Incremental Clustering for Scalable Message Processing
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Solution Overview
Problem
Current messaging and dial-in service platforms face challenges in efficiently identifying and responding to similar questions due to the difficulty in precision, which hinders real-time clustering and scalability.
Innovation Solution
A computer-implemented method using a machine learning engine to receive and scan incoming messages for similarity, identifying and assigning them to existing or new clusters, employing incremental clustering to manage memory usage and ensure scalability.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional clustering methods are used to identify similar questions, then clustering accuracy can be maintained, but memory requirements increase and scalability deteriorates
Solution Approach 1:
The patent segments the clustering process into incremental updates rather than processing all data at once. The clustering engine processes messages in batches and updates cluster assignments incrementally, allowing large datasets to be handled with limited memory by processing subsets of data sequentially rather than loading everything into memory simultaneously.
Solution Approach 2:
The patent implements dynamic cluster assignments where messages can be reassigned to different clusters as new information becomes available. The clustering engine continuously updates cluster structures and reassigns messages dynamically, allowing the system to adapt to new patterns while using memory efficiently through incremental updates rather than static batch processing.
2Speed
If real-time clustering of all messages is performed, then response speed improves, but infrastructure load increases
Solution Approach 1:
The patent applies partial action by processing only a subset of messages for clustering at any given time rather than all messages. The clustering engine selects representative samples or processes messages in batches, providing sufficient clustering accuracy for real-time responses without the full computational burden of processing every single message through the complete clustering pipeline.
Solution Approach 2:
The system segments the message stream into manageable batches for incremental processing. Rather than performing complete clustering on all accumulated messages, the engine processes segments of the message stream sequentially, maintaining real-time responsiveness while distributing the computational load over time to reduce peak infrastructure requirements.
3Measurement precision
If precise identification of similar questions is implemented, then answer accuracy improves, but processing time increases
Solution Approach 1:
The patent implements preliminary action by pre-computing and storing cluster assignments and similarity metrics during idle periods or between message bursts. The clustering engine prepares cluster structures and similarity thresholds in advance, so when new messages arrive, the system can quickly compare them against pre-computed cluster data rather than performing complete similarity calculations from scratch, maintaining precision while reducing processing time.
Data Source
AI summary
Incremental clustering of similar or related messages that otherwise requires limited use of memory for the purpose of increasing scalability. Incremental clustering includes receiving, by a machine learning (ML) engine, an incoming message from an application programming interface (API) server, and scanning, by the ML engine, a plurality of clusters for one or more messages similar to that of the incoming message. Incremental clustering also includes identifying, by a clustering engine, a cluster from the plurality of clusters. The identified cluster includes the one or more messages similar to that of the incoming message. Incremental clustering further includes assigning, by the clustering engine, the incoming message to the identified cluster.