PTAB

IPR2025-01035

Tesla Inc v. Granite Vehicle Ventures LLC

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Petition
petition

1. Case Identification

2. Patent Overview

  • Title: Self-Driving Vehicle Competence Determination
  • Brief Description: The ’004 patent discloses a self-driving vehicle (SDV) that uses a sensor system to determine a human driver's competence level. Based on this competence level, the SDV's computer system determines whether faults have occurred and selects appropriate corrective actions.

3. Grounds for Unpatentability

Ground 1: Claims 10-14 are obvious over Hampiholi, Attard, McNew, Yamada, Gunderson, Grimm, and Frazer.

  • Prior Art Relied Upon: Hampiholi (Application # 2016/0267335), Attard (Patent 9,406,177), McNew (Patent 10,377,303), Yamada (Patent 9,159,301), Gunderson (Application # 2007/0268158), Grimm (Patent 9,430,944), and Frazer (Patent 9,494,926).
  • Core Argument for this Ground:
    • Prior Art Mapping: Petitioner argued that the combination of references teaches every element of independent claim 10. Hampiholi was asserted to teach the base SDV with a sensor system (including a driver-facing camera, GPS, and thermometer) that determines a driver's competence level (a "severity rank") and takes corrective actions based on that rank falling below certain quotas. Attard was argued to add autonomous control modes and a second, world-facing camera. McNew was added for its teaching of a steering wheel grip sensor to more accurately detect driver engagement. Yamada was cited for its disclosure of a vehicle display capable of showing readings from a GPS sensor, thermometer, and speedometer. Gunderson and Grimm were introduced to teach using weighted, active learning data from a cohort of other vehicles to assess driver behavior and vehicle threats, respectively, fulfilling the "first plurality of weights" and "second plurality of weights" limitations. Finally, Frazer was asserted to teach a fault-remediation table that organizes fault conditions and corresponding corrective actions, mapping to the claimed table structure.
    • Motivation to Combine: Petitioner contended a person of ordinary skill in the art (POSITA) would combine these references to create a more robust and safe SDV, which was a well-known goal in the art. A POSITA would combine Hampiholi's driver monitoring with Attard's autonomous control features to create a vehicle that can intelligently respond to both driver inattention and external events. Adding McNew's grip sensor was presented as a predictable improvement to Hampiholi's system for detecting driver engagement. Incorporating Gunderson's and Grimm's use of weighted data from other vehicles was argued to be a known method for improving the accuracy of risk assessments. Using Frazer's table structure was presented as a well-known and efficient method for organizing the fault-and-response logic inherent in Hampiholi's system.
    • Expectation of Success: Petitioner argued a POSITA would have had a reasonable expectation of success because combining these known sensor systems, data processing techniques, and control logic involved applying known techniques to a known system to achieve predictable results.

Ground 2: Claim 15 is obvious over Hampiholi, Attard, McNew, Gunderson, Grimm, Frazer, Duncan, and Engelman.

  • Prior Art Relied Upon: The combination from Ground 1, plus Duncan (Application # 2015/0158495) and Engelman (Application # 2015/0166069).

  • Core Argument for this Ground:

    • Prior Art Mapping: This ground builds on the combination for claim 10 to address the distinct limitations of independent claim 15. The core combination was asserted to teach most elements, including the sensor system and fault remediation table with multiple fault states (from Frazer). The new references were introduced for specific profiling limitations. Duncan was argued to teach "generating a human driver profile" by disclosing a system that creates and stores an operator profile based on historical performance characteristics like attentiveness. Engelman was argued to teach "generating a control processor profile" by disclosing a system that determines and tracks whether the vehicle is operating in an autonomous or non-autonomous mode.
    • Motivation to Combine: A POSITA would be motivated to add Duncan's driver profiling to store and retrieve Hampiholi's historical driver data in a more structured way, predictably improving the system. Petitioner argued it would have been obvious to add Engelman's mode-tracking to the multi-mode system of Attard to ensure the control module is aware of which autonomous features are permitted at any given time, a necessary function for safe operation.
    • Expectation of Success: Petitioner asserted success would be expected, as integrating these features would require only straightforward software modifications to the base system, which already possessed the necessary processors and memory.

  • Additional Grounds: Petitioner asserted additional obviousness challenges for the remaining claims based on incrementally adding further prior art to the core combination. Ground 3 added Strauss (Patent 9,604,652) for teachings on semi-autonomous control. Ground 4 added Sako (Patent 10,139,824) for teachings on switching autonomous modes based on roadway type (e.g., highway vs. general road). Ground 5 added Hada (Patent 8,305,444) for teachings on displaying the position of nearby vehicles.

4. Relief Requested

  • Petitioner requests institution of an inter partes review and cancellation of claims 10-24 and 27 of the ’004 patent as unpatentable.