Accenture Global Solutions Limited

Amazon Technologies, Inc.

Guanglei Xiong

Chung-Sheng Li

Sukryool Kang

Emmanuel Munguia Tapia

Anwitha Paruchuri

Colin Connors

Neeru Narang

Lan Guan

Benjamin Nathan Grosof

Jayashree Subrahmonia

Madhura Shivaram

Mohammad Jawad Ghorbani

Kyle Patrick Johnson

Swati Tata

Kayhan Moharreri

Ashish Jain

Golnaz Ghasemiesfeh

Jingyun Fan

Suraj Govind Jadhav

Christopher Yen-Chu Chan

Pratip Samanta

Sujeong Cha

Anupam Anurag Tripathi

Ullas Balan Nambiar

Neeraj D Vadhan

Saurabh Mahadik

Jill Gengelbach-wylie

Tsunghan Wu

Louise Noreen Barrere

Thomas Wayne Hancock

Omar Razi

Wenxian Zhang

Christopher Cole

Nagendra Kumar M R

Priyanka Chowdhary

Krishna Kummamuru

Jorjeta Gueorguieva Jetcheva

Prakash Ghatage

Chinnappa Guggilla

Guruprasad Dasappa

Soujanya Soni

Yuan He

Ditty Mathew

Kavita V V Ganeshan

Sumeet Sawarkar

Mingzhu Lu

Akanksha Juneja

Anshul Solanki

Yashal Shakti Kanungo

Ivan A Wong

Anjani Kumari

Srinivasan Krishnan Rajagopalan

Rajeev Sinha

Danielle Moffat

Praveen Maniyan

Naveen Bansal

Karthik Meenakshisundaram

Sivasubramanian Arumugam Jalajam

Venkatesan Kamalakannan

Armando Ferreira Gomes

Angela Yang Sanford

Giulio Cattozzo

Jing Su Brewer

Lisa Blackwood

Harbhajan Singh

David Alfonso Guerra

Thyagarajan Delli

Aaron James Sander

Madhur Bhasini Chaini

Michael C Petersen

Tor Hamilton Steiner

Dennis P Delaney

Sravan Sripada

Michael Dekshenieks

Manoj Shroff

Sachin Aul

Sudhanshu A Dwivedi

Renuka Mannem

Rishi Vig

Ankit Pandey

Hariram Krishnamurth

Jill K Goldstein

Christopher Banschbach

Karthik Lakshminarayanan

Thomas Michael Fahey

Techniques are described here for prompt generation. An example method can include determining, using a first machine learning model, a characteristic of an item based at least in part on a textual description of the item. The method can further include receiving a first input indicating a qualifier describing a theme for the image. The method can further include generating, using the first machine learning model, a prompt for a second machine learning model based at least in part on the characteristic and the background. The method can further include generating, using the second machine learning model, the image based at least in part on the prompt, the image associated with the theme and showing the item and the background.

Text and image based prompt generation

The present disclosure relates to a system, a method, and a product for using machine learning models to quantify and/or improve trust in conversations. The system includes a non-transitory memory; and a processor in communication with the non-transitory memory. The processor executes the instructions to cause the system to: obtain a set of vocal features and a set of text features for each sample in audio samples; obtain a trust score for each sample; perform a preprocess to obtain a set of input features for each sample; determine a type of machine-learning algorithm for the machine-learning network; tune a set of hyper parameters for the machine-learning network; generate a predicated trust score by the machine-learning network with the sets of input features for each sample; and train the machine-learning network based on the predicated trust score and the trust score for each sample to obtain the training result.

Systems and methods to improve trust in conversations

Implementations are directed to receiving a set of tuples, each tuple including an entity and a product from a set of products, for each tuple: generating, by an embedding module, a total latent vector as input to a recommender network, the total latent vector generated based on a structural vector, a textual vector, and a categorical vector, each generated based on a product profile of a respective product and an entity profile of the entity, generating, by a context integration module, a latent context vector based on a context vector representative of a context of the entity, and inputting the total latent vector and the latent context vector to the recommender network, the recommender network being trained by few-shot learning using a multi-task loss function, and generating, by the recommender network, a prediction including a set of recommendations specific to the entity.

Few-shot learning for multi-task recommendation systems

The present disclosure relates to a system, a method, and a product for using deep learning models to quantify and/or improve trust in conversations. The system includes a non-transitory memory storing instructions executable to construct a deep-learning network to quantify trust scores; and a processor in communication with the non-transitory memory. The processor executes the instructions to cause the system to: obtain a trust score for each voice sample in a plurality of audio samples, generate a predicated trust score by the deep-learning network based on each voice sample in the plurality of audio samples, wherein the deep-learning network comprises a plurality of branches and an aggregation network configured to aggregate results from the plurality of branches, and train the deep-learning network based on the predicated trust score and the trust score for each voice sample to obtain a training result.

Systems and methods to improve trust in conversations with deep learning models

A system and method for lead conversion using conversational virtual avatar is disclosed. System comprising processor causes Conversation Virtual Avatar Platform (CVAP) to receive, for first entity, from lead prioritization engine, leads applicable to first entity via lead repository based on scores associated with respective leads. Processor causes CVAP to receive, through conversation management engine (CME) configured in CVAP, from leads, responses to questions pertaining to product attributes and information pertaining to lead. The processor causes CVAP to process responses to determine action and/or state, which includes whether to issue additional product-attribute based questions through Virtual Avatar (VA) using Response To Motion Module (RTME) or to, through recommender engine that uses recommendation model, in real-time, recommend products associated with first entity to respective at least one lead based on any or combination of responses received from lead, information pertaining to lead, and products ordered by entities similar to lead.

System, method, and medium for lead conversion using a conversational virtual avatar

A system for providing automated and domain specific contextual processing for context based verification may classify a plurality of extracted parameters from a set of digitized training document to assign a document similarity score with respect to a set of reference documents. The system may automatically detect a domain for the set of digitized training documents based on the document similarity score. The system may load a domain based neural model for the detected domain to generate a plurality of pre-defined contextual parameters. The system may receive a set of input documents and perform a contextual processing of the received set of documents based on the pre-defined contextual parameters to obtain an output in form of a plurality of filtered snippets, each bearing a corresponding rank. The context based verification may be performed based on the plurality of filtered snippets and the corresponding rank.

Automated contextual processing of unstructured data

A document processing system processes documents including typewritten and/or handwritten data by converting them to document images for entity extraction. A received document is initially processed to generate a deep document data structured and for classification as one of a structured or an unstructured document. If the document is classified as a structured document, it is processed for entity extraction based on a matching template and image alignment of the document image with the matching template. If the document is classified as an unstructured document, entities are extracted by obtaining nodes and providing the nodes to a self-supervised masked visual language model.

Deep document processing with self-supervised learning

The validity of engineering drawings is automatically determined based on compliance of the specifications of the engineering drawings with automatically generated rules. A document package including images of the engineering drawings and related documents is received. Rules codifying the requirements to be fulfilled by the engineering drawings are automatically generated from the related documents. Data such as specifications of the various parts of the entities in the engineering drawings are automatically extracted. The extracted data is analyzed to determine compliance with the rules to validate the engineering drawings.

Intelligent engineering data digitization

Implementations of the present disclosure include receiving a goal, providing a problem-specific knowledge graph that is responsive to at least a portion of the goal, determining a set of events from the problem-specific knowledge graph, processing data representative of events in the set of events through a first machine learning (ML) model to provide a set of event scores, each event score in the set of event scores being associated with a respective event in the set of events, determining a sub-set of events based on the set of event scores, for each event in the sub-set of events, determining at least one action by processing a sequence of actions through a second ML model, and outputting the sub-set of events and a set of actions for execution of at least one action in the set of actions.

Early pattern detection in data for improved enterprise operations

The proposed systems and methods describe an autonomous asset detection system that leverages artificial intelligence (AI) models for three-dimensional asset identification and damage detection, asset damage classification, automatic in-field asset tag readings, and real-time asset management. In some embodiments, a deep learning-based system receives a set of aerial images of one or more assets and automatically identifies each asset in the image(s) using rotational coordinates. In some embodiments, an image annotation tool labels the images either manually or automatically. The system then detects whether the asset is damaged and, if so, determine the type of damage, and further captures and stores asset tag information for the target asset. The collected and processed data is then provided to end-users via a comprehensive user interface platform for managing the assets in real-time. The platform can also be configured to generate insights on damage and repair, facilitating an expedited and thoughtful response to asset maintenance.

System and method for identifying structural asset features and damage

Examples of artificial intelligence-based reasoning explanation are described. In an example implementation, a knowledge model having a plurality of ontologies and a plurality of inferencing rules is generated. Once the knowledge model is generated, based on a real-world problem, a knowledge model from amongst various knowledge models is selected to be used for resolving a real-world problem. The data procured from the real-world problem is clustered and classified into an ontology of the determined knowledge model. Inferencing rules to be used for deconstructing the real-world problem are identified, and a machine reasoning is generated to provide a hypothesis for the problem and an explanation to accompany the hypothesis.

Explainable artificial intelligence

In an example, an ontology analyzer may generate an ontology, based on a claim adjudication request. The claim adjudication request may be processed, based on the ontology to provide an ontology based inference. A rule based analyzer may identify a predefined rule corresponding to the claim adjudication request and process the request, based on the predefined rule. A conflict resolver may resolve a conflict which may occur between the ontology based inference and the rule based inference. When a conflict is detected, a predefined criteria may be selected for resolving the conflict, the predefined criteria comprising rules to select one of the ontology based inference and the rule based inference to maximize a probability of accurately processing the claim adjudication request in case of a conflict.

Ontology and rule based adjudication

Examples of employee concierge are provided. In an example, an issue may be determined for an employee. The issue may be determined based on a query shared by the employee or upon occurrence of an unusual event. The unusual event may be indicative of a deviation in behaviour and routine of the employee. A session may be initiated and the issue may be parsed to determine a context. A bot may be selected from multiple bots for the issue where each bot includes information relating to a solution to address the issue. Data associated with the issue may be collected from a central database and other bots. The data may then be analyzed to determine a solution. The solution comprises a response to the query and a suggestion to mitigate the unusual event.

Employee concierge

Examples of analyzing documents are defined. In an example, a request to analyze a document may be received. A knowledge model corresponding to a guideline associated with the document may be obtained. The knowledge model may include at least one of a hypothetical question and a logical flow to determine an inference to the hypothetical question. The hypothetical question relates to an element of the guideline. Based on the knowledge model, data from the document may be extracted for analysis using an artificial intelligence (AI) component. The Ai component may be configured to extract and analyze data, based on the knowledge model. Based on the analysis, a report indicating whether the document falls within a purview of the guideline may be generated.

Document analyzer

Systems and methods for orchestrating a process are disclosed. In an implementation, a system is configured to extract process information associated with the process. Based on the process information, the system is configured to determine a current model of performing the process based on the process information. The system is further configured to retrieve regulatory information associated with the process, wherein the regulatory information is indicative of at least one of a predefined policy, a predefined rule, and a predefined regulation associated with the process. Further, the system is configured to update the current model based on at least one of the process information and the regulatory information for obtaining a predefined outcome of the process.

Process orchestration

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Pleasanton, CA, United States of America

Guanglei Xiong