Bank of America Corporation (20240320660). MACHINE LEARNING BASED SYSTEM FOR PROCESSING DEVICE TELEMETRY IN A DISTRIBUTED COMPUTING ENVIRONMENT simplified abstract
MACHINE LEARNING BASED SYSTEM FOR PROCESSING DEVICE TELEMETRY IN A DISTRIBUTED COMPUTING ENVIRONMENT
Organization Name
Inventor(s)
Sujatha Balaji of TamilNadu (IN)
Mullaikani Anbazhagan of TamilNadu (IN)
MACHINE LEARNING BASED SYSTEM FOR PROCESSING DEVICE TELEMETRY IN A DISTRIBUTED COMPUTING ENVIRONMENT - A simplified explanation of the abstract
This abstract first appeared for US patent application 20240320660 titled 'MACHINE LEARNING BASED SYSTEM FOR PROCESSING DEVICE TELEMETRY IN A DISTRIBUTED COMPUTING ENVIRONMENT
The abstract of the patent application describes a system for real-time transmission of IoT telemetry data to a third party, who then uses machine learning models to generate personalized recommendations for the IoT device user. Non-fungible tokens (NFTs) are minted by the IoT device manufacturer/distributor to securely link the IoT device identifier and user identifier, allowing for data transmission without revealing the user's identity.
- Machine learning models analyze IoT telemetry data to provide personalized recommendations for the IoT device user.
- Non-fungible tokens (NFTs) are used to securely link IoT device and user identifiers for data transmission.
- The system allows for real-time transmission of IoT telemetry data to a third party without revealing the user's identity.
- Recommendations generated by the machine learning models are specific to the IoT device and user.
- Smart contracts are formed between the third party and the IoT device user to define the criteria for data acquisition.
Potential Applications: - Personalized recommendations for IoT device users based on telemetry data. - Secure transmission of IoT data to third parties for analysis and insights.
Problems Solved: - Protecting user privacy while allowing for data analysis and recommendations. - Providing personalized recommendations for IoT device users.
Benefits: - Enhanced user experience with personalized recommendations. - Secure data transmission without compromising user privacy.
Commercial Applications: Title: Secure IoT Data Analysis and Personalized Recommendations System This technology can be applied in various industries such as healthcare, smart home devices, and industrial IoT for personalized recommendations and secure data transmission.
Questions about the technology: 1. How does the use of non-fungible tokens (NFTs) enhance the security of IoT data transmission? 2. What are the potential implications of personalized recommendations for IoT device users in terms of user experience and engagement?
Original Abstract Submitted
real-time transmission of iot telemetry data to a third-party and use of such data by the third-party for purposes of determining iot device-related recommendations specific to the iot device user. machine learning models receive the iot telemetry data, as a least a part of the inputs, to determine recommendations that are tied to the iot device and specific to the iot device user. non fungible tokens (nfts) are minted by the iot device manufacturer/distributor and hold a link to an iot device identifier and an iot device user identifier and include a smart contract formed between the third-party and the iot device user that defines the criteria for the telemetry data that is to be acquired by the third-party. use of the nft allows for iot gateways/data listeners to determine which iot telemetry data is to be transmitted to the third-party and allows for the transmission to occur without identifying the iot user in the transmissions.
