Abstract: A device may receive event data identifying events associated with user devices utilizing a video conferencing system, and may utilize the event data to train one or more fraud detection models. The device may receive, from a user device, user traffic including at least one of API traffic or media traffic, and may identify characteristics of the user traffic, such as activity related features, application-specific features, and dial-in number-specific features. The device may process at least one of the activity related features, the application-specific features, or the dial-in number-specific features, with the one or more fraud detection models, to determine whether the user traffic from the user device is anomalous or normal, and may block at least one of an IP address of the user device, a telephone number of the user device, or a meeting of the user device, based on determining that the user traffic is anomalous.

Abstract: A multi-party communications (MPC) analysis framework allows for the determination of participant engagement and MPC insights. In some embodiments, the MPC analysis framework allows for the determination of an engagement score indicating a level of engagement of participants to the MPC. In some embodiments, the MPC analysis framework allows for the identification of relevant participant metrics with respect to the MPC. In some embodiments, the MPC analysis framework allows for the generation of visual representations illustrating core concepts discussed during the MPC.

Abstract: A natural language processing (NLP) framework allows for receiving, recognizing, and performing actions in relation to multi-party communications (MPC) through interactive conversations with a user. In an embodiment, the NLP framework allows a user to conduct an interactive conversation with a conversation engine and provide it, through natural language inputs, with commands or requests related to MPCs.

Abstract: A natural language processing (NLP) framework enables providing content and participant insights from multi-party communications (MPC). MPC insights can include a summary of the MPC, relevant keywords or highlights of the MPC, chapter names, and a title or header of the MPC. For a given speaker, the MPC insights can include a speaker specific focused summary or relevant keywords.

Abstract: A device may receive event data identifying events associated with user devices utilizing a video conferencing system, and may utilize the event data to train one or more fraud detection models. The device may receive, from a user device, user traffic including at least one of API traffic or media traffic, and may identify characteristics of the user traffic, such as activity related features, application-specific features, and dial-in number-specific features. The device may process at least one of the activity related features, the application-specific features, or the dial-in number-specific features, with the one or more fraud detection models, to determine whether the user traffic from the user device is anomalous or normal, and may block at least one of an IP address of the user device, a telephone number of the user device, or a meeting of the user device, based on determining that the user traffic is anomalous.

Abstract: A method and a system for maximizing share-ride bookings in a geographical area in a ride-sharing system are provided. Historical share-ride demands for the geographical area are estimated. A time period is segmented into time intervals such that each time interval has an equal count of the estimated historical share-ride demands. A conversion rate and a gross merchandise value (GMV) per unit of distance are determined for a first time interval at a check point of a second time interval. Error signals are generated at the check point based on deviations in the conversion rate and the GMV per unit of distance with respect to a defined conversion rate and a defined GMV per unit of distance, respectively. A share-ride fare in the second time interval is controlled based on the error signals to maximize the share-ride bookings during the second time interval.

Abstract: A method and a system for location clustering for a transportation service are provided. A plurality of locations are clustered into a plurality of clusters, each having one or more locations of the plurality of locations. A graph is generated by connecting the plurality of clusters. A first cluster of the plurality of clusters is connected to one or more second clusters of the plurality of clusters that satisfy one or more threshold parameters. The graph is segmented into a plurality of fully-connected maximal sub-graphs based on one or more connections between the plurality of clusters. One or more fully-connected maximal sub-graphs of the plurality of fully-connected maximal sub-graphs have a set of common clusters. The plurality of fully-connected maximal sub-graphs are used for performing one or more transportation service operations of the transportation service.

Abstract: A method and a system for maximizing share-ride bookings in a geographical area in a ride-sharing system are provided. Historical share-ride demands for the geographical area are estimated. A time period is segmented into time intervals such that each time interval has an equal count of the estimated historical share-ride demands. A conversion rate and a gross merchandise value (GMV) per unit of distance are determined for a first time interval at a check point of a second time interval. Error signals are generated at the check point based on deviations in the conversion rate and the GMV per unit of distance with respect to a defined conversion rate and a defined GMV per unit of distance, respectively. A share-ride fare in the second time interval is controlled based on the error signals to maximize the share-ride bookings during the second time interval.

Abstract: A method and a system for location clustering for a transportation service are provided. A plurality of locations are clustered into a plurality of clusters, each having one or more locations of the plurality of locations. A graph is generated by connecting the plurality of clusters. A first cluster of the plurality of clusters is connected to one or more second clusters of the plurality of clusters that satisfy one or more threshold parameters. The graph is segmented into a plurality of fully-connected maximal sub-graphs based on one or more connections between the plurality of clusters. One or more fully-connected maximal sub-graphs of the plurality of fully-connected maximal sub-graphs have a set of common clusters. The plurality of fully-connected maximal sub-graphs are used for performing one or more transportation service operations of the transportation service.