DCT
1:23-cv-06142
Hayden Ai Tech Inc v. Seon Design USA Corp
I. Executive Summary and Procedural Information
- Parties & Counsel:
- Plaintiff: Hayden AI Technologies, Inc. (Delaware)
- Defendant: Seon Design (USA) Corp. (Washington)
- Plaintiff’s Counsel: Fenwick & West LLP
- Case Identification: 2:23-cv-01011, W.D. Wash., 07/07/2023
- Venue Allegations: Plaintiff alleges venue is proper because Defendant is a Washington corporation with its principal place of business in the district and conducts business there, including alleged acts of infringement.
- Core Dispute: Plaintiff alleges that Defendant’s ClearLane automated bus lane enforcement system infringes a patent related to AI-powered mobile systems for detecting traffic violations.
- Technical Context: The technology involves using vehicle-mounted cameras, GPS, and AI processing to automatically identify and document vehicles illegally obstructing restricted traffic areas, such as bus lanes.
- Key Procedural History: The complaint alleges that Plaintiff and Defendant are direct competitors in the automated bus lane enforcement market, which may be relevant to questions of willful infringement and damages.
Case Timeline
| Date | Event |
|---|---|
| 2020-10-16 | Priority Date for U.S. Patent No. 11,003,919 |
| 2021-05-11 | U.S. Patent No. 11,003,919 Issued |
| 2023-07-07 | Complaint Filed |
II. Technology and Patent(s)-in-Suit Analysis
U.S. Patent No. 11,003,919 - Systems and Methods for Detecting Traffic Violations Using Mobile Detection Devices
The Invention Explained
- Problem Addressed: The patent describes the challenge of enforcing traffic ordinances in bus and bike lanes, noting that traditional enforcement is often manual, inefficient, and reliant on stationary cameras with a limited field of view, leading to safety hazards and transit delays (’919 Patent, col. 1:13-56).
- The Patented Solution: The invention proposes a mobile detection device, mountable on a vehicle like a bus, that uses video sensors, a GNSS receiver, and onboard processors to solve this problem (’919 Patent, col. 6:35-45). The device captures video, identifies both an offending vehicle and a restricted road area, bounds them in video frames, and detects a potential violation based on the overlap of these bounds, creating an evidence package for review (’919 Patent, Abstract; Fig. 9).
- Technical Importance: This approach allows for automated, scalable, and mobile enforcement of traffic rules without requiring dedicated patrol personnel or fixed infrastructure (’919 Patent, col. 1:50-56).
Key Claims at a Glance
- The complaint asserts infringement of at least independent Claim 20 (Compl. ¶38).
- The essential elements of independent Claim 20 include:
- A device comprising one or more video image sensors and a GNSS receiver.
- One or more processors programmed to execute instructions.
- The instructions cause the processor to identify a vehicle and a restricted road area from video frames by applying functions from a computer vision library and passing the frames to a deep learning model running on the device.
- The instructions further cause the processor to bound the vehicle with a "vehicular bounding box" and the restricted area with a "road bounding box."
- Finally, the instructions cause the processor to detect a potential violation based in part on the location of the vehicle and the overlap of the two bounding boxes.
- The complaint does not explicitly reserve the right to assert dependent claims but makes general allegations of infringement of the patent (’919 Patent, Compl. ¶1).
III. The Accused Instrumentality
Product Identification
- Defendant Seon’s “ClearLane” automated bus lane enforcement system (Compl. ¶4).
Functionality and Market Context
- The complaint alleges the ClearLane system is a device installed on transit buses that uses "technologies onboard the bus" to "automatically [issue] violation notices to vehicle owners who obstruct bus lanes" (Compl. ¶¶39, 42). The system is described as comprising two types of cameras (Context and ALPR), a purpose-built computer with inertial sensors, a GPS receiver, and a cellular router (Compl. ¶40, p. 8). A marketing diagram included in the complaint shows a five-step process: (1) cameras capture license plate details; (2) the vehicle is identified; (3) advanced algorithms process business rules; (4) an evidence package is formed; and (5) the package is sent for review (Compl. ¶30, p. 6).
- The complaint positions the ClearLane system as a direct competitor to Plaintiff's Hayden ABLE System, alleging it is a "copycat product" offered at a lower price (Compl. ¶31).
IV. Analysis of Infringement Allegations
U.S. Patent No. 11,003,919 Infringement Allegations
| Claim Element (from Independent Claim 20) | Alleged Infringing Functionality | Complaint Citation | Patent Citation |
|---|---|---|---|
| one or more video image sensors configured to capture a video of a vehicle and a restricted road area; | The ClearLane product includes “two different types of cameras – context and ALPR” to capture video. | ¶40 | col. 6:36-39 |
| a global navigation satellite system (GNSS) receiver configured to determine a location of the vehicle; | The ClearLane product includes “a GPS receiver,” which is a type of GNSS receiver. | ¶41 | col. 6:39-41 |
| one or more processors programmed to execute[] instructions to: identify the vehicle and the restricted road area from frames of the video by applying a plurality of functions from a computer vision library to the frames and passing the frames to a deep learning model running on the device; | The ClearLane product uses a “purpose-built computer” that runs “advanced algorithms” and “artificial intelligence modeling” to identify the vehicle and determine it is obstructing a bus-only lane. | ¶¶42, 43 | col. 6:41-48 |
| 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; | A marketing image shows the ClearLane system identifying a vehicle and drawing a red bounding box around it, and identifying a restricted bus-only lane with a blue overlay, described as geometric bounds. | ¶44 | col. 6:49-51 |
| and detect that a potential traffic violation has occurred based in part on the location of the vehicle and overlap of the vehicular bounding box with the road bounding box. | The ClearLane system is alleged to rely on GPS data and the previously drawn bounding boxes to determine that a potential violation has occurred. | ¶45 | col. 6:51-54 |
The complaint includes a visual from a SafeFleet blog post depicting a vehicle in a bus lane with a red bounding box drawn around it and the bus lane highlighted in a blue overlay, which Plaintiff alleges demonstrates the claimed bounding functionality (Compl. ¶44, p. 11).
- Identified Points of Contention:
- Scope Questions: A central question may be whether the accused system’s alleged use of "advanced algorithms" and "artificial intelligence modeling" meets the claim limitations requiring "applying a plurality of functions from a computer vision library" and "passing the frames to a deep learning model." The specific definitions and operational characteristics of these software components will be critical.
- Technical Questions: Does the accused system's detection method constitute an "overlap of the vehicular bounding box with the road bounding box" as required by the claim? The complaint alleges the system uses GPS data and the bounding boxes to make a determination, which raises the question of whether this is the same specific geometric overlap function described in the patent.
V. Key Claim Terms for Construction
The Term: "deep learning model"
Context and Importance: This term is central to the claimed invention's data processing method. The infringement analysis will depend on whether Seon's "advanced algorithms" and "artificial intelligence modeling" constitute a "deep learning model" as understood in the patent. Practitioners may focus on this term because the complaint's allegations are based on high-level marketing descriptions rather than specific technical disclosures of the accused product's software architecture.
Intrinsic Evidence for Interpretation:
- Evidence for a Broader Interpretation: The specification states that the model "can be or comprise a neural network trained for object detection" and can be a "convolutional neural network (CNN)" (’919 Patent, col. 18:35-39). This could support an interpretation covering various types of AI models used for object detection.
- Evidence for a Narrower Interpretation: The specification provides a specific example, stating the model "can be the YOLOv3 object detection model" (’919 Patent, col. 18:55-56). A defendant may argue this example limits the term to specific, known object detection architectures rather than any generic "AI."
The Term: "bounding... the restricted road area in the frames with a road bounding box"
Context and Importance: This term defines a specific required action for identifying the restricted area. The visual evidence in the complaint shows a colored overlay on the bus lane rather than a distinct "box" (Compl. ¶44, p. 11). The dispute may turn on whether this graphical overlay functions as or is equivalent to a "road bounding box."
Intrinsic Evidence for Interpretation:
- Evidence for a Broader Interpretation: The claims require bounding the area "with a road bounding box," which could be interpreted functionally to mean any graphical designation that defines the restricted area's boundaries in the image frame for the purpose of detecting overlap.
- Evidence for a Narrower Interpretation: Figure 9 of the patent depicts distinct, four-sided boxes (900, 902) for both the vehicle and the road. A defendant could argue that the term requires a literal, geometrically-defined box, not just a colored zone or overlay.
VI. Other Allegations
- Indirect Infringement: The complaint alleges that Seon induces and contributes to infringement by actions including advertising infringing uses on its websites, providing product specifications and user manuals to customers, and offering technical support for the ClearLane product (Compl. ¶48).
- Willful Infringement: Willfulness is alleged based on Seon's status as a direct competitor in the "automated bus lane enforcement solutions market" (Compl. ¶49). The complaint asserts on information and belief that Seon knew, should have known, or was willfully blind to the existence of the ’919 Patent because the parties compete for government contracts (Compl. ¶¶49, 52). Knowledge is also alleged from the date of the complaint filing (Compl. ¶49).
VII. Analyst’s Conclusion: Key Questions for the Case
The resolution of this dispute may hinge on two central questions:
A key question of claim scope will be whether Seon’s use of "advanced algorithms" and "artificial intelligence modeling," as described in its marketing materials, falls within the patent’s more specific requirements of using a "computer vision library" and a "deep learning model." The case may turn on the evidence produced regarding the actual software architecture of the ClearLane system.
A critical evidentiary question will be one of functional operation: does the accused ClearLane system’s method of identifying a violation—allegedly by using GPS data in conjunction with visual bounding boxes—perform the same function as the claimed step of detecting an "overlap of the vehicular bounding box with the road bounding box"? The court will need to determine if there is a fundamental match or mismatch in the technical operation of the two systems.