Vector Space Alignment for Machine Learning Model Updates
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Solution Overview
Problem
Machine-learning models are typically static and cannot be updated with new data without retraining, which is resource-intensive, insecure, and time-consuming, especially when privacy or security restrictions apply to the new data.
Innovation Solution
A method to update a base machine-learning model by transforming a client model's vector space to align with the base model's vector space, allowing new data to be incorporated without retraining, using a transformation mapping matrix generated from overlapping words and normalizing vectors to avoid incremental learning issues.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a machine-learning model is retrained with new training data to improve accuracy, then the model can incorporate new information and maintain prediction accuracy, but the process becomes resource-intensive, time-consuming, and insecure when privacy restrictions apply
Solution Approach 1:
The patent divides the model updating process into two independent stages: (1) training a base model with initial training data, and (2) training a client model with new client data, then merging them through vector space transformation. This segmentation allows the base model to be updated incrementally without complete retraining, reducing time loss while maintaining accuracy.
Solution Approach 2:
The patent performs preliminary vector space transformation on the client model before merging it with the base model. By pre-aligning the vector spaces using transformation matrices derived from overlapping words, the system prepares the client model in advance, making the merging process efficient and avoiding time-consuming adjustments after integration.
2Adaptability or versatility
If a machine-learning model is retrained with new training data to incorporate new information, then the model can be updated with current data, but the process consumes significant processing resources and computational power
Solution Approach 1:
The patent applies partial action by updating only the necessary components of the base model through client model merging, rather than performing complete retraining. The vector space transformation and selective merging of word vectors based on overlapping words reduce computational resource consumption while maintaining model adaptability to new data.
Solution Approach 2:
The patent changes the parameter representation by transforming the client model's vector space to align with the base model's vector space using transformation matrices. This parameter transformation allows efficient merging without resource-intensive retraining, enabling the model to adapt to new data with reduced computational overhead.
3Measurement precision
If new training data is incorporated into the original training data for model retraining, then the model can learn from comprehensive data, but privacy or security restrictions may prevent access to the new data
Solution Approach 1:
The patent introduces an intermediary mechanism - the client model and vector space transformation - that enables knowledge transfer from client data to the base model without direct access to the raw client data. The transformation matrices serve as intermediaries that capture essential patterns from overlapping words while preserving data privacy and security restrictions.
Solution Approach 2:
The patent creates a transformed copy of the client model's vector space that aligns with the base model's vector space. This copying approach allows the base model to learn from client data patterns without accessing the actual client data, maintaining privacy security while improving learning accuracy through the copied vector representations.
4Ease of operation
If a static machine-learning model is used to maintain simplicity and ease of deployment, then the model is easy to implement and deploy, but the model cannot be updated with new data without complete retraining
Solution Approach 1:
The patent introduces dynamics into the previously static model by enabling incremental updates through client model merging. The base model can now adapt to new data by merging with client models that have been transformed to align with its vector space, maintaining deployment simplicity while gaining update capability without complete retraining.
Data Source
AI summary
The present disclosure relates to systems and methods for updating static machine-learning models (e.g., a Doc2Vec model) without needing to retrain the models. More particularly, the present disclosure relates to systems and methods that can be used to add new data to a base model by training a client model using the new data, and transforming the vector space of the client model to align with the vector space of the base model. The base model can then be updated using the realigned client model. As such, the base model can be updated with the new data without needing to retrain the base model, which can be burdensome to processing resources, insecure, and time consuming.