Summary of US Patent 12037004

Title: Controlling driving modes of self-driving vehicles

Assignee: Granite Vehicle Ventures LLC

Inventors: Michael S. Gordon, James R. Kozloski, Ashish Kundu, Peter K. Malkin, Clifford A. Pickover

Filing Date: July 17, 2023 (for application number US18/222,774)

Issue Date: July 16, 2024

Abstract:
A computer-implemented method, system, and/or computer program product for controlling a driving mode of a self-driving vehicle (SDV) is disclosed. One or more processors determine a competence level for an on-board SDV control processor and for a human driver in controlling the SDV while it experiences a current operational anomaly. These competence levels are then compared, and control of the SDV is selectively assigned to either the SDV control processor or the human driver based on which entity has a relatively higher competence level for handling the specific operational anomaly.

Plain-Language Overview of Independent Claims:

Independent Claim 1 (Method):
This claim describes a computer-implemented method for switching a self-driving vehicle (SDV) between autonomous and manual driving modes. The method involves several steps performed by one or more processors:

1. Detecting an Anomaly: The system receives sensor readings that identify an abnormal condition or issue with the SDV (an "operational anomaly").
2. Evaluating AI Competence: It determines how capable the SDV's on-board control processor is at handling the vehicle while that specific anomaly is occurring.
3. Evaluating Human Competence: It also receives information from a human driver's profile to determine how capable the human driver is at controlling the vehicle under the same anomalous condition.
4. Comparing Competence: The system then compares the competence level of the on-board control processor to that of the human driver.
5. Assigning Control: Based on which entity (the processor or the human driver) is deemed more competent to handle the vehicle with the current anomaly, control of the SDV is assigned to that entity.

Independent Claim 11 (System):
This claim describes a system, including memory and one or more processors, designed to perform the same method outlined in Claim 1. The processors are configured to:

1. Receive sensor readings detailing an SDV's current operational anomaly.
2. Determine the on-board SDV control processor's competence level in controlling the SDV during that anomaly.
3. Receive a human driver's profile, which indicates their competence level for controlling the SDV during the anomaly.
4. Compare these two competence levels.
5. Selectively assign control of the SDV to either the on-board control processor or the human driver, based on which has the higher competence level.

Independent Claim 19 (Computer Program Product):
This claim covers a computer program product stored on a computer-readable medium. The program instructions, when executed by a processor, carry out the same method as described in Claim 1. Specifically, the instructions enable the processor to:

1. Receive sensor readings about an SDV's current operational anomaly.
2. Determine the control processor's competence level for handling the SDV during the anomaly.
3. Receive a human driver's profile to ascertain their competence level for managing the SDV during the anomaly.
4. Compare the control processor's competence level to the human driver's competence level.
5. Assign control of the SDV to the control processor or the human driver, choosing the one with the relatively higher competence level for that specific anomalous situation.

Litigation Status:
US Patent 12037004 is currently active and involved in litigation. Granite Vehicle Ventures LLC, the assignee, filed a lawsuit against Tesla Inc. on December 6, 2024, in the U.S. District Court for the Eastern District of Texas, alleging infringement of this patent, along with US Patent Nos. 11,597,402 and 11,738,765. Tesla subsequently moved to dismiss some of the infringement claims and requested a transfer of the case. The case was transferred from the Eastern District of Texas to the Northern District of California on February 13, 2026.

While a direct CAFC docket specifically detailing an appeal for this patent in 2026 was not found in the live search results, the patent's information on Google Patents (as provided in the prompt) indicates a "US case filed in Court of Appeals for the Federal Circuit" with case number 26-116. Given the recent transfer of the district court case, it is likely that any CAFC activity might be a related or prior procedural matter, or an appeal yet to fully develop from the ongoing district court proceedings.

US Patent 12037004 is currently involved in litigation. Here's a summary of the known case:

Case Name: Granite Vehicle Ventures LLC v. Tesla, Inc.

• Plaintiff(s): Granite Vehicle Ventures LLC
• Defendant(s): Tesla, Inc.
• Jurisdiction: Initially filed in the U.S. District Court for the Eastern District of Texas, the case was transferred to the U.S. District Court for the Northern District of California.
• Case Number: 2:24-cv-01007 (Eastern District of Texas) and 3:26-cv-01457 (Northern District of California)
• Filing Date: December 6, 2024
• Outcome/Current Status: The case was transferred from the Eastern District of Texas to the Northern District of California on February 13, 2026. The transfer constitutes a termination of proceedings in E.D. Texas, and the litigation is expected to continue in N.D. California under the new docket. No merits verdict, damages, or injunctive relief were issued at the Eastern District level. The substantive infringement and validity questions, including potential challenges under 35 U.S.C. § 103 (obviousness) and § 112 (enablement), are yet to be resolved.

Proceedings overview

Two Inter Partes Review (IPR) proceedings, IPR2025-01035 and IPR2025-01034, have been filed against US Patent 12037004. Both petitions have a status of "Discretionary Denial," meaning the PTAB declined to institute a full review of the challenged claims. This indicates that the patent has survived these initial challenges and is hardened against these specific IPRs, providing a strong defensive posture for the patent owner against these petitioners and grounds.

IPR2025-01035 — Tesla Inc. v. Granite Vehicle Ventures LLC

• Type: Inter Partes Review
• Filed: 2025-05-28
• Status: Discretionary Denial. This means the PTAB, in its discretion, declined to institute an IPR trial. It does not reflect a ruling on the merits of the patentability challenges.
• Judge panel: Not publicly available in the provided information. However, institution decisions are typically made by the Director of the USPTO in consultation with at least three PTAB judges.
• Petition grounds: The specific claims, prior art, and statutory bases (§ 102 / § 103 / § 112) for the petition are not detailed in the provided information.
• Institution decision: Denied (Discretionary Denial) on 2025-12-04. The reasoning for discretionary denial often involves factors beyond the merits of the patentability challenge, such as the stage of parallel district court litigation or inconsistent claim construction positions by the petitioner.
• Final Written Decision: Not applicable, as institution was denied.
• Settlement / termination: Not applicable, as institution was denied.
• Appeal: Not applicable, as institution was denied. Appeals to the Federal Circuit are generally barred for institution decisions.
• Defensive value: The patent owner prevailed at the institution stage, meaning the claims challenged in this IPR were not subjected to a full PTAB review based on the arguments presented by Tesla Inc. This makes an IPR-based defense using the same or similar grounds against this patent by Tesla Inc. or its privies significantly harder due to estoppel.

IPR2025-01034 — Tesla Inc. et al. v. Granite Vehicle Ventures LLC

• Type: Inter Partes Review
• Filed: 2025-05-28
• Status: Discretionary Denial. This means the PTAB, in its discretion, declined to institute an IPR trial. It does not reflect a ruling on the merits of the patentability challenges.
• Judge panel: Not publicly available in the provided information. However, institution decisions are typically made by the Director of the USPTO in consultation with at least three PTAB judges.
• Petition grounds: The specific claims, prior art, and statutory bases (§ 102 / § 103 / § 112) for the petition are not detailed in the provided information.
• Institution decision: Denied (Discretionary Denial) on 2025-12-04. The reasoning for discretionary denial often involves factors beyond the merits of the patentability challenge, such as the stage of parallel district court litigation or inconsistent claim construction positions by the petitioner.
• Final Written Decision: Not applicable, as institution was denied.
• Settlement / termination: Not applicable, as institution was denied.
• Appeal: Not applicable, as institution was denied. Appeals to the Federal Circuit are generally barred for institution decisions.
• Defensive value: The patent owner prevailed at the institution stage, meaning the claims challenged in this IPR were not subjected to a full PTAB review based on the arguments presented. This makes an IPR-based defense using the same or similar grounds against this patent by the petitioner (Tesla Inc. et al.) or their privies significantly harder due to estoppel.

Strategic summary

Currently, all claims of US Patent 12037004 remain UNTESTED on the merits through PTAB proceedings, as both IPR2025-01035 and IPR2025-01034 resulted in discretionary denials of institution. This means no claims have been canceled or sustained by the PTAB in these proceedings. The patent owner, Granite Vehicle Ventures LLC, has successfully fended off these challenges at the initial stage.

The estoppel landscape under § 315(e)(2) will bar Tesla Inc. (and any parties in privity with them) from asserting, in any other civil action or ITC proceeding, invalidity grounds that were raised or reasonably could have been raised in IPR2025-01035 and IPR2025-01034. Since both petitions were denied institution, the specific prior-art grounds that "reasonably could have been raised" might be a point of contention, but the petitioner is generally estopped from re-litigating grounds that were presented to the PTAB. For a defendant currently being asserted against who is not Tesla Inc. or in privity with them, all prior-art grounds remain available.

Both IPRs were filed by Tesla Inc. (or Tesla Inc. et al.), indicating a concerted effort by this entity to challenge the patent. The fact that both were met with discretionary denials suggests a potential pattern related to the PTAB's discretionary denial factors, which can include the status of parallel litigation or positions taken by the petitioner in other forums.

Recommended next steps

Since both IPRs resulted in discretionary denials, there are no claims invalidated to cite in a defense. No active proceedings are pending at the trial stage for US Patent 12037004.

Inventors

• Michael S. Gordon (Employer not determinable from patent text)
• James R. Kozloski (Employer not determinable from patent text)
• Ashish Kundu (Employer not determinable from patent text)
• Peter K. Malkin (Employer not determinable from patent text)
• Clifford A. Pickover (Employer not determinable from patent text)

No unusual patterns regarding inventor departures are determinable from the provided patent text.

Original assignee

Granite Vehicle Ventures LLC is the original assignee named on the issued patent. There is no information within the patent document to determine if they ship a product embodying the claims or their primary line of business. The current status of Granite Vehicle Ventures LLC is "Active" according to the Google Patents legal status, and they are currently involved in litigation.

Assignment timeline

There are no assignment records for US12037004B2 currently available on the USPTO Assignment Center when searching by patent number. The Google Patents record indicates that the current assignee is Granite Vehicle Ventures LLC, and the original assignee was also Granite Vehicle Ventures LLC. The Google Patents record shows one reassignment event:

• 2023-07-27 / recorded 2023-07-27 - Reel (not specified)
  • Conveyance: Assignment of Assignor's Interest
  • Assignor: SLINGSHOT IOT LLC
  • Assignee: GRANITE VEHICLE VENTURES LLC
  • Correspondent: Not specified in Google Patents record.
  • Context: Transfer-to-asserter from an entity identified as part of a patent monetization network to an entity that then asserted the patent.

It is important to note that while the Google Patents record indicates this reassignment, the USPTO Assignment Center is the authoritative source for recorded assignments. The absence of a recorded assignment in the USPTO Assignment Center search results for this patent number may indicate the recording has not yet been processed or publicly made available, or there might be an issue with the search query. However, the provided Google Patents information explicitly states an assignment event.

Timeline diagram

timeline
    title Ownership of US 12037004
    2015 : Priority date (Application 14/865,393)
    2023 : Application filed (US18/222,774)
         : Assigned from Slingshot IOT LLC
         : Assigned to Granite Vehicle Ventures LLC
    2024 : Issued to Granite Vehicle Ventures LLC
         : First infringement suit filed vs Tesla
    2026 : Case transferred to N.D. California

NPE / troll-pattern signals

1. Shell-entity transfer — Present. The patent was assigned from SLINGSHOT IOT LLC to GRANITE VEHICLE VENTURES LLC on 2023-07-27. Google Patents and other sources explicitly identify Granite Vehicle Ventures LLC as a non-practicing entity (NPE) asserting patent rights in vehicle automation technology. Additionally, "Ventures LLC" in the name is a common suffix for licensing-only entities.
2. Known asserter in the chain — Present. Granite Vehicle Ventures LLC is identified as a non-practicing entity (NPE). Unified Patents has issued a contest seeking prior art for this patent, explicitly stating it is "owned and asserted by Granite Vehicle Ventures LLC, an NPE."
3. Repeat correspondent across the chain — Unclear. The Google Patents record for the 2023-07-27 assignment does not specify a correspondent. Without USPTO Assignment Center records, it's not possible to track correspondent recurrence. However, other sources mention that Granite Vehicle Ventures LLC is associated with an "Eggleston-York team" which has created other "rock-related names" LLCs for litigation purposes. This suggests a pattern of related entities possibly using the same legal counsel.
4. Cascading transfers — Not present. Only one assignment is noted in the provided information.
5. Pre-litigation transfer — Present. The patent was assigned to Granite Vehicle Ventures LLC on 2023-07-27, and the first infringement suit against Tesla was filed on December 6, 2024. This transfer occurred approximately 16 months before the lawsuit, which is outside the typical "within 6 months" window but still indicates a clear transfer to an asserting entity prior to litigation. While 16 months is longer than 6 months, the context of Slingshot IOT LLC being part of a patent monetization network and Granite Vehicle Ventures LLC being an NPE still strongly suggests the transfer was made to enable assertion.
6. Bankruptcy fire-sale — Not present. No information indicates the original assignee filed for bankruptcy.
7. Privateering — Unclear. No information is available to suggest an operating company transferred the patent to an NPE to assert on their behalf.
8. Defensive aggregator (anti-NPE) — Not present. The chain ends at Granite Vehicle Ventures LLC, an NPE, not a defensive aggregator.

Verdict

NPE — high confidence. The presence of Granite Vehicle Ventures LLC, explicitly identified as a non-practicing entity, and the pre-litigation transfer from Slingshot IOT LLC, an entity noted to be involved in patent monetization, strongly indicate an NPE pattern. The transfer to Granite Vehicle Ventures LLC occurred before the infringement suit against Tesla, further supporting that the chain was arranged for assertion.

Verification: https://assignmentcenter.uspto.gov/ (Search for patent number 12037004).

To identify the most relevant prior art for US Patent 12037004, I will search the citations listed on the patent itself. The provided patent text from Google Patents already lists "Referenced Cited U.S. Patent Documents". I will use this list.

Here are the U.S. Patent Documents cited in US12037004B2, along with their publication/filing dates and a brief description where available. Please note that detailed descriptions and specific claim anticipation would require a full analysis of each cited patent's claims against those of US12037004B2. However, I can provide a general idea of how they might relate to the disclosed invention based on their titles and typical subject matter for the given dates. The standard for anticipation under 35 U.S.C. § 102 requires that all limitations of a claim are found in a single prior art reference.

Prior Art References for US Patent 12037004:

• US 4,665,395

  • Full Citation: US 4,665,395 A
  • Publication Date: May 12, 1987
  • Brief Description: This patent, issued in the late 1980s, likely pertains to early forms of vehicle control systems, possibly involving automatic control or warning systems rather than fully autonomous driving. Given the early date, it would likely focus on basic automation or driver assistance features.
  • Potential Anticipation: Could potentially anticipate general concepts of vehicle control or fault detection if broadly construed, but it is unlikely to anticipate the nuanced comparison of AI and human driver competence in specific anomalous conditions as detailed in claims 1, 11, and 19 of US12037004B2.

• US 4,908,988

  • Full Citation: US 4,908,988 A
  • Publication Date: March 20, 1990
  • Brief Description: Similar to US 4,665,395, this patent would predate modern self-driving technology. It might cover automated vehicle features, navigation aids, or early forms of adaptive cruise control.
  • Potential Anticipation: Unlikely to anticipate the core elements of US12037004B2 regarding dynamic competence level comparison between an SDV control processor and a human driver for operational anomalies.

• US 5,975,791

  • Full Citation: US 5,975,791 A
  • Publication Date: November 2, 1999
  • Brief Description: This patent would likely deal with more advanced vehicle electronic control systems, potentially including aspects of collision avoidance, active safety, or vehicle stability control, which were emerging technologies in the late 1990s.
  • Potential Anticipation: Could potentially anticipate aspects of detecting vehicle operational anomalies or initiating some form of automated response. However, it's improbable to detail a system that compares AI and human driver competence levels to determine control in anomalous situations.

• US 6,064,970

  • Full Citation: US 6,064,970 A
  • Publication Date: May 16, 2000
  • Brief Description: This patent, similar to US 5,975,791, probably relates to automotive control systems, possibly focusing on engine management, transmission control, or early forms of driver assistance systems.
  • Potential Anticipation: Would likely not anticipate the specific method of comparing control processor and human driver competence to assign driving modes in response to operational anomalies, as claimed in US12037004B2.

• US 6,201,318

  • Full Citation: US 6,201,318 B1
  • Publication Date: March 13, 2001
  • Brief Description: This patent likely addresses electronic control units (ECUs) in vehicles, possibly relating to diagnostics, communication within vehicle networks, or advanced sensor integration for vehicle operations.
  • Potential Anticipation: While it might touch upon sensor readings and control, it is unlikely to disclose the specific comparison and dynamic assignment of driving control based on relative competence levels of an AI and a human driver in anomalous conditions.

• US 6,326,903

  • Full Citation: US 6,326,903 B1
  • Publication Date: December 4, 2001
  • Brief Description: This patent, from the early 2000s, could cover aspects of vehicle telematics, navigation systems, or initial forms of semi-autonomous features like lane-keeping assistance or parking assist.
  • Potential Anticipation: It is improbable that this patent would anticipate the core inventive concept of US12037004B2, which revolves around a sophisticated comparison of AI and human driver competence during operational anomalies to switch driving modes.

To perform a complete prior art analysis and determine which specific claims (1, 11, or 19) each cited patent might anticipate under 35 U.S.C. § 102, a thorough review of the claims of each cited patent against the claims of US12037004B2 would be necessary. This would involve examining whether every element of a claim in US12037004B2 is present in a single prior art reference. Without access to the full text and claims of each cited patent, a definitive statement on anticipation cannot be made beyond a general assessment based on the publication date and likely subject matter.

Obviousness Analysis under 35 U.S.C. § 103 for US Patent 12037004

Under 35 U.S.C. § 103, a patent claim is obvious if "the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains." The analysis requires considering the scope and content of the prior art, the differences between the prior art and the claims at issue, the level of ordinary skill in the pertinent art, and any secondary considerations of non-obviousness.

For US Patent 12037004, the independent claims (Claim 1, 11, and 19) generally describe a system and method for controlling an SDV's driving mode (autonomous or manual) by comparing the competence levels of an on-board SDV control processor and a human driver in the face of an operational anomaly. The control is then assigned to the more competent entity.

The patent itself lists several prior art documents in its "Cross-Reference to Related Applications" section:

• U.S. application Ser. No. 17/374,656, filed Jul. 13, 2021
• U.S. application Ser. No. 16/997,202, filed Aug. 19, 2020 (issued as U.S. Pat. No. 11,091,171 on Aug. 17, 2021)
• U.S. application Ser. No. 16/899,407, filed Jun. 11, 2020
• U.S. application Ser. No. 15/955,874, filed Apr. 18, 2018 (issued as U.S. Pat. No. 10,717,446 on Jul. 21, 2020)
• U.S. application Ser. No. 15/341,225, filed Nov. 2, 2016 (issued as U.S. Pat. No. 10,029,701 on Jul. 24, 2018)
• U.S. application Ser. No. 14/865,393, filed Sep. 25, 2015 (issued as U.S. Pat. No. 9,566,986 on Feb. 14, 2017)

These patents are considered prior art, as they are part of the same patent family and precede the filing date of the application for US12037004B2 (July 17, 2023).

Level of Ordinary Skill in the Art

A person having ordinary skill in the art (POSA) in the context of this patent would likely possess a strong understanding of:

• Autonomous vehicle systems, including sensors, control algorithms, and human-machine interfaces.
• Software and hardware design for embedded systems in vehicles.
• Data analysis and machine learning for evaluating performance and predicting faults.
• Safety protocols and risk assessment in vehicle operation.

Potential Combinations of Prior Art and Motivation to Combine

The core inventive step of US12037004 lies in the explicit comparison of control processor competence and human driver competence in the context of an operational anomaly, and then selectively assigning control based on this comparison. Many of the concepts individually are found in the broader field of autonomous vehicles. The key is whether combining these known concepts in the specific manner claimed would have been obvious to a POSA.

Given that US12037004B2 is a continuation of several earlier patents, it is highly probable that elements of the independent claims are individually disclosed or suggested within this family of patents.

Combination 1: US 9,566,986 and general knowledge of driver monitoring systems

• US 9,566,986 ("Controlling driving modes of self-driving vehicles"): This patent, from which US12037004B2 claims priority, explicitly discusses detecting vehicle faults and altering driving modes (manual or autonomous) based on these faults. It also mentions a "driver profile" that provides "an indication of the driver's physical or other abilities," which is used to "further determine whether the SDV should be in autonomous or manual mode." The patent describes a fault-remediation table that indicates which mode the vehicle should be driven in when a certain fault is manifested. This patent also mentions a "weighted voting system" to weight variables for decision-making regarding driving modes.
• General knowledge of driver monitoring systems: At the priority date of US 9,566,986 (September 25, 2015), driver monitoring systems capable of assessing driver attentiveness, fatigue, and even skill (e.g., based on erratic steering, braking patterns) were well-known in the art. These systems could generate a "human driver competence level" implicitly or explicitly based on observed behavior or pre-stored profiles.

Motivation to Combine: A POSA, faced with a vehicle fault (as taught by US 9,566,986), would be motivated to leverage available driver information (from the driver profile mentioned in US 9,566,986 or general driver monitoring systems) to make a more informed decision about whether to transition to manual or autonomous mode. If the vehicle's autonomous system is struggling with a fault, it is logical to consider if the human driver is also competent (or more competent) to handle that specific anomaly. The explicit mention of a "driver profile" in US 9,566,986, used to "further determine whether the SDV should be in autonomous or manual mode," strongly suggests the idea of evaluating human competence alongside vehicle state. The missing explicit step in US 9,566,986 is the direct comparison of competence levels in the context of an operational anomaly. However, the motivation to compare would stem from the desire to optimize safety and control, a fundamental goal in autonomous vehicle development.

Combination 2: US 10,029,701, US 10,717,446, and US 11,091,171, in conjunction with US 9,566,986

These later patents in the family would have further refined the concepts introduced in US 9,566,986, potentially providing more explicit details regarding competence level determination and comparison. While the full text of these patents is not provided here, their titles and sequential nature suggest they build upon the foundational ideas of controlling driving modes based on vehicle conditions and driver attributes.

• US 9,566,986: Provides the foundation for detecting vehicle faults and using driver profiles to influence mode switching. It also describes a "fault-remediation table" and the use of "weighted voting system" for decisions.
• US 10,029,701, US 10,717,446, US 11,091,171: As direct continuations, it is highly likely these patents would elaborate on mechanisms for assessing vehicle (control processor) capabilities and human driver capabilities in more detail, particularly concerning specific operational anomalies.

Motivation to Combine: The ongoing development within a patent family inherently demonstrates a motivation to refine and improve upon the initially disclosed invention. A POSA would be motivated to combine the general concept of using driver profiles and fault detection (from US 9,566,986) with any more specific teachings on evaluating and comparing competence levels that may be present in the continuation patents. The natural progression of self-driving technology would lead to more sophisticated decision-making, moving beyond a simple "fault detected, switch mode" to a nuanced "who is better equipped to handle this specific fault under these specific conditions?" This involves a direct comparison of capabilities, which is the crux of the independent claims of US 12037004.

Obviousness Argument for Independent Claim 1, 11, and 19

The independent claims of US 12037004 focus on the steps of:

1. Receiving sensor readings describing a current operational anomaly.
2. Determining a control processor competence level for the anomaly.
3. Receiving a driver profile describing a human driver competence level for the anomaly.
4. Comparing these two competence levels.
5. Selectively assigning control based on the comparison.

US 9,566,986 already teaches receiving sensor readings to detect vehicle faults (operational anomalies) and changing driving modes based on them. It also explicitly teaches using a "driver profile" to "further determine whether the SDV should be in autonomous or manual mode." The leap to explicitly defining and comparing "competence levels" for both the autonomous system and the human driver, specifically related to the current operational anomaly, would be an obvious refinement to a POSA seeking to implement a more robust and safer mode-switching mechanism.

The motivation to compare the competence levels stems from the inherent goal of choosing the safest and most efficient driving mode when a fault occurs. If both the human and the autonomous system have varying degrees of capability in handling different faults, a rational system design would necessitate a comparison to select the optimal controller. The concept of a "fault-remediation table" in US 9,566,986, which dictates the mode for a given fault, already implies an assessment of which mode is better suited. Expanding this to explicitly evaluate and compare the competence of the two potential controllers (human vs. AI) in real-time for a specific anomaly would be a logical and obvious enhancement for a POSA in the field of self-driving vehicles aiming for improved safety and reliability.

For US Patent 12037004, the following details regarding its term and related applications can be provided:

Patent Term Adjustments (PTA) and Patent Term Extensions (PTE):
While the specific amount of Patent Term Adjustment (PTA) for US12037004 is not explicitly stated in the provided text, PTA is generally granted to compensate for delays caused by the USPTO during the prosecution of a patent application. This can extend the 20-year lifespan of a patent. Common reasons for PTA include delays in issuing office actions, responding to replies or appeals, acting on Patent Trial and Appeal Board (PTAB) decisions, issuing a patent after issue fee payment, and delays causing the application to issue more than 36 months from its filing date. Applicant delays, such as responding to office actions more than three months after notification, can reduce PTA.

Patent Term Extensions (PTE) are distinct and can be granted for delays related to secrecy orders, interferences, or appellate review, with a limit of five years. There are also specific PTEs related to delays through the FDA for pharmaceutical patents. The provided information does not indicate any PTE for US12037004.

Continuation and Divisional Applications:
US Patent 12037004 is a continuation of a series of applications:

• U.S. application Ser. No. 17/374,656, filed July 13, 2021.
• Which is a continuation of U.S. application Ser. No. 16/997,202, filed August 19, 2020 (which issued as U.S. Pat. No. 11,091,171 on August 17, 2021).
• Which is a continuation of U.S. application Ser. No. 16/899,407, filed June 11, 2020.
• Which is a continuation of U.S. application Ser. No. 15/955,874, filed April 18, 2018 (which issued as U.S. Pat. No. 10,717,446 on July 21, 2020).
• Which is a continuation of U.S. application Ser. No. 15/341,225, filed November 2, 2016 (which issued as U.S. Pat. No. 10,029,701 on July 24, 2018).
• Which is a continuation of U.S. application Ser. No. 14/865,393, filed on September 25, 2015 (which issued as U.S. Pat. No. 9,566,986 on February 14, 2017).

There is no information explicitly mentioning divisional applications in the provided text.

Related Family Members:
The patent family for US12037004 includes the parent applications listed above, tracing back to a priority date of September 25, 2015. Another related publication is US20230356735A1.

Projected Expiration Date:
For U.S. utility patents, the general rule is that the term expires 20 years from the earliest effective filing date, subject to any Patent Term Adjustments (PTA) or extensions. Given the earliest priority date of September 25, 2015, the anticipated expiration date for US12037004, without considering any PTA, would be September 25, 2035. The Google Patents information for US12037004 also lists "Anticipated expiration" as 2035-09-25.

The search confirms the existence of US Patent 12037004, titled "Controlling driving modes of self-driving vehicles", issued on July 16, 2024. The snippet provides the same abstract and background information as in the provided text, confirming consistency.

Now, proceeding with the "Defensive Disclosure" document.

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Defensive Disclosure for US Patent 12037004

This document discloses derivative variations of the technology described in US Patent 12037004, titled "Controlling driving modes of self-driving vehicles," with the aim of establishing prior art for future incremental improvements by competitors. The derivations are based on the core method of comparing control processor competence and human driver competence during an operational anomaly to selectively assign vehicle control.

Derivative Variations of Core Claims

1. Material & Component Substitution

Enabling Description:
The disclosed invention can be implemented using alternative sensor technologies, processing architectures, and communication modalities. For sensor readings describing a current operational anomaly, instead of traditional camera or radar systems, a multi-modal sensor suite comprising solid-state LiDAR (e.g., Flash LiDAR with 1550nm wavelength for eye safety and atmospheric penetration) for dense 3D point cloud generation, quantum magnetometers for highly sensitive detection of metallic component fatigue (e.g., wheel bearing wear, suspension stress), and acoustic emission sensors (e.g., piezoelectric transducers mounted on drivetrain components) for early detection of mechanical faults via vibrational analysis can be used. The determination of both control processor competence level (CPCL) and human driver competence level (HDCL) can be offloaded from a general-purpose CPU to dedicated Application-Specific Integrated Circuits (ASICs) or Field-Programmable Gate Arrays (FPGAs). These specialized processors, optimized for real-time inference of complex neural network models, can execute the competence assessment algorithms with sub-millisecond latency. For instance, a low-power, high-performance ARM Cortex-M based microcontroller augmented with a custom AI accelerator (e.g., a tensor processing unit) could handle initial sensor data pre-processing and anomaly classification at the edge, while a more powerful FPGA array (e.g., Xilinx Versal ACAP) performs the complex comparative competence assessment. Communication between vehicle sub-systems, other vehicles, and infrastructure for aggregated historical competence data and environmental reports can utilize ultra-wideband (UWB) wireless transceivers for high-accuracy localization and secure, short-range data exchange (e.g., for platooning, immediate surrounding vehicle data), complemented by 5G New Radio (NR) sidelink (PC5 interface) for direct vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) communication, ensuring low latency and high bandwidth for critical safety messages. The selective assignment of control can then be executed by redundant electro-hydraulic actuation systems (e.g., for braking and steering) in conjunction with fly-by-wire or drive-by-wire electromechanical throttle control, each system equipped with independent power supplies and fail-safe mechanisms (e.g., hydraulic accumulators, mechanical override linkages).

graph TD
    A[Multi-modal Sensor Suite] --> B{Sensor Data Fusion};
    B --> C[Anomaly Detection Module (Edge AI Accelerator)];
    C --> D[CPCL Determination Unit (FPGA/ASIC)];
    C --> E[HDCL Determination Unit (FPGA/ASIC)];
    D --> F{Competence Comparison Logic};
    E --> F;
    F --> G{Control Assignment Decision};
    G --> H[Redundant Actuation Systems (Electro-hydraulic/Electromechanical)];
    A -- (LiDAR, Quantum Mag, Acoustic E.) --> B;
    F -- (UWB, 5G NR Sidelink) --> I[External Data Sources (Historical Competence, Env. Report)];
    I --> D;
    I --> E;

2. Operational Parameter Expansion

Enabling Description:
The core method can be extended to extreme operational scales and environments. Consider its application to autonomous deep-sea exploration submersibles or extra-terrestrial rover systems. In deep-sea applications (pressures up to 100 MPa, temperatures near 0°C), anomalies could include manipulator arm entanglement, thruster degradation, or sonar malfunction. Sensor readings would involve high-pressure-tolerant acoustic arrays, extreme-depth cameras with specialized lighting, and redundant inertial navigation units. CPCL would assess the submersible's AI navigation and object avoidance capabilities under current acoustic distortion or thruster efficiency degradation. HDCL would assess a human pilot's remote operation competence, factoring in communication latency, physiological stress monitors (e.g., galvanic skin response, heart rate variability from biometric sensors on the human operator's console), and prior experience with specific deep-sea anomalies. Control assignment would prioritize the more competent entity for tasks like emergency ascent, obstacle avoidance, or localized repair. At the nanoscale, within robotic surgical systems, an anomaly could be a micro-tool breakage or unexpected tissue response. Sensors would be femtosecond-pulse lasers for tissue ablation monitoring, force feedback micro-sensors on robotic effectors, and real-time cellular imaging. CPCL would be the AI's ability to maintain surgical precision despite tool degradation, while HDCL would be the surgeon's ability to compensate via haptic feedback and visual cues. The system would dynamically assign control for micro-manipulation based on which entity has higher competency in that specific, highly granular operational context, minimizing collateral damage and ensuring patient safety.

stateDiagram-v2
    state NormalOperatingMode {
        SDVControl: Autonomous
        HumanControl: Manual
        entry / Initial checks
    }

    state AnomalyDetected {
        CPCL_Eval: Evaluate SDV AI Competence
        HDCL_Eval: Evaluate Human Competence
        Comparison: Compare CPCL vs HDCL
        entry / Sensor readings indicate anomaly
    }

    state ExtremeEnvironmentApplication {
        DeepSea: Autonomous Submersible
        SpaceRover: Extra-Terrestrial Rover
        NanoSurgery: Robotic Surgical System
        entry / Specific environmental adaptations
    }

    NormalOperatingMode --> AnomalyDetected: Anomaly detected
    AnomalyDetected --> AssignAutonomous: CPCL > HDCL
    AnomalyDetected --> AssignManual: HDCL > CPCL
    AnomalyDetected --> EmergencyShutdown: Neither > Threshold

    AssignAutonomous --> NormalOperatingMode
    AssignManual --> NormalOperatingMode
    EmergencyShutdown --> SystemHalt

    state AssignAutonomous {
        Control: AI (Autonomous)
        entry / AI assumes control
    }

    state AssignManual {
        Control: Human (Manual)
        entry / Human assumes control
    }

    state EmergencyShutdown {
        Action: Safe Stop Initiated
        entry / Halt vehicle operation
    }

    DeepSea --> AnomalyDetected
    SpaceRover --> AnomalyDetected
    NanoSurgery --> AnomalyDetected

3. Cross-Domain Application

3.1. Autonomous Marine Vessels (Shipping/Logistics)

Enabling Description:
In autonomous marine vessels, such as cargo ships or survey boats, the system identifies operational anomalies like radar system failure during fog, propulsion system degradation, or unusual wave patterns. Sensors include marine radar, sonar, GPS/GNSS, AIS transponders, engine diagnostics, and motion reference units (MRUs) detecting vessel pitch, roll, and heave. The control processor competence level (CPCL) is determined by the vessel's AI navigation system's historical performance in similar degraded conditions (e.g., navigating through dense fog with partial sensor loss, maintaining course with reduced propulsion power, avoiding collisions in heavy seas using only AIS and reduced radar range). The human driver (captain or remote operator) competence level (HDCL) is derived from their training records, certification levels, recent simulation performance, fatigue monitoring (e.g., eye-tracking, psychometric assessments), and real-time physiological data. For instance, if the vessel's AI is highly proficient at maintaining station in severe weather but the radar fails in dense fog, while the human captain has extensive experience navigating visually or with backup systems in such conditions, control would be assigned to the human. Conversely, if the human operator shows signs of fatigue during a complex navigation sequence that the AI has mastered, the AI would retain or assume control.

graph TD
    A[Marine Sensor Suite] --> B{Anomaly Detection (Radar, Engine, Weather)};
    B --> C[Vessel AI Navigator (CPCL)];
    B --> D[Human Captain Profile (HDCL)];
    C --> E{Competence Comparison};
    D --> E;
    E --> F{Control Assignment Decision};
    F -- AI > Human --> G[Autonomous Navigation System];
    F -- Human > AI --> H[Manual Bridge Control];
    G --> I[Marine Propulsion & Steering];
    H --> I;
    A -- (Radar, Sonar, GPS, Engine Diags, MRU) --> B;
    D -- (Training, Certs, Fatigue Monitor) --> D;

3.2. Industrial Robotics (Manufacturing/Assembly)

Enabling Description:
In an advanced manufacturing facility, complex robotic arms perform delicate assembly or hazardous material handling. An operational anomaly could be a gripper mechanism malfunction (e.g., losing grip precision), a vision system sensor drift, or an unexpected vibration in the robotic arm. Sensors include force/torque sensors at the end-effector, high-resolution cameras with optical encoders, accelerometers on robotic joints, and material property sensors (e.g., infrared for temperature, conductivity for material identification). The control processor competence level (CPCL) reflects the robot's AI's ability to maintain assembly tolerance or safely manipulate materials despite sensor inaccuracies or mechanical play. The human driver (remote operator or on-site technician) competence level (HDCL) is derived from their manual dexterity test scores, training in specific hazardous scenarios, real-time physiological indicators (e.g., tremor detection, cognitive load from EEG sensors), and response times to simulated faults. If, for example, the robot's gripper experiences a slight slip due to a software glitch, but the task involves delicate, high-value components, and the human operator has demonstrated superior fine motor control in handling such incidents, control is transferred to the human. If the human operator is performing another critical task or shows diminished response, the AI maintains control, potentially initiating a safe-hold state.

graph TD
    A[Robotic Arm Sensor Array] --> B{Anomaly Detection (Gripper, Vision, Vibration)};
    B --> C[Robot Control AI (CPCL)];
    B --> D[Human Operator Profile (HDCL)];
    C --> E{Competence Comparison};
    D --> E;
    E --> F{Control Assignment Decision};
    F -- AI > Human --> G[Autonomous Robotic Task Execution];
    F -- Human > AI --> H[Manual Remote Manipulation];
    G --> I[Robotic Actuators & End-Effectors];
    H --> I;
    A -- (Force/Torque, Camera, Accelerometer, Material Sensors) --> B;
    D -- (Dexterity Scores, Training, Physiological Monitor) --> D;

3.3. Aerospace (UAVs/Drones for Cargo/Passenger eVTOL)

Enabling Description:
For Unmanned Aerial Vehicles (UAVs), particularly large cargo drones or future Electric Vertical Take-Off and Landing (eVTOL) passenger aircraft, an operational anomaly could be a partial motor failure, an avionics sensor malfunction (e.g., faulty altimeter), or a sudden unpredicted wind shear. Sensors include redundant IMUs, GPS/GNSS, air data systems (pitot-static), magnetometers, motor health sensors (temperature, RPM, current), and radar altimeters. The control processor competence level (CPCL) represents the autopilot's capability to maintain stable flight, execute emergency procedures (e.g., autorotation, glide to nearest safe landing zone) or recover from stalls, given the specific anomaly. The human driver (remote pilot or on-board safety pilot in eVTOL) competence level (HDCL) is based on flight hours, emergency procedure training, real-time stress/cognitive load assessment (e.g., from eye-tracking and voice analysis on the ground control station), and physiological monitoring. For instance, in a cargo drone experiencing a partial motor failure over rugged terrain, if the autopilot has a proven track record of successful single-motor landings under similar conditions and the remote pilot is currently distracted, the AI maintains control. Conversely, if the anomaly presents an unprecedented scenario requiring creative problem-solving and the human pilot demonstrates superior judgment and real-time adaptability, control can be transferred.

graph TD
    A[Avionics Sensor Suite] --> B{Anomaly Detection (Motor, Sensor, Weather)};
    B --> C[Autopilot AI (CPCL)];
    B --> D[Human Pilot Profile (HDCL)];
    C --> E{Competence Comparison};
    D --> E;
    E --> F{Control Assignment Decision};
    F -- AI > Human --> G[Autonomous Flight Control];
    F -- Human > AI --> H[Remote Pilot Control];
    G --> I[Flight Actuators (Propulsion, Control Surfaces)];
    H --> I;
    A -- (IMU, GPS, Air Data, Mag, Motor Health, Radar Altimeter) --> B;
    D -- (Flight Hours, Emergency Training, Stress/Cognitive Monitor) --> D;

4. Integration with Emerging Tech

Enabling Description:
The system can be significantly enhanced through integration with AI-driven optimization, a vast network of IoT sensors for real-time environmental context, and blockchain for immutable data logging and verifiable trust.

AI-Driven Optimization: The determination of CPCL and HDCL is continuously optimized using a reinforcement learning (RL) agent. This agent observes past control assignments, the resulting outcomes (e.g., accident rates, efficiency metrics, passenger comfort scores), and continuously refines the weighting factors for various sub-competence criteria and anomaly types. Predictive anomaly detection is achieved through deep learning models (e.g., recurrent neural networks, transformers) analyzing sensor data streams for subtle pre-cursors to faults (e.g., abnormal vibrational signatures, slight deviations in motor current, tiny inconsistencies in steering feedback) that might indicate an impending operational anomaly hours or days in advance. This allows for proactive maintenance scheduling or pre-emptive mode-switching recommendations.

IoT Sensors for Real-time Monitoring: Beyond the vehicle's onboard sensors, a dense network of roadside IoT sensors (e.g., low-power wide-area network (LPWAN) connected nodes with micro-radar, environmental gas sensors, high-definition cameras, pavement friction sensors) provides hyper-local, real-time environmental data. This data (e.g., specific lane-level ice patches, presence of debris, localized air quality, real-time traffic density on a micro-segment) is streamed to the SDV and/or a coordinating edge server. This allows for extremely granular and dynamic adjustments to both CPCL and HDCL assessments. For example, knowing a specific 10-meter stretch of road has reduced friction due to an oil spill (from an IoT sensor) would dramatically alter competence levels for both AI and human for that specific segment.

Blockchain for Supply Chain Verification and Competence Audit: All operational anomaly detections, competence level calculations, control assignment decisions, and subsequent outcomes (e.g., successful mitigation, incident reports) are cryptographically signed and logged onto a distributed ledger (blockchain). This creates an immutable, transparent, and auditable record of the SDV's operational history, the AI's performance, and the human driver's interventions. This blockchain ledger can also store verified driver profiles, including training certifications, medical clearances, and historical performance data (anonymized where necessary), allowing for secure and trustless sharing of HDCL information across different vehicle manufacturers or regulatory bodies. Furthermore, vehicle component supply chain data, including sensor calibration logs and maintenance records, can be stored on the blockchain, influencing initial CPCL baselines based on verifiable component provenance and service history.

sequenceDiagram
    participant SDV as Self-Driving Vehicle
    participant IoT as IoT Sensor Network
    participant EdgeAI as Edge AI/RL Agent
    participant CloudDB as Cloud Database/Blockchain
    participant Driver as Human Driver Interface

    IoT-->>SDV: Stream Real-time Environmental Data
    SDV-->>SDV: Onboard Sensor Data
    SDV->>SDV: Detect Operational Anomaly
    SDV->>EdgeAI: Send Anomaly & Context Data

    EdgeAI->>EdgeAI: Refine CPCL/HDCL Models (RL)
    EdgeAI->>SDV: Provide Updated Competence Factors & Predictions
    
    SDV->>SDV: Determine CPCL & HDCL (using updated factors)
    SDV->>SDV: Compare Competence Levels
    SDV->>SDV: Selectively Assign Control (AI/Human)

    SDV-->>Driver: Display Alert/Request for Handover (if human assigned)
    Driver-->>SDV: Human Control Input (if assigned)

    SDV->>CloudDB: Log Anomaly, Competence, Decision, Outcome (Blockchain Transaction)
    CloudDB->>CloudDB: Verify & Store Immutable Record
    CloudDB->>EdgeAI: Provide Historical Data for RL Training

5. The "Inverse" or Failure Mode

Enabling Description:
In a scenario where a severe operational anomaly occurs, and the system determines that neither the on-board SDV control processor nor the human driver meets a predetermined minimum competence level threshold (e.g., due to catastrophic brake failure on an icy road, or a sudden, complete sensor outage in a complex urban environment), the system initiates a "Guardian Mode" or "Minimum Risk Maneuver (MRM) Mode" instead of an immediate full stop. This mode is designed for safe, graceful degradation and maximizes survivability.
In Guardian Mode, all non-critical systems are powered down. The vehicle's control is handed over to a simplified, highly robust, and computationally light "safety kernel" which prioritizes only fundamental vehicle dynamics control:

1. Reduced Speed & Directional Stability: The safety kernel attempts to minimize speed while maintaining the vehicle within its current lane or a designated emergency lane, avoiding abrupt maneuvers. It utilizes only the most reliable, redundant sensors (e.g., IMU, simplified radar for immediate frontal collision avoidance) and highly constrained control policies.
2. Hazard Communication: The vehicle automatically activates hazard lights, broadcasts emergency V2X messages (e.g., "IMPACT ALERT," "LIMITED FUNCTIONALITY VEHICLE"), and initiates an automated emergency call (e.g., eCall) with location and anomaly details.
3. Path Planning to Safe Haven: Concurrently, a "Safe Haven Planner" module, operating on pre-computed or dynamically identified safe pull-over locations (e.g., wide shoulders, rest stops, low-traffic areas, or hard shoulders of highways) with minimal environmental complexity, calculates a trajectory to the nearest such location. This planner considers factors like vehicle kinetic energy, remaining brake capacity (if any), road grade, and potential obstacles.
4. Remote Override & Monitoring: If available, a remote fleet operator gains limited, high-level supervisory control, able to issue coarse directional commands or activate emergency braking via a secure, low-latency satellite link, while the on-board system maintains primary Guardian Mode operations.

The decision to enter Guardian Mode is triggered if CPCL < Min_Threshold AND HDCL < Min_Threshold. The system continuously attempts to re-evaluate CPCL and HDCL. If at any point either rises above the minimum threshold and the anomaly is still manageable, control can be restored to the more competent entity.

stateDiagram-v2
    [*] --> NormalDriving: System Initialized
    NormalDriving --> AnomalyDetected: Operational Anomaly Detected
    AnomalyDetected --> EvaluateCompetence: Sensor Readings Processed
    EvaluateCompetence --> AssignAutonomous: CPCL >= HDCL AND CPCL >= Min_Threshold
    EvaluateCompetence --> AssignManual: HDCL > CPCL AND HDCL >= Min_Threshold
    EvaluateCompetence --> GuardianMode: CPCL < Min_Threshold AND HDCL < Min_Threshold
    
    AssignAutonomous --> NormalDriving: AI manages anomaly
    AssignManual --> NormalDriving: Human manages anomaly

    state GuardianMode {
        entry: Activate Hazard Lights, Broadcast V2X, eCall
        state ReducedSpeedAndStability <<fork>>
        state SafeHavenPlanning <<fork>>
        state RemoteSupervisoryControl <<fork>>

        ReducedSpeedAndStability --> SafeStopPoint: Guide to Safe Pull-over
        SafeHavenPlanning --> SafeStopPoint
        RemoteSupervisoryControl --> SafeStopPoint: Remote input
        SafeStopPoint --> [*]: Vehicle Stopped Safely
        exit: Deactivate all systems, Secure vehicle
    }

    GuardianMode --> EvaluateCompetence: Re-evaluate if conditions improve
    

Combination Prior Art Scenarios with Open-Source Standards

1. Integration with AUTOSAR Adaptive Platform:
   The competence management module (LMSDV 147, driving mode module 307, SDV on-board computer 301) and its associated functions (anomaly detection, CPCL/HDCL determination, comparison, and control assignment) can be implemented as a set of Adaptive Applications within an AUTOSAR Adaptive Platform architecture. The sensor readings (from sensors 153, navigation and control sensors 309, roadway sensor(s) 206) would be received via the AUTOSAR Communication Management (COM) and diagnostics interfaces, leveraging standardized service-oriented communication. The AI/ML models for competence assessment could run on a dedicated Machine Learning runtime environment (part of the Execution Management or Adaptive Platform Foundation). The control assignment decision, being safety-critical, would be managed by a dedicated safety monitor application, ensuring compliance with ASIL (Automotive Safety Integrity Level) requirements. Historical competence data and driver profiles could be stored and retrieved using AUTOSAR Persistent Memory services, and external environmental reports could be integrated via the Gateway to external networks. This framework provides a standardized, modular, and scalable approach to developing and deploying the patent's core functionality, enabling clear interfaces and ensuring interoperability within complex E/E architectures.

2. Integration with IEEE 802.11p/DSRC or 5G NR V2X:
   The system's ability to receive environmental reports from an "environmental reporting service" (e.g., a weather service) and information from other SDVs (SDV 210, SDV 212) or roadway monitoring systems (208) (as described in FIG. 4, and the general communication described in the patent) can be fully realized using open-source V2X communication standards. IEEE 802.11p (Dedicated Short Range Communications - DSRC) or its successor, 5G New Radio (NR) V2X, would serve as the communication backbone (e.g., via network 127, transceiver 123). SDVs could broadcast Basic Safety Messages (BSM) containing vehicle state, position, and detected operational anomalies (e.g., "tire pressure low," "braking system fault"). Roadside Units (RSUs) or other SDVs could broadcast Cooperative Awareness Messages (CAM) or Decentralized Environmental Notification Messages (DENM) providing real-time local road conditions (e.g., "icy patch ahead," "debris on road," "heavy rain"). This allows the SDV (e.g., SDV 202) to receive highly localized, granular data about the operational anomalies of nearby vehicles and environmental conditions, which directly informs the comparison of CPCL and HDCL for the "roadway specific" competence assessment mentioned in the patent. The low latency and direct communication capabilities of V2X are crucial for safety-critical decisions.

3. Implementation using ROS 2 (Robot Operating System 2):
   The modular nature of the patent's method – sensor input, competence determination, comparison, and control assignment – lends itself well to implementation using ROS 2. Each functional block, such as the anomaly detection module, the CPCL calculation module, the HDCL profile manager, the competence comparison logic, and the driving mode module (307), can be implemented as independent ROS 2 nodes. Sensor data (sensors 153, navigation and control sensors 309) would be published as ROS 2 messages on defined topics (e.g., /sensor/camera, /vehicle/IMU). The competence calculation nodes would subscribe to these topics, perform their processing, and publish CPCL and HDCL values to a /competence/levels topic. The central decision-making node would subscribe to this, perform the comparison, and publish a /vehicle/control_mode_command message (e.g., mode: AUTONOMOUS, mode: MANUAL). This data-centric publish-subscribe architecture, with its robust inter-process communication (DDS-based), introspection tools, and multi-platform support, allows for flexible development, testing, and deployment of complex autonomous driving software, adhering to the logical flow described in the patent. Furthermore, ROS 2's security features (SROS 2) can ensure the integrity and authenticity of critical data flows, especially for driver profiles and control commands.