How AI and Blockchain Team Up to Predict What You'll Buy

This patent describes a system where different companies can privately share insights from their customer data using AI and blockchain to build a better model for predicting if a customer will buy a product.

What it covers

This system helps improve how well businesses can predict if a customer will buy a product. A central 'supervisor node' receives a request from an 'initiator node' (like an online store) that wants to build a better prediction model (Claim 1). The supervisor then broadcasts this request across a secure 'blockchain federation' to find other 'participant nodes' (other businesses) willing to contribute their data insights. Instead of sharing raw customer data, these nodes only share 'representation data' – a summary or pattern of their customer information. Based on this shared representation data, the supervisor decides the best way to combine their knowledge, choosing between a 'longitudinal' or 'horizontal' federated learning strategy (Claim 3). Finally, the supervisor coordinates the initiator and participant nodes to collaboratively train a 'conversion rate model' without any single party seeing all the raw data. For example, an e-commerce site could use this to predict if a user will purchase a specific item after viewing it, by combining its own data insights with those from other related businesses.

What it doesn't cover

• —Does not cover traditional machine learning systems where all raw user data is collected and processed in one central location.
• —Does not cover federated learning systems that do not use a blockchain for coordination, broadcasting requests, or recording rewards.
• —Does not cover predicting general user behaviors; it specifically focuses on predicting a 'product conversion rate' for a 'preset product'.
• —Does not cover systems that directly share raw user data between nodes; it emphasizes sharing 'representation data' instead.
• —Does not cover a system without a designated 'supervisor node' to coordinate the federated learning process.

The clever bit

The clever part is the integration of blockchain for secure and transparent coordination of federated learning, especially the dynamic selection of a 'longitudinal' or 'horizontal' learning strategy based on how similar the participating datasets are.

Why it matters

Predicting what customers will buy is crucial for businesses, but privacy concerns often limit how much data can be shared and used. This patent addresses that challenge by combining federated learning, which allows AI models to learn from decentralized data without direct sharing, with blockchain technology for secure and transparent coordination. This approach could enable more accurate prediction models by leveraging a wider range of data insights, while still protecting user privacy and complying with data regulations.

Real-world examples

• 1.Personalized product recommendations on e-commerce platforms
• 2.Targeted advertising campaigns in online retail
• 3.Fraud detection systems in financial services
• 4.Customer churn prediction in subscription services