International Business Machines Corporation

Xinyun Zhao

Rahul P Akolkar

Joseph Lindsey Sharpe, Iii

Kristi A Farinelli

Stefan Van Der Stockt

Brian E Bissell

Stefan A G Van Der Stockt

Manali Jairam Chanchlani

Sihang B Fang

Sai Karthik Reddy Ginni

Technology for determining an amount of time to wait to retry requests to a representational state transfer (REST) server system for a REST resource, where the time to wait is always chosen to be a prime number of time units (for example, slots, milliseconds). While currently conventional systems will sometimes use a prime number of time units to wait for a retry request, various embodiments of the present invention will always, and invariably, use a prime number of time units. The REST resource may be, for example, a REST application programming interface (API) that is requested by and delivered to a client system using hypertext transfer protocol (HTTP).

Service retry collision prevention using prime numbers

A prescriptive meeting resource recommendation engine automatically learns participant and resource preferences in the context of given meeting input data using natural language features, and automatically recommends all relevant participants and resources (teleconferences, web meetings, links, etc.) to the meeting creator. The engine uses a feature data store to associate historical persons and historical resources with various natural language features, e.g., chargrams. As the host enters text in an invitation template (such as in the title field), the engine extracts current natural language features and computes current participant scores and current resource scores based on the current natural language features. A 'forgetfulness' routine is applied to the feature data store to phase out the influence of stale data. After receiving user confirmation, the system takes appropriate action such as electronically sending invitations to the recommended participants and making reservations for the recommended resources.

Prescriptive meeting resource recommendation engine using calendar invite content

A method, system, and computer program product are provided for classifying spoken audio content with a cognitive audio classifier by applying a set of distorted audio resources through a set of speech-to-text models STTi (STT. . . STTn) to get a set of interference coherence scores based on the transcript for each speech-to-text model STTi, thereby generating a measured baseline Mi (M. . . Mn) and a practical baseline Pi (P. . . Pn) that is associated with a coherence matrix for the audio effects AEj (AE. . . AEk) that were used to generate the distorted audio resources, thereby generating training data for use in training a cognitive audio classifier which classifies input spoken audio content to measure a quality of detected vocabulary elements from the spoken audio content under the set of audio distortion effects for each speech-to-text model STTi.

Cognitive audio classifier

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Austin, TX, United States of America

Xinyun Zhao