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Data mining to determine online user responses to broadcast messages

  • US 10,248,960 B2
  • Filed: 11/11/2011
  • Issued: 04/02/2019
  • Est. Priority Date: 11/16/2010
  • Status: Active Grant
First Claim
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1. In a system wherein computing devices are used by users at a plurality of nodes at least to initiate messages that flow between nodes of the plurality of nodes, a method of measuring advertising effectiveness, the method comprising:

  • monitoring, by a processor, at least one aggregate activity stream following a broadcast time of a broadcast advertisement, wherein the at least one aggregate activity stream represents one or more messages sent by an aggregation of users that form a statistical group over which advertising effectiveness is to be measured, wherein each of the one or more messages representing the at least one aggregate activity stream do not include the broadcast advertisement;

    filtering, by the processor, the one or more messages representing the at least one aggregate activity stream to identify messages that appear to be responsive to the broadcast advertisement, wherein filtering the one or more messages includes determining which of the identified messages were sent before the broadcast time of the broadcast advertisement, and determining which of the identified messages are mechanically generated and which of the identified messages are not mechanically generated, wherein a mechanically generated message includes a message that was previously sent at a prior time and that is resent following the broadcast time, and wherein a non-mechanically generated message includes a message that is sent following the broadcast time and that is based on a human response from an observation of the broadcast advertisement;

    assigning, by the processor, a first weight value to the identified messages that are mechanically generated and assigning a second weight value to the identified messages that are non-mechanically generated, wherein the first weight value is less than the second weight value;

    classifying, by the processor, the identified messages into a plurality of groups, wherein the identified messages are classified into a group based at least in part on a word pattern or phrase associated with an author;

    determining, by the processor, a demographic for each of the plurality of groups; and

    for each demographic;

    determining, by the processor, a measure of favorability based on the identified messages in one or more groups corresponding to the demographic and the weight values assigned to the identified messages in the one or more groups.

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