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.
Original patent title: “Methods and systems for imrpoving a product conversion rate based on federated learning and blockchain”
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. Owned by Hangzhou Tonghuashun Data Processing Co with 23 claims and 12 forward citations, and it is expected to expire in 2043.
Coverage
What does this patent actually cover?
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 (ClaimclaimA numbered sentence at the end of a patent that legally defines what the inventor owns. The most important section.Read more → 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.
The gap
What does this patent NOT 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.
These exclusions are unique to PatentBrief — derived from the actual claim language, not patent-office boilerplate.
Key facts
What made this novel
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.
The Patent Drawing
Schematic visualization of the patent's claim structure. Hand-drawn diagrams in progress for each landmark patent.
Where you've seen this
Real-world examples
Personalized product recommendations on e-commerce platforms
Targeted advertising campaigns in online retail
Fraud detection systems in financial services
Customer churn prediction in subscription services
Why it matters
The bigger picture
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.
Filed
May 22, 2023
Market context
Who's building on this
Companies in this space
Companies like Google, NVIDIA, and IBM are actively developing federated learning technologies for various applications, including advertising and healthcare, to address data privacy concerns. Startups in the Web3 and AI space are also exploring how blockchain can enhance data security and collaboration in AI model training. Hangzhou Tonghuashun Data Processing Co., the assigneeassigneeThe entity that owns the patent — usually the inventor's employer or a company.Read more →, is a major financial information service provider in China, indicating its interest in leveraging these technologies for financial product conversion.
Market impact
This technology aims to overcome data silos and privacy regulations that hinder the development of highly accurate AI models. By enabling secure, privacy-preserving collaboration, it could lead to more effective advertising, personalized services, and risk assessment across industries. This approach could also reduce the need for companies to centralize sensitive user data, potentially lowering compliance costs and increasing consumer trust in AI-driven services.
Claim 1 — Plain English
What this patent 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.
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.
What it does not 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.
Patent timeline
Application submitted to the patent office
Patent enters public domain
PatentBrief Score
Impact Score
Early stage
Citation count
22/40
Moderately cited
Claim breadth
15/20
Broad claimsclaimsThe numbered statements at the end of a patent that legally define what the inventor owns.Read more →
Recency
0/20
Older than 20 years
Assignee scale
0/20
Independent or smaller assigneeassigneeThe entity that owns the patent — usually the inventor's employer or a company.Read more →
PatentBrief Impact Score — based on citation count, claim breadth, recency, and assignee scale. Not a legal assessment.
Heuristic Value Estimate
What this patent might be worth
$105K – $335K
Midpoint $210K · 16.7 yr remaining · industry ×1.4
Heuristic only — blends forward/backward citation counts, claim scope, time remaining, litigation history, and CPC-derived industry baseline. Real valuations need a professional appraisal.
Claim text not yet imported for this patent
The original legal language
Original claims
23 claims as filed with the patent office.
Concepts involved
Citations
Patent lineage
Cite this patent
WEN, L., YAO, Y., Chen, M., MA, T., & BAO, Y. How AI and Blockchain Team Up to Predict What You'll Buy (U.S. Patent No. 20,230,419,182). U.S. Patent and Trademark Office. https://patentbrief.org/patent/us/20230419182/methods-and-systems-for-imrpoving-a-product-conversion-rate-based-on-federated-l
Auto-generated from the patent record. Double-check author order and the issue date against the official USPTO document before submitting.
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Common Questions
Frequently Asked Questions
What does How AI and Blockchain Team Up to Predict What You'll Buy cover?
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.
Who owns patent US 20230419182?
This patent is owned by Hangzhou Tonghuashun Data Processing Co.
When does this patent expire?
This patent is expected to expire on May 22, 2043, when the invention enters the public domain.
What is patent US 20230419182 cited by?
This patent has been cited by 12 later patents that build on its ideas.
What problem does this patent solve?
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.
What does this patent NOT cover?
Does not cover traditional machine learning systems where all raw user data is collected and processed in one central location.
Patent monitoring