The patent badge is an abbreviated version of the USPTO patent document. The patent badge does contain a link to the full patent document.
The patent badge is an abbreviated version of the USPTO patent document. The patent badge covers the following: Patent number, Date patent was issued, Date patent was filed, Title of the patent, Applicant, Inventor, Assignee, Attorney firm, Primary examiner, Assistant examiner, CPCs, and Abstract. The patent badge does contain a link to the full patent document (in Adobe Acrobat format, aka pdf). To download or print any patent click here.
Patent No.:
Date of Patent:
Jan. 23, 2018
Filed:
Oct. 17, 2016
Microsoft Technology Licensing, Llc, Redmond, WA (US);
Gursharan S. Sidhu, Seattle, WA (US);
Thomas Kuehnel, Seattle, WA (US);
Rao Salapaka, Sammamish, WA (US);
Vishal Soni, Redmond, WA (US);
Ranveer Chandra, Bellevue, WA (US);
Mansoor Jafry, Kirkland, WA (US);
Anish Desai, Bellevue, WA (US);
Ruchir Astavans, Redmond, WA (US);
Humayun Khan, Issaquah, WA (US);
John Mark Miller, Kirkland, WA (US);
Microsoft Technology Licensing, LLC, Redmond, WA (US);
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.