Accenture Global Solutions Limited

Souvik Chakraborty

Gurpreet Singh Bawa

Kaustav Pakira

Sanjay Sharma

Kaushik Sanyal

Rebecca Lynn Ruliffson

Aspects of the present disclosure provide systems, methods, and computer-readable storage media that support automated summarization of documents using machine learning and artificial intelligence. The summaries include document category-specific summaries for different categories of documents. To illustrate, a document processing device may receive input data representing a document and provide first feature data based on the input data to one or more machine learning (ML) models to select a document category of a plurality of predefined document categories. The document processing device may provide second feature data based on the input data to a second set of ML models associated with the selected document category to generate annotation data associated with the document. The document processing device may provide the annotation data to a third set of ML models to generate a summary of the document, and the document processing device may generate an output based on the summary.

Automated categorization and summarization of documents using machine learning

Systems and methods for establishing user persona from audio interactions are disclosed, including a voice-based conversational AI platform having an acoustic analytical record engine and audio signal codification optimizer. The engine receives an audio sample indicative of voice conversation between an end user and a bot and transforms it into quantifiable and machine-ingestible power spectrum and acoustic indicators that uniquely represent the audio sample in the form of a feature vector. The optimizer ingests and processes the indicators to estimate likelihood of an attribute value representing the audio sample by constructing a convolutional neural network model for each attribute category. The optimizer establishes user persona attribute values across different attribute categories for the audio sample based on the estimated likelihood. Finally, a Textual Latent Value Extractor of the system determines the issue's context window and estimates the statements polarity to provide distinguishable insight in business strategy and development.

Establishing user persona in a conversational system

An utterance recommendation system may obtain a plurality of utterance influencing attributes influencing a response recommended for a user query from a plurality of sources. The system may collate the utterance influencing attributes to provide enriched user data and may identify a set of preconfigured potential utterance options associated with the user query. The system may implement a plurality of utterance recommendation techniques to analyze the enriched user data based on associated predefined rules, and provide a preferential rank ordering of the preconfigured potential utterance options to be recommended based on the analysis. The system may determine a single optimized rank ordering of the preconfigured potential utterance options representing a ranking order of the preconfigured potential utterance options. The system may provide the response to the user query, based on a potential utterance option ranked highest in the single optimized rank ordering.

Utterance recommendation in a conversational artificial intelligence platform

Examples of network bandwidth management are provided. A system may access, time series distribution data including a distribution of data points corresponding to an average count of network packets. Further, the system may determine, from the time series distribution data, trend data component, seasonal data component, cyclical data component, and irregular data component. Furthermore, the system may compute, baseline distribution traffic data based on the trend data component, the cyclical data component, and the seasonal data component. Furthermore, the system may identify auto regression data component and moving average data component based on the irregular data component. The system may further, determine an estimated network traffic for a future time interval based on the baseline distribution traffic data, a lagged covariate factor, and covariate metrics. Furthermore, the system may provide the estimated bandwidth based on the estimated network traffic to configure the server.

Network bandwidth management

In some implementations, a device may receive unstructured interaction data identifying an interaction of a user with a user device. The device may receive historical unstructured interaction data identifying historical interactions of users and historical unstructured resolution data identifying historical resolutions to the historical interactions. The device may process the historical unstructured interaction data and the historical unstructured resolution data to determine historical structured interaction data and historical structured resolution data. The device may process the unstructured interaction data and the historical structured interaction data to determine pretext identifiers for the interaction of the user. The device may process the pretext identifiers and the historical structured resolution data to generate a resolution network identifying possible resolutions to the interaction of the user. The device may process the pretext identifiers and the resolution network to determine a resolution path identifying a resolution to the interaction of the user.

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Kolkata, India

Souvik Chakraborty