Digital Credential Vector Mapping for Verification
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Solution Overview
Problem
The increasing use of alternative learning sources for obtaining technical skills and proficiencies has created challenges in verifying and tracking the authenticity of digital credentials, as traditional physical certificates are no longer relied upon, and existing methods lack efficiency in publishing, verifying, and tracking digital credentials.
Innovation Solution
A digital credential platform server uses vector space modeling to analyze and map digital credential objects to field data objects, transforming them into vectors within a multi-dimensional space to select closest-matching field data objects, thereby generating mappings that can be used to verify and track digital credentials effectively.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If digital credentials are issued and verified using traditional methods, then the system is simple to implement, but the verification and tracking efficiency is low
Solution Approach 1:
The patent replaces traditional manual credential verification methods with automated vector space modeling and machine learning algorithms. The system transforms credential data into vector representations and uses computational algorithms to automatically match credentials with corresponding fields, eliminating manual verification processes and significantly improving efficiency.
Solution Approach 2:
The patent transforms credential data from traditional structured formats into vector space representations with multiple dimensions. By changing the data representation parameters from simple text fields to multi-dimensional vectors, the system enables more sophisticated matching and verification capabilities while improving processing efficiency through optimized vector operations.
2Adaptability or versatility
If digital credentials are mapped to field data objects using exact matching, then the mapping precision is high, but the adaptability to different credential types is low
Solution Approach 1:
The patent extends the mapping process from traditional single-dimensional text matching to multi-dimensional vector space matching. By representing both credentials and field data objects as vectors with multiple dimensions (including semantic meaning, context, and hierarchical relationships), the system achieves both high adaptability to different credential types and maintained mapping precision through vector similarity calculations.
Solution Approach 2:
The patent introduces vector space representations as an intermediary layer between raw credential data and field data objects. This intermediary transformation allows the system to handle diverse credential types uniformly while preserving the semantic relationships needed for accurate mapping, bridging the gap between adaptability and precision.
3Loss of information
If credential data is processed and stored in detailed formats, then the information completeness is high, but the processing and searching time is long
Solution Approach 1:
The patent performs preliminary transformation of credential data into vector representations during the credential issuance and registration process. By pre-processing and pre-storing credentials in vector format with embedded semantic information, the system eliminates the need for time-consuming text processing during verification, achieving both information completeness and fast processing through advance preparation.
Solution Approach 2:
The patent replaces traditional text-based searching and processing mechanisms with vector-based computational operations. Vector similarity searches and mathematical operations are computationally more efficient than text parsing and pattern matching, enabling the system to maintain complete information while dramatically reducing processing and searching time through substituted computational methods.
Data Source
AI summary
Techniques described herein relate to mapping of digital credential objects to various field data objects. For example, requests may be received by a digital credential platform server from digital credential template owner devices, issuer devices, and/or receiver devices. In response, the digital credential platform server may determine and transmit back mappings between the digital credentials and the selected field data objects. To generate mappings, digital credential objects may be tokenize and transformed into vectors within a multi-dimensional vector space. Individual field data objects stored within a high-performance text search engine also may be transformed into vectors within the same multi-dimensional vector space, and the distances between the vectors may be calculated to select a number of field data objects corresponding to the digital credential objects.