System and method for displaying relationships between electronically stored information to provide classification suggestions via inclusion

A method for providing reference items as a suggestion for classifying uncoded electronically stored information items, comprising the steps of:

• designating a set of reference electronically stored information items each associated with one of a plurality of classification codes and a visual representation of that classification code comprising at least one of a shape and a symbol, wherein the visual representation of each of the classification codes is different from the visual representations of the remaining classification codes;
• obtaining a set of uncoded electronically stored information items each associated with a visual representation different from the visual representations of the classification codes;
• combining one or more of the coded reference electronically stored information items with the set of the uncoded electronically stored information items;
• grouping the combined uncoded electronically stored information items and one or more coded reference electronically stored information items into clusters;
• and
• visually depicting relationships between the uncoded electronically stored information items and one or more coded reference electronically stored information items in at least one of the clusters as suggestions for classifying the uncoded electronically stored information items in that cluster, comprising displaying the visual representation associated with each of the coded reference electronically stored information items in that cluster and the visual representation associated with each of the uncoded electronically stored information items in that cluster,
• wherein the steps are performed by a suitably programmed computer.

A system for clustering reference documents to generate suggestions for classification of uncoded documents, comprising:

• a set of reference documents each associated with one of a plurality of classification codes and a visual representation of that classification code comprising at least one of a shape and a symbol, wherein the visual representation of each of the classification codes is different from the visual representations of the remaining classification codes;
• a set of uncoded documents each associated with a visual representation different from the visual representations of the classification codes;
• a processor to execute modules, comprising:
• a clustering module to select one or more of the coded reference documents, to combine the one or more coded reference documents selected with the uncoded documents as a set of combined documents, and to group the combined set of documents into clusters, further comprising:
• a cluster similarity module to determine a similarity between each document;
• and
• a grouping module to group the documents into the clusters based on the similarity;
• an identification module to identify at least one of the clusters with the coded reference documents;
• and
• a display to visually depict relationships between the uncoded documents and the one or more coded reference documents in the at least one cluster as suggestions for classifying the uncoded documents in that cluster by displaying the visual representation associated with each of the coded reference documents in that cluster and the visual representation associated with each of the uncoded documents in that cluster.

A method for clustering reference documents to generate suggestions for classification of uncoded documents, comprising the steps of:

• designating a set of reference documents each associated with one of a plurality of classification codes and a visual representation of that classification code comprising at least one of a shape and a symbol, wherein the visual representation of each of the classification codes is different from the visual representations of the remaining classification codes;
• obtaining a set of uncoded documents each associated with a visual representation different from the visual representations of the classification codes;
• selecting one or more of the coded reference documents and combining the one or more coded reference documents selected with the uncoded documents as a set of combined documents;
• grouping the combined set of documents into clusters, comprising:
• determining a similarity between each document;
• and
• grouping the documents into the clusters based on the similarity;
• identifying at least one cluster of the clusters with the coded reference documents;
• and
• visually depicting relationships between the uncoded documents and the one or more coded reference documents in the at least one cluster as suggestions for classifying the uncoded documents in that cluster, comprising displaying the visual representation associated with each of the coded reference documents in that cluster and the visual representation associated with each of the uncoded documents in that cluster,
• wherein the steps are performed by a suitably programmed computer.