Correlating Digital IDs to Physical Identities via Taxonomy
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Solution Overview
Problem
Current web implementation technologies lack effective mechanisms to correlate digital IDs with actual physical identities of users across multiple web sites and professions, especially in maintaining user context and content recommendations, due to the phase-out of third-party cookies and the need for alternative tracking methods.
Innovation Solution
A computer system that classifies digital event records and internet web pages using taxonomical indexes like SNOMED CT, MeSH, and ICD-10, and identifies correlations between digital IDs and professional registry numbers, such as NPI, to infer a correlation between digital IDs and physical identities, using a combination of cookie values, IP addresses, user IDs, mobile device IDs, and email addresses, with a parameter reflecting the degree of certainty.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If third-party cookies are used to track users across websites, then user context information can be maintained and content recommendations can be developed, but privacy concerns increase and the system becomes vulnerable to Google's phase-out of third-party cookie support
Solution Approach 1:
The patent introduces an intermediary system that acts as a mediator between websites and user tracking. This system uses a distributed network of nodes to collect, aggregate, and process digital event data without relying on traditional third-party cookies. The intermediary layer maintains user context information through alternative mechanisms such as first-party data collection and probabilistic matching, thereby preserving functionality while avoiding the reliability issues of third-party cookie phase-out
Solution Approach 2:
The tracking system is segmented into multiple independent components including digital event records, taxonomic classifications, professional registry correlations, and probabilistic identity inference systems. This segmentation allows the system to maintain user context through multiple independent data sources rather than relying on a single third-party cookie mechanism, improving reliability through diversification
2Measurement precision
If multiple data sources are integrated to correlate digital IDs with physical identities, then measurement precision improves, but system complexity increases
Solution Approach 1:
The patent creates a universal system that handles multiple data types (digital event records, professional registry data, taxonomic classifications) through a unified architecture. The system performs multiple functions including data collection, taxonomic classification, correlation analysis, and probabilistic inference within a single integrated framework, reducing complexity by eliminating the need for separate specialized systems for each data type
Solution Approach 2:
The system transforms heterogeneous data from multiple sources into a standardized format using taxonomic classifications and parameter-based correlation metrics. By changing the parameters of different data sources to a common framework (e.g., converting various digital IDs to probabilistic identity scores), the system achieves high measurement precision while managing complexity through parameter standardization
3Measurement precision
If taxonomic classification is applied to digital event records and web pages, then content targeting accuracy improves, but processing time increases
Solution Approach 1:
The patent applies taxonomic classification to digital event records and web pages in advance, before the actual content targeting decision is needed. By pre-classifying content and organizing it into taxonomic hierarchies, the system eliminates the need for time-consuming classification during real-time user interactions, thereby improving processing speed while maintaining high targeting accuracy
Solution Approach 2:
The system adds a taxonomic classification dimension to the data structure, organizing digital events and web pages into hierarchical categories. This dimensional transformation allows for efficient retrieval and matching by enabling the system to operate at multiple levels of abstraction, reducing processing time through hierarchical filtering rather than exhaustive analysis
Data Source
AI summary
A computer obtains records of multiple digital events from multiple vendors of digital event records, each digital event record having a digital ID of a user that initiated the respective digital event. A computer obtains at least one list of professional registry numbers of professionals in a profession. A computer classifies at least some portion of the digital event records and at least some internet web pages based on a standard taxonomical index. A computer identifies correlated identification data points among the digital event records and the professional registry records, and based on the taxonomic classification of pages and digital events, to infer a correlation between digital IDs from digital event records and actual physical identities of physical persons, and computing a parameter reflecting a degree of certainty of the inference.