Kofax, Inc.

Webroot Inc.

Christopher K Harris

Mauritius A R Schmidtler

Hal Lonas

Anthony Macciola

Roland G Borrey

Sameer Samat

Ron Hegli

Scott S Texeira

A method and system for controlling access to an Internet resource is disclosed herein. When a request for an Internet resource, such as a Web site, is transmitted by an end-user of a LAN, a security appliance for the LAN analyzes a reputation index for the Internet resource before transmitting the request over the Internet. The reputation index is based on a reputation vector which includes a plurality of factors for the Internet resource such as country of domain registration, country of service hosting, country of an internet protocol address block, age of a domain registration, popularity rank, internet protocol address, number of hosts, to-level domain, a plurality of run-time behaviors, JavaScript block count, picture count, immediate redirect and response latency. If the reputation index for the Internet resource is at or above a threshold value established for the LAN, then access to the Internet resource is permitted. If the reputation index for the Internet resource is below a threshold value established for the LAN, then access to the Internet resource is denied.

System and method for developing a risk profile for an internet resource

A method and system for delineating document and/or subdocument boundaries and identifying document and/or subdocument types, the method comprising: automatically generating at least one identifier for identifying which of a plurality of document and/or subdocument images belongs to which of a plurality of categories. The method and/or system optionally may include automatically categorizing a plurality of document and/or subdocument images into a plurality of predetermined categories in accordance with classification rules for said categories.

Automatic document separation

A method and system a method for compressing and searching a plurality of strings. The method includes inputting a plurality of strings into a compression engine. The method also includes converting each of the plurality of strings into a new, prefix-preserving compressed string, using the compression engine. For every string P that is a strict prefix of a string S, P's resulting compressed string is a strict prefix of S's resulting compressed string.

System and method for performing longest common prefix strings searches

A method and system for delineating document boundaries and identifying document types by analyzing digital images of one or more documents, automatically categorizing one or more pages or subdocuments within the one or more documents and automatically generating delineation identifiers, such as computer-generated images of separation pages inserted between digital images belonging to different categories, a description of the categorization sequence of the digital images, or a computer-generated electronic label affixed or associated with said digital images.

System and method for developing a risk profile for an internet service

A system, method, data processing apparatus, and article of manufacture are provided for classifying data. Labeled data points are received, each of the labeled data points having at least one label indicating whether the data point is a training example for data points for being included in a designated category or a training example for data points being excluded from a designated category; receiving unlabeled data points; receiving at least one predetermined cost factor of the labeled data points and unlabeled data points; training a transductive classifier using MED through iterative calculation using the at least one cost factor and the labeled data points and the unlabeled data points as training examples; applying the trained classifier to classify at least one of the unlabeled data points, the labeled data points, and input data points; and outputting a classification of the classified data points, or derivative thereof.

Methods and systems for transductive data classification

Methods and systems for improved transductive maximum entropy discrimination classification

An improved method of classifying examples into multiple categories using a binary support vector machine (SVM) algorithm. In one preferred embodiment, the method includes the following steps: storing a plurality of user-defined categories in a memory of a computer, analyzing a plurality of training examples for each category so as to identify one or more features associated with each category; calculating at least one feature vector for each of the examples; transforming each of the at least one feature vectors so as reflect information about all of the training examples; and building a SVM classifier for each one of the plurality of categories, wherein the process of building a SVM classifier further includes: assigning each of the examples in a first category to a first class and all other examples belonging to other categories to a second class, wherein if anyone of the examples belongs to another category as well as the first category, such examples are assigned to the first class only, optimizing at least one tunable parameter of a SVM classifier for the first category, wherein the SVM classifier is trained using the first and second classes; and optimizing a function that converts the output of the binary SVM classifier into a probability of category membership.

Effective multi-class support vector machine classification

An improved method of classifying examples into multiple categories using a binary support vector machine (SVM) algorithm. In one preferred embodiment, the method includes the following steps: storing a plurality of user-defined categories in a memory of a computer; analyzing a plurality of training examples for each category so as to identify one or more features associated with each category; calculating at least one feature vector for each of the examples; transforming each of the at least one feature vectors so as reflect information about all of the training examples; and building a SVM classifier for each one of the plurality of categories, wherein the process of building a SVM classifier further includes: assigning each of the examples in a first category to a first class and all other examples belonging to other categories to a second class, wherein if any one of the examples belongs to another category as well as the first category, such examples are assigned to the first class only; optimizing at least one tunable parameter of a SVM classifier for the first category, wherein the SVM classifier is trained using the first and second classes; and optimizing a function that converts the output of the binary SVM classifier into a probability of category membership.

Growing community of inventors

San Diego, CA, United States of America

Christopher K Harris