Accenture Global Solutions Limited

Accenture Global Services Gmbh

Infosys Technologies Limited

Shubhashis Sengupta

Roshni Ramesh Ramnani

Janardan Misra

Annervaz Karukapadath Mohamedrasheed

Vikrant Shyamkant Kaulgud

Anutosh Maitra

Sanjay Podder

Vibhu Saujanya Sharma

Abhisek Mukhopadhyay

Annervaz K M

Milind Ravikiran Savagaonkar

Surya Kumar Ivg

Harshawardhan Madhukar Wabgaonkar

Ajay Chatterjee

Andrew E Fano

Sanjay Tiwari

Venkatesh K Subramanian

Shimon Modi

Anurag Dwarakanath

Mathangi Sandilya

Elvis Hovor

Divya Rawat

Badarayan Panigrahi

Poulami Debnath

Gary Titus

Shantha Maheswari

Subhabrata Das

Tarang Kumar

Tom Geo Jain

Tirupal Rao Ravilla

Sumitraj Ganapat Patil

Marcio Miranda Theme

Alpana A Dubey

Neville Dubash

Kumar Abhinav

Sakshi Jain

Anitha Chandran

Gaurav Khanduri

Anirban Chakrabarti

Chethana Dinakar

Annervaz Km

Aravindan Thoppe Santharam

Jayati Deshmukh

Satish Nagasamudram

Sneha Singhania

Rakesh Thimmaiah

Moushumi Mahato

David E Ingram

Piyush Goenka

Pushpak Bhattacharyya

Neetu Pathak

Sanjoy Paul

Madhura Shivaram

Kapil Singi

Aniya Aggarwal

Srikanth G Rao

Ranjana Bhalchandra Narawane

Suma Mani Kuriakose

Amitabh Saxena

Tarun Singhal

Priyavanshi Pathania

Dongay Choudary Nuvvula

Tanmoy Chakraborty

Aditya Bhola

Rachit Bansal

Siddesha Swamy

Sowmya Rasipuram

Shrikanth Narayanaswamy Chandrasekaran

Nidhi

Karthik Acharyulu

Vedamati Sahu

Srinivas Keshava Murthy

Hitanshu Rakeshkumar Tiwari

Niharika Gupta

Tulika Saha

Robert E Kress

Ashwin Menon

Reeja Jose

Chakravarthy Lakshminarasimhan

Chaitra Hareesh

Manisha Kapur

Jovin George

Shilpi Jain

Mansi Srivastava

Vaidya Sumanth

Sarabjit Singh Gugneja

Rajeh Ganesh Natrajan

Leonardo Orlando

Donal P Smith

Shivam Garg

Subani Bhasha Nure

Sushil Kumar Shah

Sukanti Beer

Chiranjeevi Nalam

Brian Ahern

K M Annervaz

Anish Damodaran

Ankur Gakhar

Sarvesh Maheshwari

Soumya Chandran

Techniques for building knowledge graphs from conversational data are disclosed. The systems include a high-performance relation classifier developed with active learning and requiring minimal supervision. The classifier is used to classify relation triples extracted from conversational text, which are then used to populate the knowledge graph. A heuristic for constructing the knowledge graph is also disclosed. The proposed embodiments provide a way to efficiently build and/or augment knowledge graphs and improve the quality of the generated responses by a dialogue agent despite a sparsity of data.

Generating knowledge graphs from conversational data

Methods, systems and apparatus, including computer programs encoded on computer storage medium, for allocating computation resources using ESG reporting. In one aspect a method includes obtaining data from a knowledge source for an entity, the knowledge source comprising a plurality of ESG disclosures that relate to one or more ESG dimensions; computing vulnerability indicator scores that represent measures of latent vulnerability with respect to the ESG dimensions; computing descriptive distribution scores that represent distributions of descriptions of the ESG dimensions within the knowledge source; determining, using the vulnerability indicator scores and the descriptive distribution scores, an allocation of computational resources to ESG computational processes associated with the ESG dimensions that achieves an increased gain in sustainability for the entity; and initiating allocation of the computational resources to the ESG computational processes according to the determined allocation.

System and method for ESG reportng based optimized resource allocation across ESG dimensions

A device may receive a three-dimensional (3D) computer-aided design (CAD) model, and may generate an assembly graph with nodes that represent components and edges that represent contact between the components. The device may generate component graphs for the components, and may generate an assembly descriptor based on the assembly graph and the component graphs. The device may process the assembly descriptor, with a graph convolution network model, to generate node embeddings, and may apply pooling to the node embeddings to generate graph embeddings. The device may calculate a cross attention between the components to generate component interrelations, and may utilize the graph embeddings and the component interrelations to predict links between the components. The device may predict poses and joint axes for the components, and may generate assembly instructions based on the graph embeddings, the component interrelations, the links, the poses, and the joint axes.

Generating robotic assembly instructions from three-dimensional computer-aided design models

In some implementations, a robot host may receive a video associated with assembly using a plurality of sub-objects. The robot host may determine spatio-temporal features based on the video and may identify a plurality of actions represented in the video based on the spatio-temporal features. The robot host may map the plurality of actions to the plurality of sub-objects to generate an assembly plan and may combine output from a point cloud model and output from a color embedding model to generate a plurality of sets of coordinates corresponding to the plurality of sub-objects. The robot host may perform object segmentation to estimate a plurality of grip points and a plurality of widths corresponding to the plurality of sub-objects. Accordingly, the robot host may generate instructions, for robotic machines, based on the assembly plan, the plurality of sets of coordinates, the plurality of grip points, and the plurality of widths.

Robotic assembly instruction generation from a video

Provided are systems, methods, and computer-readable storage media that leverage artificial intelligence and machine learning to identify, diagnose, and mitigate occurrences of network faults or incidents within a network. Historical network incidents may be used to generate a model that may be used to evaluate real-time occurring network incidents, such as to identify a cause of the network incident. Clustering algorithms may be used to identify portions of the model that share similarities with a network incident and then actions taken to resolve similar network incidents in the past may be identified and proposed as candidate actions that may be executed to resolve the cause of the network incident. Execution of the candidate actions may be performed under control of a user or automatically based on execution criteria and the configuration of the fault mitigation system.

Intelligent network operation platform for network fault mitigation

Systems and methods that offer significant improvements to current virtual agent (VA) conversational experiences are disclosed. The proposed systems and methods are configured to manage conversations in real-time with human customers while accommodating a dynamic goal. The VA includes a goal-driven module with a reinforcement learning-based dialogue manager. The VA is an interactive tool that utilizes both task-specific rewards and sentiment-based rewards to respond to a dynamic goal. The VA is capable of handling dynamic goals with a significantly high success rate. As the system is trained primarily with a user simulator, it can be readily extended for applications across other domains.

Dynamic goal-oriented dialogue with virtual agents

Automated response generation systems and methods are disclosed. The systems can include a deep learning model specially configured to apply inferencing techniques to redesign natural language querying systems for use over knowledge graphs. The disclosed systems and methods provide a model for inferencing referred to as a Hierarchical Recurrent Path Encoder (HRPE). An entity extraction and linking module as well as a data conversion and generation module process the content of a given query. The output is processed by the proposed model to generate inferred answers.

Method and system for inferring answers from knowledge graphs

A device may receive text data, audio data, and video data associated with a user, and may process the received data, with a first model, to determine a stress level of the user. The device may process the received data, with second models, to determine depression levels of the user, and may combine the depression levels to identify an overall depression level. The device may process the received data, with a third model, to determine a continuous affect prediction, and may process the received data, with a fourth model, to determine an emotion of the user. The device may process the received data, with a fifth model, to determine a response to the user, and may utilize a sixth model to determine a context for the response. The device may utilize seventh models to generate contextual conversation data, and may perform actions based on the contextual conversational data.

Utilizing machine learning models to generate automated empathetic conversations

A system and method for automatically detecting misinformation is disclosed. The misinformation detection system is implemented using a cross-stitch based semi-supervised end-to-end neural attention model which is configured to leverage the large amount of unlabeled data that is available. In one embodiment, the model can at least partially generalize and identify emerging misinformation as it learns from an array of relevant external knowledge. Embodiments of the proposed system rely on heterogeneous information such as a social media post's text content, user details, and activity around the post, as well as external knowledge from the web, to identify whether the content includes misinformation. The results of the model are produced via an attention mechanism.

Method and system for detection of misinformation

The disclosed system and method focus on automatically generating questions from input of written text and/or audio transcripts (e.g., learning materials) to aid in teaching people through testing their knowledge about information they have previously been presented with. These questions may be presented to an end user via a conversational system (e.g., virtual agent or chatbot). The user can iterate through each question, provide feedback for the question, attempt to answer the question, and/or get an answer score for each answer. The disclosed system and method can generate questions tailored to a particular subject by using teaching materials as input. The disclosed system and method can further curate the questions based on various conditions to ensure that the questions are automatically selected and arranged in an order that best suits the subject taught and the learner answering the questions.

Virtual agent conducting interactive testing

A device may receive training data that includes datasets associated with natural language processing, and may mask the training data to generate masked training data. The device may train a masked event C-BERT model, with the masked training data, to generate pretrained weights and a trained masked event C-BERT model, and may train an event aware C-BERT model, with the training data and the pretrained weights, to generate a trained event aware C-BERT model. The device may receive natural language text data identifying natural language events, and may process the natural language text data, with the trained masked event C-BERT model, to determine weights. The device may process the natural language text data and the weights, with the trained event aware C-BERT model, to predict causality relationships between the natural language events, and may perform actions, based on the causality relationships.

Utilizing machine learning models and in-domain and out-of-domain data distribution to predict a causality relationship between events expressed in natural language text

Embodiments of the present disclosure provide systems, methods, and computer-readable storage media that leverage artificial intelligence and machine learning to identify, diagnose, and mitigate occurrences of network faults or incidents within a network. Historical network incidents may be used to generate a model that may be used to evaluate real-time occurring network incidents, such as to identify a cause of the network incident. Clustering algorithms may be used to identify portions of the model that share similarities with a network incident and then actions taken to resolve similar network incidents in the past may be identified and proposed as candidate actions that may be executed to resolve the cause of the network incident. Execution of the candidate actions may be performed under control of a user or automatically based on execution criteria and the configuration of the fault mitigation system.

Systems and methods that offer significant improvements to current chatbot conversational experiences are disclosed. The proposed systems and methods are configured to manage conversations in real-time with human customers based on a dynamic and unscripted conversation flow with a virtual assistant. In one embodiment, a knowledge graph or domain model represents the sole or primary source of information for the virtual assistant, thereby removing the reliance on any form of conversational modelling. Based on the information provided by the knowledge graph, the virtual agent chatbot will be equipped to answer customer queries, as well as demonstrate reasoning, offering customers a more natural and efficacious dialogue experience.

Dynamic and unscripted virtual agent systems and methods

The present system and method may generally include organizing the task flow of a virtual agent in a way that is controlled by a set of rules and set of conditional probability distributions. The system and method may include receiving a user utterance including a first task, identifying the first task from the user utterance, and obtaining a set of rules related to the plurality of tasks. The set of rules may determine whether pre-tasks and/or pre-conditions are to be executed before executing the first task. The set of rules may also determine whether post-tasks and/or post-conditions are to be executed after executing the first task. The system and method may include executing the task; running a probabilistic graphical model on the plurality of tasks to determine a second task based on the first task; suggesting to the user the second task; and updating the probabilistic graphical model after a threshold number of runs.

Artificial intelligence based system and method for controlling virtual agent task flow

A virtual agent with a dialogue management system and a method of training the dialogue management system is disclosed. The dialogue management system is trained using a deep reinforcement learning process. Training involves obtaining or simulating training dialogue data. During the training process, actions for the dialogue management system are selected using a Deep Q Network to process observations. The Deep Q Network is updated using a target function that includes a reward. The reward may be generated by considering one or more of the following metrics: task completion percentage, dialogue length, sentiment analysis of the user's response, emotional analysis of the user's state, explicit user feedback, and assessed quality of the action. The set of actions that the dialogue management system can take at any time may be limited by an action screener that predicts the subset of actions that the agent should consider for a given state of the system.

Virtual agent with a dialogue management system and method of training a dialogue management system

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Bengaluru, India

Shubhashis Sengupta