Financial Utterance Clustering Using Mass and Torque Adjustment
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Solution Overview
Problem
Existing clustering techniques for financial utterances in AI systems often produce either too many overlapping clusters or too few clusters, leading to ambiguity and inefficiency in understanding user intent and generating appropriate actions, due to the nuanced and context-dependent nature of financial language.
Innovation Solution
A torque-based clustering method that assigns mass values to data points and adjusts their positions based on mutual forces and distances, allowing for parameter-free identification of dense clusters without requiring prior specifications of cluster count or distance thresholds, thereby improving cluster coherency and distinction.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If existing clustering techniques are used for financial utterances, then the system can process and group user inputs, but the clusters become either too many and overlapping or too few, leading to ambiguity in understanding user intent
Solution Approach 1:
The clustering system automatically determines optimal cluster parameters by treating data points as mass-bearing objects that self-organize through torque-based interactions. The system computes mass values for each data point based on local density, then iteratively adjusts cluster assignments by calculating torques from mass-bearing neighbors, eliminating the need for manual parameter specification and achieving self-optimized cluster coherency
Solution Approach 2:
The system dynamically changes the parameter of mass assignment by computing mass values based on local density estimates rather than using uniform or pre-specified parameters. This parameter transformation allows data points to self-organize into coherent clusters without requiring manual tuning of cluster count or distance thresholds
2Productivity
If traditional clustering methods are applied to financial utterances, then the processing can be completed, but manual tuning of parameters is required to achieve acceptable cluster quality
Solution Approach 1:
The system performs self-service by automatically determining optimal clustering parameters through iterative torque-based adjustments. Mass values are computed from local density, and the system autonomously refines cluster assignments without human intervention, completely eliminating manual tuning time while maintaining high productivity in cluster formation
3Measurement precision
If mass-based torque adjustments are applied to compressed vectors, then cluster coherency improves with singular actionable labels, but the computational process becomes more complex
Solution Approach 1:
The system introduces mass values as an intermediary property that mediates the interaction between data points. By assigning mass based on local density and using it to compute torques in the iterative adjustment process, the system achieves better actionable label accuracy while the complexity is managed through the physical analogy of mass-bearing objects interacting through torque forces
Data Source
AI summary
A set of embeddings is generated from the data of a set of utterances originating in a financial data processing environment. An embedding corresponds to an utterance and includes a multidimensional vector whose dimensionality is reduced forming a compressed vector. A mass value is assigned to the compressed vector (mass-bearing data point (MD)). A set of MDs corresponds to the set of utterances. For a neighborhood of the MID, a torque value is iteratively adjusted using mass values and pairwise distances between pairs of members of the neighborhood of MD. After reaching an exit condition, a cluster is output with a better coherency correspondence with a singular actionable label as compared to another coherency correspondence of another cluster formed without the mass assignment or torque based adjustment. An operation is triggered from the actionable label in the financial data processing environment.