Sas Institute Inc.

Yi Liao

William Robert Nadolski

Heather Michelle GoodyKoontz

Manoj Keshavmurthi Chari

Jin-Whan Jung

David James Wheaton

Adheesha Sanjaya Arangala

Balaji Gopalakrishnan

Michelle Gail Opp

Charles Franklin Board

Karthik Nakkeeran

Adheesha Sanjuaya Arangala

Phoemphun Oothongsap

Artin Armagan

Brian Christopher Hare

A system and method include receiving a first set of variables associated with a real-time request, extracting a predetermined subset of the first set of variables for generating a second set of variables, identifying historical request data, computing a set of parameters based on the first set of variables and the historical request data, generating a plurality of numeric sequences and a plurality of string sequences for the real-time request, converting each of the plurality of string sequences into an encoded string sequence to obtain a plurality of encoded string sequences, inputting the plurality of numeric sequences and the plurality of encoded string sequences into a trained deep machine learning model, and computing a score from the trained deep machine learning model, the score indicative of a likelihood that the real-time request belongs to an unauthorized classification.

Predicting likelihood of request classifications using deep learning

Various embodiments are generally directed to techniques for image content extraction. Some embodiments include extracting contextually structured data from document images, such as by automatically identifying document layout, document data, document metadata, and/or correlations therebetween in a document image, for instance. Several embodiments include extracting contextually structured data from table images, such as gridded and non-gridded tables. For example, the contents of cells may be extracted from a table image along with structural context including the corresponding row and column information. Many embodiments are directed to generating and utilizing a document template database for automatically extracting document image contents into a contextually structured format. Several embodiments are directed to automatically identifying and associating document metadata with corresponding document data in a document image to generate a machine-facilitated annotation of the document image. In some embodiments, the machine-facilitated annotation may be used to generate a template for the template database.

Techniques for image content extraction

Various embodiments are directed to techniques for countering oscillation in community assignments of nodes in a network during detection of its communities. A computer-program product tangibly embodied in a non-transitory machine-readable storage medium includes instructions operable to cause a computing device to derive a first connectedness metric of a first community to which a first node of a network belongs and a second connectedness metric of a second community to which a second node of the network belongs in parallel in an iteration of parallel detection of communities in the network, wherein the first and second nodes are connected in the network; randomly pin the first node to prevent its reassignment to another community during the iteration; compare the first and second connectedness metrics during the iteration; and reassign the second node from the second community to the first community based on the comparison. Other embodiments are described and claimed.

Parallel community detection

Computer-implemented systems and methods are provided for determining an action item from a global set of action items for a plurality of customers based on an objective function, a plurality of individual constraints, and a plurality of aggregate constraints. A plurality of offer sets is generated for each customer. An approximate highest reduced adjusted objective for each of the offer sets for each customer is calculated, and the customers are bucketed based on the highest adjusted objective value associated with each customer. The buckets are collapsed into a single bucket record containing a plurality of aggregate offer set columns, and an aggregate offer set column is selected from each bucket record for each bucket. Each bucket associated with a selected offer set is disaggregated, and the action item included in the selected offer set is stored in a computer-readable memory.

Computer-implemented marketing optimization systems and methods

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Apex, NC, United States of America

Yi Liao