Proofpoint, Inc.

Theta Lake, Inc.

Sharon Huffner

Ali Mesdaq

Richard Banks Sutton

Gaurav Mitesh Dalal

Harold Nguyen

Daniel Oshiro Nadir

Andrea Varga

Disclosed is an effective domain name defense solution in which a domain name string may be provided to or obtained by a computer embodying a visual domain analyzer. The domain name string may be rendered or otherwise converted to an image. An optical character recognition function may be applied to the image to read out a text string which can then be compared with a protected domain name to determine whether the text string generated by the optical character recognition function from the image converted from the domain name string is similar to or matches the protected domain name. This visual domain analysis can be dynamically applied in an online process or proactively applied in an offline process to hundreds of millions of domain names.

Visual domain detection systems and methods

Disclosed is a domain engineering analysis solution that determines relevance of a domain name to a brand name in which a domain name, brand name, and identification of a substring of the domain name may be provided to or obtained by a computer embodying a domain engineering analyzer. A list of features may be determined. The list of features may include a lexicon, or a set of key-value pairs that encode information about terms included as substrings in the domain name. Determining the features may include obtaining a language model for each term, analyzing a cluster of language models closest to the obtained language model, and determining and scoring a relevance of each term to the brand name. The determined relevance and score of each term may be provided to a client. This relevance analysis can be dynamically applied in an online process or proactively applied in an offline process.

Domain name classification systems and methods

Systems and methods are disclosed for using natural language processing to generate sound-alikes and look-alikes for potentially relevant terms that could be mis-transcribed in a transcript. The disclosed framework combines the advantages of several measures aimed at tackling a wide range of transcription errors including but not limited to word boundary errors, phonetic confusion of words, spelling mistakes, grammatical errors, character drops, etc. In some examples, morphological similarity and phonetic similarity are used to address the word boundary errors and phonetic confusion of words. In some examples, a spell checker, word formation, and look-alike sound-alike generator are used to address errors such as phonetic confusion, spelling mistakes and grammatical errors. The generated sound-alikes can be ranked, which enables a flexible application of the generated phrases.

System and method for disambiguating data to improve analysis of electronic content

Growing community of inventors

Munich, Germany

Sharon Huffner