Abstract: Disclosed here is a system to automatically predict and resolve issues within a telecommunication network. Initially, the system builds a service registry to store dependence information within the network, which can include software components and hardware components. Various components of the network create logs of their operations. Machine learning models examine the logs and detect any issues. Upon detecting an issue or abnormal event, the system can automatically resolve the issue by determining the most similar issue occurring previously and determining a solution that resolved the previous most similar issue. In addition, the system can propagate the fix to dependent systems and/or notify the dependent systems of the issue.

Abstract: This disclosure describes techniques that facilitate selectively interacting with a computing resource based on receipt of an incoming voice command. Particularly, a voice control integration system may parse content of an incoming voice command to authenticate an identity of the client, and further determine an intended meaning of the incoming voice command. In doing so, the voice control integration system may interact with a computing resource to perform an action that fulfills a client request. Computing resources may be associated with service providers or client devices of a client. Further, the voice control integration system may authenticate a client identity based on a one or two-factor authentication protocol, of which one may correspond to a biometric analysis of the incoming voice command. Further, a second-factor of the two-factor authentication protocol may be implemented via a voice interaction device, another client device accessible to the client, or a combination of both.

Abstract: Disclosed here is a system to automatically predict and resolve issues within a telecommunication network. Initially, the system builds a service registry to store dependence information within the network, which can include software components and hardware components. Various components of the network create logs of their operations. Machine learning models examine the logs and detect any issues. Upon detecting an issue or abnormal event, the system can automatically resolve the issue by determining the most similar issue occurring previously and determining a solution that resolved the previous most similar issue. In addition, the system can propagate the fix to dependent systems and/or notify the dependent systems of the issue.

Abstract: This disclosure describes techniques that facilitate selectively automating a response to a client request for vendor services based on an intended meaning of the client request, a category of vendor services associated with the client request, and one or more factors such as an urgency index and a health index. The vendor services system may parse audio, text, and image data of a client request to determine an intended meaning. In some examples, the vendor services system may automate a response that resolves the client request, or automate a response that requests additional information from the client device. Alternatively, or additionally, the vendor services system may direct the client request to a vendor representative for further processing. In doing so, the vendor services system may monitor dialogue between the client and vendor representative, and selectively provide the vendor representative with support data that may help resolve the client request.

Abstract: A text representation is received from a virtual assistant application on a user device. The text representation may be generated via a speech-to-text engine of the virtual assistant application from audio speech spoken by a customer. Device diagnostic data of the user device is also received from the virtual assistant application. An identifier of the customer is placed in an assistance queue. At least information in the text representation and the device diagnostic data is analyzed to determine an issue associated with the user device and a solution for resolving the issue, so that the solution is applied. In response to the identifier of the customer reaching a front of the assistance queue, session state information that includes at least the text representation and a description of the issue is provided to a support application. A voice support session is initiated between the support application and the virtual assistant application.

Abstract: A text representation is received from a virtual assistant application on a user device. The text representation may be generated via a speech-to-text engine of the virtual assistant application from audio speech spoken by a customer. Device diagnostic data of the user device is also received from the virtual assistant application. An identifier of the customer is placed in an assistance queue. At least information in the text representation and the device diagnostic data is analyzed to determine an issue associated with the user device and a solution for resolving the issue, so that the solution is applied. In response to the identifier of the customer reaching a front of the assistance queue, session state information that includes at least the text representation and a description of the issue is provided to a support application. A voice support session is initiated between the support application and the virtual assistant application.

Abstract: This disclosure describes techniques that facilitate selectively automating a response to a client request for vendor services based on an intended meaning of the client request, a category of vendor services associated with the client request, and one or more factors such as an urgency index and a health index. The vendor services system may parse audio, text, and image data of a client request to determine an intended meaning. In some examples, the vendor services system may automate a response that resolves the client request, or automate a response that requests additional information from the client device. Alternatively, or additionally, the vendor services system may direct the client request to a vendor representative for further processing. In doing so, the vendor services system may monitor dialogue between the client and vendor representative, and selectively provide the vendor representative with support data that may help resolve the client request.

Abstract: Disclosed here is a system to automatically predict and resolve issues within a telecommunication network. Initially, the system builds a service registry to store dependence information within the network, which can include software components and hardware components. Various components of the network create logs of their operations. Machine learning models examine the logs and detect any issues. Upon detecting an issue or abnormal event, the system can automatically resolve the issue by determining the most similar issue occurring previously and determining a solution that resolved the previous most similar issue. In addition, the system can propagate the fix to dependent systems and/or notify the dependent systems of the issue.

Abstract: This disclosure describes techniques that facilitate selectively automating a response to a client request for vendor services based on an intended meaning of the client request, a category of vendor services associated with the client request, and one or more factors such as an urgency index and a health index. The vendor services system may parse audio, text, and image data of a client request to determine an intended meaning. In some examples, the vendor services system may automate a response that resolves the client request, or automate a response that requests additional information from the client device. Alternatively, or additionally, the vendor services system may direct the client request to a vendor representative for further processing. In doing so, the vendor services system may monitor dialogue between the client and vendor representative, and selectively provide the vendor representative with support data that may help resolve the client request.

Abstract: This disclosure sets forth systems and techniques that integrates social networking services with vendor services, such that a user may directly or indirectly communicate their experience, impression, or intention to a vendor via a user's preferred social networking service. In some examples, the social networking services may facilitate a vendor providing technical service support for a product or service. In other examples, a user may initiate a transaction with the vendor via a preferred social networking service. The user may initiate payment for an outstanding bill, initiate a purchase of vendor products or services, solicit a trade-in estimate for a particular product, query an order status, or initiate a data-allowance transfer to a recipient of a telecommunications vendor. In each of these examples, the user may communicate with a vendor by via social media posts on a social networking services authorized via a vendor platform.

Abstract: This disclosure describes techniques that facilitate selectively interacting with a computing resource based on receipt of an incoming voice command. Particularly, a voice control integration system may parse content of an incoming voice command to authenticate an identity of the client, and further determine an intended meaning of the incoming voice command. In doing so, the voice control integration system may interact with a computing resource to perform an action that fulfills a client request. Computing resources may be associated with service providers or client devices of a client. Further, the voice control integration system may authenticate a client identity based on a one or two-factor authentication protocol, of which one may correspond to a biometric analysis of the incoming voice command. Further, a second-factor of the two-factor authentication protocol may be implemented via a voice interaction device, another client device accessible to the client, or a combination of both.

Abstract: This disclosure describes techniques that facilitate selectively interacting with a computing resource based on receipt of an incoming voice command. Particularly, a voice control integration system may parse content of an incoming voice command to authenticate an identity of the client, and further determine an intended meaning of the incoming voice command. In doing so, the voice control integration system may interact with a computing resource to perform an action that fulfills a client request. Computing resources may be associated with service providers or client devices of a client. Further, the voice control integration system may authenticate a client identity based on a one or two-factor authentication protocol, of which one may correspond to a biometric analysis of the incoming voice command. Further, a second-factor of the two-factor authentication protocol may be implemented via a voice interaction device, another client device accessible to the client, or a combination of both.

Abstract: Techniques for identifying a likely subject of a user contact with a user support network and routing the user contact to an agent or an application that is determined to have relevant experience with the subject or issue the user is experiencing. To identify a likely subject of the user contact, data is gathered from a user device as well as from secondary user devices located near the user device. Also, data related to network resources the user device is using, history data of a user's previous contacts with the user support network, and account data associated with the user device is also collected. As a result, the user contact is routed to a knowledgeable agent who is likely to resolve the user's issue without further routing or consultation, thus saving network bandwidth, user device resources, agent time, user time, etc.

Abstract: Techniques for identifying a likely subject of a user contact with a user support network and routing the user contact to an agent or an application that is determined to have relevant experience with the subject or issue the user is experiencing. To identify a likely subject of the user contact, data is gathered from a user device as well as from secondary user devices located near the user device. Also, data related to network resources the user device is using, history data of a user's previous contacts with the user support network, and account data associated with the user device is also collected. As a result, the user contact is routed to a knowledgeable agent who is likely to resolve the user's issue without further routing or consultation, thus saving network bandwidth, user device resources, agent time, user time, etc.

Abstract: A customer care system that uses machine learning to surface relevant information for resolving customer issues regarding cellular network service is provided. The system receives a set of incoming data that includes current information of a cellular network. The system applies the set of incoming data as input to a machine learning model to produce a set of predicted conclusions. The machine learning model is trained by using one or more sets of orthogonal datasets that includes historical information of the cellular network. The system maps the set of predicted conclusions to a remedial action and performs the remedial action to effectuate a change in the cellular network.

Abstract: Cellular networks regularly operate with trusted devices, which typically are trusted because of the integration of a SIM card therewith. Untrusted devices typically do not interface with a SIM card, and rely on user input through traditional user interfaces for authentication. Recently, the use of hands-free, always-on digital assistant devices have become more common. Such devices typically have only a voice user interface that may be used by a number of people in close proximity to the device. Particular problems arise in such a scenario when a user wants to access a secure service that requires user authentication. Such problems are addressed with multiple techniques described herein.

Abstract: Cellular networks regularly operate with trusted devices, which typically are trusted because of the integration of a SIM card therewith. Untrusted devices typically do not interface with a SIM card, and rely on user input through traditional user interfaces for authentication. Recently, the use of hands-free, always-on digital assistant devices have become more common. Such devices typically have only a voice user interface that may be used by a number of people in close proximity to the device. Particular problems arise in such a scenario when a user wants to access a secure service that requires user authentication. Such problems are addressed with multiple techniques described herein.

Abstract: This disclosure describes techniques that facilitate selectively automating a response to a client request for vendor services based on an intended meaning of the client request, a category of vendor services associated with the client request, and one or more factors such as an urgency index and a health index. The vendor services system may parse audio, text, and image data of a client request to determine an intended meaning. In some examples, the vendor services system may automate a response that resolves the client request, or automate a response that requests additional information from the client device. Alternatively, or additionally, the vendor services system may direct the client request to a vendor representative for further processing. In doing so, the vendor services system may monitor dialogue between the client and vendor representative, and selectively provide the vendor representative with support data that may help resolve the client request.

Abstract: Cellular networks regularly operate with trusted devices, which typically are trusted because of the integration of a SIM card therewith. Untrusted devices typically do not interface with a SIM card, and rely on user input through traditional user interfaces for authentication. Recently, the use of hands-free, always-on digital assistant devices have become more common. Such devices typically have only a voice user interface that may be used by a number of people in close proximity to the device. Particular problems arise in such a scenario when a user wants to access a secure service that requires user authentication. Such problems are addressed with multiple techniques described herein.

Abstract: This disclosure describes techniques that facilitate selectively interacting with a computing resource based on receipt of an incoming voice command. Particularly, a voice control integration system may parse content of an incoming voice command to authenticate an identity of the client, and further determine an intended meaning of the incoming voice command. In doing so, the voice control integration system may interact with a computing resource to perform an action that fulfills a client request. Computing resources may be associated with service providers or client devices of a client. Further, the voice control integration system may authenticate a client identity based on a one or two-factor authentication protocol, of which one may correspond to a biometric analysis of the incoming voice command. Further, a second-factor of the two-factor authentication protocol may be implemented via a voice interaction device, another client device accessible to the client, or a combination of both.