Telecom Interaction Tagging Using Transcript-Summary Term Association
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Solution Overview
Problem
Existing tagging systems for telecommunication interactions are inefficient and inaccurate, particularly when dealing with vague, lengthy, or noisy transcripts, leading to excessive computing resource consumption.
Innovation Solution
A system that utilizes both transcripts and agent-prepared summaries to generate indices of terms, determines association scores using pointwise mutual information, and extracts candidate key phrases for accurate tagging, conserving computing resources.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of energy
If existing tagging systems process telecommunication transcripts, then tagging functionality is provided, but computing resource consumption is excessive
Solution Approach 1:
The patent extracts and removes stop words (common, meaningless words) from the transcript before processing. This extraction step eliminates unnecessary computational overhead while preserving the meaningful content needed for accurate tagging, thus reducing computing resource consumption without sacrificing tagging accuracy.
Solution Approach 2:
The patent performs preliminary indexing of terms from both transcripts and agent-prepared summaries before the actual tagging process. By pre-computing term frequencies and associations, the system avoids redundant processing during tagging operations, significantly reducing computing resource consumption while maintaining or improving tagging accuracy through the use of pre-analyzed term relationships.
2Measurement precision
If tagging systems process vague and noisy transcripts, then comprehensive tagging is attempted, but tagging accuracy deteriorates
Solution Approach 1:
The patent introduces agent-prepared summaries as an intermediary between the noisy transcripts and the tagging process. These summaries provide a cleaned, structured representation of the transcript content, serving as a mediator that filters out noise and vagueness while preserving key information, thereby improving tagging accuracy without requiring complex noise-handling mechanisms.
Solution Approach 2:
The patent dynamically adjusts the tagging process by using term association scores that are computed based on co-occurrence patterns in both transcripts and summaries. This dynamic approach allows the system to adapt to varying transcript qualities, automatically weighting terms based on their association strength rather than using fixed processing rules, thus improving accuracy while managing complexity.
3Productivity
If traditional tagging approaches are used, then simple processing is maintained, but locating relevant transcripts requires excessive attempts
Solution Approach 1:
The patent performs preliminary indexing and term association analysis on both transcripts and agent-prepared summaries before tagging. This pre-processing creates an optimized data structure that enables efficient retrieval and matching, dramatically reducing the number of attempts needed to locate relevant transcripts while decreasing overall computing resource consumption through avoided redundant processing.
Solution Approach 2:
The patent replaces traditional mechanical search approaches with an AI-based tagging system that uses learned term associations and relevance scoring. This substitution enables more intelligent and efficient transcript location by leveraging semantic relationships rather than relying on brute-force searching, thereby improving productivity while reducing energy consumption.
Data Source
AI summary
In some implementations, a device may obtain a data set that indicates scores for first terms based on association scores between the first terms and second terms. The association scores may be based on occurrences of the first terms in electronic documents that provide transcripts relating to telecommunication interactions, and occurrences of the second terms in electronic records that provide summaries relating to the telecommunication interactions. The device may process an electronic document, that provides a transcript relating to a new telecommunication interaction involving an agent device and a user device, to extract candidate key phrases from the transcript. The device may determine a relevance score for each candidate key phrase based on the score(s), of the data set, associated with the first term(s) in the candidate key phrase. The device may generate tag(s) for the new telecommunication interaction based candidate key phrase(s) associated with highest relevance score(s).