Tesla Inc v. Granite Vehicle Ventures LLC

Prosecution History analysis

Examiner Search analysis

Invalidity Framework analysis

Petition

Petition Analysis

## 1. Case Identification
- Case Number: [IPR2025-01034](https://ai-lab.exparte.com/case/ptab/IPR2025-01034)
- Patent #: 12,037,004
- Filed: May 28, 2025
- Petitioner(s): [Tesla](https://ai-lab.exparte.com/party/tesla-inc), Inc.
- Patent Owner(s): [Granite Vehicle Ventures](https://ai-lab.exparte.com/party/granite-vehicle-ventures-llc) LLC
- Challenged Claims: 1-9, 29-39

## 2. Patent Overview
- Title: Systems and Methods for Controlling a Self-Driving Vehicle
- Brief Description: [The ’004 patent](https://ai-lab.exparte.com/case/ptab/IPR2025-01034/doc/1001) relates to a computer program product for controlling a driving mode of a self-driving vehicle (SDV). The system determines separate competence levels for the human driver and the SDV's control processor, and takes corrective actions based on comparing these competence levels to various thresholds to manage operational anomalies.

## 3. Grounds for Unpatentability

#### Ground 1: Obviousness of Claim 1 over Core Driver/Vehicle Monitoring References - Claim 1 is obvious over Hampiholi in view of Attard, McNew, and Frazer.
  - **Prior Art Relied Upon:** [Hampiholi](https://ai-lab.exparte.com/case/ptab/IPR2025-01034/doc/1007) (U.S. Patent Application Publication No. 2016/0267335), [Attard](https://ai-lab.exparte.com/case/ptab/IPR2025-01034/doc/1004) (Patent 9,406,177), [McNew](https://ai-lab.exparte.com/case/ptab/IPR2025-01034/doc/1006) (Patent 10,377,303), and [Frazer](https://ai-lab.exparte.com/case/ptab/IPR2025-01034/doc/1005) (Patent 9,494,926).
  - **Core Argument for this Ground:**
    - **Prior Art Mapping:** Petitioner argued that the combination teaches the core concepts of claim [1](https://ai-lab.exparte.com/docs/ptab/IPR2025-01034/1). [Hampiholi](https://ai-lab.exparte.com/case/ptab/IPR2025-01034/doc/1007) was asserted to teach monitoring driver distraction with sensors to determine a "severity rank R," which corresponds to the claimed human driver competence level (HDCL). [Hampiholi](https://ai-lab.exparte.com/case/ptab/IPR2025-01034/doc/1007) further teaches comparing this rank to thresholds to trigger corrective actions, mapping to the patent's fault detection logic. [Attard](https://ai-lab.exparte.com/case/ptab/IPR2025-01034/doc/1004) was argued to supply the claimed SDV competence level (CPCL) through its disclosure of a confidence value "Φ" that indicates the SDV's capability to operate autonomously. To meet the steering wheel sensor limitation, Petitioner added [McNew](https://ai-lab.exparte.com/case/ptab/IPR2025-01034/doc/1006), which teaches using a steering wheel grip sensor to monitor driver attention. Finally, [Frazer](https://ai-lab.exparte.com/case/ptab/IPR2025-01034/doc/1005) was cited for its disclosure of a fault-remediation table (an event table mapping conditions to actions), which Petitioner argued is a known and obvious way to implement the if-then corrective action logic.
    - **Motivation to Combine:** A person of ordinary skill in the art (POSITA) would combine [Hampiholi](https://ai-lab.exparte.com/case/ptab/IPR2025-01034/doc/1007) and [Attard](https://ai-lab.exparte.com/case/ptab/IPR2025-01034/doc/1004) to create a more robust safety system that monitors both driver and vehicle states. A POSITA would incorporate [McNew's](https://ai-lab.exparte.com/case/ptab/IPR2025-01034/doc/1006) sensor as a known method to improve driver monitoring. Implementing the system's logic using [Frazer's](https://ai-lab.exparte.com/case/ptab/IPR2025-01034/doc/1005) table-based approach was argued to be an obvious design choice to improve efficiency and reduce latency, as expressly taught by [Frazer](https://ai-lab.exparte.com/case/ptab/IPR2025-01034/doc/1005).
    - **Expectation of Success:** A POSITA would have had a reasonable expectation of success because combining these elements involves applying known techniques (sensor-based monitoring, competence assessment, lookup tables) from the same field of automotive automation to achieve the predictable result of a safer, more comprehensive control system.

#### Ground 2: Obviousness of Claims 2-5 over Weighted Voting and Active Learning References - Claims 2-5 are obvious over the Ground 1 combination in view of DeRuyck, Gunderson, An, Kang, and Schunder.
  - **Prior Art Relied Upon:** The combination for Ground [1](https://ai-lab.exparte.com/docs/ptab/IPR2025-01034/1), plus [DeRuyck](https://ai-lab.exparte.com/case/ptab/IPR2025-01034/doc/1011) (Patent 9,714,037), [Gunderson](https://ai-lab.exparte.com/case/ptab/IPR2025-01034/doc/1012) (’158 application), [An](https://ai-lab.exparte.com/case/ptab/IPR2025-01034/doc/1008) (Patent 9,063,543), [Kang](https://ai-lab.exparte.com/case/ptab/IPR2025-01034/doc/1009) (Patent 9,688,145), and [Schunder](https://ai-lab.exparte.com/case/ptab/IPR2025-01034/doc/1010) (Patent 9,451,030).
  - **Core Argument for this Ground:**
    - **Prior Art Mapping:** This ground addressed dependent claims requiring a "weighted voting system" and "active learning data." Petitioner argued [DeRuyck](https://ai-lab.exparte.com/case/ptab/IPR2025-01034/doc/1011) teaches a weighted voting system to determine a driver's competence from multiple data sources. To meet the limitation that weights are based on "active learning data" from a "cohort of human drivers of other SDVs" (claim [2](https://ai-lab.exparte.com/docs/ptab/IPR2025-01034/2)(c)), Petitioner cited [Gunderson](https://ai-lab.exparte.com/case/ptab/IPR2025-01034/doc/1012), which teaches analyzing data from a group of drivers and assigning weights to risk factors based on their prevalence. For claim [2](https://ai-lab.exparte.com/docs/ptab/IPR2025-01034/2)(h)'s requirement of using condition data from other SDVs, Petitioner combined [Kang](https://ai-lab.exparte.com/case/ptab/IPR2025-01034/doc/1009) (weighting inputs based on weather) with [Schunder](https://ai-lab.exparte.com/case/ptab/IPR2025-01034/doc/1010) (receiving real-time weather data from other vehicles on the same roadway).
    - **Motivation to Combine:** A POSITA would combine these references to create a more nuanced and accurate competence assessment than simple thresholding. Using weighted voting ([DeRuyck](https://ai-lab.exparte.com/case/ptab/IPR2025-01034/doc/1011)) and incorporating data from a cohort of other vehicles ([Gunderson](https://ai-lab.exparte.com/case/ptab/IPR2025-01034/doc/1012), [Schunder](https://ai-lab.exparte.com/case/ptab/IPR2025-01034/doc/1010)) and environmental conditions ([Kang](https://ai-lab.exparte.com/case/ptab/IPR2025-01034/doc/1009)) are all known techniques to enhance the reliability and safety of an autonomous system.
    - **Expectation of Success:** The combination was presented as a straightforward application of known data processing techniques to the base system of Ground [1](https://ai-lab.exparte.com/docs/ptab/IPR2025-01034/1), yielding the predictable result of a more robust and context-aware system.

#### Ground 3: Obviousness of Claim 29 over Sensor Display References - Claim 29 is obvious over Hampiholi, Attard, McNew, and Frazer in view of Hada.
  - **Prior Art Relied Upon:** The combination for Ground [1](https://ai-lab.exparte.com/docs/ptab/IPR2025-01034/1), plus [Hada](https://ai-lab.exparte.com/case/ptab/IPR2025-01034/doc/1043) (Patent 8,305,444).
  - **Core Argument for this Ground:**
    - **Prior Art Mapping:** Petitioner argued this combination renders independent claim 29 obvious. The base combination from Ground [1](https://ai-lab.exparte.com/docs/ptab/IPR2025-01034/1) was asserted to teach the claimed system of receiving various sensor readings to determine human and SDV competence levels. To meet the additional limitation of "displaying, on a display screen, one or more sensor readings of the sensor readings," Petitioner added [Hada](https://ai-lab.exparte.com/case/ptab/IPR2025-01034/doc/1043). [Hada](https://ai-lab.exparte.com/case/ptab/IPR2025-01034/doc/1043) was asserted to teach displaying sensor data such as the location of nearby vehicles, lane markings, and traffic signals on a graphical display to improve driver awareness.
    - **Motivation to Combine:** A POSITA would be motivated to add [Hada's](https://ai-lab.exparte.com/case/ptab/IPR2025-01034/doc/1043) display functionality to the base system to improve vehicle safety, a known and direct benefit of providing drivers with enhanced situational awareness. Petitioner argued this modification was a simple use of a known technique (displaying sensor data) to improve a known system.
    - **Expectation of Success:** A reasonable expectation of success existed because the base system in [Hampiholi](https://ai-lab.exparte.com/case/ptab/IPR2025-01034/doc/1007) already included a display and the hardware for processing sensor data. Modifying the system's programming to show additional sensor data as taught by [Hada](https://ai-lab.exparte.com/case/ptab/IPR2025-01034/doc/1043) would be a straightforward task for a POSITA.
- **Additional Grounds:** Petitioner asserted additional obviousness challenges based on combinations including [Sako](https://ai-lab.exparte.com/case/ptab/IPR2025-01034/doc/1040) (for roadway type detection), [Coelingh](https://ai-lab.exparte.com/case/ptab/IPR2025-01034/doc/1041) (for V2V sensor diagnosis), [Nagasaka](https://ai-lab.exparte.com/case/ptab/IPR2025-01034/doc/1042) (for autonomous turn signal use), and [Duncan](https://ai-lab.exparte.com/case/ptab/IPR2025-01034/doc/1045) (for retrieving driver profiles from a database) to meet various other dependent claim limitations.

## 4. Relief Requested
- Petitioner requests the institution of an inter partes review and the cancellation of claims 1-9 and 29-39 of Patent [12037004](https://ai-lab.exparte.com/patent/12037004) as unpatentable under 35 U.S.C. §103.

Tesla targets self-driving vehicle control patent: Hampiholi & Attard prior art cited for 20 claims as obvious over driver/vehicle competence monitoring.