Language Model Neural Network for Tailored Communication Suggestions
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Solution Overview
Problem
Existing techniques for generating suggested communications in multi-actor interactions fail to consider the varying objectives of different interactions and lack long-term impact analysis, resulting in short-term predictions that are not tailored to the specific interaction goals.
Innovation Solution
The system uses a language model neural network to simulate interactions by evaluating multiple parameterizations of context variables for all actors involved, selecting the parameterization that best satisfies the interaction objectives, and generating suggested communications that are more effective and tailored to the current interaction.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If existing auto-completion or next turn prediction techniques are used to generate suggested communications, then the system can provide quick suggestions based on previous communications, but the suggestions fail to consider interaction objectives and long-term impact, resulting in generic and less effective recommendations
Solution Approach 1:
The system performs preliminary simulations of multiple possible interaction trajectories before generating the final suggestion. By pre-evaluating different parameterizations of context variables and simulating their long-term impacts on interaction objectives, the system prepares informed recommendations that balance speed with objective-awareness, resolving the contradiction between quick suggestions and tailored effectiveness
Solution Approach 2:
The system creates simulated copies of the interaction by generating multiple parameterizations of context variables representing different possible interaction paths. These simulated interactions allow the system to evaluate long-term impacts without actually committing to a single suggestion path, enabling both rapid suggestion generation and comprehensive objective evaluation
2Adaptability or versatility
If the system simulates interactions by evaluating multiple parameterizations of context variables using a language model neural network, then the suggestions become more tailored to interaction objectives and consider long-term impact, but the computational complexity and processing time increase
Solution Approach 1:
The simulation process is segmented into discrete time steps, with the language model neural network evaluating context variables at each step independently. This segmentation allows the complex simulation to be broken down into manageable computational units, reducing overall complexity while maintaining the ability to evaluate multiple parameterizations and their long-term impacts on interaction objectives
Solution Approach 2:
The system evaluates multiple parameterizations of context variables, but focuses computational resources on the most promising simulation paths identified at each time step. By performing partial evaluations of all possible parameterizations and deeply analyzing only the most relevant ones, the system achieves thorough objective assessment without the full computational burden of exhaustively simulating every possible interaction trajectory
Data Source
AI summary
Methods, systems, and apparatus, including computer programs encoded on computer storage media, for generating suggested communications during a multi-agent interaction using a language model neural network.