Wells Fargo & Co has been granted a patent for a system utilizing post-quantum cryptography (PQC). The system monitors encrypted data structures, identifies changes in risk profiles, and adapts cryptographic techniques based on performance information and generated encryption attributes to enhance data security in various environments. GlobalData’s report on Wells Fargo & Co gives a 360-degree view of the company including its patenting strategy. Buy the report here.
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According to GlobalData’s company profile on Wells Fargo & Co, Virtual banking assistant was a key innovation area identified from patents. Wells Fargo & Co's grant share as of July 2024 was 86%. Grant share is based on the ratio of number of grants to total number of patents.
Post-quantum cryptography systems and methods for data encryption
The granted patent US12073300B2 outlines a comprehensive system for post-quantum cryptography (PQC) designed to enhance data security in a rapidly evolving technological landscape. The system includes data monitoring circuitry that tracks an enveloped data structure, which contains encrypted data. This monitoring capability allows the system to identify changes in the data structure, including shifts in associated risk profiles. In response to these changes, the system retrieves performance information related to various PQC cryptographic techniques and generates a new set of encryption attributes. Subsequently, the data is re-encrypted using a second cryptographic technique selected based on these updated attributes, ensuring that the data remains secure against potential quantum computing threats.
Additionally, the patent details various components that contribute to the system's functionality, such as cryptographic attribute generation circuitry, data attribute generation circuitry, and risk profile generation circuitry. These components work together to create a dynamic and responsive cryptographic environment. The system can also maintain a cryptographic spawn log to track the encryption attributes used over time, facilitating compliance with regulatory requirements. Furthermore, the patent emphasizes the importance of machine learning in optimizing cryptographic techniques, allowing the system to adapt to new threats and performance metrics. Overall, this patent presents a robust framework for implementing PQC, addressing both current and future challenges in data security.
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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.
