Abstract: Apparatuses, systems, and techniques are presented to analyze objects in images including digital representations of those objects. In at least one embodiment, one or more diagnostic processes are determined based, at least in part, on one or more neural networks used to identify one or more medical images.

Abstract: In various examples, a virtualized computing platform for advanced computing operations—including image reconstruction, segmentation, processing, analysis, visualization, and deep learning—may be provided. The platform may allow for inference pipeline customization by selecting, organizing, and adapting constructs of task containers for local, on-premises implementation. Within the task containers, machine learning models generated off-premises may be leveraged and updated for location specific implementation to perform image processing operations. As a result, and using the virtualized computing platform, facilities such as hospitals and clinics may more seamlessly train, deploy, and integrate machine learning models within a production environment for providing informative and actionable medical information to practitioners.

Abstract: An audio labeling tool is described for rapidly and efficiently building a labeled data set preferably comprising audio files with annotations denoting speaker transcriptions, speaker identity, periods of silence, background noise, and speaker emotion labels. The tool provides a configurable user interface (UI) with keyboard shortcuts and menu items for streamlined user-guided markup of audio files with context specific labeling and transcription notes. Human audio file labelers will preferably leverage the labeling tool and configurable user interface and menu to rapidly annotate, validate, and build labeled data sets, at scale, of time sliced audio files. The audio labeling tool is preferably applied to audio files and provides an automated means for adding notations, markup, text transcription, and feature labeling to an audio waveform, spectrogram, or other audio data visualization domain for amplifying and highlighting audio feature details.

Abstract: A system and method for comprehensive automated call center customer/agent interaction monitoring and service level agreement (SLA) compliance. The system reduces a massive volume of call center activity into readable data points and SLA metrics for measuring agent and overall call center performance levels. The system allows for the scaling up of the SLA compliance process, which is currently done manually by quality assurance personnel for a limited sample set. With the system, customer calls are computationally sampled for speaker diarization and voice isolation, speech emotion recognition, unique salient feature extraction, reference pattern template matching, and automatic speech recognition. The system is adaptively programmable for recognizing and predicting SLA metrics such as: customer satisfaction, issue resolution, appropriate agent greeting and identification, customer understanding, acknowledgment, abandonment, sales attempts, and customer retention, etc.

Abstract: A system and method for providing merchants and service providers automated identification of proximate clients to provide relevant client data and to authorize transactions. Whereupon a client is discreetly identified by the system through facial recognition, thumbprint, voice sample, iris scan, or other biometric sample. Multi-level authorization and authentication are provided using client metadata, such as email, phone number, mobile device, location, and payment information. The service provider is shown client preferences and transaction history in order to facilitate personalized service. The client is provided with relevant options for available goods or services as recommended by the system. The system provides client sentiment analysis to generate dynamic personalization, customer feedback, and intention projection. Service providers and merchants in the network are curated such that they may be presented to a customer in an orderly fashion.