Knowledge Network Platform Model Recommendation
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Machine learning models require large amounts of raw data for training, which are often privately owned and not publicly available, making it difficult to share and adapt models across different classification domains.
Innovation Solution
A managed knowledge network platform that computes model dissimilarity values and path lengths between user and model nodes, recommending models based on these values to facilitate secure sharing and adaptation of machine learning models across users and communities.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If machine learning models are trained using privately owned training datasets, then model accuracy is improved, but model sharing and adaptation across different classification domains is hindered
Solution Approach 1:
The patent segments the training dataset into multiple subsets, each associated with different users or organizations. This allows the overall training data to be divided into manageable, shareable portions while maintaining the integrity and accuracy of the complete dataset. The segmentation enables selective sharing of specific data subsets without requiring full data disclosure.
Solution Approach 2:
The patent introduces a trusted intermediary entity (such as a data trust or centralized coordination platform) that facilitates model sharing and adaptation. This intermediary manages the privately owned training datasets, enables controlled access and sharing between parties, and coordinates the adaptation process across different classification domains while preserving data ownership and privacy.
2Measurement precision
If a model is developed for one classification domain, then specialization is achieved, but adaptation to different classification domains becomes more difficult
Solution Approach 1:
The patent designs models with universal components that can function across multiple classification domains. By creating a base model structure that is domain-agnostic and can be adapted to different tasks, the system maintains high specialization accuracy for each domain while reducing the complexity of adaptation. The universal model serves multiple functions across different classification domains.
Solution Approach 2:
The patent performs preliminary actions by pre-training models on diverse, multi-domain datasets or by establishing a framework that anticipates future adaptation needs. This preliminary preparation reduces the complexity of subsequent domain-specific adaptations, as the base model already possesses transferable knowledge and structural flexibility.
3Reliability
If training datasets are kept private, then data security and ownership are protected, but collaboration and knowledge sharing are reduced
Solution Approach 1:
The patent employs trusted intermediaries that enable collaboration and knowledge sharing while preserving data privacy and ownership. These intermediaries facilitate the exchange of insights, patterns, and model adaptations without requiring direct access to private training datasets, thus maintaining security while enabling information flow.
Solution Approach 2:
The patent uses copying mechanisms where models or model components are replicated and shared across different users and domains. Instead of sharing the actual private training data, the system shares copies or derivations (such as trained models, feature extractors, or learned representations) that capture the essential knowledge without exposing the underlying private datasets.
Data Source
AI summary
An apparatus and method are provided for a managed knowledge network platform (KNP). Model dissimilarity values for model pairs are obtained, each model pair including a first model of a plurality of models in a KNP and a different model in the plurality of models. Path lengths between a first model node of a plurality of model nodes in the KNP and each one of other model nodes are computed, where the first model node represents the first model and the first model node is connected to a first user node of a plurality of user nodes representing users of the KNP. At least one of the different models is selected based on the model dissimilarity values and the path lengths. A recommendation that includes the at least one model is generated for a first user represented by the first user node.