Accenture Global Solutions Limited

Gopali Raval Contractor

Subhashini Lakshminarayanan

Bhushan Gurmukhdas Jagyasi

Parag Rane

Rajashree Chandrashekar

Shiladitya Sengupta

Shantanu Shirish Sahasrabudhe

Maria Lopes

Rajendra Tanniru Prasad

Jayant Swamy

Nitin Madhukar Sawant

Selvakuberan Karuppasamy

Deepmala Gupta

Shaun Cyprian D'Souza

Surajit Sen

Prasanna Srinivasa Rao

Bhavin Prafulchandra Mehta

Arati Deo

Jyoti Bishnoi

Siva Rama Sarma Theerthala

Raghuram Yaddanapudi

Arjun Marri

Jayateerth Kulkarni

Aditi Debsharma

Rosalin Parida

Manish Vijaywargiya

Adnan Khan

Mukesh Kumar Chaudhary

Rahul Sarkar

Saurabh Juneja

Venkatraman Viswanathan

Tapan Kumar Jain

Kishore Kumar Eswara

Shantanu Ss

Azimullah Shahzad

Suma S Joshi

Soumit Bhowmick

Prasanna S R

Vallinath Sriramula

Saurabh Pashine

Payal Agarwal

This application discloses a system and method for federated collaborative machine learning model development using local training datasets that are not shared. An adaptive and evolutionary approach is used to select local training nodes that are most fit from one training round to the next training round to optimize an overall cost and performance function for the federated learning, to cross-over model architecture between local training nodes, and to perform model architecture mutation within local training nodes. The local training nodes are further clustered to account for the inhomogeneity in the local datasets. Such adaptive, evolutionary, and collaborative federated learning thus provides cost-effective and high-performance model development.

Adaptive and evolutionary federated learning system

In some implementations, an olfaction system may receive partition coefficients associated with one or more molecules detected in a headspace of a sample captured from an environment. The olfaction system may generate a quantum-ready dataset based on the partition coefficients using a partial quantum autoencoder that includes one or more quantum gate layers. The olfaction system may use a quantum approximate optimization algorithm to identify, within a spectrum of potential smells simulated by a quantum circuit, a set of smells emitted by the sample based on the quantum-ready dataset. The olfaction system may map a set of objects to the set of smells emitted by the sample. The olfaction system may predict a future state associated with the set of smells emitted by the sample using one or more hybrid quantum machine learning models.

Virtual nose using quantum machine learning and quantum simulation

A method and system for emotion recognition and forecasting are disclosed. The method may include obtaining an audio data of a conversation involving a plurality of speakers and identifying a plurality of turns of the conversation from the plurality of utterances. The method may further include extracting audio embedding features from the plurality of turns, obtaining a plurality of text segments associated with the audio data, extracting text embedding features from the plurality of text segments, obtaining and concatenating speaker embedding features associated with the audio data, obtaining and concatenating a plurality of emotion features corresponding to the plurality of turns. The method further comprises executing a tree-based prediction model to predict emotion features of the plurality of speakers for a subsequent turn of the ongoing conversation based on the audio embedding features, text embedding features, the concatenated speaker embedding features, and the concatenated emotion features.

Machine learning based emotion prediction and forecasting in conversation

An automated system and method of converting legacy decision logic to a target format. The legacy files are received by the decision logic translation system, which outputs the business rule content in a standard rule structure, according to the selected target format. The process involves decision logic-based rule extraction. In general, methods or processes for extracting business rules have been difficult to reproduce and do not present clearly the extracted rules regarding the concepts of business rules, their composition and categorization. These drawbacks lead to incomplete extraction of rules and massive manual effort to achieve a complete extraction and verification. In contrast, the proposed system overcomes these drawbacks, and outputs files that can be easily used to migrate the business rules to a new platform.

Decision logic translation system and method

In some implementations, a device may receive, from a user device, a query for information from a user. The device may receive, from an enterprise system, data identifying products and services to be offered by the user. The device may process the query and the data, with a model, to identify a subject of the query and a context for the subject. The device may process the subject and the context, with a model, to determine a workflow for the user. The device may process the data, the subject of the query, and the context for the subject, with a model, to determine a key performance indicator or a decision associated with one of the products or the services. The device may provide information identifying the workflow, the key performance indicator, and/or the decision to the user device via a virtual assistant or a user interface.

Utilizing machine learning models for identifying a subject of a query, a context for the subject, and a workflow

A device may consolidate the input data associated with vehicle insurance claims to generate processed input data. The device may process claims data, treatment data, and repair shop data to resolve ambiguities in the processed input data and to generate resolved data. The device may process the resolved data to generate related data identifying relations between persons and vehicle accidents. The device may process notes of claims adjusters and vehicle accident descriptions to extract feature data identifying features. The device may process the feature data to determine contradiction data identifying contradictions in the feature data. The device may process weather data, location data, and telematics data to determine weather conditions and locations associated with the accidents. The device may process the related data, the contradiction data, the weather conditions, and the locations to generate a knowledge graph. The device may identify a fraud ring based on the knowledge graph.

Utilizing machine learning models, predictive analytics, and data mining to identify a vehicle insurance fraud ring

Examples of a sentence phrasing system are provided. The system may obtain a user question from a user. The system may obtain question entailment data from a plurality of data sources. The system may implement an artificial intelligence component to identify a word index from the question entailment data and to identify a question premise from the user question. The system may implement a first cognitive learning operation to determine an answer premise corresponding to the question premise comprising a second-word data set. The system may determine a subject component corresponding to the question premise. The system may generate an object component and a predicate component from the second-word data set corresponding to the subject component. The system may generate an integrated answer relevant for resolving the user question and comprising the subject component, the object component, and the predicate component concatenated to form an answer sentence.

Sentence phrase generation

A device may receive transaction data associated with transactions. The transaction data may be associated with transaction entries that are associated with the transactions. The device may process, using a matching model, the transaction entries to classify the transaction entries into a set of matched transaction entries and a set of unmatched transaction entries. The device may update a transaction grouping model based on the set of matched transaction entries to create an updated transaction grouping model. The device may determine, using the updated transaction grouping model, that a subset of the set of unmatched transaction entries are associated with a same transaction. The device may classify the subset of the set of unmatched transaction entries as grouped transaction entries. The device may provide an indication that the grouped transaction entries and the set of matched transaction entries are reconciled transactions.

Machine learning system for transaction reconciliation

A domain-specific question answering system receives an end-user's question, identifies relevant information from domain-specific documents, composes a natural-language based response including the relevant information to be provided to the end-user. A knowledge base includes a domain-specific ontology and is built from data structures configured to preserve the hierarchy of the informational elements within the domain-specific documents. The question received from the end-user is analyzed to extract the features and the key phrases which are used to identify the relevant data structures. A pre-trained model that is further trained on domain-specific data is used to identify the sentences that include information responsive to the question. A response to the end-user's question is composed from the sentences. The question answering system also enables validation of the answers provided to the users' questions.

Automatically generating natural language responses to users' questions

A device may receive issue resolution information, associated with a cognitive model, including an item of issue resolution information that describes an issue and a resolution to the issue. The device may assign the item of issue resolution information to a domain hierarchy, where the assigning is associated with a first user. The device may generate a question and an answer corresponding to the item of issue resolution information, where the generating of the question and the answer is associated with a second user. The device may approve the question and the answer, where the approving is associated with a third user. The device may generate a question/answer (QA) pair for the question and the answer. The device may create a data corpus including the QA pair, and provide the data corpus to cause the cognitive model to be trained based on the data corpus.

Processing data for use in a cognitive insights platform

A virtual cognitive agent (VCA) system comprises social media communication channels integrated with machine cognition engines. The VCA system connects to an external message platform and accesses posted messages. An intent is determined and sentiment analysis is performed on text elements from the intercepted messages to determine handling of the message. The integrated machine cognition engines determine a response to the captured message. The VCA system may access a corpus or exchange data with the originator of the post or another entity to determine the response. The integrated social media communication channels may connect to an external query response platform and communicate the response to the query response platform.

Integrated virtual cognitive agents and message communication architecture

Growing community of inventors

Mumbai, India

Gopali Raval Contractor