Microsoft Technology Licensing, LLC

Stuart H Marshall

Neeraj Garg

David John Brennan

Indrojit Deb

Michael T Daly

Rajesh Kuppuswamy

Mahesh Jayaram

Yunkai Zhou

Kamen Penev

Xingheng Wang

A system for periodic and real-time processing of accounts. The system operates in a bulk mode and an error mode. A bulk component concurrently processes a set of eligible accounts with a set of dependent tasks, and a removal component removes one or more accounts from the eligible accounts if an error is associated therewith. In bulk mode, each task fetches only the required set of accounts/subscriptions/payment instruments for processing. In error mode (retry/reprocess mode), if an account encounters an error for a task in the bulk mode, the account drops is removed from bulk mode and becomes ineligible for fetching in any future bulk-mode task processing. Error-mode processing tests and processes all tasks sequentially for an errored account until the account state is in par with the accounts being processed by bulk mode. Then the account is removed from error mode processing and is picked up appropriately by bulk-mode processing.

Account related task processing

The Na&#xef;ve Bayes Classifier predicts the classification of a set of data based on the features of that data and a series of counts reflecting the information obtained from prior data sets, with one count per feature per class. An external boost can be applied to the counts generated by the NBC to account for external information. Such a boost is added to the counts generated by the NBC, and the boosted counts are then used by the NBC. A boost can be applied to some or all of the counts and the boost for each count can be applied independently. Likewise, the counts can be periodically aged by multiplying the counts with an aging factor of between 0 and 1 per period. Aging factors can be applied uniformly across all counts, or can be individually applied, enabling some counts to age more than others.

Classifying knowledge aging in emails using Na&#xef;ve Bayes Classifier

Various technologies and techniques are disclosed that improve an ecommerce payment process. The system receives a selection from a user to perform a checkout operation for items in a shopping cart. The system receives a payment type selection from a user and creates an encrypted packet of order information. The user is redirected to a URL of a third party payment processing system that supports the selected payment type for the user's country. The encrypted information is posted securely to the payment system and includes details standard across payment types. A user interface theme identifier is included to allow the third party system to provide a consistent user interface. The third party system accepts the standard details in the encrypted packet and implements the payment type specific details. The ecommerce system receives notification that the payment process succeeded and provisioning resumes by providing the purchased items to the user.

Framework for adding billing payment types

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Seattle, WA, United States of America

Stuart H Marshall