NLP Named Entity Detection for Manual Data Entry Error Correction
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Solution Overview
Problem
Manual data entry in call centers often results in errors, which can lead to significant impacts such as incorrect customer information, missed notifications, and ultimately, customer dissatisfaction and loss of business opportunities.
Innovation Solution
A system utilizing natural language processing (NLP) and machine learning to detect named entities in conversations and compare them with manually entered data on a service platform, alerting service agents of any mismatches.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual data entry is used by service agents, then data can be entered into the system, but errors occur in the entered data
Solution Approach 1:
The system records conversations, extracts named entities through NLP, compares extracted data with manually entered data, and provides feedback alerts to service agents when mismatches are detected. This closed-loop feedback mechanism enables real-time error detection and correction, improving data accuracy without requiring complete system redesign
Solution Approach 2:
The patent introduces an intermediary system comprising speech-to-text converters, named entity recognition models, and data comparison mechanisms. This intermediary layer sits between the manual data entry process and the final data storage, automatically verifying entered data against conversation content without replacing the manual entry process entirely
2Productivity
If manual data entry is performed, then service agents can input customer information, but time is lost due to errors and corrections
Solution Approach 1:
The system performs preliminary actions by recording the entire conversation and pre-extracting named entities during or immediately after the call. This preparation work is done before the service agent needs to enter data, so when data entry occurs, the verification system already has the reference data ready for immediate comparison, eliminating post-entry correction time
Solution Approach 2:
Real-time or near-real-time feedback alerts notify service agents of potential errors immediately after data entry, enabling prompt correction before data is finalized. This prevents the time loss associated with discovering errors later in the process
3Measurement precision
If automated speech-to-text and named entity recognition are implemented, then data accuracy can be improved, but system complexity increases
Solution Approach 1:
The system employs universal components that perform multiple functions: the speech-to-text converter transcribes conversation and enables text analysis; the named entity recognition model extracts various types of data (names, addresses, phone numbers, etc.) using the same underlying technology; the comparison mechanism works across different data types. This multi-functionality reduces overall system complexity despite the advanced capabilities
Solution Approach 2:
The system is self-sufficient in that it automatically records conversations, transcribes speech to text, extracts named entities, compares extracted data with manual entry, and generates alerts without requiring external intervention. This automation eliminates the need for additional manual verification steps, offsetting the complexity through operational simplicity
Data Source
AI summary
Systems and computer-implemented methods disclosed herein relate to detecting errors in manually entered data. In one embodiment, the system can identify a named entity automatically from a conversation between a customer and service agent with a named entity recognition model that employs natural language processing and machine learning to detect a word or string of words in the conversation that corresponds to a named entity category. In another embodiment, the system can determine whether data entered into a field on a service platform by the service agent includes an error by comparing the data entered with the named entity. In another embodiment, the system can transmit an alert to the service agent through the service platform when there is a mismatch between the named entity and the data entered.