Task Scheduling Identification for Continual Learning Adaptation

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Solution Overview

Problem

Existing task-agnostic continual learning methods struggle to identify the task scheduling regime of a data stream and efficiently adapt their parameters on-the-fly, leading to suboptimal performance.

Innovation Solution

A method comprising an initialization phase, a datastream monitoring phase, and a scheduling identification and model adaptation phase, where prompt pool parameters and scheduling identification parameters are defined, and the task-agnostic continual learning model is trained. The method monitors the data stream, builds collections of instances, and identifies the scheduling type as either continuous or discrete based on frequent sequence analysis and Jaccard distance comparison.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Adaptability or versatility

If task-agnostic continual learning methods are used without scheduling identification, then the method can handle unknown task identities and boundaries, but the algorithm performance becomes directly dependent on task scheduling which cannot be identified

Engineering Contradiction:

Improvetask-agnostic learning capabilityVSAvoidalgorithm performance consistency

Core Design Contradiction:

Adaptability or versatilityVSReliability

Solution Approach 1:

The system continuously monitors the data stream and uses frequent sequence analysis to identify scheduling patterns, providing feedback about the current scheduling regime. This feedback mechanism allows the algorithm to adapt its parameters dynamically based on the identified scheduling type (discrete or continuous), resolving the contradiction between handling unknown tasks and maintaining performance consistency.

Inventive Principle:

Principle #23Feedback

Solution Approach 2:

The patent implements dynamic parameter adaptation where the continual learning algorithm adjusts its parameters on-the-fly based on the identified scheduling regime. The system transitions from a static parameter configuration to a dynamic one that responds to changing scheduling conditions, enabling the algorithm to maintain optimal performance across different scheduling scenarios while preserving task-agnostic capability.

Inventive Principle:

Principle #15Dynamics

2Productivity

If conventional continual learning methods are used, then the method can learn continuously without knowing task boundaries, but the inability to identify scheduling regime prevents efficient parameter adaptation

Engineering Contradiction:

Improvecontinuous learning capabilityVSAvoidparameter adaptation efficiency

Core Design Contradiction:

ProductivityVSAdaptability or versatility

Solution Approach 1:

The system performs preliminary analysis of the data stream by building collections of instances and applying frequent sequence analysis to identify scheduling patterns before adjusting model parameters. This preliminary identification action enables efficient subsequent parameter adaptation, as the scheduling regime is determined in advance of parameter updates, allowing the algorithm to optimize its learning process based on anticipated scheduling characteristics.

Inventive Principle:

Principle #10Preliminary action

Solution Approach 2:

The patent replaces manual or heuristic parameter adjustment mechanisms with an automated scheduling identification system based on frequent sequence analysis and Jaccard distance comparison. This substitution of the parameter adaptation mechanism with a data-driven identification system significantly improves adaptability efficiency while maintaining continuous learning productivity.

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

3Device complexity

If the algorithm parameters are fixed, then the implementation is simple, but the algorithm cannot adapt to different scheduling regimes in dynamic learning environments

Engineering Contradiction:

Improveimplementation simplicityVSAvoidscheduling regime adaptability

Core Design Contradiction:

Device complexityVSAdaptability or versatility

Solution Approach 1:

The system introduces dynamic parameter adjustment capability while maintaining relatively simple implementation through a modular architecture. The scheduling identification module operates independently and provides inputs to the parameter adaptation mechanism, allowing the system to handle multiple scheduling regimes without requiring complete redesign of the continual learning framework, thus balancing complexity and adaptability.

Inventive Principle:

Principle #15Dynamics

Data Source

PatentUS20250138876A1Task scheduling identification for efficient task-agnostic continual learning

Publication Date: 2025.05.01 DELL PROD LP

AI summary

One example method includes training a task-agnostic continual learning (CL) model using prompt pool parameters and scheduling identification parameters, where the CL model comprises a machine learning model that is operable to perform tasks, monitoring a datastream that is provided to the CL model, and identifying every c instances of the datastream as a collection so that one or more collections are defined, and identifying, based on analysis of the datastream, a task scheduling type embodied in the datastream.