Hayden AI Technologies, Inc. v. Safe Fleet Holdings LLC, Safe Fleet Acquisition Corp. and Seon Design (USA) Corp.

District Court, E.D. New York·Decided August 13, 2026·No. 1:23-cv-03471·Unknown

Opinion

UNITED STATES DISTRICT COURT EASTERN DISTRICT OF NEW YORK --------------------------------------------------------------------- HAYDEN AI TECHNOLOGIES, INC.,

Plaintiff, MEMORANDUM -against- AND ORDER

SAFE FLEET HOLDINGS LLC, SAFE FLEET No. 23-CV-3471-EK-JRC ACQUISITION CORP. and SEON DESIGN (USA) CORP.,

Defendants. --------------------------------------------------------------------- JAMES R. CHO, United States Magistrate Judge:

This Order sets forth the Court’s patent claim constructions pursuant to Markman v. Westview Instruments, Inc., 517 U.S. 370 (1996).1 Plaintiff Hayden AI Technologies, Inc. (“plaintiff” or “Hayden AI”) alleges that defendants Safe Fleet Holdings LLC, Safe Fleet Acquisition Corp. and Seon Design (USA) Corp. (collectively, “defendants” or “Safe Fleet”) infringed upon its interest in U.S. Patent No. 11,003,919 (“the ’919 patent”), Dkts. 144-2, 145-1, which describes “Systems and Methods for Detecting Traffic Violations Using Mobile Detection Devices,” and U.S. Patent No. 11,164,014 (“the ’014 patent”), Dkts. 144-3, 145-2, which describes “Lane Violation Detection Using Convolutional Neural Networks.” See generally Fourth Am. Compl. (“FAC”), Dkt. 172.2 As explained in further detail below, the parties dispute the appropriate construction of eleven terms in the patent claims. See Parties’ Revised Joint Claim Construction Chart (“Joint Claim Construction Chart”), Dkt. 141-1. On January 17, 2025, the parties filed their opening claim construction briefs. See Dkts. 142, 144, 145. On January 31, 2025, both parties filed their responsive claim construction briefs.

1 The parties consented to the undersigned for purposes of claim construction. See Dkt. 185-1. 2 Hayden AI also alleges trade secret misappropriation. See FAC ¶ 7. See Dkts. 150, 151. The undersigned held a day-long Markman hearing on February 21, 2025. See Min. Entry dated Feb. 21, 2025; Tr. of Markman Hearing (“Tr.”), Dkt. 163. For the reasons set forth below, the Court construes the disputed terms as follows: 1. “Edge device[s]” as “a mobile edge device”; 2. “Bounding box” or “bounding boxes” (with or without modifiers) as “rectangular or quadrilateral shapes enclosing a detected object”; 3. “Vehicle attributes” according to its plain and ordinary meaning3; 4. “Computer vision library” according to its plain and ordinary meaning; 5. “Plurality of functions” according to its plain and ordinary meaning; 6. “Docker container image” as “a lightweight, standalone, and executable package of software or data that comprises everything needed to run the software or read or manipulate the data including the software code, runtime instructions, system tools, system libraries, and system settings” and “docker container” according to its plain and ordinary meaning; and

7. The “bounding” limitations as follows: a. “Bounding, using the one more processors of the [first or second] edge device, the vehicle and the restricted road area in the [first or second] frame in a plurality of [first or second] bounding boxes outputted by the deep learning model”; b. “Bounding the vehicle and the restricted road area further comprises bounding the vehicle using a vehicular bounding box and bounding the restricted road area using a road bounding box outputted by the deep learning model”; c. “Bound the vehicle in the first frame using a vehicular bounding box; bound the restricted road area in the first frame using a road bounding box outputted by the deep learning model”; d. “Bound the vehicle and the restricted road area in the [first or second] frame in a plurality of [first or second] bounding boxes outputted by the deep learning model”; and e. “Bound the vehicle in the frames with a vehicular bounding box and bound the restricted road area in the frames with a road bounding box outputted by

3 For the reasons discussed below, the Court finds that the plain and ordinary meaning of “attribute” is “a quality, character, or characteristic.” the deep learning model.” Background Hayden AI is “the leading provider of mobile automated bus lane and bus stop enforcement systems in the United States.” FAC ¶ 4. Defendants compete with Hayden AI for government contracts for automated bus lane enforcement services. See Defs.’ Opening Claim

Constr. Br. (“Defs.’ Br.”) at 1, Dkt. 142. Hayden AI alleges that Safe Fleet’s “ClearLane” automated bus lane enforcement system infringes upon one or more claims in the ’919 and ’014 patents. See FAC ¶¶ 1, 5, 6. 1. The ’919 Patent The ’919 patent has 32 claims, including independent claims 1, 11, and 20. The patent describes “systems and methods for detecting traffic violations using mobile detection devices.” ’919 patent4 col.1 ll.8–10; see also FAC ¶ 94 (“The ’919 Patent is generally directed to systems and methods for detecting traffic violations involving a vehicle and a restricted road area, using mobile detection devices and the construction of semantic annotated maps using data and information received from the mobile detection devices.”). The invention uses deep learning

models and computer vision technologies to analyze video images captured by the mobile detection devices to identify potential traffic violations. See id. ¶¶ 95, 98. The invention therefore reduces the high false positive rates associated with “logic-based” bus lane enforcement technologies. See id. ¶ 96; ’919 patent col.1 ll.40–44. The invention also improves upon “[t]raditional traffic enforcement technology and approaches” that often use “traffic enforcement cameras . . . set up near crosswalks or intersections and [that] are not suitable for enforcing lane violations beyond the cameras’ fixed

4 Citations to the patents refer to numbered columns and line numbers, rather than page numbers. field of view.” ’919 patent col.1 ll.35–40. Plaintiff’s claimed invention solves for this problem by “using a mobile edge device mounted to carrier vehicle to detect when other vehicles are in a restricted road area (e.g., a bus lane).” Pl.’s Opening Claim Constr. Br. (“Pl.’s Br.”) at 5, Dkt. 144. The edge device captures videos of vehicles and restricted road areas, and the location of the vehicle is determined using a positioning unit. Id.; see ’919 patent col.1 l.59–col.2 l.43.

“The edge device identifies vehicles and restricted road areas in the video using a plurality of functions from a computer vision library and a deep learning model.” Pl.’s Br. at 5; see ’919 patent col.1 l.59–col.2 l.43. Both the identified vehicle and the restricted road area are bound using “bounding boxes.” See Pl.’s Br. at 6. The system identifies potential traffic violations based on the overlap between the bounding boxes. See id. 2. The ’014 Patent Like the ’919 patent, the ’014 patent describes a system for detecting traffic lane violations. See ’014 patent col.1 ll.15–17. The ’014 patent is designed to solve for the same limitations of the prior art. See id. col.1 ll.43–54. In addition, the ’014 patent notes that “lane

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Hayden AI Technologies, Inc. v. Safe Fleet Holdings LLC, Safe Fleet Acquisition Corp. and Seon Design (USA) Corp., (E.D.N.Y. 2026).

Hayden AI Technologies, Inc. v. Safe Fleet Holdings LLC, Safe Fleet Acquisition Corp. and Seon Design (USA) Corp. (Hayden AI Technologies, Inc. v. Safe Fleet Holdings LLC, Safe Fleet Acquisition Corp. and Seon Design (USA) Corp.) — published by Counsel Stack Legal Research, free access to 12M+ legal documents.

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