Multitask Machine Learning Model for Electronic Message Data Extraction
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Solution Overview
Problem
Current systems require manual generation of data extraction rules for electronic messages, which is costly, time-consuming, and not scalable due to the lack of standardization across senders and message types, making it impractical to extract data from the vast volume of machine-generated emails.
Innovation Solution
A novel multitask machine learning model is used to automatically extract data from electronic messages by translating message contents into structured information, eliminating the need for human editors and enabling efficient data extraction across various message types and senders.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual generation of data extraction rules is used, then extraction accuracy can be maintained, but the cost and time consumption increase significantly
Solution Approach 1:
The system performs preliminary actions by pre-training machine learning models on large corpora of electronic messages before actual data extraction is needed. The model learns extraction patterns in advance, so when actual messages are processed, the extraction can occur rapidly without manual rule generation for each message type.
Solution Approach 2:
The machine learning model performs self-service by automatically learning and adapting to different message formats and senders without requiring manual intervention. The system extracts data from new message types autonomously by generalizing from training data, eliminating the need for human editors to create extraction rules for each new message variant.
2Reliability
If manual generation of data extraction rules is used, then extraction quality can be ensured, but scalability is severely limited
Solution Approach 1:
The machine learning model achieves universality by being capable of extracting data from multiple different message types and senders using a single trained model. Instead of requiring separate manual rules for each sender and message type, the model generalizes across diverse formats, making the system scalable to handle vast volumes of machine-generated emails from various sources.
Solution Approach 2:
The system handles parameter changes in message formats by training the model on varied examples during preprocessing. When senders change their message templates or formats, the model can adapt to these parameter changes without requiring manual rule updates, maintaining extraction quality across evolving message structures.
3Measurement precision
If human editors are used to update extraction rules, then extraction accuracy is maintained, but the cost becomes prohibitive
Solution Approach 1:
The system eliminates the need for human editors by implementing self-service through automated machine learning models. The model continuously improves extraction accuracy through automated training on message corpora, performing what would traditionally require human editorial work without incurring labor costs.
Solution Approach 2:
The patent substitutes the mechanical system of human editorial work with an automated computational system. Instead of human editors manually analyzing and creating extraction rules, machine learning algorithms automatically learn extraction patterns from data, replacing the manual process with an automated intelligent system that scales without proportional cost increases.
4Measurement precision
If manual extraction rules are generated for each sender and message type, then extraction precision is maintained, but device complexity increases
Solution Approach 1:
The machine learning model serves as a universal extraction system that handles multiple senders and message types through a single unified model. Instead of maintaining separate extraction rule sets for each sender and message type, the model learns common and specific patterns during training, simplifying the system architecture while maintaining precision across diverse message formats.
Data Source
AI summary
Techniques for automatic intelligent information extraction from electronic messages are disclosed. In one embodiment, a computerized method is disclosed comprising obtaining a corpus of electronic messages, generating training data using the corpus of electronic messages, training an attribute generation model using the training data, analyzing an electronic message from a message folder and generating model input based on the analysis, obtaining model output from the attribute generation model based on the model input, the model output comprising, in connection with a respective type of information, a set of attribute values for a set of attributes corresponding to the respective type of information, and generating a presentation, for display at a user computing device, the presentation comprising information based at least in part on the set of attribute values associated with the set of attributes.