Temporal Transformer for Context-Aware Communication Protocol Selection
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Solution Overview
Problem
Conventional machine learning models, particularly convolutional neural networks (CNNs), face challenges in scalability, capturing temporal and contextual relationships, integrating diverse data sources, and optimizing communication protocols across disparate networks, leading to suboptimal communication strategies in pharmaceutical contexts.
Innovation Solution
A customized transformer-based neural network, enhanced with temporal and domain-specific contextual embeddings, and an optimization framework, addresses these challenges by effectively capturing temporal dynamics and contextual nuances, providing a scalable and accurate solution for pharmaceutical omnichannel orchestration.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If conventional CNNs are used for communication protocol selection, then implementation simplicity is maintained, but the model fails to capture temporal and contextual relationships in multi-dimensional sequential data
Solution Approach 1:
The patent transforms the input data by creating temporal features (time since last event, event frequency) and contextual features (customer segment, channel preferences) that encode temporal and contextual relationships into static vectors suitable for CNN processing, thereby resolving the contradiction between model simplicity and information capture
2Power
If conventional CNNs are used, then computational efficiency is maintained, but scalability to increasing data dimensions becomes problematic due to matrix sparsity
Solution Approach 1:
The patent performs preliminary data aggregation and feature engineering to consolidate multi-dimensional sequential data into condensed customer journey vectors with pre-computed temporal and contextual features before inputting to the CNN, enabling the model to scale to increasing dimensions without suffering from matrix sparsity issues
3Productivity
If existing models are used for communication protocol selection, then implementation speed is maintained, but integration of real-world evidence and contextual information becomes suboptimal
Solution Approach 1:
The patent merges multiple data sources including CRM interactions, email data, third-party vendor activities, and real-world evidence into a unified customer journey representation that feeds into the CNN model, achieving both fast implementation and improved protocol selection accuracy through integrated contextual information
Data Source
AI summary
A computer-implemented system can implement a temporal and context-based transformer for improved communication protocol selection across disparate networks. The system can train a transformer-based neural network using embeddings generated separately from, respectively, profile attributes, channel-specific behaviors, external clinical events and/or observed outcomes. The transformer-based neural network can be trained to reconstruct masked events and inter-event time gaps, then fine-tuned with a dual-loss objective that simultaneously preserves behavioral grammar and maximizes outcome prediction accuracy. During live operation the model ingests current profile, real-time telemetry and new contextual events to temporally and contextually select communication channels to generate events across disparate networks.