Abstract: Controlling autonomous vehicle (AV) functions includes receiving occupant identification data from device(s) of an AV and identifying occupant(s) of the AV based on the received occupant identification data, determining a respective prioritization level for each occupant of the occupant(s), the prioritization level for each occupant dictating priority of the AV in performing commands provided to the AV by that occupant to control AV functions, receiving, from an occupant, input of a command for performance by the AV to control a function of the AV, determining whether to perform the command to control the function of the AV based on the prioritization level for the occupant, and performing processing based on the determining whether to perform the command.

Abstract: A computer-implemented method reduces toil in chat systems. The method includes receiving, from a sending account, a message to be sent in a chat environment, where the chat environment includes a plurality of accounts including the sending account. The method also includes determining, by a learning model, a category of the message. The method further includes generating, for each account of the plurality accounts in a channel of the chat environment, a score, where the score indicates a likelihood an associated account will respond to a post with relevant information. The method includes recommending, based on the score, one or more accounts of the plurality of accounts to an at message, where the at message is configured to cause a notification to the associated account. The method further includes posting the message in the channel of the chat environment while at-mentioning the one or more accounts.

Abstract: A computer-implemented method reduces toil in chat systems. The method includes receiving, from a sending account, a message to be sent in a chat environment, where the chat environment includes a plurality of accounts including the sending account. The method also includes determining, by a learning model, a category of the message. The method further includes generating, for each account of the plurality accounts in a channel of the chat environment, a score, where the score indicates a likelihood an associated account will respond to a post with relevant information. The method includes recommending, based on the score, one or more accounts of the plurality of accounts to an at message, where the at message is configured to cause a notification to the associated account. The method further includes posting the message in the channel of the chat environment while at-mentioning the one or more accounts.

Abstract: An image of a real-world environment including one or more users, is received from an image capture device. A mask status of a first user of is determined by a processor based on the image. A stream of audio including speech from one or more users is captured from one or more audio transceivers. A first user speech from the stream of audio identified by the processor. The stream of audio is parsed, by the processor and based on the first user speech and based on an audio processing technique, to create a first user speech element. An augmented view that includes the first user speech element is generated, for a wearable computing device, based on the first user speech and based on the mask status.

Abstract: An acoustic signal is received from a first user. The acoustic signal of the first user includes a verbal utterance related to the first user. A set of one or more non-verbal sensor readings may be detected from a set of one or more sensors of a wearable device. The wearable device is worn by the user concurrent with the verbal utterance. A verbal distortion factor may be determined based on an acoustic adjustment model and in response to the set of non-verbal sensor readings. An adjusted verbal utterance related to the first user may be generated. The adjusted verbal utterance may be generated from the verbal utterance and based on the verbal distortion factor. The adjusted verbal utterance may be provided to a voice output.

Abstract: In an approach for building a machine learning model that predicts the appropriate action to resolve a malfunction or system error, a processor receives an alert that a malfunction or a system error has occurred. A processor creates a workspace on a ChatOps platform integrated with a chatbot and one or more tools. A processor inputs data relating to the alert in a natural language format. A processor processes the data using a natural language processing algorithm. Responsive to determining a pre-set threshold for outputting the appropriate action is not met, a processor establishes a conversation between two or more support service agents in the workspace. A processor monitors the conversation using the natural language processing algorithm. A processor analyzes a transcript of the conversation using text analytics or pattern matching. A processor creates and trains a machine learning model to predict the appropriate action in future iterations.

Abstract: In an approach for building a machine learning model that predicts the appropriate action to resolve a malfunction or system error, a processor receives an alert that a malfunction or a system error has occurred. A processor creates a workspace on a ChatOps platform integrated with a chatbot and one or more tools. A processor inputs data relating to the alert in a natural language format. A processor processes the data using a natural language processing algorithm. Responsive to determining a pre-set threshold for outputting the appropriate action is not met, a processor establishes a conversation between two or more support service agents in the workspace. A processor monitors the conversation using the natural language processing algorithm. A processor analyzes a transcript of the conversation using text analytics or pattern matching. A processor creates and trains a machine learning model to predict the appropriate action in future iterations.

Abstract: An acoustic signal is received from a first user. The acoustic signal of the first user includes a verbal utterance related to the first user. A set of one or more non-verbal sensor readings may be detected from a set of one or more sensors of a wearable device. The wearable device is worn by the user concurrent with the verbal utterance. A verbal distortion factor may be determined based on an acoustic adjustment model and in response to the set of non-verbal sensor readings. An adjusted verbal utterance related to the first user may be generated. The adjusted verbal utterance may be generated from the verbal utterance and based on the verbal distortion factor. The adjusted verbal utterance may be provided to a voice output.

Abstract: A computer-implemented method of cross-environment event correlation includes determining one or more correlated events about an issue across a plurality of domains. A knowledge data is extracted from the issue determined from the one or more correlated events is performed. A correlation graph is generated from the extracted knowledge to trace the issue and group the correlated events into one or more event groups to represent their relationship with the issue. A logical reasoning description is constructed based on the generated correlation graph for a domain-space exploration related to how the issue in one domain affects another domain of the plurality of domains. The one or more event groups of correlated events is provided with an explanation about a cause of the issue based on the logical reasoning description.