Customer feedback acquisition and processing system
First Claim
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1. A customer feedback acquisition and processing system comprising:
- a service representative terminal for receiving customer feedback messages;
a data acquisition processor in communication with the service representative terminal for developing electronic records including text representative of the customer feedback messages;
a database in communication with the data acquisition processor for storing the records developed by the data acquisition processor;
a data analyst terminal for receiving query inputs; and
a database processor in communication with the database and the data analyst terminal, the database processor being responsive to a query input received from the data analyst terminal to analyze the text in the records stored in the database to identify a trend in the customer feedback messages, the trends associated with a plurality of different customers, the database processor also producing a first search queue from the search result, the first search queue including a plurality of concepts.
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Abstract
A customer feedback acquisition and processing system is disclosed. Customer feedback, which may optionally include voice signals, is captured and stored in a database. The database can be searched to develop a subset of records pertaining to an area of interest. A data mining tool can then be used on the subset to identify trend(s) in the customer feedback records. The database tool assigns relevance scores to each word (“concept”) in one or more fields of the records in the subset. It then combines the concepts and develops new relevance scores for the combined concepts to identify trend(s) in the customer feedback records.
176 Citations
36 Claims
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1. A customer feedback acquisition and processing system comprising:
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a service representative terminal for receiving customer feedback messages;
a data acquisition processor in communication with the service representative terminal for developing electronic records including text representative of the customer feedback messages;
a database in communication with the data acquisition processor for storing the records developed by the data acquisition processor;
a data analyst terminal for receiving query inputs; and
a database processor in communication with the database and the data analyst terminal, the database processor being responsive to a query input received from the data analyst terminal to analyze the text in the records stored in the database to identify a trend in the customer feedback messages, the trends associated with a plurality of different customers, the database processor also producing a first search queue from the search result, the first search queue including a plurality of concepts. - View Dependent Claims (2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22)
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23. A method of acquiring and analyzing customer feedback comprising the steps of:
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receiving customer feedback messages;
developing electronic records including text representative of the customer feedback messages;
storing the developed records in a database;
receiving a query input; and
responding to the query input by analyzing the text in the records to identify a trend in the customer feedback messages from a plurality of different customers;
producing a search result identifying a sub-set of the records in the database; and
producing a first search queue from the search result, the first search queue including a plurality of concepts. - View Dependent Claims (24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36)
producing a search result identifying a subset of the records in the database; and
producing a first search queue from the search result, the first search queue including a plurality of concepts, each concept in the first search queue corresponding to a unique word contained in at least one predetermined portion of at least one of the records in the search result.
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27. A method as defined in claim 26 wherein the step of responding to the query input by analyzing the text in the records further comprises the step of computing a relevance score for a given concept in the first search queue.
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28. A method as defined in claim 27 wherein the step of computing a relevance score for a given concept in the first search queue further comprises the steps of:
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developing a set count indicative of a number of records in the database containing the given concept in the at least one predetermined portion of the records; and
developing a subset count indicative of a number of records in the search result containing the given concept in the at least one predetermined portion of the records.
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29. A method as defined in claim 28 wherein the step of computing a relevance score for a given concept in the first search queue further comprises the steps of:
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generating a global frequency for the given concept by dividing the set count for the given concept by a total number of records in the database; and
generating a local frequency for the given concept by dividing the subset count for the given concept by a total number of records identified in the search result.
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30. A method as defined in claim 29 wherein the step of computing a relevance score for a given concept in the first search queue further comprises the steps of calculating the relevance score for the given concept by dividing the local frequency by the global frequency.
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31. A method as defined in claim 27 further comprising the step of sorting the concepts in the first search queue based on the relevance scores.
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32. A method as defined in claim 26 further comprising the step of creating a compound concept by combining at least two concepts in the first search queue.
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33. A method as defined in claim 32 further comprising the steps of:
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computing a relevance score for at least one concept in the first search queue; and
calculating a relevance score for the compound concept.
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34. A method as defined in claim 33 further comprising the step of selectively creating a second search queue by adding the compound concept to the first search queue if the relevance score of the compound concept is greater than the relevance score of each of the at least two concepts.
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35. A method as defined in claim 34 further comprising the steps of:
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developing a new subset list for the compound concept from at least two subset lists corresponding to the at least two concepts in the first search queue, wherein each of the at least two subset lists identify each record in the search result containing the corresponding concept, and wherein the new subset list identifies each record appearing in each of the at least two subset lists;
developing a new subset count for the compound concept by counting the records identified in the new subset list;
generating a global frequency for the compound concept;
generating a local frequency for the compound concept by dividing the subset count for the compound concept by a total number of records identified in the search result; and
calculating the relevance score for the compound concept by dividing the local frequency by the global frequency.
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36. A method as defined in claim 35 wherein the step of generating a global frequency for the compound concept further comprises the steps of:
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determining a global frequency for each of the at least two concepts comprising the compound concept; and
multiplying the global frequencies of the at least two concepts together to estimate the global frequency of the compound concept.
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Specification