Methods and apparatus for deep interaction analysis
First Claim
1. A computerized method for automatically sectioning an audio signal of an interaction held in a call center, into sections representing the flow of the interaction, the method comprising the steps of:
- receiving at least a part of the audio signal from a logging and capturing unit comprising a computing platform and associated with the call center, the at least a part of the audio signal comprises a non-training production run-time interaction;
performing audio analysis on the at least a part of the audio signal for obtaining run-time data;
segmenting the at least a part of the audio signal into at least one context unit;
extracting a feature vector as a multi-valued construct comprising at least one run-time feature of the at least one context unit, using the run-time data;
classifying the at least one context unit using a sectioning model and the feature vector, to obtain at least one section label to be associated with the at least one context unit; and
subsequentlygrouping context units assigned identical labels into one section,wherein the method is carried out by an at least one processing apparatus.
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Abstract
A method and apparatus for automatically sectioning an interaction into sections, in order to get more insight into interactions. The method and apparatus include training, in which a model is generated upon training interactions and available tagging information, and run-time in which the model is used towards sectioning further interactions. The method and apparatus operate on context units within the interaction, wherein each context unit is characterized by a feature vector relate to textual, acoustic or other characteristics of the context unit.
184 Citations
20 Claims
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1. A computerized method for automatically sectioning an audio signal of an interaction held in a call center, into sections representing the flow of the interaction, the method comprising the steps of:
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receiving at least a part of the audio signal from a logging and capturing unit comprising a computing platform and associated with the call center, the at least a part of the audio signal comprises a non-training production run-time interaction; performing audio analysis on the at least a part of the audio signal for obtaining run-time data; segmenting the at least a part of the audio signal into at least one context unit; extracting a feature vector as a multi-valued construct comprising at least one run-time feature of the at least one context unit, using the run-time data; classifying the at least one context unit using a sectioning model and the feature vector, to obtain at least one section label to be associated with the at least one context unit; and
subsequentlygrouping context units assigned identical labels into one section, wherein the method is carried out by an at least one processing apparatus. - View Dependent Claims (2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14)
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15. An apparatus for automatically sectioning an interaction held in a call center, into sections representing the flow of the interaction, based on at least one training interaction, the apparatus comprising:
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an interaction receiving component arranged to receive at least a part of at least one first audio signal representing the interaction as a non-training production run-time interaction, or at least one second audio signal representing the training interaction; an extraction component arranged to extract data from the at least a part of the at least one first audio signal or the at least one second audio signal; a context unit segmentation component arranged to segment the at least a part of the at least one first audio signal or the at least one second audio signal into context units; a feature vector determination component arranged to generate a feature vector as a multi-valued construct comprising at least one feature based on the data extracted from the at least a part of the at least one first audio signal or the at least one second audio signal; and a sectioning component arranged to apply a sectioning model on the feature vector. - View Dependent Claims (16, 17, 18, 19)
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20. A non-transitory computer readable storage medium containing a set of instructions for a general purpose computer, the set of instructions comprising:
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receiving at least a part of an audio signal representing an interaction as a non-training production run-time interaction captured within a call center; performing audio analysis on the at least a part of the audio signal for obtaining tested data; segmenting the at least a part of the audio signal into at least one tested context unit; extracting a tested feature vector as a multi-valued construct comprising at least one feature of the at least one context unit, using the data; classifying the at least one context unit using a sectioning model and the tested feature vector, to obtain at least one section label to be associated with the at least one context unit; and
subsequentlygrouping context units assigned identical labels into one section.
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Specification