Rephrasing Model for Extracting Actionable Data from Sales Conversations
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Solution Overview
Problem
Current natural language processing techniques struggle to accurately identify and translate textual data from sales communications into actionable insights due to limitations in keyword identification and context understanding, leading to inefficiencies in data analysis and missed follow-up actions.
Innovation Solution
A rephrasing model is developed to generate actionable data from textual conversations by training on a dataset with control signals, using a two-step configuration of extraction and rephrasing models to improve accuracy and consistency, and fine-tune the model for specific contexts and industries.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Extent of automation
If natural language processing techniques are used to identify keywords and translate textual data into actionable insights, then data analysis can be automated, but the accuracy of identification and context understanding is limited
Solution Approach 1:
The system segments the textual data processing into distinct stages: keyword identification, context analysis, and actionable insight generation. By dividing the complex NLP task into manageable components, each stage can be optimized independently to improve overall accuracy while maintaining automation.
Solution Approach 2:
The system introduces intermediary structures such as structured data formats and intermediate representation layers between the raw textual data and the final actionable insights. These intermediaries enable more precise processing and validation at each stage, improving identification accuracy without reducing automation.
2Loss of information
If comprehensive analysis of all textual data is performed to extract meaningful insights, then complete information can be obtained, but the processing time and resource consumption increase significantly
Solution Approach 1:
The system performs preliminary actions by pre-processing textual data into structured formats, pre-identifying potential keywords and entities, and pre-filtering irrelevant information before the main analysis stage. This preliminary preparation reduces the complexity of subsequent processing while ensuring no meaningful information is lost.
Solution Approach 2:
The system applies partial action by focusing analysis on the most relevant portions of textual data identified through preliminary filtering, rather than processing every single piece of data equally. This selective approach maintains information completeness for critical elements while reducing overall processing time.
3Reliability
If detailed processing and analysis of textual corpus is conducted to derive insights, then comprehensive understanding can be achieved, but the complexity and time-consuming nature of the process increases
Solution Approach 1:
The processing system is segmented into modular components, each responsible for a specific function such as tokenization, entity recognition, relationship extraction, and insight generation. This modular architecture reduces system complexity by making each component independent and manageable while maintaining comprehensive analysis capabilities.
Solution Approach 2:
The system employs universal processing frameworks and standardized data structures that can handle multiple types of textual data and analysis tasks through the same core mechanisms. This multi-functionality reduces overall system complexity by avoiding the need for separate specialized systems for different analysis functions.
4Measurement precision
If manual review of transcribed calls and emails is performed to extract sales information, then high accuracy can be achieved, but the process is time-consuming and most information cannot be exploited
Solution Approach 1:
The system enables self-service by automatically performing data extraction, analysis, and insight generation without requiring manual intervention for each record. The automated system serves itself by continuously processing textual data, identifying patterns, and generating actionable insights at scale, achieving both high accuracy and high productivity.
Solution Approach 2:
The system incorporates feedback mechanisms where the results of automated processing are continuously validated and refined. By comparing automated extraction results with expected outcomes and using this feedback to improve the processing algorithms, the system achieves manual-level accuracy while maintaining automated processing speed and throughput.
Data Source
AI summary
A system and method for generating a rephrasing model for rephrased actionable data extracted from conversations is presented. The method includes receiving a training dataset including a plurality of training samples, wherein each training sample includes a textual data extracted from recorded conversations and at least one action item, wherein the at least one action item is a portion of the textual data; associating a control signal to each training sample of the training dataset, wherein the control signal is added to the associated training sample; and training a rephrasing model using the training dataset, wherein the rephrasing model is trained to paraphrase the at least one action item to output at least one actionable data, wherein each training sample of the training dataset is iteratively fed into the machine learning algorithm of the rephrasing model.