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Expert recommendation leveraging topic clusters derived from unstructured text data

  • US 10,235,452 B1
  • Filed: 03/27/2015
  • Issued: 03/19/2019
  • Est. Priority Date: 03/27/2015
  • Status: Active Grant
First Claim
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1. An apparatus comprising:

  • a processing platform comprising one or more processing devices each comprising a processor coupled to a memory;

    the processing platform being configured;

    to receive information relating to a communication from a user device, the communication comprising a service request;

    to identify at least one subject matter expert for the communication based on the received information and unstructured text data of a service events database, the unstructured text data comprising a plurality of documents associated with previous service requests, the plurality of documents comprising at least one unstructured service request summary comprising one or more problem summaries and one or more corresponding solution summaries;

    to separate the unstructured text data into topic clusters for a plurality of topics, at least a subset of the plurality of topics being determined automatically from the unstructured text data without reference to a set of rules characterizing predefined topics; and

    to connect the user device with an expert device corresponding to the identified subject matter expertwherein determining at least the subset of the plurality of topics automatically from the unstructured text data without reference to a set of rules characterizing predefined topics comprises;

    processing the unstructured service request summaries of the plurality of documents to construct a term index of terms utilized in the unstructured service request summaries;

    generating, for a domain comprising the unstructured service request summaries of the plurality of documents, an in-domain dictionary by processing the term index utilizing automatic lemmatization and synonym extraction;

    constructing a topic model by processing the in-domain dictionary; and

    determining a list of topics utilizing the topic model, wherein the list of topics comprises at least one topic elevated as a set of related terms from the unstructured request summaries of the plurality of documents;

    wherein connecting the user device with the expert device corresponding to the identified subject matter expert further comprises delivering one or more visualizations to the expert device, the one or more visualizations comprising at least one of;

    a bigram view visualization of a plurality of term pairs from a selected topic cluster;

    a summarization view visualization of representative term sequences from the selected topic cluster; and

    a unigram and aggregate probability view visualization of a plurality of individual terms from the selected topic cluster, the aggregate probability comprising a combination of individual probabilities that respective ones of the terms appear in the selected topic cluster.

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