Vermont Patent of the Month – August 2021
When training AI and machine learning algorithms, it is often unrealistic to achieve the desired effect with real social media accounts. For instance, learning how users interact with posts may not be efficient since real users are often inconsistent in their interactions, too low in volume, or lacking the dimensionality needed for the AI to gain a broad education. As such, Intellidimension, Inc. has designed a method of simulating network systems to help relate users in real world e-commerce.
This methodology compares a real-world computer-based social or e-commerce network user to a computerized simulated network. In order to achieve this, a computerized simulated network defines a comparison profile for each real-world user or real-world e-commerce system and then structures a computerized simulated network with simulated users. These simulated networks can help inform real world e-commerce of real world activity. For instance, it can help to provide a recommendation of an item to a user through simulating predictive interests. The information used in the simulated network is obtained by simulated sharing and spreading of simulated items amongst simulated users in simulated networks. That’s a lot of simulation – but it helps to build a database that offers direct input into interacting with real users without falling short by the human limitations and errors or real users. Machine learning is then able to expand itself as broadly as possible and better interact with real users.
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