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Blockchain + IoT + AI Integration Insights from Patents


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Convergence of IoT, AI and Blockchain
IoT, AI and Blockchain are Catalysts for Digital Transformation
A decentralized AI market is emerging from the combination of blockchain, on-device AI and Internet of Things (IoT).

Published in: Software
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Blockchain + IoT + AI Integration Insights from Patents

  1. 1. �2018 TechIPm, LLC All Rights Reserved 1 Blockchain + IoT + AI Integration Insights from Patents Alex G. Lee ( Blockchain technology enables a unified decentralized peer-to-peer trusted IoT network. US20170046669 illustrates a device-sharing platform (e.g., rental car operation system) that performs operations consistent with services provisioned to the shared device based on real-time sensordata indicative of and characterizing a user's operation of the device. For example, the provisioned services can be a payment service that enables a vehicle rented by user to initiate periodic micro-payment transactions that reflect a real-time instantaneous usage or consumption of vehicle-specific resources detected by sensors embedded in the vehicle. The sensors can include a fuel-level sensor configured to monitor the vehicle's real-time consumption of liquid fuel, sensors to monitor a working speed of the vehicle's engine, and sensors for monitoring a braking activity of the vehicle. The device-sharing platform can record the captured sensordata within several blocks of a blockchain ledger, which can be generated and maintained by peer computing systems connected via vehicle networks, to initiate and settle periodic micro-payment transactions reflecting a real-time consumption or usage of various resources. The peer computing systems can receive the data package and digital signature, validate the received information, and generate a new block of the current blockchain ledger that includes the captured sensordata. Forexample, the peer
  2. 2. �2018 TechIPm, LLC All Rights Reserved 2 computing system can validate the received data package and digital signature using a public key of user, and in responseto a successfulvalidation, generate a new block that includes the captured sensor data. The peer computing system can generate cryptographic hashes appropriate to and representative of the generated block to update the blockchain ledger.
  3. 3. �2018 TechIPm, LLC All Rights Reserved 3
  4. 4. �2018 TechIPm, LLC All Rights Reserved 4 Big data monetization is to generate revenue from available big data products foruse by third parties by instituting the discovery, capture, storage, analysis, dissemination, and use of the big data. Trading of big data products through blockchain enables secure transaction of big data products. For example, US20140344015 illustrates a big data trading platform for enabling consumers to monetize their personal data while managing their online privacy. The platform uses cryptocurrencythrough blockchain as the means for monetizing the value of personal data. US20150379510 illustrates another big data trading platform to monetize big data using the blockchain infrastructure. The platform matches a producer's data product with a data buyer's specifications. The platform enables micropayments and the maintenance of privacy of personal information. The platform enables fair and transparent marketplace for data producers and data buyers using the redundant distributed ledgers of transactions on peer to peer networks.
  5. 5. �2018 TechIPm, LLC All Rights Reserved 5
  6. 6. �2018 TechIPm, LLC All Rights Reserved 6 US20180094953 illustrates a distributed manufacturing platform that can connect designers, manufacturers, shippers, and other entities and simplifies the process ofmanufacturing and supplying new products.A blockchain records transactions, executes smart contracts, and performs other operations to increase transparency and integrity of supply chain. A machine learning model (e.g., deep learning techniques, neural language models, convolutional neural networks, or other machine learning model) can be used to categorize and match orders with manufacturers (Ref. Ristoski et al., "A Machine Learning Approachfor ProductMatching and Categorization," Data and Web Science Group, University of Mannheim, B6, 26, 68159 Mannheim, Oct. 11, 2016).
  7. 7. �2018 TechIPm, LLC All Rights Reserved 7
  8. 8. �2018 TechIPm, LLC All Rights Reserved 8