Accenture Global Solutions Limited

Accenture Global Services Gmbh

Siddesha Swamy

Shubhashis Sengupta

Prakash Ghatage

Nirav Sampat

Kumar Viswanathan

Annervaz Karukapadath Mohamedrasheed

Manish Sharma Kolachalam

Manisha Dubey

Kamalesh Kuppusamy Kuduva

Kanakalata Narayanan

Rajnikant Dutt

Ranjan Kumar Jena

Chakravarthy Lakshminarasimhan

Suket Kumar Jain

Mohd Israil Khan

Anindya Dutt

Manisha Kapur

Ankur Narain

Jovin George

Prashanth Ramesh

Mansi Srivastava

Vaidya Sumanth

Rajeh Ganesh Natrajan

A system for issue prediction based on multidimensional data analysis includes a model generator that receives a resolved data item relating to a service issue. The resolved data item includes different attributes corresponding to multiple data dimensions and adjusts a population of attributes based on a statistical data model and a deep learning data model operating independent of each other. The statistical data model operates on the attributes for providing a predictive feature and the deep learning data model operates on the attributes for providing a predictive label based on performance metrics related to the data dimensions. The predictive feature and the predictive label collectively define training data. The model generator also trains a classification model based on the training data for predicting a potential issue related to an unresolved data item. The trained data model provides a trigger based on the potential issue being related to the performance metrics.

Multidimensional data analysis for issue prediction

A system and method provide a trained model that uses vectorized word embeddings that are averaged or summed to form representations for sentences and phrases. The representations are processed in a Siamese neural network including multiple LSTM stages to find semantically related matches in catalogs for non-catalog queries. The model is trained using catalog data and randomized data using a contrastive loss function to generate similarity metrics for catalog-non-catalog pairs.

Catalog adoption in procurement

An Artificial Intelligence (AI) based virtual automated assistance system provides services pertaining to component processes of a task that is to be automatically executed. The virtual automated assistance system includes a pipeline studio that enables generating the services. Historical data pertaining to a service is accessed for training and validating various ML models. The ML models are scored and a selected ML model is registered as a service on the virtual automated assistance system. The services thus registered are represented as process blocks within the pipeline studio wherein the process blocks pertaining to the component processes of the task are arranged in order to form a pipeline. The pipeline thus constructed enables automatic execution of the task by receiving and processing a request pertaining to the task via the services that form the pipeline.

Artificial intelligence based virtual automated assistance

A computer-implemented method, a processing pipeline and a system create a hierarchical semantic map of a document and extracted information. The method includes apportioning the document into major sections by accessing the document, recognizing a hierarchical structure of the document, and dividing the document into the major sections by using a data profiler and a machine learning module, classifying the major sections, and mapping the major sections to key elements in one of the multiple levels, searching one major section, and identifying sub-sections from the one major section to achieve a maximum confidence score indicates that the sub-sections associate with the key element, extracting the information from the identified sub-sections by using sequence modelers and linguistic characteristics provided by the data profiler, generating the hierarchical semantic map of the document by using the extracted information, and displaying in a user interface drop down selections of the key elements.

System and method for automating information abstraction process for documents

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Bengaluru, India

Siddesha Swamy