×

Classifying electronic messages using individualized artificial intelligence techniques

  • US 9,946,789 B1
  • Filed: 04/28/2017
  • Issued: 04/17/2018
  • Est. Priority Date: 04/28/2017
  • Status: Expired due to Fees
First Claim
Patent Images

1. A system, comprising:

  • a non-transitory memory; and

    one or more hardware processors coupled to the non-transitory memory and configured to execute instructions to perform operations comprising;

    obtaining a plurality of electronic messages associated with a user, the plurality of electronic messages including a first electronic message and a second electronic message;

    identifying a plurality of message labels associated with the plurality of electronic messages, the plurality of message labels including a first message label and a second message label;

    identifying, based on a classification model specific to a first user, a first message label associated with the first electronic message and a second message label associated with the second electronic message;

    producing one or more tokens from the second electronic message;

    detecting a finger gesture by the first user on the second electronic message to apply the first message label to the second electronic message;

    responsive to detecting the finger gesture, re-training the classification model using a computer based on the one or more tokens produced from the second electronic message to produce an updated classification model specific to the first user;

    re-training the classification model comprising;

    applying one or more natural language processing techniques to the plurality of electronic messages to produce a plurality of tokens;

    generating a first message cluster and a second message cluster based on the plurality of tokens, the first message cluster including one or more electronic messages sharing a predefined number of similarities with tokens produced from the second electronic message;

    assigning, the first message label, to messages included in the first message cluster; and

    updating the classification model based on the first message label and the messages included in the first message cluster;

    after re-training the classification model is completed, detecting an incoming electronic message not included in the plurality of electronic messages, the incoming electronic message being associated with a timestamp that is later in time than timestamps associated with the first electronic message and the second electronic message;

    comparing the incoming electronic message with the one or more tokens produced from the second electronic message;

    determining that the incoming electronic message shares a predefined number of similarities with the one or more tokens produced from the second electronic message; and

    applying, based on the updated classification model, the first message label to the incoming electronic message.

View all claims
  • 2 Assignments
Timeline View
Assignment View
    ×
    ×