ML-Based Mixed-Modality Message Classification for Resource Optimization
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Solution Overview
Problem
Existing software applications face inefficiencies in transmitting mixed-modality messages due to resource overhead, as the effectiveness of these messages is not accurately predicted, leading to wasted resources on ineffective messages.
Innovation Solution
The use of machine learning models to generate embedding representations of mixed-modality messages and classify them as effective or ineffective, allowing for managed transmission based on predicted effectiveness, with a first model generating embeddings and a second model classifying the messages.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If mixed-modality messages are transmitted to users, then message effectiveness and user engagement are improved, but resource overhead (network bandwidth, storage, power, processing) increases significantly
Solution Approach 1:
The system performs preliminary classification of messages using machine learning models before transmission. The first model generates embedding representations and the second model classifies messages as effective or ineffective, allowing the system to pre-determine which messages warrant transmission and which should be discarded, thus avoiding wasted resource overhead on ineffective messages
Solution Approach 2:
The machine learning models automatically evaluate and classify message effectiveness without requiring manual review. The system serves itself by using the trained models to autonomously determine message transmission decisions, reducing the need for human intervention while optimizing resource allocation
2Loss of energy
If machine learning models are used to classify messages, then resource wastage is reduced by transmitting only effective messages, but computational overhead and processing time increase
Solution Approach 1:
The classification process is segmented into two distinct machine learning models: the first model generates embedding representations of messages, and the second model performs the actual classification. This segmentation allows each model to specialize in a specific task, improving overall efficiency and reducing the computational burden compared to using a single complex model
Solution Approach 2:
The system uses embedding representations as intermediate copies of message features. Instead of processing raw messages directly through complex classification logic, the system creates compressed vector representations (embeddings) that capture essential message characteristics, making subsequent classification computationally efficient
Data Source
AI summary
Certain aspects of the present disclosure provide techniques for managing the transmission of mixed-modality messages using machine learning models. An example method generally includes generating, using a first machine learning model, an embedding representation of a mixed-modality message. The mixed-modality message is classified as an effective message or an ineffective message using a second machine learning model and the embedding representation of the mixed-modality message. One or more actions are taken to manage transmission of the mixed-modality message based on the classifying the mixed-modality message as an effective message or an ineffective message.