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Patent Agency Ranking
公开(公告)号：US20170034754A1
公开(公告)日：2017-02-02
申请号：US15295174
申请日：2016-10-17
Applicant: Microsoft Technology Licensing, LLC
Inventor： Gursharan S. Sidhu , Thomas Kuehnel , Rao Salapaka , Vishal Soni , Ranveer Chandra , Mansoor Jafry , Anish Desai , Ruchir Astavans , Humayun Khan , John Mark Miller
IPC: H04W36/14 , H04W36/00 , H04W40/26 , H04L12/26 , H04B17/318
CPC classification number: H04W36/14 , H04B17/318 , H04L43/16 , H04M7/006 , H04M7/122 , H04W36/0083 , H04W40/26
Abstract: A continual learning process is applied to a class of risk estimate-based algorithms and associated risk thresholds used for deciding when to initiate a handoff between different types of network connections that are available to a mobile device having telephony functionality. The process is implemented as a virtuous loop providing ongoing tuning and adjustment to improve call handoff algorithms and risk thresholds so that handoffs can be performed with the goals of minimizing dropped calls and unacceptable degradation in call quality as well as avoiding premature handoffs. Device characteristics, environmental context, connection measurements, and outcomes of call handoff decisions are crowd-sourced from a population of mobile devices into a cloud-based handoff decision enabling service. The service evaluates potentially usable handoff decision algorithms and risk thresholds against archived crowd-sourced data to determine how they would have performed in real world situations and delivers improved algorithms and risk thresholds to the mobile devices.
Abstract translation: 连续学习过程被应用于一类基于风险估计的算法和相关联的风险阈值，用于决定什么时候在具有电话功能的移动设备可用的不同类型的网络连接之间进行切换。 该过程被实现为良性循环，其提供持续的调谐和调整以改善呼叫切换算法和风险阈值，使得可以以最小化呼叫质量和拒绝过早切换的不可接受的降级的目标来执行切换。 设备特性，环境背景，连接测量以及呼叫切换决策的结果都是从移动设备群体中大量涌入基于云的切换决策启用服务。 该服务评估潜在可用的切换决策算法和风险阈值，以防止存档的人群来源数据，以确定它们将如何在现实世界中执行，并为移动设备提供改进的算法和风险阈值。
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公开(公告)号：US20160112468A1
公开(公告)日：2016-04-21
申请号：US14976030
申请日：2015-12-21
Applicant: Microsoft Technology Licensing, LLC
Inventor： Guo-Wei Sheih , Srivatsa K. Srinivisan , Senthil K. Velayutham , Rajneesh Mahajan , Subhashri Iyer , Humayun Khan
IPC: H04L29/06
CPC classification number: H04L65/1069 , H04L65/4023
Abstract: Real-time media optimization may be provided. First, a remote session may be established with a remote computing device. Then, during the remote session, non-real-time media data may be exchanged with the remote computing device over a server path. Moreover, real-time media data may be exchanged with the remote computing device over a media path during the remote session.
Abstract translation: 可以提供实时媒体优化。 首先，可以使用远程计算设备来建立远程会话。 然后，在远程会话期间，非实时媒体数据可以通过服务器路径与远程计算设备进行交换。 此外，在远程会话期间，实时媒体数据可以通过媒体路径与远程计算设备交换。
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公开(公告)号：US09699225B2
公开(公告)日：2017-07-04
申请号：US14976030
申请日：2015-12-21
Applicant: Microsoft Technology Licensing, LLC
Inventor： Guo-Wei Sheih , Srivatsa K. Srinivasan , Senthil K. Velayutham , Rajneesh Mahajan , Subhashri Iyer , Humayun Khan
CPC classification number: H04L65/1069 , H04L65/4023
Abstract: Real-time media optimization may be provided. First, a remote session may be established with a remote computing device. Then, during the remote session, non-real-time media data may be exchanged with the remote computing device over a server path. Moreover, real-time media data may be exchanged with the remote computing device over a media path during the remote session.
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公开(公告)号：US09935787B2
公开(公告)日：2018-04-03
申请号：US14141025
申请日：2013-12-26
Applicant: Microsoft Technology Licensing, LLC
Inventor： John D. Bruner , Jeffrey Kay , Gursharan Sidhu , Anish Desai , Humayun Khan , Mansoor Jafry , Ray Froelich , Eric Hamilton , Eugen Pajor , Kerry Woolsey , Ganapathy Raman , Krishnan Ananthanarayanan , Mahendra Sekaran
CPC classification number: H04L12/4633 , H04L65/1006 , H04L65/1053 , H04L65/1069 , H04L65/403 , H04M7/006 , H04W76/20
Abstract: Signaling from a mobile device is transparently tunneled through a cellular voice network to a Voice over Internet Protocol (“VoIP”) core network so that multi-party calls, including conference calls and call waiting, can be managed entirely within the VoIP core network. The tunneled signals enable call control to be implemented in the VoIP core network and also establish a way to communicate requests, instructions, and call state. The signaling is transparent to the cellular network because that network does not receive and interpret the signaling. Instead, the cellular network's existing and unmodified control plane is repurposed by the mobile device by placing new, brief outgoing calls through the cellular network to the VoIP core network where the called party number (i.e., the caller-ID) encodes specific information. The VoIP core network immediately releases the new cellular call once the caller-ID is received and the encoded information is interpreted.
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公开(公告)号：US09877250B2
公开(公告)日：2018-01-23
申请号：US15295174
申请日：2016-10-17
Applicant: Microsoft Technology Licensing, LLC
Inventor： Gursharan S. Sidhu , Thomas Kuehnel , Rao Salapaka , Vishal Soni , Ranveer Chandra , Mansoor Jafry , Anish Desai , Ruchir Astavans , Humayun Khan , John Mark Miller
CPC classification number: H04W36/14 , H04B17/318 , H04L43/16 , H04M7/006 , H04M7/122 , H04W36/0083 , H04W40/26
Abstract: A continual learning process is applied to a class of risk estimate-based algorithms and associated risk thresholds used for deciding when to initiate a handoff between different types of network connections that are available to a mobile device having telephony functionality. The process is implemented as a virtuous loop providing ongoing tuning and adjustment to improve call handoff algorithms and risk thresholds so that handoffs can be performed with the goals of minimizing dropped calls and unacceptable degradation in call quality as well as avoiding premature handoffs. Device characteristics, environmental context, connection measurements, and outcomes of call handoff decisions are crowd-sourced from a population of mobile devices into a cloud-based handoff decision enabling service. The service evaluates potentially usable handoff decision algorithms and risk thresholds against archived crowd-sourced data to determine how they would have performed in real world situations and delivers improved algorithms and risk thresholds to the mobile devices.
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