Abstract: A management server measures network activity of user devices to determine activities of the users associated with each user device. The management server generates digital model personas corresponding to the users based on one or more activities of the user. The management server clusters the digital model personas to generate user groups based on similar activities, and compares a first digital model persona from a first user with at least one second digital model persona.

Abstract: A network management system tests the availability of a network resource before a user performs a task with the network resource. The system measures network activity of a user performing one or more tasks. The network activity includes communication between a user device of the user and each network resource associated with a corresponding task performed by the user. The system also generates a digital model persona of the user based on the tasks performed by the user, and determines a schedule of the tasks performed the user. Each particular task is associated with a corresponding execution time for the user. The system further configures the digital model persona to test the network resource associated with each corresponding task at a testing time that is a predetermined length of time prior to the execution time for the user.

Abstract: Presented herein are techniques associated with proactively scheduling shared resources or responsibilities among multiple users. In one example a method is provided that may include sharing masked calendar information between a first device of a first user and at least one second device of at least one second user based on the first device being proximate to the at least one second device; identifying a conflict involving use of a shared resource at a particular time; comparing first priority metadata for the first user and second priority metadata for the at least one second user; and assigning the use of the shared resource to one of the first user or the at least one second user based, at least in part, on determining that the first user or the at least one second user has a highest priority for use of the shared resource.

Abstract: A method, computer system, and computer program product are provided for improving user speech. A data sample of a user speaking one or more words is received, wherein the data sample includes video data and audio data of the user speaking. The data sample is analyzed to determine a correct articulation of a mouth when speaking the one or more words. A synthetic video of the user performing the correct articulation is generated. The synthetic video of the user is presented to the user. A live video of the user is presented to the user while the synthetic video is presented.

Abstract: A computer executed process for mimicking human dialog, referred to herein as a “humanoid” or “humanoid system,” can be configured to provide automated customer support. The humanoid can identify a support issue for a customer, as well as a customer support campaign corresponding to the support issue. The humanoid can identify at least one machine learning model associated with the customer support campaign and can communicate with the customer using the at least one machine learning model. The humanoid can execute a support action to resolve the support issue.

Abstract: Presented herein are techniques to perform call failure diagnostics. A method includes receiving, at a network device, an indication of calls-of-interest, detecting, at the network device, a failure of one of the calls-of-interest, triggering, in response to the detecting, at the network device, diagnostics data analysis of data associated with the failure of one of the calls-of-interest, determining, based on the diagnostics data analysis, a cause of the failure of the one of the calls-of-interest, and notifying, by the network device, a management system of the cause of the failure of the one of the calls-of-interest and of recent configuration changes on the network device that are related to the cause of the failure of the one of the calls-of-interest.

Abstract: Techniques for identifying a trusted SSID for a wireless network are disclosed. Prior to establishing a connection with a wireless network, a first network message is received from a first access point (AP) identifying a first service set identifier (SSID) associated with a first wireless network, a second network message is received from a second AP identifying a second SSID associated with a second wireless network, and a visual similarity is determined between a first visual representation of the first SSID and a second visual representation of the second SSID. The second SSID is designated as suspicious based on the determined visual similarity.

Abstract: A computer executed process for mimicking human dialog, referred to herein as a “humanoid” or “humanoid process software,” can be configured to participate in multi-parry conversations. The humanoid can monitor electronic communications in a conversation involving the humanoid and at least one other party. The humanoid can model the electronic communications by uniquely identifying each of the electronic communications as a stream of data. For example, the data can be labeled and sorted in a database and/or arranged in a nodal graph representation. The humanoid can participate in the conversation based on the modeling.

Abstract: A meeting system facilitates spontaneous social encounter between users with a meeting server. The meeting server obtains calendar data, user preferences, and an organization chart associated with each user of a plurality of users. The meeting server selects a first user and a second user for a social encounter based on the calendar data, user preferences, and roles in the organization chart. The meeting server prompts a first user device associated with the first user, and a second user device associated with the second user, for a social encounter. Responsive to obtaining acceptances from the first user and the second user, the meeting server facilitates the social encounter between the first user and the second user.

Abstract: A computer executed process for mimicking human dialog, referred to herein as a “humanoid” or “humanoid process software,” can be configured to participate in multi-party conversations. The humanoid can monitor electronic communications in a conversation involving the humanoid and at least one other party. The humanoid can model the electronic communications by uniquely identifying each of the electronic communications as a stream of data. For example, the data can be labeled and sorted in a database and/or arranged in a nodal graph representation. The humanoid can participate in the conversation based on the modeling.

Abstract: This disclosure describes techniques for diagnosing a presence or malfunction of a network node. In an example method, a first network node receives an indication of a diagnostic transmission originating from a second network node. The second network node further receives a forwarded transmission corresponding to the diagnostic transmission. The first network node diagnoses at least one of a presence or a malfunction of an intermediary node between the first network node and the second network node based on at least one of the indications of the diagnostic transmission or the forwarded transmission.

Abstract: A computer executed process for mimicking human dialog, referred to herein as a “humanoid” or “humanoid system,” can be configured to provide automated customer support. The humanoid can identify a support issue for a customer, as well as a customer support campaign corresponding to the support issue. The humanoid can identify at least one machine learning model associated with the customer support campaign and can communicate with the customer using the at least one machine learning model. The humanoid can execute a support action to resolve the support issue.

Abstract: Techniques for identifying a trusted SSID for a wireless network are disclosed. Prior to establishing a connection with a wireless network comprising a service set identifier (SSID), a network message is received at a wireless station (STA), from an access point (AP) associated with the wireless network. The STA identifies an encrypted identifier in the network message. The STA validates the encrypted identifier, and in response determines that the AP corresponds to a trusted wireless network. The SSID is designated as trusted, at the STA.

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 network management station is configured to test a computer network through digital model personas. The network management station obtains network behavior data corresponding to user(s) of the computer network and generates digital model(s) based on the network behavior. The network management station deploys a first digital model persona based on a first digital model among the generated digital models. The first digital model persona operates at a first network location to test the computer network.

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: 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: A virtual collaboration system provides communication between a plurality of participants via a collaboration session that includes a corresponding plurality of participant devices. The collaboration session facilitates communication between the participant devices via a plurality of communication channels over which the participants communicate. For example, in some embodiments, the channels include one or more of a video channel, audio channel, or document sharing channel. A participant designates in which of the plurality of channels they will participate, and the designations are communicated to other participants. This provides a broader understanding of each participant's circumstances and ability to engage across the different channels with the plurality of participants.

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: A meeting system facilitates spontaneous social encounter between users with a meeting server. The meeting server obtains calendar data, user preferences, and an organization chart associated with each user of a plurality of users. The meeting server selects a first user and a second user for a social encounter based on the calendar data, user preferences, and roles in the organization chart. The meeting server prompts a first user device associated with the first user, and a second user device associated with the second user, for a social encounter. Responsive to obtaining acceptances from the first user and the second user, the meeting server facilitates the social encounter between the first user and the second user.