System and method for wireless network offloading

Information

  • Patent Grant
  • 11425580
  • Patent Number
    11,425,580
  • Date Filed
    Monday, September 28, 2020
    3 years ago
  • Date Issued
    Tuesday, August 23, 2022
    a year ago
Abstract
Wireless offloading provides tools to a service provider to encourage or direct a subscriber to offload from a first network, e.g., a cellular network, to a second network, e.g., a Wi-Fi network. The cellular service provider can use network data to determine wireless offloading priorities for cellular subscribers on an individual or group basis. The cellular service provider may use wireless network data it has and/or wireless network data it learns about networks from the wireless devices (which may obtain Wi-Fi network data from beacon frames of Wi-Fi networks or active scanning and which may report to the cellular service provider). Each wireless device can be given scanning assignments to ensure that the reporting task is shared among subscribers or adjusted to fill in gaps in data. With the network data, the cellular service provider is capable of generating useful prioritized network lists for wireless devices, either individually or as a group. Preferences can be encouraged in the form of incentive offers to subscribers to, e.g., offload from the cellular network to a Wi-Fi network. Incentive offers can include offers to lower service costs or provide additional or improved services.
Description
COPYRIGHT NOTICE

A portion of the disclosure of this patent document contains material which is subject to copyright protection. The copyright owner has no objection to the facsimile reproduction by anyone of the patent document or the patent disclosure, as it appears in the Patent and Trademark Office patent file or records, but otherwise reserves all copyright rights whatsoever.


BACKGROUND

Wireless networks, such as Wi-Fi, 2G, 3G, 4G and WiMAX, whether governed by standards or proprietary protocols, often overlap with one another. Multiple wireless networks of the same type, perhaps with configuration-specific differences, also often overlap with one another.


A wireless device chooses an available wireless network to associate with. The choice is generally made based on user selection, whether or not a better selection is available for a given situation.





BRIEF DESCRIPTION OF THE DRAWINGS


FIG. 1 depicts a diagram of an example of a system including a wireless network offloading engine.



FIG. 2 depicts a diagram of an example of a system for providing a prioritized network list to stations on a wireless network.



FIG. 3 depicts a diagram of an example of a system for generating temporally adjusted prioritized network lists.



FIG. 4 depicts a diagram of an example of a system for monitoring performance of networks on a prioritized network list.



FIG. 5 depicts a diagram of an example of a system for using a motion trace to prioritize networks on a network map.



FIG. 6 depicts a diagram of an example of a system for using knowledge of subscriber network connections to prioritize network lists for subscribers.



FIG. 7 depicts a diagram of an example of a system for using performance history to customize a prioritized network list.



FIG. 8 depicts a diagram of an example of a system for selecting network connections based on network prioritization.



FIG. 9 depicts a conceptual display associated with incentivized network selection.



FIG. 10 depicts a diagram of an example of a system for offering incentives to a subscriber to connect to a network.



FIG. 11 depicts a diagram of an example of a system for repeatedly cycling through performance tests.



FIG. 12 depicts a diagram of an example of a system capable of wireless network offloading.



FIG. 13 depicts an example of a computer system on which techniques described in this paper can be implemented.



FIG. 14 depicts a flowchart of an example of a method for prioritized wireless offloading.



FIG. 15 depicts a flowchart of an example of a method for using device assisted services to facilitate wireless offloading.





DETAILED DESCRIPTION

In the following description, several specific details are presented to provide a thorough understanding of embodiments of the invention. One skilled in the relevant art will recognize, however, that embodiments of the invention can be practiced without one or more of the specific details, or in combination with other components, etc. In other instances, well-known implementations or operations are not shown or described in detail to avoid obscuring aspects of various embodiments.


A technique for wireless offloading provides tools to a service provider to encourage or direct a subscriber to offload from a first network to a second network. For the purposes of this introductory example, the service provider may be referred to as a cellular service provider, the first network may be referred to as a cellular network, and the second network may be referred to as a Wi-Fi network.


The cellular service provider can use network data to determine wireless offloading priorities for cellular subscribers on an individual or group basis. In order to determine wireless offloading priorities, the cellular service provider may use wireless network data it has and/or wireless network data it learns about networks from the wireless devices (which may obtain Wi-Fi network data from beacon frames of Wi-Fi networks or active scanning and which may report to the cellular service provider). Each wireless device can be given scanning assignments to ensure that the reporting task is shared among subscribers or adjusted to fill in gaps in data. With the network data, the cellular service provider is capable of generating useful prioritized network lists for wireless devices, either individually or as a group. These prioritized network lists can be represented as a network map.


The cellular service provider can obtain more than just network data. For example, wireless devices can provide connection data, such as the probability that an authentication request will result in an eventual connection or the delay in the access grant. The wireless device can timestamp certain data to enable the service provider to determine how network or otherwise relevant characteristics can vary by, for example, time of day or day of the week. Other data can include the location of the wireless device, which can provide data useful for determining zones of coverage for a service area with different performance or other characteristics. Using a combination of the timestamp and location data, the server can derive a motion trace, or the motion trace can be explicitly provided by subscribers, that is representative of the velocity at which a subscriber is moving. All of this data can be useful for generating more useful prioritized lists for the wireless devices.


The cellular service provider can also obtain subscriber-specific data. Some such data may be available from a subscriber account or the parameters of a service plan. Other such data can be in the form of user preferences or performance history for a wireless device. Rules for adjusting network priorities can take into account a cost function with parameters that may vary by implementation, configuration, or preference. Preferences can be encouraged in the form of incentive offers to subscribers to, e.g., offload from the cellular network to a Wi-Fi network. Incentive offers can include offers to lower service costs or provide additional or improved services.



FIG. 1 depicts a diagram of a system 100 including a wireless network offloading engine 106. The system 100 includes wireless devices 102-1 to 102-N (referred to collectively as the wireless devices 102), wireless networks 104-1 to 104-N (referred to collectively as the wireless networks 104), and a wireless network offloading engine 106.


The wireless devices 102 will at a minimum include a processor, memory (though the memory could be implemented in the processor), a radio, and a radio interface (though the radio interface could be implemented as “part of” the radio). The wireless devices 102 will typically have at least one input device and at least one output device, including input and output interfaces, if applicable.


The wireless devices 102 can be implemented as stations. A station, as used herein, may be referred to as a device with a media access control (MAC) address and a physical layer (PHY) interface to the wireless medium that comply with, e.g., the IEEE 802.11 standard. A station can be described as “IEEE 802.11-compliant” when compliance with the IEEE 802.11 standard is intended to be explicit (i.e., a device acts as described in at least a portion of the IEEE 802.11 standard.) One of ordinary skill in the relevant art would understand what the IEEE 802.11 standard comprises today and that the IEEE 802.11 standard can change over time, and would be expected to apply techniques described in this paper in compliance with future versions of the IEEE 802.11 standard if an applicable change is made. IEEE Std. 802.11™-2007 (Revision of IEEE Std. 802.11-1999) is incorporated by reference. IEEE 802.11k-2008, IEEE 802.11n-2009, IEEE 802.11p-2010, IEEE 802.11r-2008, IEEE 802.11w-2009, and IEEE 802.11y-2008 are also incorporated by reference.


In alternative embodiments, one or more of the wireless devices 102 may comply with some other standard or no standard at all, and may have different interfaces to a wireless or other medium. It should be noted that not all standards refer to wireless devices as “stations,” but where the term is used in this paper, it should be understood that an analogous unit will be present on all applicable wireless networks. Thus, use of the term “station” should not be construed as limiting the scope of an embodiment that describes wireless devices as stations to a standard that explicitly uses the term, unless such a limitation is appropriate in the context of the discussion.


The wireless networks 104 will typically include an internetworking unit (IWU) that interconnects wireless devices on the relevant one of the wireless networks 104 with another network, such as a wired LAN. The IWU is sometimes referred to as a wireless access point (WAP). In the IEEE 802.11 standard, a WAP is also defined as a station. Thus, a station can be a non-WAP station or a WAP station. In a cellular network, the WAP is often referred to as a base station.


The wireless networks 104 can be implemented using any applicable technology, which can differ by network type or in other ways. The wireless networks 104 can be of any appropriate size (e.g., metropolitan area network (MAN), personal area network (PAN), etc.). Broadband wireless MANs may or may not be compliant with IEEE 802.16, which is incorporated by reference. Wireless PANs may or may not be compliant with IEEE 802.15, which is incorporated by reference. The wireless networks 104 can be identifiable by network type (e.g., 2G, 3G, 4G, and Wi-Fi), service provider, WAP/base station identifier (e.g., Wi-Fi SSID, base station and sector ID), geographic location, or other identification criteria.


The wireless networks 104 may or may not be coupled together via an intermediate network. The intermediate network can include practically any type of communications network, such as, by way of example but not limitation, the Internet, a public switched telephone network (PSTN), or an infrastructure network (e.g., private LAN). The term “Internet” as used herein refers to a network of networks which uses certain protocols, such as the TCP/IP protocol, and possibly other protocols such as the hypertext transfer protocol (HTTP) for hypertext markup language (HTML) documents that make up the World Wide Web (the web).


In the example of FIG. 1, the wireless network offloading engine 106 is coupled to the wireless device 102-1. In a specific implementation, the wireless network offloading engine 106 is implemented on a server and is coupled to the wireless device 102-1 through the Internet. However, at least a portion of the wireless network offloading engine 106, described in more detail later with reference to FIG. 2, can alternatively be implemented on the wireless device 102-1, with or without a connection to a server that includes another portion (e.g., a server portion) of the wireless network offloading engine 106.


In an example of operation, periodically, occasionally, or when instructed, the wireless device 102-1 performs an available network characterization scan (ANCS) on one or more of the wireless networks 104. Other devices, such as the wireless device 102-2 or some other station, may or may not also perform an ANCS. The ANCS can be used to characterize available performance for each network (e.g., data rate, bit rate variability, latency, latency jitter, quality of service (QoS), response time, etc.).


Some objective criteria for measuring performance exist (e.g., throughput). Intelligent network monitoring can enable real-time monitoring of network service usage (e.g., at the packet level/layer, network stack application interface level/layer, and/or application level/layer) of the wireless network (e.g., radio access networks and/or core networks) and to effectively manage the network service usage for protecting network capacity (e.g., while still maintaining an acceptable user experience). Using Device Assisted Services (DAS) techniques, and in some cases, network assisted/based techniques, to provide for network service usage monitoring of devices, network carriers/operators would be provided greater insight into what devices, which users and what applications, and when and where network congestion problems occur, enabling operators to intelligently add additional resources to certain areas when necessary (e.g., offloading data traffic onto femto cells or WiFi hotspots and adding more network resources), to differentially control network service usage, and/or to differentially charge for network service usage based on, for example, a network busy state, for protecting network capacity.


Performance need not be based on network performance alone. For example, a subscriber may be interested in economic performance (e.g., price). Accordingly, in this paper, performance is sometimes characterized using a cost function that can include various parameters, including network performance, economic performance, reliability, and/or other parameters that are indicative of preferences of a user or service provider. Where a particular type of performance is applicable, the meaning can be made explicit (e.g., by making reference to “network performance” as opposed to simply “performance”) or can be derived from context.


The wireless device 102-1 generates an ANCS report using results of the ANCS in order to characterize available performance for each scanned network of the wireless networks 104. The ANCS report can also include an identification of currently available networks for the wireless device 102-1, location, time, and potentially some performance characterization. The wireless device 102-1 makes the ANCS report available to the wireless network offloading engine 106. The wireless device 102-1 can also make device-specific information available, such as location, performance thresholds, a motion trace, knowledge about other devices or interference, a performance history, applications (e.g., a VoIP or streaming media application), device-specific rules related to when the device will link to a network or offload (e.g., based on reliability, performance state, congestion state, QoS, incentive state, et al.), or a cost function (e.g., based on signal strength, channel strength, basic radio bit rate, network speed, network throughput, speed jitter, throughput jitter, network delay, delay jitter, network availability, network reliability in access grant percentage, network reliability in delay in access grant, variation in performance as a function of position, et al.). Alternatively, some device-specific information may or may not be shared with the wireless network offloading engine 106, and used to customize a priority list or multi-dimensional network map that is generated or received at the wireless device 102-1.


The wireless network offloading engine 106 generates a multi-dimensional network map from the ANCS report and/or other data that is known to the wireless network offloading engine 106. The wireless network offloading engine 106 can provide the multi-dimensional network map to the wireless device 102-1, from which the wireless device 102-1 can generate or modify a wireless operation instruction set. Alternatively, the wireless network offloading engine 106 can generate an instruction set from the multi-dimensional map, which it makes available to the wireless device 102. The instruction set can be an implementation of a general algorithm that is customized by the wireless device 102-1 after it is received, or the instruction set can be generated specifically for the wireless device 102-1 or a set of devices that includes the wireless device 102-1, to be executed on-device in accordance with device-specific parameters (e.g., power saving settings, location, time of day, etc.). Advantageously, the wireless device 102-1 is able to use the instruction set to enable intelligent offloading of the wireless device 102-1 from one of the wireless networks 104 to another. In some embodiments, the wireless device 102-1 is capable of modifying the multi-dimensional network map before making a network selection decision. The wireless network offloading engine may provide one or more parameters and/or algorithms to the wireless device 102-1 for making the network selection decision.


Differential network access control for protecting network capacity includes applying policies to determine which network a service activity should be connected to (e.g., 2G, 3G, 4G, home or roaming, WiFi, cable, DSL, fiber, wired WAN, and/or another wired or wireless or access network), and applying differential network access control rules (e.g., traffic control rules) depending on which network to which the service activity is connected. In some embodiments, differential network access control for protecting network capacity includes differentially controlling network service usage activities based on the service usage control policy and a user input (e.g., a user selection or user preference). Depending upon the implementation, network service usage control policy can consider availability of alternative networks, policy rules for selecting alternative networks, network busy state or availability state for alternative networks, specific network selection or preference policies for a given network service activity or set of network service activities, to name several.


In a specific implementation, the wireless device 102 aids in determining (e.g., measuring and/or characterizing) a network busy state experienced by the device (e.g., which can be used to determine the network access control policy for one or more network capacity controlled services). For example, the network busy state experienced by the device can be recorded by the device and included in a network busy state report that is sent to a network element/function (e.g., a wireless network offloading engine 106 as described herein). The network busy state report can include, for example, data rate, average throughput, minimum throughput, throughput jitter, latency, latency jitter, bit error rate, data error rate, packet error rate, packet drop rate, number of access attempts, number of access successes, number of access failures, QoS level availability, QoS level performance, variability in any of the preceding parameters, and/or the historic statistics of any of the preceding parameters, to name several by way of example. The network busy state report can include, for example, 2G, 3G, 4G or WiFi base station ID, SSID, cell sector ID, CDMA ID, FDMA channel ID, TDMA channel ID, GPS location, and/or physical location to identify the edge network element that is associated with the network busy state report to a network element, to name several by way of example. In a specific implementation, the network busy state is monitored by one or more network elements that can measure and/or report network busy state (e.g., wireless network offloading engine 106, BTS, BTSC, access point, base station monitor, and/or airwave monitor).


As a clarifying example embodiment, the wireless device 102 (e.g. a network performance characterization software or hardware agent on the device) acts in conjunction with a network element (e.g. a wireless network offloading engine 106) to characterize the network busy state of an alternative network access point or base station resource. In such embodiments the device can sense an available alternative network, connect to a network element (e.g. a wireless network offloading engine 106) through the alternative network, conduct a download and/or upload sequence during which the network performance is monitored, and then cause the performance to be characterized and recorded. The performance can be characterized by the network element (e.g. a wireless network offloading engine 106), by the wireless device 102 (e.g. a network performance characterization software or hardware agent) or by both.


As another clarifying embodiment, the wireless device 102 (e.g. a network performance characterization software or hardware agent on the device) can sense an available alternative network, connect to the alternative network, allow the user to use the network connection services, monitor the resulting network performance and record the performance results.


In a specific implementation, one or more of the wireless devices that use wireless services on the one or more main networks and/or alternative networks are used as described herein to collect alternative network performance, busy state and/or QoS state information.


In a specific implementation, the main networks and/or alternative networks can be monitored and characterized by devices that are permanently located in the vicinity of one or more alternative network base stations or access points and configured to communicate with a wireless network offloading engine 106. A permanently located mobile terminal can provide network monitors for reporting, for example, network busy state, to a central network element, such as the wireless network offloading engine 106, which can, for example, aggregate such network busy state information to determine network busy state for one or more network coverage areas.


For example, airwave monitors and/or base station monitors can be provided to facilitate a reliable characterization of network busy state in a coverage area of one or more base stations and/or base station sectors and/or WiFi access points, such as affixed mobile terminals (e.g., trusted terminals that can include additional network busy state monitoring and/or reporting functionality) installed (e.g., temporarily or permanently) in the coverage area of one or more base stations and/or base station sectors (e.g., in which a sector is the combination of a directional antenna and a frequency channel) so that the mobile terminals perform network busy state monitoring and reporting to the wireless network offloading engine 106, the local base station, and/or other network element(s)/function(s). In some embodiments, the permanently affixed mobile terminals provide network monitors for reporting, for example, network busy state (or performance, reliability or QoS), to a central network element, such as the wireless network offloading engine 106, which can, for example, aggregate such network busy state information to determine network busy state for one or more network coverage areas. In some embodiments, the mobile terminals are always present in these locations where installed and always on (e.g., performing network monitoring), and can be trusted (e.g., the mobile terminals can be loaded with various hardware and/or software credentials). For example, using the mobile terminals, a reliable characterization of network busy state can be provided, which can then be reported to a central network element and aggregated for performing various network busy state related techniques as described herein with respect to various embodiments.


In a specific implementation, the wireless network offloading engine 106 uses the network busy state reports (or performance reports or QoS reports) from user devices and/or permanent mobile terminals connected to the same alternative network to determine the network busy state for an alternative network edge element connected to the device.


In some embodiments, a network element/function (e.g. a wireless access point or base station) sends a busy state report for the network edge element to the device (e.g., and to other devices connected to the same network edge element), which the device can then use to implement differential network access control policies (e.g., for network capacity controlled services) based on the network busy state. In some embodiments, a network busy state is provided by a network element (e.g., wireless network offloading engine 106 or service cloud) and broadcast to the device (e.g., securely communicated to the wireless device 102).


In some embodiments, the wireless device 102 (e.g. a network performance characterization software or hardware agent) selects the access network connection in accordance with a network service profile setting that determines which network the device should choose between available alternative WWAN, WLAN, WPAN, Ethernet and/or DSL network connections. This choice can be based on the performance, reliability, busy state or QoS capability of one or more alternative networks. The characterization of the alternative networks can be based on an end to end performance, and not just the over the air or radio frequency performance. For example, service profile settings can be based on the performance of the actual access network (e.g., home DSL/cable, coffee shop, shopping center, public WiFi hot spot or work network) behind the Wi-Fi not the fact that it is Wi-Fi (e.g., or any other network, such as DSL/cable, satellite, or T-1), which is viewed as different than accessing a Wi-Fi network at the coffee shop. For example, in a Wi-Fi hotspot situation in which there are a significant number of users on a DSL or T-1 backhaul, the wireless network offloading engine 106 can sit in a service provider cloud or an MVNO cloud, the service controls can be provided by a VSP capability offered by the service provider or the wireless network offloading engine 106 can be owned by the hotspot service provider that uses the wireless network offloading engine 106 on their own without any association with an access network service provider.



FIG. 2 depicts a diagram an example of a system 200 for providing a prioritized network list to stations on a wireless network. In the example of FIG. 2, the system 200 includes a network 202, a point-of-presence (PoP) 204, a network switch 206, wireless networks 208-1 to 208-N (collectively referred to as wireless networks 208), and a communications service provider (CSP) 210. The wireless network 208-1 includes a WAP 212 and, in operation, stations 214-1 to 214-N (collectively referred to as stations 214). The CSP 210 includes a prioritized network list provisioning engine 216.


The network 202 can include any applicable network that is capable of coupling the station 214-1 to the CSP 210. The PoP 204 is coupled to the network 202. The term “PoP” is often used to refer to a PoP on the Internet. However, the term as used with reference to FIG. 2 is intended to mean a PoP on the network 202, regardless of the type of network. The network switch 206 can be referred to as a wireless network switch because it couples the WAP 212 to a (typically) wired network, such as a LAN. The term “WAP” is often used with reference to AP stations in an IEEE 802.11-compatible network. However, the term should be construed to include the relevant node when the wireless network makes use of some other access technology (e.g., the term “base station” is often used to refer to the access node of a cellular network). In some cases, one or more of the PoP 204, network switch 206, and WAP 212 can be co-located.


The wireless networks 208 can be of an applicable known or convenient wireless network type. The basic service set (BSS) is a term used in IEEE 802.11 to refer to a group of stations that communicate with one another. The basic service area is defined by the propagation characteristics of the wireless medium. (Note: the term “area” is typically used to describe the three-dimensional space of a basic service area.) A station in the basic service area can communicate with other stations in the BSS. A BSS with a WAP, as is depicted in the example of FIG. 2 for the wireless network 208-1, can be referred to as an infrastructure BSS. To avoid confusion with the acronym IBSS, which refers to an independent BSS (also known as an ad hoc BSS), an infrastructure BSS is not referred to as an IBSS. An infrastructure BSS is defined by the distance from the WAP; so the stations 214, which are all on the wireless network 208-1, are within reach of the WAP 212 (as illustrated by the stations 214 being depicted as inside the cloud associated with the wireless network 208-1). In an infrastructure BSS, stations must associate with a WAP to obtain network services. The stations typically initiate the process and the WAP decides whether to grant or deny access based on the contents of an association request. Although this process is described in the context of IEEE 802.11 language, a similar description is applicable to other wireless network technologies.


The wireless network 208-1 is constrained in size by the range of the WAP 212, though multiple WAPs (not shown) could be used to increase the size of the wireless network 208-1. An extended service set (ESS) can comprise multiple BSSs, each connected to a backbone network. All of the WAPs in an ESS are given the same service set identifier (SSID), which is can be considered to be the “name” of the wireless network. The degree to which basic service areas overlap in an extended service area is implementation- and/or technology-specific.


The WAP 212 may or may not support multiple wireless networks with the same radio. Within the WAP 212, each SSID would be associated with a virtual LAN (VLAN). A relatively common implementation of this is when the WAP 212 supports a guest network (a first VLAN) and an internal network (a second VLAN). The stations 214 would likely see two separate networks in the radio domain. Thus, the wireless networks 208 may or may not have separate WAPs. A WAP that supports multiple networks may or may not have the same range for each network, particularly if the broadcast power or frequency bands are different (e.g., a WAP could be 802.11a and 802.11b/g-compatible).


In the example of FIG. 2, the stations 214 are within a service area of the wireless networks 208. As is shown by way of example, some of the stations, e.g., station 214-N, can be within the service area of a different wireless network, e.g., wireless network 208-N, than the other stations 214. The stations 214 can send information about a subset of the wireless networks 208 if the stations 214 are in the respective service areas of the wireless networks 208. By subset, it is intended that, depending upon the implementation or station capabilities, a station may or may not send information about all of the wireless networks 208 if in the respective service areas, and may or may not send information about any of the wireless networks 208. Depending upon the implementation or station capabilities, a station may or may not send information about a network when no longer in a service area of the wireless network, such as, e.g., when a WAP fails or the station is moved out of the service area. As shown by way of example, the station 214-1 is in the service area of wireless networks 208-1 and 208-2. So the station 214-1 can send information about the wireless networks 208-1 and 208-2, either the wireless network 208-1 or the wireless network 208-2, or neither of the wireless networks 208-1 and 208-2; the station 214-1 may or may not also send information about the wireless network 208-N, e.g., based on historical data, data received from station 214-N, or data received from another source, even though the station 214-1 is not currently within the service area of the wireless network 208-N.


The stations 214 are operationally connected to the CSP 210 through the WAP 212. Where the CSP 210 is part of an enterprise network that includes the wireless network 208-1, the stations 214 may or may not actually be coupled to the CSP 210 through the PoP 204 because the CSP 210 could be on the wired backbone network to which the WAP 212 is connected. However, this observation does not make an understanding of the example of FIG. 2 difficult to one of ordinary skill in the relevant art.


The CSP 210 can be part of a public or private entity in, e.g., telecom (landline or wireless), Internet, cable, satellite, and/or managed services businesses. CSPs often specialize in an industry, such as telecommunications, entertainment and media, and Internet/Web services, though service providers can operate in multiple areas. While it is likely that a CSP would be able to best implement the prioritized network list provisioning engine 216 due to the data available to the CSP, it is also possible to offer the prioritized network list provisioning engine 216 through an application service provider (ASP), if the ASP is given sufficient data either from stations or CSPs, or perhaps a managed service provider (MSP) providing services on behalf of the CSP or some other entity. Alternatively, the prioritized network list provisioning engine 216 could be implemented on a private network, or on some other server.


In the example of FIG. 2, it is assumed that the stations 214 are known to the CSP 210. If the CSP 210 provides services to each of the stations 214, the CSP 210 can have account information associated with each of the stations 214, can be made aware of device-specific data (e.g., roaming, bandwidth consumption, application use, etc.), and can receive additional information associated with the stations 214 and/or networks near the stations 214 over time. How the stations 214 are known and what information is made available to the CSP 210 can depend upon the implementation. For example, the CSP 210 could be controlled by a mobile wireless communication company that provides cellular services to the stations 214 on, e.g., a 4G network. (As was previously mentioned, some services could be provided through an ASP; so it should be borne in mind that this is simply one example and other applicable implementations should be understood to have appropriate variations.)


In the example of FIG. 2, the prioritized network list provisioning engine 216 provides a prioritized network list to the stations 214, which is represented in the example of FIG. 2 as a dashed line 218. The list need not be identical for each of the stations 214. For example, the prioritized network list provisioning engine 216 could customize the list sent to the station 214-1 based upon account parameters, current device-specific parameters, or historical device-specific parameters. Alternatively, the list sent to each of the stations 214 could be customized (or not) at the stations 214.


The prioritized list can be provided through an applicable channel. For example, the prioritized network list provisioning engine 216 could push the prioritized list to a station through a cellular network provided by a company that controls the CSP 210, through a public network out of the control of the company, through a private network, or through some other channel. The station could also pull the prioritized list from the prioritized network list provisioning engine 216. While it is likely the prioritized list will be provided on a wireless network periodically or as needed, it is also possible to provide the prioritized list in advance, which means it could be, for example, provided when a wireless device is wire-connected to a computer that has been provided or can obtain the prioritized list.


Advantageously, the prioritized list can include information that is not available to the stations 214 at a given point in time. For example, the stations 214 can perform a passive scan of nearby network service areas. The stations 214 can sort the list of applicable wireless networks based on, for example, a received signal strength indicator (RSSI) for each of the wireless networks. This type of list is referred to in this paper as a “sorted list,” which is intended to mean a list that has been sorted in accordance with a current key value. However, certain data is not used when sorting the list of wireless networks. The certain data can be categorized as “historical data,” which is previously obtained data about characteristics of a subset of the wireless networks, and “remotely obtained data,” which is data of which one or more of the stations 214 did not collect on their own. (Data collected by a station can be referred to as “locally obtained data.”) A “prioritized list” is defined as a sorted list that is further sorted using historical and/or remotely obtained data. Where it is desirable to explicitly indicate the type of prioritized list, the prioritized list can be referred to as a historically and contemporaneously prioritized list, a remotely and locally prioritized list, or (where both types of data are used to create the prioritized list) a historically and contemporaneously, remotely and locally prioritized list. A prioritized list that can include any of these types is referred to as a “prioritized list.” Advantageously, the stations 214 can use a prioritized list that is provided from the prioritized network list provisioning engine 216 to guide network association behavior.


The stations 214 can obtain data by scanning. Passive scans can identify wireless networks that use beacon frames, which will include some information about the wireless network. Active scans can generally obtain more data than a passive scan. The data obtained can be used to modify the prioritized list. In an embodiment in which a station can generate its own prioritized list (in addition to or instead of receiving the prioritized list from the prioritized network list provisioning engine 216 on the CSP 210, for example) the station will use historical data accumulated with scans, and additional historical and/or remotely obtained data could be provided from a server or other source.


In an example in which the stations 214 are serviced by the CSP 210 or other communication service provider, the CSP 210 can optimize capacity for the stations 214 as a group. Capacity for the stations 214 can be optimized for the stations as a group by the CSP 210 having information about the networks 208 and deciding a prioritized list for each of the stations 214 that results in the stations 214 choosing to associate with the networks 208 such that the stations 214 have, in the aggregate, greater performance. The CSP 210 can take into account network loading on the networks 208 when generating the prioritized lists provided by the prioritized network list provisioning engine 216 to the stations 214. In this way, the CSP 210 can determine which of the networks 208 have more available bandwidth, and can optionally determine what the loading of the networks 208 will be after the stations 214 make use of the prioritized lists. Advantageously, the CSP 210 can use the current network load to predict load on the networks 208 based upon data provided by the stations, historical data, and prioritized lists that have not yet been sent. The CSP 210 can also consider station-specific data, such as applications that are being used, QoS requirements, historical bandwidth consumption, a cost function, etc., when determining how to generate the prioritized lists.


The stations 214 can have a network optimization engine (not shown) in which an algorithm is implemented to optimize capacity. The network optimization engine can reorganize a prioritized list based upon device-specific parameters and/or user preferences.



FIG. 3 depicts a diagram of an example of a system 300 for generating temporally adjusted prioritized network lists. In the example of FIG. 3, the system 300 includes a network interface 302, a network statistics datastore 304, a network statistics characterization engine 306, a subscriber datastore 308, a subscriber-specific characterization engine 310, a temporal adjustment engine 312, and a prioritized network list generation engine 314.


The network interface 302 is intended to include an applicable known or convenient interface to a network. The network interface 302 can have a variety of implementations, including a network interface card (NIC), a modem, or some other technology that facilitates interconnection with a network.


The network statistics datastore 304, and other datastores described in this paper, can be implemented, for example, as software embodied in a physical computer-readable medium on a general-purpose or specific-purpose machine, in firmware, in hardware, in a combination thereof, or in an applicable known or convenient device or system. Datastores in this paper are intended to include any organization of data, including tables, comma-separated values (CSV) files, traditional databases (e.g., SQL), or other applicable known or convenient organizational formats. Datastore-associated components, such as database interfaces, can be considered “part of” a datastore, part of some other system component, or a combination thereof, though the physical location and other characteristics of datastore-associated components is not critical for an understanding of the techniques described in this paper.


The network statistics datastore 304 can store network statistics data structures. As used in this paper, a data structure is associated with a particular way of storing and organizing data in a computer so that it can be used efficiently within a given context. Data structures are generally based on the ability of a computer to fetch and store data at any place in its memory, specified by an address, a bit string that can be itself stored in memory and manipulated by the program. Thus some data structures are based on computing the addresses of data items with arithmetic operations; while other data structures are based on storing addresses of data items within the structure itself. Many data structures use both principles, sometimes combined in non-trivial ways. The implementation of a data structure usually entails writing a set of procedures that create and manipulate instances of that structure.


The network statistics datastore 304 can store data structures having data that is received or derived from stations on a network. The amount of data that a station can obtain and provide to the system 300 will depend upon the capabilities of the station, the type of network, device-specific settings (e.g., active scan settings), and other factors. Data can include such values as RSSI, channel strength, basic radio bit rate, loading, network speed, network throughput, speed jitter, throughput jitter, network delay, delay jitter, network availability, successful network access grant, delay in access grant, location, to name several. The network statistics datastore 304 can store data from a plurality of stations to create a store of remotely obtained data. Over time, the network statistics datastore 304 can obtain a large store of historical data.


The network statistics characterization engine 306 can use network statistics to characterize networks. For example, the network statistics characterization engine 306 can, e.g., analyze location and RSSI for stations to determine a variation in performance as a function of position, analyze access grant data to determine an access grant likelihood, analyze number of stations associated to a network, applications in use at the stations, and the capacity of a network to determine available capacity for the network, or the like. Thus, the network statistics characterization engine 306 can take standard network measurements, combine the network measurements with historical network data and network data that is remotely obtained relative to a particular station, and transform the network statistics into a more useful form. Characterized network statistic data structures can be stored in the network statistics datastore 304 (an arrow indicating such storage is not shown in the example of FIG. 3 in order to avoid disrupting the illustrative flow).


Where the system 300 is on a private network managed by a service provider (e.g., a mobile service provider), subscribers will typically have an account. The subscriber datastore 308 can store account data structures (or subscriber data structures). Advantageously, the account data structures can include data that is useful for generating prioritized lists. For example, an account could include cost function parameters that are indicative of when a subscriber would wish to offload from one network to another. Such data can be used to customize a prioritized network list for a particular subscriber. As another example, an account could include performance or favored network preferences that enable prioritizing networks based upon subscriber preferences. As another example, the subscriber datastore 308 could include a motion trace useful to predict movement between coverage areas. It should be noted that some or all of the contents of the subscriber datastore 308 could instead be stored on a device, and a prioritized list could be customized based on the device-specific settings, movement (e.g., the motion trace), or the environment.


The subscriber-specific characterization engine 310 can use subscriber-specific data to modify network list priorities. For example, a subscriber can indicate what applications are used on a mobile device. The subscriber-specific characterization engine 310 can determine from the applications which networks are more desirable given the operational parameters of the application.


As another example, if a motion trace suggests that a subscriber is on a train because it is moving relatively fast, the subscriber-specific characterization engine 310 may strongly prioritize a cellular network over a shorter-range network (e.g., Wi-Fi). By “relatively fast,” what is meant is that the subscriber is moving at a rate that suggests hand-off from one network to another will be required with relatively high probability due to the subscriber's motion. It is possible for a motion trace to show relatively high velocity, but relatively low risk of hand-off (e.g., if a subscriber is riding a carousel). Hand-off from one access point of a network to another access point of the same network is likely not as large a concern as hand-off from one network type (e.g., Wi-Fi) to another network type (e.g., cellular) or from two different networks of the same type (e.g., a first private Wi-Fi network and a second private Wi-Fi network). The motion trace itself can be considered a subscriber-specific characterization in the sense that the subscriber datastore 308 can receive location data from, e.g., a mobile device of the subscriber, and the subscriber-specific characterization engine 310 can determine velocity from the change in location over time to establish that a subscriber is moving relatively fast.


The temporal adjustment engine 312 can adjust network priorities based on, e.g., time of day. For example, if the networks statistics datastore 304 has historical data that shows certain networks have high loads at certain times of day, the temporal adjustment engine 312 can prioritize networks that have lower loads in the near future. The temporal adjustment engine 312 can also change priorities using data from the subscriber datastore 308. For example, if a subscriber indicates they have a preference for not switching networks once associated, the temporal adjustment engine 312 can use subscriber historical activity to determine a likely amount of time the subscriber will be connected to a network and network historical data to determine likely loads on various networks during that time, and prioritize networks such that the subscriber can be connected to a network that will meet minimal performance preferences for the duration of the connection.


To the extent the subscriber datastore 308 is on a client device, the temporal adjustment engine 312 could provide priorities based upon time, and the client device could customize the prioritized network list. In an alternative implementation, the temporal adjustment engine 312 is on the client device and the client device receives prioritized lists that are different at different times, then the temporal adjustment engine 312 customizes (or picks the appropriate) prioritized list based upon the current time.


The prioritized network list generation engine 314 generates a network list in accordance with the network statistics characterization engine 306 and, if applicable, the subscriber-specific characterization engine 310 and temporal adjustment engine 312. The prioritized network list can be provided to devices through the network interface 302.


Advantageously, the system 300 can characterize the statistics of available capacity for a network and determine how much if any reliable capacity is typically available on that network. This is accomplished by having devices report network data, e.g., how many devices are connected to the network, and prioritizes the network such that one or more devices will connect to or disconnect from the network based on an algorithm to optimize the (e.g., average, worst case, median, etc.) capacity offered to a group of devices serviced by the system 300. The algorithm can take into account loading of one or more alternative networks before sending the prioritized network list or otherwise communicating with a device to connect to or disconnect from the network. The system 300 can thereby characterize statistics of available capacity and provide prioritized network lists with reliable capacity as a function of time to adjust an available capacity factor. This technique is applicable to one or more devices optimized in the aggregate.



FIG. 4 depicts a diagram of an example of a system 400 for monitoring performance of prioritized network lists. In the example of FIG. 4, the system 400 includes a radio interface 402, a radio 404, a geo-location engine 406, a geo-prioritized networks datastore 408, a geo-analysis connection engine 410, a performance threshold datastore 412, a selective network monitoring engine 414, and an ANCS reporting engine 416.


In the example of FIG. 4, the radio interface 402 includes applicable known or convenient technology sufficient to enable a wireless device to use a radio to connect to a wireless network. Devices that use something other than a radio are theoretically possible; the term “radio interface” is used with the understanding that the communication device may or may not be limited to a specific subset of the electromagnetic (EM) spectrum, i.e., radio waves. The radio interface 402 can include multiple interfaces for use with multiple radios and/or different radio frequencies or wireless protocols.


In the example of FIG. 4, the radio interface 402 is coupled to a radio 404. The radio 404 can include multiple radios for use with different radio frequencies or wireless protocols. For illustrative simplicity, the radio 404 will generally be treated as if operating consistently over one channel (potentially with multiple subchannels). In an alternative, the radio 404 can send reports or scan on one frequency, and send/receive other communications on another frequency.


In the example of FIG. 4, the geo-location engine 406 receives a prioritized list and modifies the list using device location. The geo-location engine 406 can use location to determine what networks should be included on the network list and what priorities of the networks should be. In a specific implementation, the geo-location engine 406 can be used in conjunction with a server that sends a geo-prioritized list that the geo-location engine 406 customizes at the device. For example, the server could send a geo-prioritized list for a geographical area that the geo-location engine 406 can adjust or use in accordance with current device location and/or a motion trace. Geo-prioritization can be in accordance with a cost function, where parameters of the cost function vary depending upon location (e.g., network performance can vary as a function of position).


In an alternative, the geo-location engine 406 could be implemented on a server, and used to generate geo-prioritized network lists for provisioning to subscribers. Using known locations of devices, the server can, depending upon the implementation, send a geo-prioritized network list for a local geographical area near the device or for geographical areas that have historically been frequented by the device.


In the example of FIG. 4, the geo-prioritized networks datastore 408 includes network data structures that are organized by priority, where the determination of priority includes a consideration of device location. A prioritized list could be stored as data structures in the geo-prioritized networks datastore 408 initially, and the data structures transformed later in accordance with geo-location data, or the data structures could be generated with the relevant priority. In either case, when device location changes enough, the geo-priority will change, and the data structures can be transformed (or new data structures generated) to have the updated geo-priority.


In the example of FIG. 4, the geo-analysis connection engine 410 uses the geo-prioritized network list stored in the geo-prioritized networks datastore 408 to instruct the radio 404 to connect to a highest priority network that is available. Alternatively, the geo-analysis connection engine 410 could form a connection using the prioritized list as received from a server and use the geo-prioritized network list for subsequent connection determinations. As was previously noted, it is also possible that the geo-location engine 406 could be at least partially located at a server, and the prioritized list could include device location when prioritizing the network list.


As device location changes, performance of network can also change. The geo-analysis connection engine 410 can determine whether performance has dropped below a performance threshold using the performance threshold datastore 412. When performance drops below the performance threshold, the geo-analysis connection engine 410 can connect to a second network. The second network can be the next network on the geo-prioritized network list. It may be noted that the geo-location engine 406 can update the geo-prioritized networks datastore 408 so that network priorities change while a device is connected to a first network. So when performance drops below the performance threshold, the geo-analysis connection engine 410 can use the updated geo-prioritized network list to find a highest priority network that is available and instruct the radio 404 to connect to it. So the second network may or may not be the next highest priority network in the geo-prioritized list that was used when a connection to the first network was established.


Advantageously, the performance threshold setting can avoid frequent hopping between networks. Even if a second network has a higher geo-priority than a first network for which a device has a current connection, it may not be desirable to switch because of the risk of switching back and forth as performance fluctuates for the first and second (or other) networks. Thus, the performance threshold can be indicative of a performance that is “good enough” even if predicted performance of a second network exceeds the performance of the first network.


The performance threshold can be dynamically adjusted. While it is desirable to avoid frequent hopping between networks, a change in location can result in significantly higher performance on a second network. Even if the performance on the first network is “good enough,” the predicted performance of the second network may be sufficiently superior that the desire to avoid frequent hopping is eclipsed by the potential improved performance of the second network. Thus, the performance threshold can be a function of current performance on a first network and a predicted performance of a second network in addition to or instead of a performance threshold network switching preference.


When the performance threshold takes into account the performance of a first network to which a device is connected and a performance of a second network, the performance parameters of the first network and the second network need not be the same. For example, performance of the second network could include an access grant reliability parameter and a predicted delay in access grant parameter, while no such parameters are used to characterize performance of the first network. Other parameters may or may not be considered for characterizing both networks (e.g., post-connection network performance parameters or economic performance parameters).


In the example of FIG. 4, the selective network monitoring engine 414 can monitor networks other than a first network to which a subscriber is connected. Monitoring can include passive scans, which entail listening for beacon frames (or equivalent transmissions) from a WAP. The information available from beacon frames can vary depending upon network-specific variables. Active scanning typically produces more network information, but consumes more resources (e.g., wireless bandwidth, battery power, etc.).


The selective network monitoring engine 414 can monitor networks that are on the geo-prioritized networks list. Not all networks are necessarily treated equally when determining which to monitor, which is why the selective network monitoring engine 414 is called “selective.” For example, a prioritized list could indicate a preference for monitoring certain networks (not necessarily based upon the priority of the network). The selective monitoring of certain networks can be in order to limit the number of networks scanned by each of a plurality of devices that are relatively close to one another, to check on a network that has been flagged as a poor performer to see if performance has changed, to keep the device aware of relatively high priority networks in case performance of a current network dips below a performance threshold, to obtain additional information about a network, or the like.


The selective network monitoring engine 414 can work in coordination with the geo-analysis connection engine 410. For example, the selective monitoring can be of networks that are high on the geo-prioritized networks list in order to keep network priorities as up-to-date as possible. The selective network monitoring engine 414 can also ensure that a dynamic performance threshold is updated with the most current network data. Data from selective network monitoring can be used at the device or sent to a server and provided in the form of a prioritized list after processing at the server.


The ANCS reporting engine 416 generates reports from ANCS of the selective network monitoring engine 414. The ANCS reporting engine 416 provides the ANCS reports to the radio 404 for transmission through the radio interface 402 to a server. The server can ensure that future prioritized lists are relatively current and, assuming an indication is provided by the server rather than derived from rules at the device, that selective network scanning indicators enable the device to scan networks in coordination with other devices or at least without wastefully consuming resources by providing less useful data regarding networks compared to more useful data that the server could use to more effectively prepare prioritized network lists for subscribers.


Advantageously, the system 400 provides location data and ANCS reports to a server to enable the server to generate prioritized network lists using the location and ANCS reports for the device sending the ANCS report and other subscribers (regardless of whether the other subscribers also send ANCS reports). The CSP 210 of FIG. 2 could, for example, include such a server.


Advantageously, the system 400 can customize prioritized network lists using a device's current location. For example, the geo-location engine 406 can customize prioritized network lists for a large geographic area in accordance with a device's current location, a motion trace (e.g., predictor of future location), or knowledge regarding historical network connection preferences. Alternatively, the geo-location engine 406 can receive a prioritized network list for a local geographic area dependent on a device's current location and/or historical network connection preferences. Alternatively, the geo-location engine 406 can choose between multiple local geographic area network maps in accordance with a device's current location and/or historical network connection preferences.


Advantageously, the system 400 enables selective monitoring of networks on a prioritized network list to identify networks for which it is most optimal for a device to connect in a given geographic area. A device can apply implemented rules to determine an optimal network using a prioritized network list. The device can also selectively scan other networks to update the prioritized network list in accordance with what is discovered. This can benefit both the device and other subscribers.


Advantageously, the system 400 can reduce the likelihood of frequent jumping from one network to another as the network priority list changes or the performance on a given network fluctuates over time. The geo-analysis connection engine 410 can ensure a device remains connected to a network until performance drops below a minimum performance threshold.



FIG. 5 depicts a diagram of an example of a system 500 for using a motion trace to prioritize networks on a network map. In the example of FIG. 5, the system 500 a location detection engine 502, a location datastore 504, a location trace generation engine 506, a location trace datastore 508, a location trace reporting engine 510, a radio 512, a radio interface 514, and a location trace application engine 516.


In the example of FIG. 5, the location detection engine 502 is capable of determining a current location of a device. Although in this paper the location of the device is treated as a known value, it should be understood that location detection is often an estimate of current location. For example, a GPS system is not always capable of pinpoint accuracy. As another example, three WAPs could detect three signals having three different signal strengths from the device and determine location based on the distance, e.g., RSSI seems to indicate, but this triangulation technique is typically fairly inaccurate. However, any applicable known or convenient location estimation technique, regardless of its accuracy, can be sufficient if it sufficiently accurate to enable application of techniques described in association with location detection in this paper.


In the example of FIG. 5, the location detection engine 502 stores the detected location in the location datastore 504. The data structures of the location datastore 504 can be as simple as coordinates in two-dimensional or three-dimensional space. It may be noted that while networks have ranges that extend into three-dimensional space, it may be useful to simplify to two-dimensional space (typically as an overlay over the ground or a floor of a building). More important than whether a z-axis component (altitude) is recorded is a timestamp for a given location. Thus, a minimalist location data structure will include an x-axis component (e.g., longitude), a y-axis component (e.g., latitude), and a timestamp, and a useful variant can include a z-axis component (e.g., altitude). The units of the axis components need not be the same. For example, the x- and y-axis components could be GPS coordinates and the x-axis component could be in feet (or meters) or a more abstract value, such as floors of a building.


In the example of FIG. 5, the location trace generation engine 506 can use historical location data to determine changes in location over time. By comparing the location associated with a first timestamp to a location associated with a second timestamp, it is possible to determine velocity as well as distance.


Velocity can be recorded in a vector data structure in the location trace datastore 508. As is true for datastores described in this paper in general, the location datastore 504 and the location trace datastore 508 can be implemented as the same datastore. For example, locations estimated by the location detection engine 502 could be stored as nodes and vectors calculated by the location trace generation engine 506 could be stored as edges between temporally adjacent nodes, in a single datastore. Alternatively, edges could be calculated on the fly such that only the nodes, with timestamps, are stored in non-volatile memory.


The location trace reporting engine 510 can generate a report for a server. The contents of the report can vary somewhat based upon implementation, but a minimal report will include at least the current location of the device and a timestamp. The server may or may not be capable of generating a location trace, which means in an alternative at least a portion of the location trace generation engine 506 can be located at a server.


The radio 512 can send the location trace report through the radio interface 514 to a server. In response to receiving the location trace report, the server can provide a network map. In an alternative, the server need not receive the location trace in order to provide the network map; so the network map is not provided in response to receiving the location trace. The network map can be generated using ANCS reports from the device or from other devices. The network map may or may not be customized at the server using the location trace of the device.


The network map is a multi-dimensional map of networks to which the device can connect. The dimensions can include two or three spatial dimensions, time, network continuity, station velocity, device-specific history, or other parameters. Advantageously, the network map can be combined with device-specific characteristics to enable intelligent and reliable switching to or from wireless networks represented in the network map.


In the example of FIG. 5, the location trace application engine 516 can use the network map and location traces to choose a network for connection from the network map. Specifically, the location trace application engine 516 can use the motion trace to predict movement into or out of network service areas, and select networks that are appropriate for the predicted movement. Further processing of location traces beyond a determination of velocity can be useful. For example, high velocity followed by a short period of rest can be indicative of travel in a car, followed by stopping at a stoplight. In such a case, it may be desirable to avoid offloading even while the subscriber is stationary. As another example, a connection history could be used to show that some locations are typically passed through fairly quickly (e.g., a subscriber might walk to work through certain areas, making certain networks unappealing targets for offloading due to the likelihood that the subscriber will continue through the network relatively soon).


In a specific implementation, the network map can include zones of reliable coverage, which may be contiguous or disjoint. Thus, the location trace application engine 516 can use a network map of reliable networks and the location (or location trace) of the device to remove networks that the device is likely to move in and out of coverage faster than a reliability threshold. The reliability threshold datastore 518 can store a data structure can include subscriber or service provider preferences for how quickly after a pause or slow movement to offload to another network. If the location trace velocity exceeds the reliability threshold, the device will not offload to certain networks (e.g., shorter-range networks).


As was mentioned previously, the location trace application engine 516 can make use of other information, such as connection history for a subscriber, activity that is indicative of being in a car or on public transportation, etc. to use a constructive velocity in the determination. Thus, even if the actual velocity of a subscriber is zero (e.g., when the subscriber is at a stop sign), the constructive velocity can have a higher value representative of the predicted future velocity. Constructive velocity can also be “net velocity” found by adding vectors over a period of time such that movement back- and forth (e.g., if a subscriber is pacing). That is absolute velocity, or speed, of a subscriber over a relatively short period of time may not be as significant as the net velocity for the purpose of comparison to the reliability threshold.


When the location trace is applied to the network map to find a highest priority network to which the device can connect, the radio 512 can be instructed to authenticate and associate with the chosen network. Thus, offloading from one network to another can be achieved using a location trace of the device and a multi-dimensional network map.



FIG. 6 depicts a diagram of an example of a system 600 for using knowledge of subscriber network connections to prioritize network lists for subscribers. In the example of FIG. 6, the system 600 includes subscribers 602-1 to 602-N (collectively, subscribers 602), wireless networks 604-1 to 604-N (collectively, wireless networks 604), a subscriber interface 606, a connection tracking engine 608, a subscriber connections datastore 610, and a prioritized network list provisioning engine 612.


In the example of FIG. 6, the subscribers 602 can include stations that are capable of connecting to wireless networks. Depending upon the context, a subscriber can refer to a device or a person using the device. It is occasionally expedient for illustrative purposes to refer to subscriber data, which can include data about the user of the device, and the existence of a subscriber record is not necessarily indicative of the existence of a device. However, the techniques described in this paper are generally applicable to a subscriber who can connect to a wireless network. Thus, the subscriber will, at least as used in the description of operation, always include a device.


In the example of FIG. 6, the wireless networks 604 can include a variety of different types of networks. For example, the wireless network 604-1 could be a Wi-Fi network and the wireless network 604-2 could be a 3G (cellular) network.


In the example of FIG. 6, the subscriber interface 606 is assumed to be on a server. It should be noted that details regarding how the subscribers 602 connect to the subscriber interface 606 are omitted. For example, the connection between the subscribers 602 can be through intervening networks including the Internet and/or a PSTN. In order for the subscribers 602 to connect to one of the wireless networks 604, the subscribers 602 may also have to connect through a WAP or base station. In an alternative, the subscriber interface 606 could be on a peer device (e.g., a station in an IBSS).


In the example of FIG. 6, the connection tracking engine 608 can receive data from the subscribers 602. The data can include ANCS reports and authentication data, but for the purpose of this example, the data includes data sufficient to identify the wireless networks 604 to which the subscribers 602 are connected. For example, subscribers 602-1 and 602-2 may indicate that they are connected to the wireless network 604-1, a Wi-Fi network in this example. Some of the subscribers 602 may not be connected with any of the wireless networks 604 at a given point in time, but are nevertheless known to the server due to authentication attempts, wireless transmissions, a wired connection, or for other applicable reasons.


In the example of FIG. 6, the subscriber connections datastore 610 stores a data structure that includes data sufficient to identify the wireless networks 604 with which the subscribers 602 are connected. The connection tracking engine 608 can modify the relevant data structure when one of the subscribers 602 disconnects from or connects to one of the wireless networks 604. The data structure may or may not also include data associated with networks for which the subscribers are within range, though this information could also be derived from knowledge of a subscriber's location and a network map.


In the example of FIG. 6, the prioritized network list provisioning engine 612 can use data from the subscriber connections datastore 610 to determine, for example, how many of the subscribers 602 are connected to a given network, such as the wireless network 604-1. When generating a prioritized network list the prioritized network list provisioning engine 612 can use this information to steer subscribers away from wireless networks that have a relatively large number of connections and/or toward wireless networks that have a relatively small number of connections. A technique of a similar type is often refereed as network load balancing.


For example, assume subscribers 602-1 to 602-2 are connected to the wireless network 604-1 (a Wi-Fi network in this example) and the subscriber 602-N can be offloaded to the wireless network 604-1 from the wireless network 604-2 (a cellular network in this example). The prioritized network list provisioning engine 612 can use the knowledge of the number of devices 602-1 to 602-2 to prioritize the wireless network 604-1 in a prioritized network list that is to be provided to the subscriber 602-N. For the purposes of this example, the subscriber 602-N is in the service area of each of the wireless networks 604; so the prioritized network list can potentially include any or all of the wireless networks 604. If the prioritized network list provisioning engine 612 determines that the number of devices connected to the wireless network 604-1 exceeds an optimal number of connections threshold, the wireless network 604-1 can have a reduced priority in the prioritized list that is provided to the subscriber 602-N (or the wireless network 604-1 could be omitted from the prioritized list). In this way, the server can effectively advise devices contemplating a connection to a first network based upon the number of devices connected to the first network.


In the example of FIG. 6, the connections threshold 614 includes a data structure indicative of the number of connections that are acceptable. The number of connections that are acceptable may or may not vary by network. For example, some networks may be capable of supporting a larger number of connections. Also, some networks might be more predictably impacted by subscriber connections (e.g., a network that services a relatively large number of subscribers can improve predictability for a server that only receives connection information for the subscribers and not for other wireless devices on the network), making connection data more useful to the prioritized network list provisioning engine 612 when weighting the various factors used to determine priority for networks.



FIG. 7 depicts a diagram of an example of a system 700 for using performance history to customize a prioritized network list. In the example of FIG. 7, the system 700 includes a prioritized list datastore 702, a historical performance evaluation engine 704, a performance history engine 706, a network connection engine 708, a radio 710, a performance monitoring engine 712, and a reliability threshold datastore 714.


In the example of FIG. 7, the prioritized list datastore 702 includes a prioritized network data structures. For the purposes of this example, the prioritized list datastore 702 is treated as including data structures with data sufficient to identify networks having service areas in which a device having the system 700 at least partially implemented is located and the priority of the networks. Of course, an actual implementation of the prioritized list datastore 702 could include additional data. The prioritized list datastore 702 can be populated by a server that sends a prioritized network list (not shown), the prioritized list could be generated at the device, or the prioritized list could be obtained in some other manner.


In the example of FIG. 7, the historical performance evaluation engine 704 can customize the prioritized list in the prioritized list datastore 702. In this way, in addition to using a prioritized list that has been prioritized based on reliability, location, time of day, or other factors that are described elsewhere in this paper, the device is capable of fine-tuning the prioritized list using on-device data.


In the example of FIG. 7, the performance history datastore 706 includes a data structure that is instructive regarding past performance for a given network. To the extent a network data structure exists in both the prioritized list datastore 702 and the performance history datastore 706, the historical performance evaluation engine 704 can compare the priority of the network to an actual performance history. Other networks in the prioritized list datastore 702 and the performance history datastore 706 can be similarly compared. Depending upon the implementation, the prioritized list datastore 702 can be updated with a customized prioritized list that adjusts networks in the prioritized list based upon past performance. It is not necessarily the case that a network having superior network performance will have the highest priority (e.g., superior economic performance could be more important), and depending on the implementation, the subscriber may be able to adjust performance preferences as it relates to changing prioritization of networks.


In the example of FIG. 7, the network connection engine 708 can use the (now) customized prioritized list to select a network. The rules used to make the selection can be as simple as choosing the highest priority network from the customized prioritized network list. However, the network connection engine 708 could also have, e.g., an offload priority threshold that must be met in order to offload to, e.g., a Wi-Fi network from a cellular network. In other words, a cellular network could be a default and other networks would have to have, e.g., a performance advantage sufficient to merit offloading, regardless of prioritization. The network connection engine 708 could also be configured to connect to the highest priority network of the prioritized network list (prior to customization) and only use the customized prioritized list after some performance monitoring.


In the example of FIG. 7, the radio 710 is instructed to connect to a network that is selected by the network connection engine. Over time, the radio 710 will receive at least some network data (e.g., from packets received over the wireless medium) that can be used to monitor performance on the selected network. The radio 710 can also be instructed to scan other networks, as is described elsewhere in this paper, and the data obtained can be used to monitor performance on the other networks.


In the example of FIG. 7, the performance monitoring engine 712 at least monitors performance on the selected network, and may or may not also monitor performance on other networks. The data obtained can be stored in the performance history datastore 706 and used by the historical performance evaluation engine 704 to customize the prioritized list. The historical performance evaluation engine 704 and the performance monitoring engine 712 can operate in parallel or in some other fashion.


In the example of FIG. 7, the reliability threshold datastore 714 includes a data structure indicative of when the performance monitoring engine 712 will trigger the network connection engine 708 to switch networks. When the performance monitoring engine 712 determines that a network is, for example, sufficiently reliable, the network connection engine 708 can offload from, e.g., a cellular network, to, e.g., a sufficiently reliable Wi-Fi network. What is meant by “sufficiently reliable” is that a reliability threshold is established based upon user preferences for reliability, network configurations, or other factors that, when met, are indicative of sufficient reliability for an offload target. The reliability threshold is described elsewhere in this paper.


Advantageously, the system 700 enables a device to perform a network performance evaluation before deciding to connect to a network. The system 700 can then offload from a first network to a sufficiently reliable second network. The device can then continue to evaluate performance and decide whether to switch to another network based on performance. FIG. 8 depicts a diagram of an example of a system 800 for selecting network connections based on network prioritization. In the example of FIG. 8, the system 800 includes a subscriber user interface (UI) 802, a preference selection engine 804, a performance preferences datastore 806, an incentivized network selection engine 808, a prioritized list 810, a network connection engine 812, and a radio 814.


The subscriber UI 802 enables a user to view information about networks, preferences, and incentives, and to input data for use by the device. As such, the UI is presumed to include a display device (with drivers, if applicable) and an input device (with drivers, if applicable). By way of example but not limitation, the subscriber UI 802 could include a touchscreen input/output (I/O) device, a liquid crystal display (LCD) and keypad, or some other applicable known or convenient combination or collection of I/O device(s).


The preference selection engine 804 displays options on the subscriber UI. The options can include, for example, rules that dictate when to switch to or from networks or network types. For example, the user could define reliability, congestion state, QoS, performance, or some other parameter value. The user can also define incentive states. These settings can be in association with a specific network (e.g., a subscriber may have a high preference for offloading to home or office Wi-Fi networks, which can be explicitly identified) or in association with a network type (e.g., a subscriber may have differing preferences for offloading to an 802.11a network or an 802.11b/g/n network).


The performance preferences datastore 806 stores data structures indicative of the performance and/or incentive settings selected at the preference selection engine 804. In a specific implementation, a user can update preferences at any time by, for example, triggering the preference selection engine 804 with a menu selection. Performance preferences can also be dynamic settings that can change in accordance with operational changes. For example, preferences may be different when a device has a full battery relative to when the device is running out of power. Thus, the preferences can by used in conjunction with or stored as rules for controlling operation of the device, specifically in this example, network connection selections by the device.


The incentivized network selection engine 808 uses a prioritized network list, which can be stored in the prioritized list datastore 810, and preferences and/or rules in the performance preferences datastore 806 to select a network and prompt the network connection engine 812 to control the radio 814 to connect to the selected network. In the example of FIG. 8, the subscriber can be provided with options that are displayed at the subscriber UI 802 and the subscriber can input data associated with those options. The amount of information provided to the subscriber can vary with implementation, but can include a list of all available networks, all available reliable networks, one or more aspects of network performance for displayed networks, or the like.



FIG. 9 depicts a conceptual display 900 associated with incentivized network selection. The display 900 includes a list of prioritized networks 902-1 to 902-N (collectively, prioritized network list 902), radio buttons 904, and state indicators 906. The prioritized network list 902 may or may not include all available networks, depending upon implementation- or configuration-specific parameters. For example, the subscriber may or may not be able to limit the list only to networks that meet certain performance or incentive specifications, or a service provider may or may not have a similar ability to prune the list of available networks. In the example of FIG. 9, the prioritized network list 902 is presumed to be ordered by priority, but a priority indicator other than order could be used instead (e.g., priority could be indicated by a number in a column, text or background color, etc.).


In the example of FIG. 9, the radio buttons 904 are intended to illustrate a network selection mechanism. An applicable known or convenient mechanism for selecting one of the networks of the prioritized network list 902 could be used instead (e.g., the text of the prioritized network list 902 could be selectable such that if a user “clicked” on a network, that network would be selected). It should be noted that in a specific implementation the choice of network can be made by the device based upon a set of rules decided upon by a subscriber regarding when to connect to a network or switch to a new network.


In the example of FIG. 9, the state indicators 906 are intended to illustrate information that could be provided in association with a prioritized network list display. In the example of FIG. 9, the state indicators 906 include a column of performances 908-1 to 908-N (collectively, performance states 908), a column of availabilities 910-1 to 910-N (collectively, network availability states 910), and a column of incentives 912-1 to 912-N (collectively, incentive states 912). The state indicators 906 need not be displayed in a columnar or tabular form (e.g., data could be displayed by hovering over a network in the prioritized network list 902). The data can also be represented by color-coding (e.g., networks in the prioritized network list 902 could be displayed with red text if a corresponding congestion state of the network is high and green text if a corresponding congestion state of the network is low), or using some other applicable known or convenient technique to convey information about the state of a network.


As was mentioned elsewhere in this paper, performance can have many different meanings (e.g., network performance, economic performance, access grant performance, etc.). Thus, although there is one column of performance states 908, there could be several columns to indicate state or estimates for different types of performance. Within each type of performance, there may be additional subcategorizations (e.g., network performance can be measured in more than one way, including throughput, QoS, congestion, etc.) Performance can be summarized for a subscriber and presented as a single value (e.g., a number that is indicative of the relative performance of the network) or more explicit data can be provided (e.g., the basic radio bit rate of the network).


The network availability states 910 are related to performance, but are represented in a separate column due to some distinctions. Performance can be indicative of what can be expected if a connection is established with the corresponding network. Availability can be indicative of the likelihood with which a connection can be established. Reliability (not shown) can also be distinguished because it is indicative of the likelihood that performance will be consistent or a connection can be maintained over time (e.g., in consideration of a motion trace or zone of reliability based on time of day), which is somewhat different from both performance and availability. Reliability can be obviated as an indicator in an implementation in which only reliable networks are in the prioritized network list 902.


The incentive states 912 can indicate to a subscriber an “incentive offer” that may entice the subscriber to choose one network over another, regardless of prioritization.



FIG. 10 depicts a diagram of an example of a system 1000 for offering incentives to a subscriber to connect to a network. In the example of FIG. 10, the system 1000 includes a radio interface 1002, a radio 1004, an incentivized network selection engine 1006, a subscriber UI 1008, and a network connection engine 1010.


The radio 1004 receives an incentive offer from or on behalf of a network through the radio interface 1002. The incentive offer can be provided in a number of different ways, such as in beacon frames, in frames identifiable as “incentive frames,” in the body or header of a message, etc. It will typically be more valuable to send incentives to devices that are in a service area of a network, but depending upon implementation, incentives could be sent based upon predicted movement, probably in the immediate future, based upon connection history or a motion trace. In an alternative, the incentive offer is not received over the radio interface 1002, and is instead generated at the system 1000 in the incentivized network selection engine 1006 (or in an incentive offer generation engine, not shown).


The incentivized network selection engine 1006 enables a user to select the incentivized network through the subscriber UI 1008. The selection could also be made based upon rules or preferences that were previously input by the subscriber or a service provider. The network selection option could be presented as a pop-up window prompting a user to select whether to connect to the applicable network in exchange for the incentive offer. Alternatively, the incentive offer could trigger a display similar to the display depicted by way of example in FIG. 9. Regardless of the mechanism used to provide the choice to the subscriber, the network connection engine 1010 can connect to the network in accordance with the subscriber's choice.


Advantageously, a service provider can identify one or more networks (e.g., Wi-Fi networks) that the service provider would like a subscriber to offload to. In the case of a cellular provider, this can enable the service provider to reduce load on the cellular network. By incentivizing the offloading, the service provider can expect a larger number of subscribers to offload than if no incentive was offered. The incentive offer can explain advantages of switching networks to the subscriber, which can include, for example, traffic charges are free or less expensive, one or more service capabilities or activities are available on, e.g., Wi-Fi that are not available or have a lower performance on, e.g., cellular, the subscriber gets a discount or credit for switching, etc.



FIG. 11 depicts a diagram of an example of a system 1100 for repeatedly cycling through performance tests. In the example of FIG. 11, the system 1100 includes a radio interface 1102, a radio 1104, a prioritized network selection engine 1106, a network connection engine 1108, a selective network monitoring engine 1110, and an ANCS reporting engine 1112.


The radio 1104 receives a prioritized list from a server through the radio interface 1102. The prioritized list could alternatively be generated at least in part at a device one which the system 1100 is implemented.


The prioritized network selection engine 1106 selects a priority network in accordance with any techniques described previously in this paper. The network connection engine 1108 controls the radio 1104 to connect to the applicable network. The network connection engine 1108 can perform a scan to determine available networks before or after obtaining the prioritized list.


The selective network monitoring engine 1110 can cycle through one or more network performance tests for a subset of the available networks. The ANCS reporting engine 1112 can report the results of the tests to a server through the radio 1104 and radio interface 1102. The server could then perform a selection algorithm to select the network that best meets a network selection cost function and prioritize the network accordingly and provide another prioritized list. Alternatively, the device implementing the system 1100 can use the ANCS to customize the prioritized list. If the prioritized network selection engine 1106 selects a new network, the network connection engine 1108 can control the radio 1104 to connect to the selected network.


The selective network monitoring engine 1110 can repeatedly generate ANCS such that the prioritized list is continuously updated. In an alternative, the ANCS reports can be uploaded to a service controller function.


The embodiments illustrated in FIGS. 1-11 include components that can be selectively combined with one another. The cost functions of the various embodiments can include such parameters as signal strength, channel strength, basic radio bit rate, network speed, network throughput, speed jitter, throughput jitter, network delay, delay jitter, network availability, network reliability in successful network access grant percentage, delay in access grant, variation in performance as a function of performance, to name several.



FIG. 12 depicts a diagram of an example of a system 1200 capable of wireless network offloading and of enabling carriers to establish the wireless network offloading service. In the example of FIG. 12, the system 1200 includes a network 1202, a server 1204, an intelligent wireless offloading client 1206, and a service design center (SDC) 1208. The network 1202 will include a wireless network to which the intelligent wireless offloading client 1206 is connected, but can otherwise include any applicable known or convenient network suitable for linking the components of the system 1200. The server 1204 can be a server of a CSP or other service provider. The intelligent wireless offloading client 1206 can include capabilities of a wireless device and can include an implementation of any subset of the techniques described in this paper.


In one embodiment, the SDC 1208 acts as the portal to enable the service providers to set service plan parameters for the wireless networking offloading functionality. The SDC 1208 can enable the service providers to set charging rates for each of the different wireless network connections, such as a charging rate for Wi-Fi networks, a charging rate for 3G networks, a charging rate for 4G networks, etc. Each service provider may set different charging rates for the same or different network connections. Each service provider may establish different service plans, each having different charging rates for the different wireless connections. For example, a service provider may have a service plan that benefits the highly mobile user, charging less for cell connections. A service provider may have a service plan that benefits those who anticipate reduced usage of cell connections.


In one embodiment, the SDC 1208 acts as the portal to enable the service providers to set notification parameters. For example, each service provider can set different notifications to motivate users to switch between wireless connections. These notifications and incentives can be temporal, geo-specific, service plan specific, etc.


In one embodiment, the SDC 1208 acts as the portal to enable the service providers to set access parameters. For example, each service provider can enable the various devices to access only a subset of available network connections, to offload to only certain network connections, etc.


The SDC 1208 further can provide functionality that may not be provided by the server 1204 or the intelligent wireless offloading client 1206. For example, the SDC 1208 can load algorithms for use at the client or server, set periodicity of scans by the client, set matrices, establish geographic boundaries of networks, set periodicity of reporting, etc.


Examples of the SDC 1208 can be found in the following related published applications, which are hereby incorporated by reference: U.S. publication No. 2010/0188975, filed Mar. 2, 2009, entitled “Verifiable Device Assisted Service Policy Implementation,” U.S. publication No. 2010/0192170, filed Mar. 2, 2009, entitled “Device Assisted Service Profile Management with User Preference, Adaptive Policy, Network Neutrality, and User Privacy,” U.S. publication No. 2010/0191612, filed Mar. 2, 2009, entitled “Verifiable Device Assisted Service Usage Monitoring with Reporting, Synchronization, and Notification,” U.S. publication No. 2010/0191576, filed Mar. 2, 2009, entitled “Verifiable Device Assisted Service Usage Billing with Integrated Accounting, Mediation Accounting, and Multi-Account,” U.S. publication No. 2010/0188991, filed Mar. 2, 2009, entitled “Network Based Service Policy Implementation with Network Neutrality and User Privacy,” U.S. publication No. 2010/0188990, filed Mar. 2, 2009, entitled “Network Based Service Profile Management with User Preference, Adaptive Policy, Network Neutrality and User Privacy,” U.S. publication No. 2010/0192212, filed Mar. 2, 2009, entitled “Automated Device Provisioning and Activation,” U.S. publication No. 2010/0191604, filed Mar. 2, 2009, entitled “Device Assisted Ambient Services,” U.S. publication No. 2010/0191575, filed Mar. 2, 2009, entitled “Network Based Ambient Services,” U.S. publication No. 2010/0188993, filed Mar. 2, 2009, entitled “Network Tools for Analysis, Design, Testing, and Production of Services,” U.S. publication No. 2010/0190470, filed Mar. 2, 2009, entitled “Roaming Services Network and Overlay Networks,” U.S. publication No. 2010/0192120, filed Mar. 2, 2009, entitled “Open Development System for Access Service Providers,” U.S. publication No. 2010/0192207, filed Mar. 2, 2009, entitled “Virtual Service Provider Systems,” U.S. publication No. 2010/0191613, filed Mar. 2, 2009, entitled “Open Transaction Central Billing System,” U.S. publication No. 2010/0188995, filed Mar. 2, 2009, entitled “Verifiable and Accurate Service Usage Monitoring for Intermediate Networking Devices,” U.S. publication No. 2010/0188994, filed Mar. 2, 2009, entitled “Verifiable Service Billing for Intermediate Networking Devices,” U.S. publication No. 2010/0191846, filed Mar. 2, 2009, entitled “Verifiable Service Policy Implementation for Intermediate Networking Devices,” U.S. publication No. 2010/0188992, filed Mar. 2, 2009, entitled “Service Profile Management with User Preference, Adaptive Policy, Network Neutrality and User Privacy for Intermediate Networking Devices,” U.S. publication No. 2010/0191847, filed Mar. 2, 2009, entitled “Simplified Service Network Architecture,” U.S. publication No. 2010/0197266, filed Jan. 27, 2010, entitled “Device Assisted CDR Creation, Aggregation, Mediation, and Billing,” U.S. publication No. 2010/0198698, filed Jan. 27, 2010, entitled “Adaptive Ambient Services,” U.S. publication No. 2010/0199325, filed Jan. 27, 2010, entitled “Security Techniques for Device Assisted Services,” U.S. publication No. 2010/0197267, filed Jan. 27, 2010, entitled “Device Group Partitions and Settlement Platform,” U.S. publication No. 2010/0198939, filed Jan. 27, 2010, entitled “Device Assisted Services Install,” U.S. publication No. 2010/0195503, filed Jan. 27, 2010, entitled “Quality of Service for Device Assisted Services,” and U.S. publication No. 2010/0197268, filed Jan. 28, 2010, entitled “Enhanced Roaming Services and Converged Carrier Networks with Device Assisted Services and a Proxy.”



FIG. 13 depicts an example of a computer system 1300 on which techniques described in this paper can be implemented. The computer system 1300 may be a conventional computer system that can be used as a client computer system, such as a wireless client or a workstation, or a server computer system. The computer system 1300 includes a computer 1302, I/O devices 1304, and a display device 1306. The computer 1302 includes a processor 1308, a communications interface 1310, memory 1312, display controller 1314, non-volatile storage 1316, and I/O controller 1318. The computer 1302 may be coupled to or include the I/O devices 1304 and display device 1306.


The computer 1302 interfaces to external systems through the communications interface 1310, which may include a modem or network interface. It will be appreciated that the communications interface 1310 can be considered to be part of the computer system 1300 or a part of the computer 1302. The communications interface 1310 can be an analog modem, ISDN modem, cable modem, token ring interface, satellite transmission interface (e.g. “direct PC”), or other interfaces for coupling a computer system to other computer systems.


The processor 1308 may be, for example, a conventional microprocessor such as an Intel Pentium microprocessor or Motorola power PC microprocessor. The memory 1312 is coupled to the processor 1308 by a bus 1370. The memory 1312 can be Dynamic Random Access Memory (DRAM) and can also include Static RAM (SRAM). The bus 1370 couples the processor 1308 to the memory 1312, also to the non-volatile storage 1316, to the display controller 1314, and to the I/O controller 1318.


The I/O devices 1304 can include a keyboard, disk drives, printers, a scanner, and other input and output devices, including a mouse or other pointing device. The display controller 1314 may control in the conventional manner a display on the display device 1306, which can be, for example, a cathode ray tube (CRT) or liquid crystal display (LCD). The display controller 1314 and the I/O controller 1318 can be implemented with conventional well known technology.


The non-volatile storage 1316 is often a magnetic hard disk, an optical disk, or another form of storage for large amounts of data. Some of this data is often written, by a direct memory access process, into memory 1312 during execution of software in the computer 1302. One of skill in the art will immediately recognize that the terms “machine-readable medium” or “computer-readable medium” includes any type of storage device that is accessible by the processor 1308 and also encompasses a carrier wave that encodes a data signal.


The computer system 1300 is one example of many possible computer systems which have different architectures. For example, personal computers based on an Intel microprocessor often have multiple buses, one of which can be an I/O bus for the peripherals and one that directly connects the processor 1308 and the memory 1312 (often referred to as a memory bus). The buses are connected together through bridge components that perform any necessary translation due to differing bus protocols.


Network computers are another type of computer system that can be used in conjunction with the teachings provided herein. Network computers do not usually include a hard disk or other mass storage, and the executable programs are loaded from a network connection into the memory 1312 for execution by the processor 1308. A Web TV system, which is known in the art, is also considered to be a computer system, but it may lack some of the features shown in FIG. 13, such as certain input or output devices. A typical computer system will usually include at least a processor, memory, and a bus coupling the memory to the processor.


In addition, the computer system 1300 is controlled by operating system software which includes a file management system, such as a disk operating system, which is part of the operating system software. One example of operating system software with its associated file management system software is the family of operating systems known as Windows® from Microsoft Corporation of Redmond, Wash., and their associated file management systems. Another example of operating system software with its associated file management system software is the Linux operating system and its associated file management system. The file management system is typically stored in the non-volatile storage 1316 and causes the processor 1308 to execute the various acts required by the operating system to input and output data and to store data in memory, including storing files on the non-volatile storage 1316.


Some portions of the detailed description are presented in terms of algorithms and symbolic representations of operations on data bits within a computer memory. These algorithmic descriptions and representations are the means used by those skilled in the data processing arts to most effectively convey the substance of their work to others skilled in the art. An algorithm is here, and generally, conceived to be a self-consistent sequence of operations leading to a desired result. The operations are those requiring physical manipulations of physical quantities. Usually, though not necessarily, these quantities take the form of electrical or magnetic signals capable of being stored, transferred, combined, compared, and otherwise manipulated. It has proven convenient at times, principally for reasons of common usage, to refer to these signals as bits, values, elements, symbols, characters, terms, numbers, or the like.


One skilled in the art should recognize that terms used are to be associated with the appropriate physical quantities and are merely convenient labels applied to these quantities. Unless specifically stated otherwise as apparent from the following discussion, it is appreciated that throughout the description, discussions utilizing terms such as “processing” or “computing” or “calculating” or “determining” or “displaying” or the like, refer to the action and processes of a computer system, or similar electronic computing device, that manipulates and transforms data represented as physical (electronic) quantities within the computer system's registers and memories into other data similarly represented as physical quantities within the computer system memories or registers or other such information storage, transmission or display devices.


The present invention, in some embodiments, also relates to apparatus for performing the operations herein. This apparatus may be specially constructed for the required purposes, or it may comprise a general purpose computer selectively activated or reconfigured by a computer program stored in the computer. Such a computer program may be stored in a computer readable storage medium, such as, but is not limited to, read-only memories (ROMs), random access memories (RAMs), EPROMs, EEPROMs, magnetic or optical cards, any type of disk including floppy disks, optical disks, CD-ROMs, and magnetic-optical disks, or any type of media suitable for storing electronic instructions, and each coupled to a computer system bus.


The algorithms and displays presented herein are not inherently related to any particular computer or other apparatus. Various general purpose systems may be used with programs in accordance with the teachings herein, or it may prove convenient to construct more specialized apparatus to perform the required method steps. The required structure for a variety of these systems will appear from the description below. In addition, the present invention is not described with reference to any particular programming language, and various embodiments may thus be implemented using a variety of programming languages.



FIG. 14 depicts a flowchart 1400 of an example of a method for prioritized wireless offloading. The method is organized as a sequence of modules in the flowchart 1400. However, it should be understood that these and other modules associated with other methods described herein may be reordered for parallel execution or into different sequences of modules.


In the example of FIG. 14, the flowchart 1400 starts at module 1402 with obtaining wireless network data. The wireless network data can be obtained through ANCS at a wireless device. The ANCS can be used at the wireless device and/or can be provided to a server in an ANCS report. In an implementation that makes use of a server, the server can receive ANCS reports from multiple wireless devices. This can enable the server to generate prioritized lists for subscribers making use of network data that is remotely obtained relative to a subscriber.


In the example of FIG. 14, the flowchart 1400 continues to module 1404 with generating a prioritized network list from the wireless network data. In an implementation that makes use of a server, the server can perform an algorithm in memory to optimize capacity to a group of subscribers of a service provider associated with the server. The optimization can take into account network loading, wireless device location, wireless device connections, performance history (including, e.g., a time of day associated with a particular performance for a network), a network map over a geographic area, motion traces of wireless devices, subscriber preferences, incentives, and cost functions to name several. The prioritized list can take the form of a network map, which can be treated as a subset of prioritized list (with an added geo-location component).


In the example of FIG. 14, the flowchart 1400 continues to module 1406 with connecting to a network from the prioritized network list. A device may or may not customize a prioritized network list that is provided from a server in accordance with device-specific parameters. Where customization does not occur, the server may take into account the device-specific parameters (as well as, e.g., account-specific parameters) when generating the prioritized list. Where customization does occur, in an implementation that includes a server, the prioritized list can still be partially customized at the server. Customization can be in accordance with monitored performance of networks within range of the device, subscriber-specified rules, service provider-specified rules, a location trace, performance history, environmental conditions, cost function, or incentives, to name several.


In the example of FIG. 14, the flowchart 1400 continues to module 1408 with monitoring network performance. The monitoring can be of the network to which the device is connected. The device can also monitor other networks, either passively or actively, in accordance with network monitoring rules. The rules can be provided by a service provider, SDC, or input directly.


In the example of FIG. 14, the flowchart 1400 returns to module 1402 and continues as described previously. It is not necessary that the same elements perform the same tasks described. For example, a server could initially generate a prioritized network list (1404), but on a second iteration, a wireless device could generate a (customized) prioritized network list without receiving a new prioritized list from the server. Also, there may be additional or fewer actions or determinations on a second iteration. For example, when a device first connects to a network (1406), it may be unnecessary to compare performance or some other parameter of a network with a threshold value to determine whether to switch to another network, but when the device considers switching from one network to another, it may be desirable to compare current performance with a threshold value to ensure it is “worth it” to switch to a (currently) more highly prioritized network.



FIG. 15 depicts a flowchart 1500 of an example of a method for using device assisted services (DAS) to facilitate wireless offloading. In the example of FIG. 15, the flowchart 1500 starts at module 1402 with monitoring network service usage activities of a device. The network service usage activities can be monitored with a verified/verifiable network performance characterization software (implemented in hardware) or hardware agent. The agent can be implemented on the device in question, on a different device, or can have components that are implemented on more than one device. The monitoring can be accomplished using a radio and can be selective. An example of an agent that performs selective monitoring is the selective network monitoring engine 414 or the selective network monitoring engine 1110, respectively described by way of example with reference to FIGS. 4 and 11, or the performance monitoring engine 712 described by way of example with reference to FIG. 7.


In the example of FIG. 15, the flowchart 1500 continues to module 1504 with determining a network busy state based on the monitored network service usage activities. Network statistics can be stored in a network statistics datastore, such as the network statistics datastore 304 described by way of example with reference to FIG. 3. The network busy state can also be stored in a network statistics datastore or can be derived from statistics that are stored in the network statistics datastore. The network busy state can include a measure of network capacity, availability, and/or performance, and can be derived using techniques described in this paper. The network busy state can be determined with a network performance characterization software (implemented in hardware) or hardware agent, which can measure and/or characterize a network busy state experienced by a device. An example of an agent that performs network busy state determination is the network statistics characterization engine 306, such as is described by way of example with reference to FIG. 3 or the historical performance evaluation engine 704, such as is described by way of example with reference to FIG. 7.


In the example of FIG. 15, the flowchart 1500 continues to module 1506 with reporting the network busy state to a network element/function. The network busy state can be included in any of the reports described in this paper (e.g., a network busy state report, ANCS report, etc.). Depending on the implementation, the network busy state can be used by a network element/function on a wireless device, such as the wireless device that at least in part monitored network service usage activities and/or determined a network busy state, on a server, or on some other applicable device. An example of such a network element/function includes the wireless network offloading engine 106, such as is described by way of example with reference to FIG. 1.


In the example of FIG. 15, the flowchart 1500 continues to module 1508 with setting network access control policy for one or more network capacity controlled services using the network busy state. The network access control policy can be acted upon by the geo-analysis connection engine 410, the network connection engine 708, the incentivized network selection engine 808 and/or the network connection engine 812, the incentivized network selection engine 1006 and/or the network connection engine 1010, the prioritized network selection engine 1106 and/or the network connection engine 1108, such as are respectively described by way of example with reference to FIGS. 4, 7, 8, 10, and 11.


Data on a wireless network is often encrypted. However, data may also be sent in the clear, if desired. With encrypted data, a rogue device will have a very difficult time learning any information (such as passwords, etc.) from clients before countermeasures are taken to deal with the rogue. The rogue may be able to confuse the client, and perhaps obtain some encrypted data, but the risk is minimal (even less than for some wired networks).


The following example illustrates possible benefits of this system. In one embodiment, a subscriber turns on a smart phone, the smart phone notices that the subscriber's home network is available. Assuming that the subscriber is connected to the cellular network and not connected to the home network, the cellular service provider sends the subscriber an incentive offer: a reduction in service fees if the subscriber offloads from the cellular network to his home network.


Upon traveling to work, the smart phone recognizes that the subscriber is no longer in the service area of his home network, but is within the service area of three of his neighbors' home networks and the cellular network. The smart phone recognizes that his motion trace (velocity) indicates movement that will move the subscriber out of the range of all three of his neighbors' home networks quickly. Thus, the smart phone may be configured to connect to the cellular network. Upon recognizing that the smart phone is stationary, e.g., at a stoplight, the smart phone may be configured to wait a predetermined period of time before considering to offload to a Wi-Fi network (especially if the smart phone knows the subscriber was moving). Accordingly, the smart phone may be configured to remain connected to the cellular network.


Upon reaching a destination, the smart phone recognizes that the motion trace becomes stationary or relatively slow and that the smart phone is proximate to two local Wi-Fi networks. In one embodiment, the beacon frames of the first Wi-Fi network may have higher received signal strength indicators (RSSI). However, other subscribers may have provided network data about the first network that indicate the first network is typically severely congested at this time. Thus, the smart phone may be configured to indicate that the second network has a higher priority than the first network, despite the high RSSI.


In some embodiments, the smart phone receives a prioritized network list that indicates the second network as having a higher priority than the first network. In some embodiments, the smart phone is configured to connect to a wireless network in accordance with an incentive offer, to connect based on preferences set by the subscriber, or to wait for the subscriber to select a network from the prioritized network list.


To assist with information gathering, the smart phone may be configured to gather information about another local wireless network, e.g., about the first wireless network, and may report the information to the cellular service provider. While the smart phone is in range of the other local wireless network, the smart phone may passively or actively scan the other network. In some embodiments, the smart phone is configured to perform active scans only when the smart phone is plugged into a power source.


INCORPORATION BY REFERENCE

This application incorporates by reference the following U.S. Patent Applications for all purposes: U.S. Ser. No. 13/134,005, filed May 25, 2011, entitled “System and Method for Wireless Network Offloading”; U.S. Ser. No. 12/380,778 filed Mar. 2, 2009, entitled “Verifiable Device Assisted Service Usage Billing with Integrated Accounting, Mediation Accounting, and Multi-Account”; U.S. Ser. No. 12/380,780 filed Mar. 2, 2009, entitled “Automated Device Provisioning and Activation”; U.S. Ser. No. 12/695,019 filed Jan. 27, 2010, entitled “Device Assisted CDR Creation, Aggregation, Mediation and Billing”; U.S. Ser. No. 12/695,020 filed Jan. 27, 2010, entitled “Adaptive Ambient Services”; U.S. Ser. No. 12/694,445 filed Jan. 27, 2010, entitled “Security Techniques for Device Assisted Services”; U.S. Ser. No. 12/694,451 filed Jan. 27, 2010, entitled “Device Group Partitions and Settlement Platform”; U.S. Ser. No. 12/694,455 filed Jan. 27, 2010, entitled “Device Assisted Services Install”; U.S. Ser. No. 12/695,021 filed Jan. 27, 2010, entitled “Quality of Service for Device Assisted Services”; and U.S. Ser. No. 12/695,980 filed Jan. 28, 2010, entitled “Enhanced Roaming Services and Converged Carrier Networks with Device Assisted Services and a Proxy”.


This application also incorporates by reference the following U.S. provisional applications: U.S. provisional application Ser. No. 61/206,354, filed Jan. 28, 2009, entitled “Services Policy Communication System and Method”; U.S. provisional application Ser. No. 61/206,944, filed Feb. 4, 2009, entitled “Services Policy Communication System and Method”; U.S. provisional application Ser. No. 61/207,393, filed Feb. 10, 2009, entitled “Services Policy Communication System and Method”; U.S. provisional application Ser. No. 61/207,739, filed Feb. 13, 2009, entitled “Services Policy Communication System and Method”; U.S. provisional application Ser. No. 61/270,353, filed Jul. 6, 2009, entitled “Device Assisted CDR Creation, Aggregation, Mediation and Billing”; U.S. provisional application Ser. No. 61/275,208, filed Aug. 25, 2009, entitled “Adaptive Ambient Services”; U.S. provisional application Ser. No. 61/237,753, filed Aug. 28, 2009, entitled “Adaptive Ambient Services”; U.S. provisional application Ser. No. 61/252,151, filed Oct. 15, 2009, entitled “Security Techniques for Device Assisted Services”; U.S. provisional application Ser. No. 61/252,153, filed Oct. 15, 2009, entitled “Device Group Partitions and Settlement Platform”; U.S. provisional application Ser. No. 61/264,120, filed Nov. 24, 2009, entitled “Device Assisted Services Install,” and U.S. provisional application Ser. No. 61/264,126, filed Nov. 24, 2009, entitled “Device Assisted Services Activity Map”; U.S. provisional application Ser. No. 61/348,022, filed May 25, 2010, entitled “Device Assisted Services for Protecting Network Capacity”; U.S. provisional application Ser. No. 61/381,159, filed Sep. 9, 2010, entitled “Device Assisted Services for Protecting Network Capacity”; U.S. provisional application Ser. No. 61/381,162, filed Sep. 9, 2010, entitled “Service Controller Interfaces and Workflows”; U.S. provisional application Ser. No. 61/384,456, filed Sep. 20, 2010, entitled “Securing Service Processor with Sponsored SIMs”; U.S. provisional application Ser. No. 61/389,547, filed Oct. 4, 2010, entitled “User Notifications for Device Assisted Services”; U.S. provisional application Ser. No. 61/385,020, filed Sep. 21, 2010, entitled “Service Usage Reconciliation System Overview”; U.S. provisional application Ser. No. 61/387,243, filed Sep. 28, 2010, entitled “Enterprise and Consumer Billing Allocation for Wireless Communication Device Service Usage Activities”; U.S. provisional application Ser. No. 61/387,247, filed Sep. 28, 2010, entitled “Secured Device Data Records”; U.S. provisional application Ser. No. 61/407,358, filed Oct. 27, 2010, entitled “Service Controller and Service Processor Architecture”; U.S. provisional application Ser. No. 61/418,507, filed Dec. 1, 2010, entitled “Application Service Provider Interface System”; U.S. provisional application Ser. No. 61/418,509, filed Dec. 1, 2010, entitled “Service Usage Reporting Reconciliation and Fraud Detection for Device Assisted Services”; U.S. provisional application Ser. No. 61/420,727, filed Dec. 7, 2010, entitled “Secure Device Data Records”; U.S. provisional application Ser. No. 61/422,565, filed Dec. 13, 2010, entitled “Service Design Center for Device Assisted Services”; U.S. provisional application Ser. No. 61/422,572, filed Dec. 13, 2010, entitled “System Interfaces and Workflows for Device Assisted Services”; U.S. provisional application Ser. No. 61/422,574, filed Dec. 13, 2010, entitled “Security and Fraud Detection for Device Assisted Services”; U.S. provisional application Ser. No. 61/435,564, filed Jan. 24, 2011, entitled “Framework for Device Assisted Services”; and U.S. provisional application Ser. No. 61/472,606, filed Apr. 6, 2011, entitled “Managing Service User Discovery and Service Launch Object Placement on a Device.”

Claims
  • 1. A method performed by a first wireless end-user device, the method comprising: identifying one or more alternative wireless networks;obtaining current performance data on the one or more alternative wireless networks;obtaining geographically-keyed wireless offloading historical performance data on a plurality of networks and network types, including on the one or more alternative wireless networks, from a network element that aggregates geographically-keyed measured performance data received from a plurality of wireless end-user devices;characterizing a performance state of each of the one or more alternative wireless networks based on location data for the first wireless end-user device, on the current performance data and on the historical performance data;applying rules involving the characterized performance state to determine whether to switch from a first wireless network to a particular wireless network of the one or more alternative wireless networks; andswitching the first wireless end-user device from the first wireless network to the particular wireless network in response to applying the rules.
  • 2. The method of claim 1, wherein the geographically-keyed wireless offloading historical performance data comprises a multi-dimensional network map.
  • 3. The method of claim 2, wherein the multi-dimensional network map is a customized map generated for the first wireless end-user device.
  • 4. The method of claim 1, wherein the geographically-keyed wireless offloading historical performance data comprises a wireless operation instruction set.
  • 5. The method of claim 4, further comprising receiving the rules involving the characterized performance state from the network element.
  • 6. The method of claim 1, further comprising compiling the current performance data along with corresponding location data and timestamp data into a report, and sending the report to the network element.
  • 7. The method of claim 6, further comprising receiving from the network element a scanning assignment request for performance data on specific wireless networks, the first wireless end-user device using the request to determine which current performance data to include in the report.
  • 8. The method of claim 6, wherein the current performance data comprises network busy state information.
  • 9. The method of claim 6, wherein the current performance data further comprises performance data for the first wireless network.
  • 10. The method of claim 1, wherein the geographically-keyed wireless offloading historical performance data comprises a prioritized network list.
  • 11. The method of claim 10, wherein the prioritized network list is selected, from a plurality of lists, for the first wireless end-user device.
  • 12. The method of claim 11, wherein the prioritized network list is selected in part based on a reported location of the first wireless end-user device.
  • 13. The method of claim 11, wherein the prioritized network list is selected in part based on reported motion information for the first wireless end-user device.
  • 14. The method of claim 13, wherein the reported motion information comprises predicted future motion information.
US Referenced Citations (1728)
Number Name Date Kind
5131020 Liebesny et al. Jul 1992 A
5283904 Carson et al. Feb 1994 A
5325532 Crosswy et al. Jun 1994 A
5572528 Shuen Nov 1996 A
5577100 McGregor et al. Nov 1996 A
5594777 Makkonen et al. Jan 1997 A
5617539 Ludwig et al. Apr 1997 A
5630159 Zancho May 1997 A
5633484 Zancho et al. May 1997 A
5633868 Baldwin et al. May 1997 A
5751719 Chen et al. May 1998 A
5754953 Briancon et al. May 1998 A
5774532 Gottlieb et al. Jun 1998 A
5794142 Vanttila et al. Aug 1998 A
5814798 Zancho Sep 1998 A
5889477 Fastenrath Mar 1999 A
5892900 Ginter et al. Apr 1999 A
5903845 Buhrmann et al. May 1999 A
5915008 Dulman Jun 1999 A
5915226 Martineau Jun 1999 A
5933778 Buhrmann et al. Aug 1999 A
5940472 Newman et al. Aug 1999 A
5974439 Bollella Oct 1999 A
5983270 Abraham et al. Nov 1999 A
6035281 Crosskey et al. Mar 2000 A
6038452 Strawczynski et al. Mar 2000 A
6038540 Krist et al. Mar 2000 A
6047268 Bartoli et al. Apr 2000 A
6047270 Joao et al. Apr 2000 A
6058434 Wilt et al. May 2000 A
6061571 Tamura May 2000 A
6064878 Denker et al. May 2000 A
6078953 Vaid et al. Jun 2000 A
6081591 Skoog Jun 2000 A
6098878 Dent et al. Aug 2000 A
6104700 Haddock et al. Aug 2000 A
6115823 Velasco et al. Sep 2000 A
6119933 Wong et al. Sep 2000 A
6125391 Meltzer et al. Sep 2000 A
6141565 Feuerstein et al. Oct 2000 A
6141686 Jackowski et al. Oct 2000 A
6148336 Thomas et al. Nov 2000 A
6154738 Call Nov 2000 A
6157636 Voit et al. Dec 2000 A
6185576 Mcintosh Feb 2001 B1
6198915 McGregor et al. Mar 2001 B1
6219786 Cunningham et al. Apr 2001 B1
6226277 Chuah May 2001 B1
6246870 Dent et al. Jun 2001 B1
6263055 Garland et al. Jul 2001 B1
6292828 Williams Sep 2001 B1
6317584 Abu-Amara et al. Nov 2001 B1
6370139 Redmond Apr 2002 B2
6381316 Joyce et al. Apr 2002 B2
6393014 Daly et al. May 2002 B1
6397259 Lincke et al. May 2002 B1
6401113 Lazaridis et al. Jun 2002 B2
6418147 Wiedeman Jul 2002 B1
6438575 Khan et al. Aug 2002 B1
6445777 Clark Sep 2002 B1
6449479 Sanchez Sep 2002 B1
6466984 Naveh et al. Oct 2002 B1
6477670 Ahmadvand Nov 2002 B1
6502131 Vaid et al. Dec 2002 B1
6505114 Luciani Jan 2003 B2
6510152 Gerszberg et al. Jan 2003 B1
6522629 Anderson, Sr. Feb 2003 B1
6526066 Weaver Feb 2003 B1
6532235 Benson et al. Mar 2003 B1
6532579 Sato et al. Mar 2003 B2
6535855 Cahill et al. Mar 2003 B1
6535949 Parker Mar 2003 B1
6539082 Lowe et al. Mar 2003 B1
6542465 Wang Apr 2003 B1
6542500 Gerszberg et al. Apr 2003 B1
6542992 Peirce et al. Apr 2003 B1
6546016 Gerszberg et al. Apr 2003 B1
6563806 Yano et al. May 2003 B1
6570974 Gerszberg et al. May 2003 B1
6574321 Cox et al. Jun 2003 B1
6574465 Marsh et al. Jun 2003 B2
6578076 Putzolu Jun 2003 B1
6581092 Motoyama Jun 2003 B1
6591098 Shieh et al. Jul 2003 B1
6598034 Kloth Jul 2003 B1
6601040 Kolls Jul 2003 B1
6603969 Vuoristo et al. Aug 2003 B1
6603975 Inouchi et al. Aug 2003 B1
6606744 Mikurak Aug 2003 B1
6628934 Rosenberg et al. Sep 2003 B2
6631122 Arunachalam et al. Oct 2003 B1
6636721 Threadgill et al. Oct 2003 B2
6639975 O'Neal et al. Oct 2003 B1
6640097 Corrigan et al. Oct 2003 B2
6640334 Rasmussen Oct 2003 B1
6650887 McGregor et al. Nov 2003 B2
6651101 Gai et al. Nov 2003 B1
6654786 Fox et al. Nov 2003 B1
6654814 Britton et al. Nov 2003 B1
6658254 Purdy et al. Dec 2003 B1
6662014 Walsh Dec 2003 B1
6678516 Nordman et al. Jan 2004 B2
6683853 Kannas et al. Jan 2004 B1
6684244 Goldman et al. Jan 2004 B1
6690918 Evans et al. Feb 2004 B2
6694362 Secor et al. Feb 2004 B1
6697821 Ziff et al. Feb 2004 B2
6704873 Underwood Mar 2004 B1
6725031 Watler et al. Apr 2004 B2
6725256 Albal et al. Apr 2004 B1
6732176 Stewart et al. May 2004 B1
6735206 Oki et al. May 2004 B1
6748195 Phillips Jun 2004 B1
6748437 Mankude et al. Jun 2004 B1
6751296 Albal et al. Jun 2004 B1
6754470 Hendrickson et al. Jun 2004 B2
6757717 Goldstein Jun 2004 B1
6760417 Wallenius Jul 2004 B1
6763000 Walsh Jul 2004 B1
6763226 McZeal, Jr. Jul 2004 B1
6765864 Natarajan et al. Jul 2004 B1
6765925 Sawyer et al. Jul 2004 B1
6782412 Brophy et al. Aug 2004 B2
6785889 Williams Aug 2004 B1
6792461 Hericourt Sep 2004 B1
6829596 Frazee Dec 2004 B1
6829696 Balmer et al. Dec 2004 B1
6839340 Voit et al. Jan 2005 B1
6842628 Arnold et al. Jan 2005 B1
6873988 Herrmann et al. Mar 2005 B2
6876653 Ambe et al. Apr 2005 B2
6879825 Daly Apr 2005 B1
6882718 Smith Apr 2005 B1
6885997 Roberts Apr 2005 B1
6901440 Bimm et al. May 2005 B1
6920455 Weschler Jul 2005 B1
6922562 Ward et al. Jul 2005 B2
6928280 Xanthos et al. Aug 2005 B1
6934249 Bertin et al. Aug 2005 B1
6934751 Jayapalan et al. Aug 2005 B2
6947723 Gumani et al. Sep 2005 B1
6947985 Hegli et al. Sep 2005 B2
6952428 Necka et al. Oct 2005 B1
6957067 Iyer et al. Oct 2005 B1
6959202 Heinonen et al. Oct 2005 B2
6959393 Hollis et al. Oct 2005 B2
6965667 Frabandt et al. Nov 2005 B2
6965872 Grdina Nov 2005 B1
6967958 Ono et al. Nov 2005 B2
6970692 Tysor Nov 2005 B2
6970927 Stewart et al. Nov 2005 B1
6982733 McNally et al. Jan 2006 B1
6983370 Eaton et al. Jan 2006 B2
6996062 Freed et al. Feb 2006 B1
6996076 Forbes et al. Feb 2006 B1
6996393 Pyhalammi et al. Feb 2006 B2
6998985 Reisman et al. Feb 2006 B2
7000001 Lazaridis Feb 2006 B2
7002920 Ayyagari et al. Feb 2006 B1
7007295 Rose et al. Feb 2006 B1
7013469 Smith et al. Mar 2006 B2
7017189 DeMello et al. Mar 2006 B1
7020781 Saw et al. Mar 2006 B1
7024200 McKenna et al. Apr 2006 B2
7024460 Koopmas et al. Apr 2006 B2
7027055 Anderson et al. Apr 2006 B2
7027408 Nabkel et al. Apr 2006 B2
7031733 Alminana et al. Apr 2006 B2
7032072 Quinn et al. Apr 2006 B1
7039027 Bridgelall May 2006 B2
7039037 Wang et al. May 2006 B2
7039403 Wong May 2006 B2
7039713 Van Gunter et al. May 2006 B1
7042988 Juitt et al. May 2006 B2
7043225 Patel et al. May 2006 B1
7043226 Yamauchi May 2006 B2
7043268 Yukie et al. May 2006 B2
7047276 Liu et al. May 2006 B2
7058022 Carolan et al. Jun 2006 B1
7058968 Rowland et al. Jun 2006 B2
7068600 Cain Jun 2006 B2
7069248 Huber Jun 2006 B2
7082422 Zirngibl et al. Jul 2006 B1
7084775 Smith Aug 2006 B1
7092696 Hosain et al. Aug 2006 B1
7095754 Benveniste Aug 2006 B2
7102620 Harries et al. Sep 2006 B2
7110753 Campen Sep 2006 B2
7113780 Mckenna et al. Sep 2006 B2
7113997 Jayapalan et al. Sep 2006 B2
7120133 Joo et al. Oct 2006 B1
7131578 Paschini et al. Nov 2006 B2
7133386 Holur et al. Nov 2006 B2
7133695 Beyda Nov 2006 B2
7136361 Benveniste Nov 2006 B2
7139569 Kato Nov 2006 B2
7142876 Trossen et al. Nov 2006 B2
7149229 Leung Dec 2006 B1
7149521 Sundar et al. Dec 2006 B2
7151764 Heinonen et al. Dec 2006 B1
7158792 Cook et al. Jan 2007 B1
7162237 Silver et al. Jan 2007 B1
7165040 Ehrman et al. Jan 2007 B2
7167078 Pourchot Jan 2007 B2
7174156 Mangai Feb 2007 B1
7174174 Boris et al. Feb 2007 B2
7177919 Truong et al. Feb 2007 B1
7180855 Lin Feb 2007 B1
7181017 Nagel et al. Feb 2007 B1
7191248 Chattopadhyay et al. Mar 2007 B2
7197321 Erskine et al. Mar 2007 B2
7200112 Sundar et al. Apr 2007 B2
7200551 Senez Apr 2007 B1
7203169 Okholm et al. Apr 2007 B1
7203721 Ben-Efraim et al. Apr 2007 B1
7203752 Rice et al. Apr 2007 B2
7209664 McNicol et al. Apr 2007 B1
7212491 Koga May 2007 B2
7219123 Fiechter et al. May 2007 B1
7222190 Klinker et al. May 2007 B2
7222304 Beaton et al. May 2007 B2
7224968 Dobson et al. May 2007 B2
7228354 Chambliss et al. Jun 2007 B2
7236780 Benco Jun 2007 B2
7242668 Kan et al. Jul 2007 B2
7242920 Morris Jul 2007 B2
7245901 McGregor et al. Jul 2007 B2
7248570 Bahl et al. Jul 2007 B2
7251218 Jorgensen Jul 2007 B2
7260382 Lamb et al. Aug 2007 B1
7266371 Amin et al. Sep 2007 B1
7269157 Klinker et al. Sep 2007 B2
7271765 Stilp et al. Sep 2007 B2
7272660 Powers et al. Sep 2007 B1
7280816 Fratti et al. Oct 2007 B2
7280818 Clayton Oct 2007 B2
7283561 Picher-Dempsey Oct 2007 B1
7283963 Fitzpatrick et al. Oct 2007 B1
7286834 Walter Oct 2007 B2
7286848 Vireday et al. Oct 2007 B2
7289489 Kung et al. Oct 2007 B1
7290283 Copeland, III Oct 2007 B2
7310424 Gehring et al. Dec 2007 B2
7313237 Bahl et al. Dec 2007 B2
7315892 Freimuth et al. Jan 2008 B2
7317699 Godfrey et al. Jan 2008 B2
7318111 Zhao Jan 2008 B2
7320029 Rinne et al. Jan 2008 B2
7320781 Lambert et al. Jan 2008 B2
7322044 Hrastar Jan 2008 B2
7324447 Morford Jan 2008 B1
7325037 Lawson Jan 2008 B2
7336960 Zavalkovsky et al. Feb 2008 B2
7340244 Osborne et al. Mar 2008 B1
7340772 Panasyuk et al. Mar 2008 B2
7346410 Uchiyama Mar 2008 B2
7349695 Oommen et al. Mar 2008 B2
7349698 Gallagher et al. Mar 2008 B2
7353533 Wright et al. Apr 2008 B2
7356011 Waters et al. Apr 2008 B1
7356337 Florence Apr 2008 B2
7366497 Nagata Apr 2008 B2
7366654 Moore Apr 2008 B2
7366934 Narayan et al. Apr 2008 B1
7369848 Jiang May 2008 B2
7369856 Ovadia May 2008 B2
7373136 Watler et al. May 2008 B2
7373179 Stine et al. May 2008 B2
7379731 Natsuno et al. May 2008 B2
7388950 Elsey et al. Jun 2008 B2
7389412 Sharma et al. Jun 2008 B2
7391724 Alakoski et al. Jun 2008 B2
7395056 Petermann Jul 2008 B2
7395244 Kingsford Jul 2008 B1
7401338 Bowen et al. Jul 2008 B1
7403763 Maes Jul 2008 B2
7409447 Assadzadeh Aug 2008 B1
7409569 Illowsky et al. Aug 2008 B2
7411930 Montojo et al. Aug 2008 B2
7418253 Kavanah Aug 2008 B2
7418257 Kim Aug 2008 B2
7421004 Feher Sep 2008 B2
7423971 Mohaban et al. Sep 2008 B1
7428750 Dunn et al. Sep 2008 B1
7433362 Mallya et al. Oct 2008 B2
7436816 Mehta et al. Oct 2008 B2
7440433 Rink et al. Oct 2008 B2
7444669 Bahl et al. Oct 2008 B1
7450591 Korling et al. Nov 2008 B2
7450927 Creswell et al. Nov 2008 B1
7454191 Dawson et al. Nov 2008 B2
7457265 Julka et al. Nov 2008 B2
7457870 Lownsbrough et al. Nov 2008 B1
7460837 Diener Dec 2008 B2
7466652 Lau et al. Dec 2008 B2
7467160 McIntyre Dec 2008 B2
7472189 Mallya et al. Dec 2008 B2
7478420 Wright et al. Jan 2009 B2
7486185 Culpepper et al. Feb 2009 B2
7486658 Kumar Feb 2009 B2
7489918 Zhou et al. Feb 2009 B2
7493659 Wu et al. Feb 2009 B1
7496652 Pezzutti Feb 2009 B2
7499438 Hinman et al. Mar 2009 B2
7499537 Elsey et al. Mar 2009 B2
7502672 Kolls Mar 2009 B1
7505756 Bahl Mar 2009 B2
7505795 Lim et al. Mar 2009 B1
7508794 Feather et al. Mar 2009 B2
7508799 Sumner et al. Mar 2009 B2
7512128 DiMambro et al. Mar 2009 B2
7512131 Svensson et al. Mar 2009 B2
7515608 Yuan et al. Apr 2009 B2
7515926 Bu et al. Apr 2009 B2
7516219 Moghaddam et al. Apr 2009 B2
7522549 Karaoguz et al. Apr 2009 B2
7522576 Du et al. Apr 2009 B2
7526541 Roese et al. Apr 2009 B2
7529204 Bourlas et al. May 2009 B2
7533158 Grannan et al. May 2009 B2
7535880 Hinman et al. May 2009 B1
7536695 Alam et al. May 2009 B2
7539132 Werner et al. May 2009 B2
7539862 Edgett et al. May 2009 B2
7540408 Levine et al. Jun 2009 B2
7545782 Rayment et al. Jun 2009 B2
7546460 Maes Jun 2009 B2
7546629 Albert et al. Jun 2009 B2
7548875 Mikkelsen et al. Jun 2009 B2
7548976 Bahl et al. Jun 2009 B2
7551921 Petermann Jun 2009 B2
7551922 Roskowski et al. Jun 2009 B2
7554983 Muppala Jun 2009 B1
7555757 Smith et al. Jun 2009 B2
7561899 Lee Jul 2009 B2
7562213 Timms Jul 2009 B1
7564799 Holland et al. Jul 2009 B2
7565141 Macaluso Jul 2009 B2
7565328 Donner Jul 2009 B1
7574509 Nixon et al. Aug 2009 B2
7574731 Fascenda Aug 2009 B2
7577431 Jiang Aug 2009 B2
7580356 Mishra et al. Aug 2009 B1
7580857 VanFleet et al. Aug 2009 B2
7583964 Wong Sep 2009 B2
7584298 Klinker et al. Sep 2009 B2
7585217 Lutnick et al. Sep 2009 B2
7586871 Hamilton et al. Sep 2009 B2
7593417 Wang et al. Sep 2009 B2
7593730 Khandelwal et al. Sep 2009 B2
7596373 Mcgregor et al. Sep 2009 B2
7599288 Cole et al. Oct 2009 B2
7599714 Kuzminskiy Oct 2009 B2
7602746 Calhoun et al. Oct 2009 B2
7603710 Harvey et al. Oct 2009 B2
7606357 Daigle Oct 2009 B2
7606918 Holzman et al. Oct 2009 B2
7607041 Kraemer et al. Oct 2009 B2
7609650 Roskowski et al. Oct 2009 B2
7609700 Ying et al. Oct 2009 B1
7610047 Hicks, III et al. Oct 2009 B2
7610057 Bahl et al. Oct 2009 B2
7610328 Haase et al. Oct 2009 B2
7610396 Taglienti et al. Oct 2009 B2
7612712 LaMance et al. Nov 2009 B2
7613444 Lindqvist et al. Nov 2009 B2
7614051 Glaum et al. Nov 2009 B2
7616962 Oswal et al. Nov 2009 B2
7617516 Huslak et al. Nov 2009 B2
7620041 Dunn et al. Nov 2009 B2
7620065 Falardeau Nov 2009 B2
7620162 Aaron et al. Nov 2009 B2
7620383 Taglienti et al. Nov 2009 B2
7627314 Carlson et al. Dec 2009 B2
7627600 Citron et al. Dec 2009 B2
7627767 Sherman et al. Dec 2009 B2
7627872 Hebeler et al. Dec 2009 B2
7633438 Tysowski Dec 2009 B2
7634253 Plestid et al. Dec 2009 B2
7634388 Archer et al. Dec 2009 B2
7636574 Poosala Dec 2009 B2
7636626 Oesterling et al. Dec 2009 B2
7643411 Andreasen et al. Jan 2010 B2
7644151 Jerrim et al. Jan 2010 B2
7644267 Ylikoski et al. Jan 2010 B2
7644414 Smith et al. Jan 2010 B2
7647047 Moghaddam et al. Jan 2010 B2
7650137 Jobs et al. Jan 2010 B2
7653394 McMillin Jan 2010 B2
7656271 Ehrman et al. Feb 2010 B2
7657920 Arseneau et al. Feb 2010 B2
7660419 Ho Feb 2010 B1
7661124 Ramanathan et al. Feb 2010 B2
7664494 Jiang Feb 2010 B2
7668176 Chuah Feb 2010 B2
7668612 Okkonen Feb 2010 B1
7668903 Edwards et al. Feb 2010 B2
7668966 Klinker et al. Feb 2010 B2
7676673 Weller et al. Mar 2010 B2
7680086 Eglin Mar 2010 B2
7681226 Kraemer et al. Mar 2010 B2
7684370 Kezys Mar 2010 B2
7685131 Batra et al. Mar 2010 B2
7685254 Pandya Mar 2010 B2
7685530 Sherrard et al. Mar 2010 B2
7688792 Babbar et al. Mar 2010 B2
7693107 De Froment Apr 2010 B2
7693720 Kennewick et al. Apr 2010 B2
7697540 Haddad et al. Apr 2010 B2
7707320 Singhai et al. Apr 2010 B2
7710932 Muthuswamy et al. May 2010 B2
7711848 Maes May 2010 B2
7719966 Luft et al. May 2010 B2
7720206 Devolites et al. May 2010 B2
7720464 Batta May 2010 B2
7720505 Gopi et al. May 2010 B2
7720960 Pruss et al. May 2010 B2
7721296 Ricagni May 2010 B2
7724716 Fadell May 2010 B2
7725570 Lewis May 2010 B1
7729326 Sekhar Jun 2010 B2
7730123 Erickson et al. Jun 2010 B1
7734784 Araujo et al. Jun 2010 B1
7742406 Muppala Jun 2010 B1
7742961 Aaron et al. Jun 2010 B2
7743119 Friend et al. Jun 2010 B2
7746854 Ambe et al. Jun 2010 B2
7747240 Briscoe et al. Jun 2010 B1
7747699 Prueitt et al. Jun 2010 B2
7747730 Harlow Jun 2010 B1
7752330 Olsen et al. Jul 2010 B2
7756056 Kim et al. Jul 2010 B2
7756509 Rajagopalan et al. Jul 2010 B2
7756534 Anupam et al. Jul 2010 B2
7756757 Oakes, III Jul 2010 B1
7760137 Martucci et al. Jul 2010 B2
7760711 Kung et al. Jul 2010 B1
7760861 Croak et al. Jul 2010 B1
7765294 Edwards et al. Jul 2010 B2
7769397 Funato et al. Aug 2010 B2
7770785 Jha et al. Aug 2010 B2
7774323 Helfman Aug 2010 B2
7774412 Schnepel Aug 2010 B1
7774456 Lownsbrough et al. Aug 2010 B1
7778176 Morford Aug 2010 B2
7778643 Laroia et al. Aug 2010 B2
7783754 Morford et al. Aug 2010 B2
7788700 Feezel et al. Aug 2010 B1
7792257 Vanier et al. Sep 2010 B1
7792538 Kozisek Sep 2010 B2
7792708 Alva Sep 2010 B2
7797019 Friedmann Sep 2010 B2
7797060 Grgic et al. Sep 2010 B2
7797204 Balent Sep 2010 B2
7797401 Stewart et al. Sep 2010 B2
7801523 Kenderov Sep 2010 B1
7801783 Kende et al. Sep 2010 B2
7801985 Pitkow et al. Sep 2010 B1
7802724 Nohr Sep 2010 B1
7805140 Friday et al. Sep 2010 B2
7805522 Schluter et al. Sep 2010 B2
7805606 Birger et al. Sep 2010 B2
7809351 Panda et al. Oct 2010 B1
7809372 Rajaniemi Oct 2010 B2
7813746 Rajkotia Oct 2010 B2
7817615 Breau et al. Oct 2010 B1
7817983 Cassett et al. Oct 2010 B2
7822837 Urban et al. Oct 2010 B1
7822849 Titus Oct 2010 B2
7826427 Sood et al. Nov 2010 B2
7826607 De Carvalho Resende et al. Nov 2010 B1
7835275 Swan et al. Nov 2010 B1
7843831 Morrill et al. Nov 2010 B2
7843843 Papp, III et al. Nov 2010 B1
7844034 Oh et al. Nov 2010 B1
7844728 Anderson et al. Nov 2010 B2
7848768 Omori et al. Dec 2010 B2
7849161 Koch et al. Dec 2010 B2
7849170 Hargens et al. Dec 2010 B1
7849477 Cristofalo et al. Dec 2010 B2
7853250 Harvey et al. Dec 2010 B2
7853255 Karaoguz et al. Dec 2010 B2
7853656 Yach et al. Dec 2010 B2
7856226 Wong et al. Dec 2010 B2
7860088 Lioy Dec 2010 B2
7865182 Macaluso Jan 2011 B2
7865187 Ramer et al. Jan 2011 B2
7868778 Kenwright Jan 2011 B2
7868814 Bergman Jan 2011 B1
7873001 Silver Jan 2011 B2
7873344 Bowser et al. Jan 2011 B2
7873346 Petersson et al. Jan 2011 B2
7873540 Arumugam Jan 2011 B2
7873705 Kalish Jan 2011 B2
7873985 Baum Jan 2011 B2
7877090 Maes Jan 2011 B2
7881199 Krstulich Feb 2011 B2
7881697 Baker et al. Feb 2011 B2
7882029 White Feb 2011 B2
7882247 Sturniolo et al. Feb 2011 B2
7882560 Kraemer et al. Feb 2011 B2
7885644 Gallagher et al. Feb 2011 B2
7886047 Potluri Feb 2011 B1
7889384 Armentrout et al. Feb 2011 B2
7890084 Dudziak et al. Feb 2011 B1
7890111 Bugenhagen Feb 2011 B2
7890581 Rao et al. Feb 2011 B2
7894431 Goring et al. Feb 2011 B2
7899039 Andreasen et al. Mar 2011 B2
7899438 Baker et al. Mar 2011 B2
7903553 Liu Mar 2011 B2
7907970 Park et al. Mar 2011 B2
7908358 Prasad et al. Mar 2011 B1
7911975 Droz et al. Mar 2011 B2
7912025 Pattenden et al. Mar 2011 B2
7912056 Brassem Mar 2011 B1
7916707 Fontaine Mar 2011 B2
7917130 Christensen et al. Mar 2011 B1
7920529 Mahler et al. Apr 2011 B1
7921463 Sood et al. Apr 2011 B2
7925740 Nath et al. Apr 2011 B2
7925778 Wijnands et al. Apr 2011 B1
7929446 Bozarth et al. Apr 2011 B2
7929959 DeAtley et al. Apr 2011 B2
7929960 Martin et al. Apr 2011 B2
7929973 Zavalkovsky et al. Apr 2011 B2
7930327 Craft et al. Apr 2011 B2
7930446 Kesselman et al. Apr 2011 B2
7930553 Satarasinghe et al. Apr 2011 B2
7933274 Verma et al. Apr 2011 B2
7936736 Proctor, Jr. et al. May 2011 B2
7937069 Rassam May 2011 B2
7937450 Janik May 2011 B2
7937470 Curley et al. May 2011 B2
7940685 Breslau et al. May 2011 B1
7940751 Hansen May 2011 B2
7941184 Prendergast et al. May 2011 B2
7944948 Chow et al. May 2011 B2
7945238 Baker et al. May 2011 B2
7945240 Klock et al. May 2011 B1
7945470 Cohen et al. May 2011 B1
7945945 Graham et al. May 2011 B2
7948952 Hurtta et al. May 2011 B2
7948953 Melkote et al. May 2011 B2
7948968 Voit et al. May 2011 B2
7949529 Weider et al. May 2011 B2
7953808 Sharp et al. May 2011 B2
7953877 Vemula et al. May 2011 B2
7957020 Mine et al. Jun 2011 B2
7957381 Clermidy et al. Jun 2011 B2
7957511 Drudis et al. Jun 2011 B2
7958029 Bobich et al. Jun 2011 B1
7962622 Friend et al. Jun 2011 B2
7965983 Swan et al. Jun 2011 B1
7966405 Sundaresan et al. Jun 2011 B2
7967682 Huizinga Jun 2011 B2
7969950 Iyer et al. Jun 2011 B2
7970350 Sheynman Jun 2011 B2
7970426 Poe et al. Jun 2011 B2
7974624 Gallagher et al. Jul 2011 B2
7975184 Goff et al. Jul 2011 B2
7978627 Taylor et al. Jul 2011 B2
7978686 Goyal et al. Jul 2011 B2
7979069 Hupp et al. Jul 2011 B2
7979889 Gladstone et al. Jul 2011 B2
7979896 McMurtry et al. Jul 2011 B2
7984130 Bogineni et al. Jul 2011 B2
7984511 Kocher et al. Jul 2011 B2
7986935 D'Souza et al. Jul 2011 B1
7987449 Marolia et al. Jul 2011 B1
7987496 Bryce et al. Jul 2011 B2
7987510 Kocher et al. Jul 2011 B2
7990049 Shioya Aug 2011 B2
8000276 Scherzer et al. Aug 2011 B2
8000318 Wiley et al. Aug 2011 B2
8005009 McKee et al. Aug 2011 B2
8005459 Balsillie Aug 2011 B2
8005726 Bao Aug 2011 B1
8005913 Carlander Aug 2011 B1
8005988 Maes Aug 2011 B2
8010080 Thenthiruperai et al. Aug 2011 B1
8010081 Roskowski Aug 2011 B1
8010082 Sutaria et al. Aug 2011 B2
8010623 Fitch et al. Aug 2011 B1
8010990 Ferguson et al. Aug 2011 B2
8015133 Wu et al. Sep 2011 B1
8015234 Lum et al. Sep 2011 B2
8015249 Nayak et al. Sep 2011 B2
8019687 Wang et al. Sep 2011 B2
8019820 Son et al. Sep 2011 B2
8019846 Roelens et al. Sep 2011 B2
8019868 Rao et al. Sep 2011 B2
8019886 Harrang et al. Sep 2011 B2
8023425 Raleigh Sep 2011 B2
8024397 Erickson et al. Sep 2011 B1
8024424 Freimuth et al. Sep 2011 B2
8027339 Short et al. Sep 2011 B2
8031601 Feroz et al. Oct 2011 B2
8032168 Ikaheimo Oct 2011 B2
8032409 Mikurak Oct 2011 B1
8032899 Archer et al. Oct 2011 B2
8036387 Kudelski et al. Oct 2011 B2
8036600 Garrett et al. Oct 2011 B2
8044792 Orr et al. Oct 2011 B2
8045973 Chambers Oct 2011 B2
8046449 Yoshiuchi Oct 2011 B2
8050275 Iyer Nov 2011 B1
8050690 Neeraj Nov 2011 B2
8050705 Sicher et al. Nov 2011 B2
8054778 Polson Nov 2011 B2
8059530 Cole Nov 2011 B1
8060017 Schlicht et al. Nov 2011 B2
8060463 Spiegel Nov 2011 B1
8060603 Gaunter et al. Nov 2011 B2
8064418 Maki Nov 2011 B2
8064896 Bell et al. Nov 2011 B2
8065365 Saxena et al. Nov 2011 B2
8068824 Shan et al. Nov 2011 B2
8068829 Lemond et al. Nov 2011 B2
8073427 Koch et al. Dec 2011 B2
8073721 Lewis Dec 2011 B1
8078140 Baker et al. Dec 2011 B2
8078163 Lemond et al. Dec 2011 B2
8085808 Brusca et al. Dec 2011 B2
8086398 Sanchez et al. Dec 2011 B2
8086497 Oakes, III Dec 2011 B1
8086791 Caulkins Dec 2011 B2
8090359 Proctor, Jr. et al. Jan 2012 B2
8090361 Hagan Jan 2012 B2
8090616 Proctor, Jr. et al. Jan 2012 B2
8091087 Ali et al. Jan 2012 B2
8094551 Huber et al. Jan 2012 B2
8095112 Chow et al. Jan 2012 B2
8095124 Balia Jan 2012 B2
8095175 Todd et al. Jan 2012 B2
8095640 Guingo et al. Jan 2012 B2
8095666 Schmidt et al. Jan 2012 B2
8098579 Ray et al. Jan 2012 B2
8099077 Chowdhury et al. Jan 2012 B2
8099517 Jia et al. Jan 2012 B2
8102814 Rahman et al. Jan 2012 B2
8103285 Kalhan Jan 2012 B2
8104080 Bums et al. Jan 2012 B2
8107953 Zimmerman et al. Jan 2012 B2
8108520 Ruutu et al. Jan 2012 B2
8108680 Murray Jan 2012 B2
8112435 Epstein et al. Feb 2012 B2
8116223 Tian et al. Feb 2012 B2
8116749 Proctor, Jr. et al. Feb 2012 B2
8116781 Chen et al. Feb 2012 B2
8121117 Amdahl et al. Feb 2012 B1
8122128 Burke, II et al. Feb 2012 B2
8122249 Falk et al. Feb 2012 B2
8125897 Ray et al. Feb 2012 B2
8126123 Cai et al. Feb 2012 B2
8126396 Bennett Feb 2012 B2
8126476 Vardi et al. Feb 2012 B2
8126722 Robb et al. Feb 2012 B2
8130793 Edwards et al. Mar 2012 B2
8131256 Martti et al. Mar 2012 B2
8131281 Hildner et al. Mar 2012 B1
8131301 Ahmed et al. Mar 2012 B1
8131840 Denker Mar 2012 B1
8131858 Agulnik et al. Mar 2012 B2
8132256 Bari Mar 2012 B2
8134954 Godfrey et al. Mar 2012 B2
8135388 Gailloux et al. Mar 2012 B1
8135392 Marcelling et al. Mar 2012 B2
8135657 Kapoor et al. Mar 2012 B2
8140690 Ly et al. Mar 2012 B2
8144591 Ghai et al. Mar 2012 B2
8144853 Aboujaoude et al. Mar 2012 B1
8145194 Yoshikawa et al. Mar 2012 B2
8146142 Lortz et al. Mar 2012 B2
8149748 Bata et al. Apr 2012 B2
8149771 Khivesara et al. Apr 2012 B2
8149823 Turcan et al. Apr 2012 B2
8150394 Bianconi et al. Apr 2012 B2
8150431 Wolovitz et al. Apr 2012 B2
8151205 Follmann et al. Apr 2012 B2
8152246 Miller et al. Apr 2012 B2
8155155 Chow et al. Apr 2012 B1
8155620 Wang et al. Apr 2012 B2
8155666 Alizadeh-Shabdiz Apr 2012 B2
8155670 Fullam et al. Apr 2012 B2
8156206 Kiley et al. Apr 2012 B2
8159520 Dhanoa et al. Apr 2012 B1
8160015 Rashid et al. Apr 2012 B2
8160056 Van der Merwe et al. Apr 2012 B2
8160554 Gosselin et al. Apr 2012 B2
8160555 Gosselin et al. Apr 2012 B2
8160556 Gosselin et al. Apr 2012 B2
8160598 Savoor Apr 2012 B2
8165576 Raju et al. Apr 2012 B2
8166040 Brindisi et al. Apr 2012 B2
8166554 John Apr 2012 B2
8170553 Bennett May 2012 B2
8174378 Richman et al. May 2012 B2
8174970 Adamczyk et al. May 2012 B2
8175574 Panda et al. May 2012 B1
8175966 Steinberg et al. May 2012 B2
8180333 Wells et al. May 2012 B1
8180881 Seo et al. May 2012 B2
8180886 Overcash et al. May 2012 B2
8184530 Swan et al. May 2012 B1
8184590 Rosenblatt May 2012 B2
8185088 Klein et al. May 2012 B2
8185093 Jheng et al. May 2012 B2
8185127 Cai et al. May 2012 B1
8185152 Goldner May 2012 B1
8185158 Tamura et al. May 2012 B2
8190087 Fisher et al. May 2012 B2
8190122 Alexander et al. May 2012 B1
8190675 Tribbett May 2012 B2
8191106 Choyi et al. May 2012 B2
8191116 Gazzard May 2012 B1
8191124 Wynn et al. May 2012 B2
8194549 Huber et al. Jun 2012 B2
8194553 Liang et al. Jun 2012 B2
8194572 Horvath et al. Jun 2012 B2
8194581 Schroeder et al. Jun 2012 B1
8195093 Garrett et al. Jun 2012 B2
8195153 Frencel et al. Jun 2012 B1
8195163 Gisby et al. Jun 2012 B2
8195661 Kalavade Jun 2012 B2
8196199 Hrastar et al. Jun 2012 B2
8200163 Hoffman Jun 2012 B2
8200200 Belser et al. Jun 2012 B1
8200509 Kenedy et al. Jun 2012 B2
8200775 Moore Jun 2012 B2
8200818 Freund et al. Jun 2012 B2
8204190 Bang et al. Jun 2012 B2
8204505 Jin et al. Jun 2012 B2
8204794 Peng et al. Jun 2012 B1
8208788 Ando et al. Jun 2012 B2
8208919 Kotecha Jun 2012 B2
8213296 Shannon et al. Jul 2012 B2
8213363 Ying et al. Jul 2012 B2
8214536 Zhao Jul 2012 B2
8214890 Kirovski et al. Jul 2012 B2
8219134 Maharajh et al. Jul 2012 B2
8223655 Heinz et al. Jul 2012 B2
8223741 Bartlett et al. Jul 2012 B1
8224382 Bultman Jul 2012 B2
8224773 Spiegel Jul 2012 B2
8228818 Chase et al. Jul 2012 B2
8229394 Karlberg Jul 2012 B2
8229914 Ramer et al. Jul 2012 B2
8230061 Hassan et al. Jul 2012 B2
8233433 Kalhan Jul 2012 B2
8233878 Gosnell et al. Jul 2012 B2
8233883 De Froment Jul 2012 B2
8233895 Tysowski Jul 2012 B2
8234583 Sloo et al. Jul 2012 B2
8238287 Gopi et al. Aug 2012 B1
8238913 Bhattacharyya et al. Aug 2012 B1
8239520 Grah Aug 2012 B2
8242959 Mia et al. Aug 2012 B2
8244241 Montemurro Aug 2012 B2
8249601 Emberson et al. Aug 2012 B2
8254880 Aaltonen et al. Aug 2012 B2
8254915 Kozisek Aug 2012 B2
8255515 Melman et al. Aug 2012 B1
8255534 Assadzadeh Aug 2012 B2
8255689 Kim et al. Aug 2012 B2
8259692 Bajko Sep 2012 B2
8264965 Dolganow et al. Sep 2012 B2
8265004 Toutonghi Sep 2012 B2
8266249 Hu Sep 2012 B2
8266269 Short et al. Sep 2012 B2
8266681 Deshpande et al. Sep 2012 B2
8270955 Ramer et al. Sep 2012 B2
8270972 Otting et al. Sep 2012 B2
8271025 Brisebois et al. Sep 2012 B2
8271045 Parolkar et al. Sep 2012 B2
8271049 Silver et al. Sep 2012 B2
8271992 Chatley et al. Sep 2012 B2
8275415 Huslak Sep 2012 B2
8275830 Raleigh Sep 2012 B2
8279067 Berger et al. Oct 2012 B2
8279864 Wood Oct 2012 B2
8280351 Ahmed et al. Oct 2012 B1
8280354 Smith et al. Oct 2012 B2
8284740 O'Connor Oct 2012 B2
8285249 Baker et al. Oct 2012 B2
8285992 Mathur et al. Oct 2012 B2
8290820 Plastina et al. Oct 2012 B2
8291238 Ginter et al. Oct 2012 B2
8291439 Jethi et al. Oct 2012 B2
8296404 McDysan et al. Oct 2012 B2
8300575 Willars Oct 2012 B2
8301513 Peng et al. Oct 2012 B1
8306505 Bennett Nov 2012 B2
8306518 Gailloux Nov 2012 B1
8306741 Tu Nov 2012 B2
8307067 Ryan Nov 2012 B2
8307095 Clark et al. Nov 2012 B2
8310943 Mehta et al. Nov 2012 B2
8315198 Corneille et al. Nov 2012 B2
8315593 Gallant et al. Nov 2012 B2
8315594 Mauser et al. Nov 2012 B1
8315718 Caffrey et al. Nov 2012 B2
8315999 Chatley et al. Nov 2012 B2
8320244 Muqattash et al. Nov 2012 B2
8320902 Moring et al. Nov 2012 B2
8320949 Matta Nov 2012 B2
8325638 Jin et al. Dec 2012 B2
8325906 Fullarton et al. Dec 2012 B2
8326319 Davis Dec 2012 B2
8326359 Kauffman Dec 2012 B2
8326828 Zhou et al. Dec 2012 B2
8331223 Hill et al. Dec 2012 B2
8331293 Sood Dec 2012 B2
8332375 Chatley et al. Dec 2012 B2
8332517 Russell Dec 2012 B2
8335161 Foottit et al. Dec 2012 B2
8339991 Biswas et al. Dec 2012 B2
8340625 Johnson et al. Dec 2012 B1
8340628 Taylor et al. Dec 2012 B2
8340644 Sigmund et al. Dec 2012 B2
8340678 Pandey Dec 2012 B1
8340718 Colonna et al. Dec 2012 B2
8346023 Lin Jan 2013 B2
8346210 Balsan et al. Jan 2013 B2
8346225 Raleigh Jan 2013 B2
8346923 Rowles et al. Jan 2013 B2
8347104 Pathiyal Jan 2013 B2
8347362 Cai et al. Jan 2013 B2
8347378 Merkin et al. Jan 2013 B2
8350700 Fast et al. Jan 2013 B2
8351592 Freeny, Jr. et al. Jan 2013 B2
8351898 Raleigh Jan 2013 B2
8352360 De Judicibus et al. Jan 2013 B2
8352630 Hart Jan 2013 B2
8352980 Howcroft Jan 2013 B2
8353001 Herrod Jan 2013 B2
8355570 Karsanbhai et al. Jan 2013 B2
8355696 Olding et al. Jan 2013 B1
8356336 Johnston et al. Jan 2013 B2
8358638 Scherzer et al. Jan 2013 B2
8358975 Bahl et al. Jan 2013 B2
8363658 Delker et al. Jan 2013 B1
8363799 Gruchala et al. Jan 2013 B2
8364089 Phillips Jan 2013 B2
8364806 Short et al. Jan 2013 B2
8369274 Sawai Feb 2013 B2
8370477 Short et al. Feb 2013 B2
8370483 Choong et al. Feb 2013 B2
8374090 Morrill et al. Feb 2013 B2
8374102 Luft et al. Feb 2013 B2
8374592 Proctor, Jr. et al. Feb 2013 B2
8375128 Tofighbakhsh et al. Feb 2013 B2
8375136 Roman et al. Feb 2013 B2
8379847 Bell et al. Feb 2013 B2
8380247 Engstrom Feb 2013 B2
8380804 Jain et al. Feb 2013 B2
8385199 Coward et al. Feb 2013 B1
8385896 Proctor, Jr. et al. Feb 2013 B2
8385964 Haney Feb 2013 B2
8385975 Forutanpour et al. Feb 2013 B2
8386386 Zhu Feb 2013 B1
8391262 Maki et al. Mar 2013 B2
8391834 Raleigh Mar 2013 B2
8392982 Harris et al. Mar 2013 B2
8396458 Raleigh Mar 2013 B2
8396929 Heitman et al. Mar 2013 B2
8401906 Ruckart Mar 2013 B2
8401968 Schattauer et al. Mar 2013 B1
8402165 Deu-Ngoc et al. Mar 2013 B2
8402540 Kapoor et al. Mar 2013 B2
8406427 Chand et al. Mar 2013 B2
8406736 Das et al. Mar 2013 B2
8406756 Reeves et al. Mar 2013 B1
8407472 Hao et al. Mar 2013 B2
8407763 Weller et al. Mar 2013 B2
8411587 Curtis et al. Apr 2013 B2
8411691 Aggarwal Apr 2013 B2
8412798 Wang Apr 2013 B1
8413245 Kraemer et al. Apr 2013 B2
8418168 Tyhurst et al. Apr 2013 B2
8422988 Keshav Apr 2013 B1
8423016 Buckley et al. Apr 2013 B2
8429403 Moret et al. Apr 2013 B2
8437734 Ray et al. May 2013 B2
8441955 Wilkinson et al. May 2013 B2
8442015 Behzad et al. May 2013 B2
8442507 Duggal et al. May 2013 B2
8443390 Lo et al. May 2013 B2
8446831 Kwan et al. May 2013 B2
8447324 Shuman et al. May 2013 B2
8447607 Weider et al. May 2013 B2
8447980 Godfrey et al. May 2013 B2
8448015 Gerhart May 2013 B2
8452858 Wu et al. May 2013 B2
8457603 El-Kadri et al. Jun 2013 B2
8461958 Saenz et al. Jun 2013 B2
8463194 Erlenback et al. Jun 2013 B2
8463232 Tuli et al. Jun 2013 B2
8468337 Gaur et al. Jun 2013 B2
8472371 Bari et al. Jun 2013 B1
8477778 Lehmann, Jr. et al. Jul 2013 B2
8478840 Skutela et al. Jul 2013 B2
8483057 Cuervo Jul 2013 B2
8483135 Cai et al. Jul 2013 B2
8483694 Lewis et al. Jul 2013 B2
8484327 Werner et al. Jul 2013 B2
8484568 Rados et al. Jul 2013 B2
8488597 Nie et al. Jul 2013 B2
8489110 Frank et al. Jul 2013 B2
8489720 Morford et al. Jul 2013 B1
8494559 Malmi Jul 2013 B1
8495181 Venkatraman et al. Jul 2013 B2
8495207 Lee Jul 2013 B2
8495227 Kaminsky et al. Jul 2013 B2
8495360 Falk et al. Jul 2013 B2
8495700 Shahbazi Jul 2013 B2
8495743 Kraemer et al. Jul 2013 B2
8499087 Hu Jul 2013 B2
RE44412 Naqvi et al. Aug 2013 E
8500533 Lutnick et al. Aug 2013 B2
8503358 Hanson et al. Aug 2013 B2
8503455 Heikens Aug 2013 B2
8504032 Lott et al. Aug 2013 B2
8504574 Dvorak et al. Aug 2013 B2
8504687 Maffione et al. Aug 2013 B2
8504690 Shah et al. Aug 2013 B2
8504729 Pezzutti Aug 2013 B2
8505073 Taglienti et al. Aug 2013 B2
8509082 Heinz et al. Aug 2013 B2
8510743 Hackborn et al. Aug 2013 B2
8510804 Bonn et al. Aug 2013 B1
8514927 Sundararajan et al. Aug 2013 B2
8516552 Raleigh Aug 2013 B2
8520589 Bhatt et al. Aug 2013 B2
8520595 Yadav et al. Aug 2013 B2
8521110 Rofougaran Aug 2013 B2
8521775 Poh et al. Aug 2013 B1
8522039 Hyndman et al. Aug 2013 B2
8522249 Beaule Aug 2013 B2
8522337 Adusumilli et al. Aug 2013 B2
8523547 Pekrul Sep 2013 B2
8526329 Mahany et al. Sep 2013 B2
8526350 Xue et al. Sep 2013 B2
8527013 Guba et al. Sep 2013 B2
8527410 Markki et al. Sep 2013 B2
8527662 Biswas et al. Sep 2013 B2
8528068 Weglein et al. Sep 2013 B1
8531954 McNaughton et al. Sep 2013 B2
8531995 Khan et al. Sep 2013 B2
8532610 Manning Cassett et al. Sep 2013 B2
8533341 Aguirre et al. Sep 2013 B2
8533775 Alcorn et al. Sep 2013 B2
8535160 Lutnick et al. Sep 2013 B2
8538394 Zimmerman et al. Sep 2013 B2
8538421 Brisebois et al. Sep 2013 B2
8538458 Haney Sep 2013 B2
8539544 Garimella et al. Sep 2013 B2
8539561 Gupta et al. Sep 2013 B2
8543265 Ekhaguere et al. Sep 2013 B2
8543814 Laitinen et al. Sep 2013 B2
8544105 Mclean et al. Sep 2013 B2
8548427 Chow et al. Oct 2013 B2
8548428 Raleigh Oct 2013 B2
8549173 Wu et al. Oct 2013 B1
8554876 Winsor Oct 2013 B2
8559369 Barkan Oct 2013 B2
8561138 Rothman et al. Oct 2013 B2
8565746 Hoffman Oct 2013 B2
8566236 Busch Oct 2013 B2
8571474 Chavez et al. Oct 2013 B2
8571501 Miller et al. Oct 2013 B2
8571598 Valavi Oct 2013 B2
8571993 Kocher et al. Oct 2013 B2
8572117 Rappaport Oct 2013 B2
8572256 Babbar Oct 2013 B2
8583499 De Judicibus et al. Nov 2013 B2
8584226 Kudla et al. Nov 2013 B2
8588240 Ramankutty et al. Nov 2013 B2
8589541 Raleigh et al. Nov 2013 B2
8589955 Roundtree et al. Nov 2013 B2
8594626 Woodson et al. Nov 2013 B1
8594665 Anschutz Nov 2013 B2
8595186 Mandyam et al. Nov 2013 B1
8600850 Zabawskyj et al. Dec 2013 B2
8600895 Felsher Dec 2013 B2
8601125 Huang et al. Dec 2013 B2
8605691 Soomro et al. Dec 2013 B2
8609911 Nicholas et al. Dec 2013 B1
8611919 Bames, Jr. Dec 2013 B2
8615507 Varadarajulu et al. Dec 2013 B2
8619735 Montemurro et al. Dec 2013 B2
8620257 Qiu et al. Dec 2013 B2
8620281 Gosselin et al. Dec 2013 B2
8621056 Coussemaeker et al. Dec 2013 B2
8630314 York Jan 2014 B2
8630925 Bystrom et al. Jan 2014 B2
8631428 Scott et al. Jan 2014 B2
8634425 Gorti et al. Jan 2014 B2
8635164 Rosenhaft et al. Jan 2014 B2
8635335 Raleigh et al. Jan 2014 B2
8639215 McGregor et al. Jan 2014 B2
8644702 Kalajan Feb 2014 B1
8644813 Gailloux et al. Feb 2014 B1
8645518 David Feb 2014 B2
8654952 Wang et al. Feb 2014 B2
8655357 Gazzard et al. Feb 2014 B1
8656472 McMurtry et al. Feb 2014 B2
8660853 Robb et al. Feb 2014 B2
8666395 Silver Mar 2014 B2
8667542 Bertz et al. Mar 2014 B1
8670334 Keohane et al. Mar 2014 B2
8670752 Fan et al. Mar 2014 B2
8675507 Raleigh Mar 2014 B2
8675852 Maes Mar 2014 B2
8676682 Kalliola Mar 2014 B2
8676925 Liu et al. Mar 2014 B1
8688671 Ramer et al. Apr 2014 B2
8688784 Zabawskyj et al. Apr 2014 B2
8693323 McDysan Apr 2014 B1
8694772 Kao et al. Apr 2014 B2
8699355 Macias Apr 2014 B2
8700729 Dua Apr 2014 B2
8701015 Bonnat Apr 2014 B2
8701080 Tripathi Apr 2014 B2
8705361 Venkataraman et al. Apr 2014 B2
8706863 Fadell Apr 2014 B2
8713535 Malhotra et al. Apr 2014 B2
8713641 Pagan et al. Apr 2014 B1
8719397 Levi et al. May 2014 B2
8719423 Wyld May 2014 B2
8724486 Seto et al. May 2014 B2
8725700 Rappaport May 2014 B2
8725899 Short et al. May 2014 B2
8730842 CoIlins et al. May 2014 B2
8731519 Flynn et al. May 2014 B2
8732808 Sewall et al. May 2014 B2
8738860 Griffin et al. May 2014 B1
8739035 Trethewey May 2014 B2
8742694 Bora et al. Jun 2014 B2
8744339 Halfmann et al. Jun 2014 B2
8761711 Grignani et al. Jun 2014 B2
8761809 Faith et al. Jun 2014 B2
8775233 Lybrook et al. Jul 2014 B1
8780857 Balasubramanian et al. Jul 2014 B2
8787249 Giaretta et al. Jul 2014 B2
8792857 Cai et al. Jul 2014 B2
8793304 Lu et al. Jul 2014 B2
8793758 Raleigh et al. Jul 2014 B2
8798610 Prakash et al. Aug 2014 B2
8799440 Zhou et al. Aug 2014 B2
8804517 Oerton Aug 2014 B2
8804695 Branam Aug 2014 B2
8811338 Jin et al. Aug 2014 B2
8811991 Jain et al. Aug 2014 B2
8812525 Taylor, III Aug 2014 B1
8818394 Bienas et al. Aug 2014 B2
8819253 Simeloff et al. Aug 2014 B2
8825109 Montemurro et al. Sep 2014 B2
8826411 Moen et al. Sep 2014 B2
8831561 Sutaria et al. Sep 2014 B2
8837322 Venkataramanan et al. Sep 2014 B2
8838686 Getchius Sep 2014 B2
8838752 Lor et al. Sep 2014 B2
8839388 Raleigh Sep 2014 B2
8843849 Neil et al. Sep 2014 B2
8845415 Lutnick et al. Sep 2014 B2
8849262 Desai et al. Sep 2014 B2
8849297 Balasubramanian Sep 2014 B2
8855620 Sievers et al. Oct 2014 B2
8856015 Mesaros Oct 2014 B2
8862751 Faccin et al. Oct 2014 B2
8863111 Selitser et al. Oct 2014 B2
8868725 Samba Oct 2014 B2
8868727 Yumerefendi et al. Oct 2014 B2
8875042 LeJeune et al. Oct 2014 B2
8880047 Konicek et al. Nov 2014 B2
8891483 Connelly et al. Nov 2014 B2
8898748 Burks et al. Nov 2014 B2
8908516 Tzamaloukas et al. Dec 2014 B2
8924469 Raleigh et al. Dec 2014 B2
8929374 Tonsingetai. Jan 2015 B2
8930238 Coffman et al. Jan 2015 B2
8930551 Pandya et al. Jan 2015 B2
8943551 Ganapathy et al. Jan 2015 B2
8948198 Nee et al. Feb 2015 B2
8948726 Smith et al. Feb 2015 B2
8949382 Cornett et al. Feb 2015 B2
8949597 Reeves et al. Feb 2015 B1
8955038 Nicodemus et al. Feb 2015 B2
8966018 Bugwadia et al. Feb 2015 B2
8971841 Menezes et al. Mar 2015 B2
8971912 Chou et al. Mar 2015 B2
8977284 Reed Mar 2015 B2
8995952 Baker et al. Mar 2015 B1
9002342 Tenhunen et al. Apr 2015 B2
9008653 Sparks et al. Apr 2015 B2
9014059 Richardson et al. Apr 2015 B2
9014973 Ruckart Apr 2015 B2
9015331 Lai et al. Apr 2015 B2
9026100 Castro et al. May 2015 B2
9030934 Shah et al. May 2015 B2
9032427 Gallant et al. May 2015 B2
9049010 Jueneman et al. Jun 2015 B2
9064275 Lu et al. Jun 2015 B1
9105031 Shen et al. Aug 2015 B2
9106414 Laves Aug 2015 B2
9107053 Davis et al. Aug 2015 B2
9111088 Ghai et al. Aug 2015 B2
9135037 Petrescu-Prahova et al. Sep 2015 B1
9137286 Yuan Sep 2015 B1
9143933 Ikeda et al. Sep 2015 B2
9172553 Dawes et al. Oct 2015 B2
9173090 Tuchman et al. Oct 2015 B2
9177455 Remer Nov 2015 B2
9183524 Carter Nov 2015 B2
9225847 Daymond et al. Dec 2015 B2
9252977 Levi et al. Feb 2016 B2
9262370 Hofstaedter et al. Feb 2016 B2
9265003 Zhao et al. Feb 2016 B2
9277433 Raleigh et al. Mar 2016 B2
9282460 Souissi Mar 2016 B2
9286469 Kraemer et al. Mar 2016 B2
9286604 Aabye et al. Mar 2016 B2
9288276 Adamczyk et al. Mar 2016 B2
9313708 Nam et al. Apr 2016 B2
9325737 Gutowski et al. Apr 2016 B2
9326173 Luft Apr 2016 B2
9344557 Gruchala et al. May 2016 B2
9350842 Swanburg et al. May 2016 B2
9363285 Kitamura Jun 2016 B2
9367680 Mahaffey et al. Jun 2016 B2
9402254 Kneckt et al. Jul 2016 B2
9413546 Meier et al. Aug 2016 B2
9418381 Ahuja et al. Aug 2016 B2
9419867 Okholm et al. Aug 2016 B2
9436805 Kravets Sep 2016 B1
9438642 Alberth, Jr. et al. Sep 2016 B2
9479917 Gota et al. Oct 2016 B1
9491199 Raleigh et al. Nov 2016 B2
9501803 Bilac et al. Nov 2016 B2
9525992 Rao et al. Dec 2016 B2
9534861 Kellgren Jan 2017 B1
9544397 Raleigh et al. Jan 2017 B2
9557889 Raleigh et al. Jan 2017 B2
9589117 Ali et al. Mar 2017 B2
9609459 Raleigh Mar 2017 B2
9609510 Raleigh et al. Mar 2017 B2
9609544 Raleigh Mar 2017 B2
9615192 Raleigh Apr 2017 B2
9634850 Taft et al. Apr 2017 B2
9642004 Wang et al. May 2017 B2
9648022 Peterka et al. May 2017 B2
9673996 Upadhyay et al. Jun 2017 B1
9680658 Goel et al. Jun 2017 B2
9681003 Kim et al. Jun 2017 B1
9691082 Bumett et al. Jun 2017 B1
9712443 Phaal Jul 2017 B1
9712476 Boynton et al. Jul 2017 B2
9749899 Raleigh et al. Aug 2017 B2
9755842 Raleigh et al. Sep 2017 B2
9766873 Steigleder Sep 2017 B2
9852426 Bacastow Dec 2017 B2
9923790 Patel et al. Mar 2018 B2
9942796 Raleigh Apr 2018 B2
9954975 Raleigh et al. Apr 2018 B2
9986413 Raleigh May 2018 B2
10002332 Spong Jun 2018 B2
10021251 Aaron et al. Jul 2018 B2
10021463 Qiu et al. Jul 2018 B2
10024948 Ganick et al. Jul 2018 B2
10034220 Silver Jul 2018 B2
10057775 Raleigh et al. Aug 2018 B2
10064033 Raleigh Aug 2018 B2
10171681 Raleigh et al. Jan 2019 B2
10171988 Raleigh et al. Jan 2019 B2
10171990 Raleigh et al. Jan 2019 B2
10178554 Pawar Jan 2019 B2
10237773 Raleigh et al. Mar 2019 B2
10248996 Raleigh Apr 2019 B2
10264138 Raleigh et al. Apr 2019 B2
10285025 Baker et al. May 2019 B1
10321515 Shen et al. Jun 2019 B2
10395216 Coffing Aug 2019 B2
10462627 Raleigh et al. Oct 2019 B2
10492102 Raleigh et al. Nov 2019 B2
10521781 Singfield Dec 2019 B1
10523726 Pantos et al. Dec 2019 B2
10536983 Raleigh et al. Jan 2020 B2
10567930 Silver Feb 2020 B2
10582375 Raleigh Mar 2020 B2
10616818 Silver Apr 2020 B2
20010048738 Baniak et al. Dec 2001 A1
20010053694 Igarashi et al. Dec 2001 A1
20020013844 Garrett et al. Jan 2002 A1
20020022472 Watler et al. Feb 2002 A1
20020022483 Thompson et al. Feb 2002 A1
20020049074 Eisinger et al. Apr 2002 A1
20020085516 Bridgelall Jul 2002 A1
20020099848 Lee Jul 2002 A1
20020116338 Gonthier et al. Aug 2002 A1
20020120370 Parupudi et al. Aug 2002 A1
20020120540 Kende et al. Aug 2002 A1
20020131397 Patel et al. Sep 2002 A1
20020131404 Mehta et al. Sep 2002 A1
20020138599 Dilman et al. Sep 2002 A1
20020138601 Piponius et al. Sep 2002 A1
20020154751 Thompson et al. Oct 2002 A1
20020161601 Nauer et al. Oct 2002 A1
20020164983 Raviv et al. Nov 2002 A1
20020176377 Hamilton Nov 2002 A1
20020188732 Buckman et al. Dec 2002 A1
20020191573 Whitehill et al. Dec 2002 A1
20020199001 Wenocur et al. Dec 2002 A1
20030004937 Salmenkaita et al. Jan 2003 A1
20030005112 Krautkremer Jan 2003 A1
20030013434 Rosenberg et al. Jan 2003 A1
20030018524 Fishman et al. Jan 2003 A1
20030028623 Hennessey et al. Feb 2003 A1
20030046396 Richter et al. Mar 2003 A1
20030050070 Mashinsky et al. Mar 2003 A1
20030050837 Kim Mar 2003 A1
20030060189 Minear et al. Mar 2003 A1
20030084321 Tarquini et al. May 2003 A1
20030088671 Klinker et al. May 2003 A1
20030133408 Cheng et al. Jul 2003 A1
20030134650 Sundar et al. Jul 2003 A1
20030159030 Evans Aug 2003 A1
20030161265 Cao et al. Aug 2003 A1
20030171112 Lupper et al. Sep 2003 A1
20030182420 Jones et al. Sep 2003 A1
20030182435 Redlich et al. Sep 2003 A1
20030184793 Pineau Oct 2003 A1
20030188006 Bard Oct 2003 A1
20030188117 Yoshino et al. Oct 2003 A1
20030191646 D'Avello et al. Oct 2003 A1
20030220984 Jones et al. Nov 2003 A1
20030224781 Milford et al. Dec 2003 A1
20030229900 Reisman Dec 2003 A1
20030233332 Keeler et al. Dec 2003 A1
20030236745 Hartsell et al. Dec 2003 A1
20040019539 Raman et al. Jan 2004 A1
20040019564 Goldthwaite et al. Jan 2004 A1
20040021697 Beaton et al. Feb 2004 A1
20040024756 Rickard Feb 2004 A1
20040030705 Bowman-Amuah Feb 2004 A1
20040039792 Nakanishi Feb 2004 A1
20040044623 Wake et al. Mar 2004 A1
20040047358 Chen et al. Mar 2004 A1
20040054779 Takeshima et al. Mar 2004 A1
20040073672 Fascenda Apr 2004 A1
20040082346 Skytt et al. Apr 2004 A1
20040098715 Aghera et al. May 2004 A1
20040102182 Reith et al. May 2004 A1
20040103193 Pandya et al. May 2004 A1
20040107360 Herrmann et al. Jun 2004 A1
20040114553 Jiang et al. Jun 2004 A1
20040116140 Babbar et al. Jun 2004 A1
20040123153 Wright et al. Jun 2004 A1
20040127200 Shaw et al. Jul 2004 A1
20040127208 Nair et al. Jul 2004 A1
20040127256 Goldthwaite et al. Jul 2004 A1
20040132427 Lee et al. Jul 2004 A1
20040133668 Nicholas, III Jul 2004 A1
20040137890 Kalke Jul 2004 A1
20040165596 Garcia et al. Aug 2004 A1
20040167958 Stewart et al. Aug 2004 A1
20040168052 Clisham et al. Aug 2004 A1
20040170191 Guo et al. Sep 2004 A1
20040176104 Arcens Sep 2004 A1
20040198331 Coward et al. Oct 2004 A1
20040203755 Brunet et al. Oct 2004 A1
20040203833 Rathunde et al. Oct 2004 A1
20040225561 Hertzberg et al. Nov 2004 A1
20040225898 Frost et al. Nov 2004 A1
20040236547 Rappaport et al. Nov 2004 A1
20040243680 Mayer Dec 2004 A1
20040243992 Gustafson et al. Dec 2004 A1
20040249918 Sunshine Dec 2004 A1
20040255145 Chow Dec 2004 A1
20040259534 Chaudhari et al. Dec 2004 A1
20040260766 Barros et al. Dec 2004 A1
20040267872 Serdy et al. Dec 2004 A1
20040268351 Mogensen et al. Dec 2004 A1
20050007993 Chambers et al. Jan 2005 A1
20050009499 Koster Jan 2005 A1
20050021995 Lal et al. Jan 2005 A1
20050041617 Huotari et al. Feb 2005 A1
20050048950 Morper Mar 2005 A1
20050055291 Bevente et al. Mar 2005 A1
20050055309 Williams et al. Mar 2005 A1
20050055595 Frazer et al. Mar 2005 A1
20050060266 Demello et al. Mar 2005 A1
20050060525 Schwartz et al. Mar 2005 A1
20050075115 Corneille et al. Apr 2005 A1
20050079863 Macaluso Apr 2005 A1
20050091505 Riley et al. Apr 2005 A1
20050096024 Bicker et al. May 2005 A1
20050097516 Donnelly et al. May 2005 A1
20050107091 Vannithamby et al. May 2005 A1
20050108075 Douglis et al. May 2005 A1
20050111463 Leung et al. May 2005 A1
20050128967 Scobbie Jun 2005 A1
20050135264 Popoff et al. Jun 2005 A1
20050163320 Brown et al. Jul 2005 A1
20050166043 Zhang et al. Jul 2005 A1
20050183143 Anderholm et al. Aug 2005 A1
20050186948 Gallagher et al. Aug 2005 A1
20050198377 Ferguson et al. Sep 2005 A1
20050216421 Barry et al. Sep 2005 A1
20050226178 Forand et al. Oct 2005 A1
20050228985 Ylikoski et al. Oct 2005 A1
20050238046 Hassan et al. Oct 2005 A1
20050239447 Holzman et al. Oct 2005 A1
20050245241 Durand et al. Nov 2005 A1
20050246282 Naslund et al. Nov 2005 A1
20050250508 Guo et al. Nov 2005 A1
20050250536 Deng et al. Nov 2005 A1
20050254435 Moakley et al. Nov 2005 A1
20050266825 Clayton Dec 2005 A1
20050266880 Gupta Dec 2005 A1
20060014519 Marsh et al. Jan 2006 A1
20060019632 Cunningham et al. Jan 2006 A1
20060020787 Choyi et al. Jan 2006 A1
20060026679 Zakas Feb 2006 A1
20060030306 Kuhn Feb 2006 A1
20060034256 Addagatla et al. Feb 2006 A1
20060035631 White et al. Feb 2006 A1
20060040642 Boris et al. Feb 2006 A1
20060045245 Aaron et al. Mar 2006 A1
20060048223 Lee et al. Mar 2006 A1
20060068796 Millen et al. Mar 2006 A1
20060072451 Ross Apr 2006 A1
20060072550 Davis et al. Apr 2006 A1
20060072646 Feher Apr 2006 A1
20060075506 Sanda et al. Apr 2006 A1
20060085543 Hrastar et al. Apr 2006 A1
20060095517 O'Connor et al. May 2006 A1
20060098627 Karaoguz et al. May 2006 A1
20060099970 Morgan et al. May 2006 A1
20060101507 Camenisch May 2006 A1
20060112016 Ishibashi May 2006 A1
20060114821 Willey et al. Jun 2006 A1
20060114832 Hamilton et al. Jun 2006 A1
20060126562 Liu Jun 2006 A1
20060135144 Jothipragasam Jun 2006 A1
20060136882 Noonan et al. Jun 2006 A1
20060143066 Calabria Jun 2006 A1
20060143098 Lazaridis Jun 2006 A1
20060156398 Ross et al. Jul 2006 A1
20060160536 Chou Jul 2006 A1
20060165060 Dua Jul 2006 A1
20060168128 Sistla et al. Jul 2006 A1
20060173959 Mckelvie et al. Aug 2006 A1
20060174035 Tufail Aug 2006 A1
20060178917 Merriam et al. Aug 2006 A1
20060178918 Mikurak Aug 2006 A1
20060178943 Rollinson et al. Aug 2006 A1
20060182137 Zhou et al. Aug 2006 A1
20060183462 Kolehmainen Aug 2006 A1
20060190314 Hernandez Aug 2006 A1
20060190987 Ohta et al. Aug 2006 A1
20060193280 Lee et al. Aug 2006 A1
20060199608 Dunn et al. Sep 2006 A1
20060200663 Thornton Sep 2006 A1
20060206709 Labrou et al. Sep 2006 A1
20060206904 Watkins et al. Sep 2006 A1
20060218395 Maes Sep 2006 A1
20060221829 Holmstrom et al. Oct 2006 A1
20060233108 Krishnan Oct 2006 A1
20060233166 Bou-Diab et al. Oct 2006 A1
20060236095 Smith et al. Oct 2006 A1
20060242685 Heard et al. Oct 2006 A1
20060258341 Miller et al. Nov 2006 A1
20060274706 Chen et al. Dec 2006 A1
20060277590 Limont et al. Dec 2006 A1
20060291419 McConnell et al. Dec 2006 A1
20060291477 Croak et al. Dec 2006 A1
20070005795 Gonzalez Jan 2007 A1
20070019670 Falardeau Jan 2007 A1
20070022289 Alt et al. Jan 2007 A1
20070025301 Petersson et al. Feb 2007 A1
20070033194 Srinivas et al. Feb 2007 A1
20070033197 Scherzer et al. Feb 2007 A1
20070035390 Thomas et al. Feb 2007 A1
20070036312 Cai et al. Feb 2007 A1
20070038763 Oestvall Feb 2007 A1
20070055694 Ruge et al. Mar 2007 A1
20070060200 Boris et al. Mar 2007 A1
20070061243 Ramer et al. Mar 2007 A1
20070061800 Cheng et al. Mar 2007 A1
20070061878 Hagiu et al. Mar 2007 A1
20070073899 Judge et al. Mar 2007 A1
20070076616 Ngo et al. Apr 2007 A1
20070093243 Kapadekar et al. Apr 2007 A1
20070100981 Adamczyk et al. May 2007 A1
20070101426 Lee et al. May 2007 A1
20070104126 Calhoun et al. May 2007 A1
20070109983 Shankar et al. May 2007 A1
20070111740 Wandel May 2007 A1
20070124077 Hedlund May 2007 A1
20070130283 Klein et al. Jun 2007 A1
20070130315 Friend et al. Jun 2007 A1
20070140113 Gemelos Jun 2007 A1
20070140145 Kumar et al. Jun 2007 A1
20070140275 Bowman et al. Jun 2007 A1
20070143824 Shahbazi Jun 2007 A1
20070147317 Smith et al. Jun 2007 A1
20070147324 McGary Jun 2007 A1
20070155365 Kim et al. Jul 2007 A1
20070165630 Rasanen et al. Jul 2007 A1
20070168499 Chu Jul 2007 A1
20070171856 Bruce et al. Jul 2007 A1
20070174490 Choi et al. Jul 2007 A1
20070191006 Carpenter Aug 2007 A1
20070192460 Choi et al. Aug 2007 A1
20070198656 Mazzaferri et al. Aug 2007 A1
20070201502 Abramson Aug 2007 A1
20070213054 Han Sep 2007 A1
20070220251 Rosenberg et al. Sep 2007 A1
20070226225 Yiu et al. Sep 2007 A1
20070226775 Andreasen et al. Sep 2007 A1
20070234402 Khosravi et al. Oct 2007 A1
20070242619 Murakami Oct 2007 A1
20070242659 Cantu et al. Oct 2007 A1
20070243862 Coskun Oct 2007 A1
20070244965 Dowling Oct 2007 A1
20070248100 Zuberi et al. Oct 2007 A1
20070254646 Sokondar Nov 2007 A1
20070254675 Zorlu Ozer et al. Nov 2007 A1
20070255769 Agrawal et al. Nov 2007 A1
20070255797 Dunn et al. Nov 2007 A1
20070255848 Sewall et al. Nov 2007 A1
20070256128 Jung et al. Nov 2007 A1
20070257767 Beeson Nov 2007 A1
20070259656 Jeong Nov 2007 A1
20070259673 Willars et al. Nov 2007 A1
20070263558 Salomone Nov 2007 A1
20070265003 Kezys et al. Nov 2007 A1
20070266422 Germano et al. Nov 2007 A1
20070274327 Kaarela et al. Nov 2007 A1
20070280453 Kelley Dec 2007 A1
20070282896 Wydroug et al. Dec 2007 A1
20070293191 Mir et al. Dec 2007 A1
20070294395 Strub et al. Dec 2007 A1
20070294410 Pandya et al. Dec 2007 A1
20070297378 Poyhonen et al. Dec 2007 A1
20070298764 Clayton Dec 2007 A1
20070299965 Nieh et al. Dec 2007 A1
20070300252 Acharya et al. Dec 2007 A1
20080005285 Robinson et al. Jan 2008 A1
20080005561 Brown et al. Jan 2008 A1
20080010379 Zhao Jan 2008 A1
20080010452 Holtzman et al. Jan 2008 A1
20080018494 Waite et al. Jan 2008 A1
20080022354 Grewal et al. Jan 2008 A1
20080025230 Patel et al. Jan 2008 A1
20080032715 Jia et al. Feb 2008 A1
20080034063 Yee Feb 2008 A1
20080034419 Mullick et al. Feb 2008 A1
20080039102 Sewall et al. Feb 2008 A1
20080049630 Kozisek et al. Feb 2008 A1
20080050715 Golczewski et al. Feb 2008 A1
20080051076 O'Shaughnessy et al. Feb 2008 A1
20080052387 Heinz et al. Feb 2008 A1
20080056273 Pelletier et al. Mar 2008 A1
20080057894 Aleksic et al. Mar 2008 A1
20080059474 Lim Mar 2008 A1
20080059743 Bychkov et al. Mar 2008 A1
20080060066 Wynn et al. Mar 2008 A1
20080062900 Rao Mar 2008 A1
20080064367 Nath et al. Mar 2008 A1
20080066149 Lim Mar 2008 A1
20080066150 Lim Mar 2008 A1
20080066181 Haveson et al. Mar 2008 A1
20080070550 Hose Mar 2008 A1
20080077705 Li et al. Mar 2008 A1
20080080457 Cole Apr 2008 A1
20080081606 Cole Apr 2008 A1
20080082643 Storrie et al. Apr 2008 A1
20080083013 Soliman et al. Apr 2008 A1
20080085707 Fadell Apr 2008 A1
20080089295 Keeler et al. Apr 2008 A1
20080089303 Wirtanen et al. Apr 2008 A1
20080095339 Elliott et al. Apr 2008 A1
20080096559 Phillips et al. Apr 2008 A1
20080098062 Balia Apr 2008 A1
20080101291 Jiang et al. May 2008 A1
20080109679 Wright et al. May 2008 A1
20080120129 Seubert et al. May 2008 A1
20080120668 Yau May 2008 A1
20080120688 Qiu et al. May 2008 A1
20080125079 O'Neil et al. May 2008 A1
20080126287 Cox et al. May 2008 A1
20080127304 Ginter et al. May 2008 A1
20080130534 Tomioka Jun 2008 A1
20080130656 Kim et al. Jun 2008 A1
20080132201 Karlberg Jun 2008 A1
20080132268 Choi-Grogan et al. Jun 2008 A1
20080134330 Kapoor et al. Jun 2008 A1
20080139210 Gisby et al. Jun 2008 A1
20080147454 Walker et al. Jun 2008 A1
20080148402 Bogineni et al. Jun 2008 A1
20080160958 Abichandani et al. Jul 2008 A1
20080161041 Pemu Jul 2008 A1
20080162637 Adamczyk et al. Jul 2008 A1
20080162704 Poplett et al. Jul 2008 A1
20080164304 Narasimhan et al. Jul 2008 A1
20080166993 Gautier et al. Jul 2008 A1
20080167027 Gautier et al. Jul 2008 A1
20080167033 Beckers Jul 2008 A1
20080168275 DeAtley et al. Jul 2008 A1
20080168523 Ansari et al. Jul 2008 A1
20080177998 Apsangi et al. Jul 2008 A1
20080178300 Brown et al. Jul 2008 A1
20080181117 Acke et al. Jul 2008 A1
20080183811 Kotras et al. Jul 2008 A1
20080183812 Paul et al. Jul 2008 A1
20080184127 Rafey et al. Jul 2008 A1
20080189760 Rosenberg et al. Aug 2008 A1
20080201266 Chua et al. Aug 2008 A1
20080207167 Bugenhagen Aug 2008 A1
20080212470 Castaneda et al. Sep 2008 A1
20080212751 Chung Sep 2008 A1
20080219268 Dennison Sep 2008 A1
20080221951 Stanforth et al. Sep 2008 A1
20080222692 Andersson et al. Sep 2008 A1
20080225748 Khemani et al. Sep 2008 A1
20080229385 Feder et al. Sep 2008 A1
20080229388 Maes Sep 2008 A1
20080235511 O'Brien et al. Sep 2008 A1
20080240373 Wilhelm Oct 2008 A1
20080242290 Bhatia Oct 2008 A1
20080250053 Aaltonen et al. Oct 2008 A1
20080256593 Vinberg et al. Oct 2008 A1
20080259924 Gooch et al. Oct 2008 A1
20080262798 Kim et al. Oct 2008 A1
20080263348 Zaltsman et al. Oct 2008 A1
20080268813 Maes Oct 2008 A1
20080270212 Blight et al. Oct 2008 A1
20080279216 Sharif-Ahmadi et al. Nov 2008 A1
20080280656 Gonikberg et al. Nov 2008 A1
20080282319 Fontijn et al. Nov 2008 A1
20080291872 Henriksson Nov 2008 A1
20080293395 Mathews et al. Nov 2008 A1
20080298230 Luft et al. Dec 2008 A1
20080305793 Gallagher et al. Dec 2008 A1
20080311885 Dawson et al. Dec 2008 A1
20080313315 Karaoguz et al. Dec 2008 A1
20080313730 Iftimie et al. Dec 2008 A1
20080316923 Fedders et al. Dec 2008 A1
20080316983 Daigle Dec 2008 A1
20080318547 Ballou et al. Dec 2008 A1
20080318550 DeAtley Dec 2008 A1
20080319879 Carroll et al. Dec 2008 A1
20080320497 Tarkoma et al. Dec 2008 A1
20090005000 Baker et al. Jan 2009 A1
20090005005 Forstall et al. Jan 2009 A1
20090006116 Baker et al. Jan 2009 A1
20090006200 Baker et al. Jan 2009 A1
20090006229 Sweeney et al. Jan 2009 A1
20090013157 Beaule Jan 2009 A1
20090016310 Rasal Jan 2009 A1
20090017809 Jethi et al. Jan 2009 A1
20090036111 Danford et al. Feb 2009 A1
20090042536 Bernard et al. Feb 2009 A1
20090044185 Krivopaltsev Feb 2009 A1
20090046707 Smires et al. Feb 2009 A1
20090046723 Rahman et al. Feb 2009 A1
20090047989 Harmon et al. Feb 2009 A1
20090048913 Shenfield et al. Feb 2009 A1
20090049156 Aronsson et al. Feb 2009 A1
20090049518 Roman et al. Feb 2009 A1
20090054030 Golds Feb 2009 A1
20090054061 Dawson Feb 2009 A1
20090065571 Jain Mar 2009 A1
20090067372 Shah et al. Mar 2009 A1
20090068984 Burnett Mar 2009 A1
20090070379 Rappaport Mar 2009 A1
20090077622 Baum et al. Mar 2009 A1
20090079699 Sun Mar 2009 A1
20090109898 Adams Apr 2009 A1
20090113514 Hu Apr 2009 A1
20090125619 Antani May 2009 A1
20090132860 Liu et al. May 2009 A1
20090149154 Bhasin et al. Jun 2009 A1
20090154348 Newman Jun 2009 A1
20090157792 Fiatal Jun 2009 A1
20090163173 Williams Jun 2009 A1
20090170554 Want et al. Jul 2009 A1
20090172077 Roxburgh et al. Jul 2009 A1
20090180391 Petersen et al. Jul 2009 A1
20090181662 Fleischman et al. Jul 2009 A1
20090197585 Aaron Aug 2009 A1
20090197612 Kiiskinen Aug 2009 A1
20090203352 Fordon et al. Aug 2009 A1
20090217065 Araujo, Jr. Aug 2009 A1
20090217364 Salmela et al. Aug 2009 A1
20090219170 Clark et al. Sep 2009 A1
20090248883 Suryanarayana et al. Oct 2009 A1
20090254857 Romine et al. Oct 2009 A1
20090257379 Robinson et al. Oct 2009 A1
20090261783 Gonzales et al. Oct 2009 A1
20090271514 Thomas et al. Oct 2009 A1
20090282127 Leblanc et al. Nov 2009 A1
20090286507 O'Neil et al. Nov 2009 A1
20090287921 Zhu et al. Nov 2009 A1
20090288140 Huber et al. Nov 2009 A1
20090291665 Gaskarth et al. Nov 2009 A1
20090292815 Gao et al. Nov 2009 A1
20090299857 Brubaker Dec 2009 A1
20090307696 Vals et al. Dec 2009 A1
20090307746 Di et al. Dec 2009 A1
20090315735 Bhavani et al. Dec 2009 A1
20090320110 Nicolson et al. Dec 2009 A1
20100010873 Moreau Jan 2010 A1
20100017506 Fadell Jan 2010 A1
20100020822 Zerillo et al. Jan 2010 A1
20100027469 Gurajala et al. Feb 2010 A1
20100027525 Zhu Feb 2010 A1
20100027559 Lin et al. Feb 2010 A1
20100030890 Dutta et al. Feb 2010 A1
20100041364 Lott et al. Feb 2010 A1
20100041365 Lott et al. Feb 2010 A1
20100041391 Spivey et al. Feb 2010 A1
20100042675 Fujii Feb 2010 A1
20100043068 Varadhan et al. Feb 2010 A1
20100046373 Smith et al. Feb 2010 A1
20100069074 Kodialam et al. Mar 2010 A1
20100071053 Ansari et al. Mar 2010 A1
20100075666 Gamer Mar 2010 A1
20100077035 Li et al. Mar 2010 A1
20100080202 Hanson Apr 2010 A1
20100082431 Ramer et al. Apr 2010 A1
20100088387 Calamera Apr 2010 A1
20100103820 Fuller et al. Apr 2010 A1
20100105378 Shi Apr 2010 A1
20100113020 Subramanian et al. May 2010 A1
20100121744 Belz et al. May 2010 A1
20100131584 Johnson May 2010 A1
20100142478 Forssell et al. Jun 2010 A1
20100144310 Bedingfield Jun 2010 A1
20100151866 Karpov et al. Jun 2010 A1
20100153781 Hanna Jun 2010 A1
20100167696 Smith et al. Jul 2010 A1
20100188975 Raleigh Jul 2010 A1
20100188990 Raleigh Jul 2010 A1
20100188992 Raleigh Jul 2010 A1
20100188994 Raleigh Jul 2010 A1
20100190469 Vanderveen et al. Jul 2010 A1
20100191576 Raleigh Jul 2010 A1
20100191612 Raleigh Jul 2010 A1
20100191846 Raleigh Jul 2010 A1
20100192170 Raleigh Jul 2010 A1
20100192212 Raleigh Jul 2010 A1
20100195503 Raleigh Aug 2010 A1
20100197268 Raleigh Aug 2010 A1
20100198698 Raleigh et al. Aug 2010 A1
20100198939 Raleigh Aug 2010 A1
20100227632 Bell et al. Sep 2010 A1
20100235329 Koren et al. Sep 2010 A1
20100241544 Benson et al. Sep 2010 A1
20100248719 Scholaert Sep 2010 A1
20100254387 Trinh et al. Oct 2010 A1
20100284327 Miklos Nov 2010 A1
20100284388 Fantini et al. Nov 2010 A1
20100287599 He et al. Nov 2010 A1
20100311402 Srinivasan et al. Dec 2010 A1
20100318652 Samba Dec 2010 A1
20100322071 Avdanin et al. Dec 2010 A1
20100325420 Kanekar Dec 2010 A1
20110004917 Saisa et al. Jan 2011 A1
20110013569 Scherzer et al. Jan 2011 A1
20110019574 Malomsoky et al. Jan 2011 A1
20110071854 Medeiros et al. Mar 2011 A1
20110081881 Baker et al. Apr 2011 A1
20110082790 Baker et al. Apr 2011 A1
20110110309 Bennett May 2011 A1
20110126141 King et al. May 2011 A1
20110145920 Mahaffey et al. Jun 2011 A1
20110159818 Scherzer et al. Jun 2011 A1
20110173678 Kaippallimalil et al. Jul 2011 A1
20110177811 Heckman et al. Jul 2011 A1
20110182220 Black et al. Jul 2011 A1
20110185202 Black et al. Jul 2011 A1
20110195700 Kukuchka et al. Aug 2011 A1
20110238545 Fanaian et al. Sep 2011 A1
20110241624 Park et al. Oct 2011 A1
20110244837 Murata et al. Oct 2011 A1
20110249668 Milligan et al. Oct 2011 A1
20110252430 Chapman et al. Oct 2011 A1
20110264923 Kocher et al. Oct 2011 A1
20110277019 Pritchard, Jr. Nov 2011 A1
20110294502 Oerton Dec 2011 A1
20120020296 Scherzer et al. Jan 2012 A1
20120029718 Davis Feb 2012 A1
20120108225 Luna et al. May 2012 A1
20120122514 Cheng May 2012 A1
20120144025 Welander et al. Jun 2012 A1
20120155296 Kashanian Jun 2012 A1
20120166364 Ahmad et al. Jun 2012 A1
20120166604 Fortier et al. Jun 2012 A1
20120195200 Regan Aug 2012 A1
20120196644 Scherzer Aug 2012 A1
20120238287 Scherzer Sep 2012 A1
20120330792 Kashanian Dec 2012 A1
20130024914 Ahmed et al. Jan 2013 A1
20130029653 Baker et al. Jan 2013 A1
20130030960 Kashanian Jan 2013 A1
20130058274 Scherzer et al. Mar 2013 A1
20130065555 Baker et al. Mar 2013 A1
20130072177 Ross et al. Mar 2013 A1
20130084835 Scherzer et al. Apr 2013 A1
20130095787 Kashanian Apr 2013 A1
20130117140 Kashanian May 2013 A1
20130144789 Aaltonen et al. Jun 2013 A1
20130176908 Baniel et al. Jul 2013 A1
20130196685 Griff Aug 2013 A1
20130225151 King et al. Aug 2013 A1
20130326356 Zheng et al. Dec 2013 A9
20140071895 Bane et al. Mar 2014 A1
20140073291 Hildner et al. Mar 2014 A1
20140198687 Raleigh Jul 2014 A1
20140241342 Constantinof Aug 2014 A1
20150039763 Chaudhary et al. Feb 2015 A1
20150181628 Haverinen et al. Jun 2015 A1
20150341226 Griff Nov 2015 A1
20160358204 Cavanaugh Dec 2016 A1
20170063695 Ferrell Mar 2017 A1
Foreign Referenced Citations (103)
Number Date Country
2688553 Dec 2008 CA
1310401 Aug 2001 CN
1345154 Apr 2002 CN
1508734 Jun 2004 CN
1538730 Oct 2004 CN
1567818 Jan 2005 CN
101035308 Mar 2006 CN
1801829 Jul 2006 CN
1802839 Jul 2006 CN
1889777 Jul 2006 CN
101155343 Sep 2006 CN
1867024 Nov 2006 CN
1878160 Dec 2006 CN
1937511 Mar 2007 CN
101123553 Sep 2007 CN
101080055 Nov 2007 CN
101115248 Jan 2008 CN
101127988 Feb 2008 CN
101183958 May 2008 CN
101335666 Dec 2008 CN
101341764 Jan 2009 CN
101815275 Aug 2010 CN
1098490 May 2001 EP
1289326 Mar 2003 EP
1463238 Sep 2004 EP
1503548 Feb 2005 EP
1545114 Jun 2005 EP
1739518 Jan 2007 EP
1772988 Apr 2007 EP
1850575 Oct 2007 EP
1887732 Feb 2008 EP
1942698 Jul 2008 EP
1978772 Oct 2008 EP
2007065 Dec 2008 EP
2026514 Feb 2009 EP
2466831 Jun 2012 EP
2154602 Jun 2017 EP
3148713 Mar 2001 JP
2005339247 Dec 2005 JP
2006041989 Feb 2006 JP
2006155263 Jun 2006 JP
2006197137 Jul 2006 JP
2006344007 Dec 2006 JP
2007318354 Dec 2007 JP
2008301121 Dec 2008 JP
2009111919 May 2009 JP
2009212707 Sep 2009 JP
2009218773 Sep 2009 JP
2009232107 Oct 2009 JP
20040053858 Jun 2004 KR
1998058505 Dec 1998 WO
1999027723 Jun 1999 WO
1999065185 Dec 1999 WO
0208863 Jan 2002 WO
2002045315 Jun 2002 WO
2002067616 Aug 2002 WO
2002093877 Nov 2002 WO
2003014891 Feb 2003 WO
2003017063 Feb 2003 WO
2003017065 Feb 2003 WO
2003058880 Jul 2003 WO
2004028070 Apr 2004 WO
2004064306 Jul 2004 WO
2004077797 Sep 2004 WO
2004095753 Nov 2004 WO
2005008995 Jan 2005 WO
2005053335 Jun 2005 WO
2005083934 Sep 2005 WO
2006004467 Jan 2006 WO
2006004784 Jan 2006 WO
2006012610 Feb 2006 WO
2006050758 May 2006 WO
2006073837 Jul 2006 WO
2006077481 Jul 2006 WO
2006093961 Sep 2006 WO
2006120558 Nov 2006 WO
2006130960 Dec 2006 WO
2007001833 Jan 2007 WO
2007014630 Feb 2007 WO
2007018363 Feb 2007 WO
2007053848 May 2007 WO
2007068288 Jun 2007 WO
2007069245 Jun 2007 WO
2007097786 Aug 2007 WO
2007107701 Sep 2007 WO
2007120310 Oct 2007 WO
2007124279 Nov 2007 WO
2007126352 Nov 2007 WO
2007129180 Nov 2007 WO
2007133844 Nov 2007 WO
2008017837 Feb 2008 WO
2008051379 May 2008 WO
2008066419 Jun 2008 WO
2008080139 Jul 2008 WO
2008080430 Jul 2008 WO
2008099802 Aug 2008 WO
2009008817 Jan 2009 WO
2009091295 Jul 2009 WO
2010088413 Aug 2010 WO
2010128391 Nov 2010 WO
2010128391 Jan 2011 WO
2011002450 Jan 2011 WO
2011149532 Dec 2011 WO
Non-Patent Literature Citations (71)
Entry
“Ads and movies on the run,” the Gold Coast Bulletin, Southport, QId, Jan. 29, 2008.
“ASA/PIX: Allow Split Tunneling for VPN Clients on the ASA Configuration Example,” Document ID 70917, Jan. 10, 2008.
“Communication Concepts for Mobile Agent Systems,” by Joachim Baumann et al.; Inst. Of Parallel and Distributed High-Performance Systems, Univ, of Stuttgart, Germany, pp. 123-135, 1997.
“End to End QoS Solution for Real-time Multimedia Application;” Computer Engineering and Applications, 2007, 43 (4):155-159, by Tan Zu-guo, Wang Wen-juan; Information and Science School, Zhanjian Normal College, Zhan jiang, Guangdong 524048, China.
“Jentro Technologies launches Zenlet platform to accelerate location-based content delivery to mobile devices,” The Mobile Internet, Boston, MA, Feb. 2008.
“The Construction of Intelligent Residential District in Use of Cable Television Network,” Shandong Science, vol. 13, No. 2, Jun. 2000.
3rd Generation Partnership Project, “Technical Specification Group Core Network and Terminals; Access Network Discovery and Selection Function (ANDSF) Management Object (MO),” Release 9, Document No. 3GPP TS 24.312, V9.1.0, Mar. 2010.
3rd Generation Partnership Project, “Technical Specification Group Services and System Aspects; General Packet Radio Service (GPRS) Enhancements for Evolved Universal Terrestrial Radio Access Network (E-UTRAN) Access,” Release 8, Document No. 3GPP TS 23.401, V8.4.0, Dec. 2008.
3rd Generation Partnership Project, “Technical Specification Group Services and System Aspects; Policy and Charging Control Architecture,” Release 8, Document No. 3GPP TS 23 203, V8.4.0, Dec. 2008.
3rd Generation Partnership Project; “Technical Specification Group Services and System Aspects; IP Flow Mobility and seamless WLAN offlload; Stage 2,” Release 10, Document No. 3GPP TS 23.261, V1 0.0, Mar. 2010.
Accuris Networks, “The Business Value of Mobile Data Offload—a White Paper”, 2010.
Ahmed et al., “A Context-Aware Vertical Handover Decision Algorithm for Multimode Mobile Terminals and Its Performance,” BenQ Mobile, Munich Germany; University of Klagenfurt, Klagenfurt, Austria; 2006.
Ahmed et al., “Multi Access Data Network Connectivity and IP Flow Mobility in Evolved Packet System (EPS),” 2010 IEEE.
Alonistioti et al., “Intelligent Architectures Enabling Flexible Service Provision and Adaptability,” 2002.
Amazon Technologies, Inc., “Kindle™ User's Guide,” 3rd Edition, Copyright 2004-2009.
Android Cupcake excerpts, The Android Open Source Project, Feb. 10, 2009.
Anton, B. et al., “Best Current Practices for Wireless Internet Service Provider (WISP) Roaming”; Release Date Feb. 2003, Version 1.0; Wi-Fi Alliance—Wireless ISP Roaming (WISPr).
Blackberry Mobile Data System, version 4.1, Technical Overview, 2006.
Byrd, Open Secure Wireless, May 5, 2010.
Chandrasekhar et al., “Femtocell Networks: A Survey,” Jun. 28, 2008.
Chaouchi et al., “Policy Based Networking in the Integration Effort of 4G Networks and Services,” 2004 IEEE.
Cisco Systems, Inc., “Cisco Mobile Exchange (CMX) Solution Guide: Chapter 2—Overview of GSM, GPRS, and UMTS,” Nov. 4, 2008.
Client Guide for Symantec Endpoint Protection and Symantec Network Access Control, 2007.
Dikaiakos et al., “A Distributed Middleware Infrastructure for Personalized Services,” Nov. 24, 2003.
Dixon et al., Triple Play Digital Services: Comcast and Verizon (Digital Phone, Television, and Internet), Aug. 2007.
Droid Wall 1.3.7 description 20100428 obtained from https://www.freewarelovers.com/android/apps/droid-wall.
Droid Wall 1.3.7 description Apr. 28, 2010; obtained from https://www.freewarelovers.com/android/apps/droid-wall.
Ehnert, “Small application to monitor IP trafic on a Blackberry—1.01.03 ”, Mar. 27, 2008; http://www.ehnert.net/MiniMoni/.
European Commission, “Data Roaming Tariffs—Transparency Measures,” obtained from EUROPA—Europe's Information Society Thematic Portal website, Jun. 24, 2011: “http://ec.europa.eu/information_society/activities/roaming/data/measures/index_en.htm.”
Farooq et al., “An IEEE 802.16 WiMax Module for the NS-3 Simulator,” Mar. 2-6, 2009.
Fujitsu, “Server Push Technology Survey and Bidirectional Communication in HTTP Browser,” Jan. 9, 2008 (JP).
Han et al., “Information Collection Services for Qos-Aware Mobile Applications,” 2005.
Hartmann et al., “Agent-Based Banking Transactions & Information Retrieval—What About Performance Issues?” 1999.
Hewlett-Packard Development Company, LP, “IP Multimedia Services Charging,” white paper, Jan. 2006.
Hossain et al., “Gain-Based Selection of Ambient Media Services in Pervasive Environments,” Mobile Networks and Applications. Oct. 3, 2008.
Jing et al., “Client-Server Computing in Mobile Environments,” GTE Labs. Inc., Purdue University, ACM Computing Surveys, vol. 31, No. 2, Jun. 1999.
Kasper et al., “Subscriber Authentication in mobile cellular Networks with virtual software SIM Credentials using Trusted Computing,” Fraunhofer-lnstitute for Secure Information Technology SIT, Darmstadt, Germany; ICACT 2008.
Kassar et al., “An overview of vertical handover decision strategies in heterogeneous wireless networks,” ScienceDirect, University Pierre & Marie Curie, Paris, France, Jun. 5, 2007.
Kim, “Free wireless a high-wire act; MetroFi needs to draw enough ads to make service add profits,” San Francisco Chronicle, Aug. 21, 2006.
Knight et al., “Layer 2 and 3 Virtual Private Networks: Taxonomy, Technology, and Standarization Efforts,” IEEE Communications Magazine, Jun. 2004.
Koutsopoulou et al., “Charging, Accounting and Billing Management Schemes In Mobile Telecommunication Networks and the Internet,” IEEE Communications Surveys & Tutorials, First Quarter 2004, vol. 6, No. 1.
Koutsopoulou et al., “Middleware Platform for the Support of Charging Reconfiguration Actions,” 2005.
Kuntze et al., “Trustworthy content push,” Fraunhofer-lnstitute for Secure Information Technology SIT; Germany WCNC 2007 proceedings, IEEE.
Kyriakakos et al., “Ubiquitous Service Provision in Next Generation Mobile Networks,” Proceedings of the 13th 1st Mobile and Wireless Communications Summit, Lyon, France, Jun. 2004.
Li, Yu, “Dedicated E-Reading Device: The State of the Art and The Challenges,” Scroll, vol. 1, No. 1, 2008.
Loopt User Guide, metroPCS, Jul. 17, 2008.
Muntermann et al., “Potentiale und Sicherheitsanforderungen mobiler Finanzinformationsdienste und deren Systeminfrastrukturen,” Chair of Mobile Commerce & Multilateral Security, Goethe Univ. Frankfurt, 2004.
NetLimiter Lite 4.0.19.0; http://www.heise.de/download/netlimiter-lite-3617703.html from vol. 14/2007.
Nilsson et al., “A Novel MAC Scheme for Solving the QoS Parameter Adjustment Problem in IEEE802.11e EDCA,” Feb. 2006.
Nuzman et al., “A compund model for TCP connection arrivals for LAN and WAN applications,” Oct. 22, 2002.
Open Mobile Alliance (OMA), Push Architecture, Candidate Version 2.2; Oct. 2, 2007; OMA-AD-Push-V2_2-20071002-C.
Oppliger, Rolf, “Internet Security: Firewalls and Bey,” Communications of the ACM, May 1997, vol. 40. No. 5.
Quintana, David, “Mobile Multitasking,” Apr. 14, 2010.
Rao et al., “Evolution of Mobile Location-Based Services,” Communication of the ACM, Dec. 2003.
Richtel, “Cellphone consumerism; If even a debit card is too slow, now you have a new way to act on impulse [National Edition],” National Post, Canada, Oct. 2, 2007.
Rivadeneyra et al., “A communication architecture to access data services through GSM,” San Sebastian, Spain, 1998.
Roy et al., “Energy Management in Mobile Devices with the Cinder Operating System”, Stanford University, MIT CSAIL, Jun. 3, 2010.
Ruckus Wireless—White Paper; “Smarter Wi-Fi for Mobile Operator Infrastructures” 2010.
Sabat, “The evolving mobile wireless value chain and market structure,” Nov. 2002.
Sadeh et al., “Understanding and Capturing People's Privacy Policies in a Mobile Social Networking Application,” ISR School of Computer Science, Carnegie Mellon University, 2007.
Schiller et al., “Location-Based Services,” The Morgan Kaufmann Series in Data Management Systems, 2004.
Sharkey, “Coding for Life-Battery Life That Is,” May 27, 2009.
Sharkey, Jeff, “Coding for Life Battery Life, That Is,” May 27, 2009.
Steglich, Stephan, “I-Centric User Interaction,” Nov. 21, 2003.
Sun et al., “Towards Connectivity Management Adaptability: Context Awareness in Policy Representation and End-to-and Evaluation Algorithm,” Dept, of Electrical and Information Engineering, Univ of Oulu, Finland, 2004.
Thurston, Richard, “WISPr 2.0 Boosts Roaming Between 3G and Wi-Fi”; Jun. 23, 2010; Web page from zdnet.com; Zdnet.com/wispr-2-0-boosts-roaming-between-3g-and-wi-fi-3040089325/.
Van Eijk, et al., “GigaMobile, Agent Technology for Designing Personalized Mobile Service Brokerage,” Jul. 1, 2002.
VerizonWireless.com news, “Verizon Wireless Adds to Portfolio of Cosumer-Friendly Tools With Introduction of Usage Controls, Usage Controls and Chaperone 2.0 Offer Parents Full Family Security Solution,” Aug. 18, 2008.
Windows? Power Management, published Apr. 2009.
Wireless Broadband Alliance, “WISPr 2.0, Apr. 8, 2010”; Doc. Ref. No. WBA/RM/WISPr, Version 01.00.
Zhu et al., “A Survey of Quality of Service in IEEE 802.11 Networks,” IEEE Wireless Communications, Aug. 2004.
Related Publications (1)
Number Date Country
20210120436 A1 Apr 2021 US
Provisional Applications (21)
Number Date Country
61348022 May 2010 US
61381159 Sep 2010 US
61381162 Sep 2010 US
61384456 Sep 2010 US
61385020 Sep 2010 US
61387243 Sep 2010 US
61387247 Sep 2010 US
61389547 Oct 2010 US
61407358 Oct 2010 US
61418507 Dec 2010 US
61418509 Dec 2010 US
61420727 Dec 2010 US
61422565 Dec 2010 US
61422572 Dec 2010 US
61422574 Dec 2010 US
61435564 Jan 2011 US
61472606 Apr 2011 US
61206354 Jan 2009 US
61206944 Feb 2009 US
61207393 Feb 2009 US
61207739 Feb 2009 US
Divisions (1)
Number Date Country
Parent 13134005 May 2011 US
Child 14156428 US
Continuations (3)
Number Date Country
Parent 16272098 Feb 2019 US
Child 17035403 US
Parent 15369542 Dec 2016 US
Child 16272098 US
Parent 14156428 Jan 2014 US
Child 15369542 US
Continuation in Parts (2)
Number Date Country
Parent 12380778 Mar 2009 US
Child 13134005 US
Parent 12380780 Mar 2009 US
Child 12380778 US