Invariant Network Learning via Knowledge Transfer

The patent applies preliminary action by pre-training an invariant network model in a source domain environment before deployment to the target domain. The pre-trained model contains learned entity representations and dependency structures that can be transferred to accelerate learning in the new environment, eliminating the need to start from scratch and reducing training time from weeks/months to days while maintaining reliability through the transfer of proven knowledge

The patent implements copying by replicating the invariant network structure, entity embeddings, and dependency relationships from the source domain to the target domain. The pre-trained model serves as a template that is copied and adapted to the new environment, allowing the target domain to inherit the learned patterns and relationships, thereby significantly reducing the time required to build a reliable network from scratch

The patent applies preliminary action by performing extensive invariant network learning in the source domain before target domain deployment. The source domain model undergoes continuous data collection and analysis to achieve high reliability, and this pre-computed knowledge is then transferred to the target domain, transforming the productivity problem by shifting the intensive learning work to a preliminary phase rather than requiring weeks/months of continuous learning in the target domain

The patent implements copying by duplicating the learned invariant network structure, entity representations, and dependency models from the source domain to the target domain. This copying mechanism allows the target domain to benefit from the source domain's extensive learning process, achieving high reliability without requiring equivalent time investment, thereby dramatically improving learning efficiency

The patent applies local quality by selectively transferring only the relevant entities and relationships from the source domain to the target domain, rather than copying everything uniformly. The system evaluates each entity's relevance to the target domain context and applies different transfer strategies to different entities, ensuring that only locally appropriate knowledge is transferred, thereby maintaining high accuracy while benefiting from reduced training time

The patent implements parameter changes by adjusting the entity transfer thresholds and selection criteria based on domain similarity metrics. The system dynamically modifies the parameters controlling which entities are transferred, adapting to the specific characteristics of the target domain. This allows the system to optimize the balance between transferring enough knowledge to reduce training time while filtering out irrelevant entities to maintain transfer accuracy

The patent applies segmentation by breaking down the complex task of domain difference determination into smaller, manageable components. The reference construction model is divided into separate modules that handle different aspects of domain comparison independently, such as entity type matching, relationship pattern analysis, and contextual similarity assessment. This segmentation reduces the overall complexity by making each sub-task more tractable while maintaining comprehensive domain variety consideration

The patent implements an intermediary approach by introducing a reference construction model that acts as a mediator between the source and target domains. This intermediary model systematically analyzes domain differences and determines entity relevance without requiring direct complex comparisons between all source entities and target contexts. The reference model serves as a filtering layer that simplifies the transfer process while ensuring accurate domain adaptation