Google Inc.

Yahoo! Inc.

Luis Garcia Pueyo

Marc-Allen Cartright

Jie Yang

Mike Bendersky

Amitabh Saikia

Vanja Josifovski

MyLinh Yang

Balint Miklos

Tobias Kaufmann

Hui Tan

Julia Proskurnia

Jinan Lou

Ivo Krka

Maureen Heymans

James Bradley Wendt

Jing Xie

Srinidhi Viswanatha

Ying Sheng

Kashyap Ramesh Puranik

Marc Alexander Najork

Alexander Johannes Smola

Roelof Van Zwol

Yifeng Lu

Amr Ahmed

Stefan Matthias Romberg

Weinan Zhang

Methods, apparatus, systems, and computer-readable media are provided for generating and applying outgoing communication templates. In various implementations a corpus of outgoing communications sent by a user may be grouped into a plurality of clusters based on one or more attributes of a context of the user. One or more segments of each outgoing communication of a particular cluster may be classified as fixed in response to a determination that a count of occurrences of the one or more segments across the particular cluster satisfies a criterion. One or more remaining segments of each communication of the particular cluster may or may not be classified as transient. Based on sequences of classified segments associated with each communication of the particular cluster, an outgoing communication template may be generated to automatically populate at least a portion of a draft outgoing communication being prepared by the user.

Generating and applying outgoing communication templates

Techniques are described herein for automatically generating data extraction templates for structured documents (e.g., B2C emails, invoices, bills, invitations, etc.), and for assigning classifications to those data extraction templates to streamline data extraction from subsequent structured documents. In various implementations, a data extraction template generated from a cluster of structured documents that share fixed content may be identified. Features of the cluster of structured documents may be applied as input to extraction machine learning model(s) trained to provide location(s) of transient field(s) in structured documents, to determine location(s) of transient field(s) in the cluster of structured documents. An association between the data extraction template and the determined transient field location(s) may be stored. Based on the association, data point(s) may be extracted from a given structured document of a user that shares fixed content with the cluster of structured documents. The extracted data point(s) may be surfaced to the user.

Template-based structured document classification and extraction

Methods, apparatus, and computer-readable media are provided for analyzing a cluster of communications, such as B2C emails, to generate a template for the cluster that defines transient segments and fixed segments of the cluster of communications. More particularly, methods, apparatus, and computer-readable media are provided for generating and/or applying a trained structured machine learning model for a generated template that can be used to determine, for one or more transient segments of subsequent communications, a corresponding probability that a given semantic label is the correct semantic label for extracted content of the transient segment(s).

Generating and applying a trained structured machine learning model for determining a semantic label for content of a transient segment of a communication

Methods and apparatus are described herein for creating associations between user interests and electronic document templates generated from B2C electronic documents. Once these associations are created, interest(s) of a user (e.g., a user profile) may be determined automatically based on B2C electronic documents addressed to the user. In various implementations, an electronic document addressed to a user may be identified. A particular electronic document template that corresponds to the electronic document addressed to the user may be selected from a plurality of electronic document templates. The selecting may be based on attribute(s) shared between the electronic document addressed to the user and the selected electronic document template. The particular electronic template may be generated from a plurality of electronic documents that share fixed content. Interest(s) associated with the particular electronic document template may be identified, and association(s) between the user and the identified interest(s) may be stored.

Template-based identification of user interest

Techniques are described herein for generating and applying event data extraction templates. In various implementations, a data extraction template may be applied to structured communications to extract, from each structured communication, event data associated with a transient markup language path indicated in the data extraction template. The data extraction template may include an event-related semantic data type assigned to the transient markup language path and a strength of association between the transient structural path and the event-related semantic data type. Feedback may be obtained concerning event data extracted from one or more of the structured communications. Based on the feedback, the strength of association between the transient markup language path and the event-related semantic data type may be altered. The data extraction template may then be applied to a subsequent structured communication to extract new event data from the structured communication based on the altered strength of association.

Generating and applying event data extraction templates

Methods, apparatus, and computer-readable media are provided for generating and applying data extraction templates. In various implementations, a corpus of structured communications such as emails may be grouped into clusters based on one or more similarities between the structured communications. A set of structural paths may be identified from structured communications of a particular cluster. One or more structural paths of the set may be classified as transient wherein a count of occurrences of one or more associated segments of text across the particular cluster satisfies a criterion. One or more transient paths may be assigned a semantic data type and/or a confidentiality designation based on various signals. A data extraction template may be generated to extract, from subsequent structured communications, segments of text associated with transient (and in some cases, non-confidential) structural paths.

Generating and applying data extraction templates

Methods, apparatus, systems, and computer-readable media are provided for selecting pattern matching segments suitable for electronic communication clustering. A set of pattern matching segments may be identified that match at least one of a corpus of electronic communication addresses. A measure of coverage of each of the set of pattern matching segments across the corpus of electronic communication addresses may be determined. A score associated with each pattern matching segment may be determined based on the measure of coverage and one or more measures of flexibility associated with each of the set of pattern matching segments. One or more of the pattern matching segments may be selected based on the determine scores. A corpus of electronic communications may then be grouped into a plurality of clusters based on a comparison of the one or more selected pattern matching segments to electronic communication addresses associated with the corpus of electronic communications.

Selecting pattern matching segments for electronic communication clustering

Methods, apparatus, systems, and computer-readable media are provided for generating and applying data extraction templates. In various implementations, a corpus of plain text communications such as emails may be grouped into clusters based on one or more similarities between the plain text communications. One or more segments of communications of a particular cluster may be classified as transient based on textual pattern matching. One or more other segments of the communications of the particular cluster may be classified as transient based on various criteria. One or more transient segments may be assigned a generic and/or specific semantic data type and/or a confidentiality designation based on various signals. A data extraction template may be generated to extract, from subsequent plain text communications, content associated with transient (and in some cases, non-confidential) segments.

Methods, apparatus, systems, and computer-readable media are provided for determining whether communications are attempts at phishing. In various implementations, a potentially-deceptive communication may be matched to one or more templates of a plurality of templates. Each template may represent content shared among a cluster of communications sent by a legitimate entity. In various implementations, it may be determined that an address associated with the communication is not affiliated with one or more legitimate entities associated with the one or more matched templates. In various implementations, the communication may be classified as a phishing attempt based on the determining.

Identifying phishing communications using templates

Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for redacting data from a document collection generated for a set of documents that include personal information. The redaction of the data is based in part on a comparison of the document collection to a set of a personal documents of users for which the users have provided explicit approval to use in the processing of the document collection.

Information redaction from document data

Methods and apparatus are described herein for generating and applying event data extraction templates. In various implementations, a set of structural paths may be identified from a corpus of communications. A first structural path of the set of structural paths, associated with a first segment of text, may be classified as transient in response to a determination that a frequency of occurrences of the first segment of text across the corpus satisfies a criterion. Event heuristics may be applied to the communications of the corpus. A determination may be made, based on the applying, that the communications of the corpus are event-related. An event data type may be assigned to the transient structural path based on the applying. An event data extraction template may be generated to extract, from one or more subsequent communications, one or more event-related segments of text associated with the transient structural path.

Methods, apparatus, systems, and computer-readable media are provided for determining whether communications are attempts at phishing. In various implementations, a potentially-deceptive communication may be matched to one or more templates of a plurality of templates. Each template may represent content shared among a cluster of communications sent by a trustworthy entity. In various implementations, it may be determined that an address associated with the communication is not affiliated with one or more trustworthy entities associated with the one or more matched templates. In various implementations, the communication may be classified as a phishing attempt based on the determining.

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San Jose, CA, United States of America

Luis Garcia Pueyo