IP Library Granted Patent US 9,628,419
Granted Patent B2
US 9,628,419 · App. 14/813,034 · Granted Apr 18, 2017

System for annotation of electronic messages with contextual information

Inventors: Nathaniel Borenstein (Wexford, PA); Marc Amphlett (Kettering, GB); Clive Jordan (Auckland, NZ); Max Linscott (London, GB); Niall O'Malley (Surrey, GB); Jacqueline Osborne (Surrey, GB); Luke Pentreath (London, GB); Oliver Scott (London, GB); Rahul Sharma (London, GB)
Assignee: MIMECAST NORTH AMERICA, INC.
H04L51/08
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Quick Facts
Patent No.
US 9,628,419
App. No.
14/813,034
Granted
Apr 18, 2017
Kind
B2
Abstract

A system that transforms electronic messages into annotated messages that include contextual information to aid a recipient in utilizing the electronic message, understanding its meaning, and responding to the message. Annotations are additions or modifications to the original message with contextual information that is related to the features and contents of the original message. Message features are extracted and used to search one or more sources of contextual information. Relevant items are retrieved and added to the message, for example as attachments, hyperlinks, or inline notes. Machine learning techniques may be used to generate or refine modules for feature extraction and information selection. Feedback components may be used to track the usage and value of annotations, in order to iteratively improve the annotation system.

Claims (68)

1. A system for annotation of electronic messages with contextual information comprising:

a computer system comprising at least one processor, at least one tangible memory device, and at least one network interface;

an interface to one or more contextual information sources that communicates over one or more of said at least one network interface;

a processor that

receives an electronic message on one or more of said at least one network interface,

wherein said electronic message contains medical information related to a medical record or legal information related to a legal case; and,

said processor selects information on other cases similar to said medical record or said legal case from an external database;

wherein said electronic message comprises one or more message artifacts, and

wherein said one or more message artifacts comprise:

one or more senders, one or more sender addresses,

one or more sender organizations,

one or more recipients,

one or more recipient addresses,

one or more recipient organizations,

a subject,

a contents,

one or more message body parts,

a message thread,

an event,

a timestamp,

a location,

one or more links,

an importance indicator,

one or more media types,

a set of message metadata, and one or more attachments;

analyzes said one or more message artifacts of said electronic message;

extracts n-grams appearing in said one or more message artifacts in said electronic message;

forms a frequency distribution of said n-grams, generates a set of features associated with said one or more message artifacts from said frequency distribution of said n-grams;

performs one or more queries against one or more external search engines, wherein said one or more queries are directed towards a medical database or a legal database and are based on said set of features using search terms, and wherein said search terms are derived from said set of features of said electronic message;

selects one or more contextual information items from said one or more contextual information sources based on said set of features;

calculates a relevance score for each of a set of available contextual information items based on said one or more features associated with said one or more message artifacts;

ranks said set of available context information items based on said relevance score;

selects a top-ranked subset of said set of available contextual information items;

transforms said electronic message by adding said one or more contextual information items to create an annotated electronic message comprising annotating said electronic message with said top-ranked subset of said set of available contextual information items to provide said electronic message with relevant data to aid said at least one or more recipients to utilize said electronic message, understand a meaning of said electronic message or respond to said electronic message;

wherein said relevance score is a similarity metric or a distance metric between said set of features associated with said one or more message artifacts and a corresponding set of features associated with or calculated from each of said available contextual information items;

transmits said annotated electronic message on one or more of said at least one network interface to at least said one or more recipients;

tracks whether and how said one or more recipients use said one or more contextual information items of said electronic message that is annotated as feedback; and,

accepts input from said one or more recipients as direct user feedback that allows users to indicate or rate a usefulness or relevance of said one or more contextual information as feedback data to improve feature extraction and information selection.

2. The system for annotation of electronic messages with contextual information of claim 1 , wherein said processor attaches said one or more contextual information items to said electronic message as attachments containing said one or more contextual information items or links or references to said one or more contextual information items.

3. The system for annotation of electronic messages with contextual information of claim 1 , wherein said processor inserts said one or more contextual information items or links or references to said one or more contextual information items into said contents of said electronic message.

4. The system for annotation of electronic messages with contextual information of claim 1 , wherein the length of each of said n-grams is between a minimum n-gram length and a maximum n-gram length selected by said processor.

5. The system for annotation of electronic messages with contextual information of claim 1 , wherein

said set of features comprises said one or more recipients, and other features extracted from said electronic message;

said one or more contextual information sources comprise access rules that indicate an accessible subset of said contextual information items that each of said one or more recipients is permitted to access; and,

said processor selects a set of contextual information items for each of said one or more recipients from the accessible subset of said contextual information items for that recipient, based on said other features extracted from said electronic message.

6. The system for annotation of electronic messages with contextual information of claim 1 , wherein said processor categorizes said electronic message or an artifact of said electronic message into one or more classes using a classifier.

7. The system for annotation of electronic messages with contextual information of claim 6 , wherein said classifier is a probabilistic classifier, and said processor annotates said electronic message only if the probability of a correct classification from said probabilistic classifier exceeds a threshold probability.

8. The system for annotation of electronic messages with contextual information of claim 1 , wherein said processor generates one or more topics associated with said electronic message using a probabilistic topic model.

9. The system for annotation of electronic messages with contextual information of claim 1 , wherein said processor further comprises a training set that develops or revises methods for feature extraction and information selection.

10. The system for annotation of electronic messages with contextual information of claim 9 , wherein said processor tracks usage of said one or more contextual information items by said one or more recipients, wherein data received as feedback by said processor is used to revise said methods for feature extraction and information selection.

11. The system for annotation of electronic messages with contextual information of claim 9 , wherein said processor

monitors the information searches of said one or more recipients after they receive said annotated electronic message; and

provides data on said information searches to revise said methods for feature extraction and information selection.

12. The system for annotation of electronic messages with contextual information of claim 1 , wherein

said electronic message contains one or more media items, each comprising one or more images, videos, audio clips, or any combinations thereof;

said processor analyzes said one or more media items to extract one or more segments of said one or more media items that represent one or more objects contained in said one or more media items; and

said processor compares said one or more segments of said one or more media items to said contextual information sources to identify said one or more objects and to select contextual information about said one or more objects.

13. The system for annotation of electronic messages with contextual information of claim 1 , wherein

said processor extracts or derives one or more locations from said one or more message artifacts;

said one or more contextual information sources comprise location-tagged contextual information items; and,

said processor selects a set of said location-tagged contextual information items with location-tags that are equal to or in proximity to said one or more locations.

14. The system for annotation of electronic messages with contextual information of claim 13 , wherein said derives one or more locations from said one or more message artifacts comprises

obtain the location of one or more of said one or more senders or of said one or more receivers from one or more of

location configuration information describing usual or standard locations for one or more of said one or more senders or of said one or more receivers;

current sender location information describing the location of said one or more senders at the time said electronic message is composed or sent; and,

current receiver location information describing the location of said one or more receivers at the time said annotated electronic message is received or read.

15. The system for annotation of electronic messages with contextual information of claim 1 , wherein said one or more message artifacts comprises a plurality of message artifacts, and wherein said processor assigns different weights to said n-grams extracted from different message artifacts of said plurality message artifacts.

16. The system for annotation of electronic messages with contextual information of claim 1 , wherein said processor preprocesses said one or more message artifacts prior to extracting said n-grams.

Assignments (6)
SECURITY INTEREST Recorded May 20, 2022
From: MIMECAST NORTH AMERICA, INC.; MIMECAST SERVICES LIMITED
To: ARES CAPITAL CORPORATION
Reel/Frame 060132/0429 →
RELEASE OF SECURITY INTEREST Recorded May 19, 2022
From: JPMORGAN CHASE BANK, N.A.
To: MIMECAST SERVICES LTD.; ETORCH INC.
Reel/Frame 059962/0294 →
SECURITY AGREEMENT Recorded Jul 23, 2018
From: MIMECAST SERVICES LIMITED
To: JPMORGAN CHASE BANK, N.A.
Reel/Frame 046616/0242 →
ASSIGNMENT OF ASSIGNOR'S INTEREST Recorded Jun 27, 2017
From: MIMECAST NORTH AMERICA, INC.
To: MIMECAST SERVICES LTD.
Reel/Frame 042821/0798 →
CORRECTIVE ASSIGNMENT TO CORRECT THE 7TH ASSIGNOR'S EXECUTION DATE AND RECEIVING PARTY'S NAME AND STREET ADDRESS PREVIOUSLY RECORDED AT REEL: 036213 FRAME: 0068. ASSIGNOR(S) HEREBY CONFIRMS THE ASSIGNEMENT. Recorded Nov 9, 2015
From: BORENSTEIN, NATHANIEL; AMPHLETT, MARC; JORDAN, CLIVE; LINSCOTT, MAX; O'MALLEY, NIALL; OSBORNE, JACQUELINE; PENTREATH, LUKE; SCOTT, OLIVER; SHARMA, RAHUL
To: MIMECAST NORTH AMERICA, INC.
Reel/Frame 037072/0589 →
ASSIGNMENT OF ASSIGNOR'S INTEREST Recorded Jul 29, 2015
From: BORENSTEIN, NATHANIEL; AMPHLETT, MARC; JORDAN, CLIVE; LINSCOTT, MAX; O'MALLEY, NIALL; OSBORNE, JACQUELINE; PENTREATH, LUKE; SCOTT, OLIVER; SHARMA, RAHUL
To: MIMECAST NORTH AMERICA INC.
Reel/Frame 036213/0068 →
Continuity (1)
Related Publication 20170034087A1 · Feb 2, 2017