Facebook, Inc.

Caere Corporation

Jun Yang

Kenneth Chan Choy

Eitan Shay

Mindy R Bokser

Richard Bill Sim

Leonard K Pon

Tapas Kanungo

Stuart Michael Bowers

Mathew Varghese

Feng Yan

Sadik Gokhan Coglar

Based on prior interactions associated with a user, an online system predicts an amount of interaction by the user with an object associated with an advertisement. Using the predicted amount of user interaction, the online system determines an expected value of presenting the advertisement to the user. The advertisement is ranked among other advertisements based on the expected values associated with the advertisements, and one or more advertisements are selected for presentation to the user based on the ranking. An advertisement may also specify a threshold amount of interaction with an associated object as targeting criteria, so the predicted amount of interaction with the object associated with the advertisement may determine if a user is eligible to be presented with the advertisement.

Predicting user interactions with objects associated with advertisements on an online system

When the social networking system selects an advertisement for presentation to the user by a client device, a content identifier associated with the selected advertisement is stored in an advertisement history associated with the user. When the user performs a conversion event associated with the selected advertisement, the social networking system receives a content identifier and a user identifier from a client device used that performed the conversion event and retrieves the user's advertisement history. If the retrieved advertisement history includes a content identifier for the selected advertisement matching the content identifier received from the client device and a time between the conversion event and providing the selected advertisement to the client device is less than a threshold time period, a fee is determined for presentation of the selected advertisement.

Monitoring conversions and fee determination of online advertisements using a social networking system

The disclosed invention utilizes a dictionary-based approach to identify languages within different zones in a multi-lingual document. As a first step, a document image is segmented into various zones, regions and word tokens, using suitable geometric properties. Within each zone, the word tokens are compared to dictionaries associated with various candidate languages, and the language that exhibits the highest confidence factor is initially identified as the language of the zone. Subsequently, each zone is further split into regions. The language for each region is then identified, using the confidence factors for the words of that region. For any language determination having a low confidence value, the previously determined language of the zone is employed to assist the identification process.

Automatic language identification system for multilingual optical

A pattern recognition system classifies images of patterns in which the definition of individual features of the pattern may have become blurred. The image is segmented into pieces of arbitrary size and shape, and various combinations are examined to determine those which represent the most likely segmentation of the pattern into its individual features. These individual features are then classified, according to known techniques. Through the use of a second order Markov model, not all possible combinations of pieces need to be examined, to determine the best ones. Rather, the examination of various combinations is limited in accordance with previously determined information, to thereby render the process more efficient. By combining multiple, independently determined probabilities, the accuracy of the overall operation is enhanced.

Pattern recognition employing arbitrary segmentation and compound

Growing community of inventors

Sunnyvale, CA, United States of America

Jun Yang