PTAB

IPR2019-00620

Tetra Tech Canada Inc v. Georgetown Rail Equipment Co

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Petition
petition

1. Case Identification

2. Patent Overview

  • Title: System and Method for Inspecting Railroad Ties
  • Brief Description: The ’956 patent discloses a vehicle-mounted inspection system for railroad tracks. The system uses a light generator (e.g., a laser) to project a line of light across the track and an optical receiver (e.g., a camera) to capture the profile, generating 2D and 3D image data that is analyzed by a processor to assess the condition of track components, particularly railroad ties.

3. Grounds for Unpatentability

Ground 1: Obviousness over Villar, Haas, and Uzarski - Claims 1-3, 7-8, 11-13, and 16-20 are obvious over Villar in view of Haas and Uzarski.

  • Prior Art Relied Upon: Villar (Patent 7,616,329), Haas (Application # 2012/0263342), and Uzarski (a 1994 publication titled “Development of Condition Indexes for Low Volume Railroad Track”).
  • Core Argument for this Ground:
    • Prior Art Mapping: Petitioner asserted that Villar, which is the parent patent to the challenged ’956 patent, discloses the foundational elements of the claimed invention. This includes a vehicle-mounted optical scanning system with a laser, camera, and processor configured to inspect railroad tracks, find tie boundaries, and detect defects, meeting the limitations of independent claims 1, 8, and 17, except for the specific grading algorithm. Petitioner argued the key missing steps—determining a series of condition metrics, applying a weight factor to each, and computing a condition grade based on a weighted summation—are not taught by Villar but were well-known in the art. Haas was cited to show an automated machine vision system for detecting the condition of rail components in accordance with safety guidelines. Petitioner contended that Uzarski provides a specific, detailed methodology for this type of assessment, teaching a "Tie Condition Index" (TCI) that explicitly uses a series of metrics (e.g., distress type, severity, density), applies weighting factors, and computes a final numerical condition grade for ties.
    • Motivation to Combine: Petitioner argued a person of ordinary skill in the art (POSITA) would have been motivated to integrate the teachings of Haas and Uzarski into Villar’s system. Villar’s background recognized the industry need for grading crossties to manage replacement logistics. Haas taught using automated systems to assess track health against established guidelines (e.g., government regulations). Petitioner contended that Uzarski provides an example of such a guideline, offering an “unbiased and reputable” TCI assessment method. A POSITA would combine Uzarski’s quantitative grading logic with Villar’s automated data-gathering system to improve its functionality and provide the objective, data-driven maintenance insights that Villar’s system lacked.
    • Expectation of Success: Petitioner asserted a POSITA would have had a reasonable expectation of success. The combination involved applying a known data analysis method (Uzarski) to a data set generated by a conventional optical inspection system (Villar). The references all relate to railroad track inspection, and implementing Uzarski’s algorithm on Villar’s processor was a straightforward application of known software programming techniques.

Ground 2: Obviousness over Villar, Haas, Uzarski, Velten, and Kanade - Claims 9-10 are obvious over the combination of Ground 1 in view of Velten and Kanade.

  • Prior Art Relied Upon: Villar (Patent 7,616,329), Haas (Application # 2012/0263342), Uzarski (1994 publication), Velten (a 1999 publication titled “Application of a Brightness-Adapted Edge Detector for Real-Time Railroad Tie Detection in Video Images”), and Kanade (a 1987 publication titled “Three-Dimensional Machine Vision”).
  • Core Argument for this Ground:
    • Prior Art Mapping: This ground builds upon the combination in Ground 1 to address dependent claims 9 and 10, which specify scanning with a "3-D optical scanning system" and generating a computer-readable image. Petitioner argued that Villar’s system—which compiles sequential 2D profiles into a 3D representation—is inherently a 3D optical scanning system. The core of this ground is that while Villar teaches basic edge detection to find tie boundaries, it would have been obvious to a POSITA to implement more advanced boundary detection algorithms as taught by Velten and Kanade. Velten discloses specific 2D edge detection algorithms for identifying the "boundary contour" of railroad ties for automated inspection. Kanade teaches that such 2D shape-recognition techniques were commonly applied to 3D range data to ascertain object boundaries.
    • Motivation to Combine: The motivation was to improve the accuracy and reliability of the tie boundary identification in Villar’s system. A POSITA would look to well-known machine vision literature to enhance the system’s performance. Incorporating Velten’s specialized railroad tie algorithms and Kanade’s methods for applying them to 3D data would have been a predictable way to further automate tie fault detection and achieve more robust results than Villar’s general disclosure of "edge detecting techniques."
    • Expectation of Success: A POSITA would have expected success because the proposed modification involved applying established and complementary image processing techniques. Using edge detection algorithms (Velten) on 3D data sets (as generated by Villar) was a standard practice in the field of machine vision, as demonstrated by the textbook-like teachings of Kanade.

4. Relief Requested

  • Petitioner requests institution of an inter partes review and cancellation of claims 1-3, 7-13, and 16-20 of the ’956 patent as unpatentable.