Abstract: A method includes: receiving protocol event data from a plurality of probes within the telecommunication system; determining a most probable cause of a call event from the protocol event data; applying the most probable cause to a trained machine learning algorithm that includes the most probable cause as its input and a telecommunication system score as its output; and in response to an output score from the trained machine learning algorithm, performing a corrective action for a plurality of network users that are expected to be affected by the most probable cause.

Abstract: A method performed by a computing system includes collecting information on transactions in a telecommunication system, using the information on transactions to create a plurality of event objects, each of the event objects associated with a telecommunication event, associating each of the event objects with a Key Performance Indicator (KPI), applying the event objects to a plurality of inference functions, each inference functions using the set of parameters as inputs and the KPIs of the event objects as outputs to create a model that infers a relationship between the set of parameters and the KPIs, and analyzing metadata from each of the inference functions to determine which of the set of parameters was used to predict an outcome leading to the KPI.

Abstract: A method performed by a computing system includes collecting information on transactions in a telecommunication system, using the information on transactions to create a plurality of event objects, each of the event objects associated with a telecommunication event, associating each of the event objects with a Key Performance Indicator (KPI), applying the event objects to a plurality of inference functions, each inference functions using the set of parameters as inputs and the KPIs of the event objects as outputs to create a model that infers a relationship between the set of parameters and the KPIs, and analyzing metadata from each of the inference functions to determine which of the set of parameters was used to predict an outcome leading to the KPI.

Abstract: A method performed by a computing system that collects information on transactions in a telecommunication system includes receiving an event object. The event object includes parameters associated with a telecommunication event. The method further includes classifying the event object, using a classification mechanism, as one of a plurality of Key Performance Indicators (KPIs) by using the parameters, including considering events from multiple protocols for classifying event objects. The method further includes, after classifying the event object, inferring relationships between a set of inputs and a set of outputs, the set of inputs including information types from the event object and the set of outputs including KPIs.

Abstract: A method includes: receiving protocol event data from a plurality of probes within the telecommunication system; determining a most probable cause of a call event from the protocol event data; applying the most probable cause to a trained machine learning algorithm that includes the most probable cause as its input and a telecommunication system score as its output; and in response to an output score from the trained machine learning algorithm, performing a corrective action for a plurality of network users that are expected to be affected by the most probable cause.

Abstract: A method includes: receiving protocol event data from a plurality of probes within the telecommunication system; determining a most probable cause of a call event from the protocol event data; applying the most probable cause to a trained machine learning algorithm that includes the most probable cause as its input and a telecommunication system score as its output; and in response to an output score from the trained machine learning algorithm, performing a corrective action for a plurality of network users that are expected to be affected by the most probable cause.

Abstract: A method performed by a computing system that collects information on transactions in a telecommunication system includes receiving an event object. The event object includes parameters associated with a telecommunication event. The method further includes classifying the event object, using a classification mechanism, as one of a plurality of Key Performance Indicators (KPIs) by using the parameters, including considering events from multiple protocols for classifying event objects. The method further includes, after classifying the event object, inferring relationships between a set of inputs and a set of outputs, the set of inputs including information types from the event object and the set of outputs including KPIs.

Abstract: A method performed by a computing system includes collecting information on transactions in a telecommunication system, using the information on transactions to create a plurality of event objects, each of the event objects associated with a telecommunication event, associating each of the event objects with a Key Performance Indicator (KPI), applying the event objects to a plurality of inference functions, each inference functions using the set of parameters as inputs and the KPIs of the event objects as outputs to create a model that infers a relationship between the set of parameters and the KPIs, and analyzing metadata from each of the inference functions to determine which of the set of parameters was used to predict an outcome leading to the KPI.

Abstract: A network device, capable of understanding communications between an end user and the core network on a RAN network is disclosed. In some embodiments, the device is able to decode the control plane and the user plane. As such, it is able to determine when the end user has requested multimedia content. Once this is known, the device can optimize the delivery of that content in several ways. In one embodiment, the device requests the content from the content server (located in the core network) and transmits this content in a just-in-time manner to the end user. In another embodiment, the device automatically changes the encoding and resolution of the content, based on overall monitored network traffic. In another embodiment, the device automatically selects or modifies the format and resolution options based on overall bandwidth limitations, independent of the end user.

Abstract: A packet scheduling method and apparatus with the knowledge of application behavior, anticipated usage/behavior based on the type of content, and underlying transport conditions during the time of delivery, is disclosed. This type of scheduling is applicable to a content server or a transit network device in wireless (e.g., 3G, WIMAX, LTE, WIFI) or wire-line networks. Methods for identifying or estimating rendering times of multi-media objects, segmenting a large media content, and automatically pausing or delaying delivery are disclosed. The scheduling reduces transit network bandwidth wastage, and facilitates optimal sharing of network resources such as in a wireless network.

Abstract: A network device, capable of understanding communications between an end user and the core network on a RAN network is disclosed. In some embodiments, the device is able to decode the control plane and the user plane. As such, it is able to determine when the end user has requested multimedia content. Once this is known, the device can optimize the delivery of that content in several ways. In one embodiment, the device requests the content from the content server (located in the core network) and transmits this content in a just-in-time manner to the end user. In another embodiment, the device automatically changes the encoding and resolution of the content, based on overall monitored network traffic. In another embodiment, the device automatically selects or modifies the format and resolution options based on overall bandwidth limitations, independent of the end user.

Abstract: A packet scheduling method and apparatus with the knowledge of application behavior, anticipated usage/behavior based on the type of content, and underlying transport conditions during the time of delivery, is disclosed. This type of scheduling is applicable to a content server or a transit network device in wireless (e.g., 3G, WIMAX, LTE, WIFI) or wire-line networks. Methods for identifying or estimating rendering times of multi-media objects, segmenting a large media content, and automatically pausing or delaying delivery are disclosed. The scheduling reduces transit network bandwidth wastage, and facilitates optimal sharing of network resources such as in a wireless network.