Prototype Classifier for Intent Detection Without Retraining
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Solution Overview
Problem
Existing automated communication systems face inefficiencies in determining user intents, requiring extensive retraining of mathematical models for adding new intents and lacking flexibility in handling unmatched messages, which can be resource-intensive and prone to overfitting.
Innovation Solution
The use of prototype vectors and a prototype classifier to determine user intents by comparing message embeddings with precomputed prototype vectors, allowing for efficient addition of new intents without retraining the model and improving handling of unmatched messages through clustering and intent discovery from usage data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If a traditional mathematical model is retrained to add new intents, then the intent classification can accommodate new categories, but the computational resources and time required increase significantly
Solution Approach 1:
The patent segments the intent classification system into two independent components: a fixed mathematical model for message embedding and separate intent prototypes. Each intent is represented as an independent prototype vector that can be added, modified, or removed without affecting the core model or other intents. This segmentation enables flexible adaptation to new intents without requiring full model retraining.
Solution Approach 2:
The patent precomputes prototype vectors for each intent category during an offline preparation phase. These prototypes are stored and ready for immediate comparison with incoming messages. When new intents need to be added, only their prototypes need to be precomputed and inserted, rather than retraining the entire model from scratch.
2Adaptability or versatility
If a traditional mathematical model is retrained to add new intents, then the intent classification can accommodate new categories, but the computational resources required increase
Solution Approach 1:
The patent segments the intent classification system into two independent components: a fixed mathematical model for message embedding and separate intent prototypes. Each intent is represented as an independent prototype vector that can be added, modified, or removed without affecting the core model or other intents. This segmentation enables flexible adaptation to new intents without requiring full model retraining.
Solution Approach 2:
The patent creates simplified copies of intent representations in the form of prototype vectors. Instead of retraining the complex mathematical model for each new intent, the system generates lightweight prototype vectors that capture the essential characteristics of each intent. These copies can be rapidly created and updated with minimal computational overhead.
3Device complexity
If a fixed number of classes is used in the classifier, then the model structure is simple, but the system cannot handle new intents without retraining
Solution Approach 1:
The patent segments the intent classification system into two independent components: a fixed mathematical model for message embedding and separate intent prototypes. Each intent is represented as an independent prototype vector that can be added, modified, or removed without affecting the core model or other intents. This segmentation enables flexible adaptation to new intents without requiring full model retraining.
Solution Approach 2:
The patent transforms the static classifier structure into a dynamic system where the number of intent classes can change over time. The prototype-based approach allows the system to dynamically add, remove, or modify intent prototypes without altering the fundamental model architecture, enabling the classifier to adapt to evolving communication patterns and new intent categories.
4Measurement precision
If traditional intent classification is used, then matched messages are classified accurately, but unmatched messages require expensive retraining or manual intervention
Solution Approach 1:
The patent implements a self-service mechanism for handling unmatched messages through prototype-based classification. When a message does not match any existing intent, the system can automatically create a new prototype from the unmatched message or cluster of similar messages, enabling self-learning and automatic expansion of the intent vocabulary without requiring manual model retraining or expert intervention.
Solution Approach 2:
The patent incorporates feedback loops where classification results (including unmatched messages) are used to improve the system. Unmatched messages can be fed back into the prototype generation process, allowing the system to learn from its mistakes and automatically create new intent prototypes. This feedback mechanism continuously improves both accuracy and the ease of handling previously unmatched message types.
Data Source
AI summary
In some applications, it may be desired to process a message to determine an intent of the message, where the intent indicates the meaning of the message. An intent classifier may be used to determine the meaning of a message by processing the message to compute a message embedding vector that represents the message in a vector space. Each possible intent may be represented by a prototype vector, and the intent of the message may be determined by comparing the message embedding to one or more prototype vectors, such as by selecting an intent whose prototype vector is closest to the message embedding. An intent classifier may be used, for example, (i) to implement an automated communications system with states where each state is associated with a subset of the possible intents or (ii) for processing usage data of a communications system to update the intents of the communications system.