Blockchain Hyper-Personalization Across Enterprises
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Solution Overview
Problem
Enterprises face challenges in providing proactive and personalized services to users due to reactive interaction models and limited insights into future user needs, relying on centralized data storage systems that are prone to risks and inefficiencies.
Innovation Solution
A system utilizing a permissioned blockchain infrastructure to execute hyper-personalized interactions across enterprises by processing user interactions, applying machine learning, and analyzing data from various sources like GPS, IoT, and social media to determine user intents and generate subsequent interactions, thereby eliminating the need for third-party intermediaries and enhancing data security and transparency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If enterprises use centralized data storage systems to collect and store user data, then they can gather abundant information for personalization, but the systems are prone to security risks and inefficiencies
Solution Approach 1:
The patent segments user data storage and processing across multiple decentralized enterprise systems rather than consolidating in a single centralized repository. Each enterprise maintains its own data locally while contributing to a shared blockchain ledger, distributing the storage burden and eliminating the single point of failure inherent in centralized systems.
Solution Approach 2:
The patent introduces a blockchain intermediary layer that mediates between enterprises and users. This blockchain acts as a trusted mediator that enables secure data sharing and verification without requiring enterprises to maintain complex centralized storage infrastructure, thereby improving security while reducing system complexity.
2Productivity
If enterprises adopt reactive interaction models to address user problems, then they can provide targeted solutions, but they cannot proactively anticipate future user needs
Solution Approach 1:
The patent implements preliminary action by enabling enterprises to proactively analyze user interaction patterns stored on the blockchain and predict future user needs before users actually express them. This allows enterprises to initiate services or communications in advance, transitioning from reactive to proactive service models while maintaining accurate user insight.
Solution Approach 2:
The patent establishes continuous feedback loops where user interactions are captured, analyzed, and fed back into the system to refine predictions of future needs. The blockchain provides an immutable record of user behavior that enables ongoing analysis and improvement of proactive service capabilities, allowing enterprises to learn and adapt over time.
3Adaptability or versatility
If enterprises share user data across multiple systems to enable hyper-personalization, then they can provide rich personalization, but data security and privacy management become more challenging
Solution Approach 1:
The patent uses the blockchain as an intermediary that enables secure data sharing across enterprises while maintaining privacy. The blockchain's cryptographic mechanisms allow verification and controlled access to user data without exposing the actual data contents, enabling hyper-personalization through aggregated insights while preserving individual data security and privacy.
4Reliability
If enterprises rely on third-party intermediaries to manage data sharing, then data security can be maintained, but system efficiency and transparency are reduced
Solution Approach 1:
The patent extracts the intermediary function from traditional third-party data brokers and reassigns it to the blockchain infrastructure itself. The blockchain provides native security, verification, and transparency mechanisms that eliminate the need for external intermediaries, thereby maintaining data security while significantly improving sharing efficiency and transparency across enterprise systems.
Data Source
AI summary
A method for executing hyper-personalized interactions across enterprises using interactions added to blockchains as transactions according to one embodiment includes determining, by a first enterprise system, an intent from a first interaction added to a blockchain via a blockchain transaction, determining, by the first enterprise system, a correlation between the intent and a set of subsequent related interactions with one or more enterprise systems different from the first enterprise system, and generating, by the first enterprise system, a second interaction with a second enterprise system of the one or more enterprise systems different from the first enterprise system, wherein the second interaction is within the set of subsequent related interactions.


