on data-attributes associated with the rules. The data-attributes may include antecedents, consequents, ranges of the antecedents, syntax and statistics of the rules and exceptions. The visualization scheme of present disclosure present an overview first, allows semantic zooming, and then shows details on demand. Further, data attributes of the rules are mapped with visual attributes of graphical elements such as shape, color, opacity to create the perceptually efficient visualization of the rules and exceptions. Initially, the visualization shows main rule highlighting the exceptions associated and properties of the exceptions. Further, a semantic zoom slider is provided for allowing a user to navigate through different exception levels of the exception. Further, an interface is provided for obtaining additional information associated with the rules and the exceptions.

System and method for interactively visualizing rules and exceptions

System and method for visual Bayesian data fusion are disclosed. In an example, a plurality of datasets associated with a topic are obtained from a data lake. Each of the plurality of datasets include information corresponding to various attributes of the topic. Further, the plurality of datasets are joined to obtain a joined dataset. Furthermore, distribution associated with a target attribute is predicted using Bayesian modeling by selecting a plurality of attributes (k) based on mutual information with the target attribute in the joined dataset, learning a minimum spanning tree based Bayesian structure using the selected attributes and the target attribute, learning conditional probabilistic tables at each node of the minimum spanning tree based Bayesian structure; and predicting the distribution associated with the target attribute by querying the conditional probabilistic tables, thereby facilitating visual Bayesian data fusion.

System and method for visual bayesian data fusion

Growing community of inventors

Gurgaon, India

Puneet Kumar Agarwal