Message Suggestion Clustering Vector Space
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Solution Overview
Problem
Large mathematical models require significant time and computational resources for retraining, limiting the frequency of updates in applications that use them, such as message suggestion systems.
Innovation Solution
Implementing a message suggestion system that uses pre-approved message encodings and conversation encodings in a vector space, with clustering techniques to select suggested messages efficiently, and processing adaptive features outside of a neural network to reduce computational requirements for retraining.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If large mathematical models are used for message suggestion, then message suggestion quality is improved, but retraining time and computational resources increase
Solution Approach 1:
The patent segments the message suggestion system into multiple components: a conversation encoding model that processes input messages, a pre-approved message set stored in vector space, and a suggestion generation module. This segmentation allows the system to use a smaller, more efficient encoding model rather than relying entirely on large pre-trained models, reducing retraining time while maintaining suggestion quality
Solution Approach 2:
The patent applies preliminary action by pre-approving and storing messages in vector space before they are needed for suggestions. Messages are pre-processed, encoded, and organized in advance, allowing the system to quickly retrieve and suggest appropriate messages without requiring complex real-time model inference, thus reducing computational resources and time
2Measurement precision
If large mathematical models are used for message suggestion, then message suggestion quality is improved, but computational resources increase
Solution Approach 1:
The patent uses copying by storing pre-approved messages in vector space representations. Instead of running large mathematical models to generate suggestions in real-time, the system creates vector copies of approved messages and uses similarity comparison to generate suggestions. This copying approach dramatically reduces computational resources while maintaining suggestion quality
Solution Approach 2:
The patent changes parameters by transforming messages into vector space representations with specific dimensions and properties. By adjusting vector space parameters such as dimensionality, similarity thresholds, and encoding parameters, the system optimizes the balance between suggestion quality and computational efficiency, reducing the need for large mathematical models
3Adaptability or versatility
If message suggestion systems are updated frequently, then adaptability is improved, but retraining computational resources increase
Solution Approach 1:
The patent applies self-service by enabling the system to update its pre-approved message set and vector space representations without requiring full retraining of large mathematical models. The system can incrementally add new messages, update existing ones, and reorganize vector space structures using minimal computational resources, allowing frequent updates while consuming few resources
Solution Approach 2:
The patent introduces dynamics by making the message suggestion system adaptable and flexible through incremental updates. The pre-approved message set and vector space representations can be dynamically modified, added, or removed based on new data, allowing the system to adapt to changing requirements without rigid retraining cycles
Data Source
AI summary
A message suggestion service may use clusters of pre-approved messages to improve the quality of messages suggested to users. During a conversation, messages of the conversation may be processed with a neural network to compute a conversation encoding vector. The neural network may also be used to compute pre-approved message encoding vectors of the pre-approved messages. Distances between the conversation encoding vector and the pre-approved message encoding vectors may be used to select one or more clusters. Distances between the conversation encoding vector and the pre-approved message encoding vectors may then be used to select one or more pre-approved messages from the selected clusters. The selected pre-approved messages may then be presented as suggested messages to a user.