{
  "patent_number": "US 20230419182",
  "country": "US",
  "title": "How AI and Blockchain Team Up to Predict What You'll Buy",
  "original_title": "Methods and systems for imrpoving a product conversion rate based on federated learning and blockchain",
  "summary": "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_does": "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_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."
  ],
  "filed": "2023-05-22",
  "granted": null,
  "expires": "2043-05-22",
  "status": "active",
  "holder": "Hangzhou Tonghuashun Data Processing Co",
  "holder_url": "https://patentbrief.org/company/hangzhou-tonghuashun-data-processing-co",
  "inventors": [
    {
      "name": "Lulu WEN",
      "url": "https://patentbrief.org/inventor/lulu-wen"
    },
    {
      "name": "Yilin YAO",
      "url": "https://patentbrief.org/inventor/yilin-yao"
    },
    {
      "name": "Ming Chen",
      "url": "https://patentbrief.org/inventor/ming-chen"
    },
    {
      "name": "Tianyi MA",
      "url": "https://patentbrief.org/inventor/tianyi-ma"
    },
    {
      "name": "Yongfei BAO",
      "url": "https://patentbrief.org/inventor/yongfei-bao"
    }
  ],
  "times_cited": 12,
  "tags": [
    "software",
    "ai_ml",
    "ecommerce",
    "telecommunications",
    "finance"
  ],
  "abstract": "The present disclosure provides systems and methods for improving a product conversion rate based on federated learning and blockchain. The system may in response to receiving a federated learning request sent by an initiator node, broadcast the federated learning request within a blockchain federation; in response to obtaining a response to the federated learning request from at least one node in the blockchain federation, determine at least one participant node; obtain first representation data related to first user data from the initiator node and second representation data related to second user data from the at least one participant node; determine a federated learning strategy corresponding to the federated learning request based on the first representation data and the second representation data; and coordinate the initiator node and the at least one participant node for federated learning based on the federated learning strategy to generate a trained conversion rate model.",
  "url": "https://patentbrief.org/patent/us/20230419182/methods-and-systems-for-imrpoving-a-product-conversion-rate-based-on-federated-l",
  "markdown_url": "https://patentbrief.org/patent/us/20230419182/methods-and-systems-for-imrpoving-a-product-conversion-rate-based-on-federated-l/md",
  "google_patents_url": "https://patents.google.com/patent/US20230419182",
  "relatedPatents": []
}