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: 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: Methods are provided for a collaborative, decentralized insight engineering based on exchanging telemetry vectors with peer network devices. Each network device independently detects a deviation in its functioning using machine learning of generated feature vectors. Specifically, the methods involve obtaining, from at least one peer network device, at least a first feature vector that represents at least one insight generated from telemetry data of a respective peer network device. The intermediate network device and the at least one peer network device are configured to forward packets of a traffic flow. The methods further involve determining whether a deviation related to one or more of the network devices, exists based at least on the first feature vector and performing the at least one predefined action based on determining that the deviation exists.

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 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: This disclosure provides chemical entities (e.g., a compound or a pharmaceutically acceptable salt, and/or hydrate, and/or cocrystal, and/or drug combination of the compound) that inhibit epidermal growth factor receptor (EGFR, ERBB 1) and/or Human epidermal growth factor receptor 2 (HER2, ERBB2). These chemical entities are useful, e.g., for treating a condition, disease or disorder in which increased (e.g., excessive) EGFR and/or HER2 activation contributes to the pathology and/or symptoms and/or progression of the condition, disease or disorder (e.g., cancer) in a subject (e.g., a human). This disclosure also provides compositions containing the same as well as methods of using and making the same.

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 method is provided to determine administrator users associated with network devices in a network. The method includes sending to one or more network devices in a network or to an entity that manages or controls access to the one or more network devices, a query to identify one or more administrator users for each of the one or more network devices. The method involves obtaining a response to the query, the response including an identifier of each the one or more administrator users for a respective network device of the one or more network devices. The method may then involve using the identifier of each of the one or more administrator users for each of the one or more network devices to send a notification to the one or more administrator users.

Abstract: Methods are provided in which a computing device obtains telemetry data associated with a network technology used in an enterprise network and an enterprise network profile that includes information about the complexity of the enterprise network. The network technology is deployed at one or more devices of the enterprise network. The methods further include the computing device determining, based on the telemetry data, for each deployment of the network technology, a current stage from a plurality of stages of an adoption lifecycle to which the network technology progressed, determining a time estimate for completing the current stage of the adoption lifecycle, based on the enterprise network profile and an adoption benchmark generated from a plurality of activities performed for progressing along the adoption lifecycle, and evaluating an adoption of the network technology, using the time estimate, to progress the network technology along the lifecycle.

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: 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: 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 method includes obtaining, from a plurality of first devices in a first time zone, data associated with hot-prefixes requested at one or more first devices of the plurality of first devices during a first time interval. The hot-prefixes are associated with network addresses that are frequently requested during the first time interval. The method further includes predicting, based on the data associated with the hot-prefixes, prefixes that will become hot-prefixes during a second time interval in a second time zone to determine predicted hot-prefixes, and transmitting an indication of the predicted hot-prefixes to a plurality of second devices configured to provide networking services in the second time zone prior to a start of the second time interval in the second time zone.

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: 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: 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.