Abstract: A real-time performance management may be used for cycling. A first computing device retrieves profile data related to a cyclist. The first computing device receives sensor readings from one or more sensors. A first sensor of the one or more sensors detects cyclist tilt. Profile data and the sensor readings from the one or more sensors determine cyclist status. A notification based on the determined cyclist status communicates to a user.

Abstract: Embodiments described herein are directed to computer-implemented methods, systems, and computer program products for calculating a metric using natural language processing tokens. A non-limiting example of the computer-implemented method includes parsing, by a processing device, user content using a natural language processing technique to extract tokens. The method further includes filtering, by the processing device, the tokens relating to a natural language processing criterion. The method further includes calculating, by the processing device, a metric based at least in part on the filtered tokens. The method further includes determining, by the processing device, whether to take an action by applying the metric to a set of rules. The method further includes taking the action responsive to determining to take the action.

Abstract: Embodiments described herein are directed to computer-implemented methods, systems, and computer program products for calculating a metric using natural language processing tokens. A non-limiting example of the computer-implemented method includes parsing, by a processing device, user content using a natural language processing technique to extract tokens. The method further includes filtering, by the processing device, the tokens relating to a natural language processing criterion. The method further includes calculating, by the processing device, a metric based at least in part on the filtered tokens. The method further includes determining, by the processing device, whether to take an action by applying the metric to a set of rules. The method further includes taking the action responsive to determining to take the action.

Abstract: Technical solutions are described for selecting and retrieving, by a media retrieval system, media items for displaying. An example method for selecting and retrieving the media items includes monitoring a conversation of a user via a user device, and extracting a conversation topic from the conversation. The method further includes determining that a metadata database of a media storage associated with the user includes the conversation topic. The method further includes adding, in response, to a topic match database, from the media storage, an identity of a media item matching with the conversation topic, and adding, to a list of media items to be played back by a media display device, the topic match database. Further yet, the method includes playing back, by the media display device, the media item.

Abstract: Technical solutions are described for selecting and retrieving, by a media retrieval system, media items for displaying. An example method for selecting and retrieving the media items includes monitoring a conversation of a user via a user device, and extracting a conversation topic from the conversation. The method further includes determining that a metadata database of a media storage associated with the user includes the conversation topic. The method further includes adding, in response, to a topic match database, from the media storage, an identity of a media item matching with the conversation topic, and adding, to a list of media items to be played back by a media display device, the topic match database. Further yet, the method includes playing back, by the media display device, the media item.

Abstract: Technical solutions are described for selecting and retrieving, by a media retrieval system, media items for displaying. An example method for selecting and retrieving the media items includes monitoring a conversation of a user via a user device, and extracting a conversation topic from the conversation. The method further includes determining that a metadata database of a media storage associated with the user includes the conversation topic. The method further includes adding, in response, to a topic match database, from the media storage, an identity of a media item matching with the conversation topic, and adding, to a list of media items to be played back by a media display device, the topic match database. Further yet, the method includes playing back, by the media display device, the media item.

Abstract: Technical solutions are described for selecting and retrieving, by a media retrieval system, media items for displaying. An example method for selecting and retrieving the media items includes monitoring a conversation of a user via a user device, and extracting a conversation topic from the conversation. The method further includes determining that a metadata database of a media storage associated with the user includes the conversation topic. The method further includes adding, in response, to a topic match database, from the media storage, an identity of a media item matching with the conversation topic, and adding, to a list of media items to be played back by a media display device, the topic match database. Further yet, the method includes playing back, by the media display device, the media item.

Abstract: Embodiments described herein are directed to computer-implemented methods, systems, and computer program products for calculating a metric using natural language processing tokens. A non-limiting example of the computer-implemented method includes parsing, by a processing device, user content using a natural language processing technique to extract tokens. The method further includes filtering, by the processing device, the tokens relating to a natural language processing criterion. The method further includes calculating, by the processing device, a metric based at least in part on the filtered tokens. The method further includes determining, by the processing device, whether to take an action by applying the metric to a set of rules. The method further includes taking the action responsive to determining to take the action.

Abstract: Embodiments described herein are directed to computer-implemented methods, systems, and computer program products for calculating a metric using natural language processing tokens. A non-limiting example of the computer-implemented method includes parsing, by a processing device, user content using a natural language processing technique to extract tokens. The method further includes filtering, by the processing device, the tokens relating to a natural language processing criterion. The method further includes calculating, by the processing device, a metric based at least in part on the filtered tokens. The method further includes determining, by the processing device, whether to take an action by applying the metric to a set of rules. The method further includes taking the action responsive to determining to take the action.

Abstract: A real-time performance management may be used for cycling. A first computing device retrieves profile data related to a cyclist. The first computing device receives sensor readings from one or more sensors. A first sensor of the one or more sensors detects cyclist tilt. Profile data and the sensor readings from the one or more sensors determine cyclist status. A notification based on the determined cyclist status communicates to a user.

Abstract: A real-time performance management may be used for cycling. A first computing device retrieves profile data related to a cyclist. The first computing device receives sensor readings from one or more sensors. A first sensor of the one or more sensors detects cyclist tilt. Profile data and the sensor readings from the one or more sensors determine cyclist status. A notification based on the determined cyclist status communicates to a user.