Inventec had seven patents in cybersecurity during Q1 2024. In Q1 2024, Inventec Corp filed patents for a federated learning system that verifies model updates through hash functions and trust values, a system for managing avatar attributes in the metaverse through fungible tokens, and an avatar carrier generating system for the metaverse based on physiological features and real-name identity information. These inventions aim to enhance security, authenticity, and identifiability in virtual environments. GlobalData’s report on Inventec gives a 360-degree view of the company including its patenting strategy. Buy the report here.
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Inventec grant share with cybersecurity as a theme is 28% in Q1 2024. Grant share is based on the ratio of number of grants to total number of patents.
Recent Patents
Application: Method for verifying model update (Patent ID: US20240080180A1)
The patent filed by Inventec Corp. describes a federated learning system that includes a moderator and client devices. Each client device verifies model updates by training a client model based on a general model and raw data, calculating the difference between the general model and the client model as an update parameter, generating a hash value using a hash function, and comparing this value with other client devices to generate a trust value. The client model is updated based on an aggregation parameter when the to-be-verified value matches the trust value, ensuring secure and accurate model updates in the federated learning system.
The method outlined in the patent involves steps for verifying model updates in a federated learning system, where client devices receive a hash function and a general model, train client models, calculate differences as update parameters, and use homomorphic encryption for secure communication. The use of aggregation parameters calculated by moderators and the comparison of to-be-verified values with trust values ensure the integrity of model updates. Additionally, the patent describes the use of KeyGen protocols for additive homomorphic encryption to generate public and private keys, enhancing the security of the federated learning system.
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GlobalData Patent Analytics tracks bibliographic data, legal events data, point in time patent ownerships, and backward and forward citations from global patenting offices. Textual analysis and official patent classifications are used to group patents into key thematic areas and link them to specific companies across the world’s largest industries.
