Automated Machine Learning with Tokenized Multi-Model Data
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Solution Overview
Problem
Existing machine learning systems do not operate on the entire available dataset due to data storage structures, leading to incomplete learning and reliance on random initialization, which makes decisions non-transparent and untraceable.
Innovation Solution
A multi-model database system that uses a document-oriented data model to store raw data without preconceptions, generates tokens from field values, and classifies them as established facts or tentative assumptions, enabling continuous learning and deterministic decision-making.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If data is stored in a database for machine learning training, then data can be reused multiple times, but the database structure requires rigid schemas that limit flexibility
Solution Approach 1:
The patent creates virtual copies of database tables through view definitions that can be dynamically modified without changing the underlying physical database structure. These virtual copies allow different machine learning algorithms to access data in different formats and structures simultaneously, resolving the contradiction between data flexibility and database complexity.
Solution Approach 2:
The system dynamically generates and modifies view definitions based on the specific requirements of different machine learning algorithms. The view definitions can be created, modified, and deleted on-the-fly without requiring changes to the rigid database schema, enabling adaptive data access patterns.
2Adaptability or versatility
If data is extracted and transformed for each machine learning algorithm, then algorithm-specific requirements are met, but data preparation time increases
Solution Approach 1:
The patent pre-defines multiple view definitions that correspond to different machine learning algorithm requirements. When a new algorithm needs to be trained, the appropriate pre-defined view can be selected and modified minimally, rather than performing complete data extraction and transformation from scratch.
Solution Approach 2:
The view mechanism provides a universal interface that can serve multiple machine learning algorithms simultaneously. A single view definition can be shared across multiple algorithms with different parameter requirements, reducing redundant data preparation work.
3Quantity of substance
If raw data is stored without transformation, then storage efficiency is improved, but data cannot be directly used by machine learning algorithms requiring specific formats
Solution Approach 1:
The patent creates virtual copies of the raw data through view definitions that present the data in algorithm-ready formats without physically transforming or duplicating the underlying stored data. This maintains storage efficiency while providing usable data formats on-demand.
Data Source
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AI summary
The invention relates to a method for automated machine learning. A pre-trained learning module (120) and a database (104) are provided. An additional dataset (108) is received and stored in a first data model (106) of the database, and one or more tokens are (109) generated and assigned to one or more token types (111). The token assignments (113) are each classified as verified fact or preliminary assumption, and an index (112) in a second data model (110) is extended using the additional token, a pointer (115) to the additional stored data set, and the token assignments.