International Business Machines Corporation

Zoom Video Communications, Inc.

Zoom Communications, Inc.

Yun Zhang

Ling Tsou

Tong Wang

Ashish Jagmohan

Ajay Ashok Deshpande

Rakesh Mohan

Ali Koc

Pavithra Harsha

Methods and systems provide for dynamic prediction of agenda item coverage in a communication session. In one embodiment, the system classifies each agenda item as a long item or a short item; for agenda items classified as long items, extracts one or more topics from the utterances, and uses a topic detection model to predict whether a topic related to each agenda item classified as a long item has been covered; for agenda items classified as short items, applies one or more matching methods to predict whether one or more of the utterances within the sentence threshold cover each agenda item classified as a short item; and transmits, to one or more client devices, a status of the agenda items for the communication session.

Dynamic agenda item coverage prediction

Methods and systems provide for dynamic prediction of agenda item coverage in a communication session. In one embodiment, the system connects to a communication session with a plurality of participants; receives one or more agenda items and a number of utterances; classifies each agenda item as a long item or a short item; for agenda items classified as long items, extracts one or more topics from the utterances, and uses a topic detection model to predict whether a topic related to each agenda item classified as a long item has been covered; for agenda items classified as short items, applies one or more matching methods to predict whether one or more of the utterances within the sentence threshold cover each agenda item classified as a short item; and transmits, to one or more client devices, a status of the agenda items for the communication session.

Dynamic prediction of agenda item coverage in a communication session

An example operation may include one or more of collecting, by a first node, a plurality of permissioned data inputs from a plurality of second nodes of a supply-chain, performing, by the first node, a granular simulation based on the permissioned data inputs to generate a plurality of key performance indicators (KPIs), and executing a smart contract to adjust order processes of the supply-chain based on the KPIs.

Supply-chain simulation

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Pittsburgh, PA, United States of America

Yun Zhang