Amazon Technologies, Inc.

Nadia C Payet

Lokesh Joshi

David R Azari

Satishkumar Kothandapani Shanmugasundaram

Viswanath Sankaranarayanan

Palanidaran Chidambaram

Aby Thomas Angilivelil

Surendran Rangasamy

Divya Mahalingam

Tanvi Milind Bhadbhade

Systems and methods are provided for optimizing a glyph-based file. Individual components may be identified within glyphs of a file. Each identified component within a glyph may be a portion of the glyph, and may be a joint component or disjoint component. Groupings of components may then be determined, where the groupings are determined based at least in part by identifying similarly shaped components. A representative component may then be selected from each grouping. Composite glyphs may be generated and stored in an optimized file, where each composite glyph includes a reference to at least one representative component.

Optimizing a glyph-based file

A panel view module receives a comic comprising one or more pages, which comprise one or more panels. The panel view module may identify candidate comic panels and determine confidence levels for the candidate comic panels. The panel view may also generate a panel view for the comic based on the confidence levels for the candidate comic panels.

Generating a panel view for comics

One or more computing systems can implement a classifier to classify content publishers as being likely to provide appropriate content or as being likely to provide inappropriate content. The classifier can gather information from previously classified publishers. The information from the previously classified publishers can used to train the classifier. Based on the training, the classifier can learn about traits, characteristics, and/or behavioral patterns, etc., associated with publishers that have been previously classified as being good as well as publishers previously classified as being bad. The classifier can then process information about an unclassified publisher to determine a classification for the unclassified publisher, as being good (and likely to provide appropriate content) or bad (and likely to provide inappropriate content).

Machine-assisted publisher classification

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Seattle, WA, United States of America

Nadia C Payet