Kyndryl, Inc.

Madiha Ijaz

Sreekrishnan Venkateswaran

Muhammad Ammar Ahmed

An embodiment extracts a set of designated entities and a set of relationships between designated entities from speech content of an audio feed of a plurality of participants of a current web conference using a machine learning model trained to classify parts of speech content. The embodiment generates a list of current action items based on the extracted set of designated entities and relationships between designated entities. The embodiment identifies a first current action item that is an updated version of an ongoing action item on a progress list of ongoing action items from past web conferences. The embodiment also identifies a second current action item that is unrelated to any of the ongoing action items on the progress list. The embodiment updates the progress list to include updates for the first current action item and by adding the second current action item.

Cognitive correlation of group interactions

An embodiment includes identifying which of a plurality of participants of a web conference is an identified participant associated with a selected cluster of a plurality of clusters of audio feed data of an audio feed of the web conference based on a self-introduction in the selected cluster. The embodiment also generates a first preliminary leadership score for the identified participant based on a speaking duration value associated with the identified participant and generates a second preliminary leadership score for the identified participant using a selected video segment as an input for a machine learning classifier model. The embodiment calculates a final leadership score for the identified participant based on the first and second preliminary leadership scores. The final leadership score is representative of a likelihood that the identified participant is a supervisor, and is indicative of the identified participant being a supervisor if it exceeds a designated threshold value.

Cognitive encapsulation of group meetings

Machine-based video classifying to identify misleading videos by training a model using a video corpus, obtaining a subject video from a content server, generating respective feature vectors of a title, a thumbnail, a description, and a content of the subject video, determining a first semantic similarities between ones of the feature vectors, determining a second semantic similarity between the title of subject video and titles of videos in the misleading video corpus in a same domain as the subject video, determining a third semantic similarity between comments of the subject video and comments of videos in the misleading video corpus in the same domain as the subject video, classifying the subject video using the model and based on the first semantic similarities, the second semantic similarity, and the third semantic similarity, and outputting the classification of the subject video to a user.

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Lahore, Pakistan

Madiha Ijaz