Document Analysis System for Predicting Subject Matter Evolution
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Solution Overview
Problem
In customer support systems, documents such as help request tickets evolve over time, often leading to incorrect initial assignment to subject matter experts due to changing content, resulting in inefficient issue resolution.
Innovation Solution
A method involving a processor that receives a document with a sequence of sub-documents, converts it into a vector format to encode word probabilities and transition probabilities, applies a machine learning algorithm to predict future subject matter, and adjusts the course of action by re-routing the document to the appropriate expert based on the prediction.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Speed
If documents are assigned to subject matter experts based on initial messages, then initial routing speed is fast, but classification accuracy deteriorates as document content evolves
Solution Approach 1:
The system performs preliminary classification based on initial messages to enable fast initial routing. As the document evolves and more messages are added, the system continuously updates the classification, allowing the document to be reassigned to the correct expert when the subject matter changes, thus resolving the contradiction between initial speed and long-term accuracy.
Solution Approach 2:
The machine learning model continuously analyzes incoming messages and provides feedback on the evolving subject matter classification. This feedback mechanism allows the system to detect changes in document content and trigger reclassification, ensuring both initial speed and ongoing accuracy are maintained.
2Measurement precision
If documents are re-evaluated as content evolves, then classification accuracy improves, but processing time increases
Solution Approach 1:
The system performs periodic re-evaluation of documents as new messages arrive. Rather than continuous re-processing, the machine learning model analyzes each new message and determines whether a reclassification is necessary, enabling accurate tracking of evolving subject matter while minimizing unnecessary processing time.
Solution Approach 2:
The machine learning model autonomously determines when reclassification is needed by analyzing the content of incoming messages. This self-service approach allows the system to maintain high accuracy without requiring constant manual intervention or exhaustive re-processing of the entire document history.
3Speed
If initial classification is based on limited information, then routing speed is fast, but reliability of assignment deteriorates
Solution Approach 1:
The classification system transitions from a static initial classification to a dynamic, evolving classification that adapts as new information becomes available. The machine learning model continuously updates the subject matter identification based on incoming messages, allowing the assignment to become more reliable over time while maintaining fast initial routing through the preliminary classification step.
Data Source
AI summary
A method includes performing, by a processor: receiving a document containing subject matter related to a course of action, the document comprising a plurality of sub-documents that are related to one another in a time sequence, converting the document to a vector format to generate a vectorized document that encodes a probability distribution of words in the document and transition probabilities between words, applying a machine learning algorithm to the vectorized document to generate an estimated vectorized document, associating the estimated vectorized document with a reference document; predicting future subject matter contained in a future sub-document of the document based on the reference document, and adjusting the course of action responsive to predicting the future subject matter.