Abstract:
In one embodiment a method for providing presence information in a smart environment is implemented on a personal computing device associated with a user and includes: broadcasting a presence indicator signal in the smart environment, where the presence indicator signal indicates presence of the user in the smart environment, and the personal computing device is not provided by an operator of the smart environment.
Abstract:
Systems and methods are described herein for logging system events within an electronic machine using an event log structured as a collection of tree-like cause and effect graphs. An event to be logged may be received. A new event node may be created within the event log for the received event. One or more existing event nodes within the event log may be identified as having possibly caused the received event. One or more causal links may be created within the event log between the new event node and the one or more identified existing event nodes. The new event node may be stored as an unattached root node in response to not identifying an existing event node that may have caused the received event.
Abstract:
This disclosure describes techniques for providing notifications about events that occur during an online meeting. For instance, a system may establish an online meeting, such as a video conferencing meeting, for users. While in the meeting, a first user may view video of a second user and determine that the second user is experiencing an emergency event. As such, a user device of the first user may receive an input indicating that the emergency event is occurring with the second user. The system may receive the indication from the user device and verify that the emergency event is occurring. Additionally, the system may then send a notification to emergency personnel that indicates at least that the emergency event is occurring and a location of the second user. Furthermore, the system may send notification(s) to contact(s) associated with the second user that indicate at least that the emergency event is occurring.
Abstract:
In one embodiment, an issue analysis service obtains telemetry data from a plurality of devices in a network across a plurality of time intervals. The service detects a failure event in which a device in the network is in a failure state. The service clusters the telemetry data obtained prior to the failure event into rounds according to time intervals in which the telemetry data was collected. Each round corresponds to a particular time interval. The service applies a machine learning-based classifier to each one of the rounds of clustered telemetry data to identify one or more common traits appearing in the telemetry data for each round. The service generates a trait change report indicating a change in the one or more common traits appearing in the telemetry data across the rounds leading up to the failure event.
Abstract:
A method is provided that is performed by a computer-implemented user support bot. The method includes obtaining from a user a support request related to software and/or hardware used, or to be used, by the user; obtaining user lifecycle journey information that tracks deployment, adoption and/or use by the user of the software and/or hardware; determining a user intent for the support request based on the user lifecycle journey information when there is ambiguity as to the user intent based solely on the support request; and providing a response to the support request based on the user intent.
Abstract:
Techniques are provided herein for remediating storage of sensitive data on a hardware device. In one example, a request to remediate storage of sensitive data on a hardware device is obtained. In response to the request, a database is automatically searched. The database correlates the hardware device with an indication of how to remediate the storage of the sensitive data on the hardware device. Based on the database, the storage of the sensitive data on the hardware device is remediated.
Abstract:
Systems and methods are described herein for logging system events within an electronic machine using an event log structured as a collection of tree-like cause and effect graphs. An event to be logged may be received. A new event node may be created within the event log for the received event. One or more existing event nodes within the event log may be identified as having possibly caused the received event. One or more causal links may be created within the event log between the new event node and the one or more identified existing event nodes. The new event node may be stored as an unattached root node in response to not identifying an existing event node that may have caused the received event.
Abstract:
A computer executed process for mimicking human dialog, referred to herein as a “humanoid” or “humanoid system,” can be configured to provision itself to provide automated customer support. The humanoid can be trained for a customer support campaign. The training can include the humanoid observing communications between a human operator and at least one customer regarding at least one customer support case in the customer support campaign. The humanoid can assess at least one confidence level of the humanoid for the customer support campaign to determine whether the humanoid is adequately trained to handle future customer support cases for the customer support campaign. The humanoid can provision itself to handle at least one future customer support case in the customer support campaign in response to determining that it is adequately trained for the customer support campaign.
Abstract:
In one embodiment, a supervisory service of a parking area may send a light fidelity (Li-Fi) based advertisement indicative of an offer to send video streams of the parking area to an autonomous vehicle. The supervisory service may receive an acceptance of the offer by the autonomous vehicle that includes an identifier for the autonomous vehicle. The supervisory service may identify one or more video streams of the parking area as associated with the autonomous vehicle based in part on a location of the autonomous vehicle in the parking area. The supervisory service may annotate the one or more identified video streams with metadata regarding a feature of the parking area. The supervisory service may send the annotated one or more video streams to the autonomous vehicle, wherein the autonomous vehicle uses the metadata of the annotated one or more video streams to avoid the feature of the parking area.
Abstract:
In one embodiment, an issue analysis service determines that an issue exists with a device in a network. The service searches a decision tree for a solution to the issue, wherein branch nodes of the decision tree comprise diagnostic checks. The service clusters, based on a determination that a solution to the issue does not exist in the decision tree, telemetry for the device with telemetry for one or more other devices that also experienced the issue. The service uses a neural network to identify a difference between the clustered telemetry and telemetry from one or more devices for which the issue was resolved. The service adds a leaf node to the decision tree with the identified difference as a solution to the issue.