A policy adjustment system changes a quality of service attribute for a user device without affecting the policy applied to the user device. The policy adjustment system applies a policy to a plurality of user devices, the policy defining one or more quality of service attributes. The policy adjustment system receives an indication that a quality of service attribute should be changed for a user device. The policy adjustment system changes the quality of service attribute for the user device without affecting the quality of service attributes for the other user devices for which the policy is applied.

Device group partitions and a settlement platform are provided. In some embodiments, device group partitions (e.g., partitions of devices based on associated device groups) are provided. In some embodiments, a settlement platform service is provided. In some embodiments, a settlement platform service is provided for partitioned devices. In some embodiments, collecting device generated service usage information for one or more devices in wireless communication on a wireless network; and aggregating the device generated service usage information for a settlement platform for the one or more devices in wireless communication on the wireless network is provided. In some embodiments, a settlement platform implements a service billing allocation and/or a service/transactional revenue share among one or more partners. In some embodiments, service usage information includes micro-CDRs, which are used for CDR mediation or reconciliation that provides for service usage accounting on any device activity that is desired. In some embodiments, each device activity that is desired to be associated with a billing event is assigned a micro-CDR transaction code, and a service processor of the device is programmed to account for that activity associated with that transaction code. In some embodiments, a service processor executing on a wireless communications device periodically reports (e.g., during each heartbeat or based on any other periodic, push, and/or pull communication technique(s)) micro-CDR usage measures to, for example, a service controller or some other network element for CDR mediation or reconciliation.

Device group partitions and a settlement platform are provided. In some embodiments, device group partitions (e.g., partitions of devices based on associated device groups) are provided. In some embodiments, a settlement platform service is provided. In some embodiments, a settlement platform service is provided for partitioned devices. In some embodiments, collecting device generated service usage information for one or more devices in wireless communication on a wireless network; and aggregating the device generated service usage information for a settlement platform for the one or more devices in wireless communication on the wireless network is provided. In some embodiments, a settlement platform implements a service billing allocation and/or a service/transactional revenue share among one or more partners. In some embodiments, service usage information includes micro-CDRs, which are used for CDR mediation or reconciliation that provides for service usage accounting on any device activity that is desired. In some embodiments, each device activity that is desired to be associated with a billing event is assigned a micro-CDR transaction code, and a service processor of the device is programmed to account for that activity associated with that transaction code. In some embodiments, a service processor executing on a wireless communications device periodically reports (e.g., during each heartbeat or based on any other periodic, push, and/or pull communication technique(s)) micro-CDR usage measures to, for example, a service controller or some other network element for CDR mediation or reconciliation.

In one example, a location of a potential bottleneck of network traffic in a network is identified. Based on the location of the potential bottleneck, a first QoE inference model is selected from a plurality of respective QoE inference models. The respective QoE inference models are each trained to infer a respective QoE of the network traffic based on one or more respective network traffic metrics generated by monitoring the network traffic at a respective location in the network. One or more first network traffic metrics of the one or more respective network traffic metrics are generated by monitoring the network traffic at a first respective location. The one or more first network traffic metrics are provided to the first QoE inference model to infer a first respective QoE.

An embodiment of the invention provides a method of estimating a body temperature of an individual where physiological data is received from at least one sensor 510. Environmental data is received and the physiological data and the environmental data are input into a model. The model generates an estimated body temperature and an estimated physiological condition based on the physiological data and the environmental data. A processor 520 compares the estimated physiological condition to a measured physiological condition in the physiological data. A controller 530 modifies at least one parameter in the model when the difference between the estimated physiological condition and the measured physiological condition is above a threshold.

An embodiment of the invention provides a method of estimating a body temperature of an individual where physiological data is received from at least one sensor 510. Environmental data is received and the physiological data and the environmental data are input into a model. The model generates an estimated body temperature and an estimated physiological condition based on the physiological data and the environmental data. A processor 520 compares the estimated physiological condition to a measured physiological condition in the physiological data. A controller 530 modifies at least one parameter in the model when the difference between the estimated physiological condition and the measured physiological condition is above a threshold.

A method (1000) for performance modeling of a plurality of microservices (215) includes deploying the plurality of microservices (215) within a network (1260). The plurality of microservices (215) are communicatively coupled to generate at least one service chain (310) for providing at least one service. Based on a resource allocation configuration, an initial set of training data for the plurality of microservices within the network (1260) is determined. At least a portion of data is excluded from the initial set of training data to generate a subset of training data. A Quality of Service (QoS) behaviour model is generated based on the subset of the training data.

A method (1000) for performance modeling of a plurality of microservices (215) includes deploying the plurality of microservices (215) within a network (1260). The plurality of microservices (215) are communicatively coupled to generate at least one service chain (310) for providing at least one service. Based on a resource allocation configuration, an initial set of training data for the plurality of microservices within the network (1260) is determined. At least a portion of data is excluded from the initial set of training data to generate a subset of training data. A Quality of Service (QoS) behaviour model is generated based on the subset of the training data.

In one embodiment, a supervisory service for a network obtains quality of experience metrics for application sessions of an online application. The supervisory service maps the application sessions to paths that traverse a plurality of autonomous systems. The supervisory service identifies, based in part on the quality of experience metrics, a particular autonomous system from the plurality of autonomous systems associated with a decreased quality of experience for the online application. The supervisory service causes application traffic for the online application to avoid the particular autonomous system.

Device Assisted Services (DAS) for protecting network capacity is provided. In some embodiments, DAS for protecting network capacity includes monitoring a network service usage activity of the communications device in network communication; classifying the network service usage activity for differential network access control for protecting network capacity; and associating the network service usage activity with a network service usage control policy based on a classification of the network service usage activity to facilitate differential network access control for protecting network capacity.