Machine Learning Tokenization for Electronic Resource Lifecycle Tracking
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Solution Overview
Problem
Existing systems struggle to securely track and manage the lifecycle of electronic resources, such as smart contracts, leading to difficulties in establishing clear ownership and inefficiencies due to expired or outdated resources.
Innovation Solution
A system utilizing a machine learning engine to parse and tokenize electronic resource segments, predict the number of tokens required, convert segments into tokens, transmit for user approval, and track expiration, thereby securing and managing the lifecycle of these resources efficiently.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If automated electronic processes are used to facilitate contractual agreements, then efficiency and speed are improved, but security tracking and lifecycle management become difficult
Solution Approach 1:
The patent segments electronic resources into discrete tokenized units, where each token represents a specific segment of the electronic resource. This segmentation enables granular tracking and management of different portions of electronic resources through unique identifiers, resolving the contradiction by making lifecycle management tractable while maintaining automated processing efficiency
Solution Approach 2:
The patent introduces a machine learning engine as an intermediary system that automatically parses electronic resources, determines segmentation, and manages tokenization. This intermediary handles the complexity of tracking and lifecycle management, allowing automated processes to maintain efficiency while the ML engine ensures reliable security tracking and management
2Reliability
If manual tracking methods are used for electronic resources, then security and ownership clarity are improved, but systems become overwhelmed and efficiency decreases
Solution Approach 1:
The patent implements self-service through automated token generation and lifecycle management systems. The machine learning engine automatically parses electronic resources, determines optimal segmentation, generates tokens, and manages expiration without manual intervention. This self-service approach maintains clear ownership tracking while preventing system overload by eliminating manual processing bottlenecks
Solution Approach 2:
The patent changes the fundamental parameters of electronic resource management by transforming continuous electronic resources into discrete tokenized units with specific attributes (identifiers, expiration dates, ownership information). This parameter transformation enables automated systems to efficiently track and manage resources while maintaining clear ownership clarity through structured data representation
3Reliability
If electronic resources are retained indefinitely for security purposes, then ownership verification is improved, but systems become overwhelmed with expired or outdated resources
Solution Approach 1:
The patent applies preliminary action by embedding expiration dates and lifecycle parameters into tokens during the tokenization process. The machine learning engine predicts optimal retention periods and automatically manages token expiration before resources become outdated. This preliminary structuring of lifecycle parameters enables reliable ownership verification while automatically preventing accumulation of expired resources
Solution Approach 2:
The patent implements automated discarding of expired tokens through the machine learning engine, which monitors token lifecycles and systematically removes outdated resources from active management. This automated discarding process maintains ownership verification reliability for active tokens while preventing system overload from accumulated expired resources, effectively managing the quantity of tracked resources
4Manufacturing precision
If complex parsing and segmentation rules are applied to electronic resources, then tokenization accuracy is improved, but computing resources and processing time increase
Solution Approach 1:
The patent replaces complex mechanical parsing and segmentation rules with a machine learning-based system. The ML engine learns optimal segmentation strategies from training data and automatically applies them to tokenize electronic resources. This substitution of ML for rule-based mechanics maintains high tokenization accuracy while significantly reducing computing resource requirements by eliminating the need for exhaustive rule evaluation
Solution Approach 2:
The patent applies preliminary action by pre-training the machine learning engine on diverse electronic resources to learn optimal segmentation patterns before actual tokenization. This preliminary training phase enables the ML engine to quickly and accurately parse new resources with minimal computing resources during operation, achieving high tokenization accuracy without excessive processing costs
Data Source
AI summary
Systems, computer program products, and methods are described herein for parsing and tokenization of designated electronic resource segments via a machine learning engine. The present invention is configured to electronically receive a request for an electronic resource into a machine learning engine, identify and parse one or more segments of the electronic resource using the machine learning engine, predictively determine a number of tokens required by analyzing the electronic resource using the machine learning engine, convert the one or more segments into corresponding one or more tokens, transmit the electronic resource to at least one user for approval, receive approval for the electronic resource from the at least one user, and designate token expiration.