Temporal Input Encoding for Intent-Aware Generative Responses
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Solution Overview
Problem
Generative models like large language models (LLMs) fail to consider the temporal characteristics of user input, leading to responses that may be over or under specified, failing to appropriately guide conversations based on user intent.
Innovation Solution
A generative model processes both the content and temporal encoding of user input, using supervised fine-tuning and reinforcement learning with human feedback to generate responses tailored to user intent, indicated by input events such as typing or speaking speed.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If generative models process only content of user input without temporal characteristics, then the model structure remains simple and processing is fast, but the response accuracy and appropriateness deteriorate
Solution Approach 1:
The patent introduces temporal encoding as an additional dimension to the traditional content-based input representation. By adding temporal characteristics (typing speed, pauses, delays) as a separate encoding layer that is concatenated with content embeddings, the system enhances response accuracy without fundamentally restructuring the model architecture, thus resolving the contradiction between precision and complexity.
Solution Approach 2:
The patent employs temporal encoding as an intermediary component that bridges the gap between raw input events and model processing. This intermediary layer captures temporal patterns from input events (such as time between keystrokes) and transforms them into a format that can be integrated with content embeddings, enabling improved response accuracy while maintaining model simplicity.
2Productivity
If generative models ignore temporal characteristics of user input, then processing speed remains high and computational resources are minimized, but the ability to guide conversation appropriately deteriorates
Solution Approach 1:
The patent segments the input processing into two independent parts: content processing and temporal processing. The content is processed through standard tokenization and embedding, while temporal characteristics are processed separately through temporal encoding. This segmentation allows each component to be optimized independently, maintaining processing speed while adding conversation guidance capability through temporal information.
Solution Approach 2:
The patent merges the temporal encoding with content embeddings by concatenating the two encoding vectors to form a combined embedding that is then processed by the generative model. This merging approach integrates temporal information into the existing processing pipeline without creating separate processing streams, thus maintaining high processing speed while enhancing conversation guidance capability.
3Quantity of substance
If generative models provide comprehensive factual information regardless of user input characteristics, then information completeness is maximized, but the response becomes over-specified and hard to digest
Solution Approach 1:
The patent changes the parameter of information quantity dynamically based on temporal encoding characteristics. When temporal encoding indicates slow typing speed or pauses (suggesting user uncertainty or need for clarity), the model adjusts the amount of factual information provided. This parameter change enables the system to provide comprehensive information when needed while avoiding over-specification when the user appears confident, thus improving response digestibility.
Data Source
AI summary
Implementations relate to generating, using a generative model (e.g., an LLM), generative model output that reflects a response that is responsive to content of user input and that is responsive to temporal characteristic(s) of providing the user input (e.g., typing speed(s)). Input event(s) that are performed by a user in providing the user input are determined, and temporal features associated with the user input are extracted from the determined input event(s). The generative model is trained to process a combined representation of a content embedding determined from content of the user input and a temporal encoding that encodes the temporal features associated with the user input, in order to generate the response. The generated response thus includes content that varies even when two queries having the same word content are received, if input events for the two queries indicate different user intents.