{"id":833405,"date":"2025-04-03T07:05:39","date_gmt":"2025-04-03T11:05:39","guid":{"rendered":"https:\/\/www.marketnewsdesk.com\/index.php\/cyngn-secures-its-22nd-u-s-patent-for-its-ai-powered-autonomous-vehicle-technologies\/"},"modified":"2025-04-03T07:05:39","modified_gmt":"2025-04-03T11:05:39","slug":"cyngn-secures-its-22nd-u-s-patent-for-its-ai-powered-autonomous-vehicle-technologies","status":"publish","type":"post","link":"https:\/\/www.marketnewsdesk.com\/index.php\/cyngn-secures-its-22nd-u-s-patent-for-its-ai-powered-autonomous-vehicle-technologies\/","title":{"rendered":"Cyngn Secures its 22nd U.S. Patent: for its AI-Powered Autonomous Vehicle Technologies"},"content":{"rendered":"
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PR Newswire<\/p>\n<\/p><\/div>\n

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Since August 2023<\/span>, Cyngn has been granted twelve additional patents, bringing the total U.S. patents granted to 22.<\/p>\n

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\n MENLO PARK, Calif.<\/span>
\n <\/span>, April 3, 2025<\/span><\/span> \/PRNewswire\/ — Cyngn Inc. (the “Company” or “Cyngn”) (Nasdaq: CYN) announced the official granting of a new patent, 12,246,733, for the Company’s innovative autonomous vehicle (AV) solutions.<\/p>\n

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\n \"Cyngn
\n <\/a>\n <\/p>\n<\/p><\/div>\n

The patent, System and Methods of Off-Board Centric Autonomous Driving Computation, introduces a cloud-based approach to autonomous vehicle operation, distributing computing tasks between the vehicle and the cloud to significantly reduce onboard hardware requirements. Vehicles transmit sensor data to cloud servers, where functions like environment mapping, decision-making, and route planning are processed before driving commands are sent back for execution. This allows the vehicle to prioritize safety-critical and time-sensitive functions.<\/p>\n

The significance of this patent lies in its ability to enable lighter, more cost-effective autonomous vehicles while enabling sophisticated driving algorithms. Furthermore, the patent includes fleet management functionality, facilitating seamless coordination of multiple autonomous vehicles through a centralized cloud infrastructure.<\/p>\n

“Since August 2023<\/span>, we’ve been granted twelve additional patents, bringing our total to 22 U.S. patents,” said Lior Tal<\/span>, CEO of Cyngn. “This milestone reflects our ongoing commitment to driving innovation and advancing autonomous technology, as we continue to build out a robust patent portfolio to protect our intellectual property. Each new patent represents the hard work of our team and our dedication to developing real-world solutions that are transforming material handling.”<\/p>\n

The grant of this patent marks a significant milestone in Cyngn’s intellectual property strategy, further bolstering its position in the AV industry. This success follows Cyngn’s recent patent issuance announcement of its 21st patent<\/a><\/u>.<\/p>\n

Cyngn provides its customers a seamless way to adopt self-driving technology into their operations without high upfront costs.<\/p>\n

\n Previous patents include:<\/b>\n <\/p>\n

\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n
\n \n <\/td>\n\n

\n Patent Number<\/span>\n <\/p>\n<\/td>\n

\n

\n Title<\/span>\n <\/p>\n<\/td>\n

\n

\n Publication Date<\/span>\n <\/p>\n<\/td>\n<\/tr>\n

\n

\n 22.<\/span>\n <\/p>\n<\/td>\n

\n

\n US-12,246,733-B2<\/span>\n <\/p>\n<\/td>\n

\n

\n SYSTEM AND METHOD OF OFF-BOARD CENTRIC AUTONOMOUS DRIVING COMPUTATION <\/span>\n <\/p>\n<\/td>\n

\n

\n 3\/11\/2025 <\/span>\n <\/p>\n<\/td>\n<\/tr>\n

\n

\n 21.<\/span>\n <\/p>\n<\/td>\n

\n

\n US-12,039,867-B2<\/span>\n <\/p>\n<\/td>\n

\n

\n ADAPTIVE OBJECT-BASED DECISION MAKING SYSTEM <\/span>\n <\/p>\n<\/td>\n

\n

\n 7\/16\/2024 <\/span>\n <\/p>\n<\/td>\n<\/tr>\n

\n

\n 20.<\/span>\n <\/p>\n<\/td>\n

\n

\n US-12,032,099-B2 <\/span>\n <\/p>\n<\/td>\n

\n

\n ADAPTIVE MOTION COMPENSATION OF PERCEPTION CHANNELS <\/span>\n <\/p>\n<\/td>\n

\n

\n 7\/09\/2024 <\/span>\n <\/p>\n<\/td>\n<\/tr>\n

\n

\n 19.<\/span>\n <\/p>\n<\/td>\n

\n

\n US-11,851,074-B2<\/span>\n <\/p>\n<\/td>\n

\n

\n SYSTEM AND METHODS OF LARGE-SCALE AUTONOMOUS<\/span>\n <\/p>\n

\n DRIVING VALIDATION<\/span>\n <\/p>\n<\/td>\n

\n

\n 12\/26\/2023<\/span>\n <\/p>\n<\/td>\n<\/tr>\n

\n

\n 18.<\/span>\n <\/p>\n<\/td>\n

\n

\n US-11,837,090-B2<\/span>\n <\/p>\n<\/td>\n

\n

\n SYSTEM AND METHODS OF ADAPTIVE TRAFFIC RULE-BASED DECISION MAKING FOR AUTONOMOUS DRIVING<\/span>\n <\/p>\n<\/td>\n

\n

\n 12\/5\/2023<\/span>\n <\/p>\n<\/td>\n<\/tr>\n

\n

\n 17.<\/span>\n <\/p>\n<\/td>\n

\n

\n US-11,837,089-B2<\/span>\n <\/p>\n<\/td>\n

\n

\n MODULAR EXTENSIBLE BEHAVIORAL DECISION SYSTEM FOR AUTONOMOUS DRIVING<\/span>\n <\/p>\n<\/td>\n

\n

\n 12\/5\/2023<\/span>\n <\/p>\n<\/td>\n<\/tr>\n

\n

\n 16.<\/span>\n <\/p>\n<\/td>\n

\n

\n US-11,767,034-B2<\/span>\n <\/p>\n<\/td>\n

\n

\n SYSTEM AND METHOD OF COMPUTATION ACCELERATION FOR AUTONOMOUS DRIVING SYSTEMS<\/span>\n <\/p>\n<\/td>\n

\n

\n 9\/26\/2023<\/span>\n <\/p>\n<\/td>\n<\/tr>\n

\n

\n 15.<\/span>\n <\/p>\n<\/td>\n

\n

\n US-11,760,368-B2<\/span>\n <\/p>\n<\/td>\n

\n

\n SYSTEM AND METHOD OF SAME-LOOP ADAPTIVE SIMULATION FOR AUTONOMOUS DRIVING<\/span>\n <\/p>\n<\/td>\n

\n

\n 9\/19\/2023<\/span>\n <\/p>\n<\/td>\n<\/tr>\n

\n

\n 14.<\/span>\n <\/p>\n<\/td>\n

\n

\n US-11,747,454-B2<\/span>\n <\/p>\n<\/td>\n

\n

\n GRANULARITY-FLEXIBLE EXISTENCE-BASED OBJECT DETECTION<\/span>\n <\/p>\n<\/td>\n

\n

\n 9\/5\/2023<\/span>\n <\/p>\n<\/td>\n<\/tr>\n

\n

\n 13.<\/span>\n <\/p>\n<\/td>\n

\n

\n US-11,745,762-B2<\/span>\n <\/p>\n<\/td>\n

\n

\n SYSTEM AND METHODS OF ADAPTIVE TRAJECTORY PREDICTION FOR AUTONOMOUS DRIVING<\/span>\n <\/p>\n<\/td>\n

\n

\n 9\/5\/2023<\/span>\n <\/p>\n<\/td>\n<\/tr>\n

\n

\n 12.<\/span>\n <\/p>\n<\/td>\n

\n

\n US-11,745,747-B2<\/span>\n <\/p>\n<\/td>\n

\n

\n SYSTEM AND METHOD OF ADAPTIVE DISTRIBUTION OF AUTONOMOUS DRIVING COMPUTATIONS<\/span>\n <\/p>\n<\/td>\n

\n

\n 9\/5\/2023<\/span>\n <\/p>\n<\/td>\n<\/tr>\n

\n

\n 11.<\/span>\n <\/p>\n<\/td>\n

\n

\n US-11,745,750-B2<\/span>\n <\/p>\n<\/td>\n

\n

\n SYSTEM AND METHOD OF LARGE-SCALE AUTOMATIC GRADING IN AUTONOMOUS DRIVING USING A DOMAIN-SPECIFIC LANGUAGE<\/span>\n <\/p>\n<\/td>\n

\n

\n 9\/5\/2023<\/span>\n <\/p>\n<\/td>\n<\/tr>\n

\n

\n 10.<\/span>\n <\/p>\n<\/td>\n

\n

\n US-11,679,726-B2<\/span>\n <\/p>\n<\/td>\n

\n

\n VEHICLE SENSOR SYSTEMS<\/span>\n <\/p>\n<\/td>\n

\n

\n 6\/20\/2023<\/span>\n <\/p>\n<\/td>\n<\/tr>\n

\n

\n 9.<\/span>\n <\/p>\n<\/td>\n

\n

\n US-11,673,577-B2<\/span>\n <\/p>\n<\/td>\n

\n

\n SYSTEM AND METHODS OF ADAPTIVE RELEVANCY PREDICTION FOR AUTONOMOUS DRIVING<\/span>\n <\/p>\n<\/td>\n

\n

\n 6\/13\/2023<\/span>\n <\/p>\n<\/td>\n<\/tr>\n

\n

\n 8.<\/span>\n <\/p>\n<\/td>\n

\n

\n US-11,668,833-B2<\/span>\n <\/p>\n<\/td>\n

\n

\n OBSTACLE DETECTION SYSTEMS<\/span>\n <\/p>\n<\/td>\n

\n

\n 6\/6\/2023<\/span>\n <\/p>\n<\/td>\n<\/tr>\n

\n

\n 7.<\/span>\n <\/p>\n<\/td>\n

\n

\n US-11,651,583-B2<\/span>\n <\/p>\n<\/td>\n

\n

\n MULTI-CHANNEL OBJECT MATCHING<\/span>\n <\/p>\n<\/td>\n

\n

\n 5\/16\/2023<\/span>\n <\/p>\n<\/td>\n<\/tr>\n

\n

\n 6.<\/span>\n <\/p>\n<\/td>\n

\n

\n US-11,614,527-B2<\/span>\n <\/p>\n<\/td>\n

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\n SELF-ADAPTIVE LIDAR-CAMERA SYNCHRONIZATION SYSTEM<\/span>\n <\/p>\n<\/td>\n

\n

\n 3\/28\/2023<\/span>\n <\/p>\n<\/td>\n<\/tr>\n

\n

\n 5.<\/span>\n <\/p>\n<\/td>\n

\n

\n US-11,592,565-B2<\/span>\n <\/p>\n<\/td>\n

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\n FLEXIBLE MULTI-CHANNEL FUSION PERCEPTION<\/span>\n <\/p>\n<\/td>\n

\n

\n 2\/28\/2023<\/span>\n <\/p>\n<\/td>\n<\/tr>\n

\n

\n 4.<\/span>\n <\/p>\n<\/td>\n

\n

\n US-11,555,928-B2<\/span>\n <\/p>\n<\/td>\n

\n

\n THREE-DIMENSIONAL OBJECT DETECTION WITH GROUND REMOVAL INTELLIGENCE<\/span>\n <\/p>\n<\/td>\n

\n

\n 1\/17\/2023<\/span>\n <\/p>\n<\/td>\n<\/tr>\n

\n

\n 3.<\/span>\n <\/p>\n<\/td>\n

\n

\n US-11,372,115-B2<\/span>\n <\/p>\n<\/td>\n

\n

\n VEHICLE LOCALIZATION<\/span>\n <\/p>\n<\/td>\n

\n

\n 6\/28\/2022<\/span>\n <\/p>\n<\/td>\n<\/tr>\n

\n

\n 2.<\/span>\n <\/p>\n<\/td>\n

\n

\n US-11,186,234-B2<\/span>\n <\/p>\n<\/td>\n

\n

\n VEHICLE SENSOR SYSTEMS<\/span>\n <\/p>\n<\/td>\n

\n

\n 11\/30\/2021<\/span>\n <\/p>\n<\/td>\n<\/tr>\n

\n

\n 1.<\/span>\n <\/p>\n<\/td>\n

\n

\n US-11,169,271-B2<\/span>\n <\/p>\n<\/td>\n

\n

\n OBSTACLE DETECTION SYSTEMS<\/span>\n <\/p>\n<\/td>\n

\n

\n 11\/9\/2021<\/span>\n <\/p>\n<\/td>\n<\/tr>\n

\n \n <\/td>\n\n \n <\/td>\n\n \n <\/td>\n\n \n <\/td>\n<\/tr>\n<\/table><\/div>\n

For a comprehensive view of Cyngn’s patents focused on modularity and flexibility of autonomous vehicle systems with multiple sensor modalities and configurations, please visit the USPTO.<\/a><\/u><\/p>\n

\n About Cyngn<\/b>\n <\/p>\n

Cyngn develops and deploys scalable, differentiated autonomous vehicle technology for industrial organizations. Cyngn’s self-driving solutions allow existing workforces to increase productivity and efficiency. The Company addresses significant challenges facing industrial organizations today, such as labor shortages, costly safety incidents, and increased consumer demand for eCommerce.<\/p>\n

Cyngn’s DriveMod Kit can be installed on new industrial vehicles at end of line or via retrofit, empowering customers to seamlessly adopt self-driving technology into their operations without high upfront costs or the need to completely replace existing vehicle investments.<\/p>\n

Cyngn’s flagship product, its Enterprise Autonomy Suite, includes DriveMod (autonomous vehicle system), Cyngn Insight (customer-facing suite of AV fleet management, teleoperation, and analytics tools), and Cyngn Evolve (internal toolkit that enables Cyngn to leverage data from the field for artificial intelligence, simulation, and modeling). For all terms referenced within, please refer to the Company’s annual report on Form 10-K with the SEC filed on March 6, 2025<\/span>.<\/p>\n

Where to find Cyngn:<\/p>\n