Dialpad, Inc.

Kevin James

James Palmer

Jonathan Johnston

Etienne Manderscheid

Nicholas Foster

Eddie Yee Tak Ma

Stefan Roesch

Functionality is provided for the automated creation, testing, training, adaptation and deployment of AI models and changes thereto. Base classes are provided that enable practicable creation of new models from existing one. New models are tested on live data sets offline from user sites. New training methods are provided for the production of particular outcomes. Efficient adaptation of new AI models is facilitated, encompassing data scientist and development team control over how fast to train and deploy new models.

Automated creation, testing, training, adaptation and deployment of new artificial intelligence (AI) models

Audio data saved at the end of client interactions are sampled, analyzed for pauses in speech, and sliced into stretches of acoustic data containing human speech between those pauses. The acoustic data are accompanied by machine transcripts made by VoiceAI. A suitable distribution of data useful for training and testing are stipulated during data sampling by applying certain filtering criteria. The resulting datasets are sent for transcription by a human transcriber team. The human transcripts are retrieved, some post-transcription processing and cleaning are performed, and the results are added to datastores for training and testing an acoustic model.

Maintainable and scalable pipeline for automatic speech recognition language modeling

A proactive autoscaling system can use heuristics and machine learning to proactively, dynamically and automatically scale computing resources allocated to applications up and down, prior to scaling events that cause changes in load levels. The proactive autoscaling system may be stateless, and may be agnostic to use case, platform, field of endeavor, or communication protocol used by the applications. The proactive autoscaling system gleans metrics in real-time. The gleaned metrics are indicative of load levels concerning one or more applications. These gleaned metrics may be in a variety of formats, and may be from different sources, both internal or external to the applications. The proactive autoscaling system automatically predicts scaling events based on gleaned metrics. Prior to the occurrence of a predicted scaling event, the proactive autoscaling system can automatically scale computing resources available to one or more target applications, in response to the predicting of the scaling event.

Universal, proactive, and dynamic scaling of computing resources

An AI platform is fully integrated with existing VoIP/AVoIP telephony infrastructure. In the course of providing VoIP/AVoIP audiovisual calls, a VoIP/AVoIP media stream of audiovisual content is processed, and transferred between endpoints. AI processing is applied to each frame of the transferred audiovisual content, in real-time while the audiovisual call is occurring. For example, automated speech recognition can be performed on the content, in which the speech of the audiovisual content is converted to text. The audiovisual call can further be automatically transcribed to a text file in real-time. Another example is the automatic detection of the occurrence of specific triggers during calls. Additional enhanced functionality is automatically provided as a result of applying the AI processing to the transferred audiovisual content. For example, in response to detecting the occurrence of a specific trigger, a corresponding directive can be automatically output on a screen of a calling device.

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San Francisco, CA, United States of America

Kevin James