Tokenized User Data Profiles for Real-Time Valuation and Access Control
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Solution Overview
Problem
Current advertising methods rely heavily on blind or semi-blind targeting, resulting in irrelevant advertisements that fail to consider the user's future intent, with only a small percentage of Internet users accounting for the majority of clicks and most advertisements going unseen.
Innovation Solution
A system that authenticates user data using biometric and network-based verification, aggregates it into a unified profile, computes a valuation score, generates a digital token, and records it on a blockchain for real-time monetization and compensation, with smart contracts managing access and token transfers.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If traditional blind advertising methods are used, then advertising coverage is broad, but advertising relevance to users is low
Solution Approach 1:
The patent segments user data into multiple dimensions including behavioral patterns, demographic characteristics, and real-time contextual information. This segmentation enables advertisers to target specific user groups with relevant advertisements rather than using broad blind advertising, thereby improving advertising relevance while utilizing available user information effectively.
Solution Approach 2:
The system performs preliminary actions by collecting and analyzing user data beforehand to create comprehensive user profiles. These pre-established profiles contain predicted user intents and preferences, allowing advertisements to be tailored in advance to match user needs before the actual advertising delivery occurs, thus improving relevance without relying on incomplete real-time data alone.
2Measurement precision
If cookie-based targeting is used, then some user behavior tracking is achieved, but future user intent prediction is inaccurate
Solution Approach 1:
The patent implements feedback mechanisms where user responses to advertisements and interactions with the platform are continuously collected and fed back into the user profile system. This feedback loop refines the prediction models over time, improving the accuracy of future intent predictions by learning from actual user behavior patterns rather than relying solely on historical cookie data.
Solution Approach 2:
The system transitions from static cookie-based tracking to dynamic user profiling that continuously updates based on real-time behavior, contextual factors, and predicted future intents. This dynamic approach allows the system to adapt to changing user preferences and accurately predict future intents by incorporating multiple evolving data sources rather than relying on fixed historical data.
3Quantity of substance
If user data is collected from multiple sources, then data completeness improves, but data verification complexity increases
Solution Approach 1:
The patent employs a universal verification framework that handles multiple data sources through a single integrated system. This multi-functional verification mechanism can authenticate data from various sources including social media, transaction records, and device sensors using common verification protocols, thereby reducing overall system complexity compared to implementing separate verification systems for each data source.
Solution Approach 2:
The system introduces intermediary verification layers that act as mediators between multiple data sources and the user profile system. These intermediaries standardize data formats, validate authenticity, and filter quality issues before data enters the main processing system, simplifying the overall verification complexity while maintaining data completeness from multiple sources.
Data Source
AI summary
Systems and methods are disclosed for dynamically valuing user-generated data assets and distributing compensation based on such valuation. A computing platform receives user data elements from disparate data sources and authenticates ownership of the data using a verification protocol. The authenticated data elements are unified into a user data profile, and a valuation score is calculated according to factors such as recency, completeness, uniqueness, and market demand. A digital token representing the user data profile is generated and stored on a distributed ledger, the token including metadata corresponding to the valuation score. Compensation to the user is automatically distributed in response to token valuation activity and third-party access transactions. In various embodiments, the valuation score is updated in real time, tokens may be bundled into composite data funds, and smart contracts control access rights, payout conditions, token expiration, and privacy-based revocation.