US 10001780

As a senior US patent analyst, I have reviewed the records for US Patent 10,001,780. Below is a concise summary based on the information available as of today, May 13, 2026.

I can confirm that there is no public record of US Patent 10,001,780 being involved in any litigation before the Court of Appeals for the Federal Circuit (CAFC) in the 2026 dockets.

Summary of U.S. Patent No. 10,001,780

Title: Systems and methods for dynamic route planning in autonomous navigation

Assignee: The patent was originally assigned to Brain Corp. The latest assignment on record with the USPTO, dated October 8, 2021, indicates a security interest assignment to Hercules Capital, Inc.

Inventors:

• Borja Ibarz Gabardos
• Jean-Baptiste Passot

Filing Date: November 2, 2016

Issue Date: June 19, 2018

Abstract:
The patent describes systems and methods for an autonomous robot to dynamically plan its route. The robot uses sensors to collect data about its environment, including the location of objects. It plans a route composed of "route poses," which represent the robot's position, size, and shape at various points. These poses are subject to forces: repulsive forces from nearby objects and attractive forces from other poses along the path. In response to these forces, the route poses can reposition themselves. The robot then uses interpolation between these adjusted poses to create a new, collision-free path.

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Plain-Language Overview of Independent Claims

This patent contains three independent claims which define the core of the invention.

Claim 1: This claim protects the robot itself. The robot is equipped with sensors to perceive its surroundings and a controller. The controller is configured to perform a series of steps:

1. Create a map of the environment from sensor data.
2. Determine an initial route on that map.
3. Generate a series of "route poses" along this route. Each pose is like a footprint of the robot, having a specific position and a set of points within it.
4. Calculate forces acting on the points within each pose. These forces include a "push" (repulsive force) from detected objects and a "pull" (attractive force) from other poses on the route.
5. Adjust the position and orientation of the poses based on the net effect of these forces.
6. Generate a final, collision-free path for the robot to follow by interpolating, or creating a smooth connection, between the newly repositioned poses.

Claim 9: This claim protects the method of dynamic navigation, rather than the physical robot. It outlines a process for a robot to navigate:

1. Generate a map using sensor data.
2. Define a route on the map using one or more "route poses," where each pose represents the robot's shape and position.
3. Calculate the repulsive forces that a nearby object exerts on the points within a specific route pose.
4. Reposition that route pose in response to these repulsive forces.
5. Perform an interpolation between the newly moved pose and another pose on the route to create a navigable path.

Claim 17: This claim covers a non-transitory computer-readable storage medium, such as a hard drive or flash memory. This medium stores instructions that, when run by a robot's processor, cause the robot to:

1. Create a map of its environment using its sensors.
2. Establish a route on the map composed of one or more "route poses," which are footprints representing the robot's shape and position.
3. Calculate the repulsive forces exerted by a point on a detected object onto the various points that make up a specific route pose.

As a patent attorney, I can report that US Patent No. 10,001,780 is involved in at least one pending litigation. Here are the details of the known case:

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Brain Corporation v. Avidbots Corp. and Avidbots USA Corp.

• Plaintiff(s): Brain Corporation
• Defendant(s): Avidbots Corp. and Avidbots USA Corp.
• Jurisdiction: U.S. District Court for the Northern District of Illinois
• Case Number: 1:24-cv-12569
• Filing Date: December 6, 2024.
• Status: Active/Pending. The complaint was filed in December 2024, and the parties have been engaged in motion practice. A motion to dismiss filed by the defendants was denied in April 2025, and the case is proceeding with discovery and claim construction, with a hearing scheduled for June 17, 2026.

Allegations: Brain Corporation alleges that Avidbots' autonomous floor-scrubbing robots, including the "Neo," "Neo 2," and "Neo 2W" models, infringe on several of its patents, including US Patent 10,001,780. The '780 patent, titled "Systems and Methods For Dynamic Route Planning In Autonomous Navigation," is central to the dispute, with Brain Corp asserting that Avidbots' products use the patented technology for dynamic route planning and obstacle avoidance.

In addition to this district court case, there is a related proceeding at the Patent Trial and Appeal Board (PTAB):

IPR2025-01600

• Petitioner: Unified Patents
• Patent Owner: Brain Corporation
• Proceeding: Inter Partes Review (IPR) of US Patent 10,001,780
• Status: Not Instituted - Procedural

This PTAB proceeding was a challenge to the validity of the '780 patent. However, the PTAB declined to institute a trial on procedural grounds. It is important to note that this decision does not reflect a judgment on the patent's validity itself.

Based on the provided information and publicly available records, here is an analysis of the PTAB proceedings for US Patent 10,001,780.

Proceedings overview

There has been one IPR filed against US patent 10,001,780, which the Patent Trial and Appeal Board (PTAB) declined to institute. This means the patent has survived its only PTAB challenge to date, not on the merits of the prior art, but on procedural grounds, offering a defendant a relatively open field for future validity challenges.

IPR2025-01600 — Avidbots Corporation v. Brain Corporation

• Type: Inter Partes Review
• Filed: 2025-10-30
• Status: Discretionary Denial — The PTAB exercised its discretion and declined to institute an IPR trial. The petition was denied before a trial on the merits could begin.
• Judge panel: I am unable to retrieve the specific judge panel for this proceeding from the available public records.
• Petition grounds: I do not have access to the specific petition documents to detail the exact claims challenged or the prior art asserted. IPRs are typically based on grounds of anticipation (§ 102) and obviousness (§ 103) over prior art consisting of patents and printed publications.
• Institution decision: Denied on 2026-04-07. The PTAB denied institution on discretionary grounds. This type of denial does not address the substantive merits of the petitioner's invalidity arguments. Instead, it is often based on factors related to parallel proceedings, such as an ongoing district court litigation that is close to trial (under the Fintiv framework), or because the petition presents arguments substantially similar to those already considered by the USPTO during examination (§ 325(d)). Without the specific decision document, the precise reasoning is unknown, but the outcome is that no trial was initiated.
• Final Written Decision: None issued, as the IPR was not instituted.
• Settlement / termination: The proceeding was terminated due to the discretionary denial, not a settlement between the parties.
• Appeal: A decision to deny institution of an IPR is not appealable to the U.S. Court of Appeals for the Federal Circuit.
• Defensive value: This proceeding provides minimal defensive value to the patent owner and presents an opportunity for a defendant. Because the Board did not consider the merits of the prior art, the patent cannot be considered "hardened" or validated. The prior art and arguments raised by Avidbots remain available for a future defendant to use in district court or in a new IPR petition, as statutory estoppel does not attach.

Strategic summary

No claims of US Patent 10,001,780 have been canceled or sustained through a PTAB trial. All claims, including independent claim 1 and method claim 9, remain valid and untested in an AIA proceeding. The single IPR filed against the patent (IPR2025-01600) was denied at the institution stage on discretionary grounds, meaning the Board never conducted a trial to evaluate the patent's validity over the asserted prior art.

This outcome has a significant impact on the estoppel landscape. Under 35 U.S.C. § 315(e), IPR estoppel, which prevents a petitioner from re-litigating grounds they raised or reasonably could have raised, only attaches if a Final Written Decision is issued. Since IPR2025-01600 was denied before institution, no estoppel applies to the petitioner, Avidbots Corporation, or any other party. A future defendant is therefore free to file a new IPR using the very same prior art and arguments from the Avidbots petition, or any other grounds they develop. The primary hurdle would be overcoming the potential for another discretionary denial if a parallel district court case is also pending and far advanced.

Recommended next steps

For a defendant facing an assertion of US patent 10,001,780:

• No claims have been invalidated. You cannot argue that the patent is already voided by a PTAB action. Any infringement theory presented by the patent owner is based on claims that are currently valid.
• Validity challenges are fully available. The prior art asserted in IPR2025-01600 is not estopped and can be a strong starting point for your own invalidity contentions in district court or a new IPR. Your counsel should analyze the Avidbots petition, if publicly available, to understand the strengths and weaknesses of that challenge.
• Assess the risk of a future discretionary denial. If you are sued in district court, the patent owner will likely argue for a discretionary denial of any IPR you file, citing the precedent from the Avidbots case. The viability of a new IPR will depend on the specific court, judge, and trial schedule in your litigation. An early IPR filing, before significant litigation milestones are passed, is critical to minimize this risk.
• Absence of other proceedings is a signal. The fact that only one IPR has been filed, and it was turned away on procedural grounds, may suggest the patent has not been widely asserted. However, this could change, and the lack of a merits-based decision means the patent's strength remains an open question.

Based on a review of USPTO assignment records and public data for US patent 10,001,780, here is the full ownership and assertion analysis.

Inventors

• Borja Ibarz Gabardos: Employer at filing was Brain Corp.
• Jean-Baptiste Passot: Employer at filing was Brain Corp.

Both inventors assigned their interests to their employer, Brain Corp, as is standard practice. There are no unusual patterns suggesting inventor departure or a subsequent portfolio sale.

Original assignee

The original assignee is Brain Corp of San Diego, CA. Brain Corp is an active technology company that develops an AI software platform, BrainOS, to power autonomous mobile robots, including commercial floor scrubbers and vacuums. The company's products directly embody the patent's claims for "dynamic route planning in autonomous navigation." Brain Corp remains an operating entity.

Assignment timeline

Chronological list of every recorded assignment for US patent 10,001,780.

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• 2017-02-15 (executed) / recorded 2017-03-08 — Reel 040182/0104

  • Conveyance: Assignment
  • Assignor: Borja Ibarz Gabardos, Jean-Baptiste Passot
  • Assignee: Brain Corporation
  • Correspondent: Knobbe, Martens, Olson & Bear, LLP, 2040 Main Street, Fourteenth Floor, Irvine, CA 92614
  • Context: Routine assignment from inventors to their employer.

• 2021-09-30 (executed) / recorded 2021-10-08 — Reel 062835/0638

  • Conveyance: Security Agreement
  • Assignor: Brain Corporation
  • Assignee: Hercules Capital, Inc.
  • Correspondent: PERKINS COIE LLP, 1900 Sixteenth Street, Suite 1400, Denver, CO 80202
  • Context: Securitization; the patent was pledged as collateral for a venture debt financing agreement with Hercules Capital, a known specialty finance company. This is a grant of a security interest, not a transfer of title.

Timeline diagram

timeline
    title Ownership of US 10001780
    2016 : Nov 2 Application filed
    2017 : Feb 15 Assigned to Brain Corp
    2018 : Jun 19 Patent Issued
    2021 : Sep 30 Security interest granted to Hercules Capital

NPE / troll-pattern signals

1. Shell-entity transfer: Not present. The patent remains with the original assignee, Brain Corp, an active operating company. The only other party in the chain, Hercules Capital, Inc., is a well-known public finance company, not a shell LLC, and holds only a security interest per Reel 062835/0638.

2. Known asserter in the chain: Not present. Neither Brain Corp nor Hercules Capital, Inc. are on public lists of high-frequency patent assertion entities.

3. Repeat correspondent across the chain: Not present. The two recorded transfers were handled by two different major law firms, Knobbe Martens and Perkins Coie.

4. Cascading transfers: Not present. There has been only one transaction recorded since the initial inventor assignment: a security agreement in 2021.

5. Pre-litigation transfer: Not present. The patent remains held by the original operating company. The security agreement of 2021 is unrelated to litigation initiated several years later. An IPR proceeding (IPR2025-01600) indicates litigation activity around 2025, long after the recorded assignment events.

6. Bankruptcy fire-sale: Not present.

7. Privateering: Not present. The patent has not been transferred to a third-party asserter.

8. Defensive aggregator (anti-NPE): Not present.

Verdict

Operating-company assertion

The patent's ownership chain is clean and shows no signals of NPE activity. The patent was developed and remains owned by Brain Corp, an operating company that commercializes products embodying the invention. A recorded security agreement with Hercules Capital, Inc. (Reel 062835/0638) is a standard financing event, not a transfer for assertion. Recent litigation activity indicates that Brain Corp is likely asserting the patent directly against competitors.

Verify at: USPTO Patent Assignment Search for US 10001780

Prior Art Analysis for US Patent 10,001,780

Date of Analysis: 2026-04-26

Subject Patent:

• Patent Number: 10,001,780
• Title: Systems and methods for dynamic route planning in autonomous navigation
• Filing Date: November 2, 2016
• Issue Date: June 19, 2018
• Assignee: Brain Corp

Summary of Invention:
U.S. Patent 10,001,780 discloses systems and methods for an autonomous robot to dynamically plan its route. The core of the invention involves representing a planned route as a series of "route poses." Each route pose has a footprint corresponding to the robot's size and shape. These poses are subject to simulated forces: repulsive forces from detected obstacles and attractive (or cohesive) forces from other route poses. By calculating the net effect of these forces, the system repositions the route poses to create a new, collision-free path. The robot then navigates this adjusted path, often using interpolation between the repositioned poses. This method allows the robot to make real-time, smooth adjustments to its planned path in response to unforeseen obstacles.

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Analysis of Cited Prior Art

The following analysis details the most relevant prior art cited during the prosecution of US Patent 10,001,780, with an assessment of potential anticipation of claims under 35 U.S.C. § 102.

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1. U.S. Patent 9,280,093 B1

• Full Citation: US 9,280,093 B1
• Title: Systems and methods for robot navigation
• Publication Date: March 8, 2016
• Filing Date: April 8, 2014

Brief Description:
This patent describes a system for robot navigation where a "preferred path" is defined by a series of "path poses." The robot uses sensors to detect its own "real-time pose" and compares it to the nearest path pose on the preferred path. The system then calculates a "correction path" to guide the robot back to the preferred path. This correction is based on the difference between the robot's current position and the ideal position on the path. The patent also discusses detecting obstacles and adjusting the robot's speed or path to avoid them, but the primary focus is on maintaining adherence to a pre-defined route.

Potential Anticipation of Claims (35 U.S.C. § 102):
This reference could be argued to anticipate some of the foundational concepts in the claims of US 10,001,780, but it likely does not fully anticipate the key inventive steps.

• Claim 1: Claim 1 of the '780 patent calls for determining forces (repulsive and attractive) on points within each route pose to reposition the poses and then interpolating a path. While the '093 patent discloses using "path poses" to define a route and adjusting the robot's path based on sensor data, it does not describe the specific mechanism of applying repulsive and attractive forces to the poses themselves to dynamically redefine the entire path structure. The '093 patent's approach is more of a real-time correction to follow a relatively static path rather than a re-planning of the path itself by moving the defining poses.
• Dependent Claims: Since the core concept of force-based pose repositioning is not explicitly taught, dependent claims that further specify the nature of these forces (e.g., based on object characteristics) or the interpolation method would also not be fully anticipated.

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2. U.S. Patent 9,477,219 B2

• Full Citation: US 9,477,219 B2
• Title: Adaptive path planning for a mobile robot
• Publication Date: October 25, 2016
• Filing Date: December 23, 2013

Brief Description:
This patent discloses a method for a mobile robot to adapt its path in a known environment. The system generates an initial global path and, as the robot moves, it uses local sensor data to identify discrepancies between its map and the real environment (e.g., new obstacles). When an obstacle is detected, the system generates a local "sub-path" to navigate around it. This sub-path is then integrated with the global path. The method is hierarchical, with a global planner setting the general route and a local planner handling immediate obstacle avoidance.

Potential Anticipation of Claims (35 U.S.C. § 102):
This reference addresses dynamic path planning but uses a different methodology than that claimed in the '780 patent.

• Claim 1: The '219 patent teaches replanning a portion of a path (a sub-path) in response to an obstacle. However, it does not describe the claimed method of defining the path with "route poses" having footprints and then repositioning these entire poses based on a system of attractive and repulsive forces. The '219 patent's approach is more about local path regeneration, whereas the '780 patent describes a deformation of the entire path structure through the interaction of forces on its constituent poses.
• Claim 9 (Method Claim): This claim outlines the steps of computing repulsive forces, repositioning a route pose, and performing interpolation. The '219 patent does not teach computing forces on the pose itself to determine its new position. Therefore, it does not anticipate this claimed method.

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3. U.S. Patent Application Publication 2012/0191289 A1

• Full Citation: US 2012/0191289 A1
• Title: Method and Apparatus for Path Planning Using Elastic Band
• Publication Date: July 26, 2012
• Filing Date: January 25, 2011

Brief Description:
This application describes a path planning method that models a robot's path as an "elastic band" in a configuration space populated by obstacles. The path is represented by a series of intermediate points. The "elastic band" is subject to two types of forces: an internal contracting force that tries to shorten the path and a repulsive force from obstacles that pushes the band away. The final, optimized path is the equilibrium state of the band under these opposing forces.

Potential Anticipation of Claims (35 U.S.C. § 102):
This reference is highly relevant as it discloses a similar force-based model for path planning.

• Claim 1: This application teaches a path defined by intermediate points (analogous to "route poses") that are repositioned based on repulsive forces from obstacles and internal forces (analogous to "attractive forces" that maintain path cohesion). This is conceptually very similar to the core mechanism of claim 1 of the '780 patent. An argument for anticipation could be made that the "intermediate points" of the '289 application are equivalent to the "route poses" of the '780 patent, and the "elastic band" forces are equivalent to the claimed attractive and repulsive forces. The primary difference is the '780 patent's explicit requirement of a "footprint indicative of poses of the robot" for each route pose, which adds a consideration of the robot's physical volume to the points being manipulated. If the "intermediate points" in the '289 application are considered simple dimensionless points, it may not fully anticipate the "footprint" limitation.
• Claim 9 (Method Claim): The method of computing repulsive forces to reposition path points is clearly taught in the '289 application. Whether it anticipates the entirety of claim 9 would again hinge on whether its disclosure of "intermediate points" is equivalent to the '780 patent's "route poses" with a "footprint."

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4. U.S. Patent Application Publication 2015/0153722 A1

• Full Citation: US 2015/0153722 A1
• Title: Method and System for Path Planning for an Autonomous Vehicle
• Publication Date: June 4, 2015
• Filing Date: November 29, 2013

Brief Description:
This publication describes a path planning system for an autonomous vehicle that generates a path as a sequence of waypoints. The system defines a "corridor" or "bounding box" around the initial path. Within this corridor, the system optimizes the path based on various cost factors, such as distance to obstacles, smoothness of the path, and vehicle dynamics. Obstacles detected by sensors add a high-cost area to a cost map, influencing the optimizer to find a path that avoids these areas while staying within the corridor.

Potential Anticipation of Claims (35 U.S.C. § 102):
This reference uses a cost-map optimization approach rather than a direct force-based model on path poses.

• Claim 1: The '722 application does not teach the concept of applying attractive and repulsive forces directly to the waypoints (or "route poses") to cause them to move. Instead, it uses a cost map to find an optimal path within a pre-defined space. While the outcome is a collision-free path, the mechanism for achieving it is different from the specific force-based repositioning of poses claimed in the '780 patent. The cost map implicitly creates "repulsion" from obstacles, but the forces are not applied to the poses themselves.
• Dependent Claims: Claims in the '780 patent that elaborate on the force function (e.g., based on distance, shape, material) would not be anticipated, as the '722 application's methodology is based on a cost function, not a direct force simulation.

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5. U.S. Patent Application Publication 2016/0114519 A1

• Full Citation: US 2016/0114519 A1
• Title: Path Planning for Mobile Robot
• Publication Date: April 28, 2016
• Filing Date: October 24, 2014

Brief Description:
This application discloses a method for real-time path planning for a mobile robot. It describes creating a "path skeleton" composed of a sequence of nodes. When an obstacle is detected, the system identifies the affected nodes on the path. It then generates alternative nodes and evaluates potential new path segments based on a cost function that considers safety and efficiency. The system selects the best alternative segment to replace the blocked portion of the path.

Potential Anticipation of Claims (35 U.S.C. § 102):
This reference describes a local path re-planning method by generating and evaluating alternative nodes, which is distinct from the force-based model of the '780 patent.

• Claim 1: The '519 application's method of generating and evaluating new nodes to find a local workaround does not involve applying continuous forces to the existing nodes ("route poses") to shift their positions. It is a discrete re-planning of a path segment rather than a continuous deformation of the overall path structure based on a force model.
• Claim 9 (Method Claim): The steps of computing forces on poses and repositioning them based on these forces are not taught in the '519 application. Therefore, it does not anticipate the claimed method.

Conclusion

Based on the analysis, U.S. Patent Application Publication 2012/0191289 A1 appears to be the most relevant prior art. It discloses a very similar conceptual framework of using repulsive forces from obstacles and internal cohesive forces to modify a path represented by a series of points. A strong argument for obviousness, if not direct anticipation, could be constructed from this reference. The key distinguishing feature in the independent claims of US 10,001,780 seems to be the explicit limitation that the "route poses" have a "footprint" representing the robot's physical dimensions, which adds a volumetric consideration to the force calculations that may not be explicitly present in the "intermediate points" of the '289 application. The other references, while related to robot navigation and obstacle avoidance, describe different technical approaches, such as path-tracking correction, local sub-path generation, or cost-map optimization, and therefore are less likely to anticipate the specific force-based pose manipulation claims of US 10,001,780.

Obviousness Analysis of US Patent 10,001,780

To: File
From: Senior US Patent Analyst
Date: May 13, 2026
Subject: Obviousness Analysis of US Patent 10,001,780 ("the '780 patent") under 35 U.S.C. § 103

I. Introduction

This memorandum provides an analysis of the patentability of the claims of US Patent 10,001,780 in view of prior art, focusing on the doctrine of obviousness under 35 U.S.C. § 103. The '780 patent, titled "Systems and methods for dynamic route planning in autonomous navigation," is directed to a system and method for an autonomous robot to dynamically adjust its path in response to obstacles. The key inventive concept appears to be the use of "route poses," which are representations of the robot's footprint along a path, and the calculation of attractive and repulsive forces on these poses to generate a new, collision-free trajectory.

This analysis is based on the legal framework established in Graham v. John Deere Co., 383 U.S. 1 (1966), and further clarified in KSR Int'l Co. v. Teleflex Inc., 550 U.S. 398 (2007). These cases require a factual inquiry into:

1. The scope and content of the prior art.
2. The differences between the prior art and the claims at issue.
3. The level of ordinary skill in the pertinent art.
4. Secondary considerations of nonobviousness, if any.

II. Understanding the '780 Patent Claims

The '780 patent includes independent claims 1 and 9, which are directed to a robot and a method for dynamic navigation, respectively.

Independent Claim 1 recites:

A robot, comprising:

• one or more sensors configured to collect data about an environment including detected points on one or more objects in the environment; and
• a controller configured to:
  • create a map of the environment based at least in part on the collected data,
  • determine a route in the map in which the robot will travel,
  • generate one or more route poses on the route, wherein each route pose comprises a footprint indicative of poses of the robot along the route and each route pose has a plurality of points disposed therein,
  • determine forces on each of the plurality of points of each route pose, the forces comprising repulsive forces from one or more of the detected points on the one or more objects and attractive forces from one or more of the plurality of points on others of the one or more route poses,
  • reposition one or more route poses in response to the forces on each point of the one or more route poses, and
  • perform interpolation between one or more route poses to generate a collision-free path between the one or more route poses for the robot to travel.

Independent Claim 9 recites a similar method for dynamic navigation of a robot. The core elements of both independent claims are:

• Mapping the environment: Using sensor data to create a map.
• Initial route planning: Defining an initial path for the robot.
• Generating "route poses": Discretizing the path into a series of poses that include the robot's footprint.
• Force-based path deformation: Calculating repulsive forces from obstacles and attractive forces between route poses.
• Repositioning poses: Adjusting the route poses based on the calculated forces.
• Interpolation for a new path: Creating a smooth, drivable path between the repositioned poses.

III. Scope and Content of the Prior Art and Level of Ordinary Skill

The field of autonomous navigation and robotics was well-developed by the priority date of the '780 patent (November 2, 2016). A person having ordinary skill in the art (PHOSITA) would typically have a bachelor's or master's degree in computer science, robotics, or a related field, along with several years of experience in autonomous navigation, path planning, and obstacle avoidance algorithms.

The prior art available at the time of the invention included numerous patents and publications related to robotic navigation. A comprehensive prior art search would be necessary for a conclusive determination, but based on general knowledge in the field, it is highly likely that prior art exists that teaches the fundamental concepts of mapping, path planning, and obstacle avoidance.

For the purpose of this analysis, we will consider hypothetical prior art references that are representative of the state of the art at the time of the invention.

• "Obstacle-Avoidance Patent" (fictional): It is reasonable to assume the existence of prior art that describes a robot that can create a map of its environment and plan a path to a destination. Such a system would likely include sensors for detecting obstacles and a controller for adjusting the robot's path to avoid them. This reference would likely not disclose the specific concept of "route poses" with footprints and force-based deformation.

• "Force-Field Paper" (fictional): The concept of using artificial potential fields for robot path planning was a known technique. This approach treats the robot as a point in a field of forces, where the goal exerts an attractive force and obstacles exert a repulsive force. A paper describing this method would teach the use of attractive and repulsive forces to guide a robot. However, it might not apply these forces to a series of poses with footprints.

IV. Obviousness Analysis of the Claims

A. Combination of Prior Art

The claims of the '780 patent would be obvious if a PHOSITA would have been motivated to combine the teachings of the "Obstacle-Avoidance Patent" and the "Force-Field Paper."

B. Motivation to Combine

A PHOSITA, familiar with the "Obstacle-Avoidance Patent," would understand the need for more sophisticated obstacle avoidance than simply stopping or making a hard turn. The "Force-Field Paper" would provide a known and elegant solution for generating smooth, continuous paths around obstacles.

The motivation to combine these references would stem from the desire to improve the robustness and efficiency of the robot's navigation. A simple obstacle avoidance system might fail in cluttered environments or produce jerky, inefficient movements. The force-field approach offers a more "natural" way for the robot to navigate, and a PHOSITA would have recognized its potential to enhance the system described in the "Obstacle-Avoidance Patent."

C. Analysis of Claim Elements

• Mapping and Initial Route Planning: These elements are likely taught by the "Obstacle-Avoidance Patent."
• Generating "Route Poses": While the exact term "route poses" might be unique to the '780 patent, the concept of discretizing a path into a series of points or waypoints is a standard technique in robotics. The addition of a "footprint" to these points is a logical and obvious extension, as it allows the planner to account for the robot's physical size and shape, which is a fundamental consideration in collision avoidance. A PHOSITA would have known that treating a robot as a single point is an oversimplification and that considering its geometry is necessary for safe navigation.
• Force-Based Path Deformation: The "Force-Field Paper" teaches the use of attractive and repulsive forces for path planning. Applying these forces to a series of waypoints (or "route poses") instead of a single point is a straightforward adaptation of this known technique. The attractive forces between poses would serve to maintain the overall shape and integrity of the path, while the repulsive forces from obstacles would push the path away from collisions. This is a predictable application of the force-field method to a discretized path.
• Repositioning Poses and Interpolation: Once the forces have been calculated, the repositioning of the poses is a direct consequence. Interpolation between the new poses to create a smooth path is a standard mathematical technique that a PHOSITA would have readily employed to generate a drivable trajectory for the robot.

V. Conclusion

Based on this analysis, there is a strong argument that the independent claims of US Patent 10,001,780 would have been obvious to a person of ordinary skill in the art at the time of the invention. The claimed invention appears to be a combination of known elements from the prior art, and a PHOSITA would have been motivated to combine these elements to achieve the predictable result of improved dynamic navigation.

A more definitive conclusion would require a thorough search and analysis of actual prior art references. However, this preliminary assessment suggests that the '780 patent may be vulnerable to an obviousness challenge. The dependent claims would need to be analyzed individually to determine if they add any non-obvious limitations, but it is likely that many of them recite further details that would also have been obvious to a PHOSITA.

Here is a technical analysis of the patent term, related applications, and family members for US patent 10,001,780.

Patent Term and Expiration Date

Based on the filing date and recorded term adjustments, the projected expiration date for US patent 10,001,780 is November 16, 2036.

A detailed breakdown of the patent term is as follows:

• Application Number: 15/341,612
• Filing Date: November 2, 2016
• Standard 20-Year Term: The patent term, without any adjustments, would normally expire 20 years from the filing date, which is November 2, 2036.
• Patent Term Adjustment (PTA): The USPTO has granted a Patent Term Adjustment of 14 days. PTA is intended to compensate for administrative delays by the USPTO during the patent's prosecution. This adjustment is added to the standard 20-year term.
• Patent Term Extension (PTE): There is no indication of any Patent Term Extension for this patent. PTE is typically granted to compensate for regulatory review delays (e.g., by the FDA) and is not applicable here.
• Projected Expiration Calculation:
  • Standard Expiration: November 2, 2036
  • Plus PTA: + 14 days
  • Adjusted Expiration Date: November 16, 2036

Related US Applications (Continuity Data)

US patent 10,001,780 is the parent of at least two subsequent continuation applications, establishing a family of related patents and applications in the United States.

• Parent Application (This Patent):

  • Application: 15/341,612
  • Filed: November 2, 2016
  • Issued as: US 10,001,780 B2

• Child Applications:

  1. Continuation Application 1:
     • Application: 16/011,499
     • Filed: June 18, 2018
     • Issued as: US 10,379,539 B2
  2. Continuation Application 2:
     • Application: 16/454,217
     • Filed: June 27, 2019
     • Published as: US 2020/0004253 A1

International Patent Family

The subject patent is part of a larger international family of patents and applications that claim priority to the original US filing through a PCT application.

• PCT Application:

  • Number: PCT/US2017/059379
  • Filed: October 31, 2017

• Foreign Family Members (based on PCT national phase entry):

  • Canada: CA3042532A1
  • China: CN110023866B
  • Europe: EP3535630A4
  • Japan: JP7061337B2
  • South Korea: KR102528869B1

Excellent. As a Senior Patent Strategist and Research Engineer, my objective is to create a robust body of prior art derived from US Patent 10,001,780. This Defensive Disclosure is designed to preemptively render incremental innovations by competitors as obvious extensions of the core concepts, thereby securing freedom to operate.

This document does not summarize US Patent 10,001,780; it builds upon its foundational claims to disclose novel and non-obvious variations. The core inventive concept is understood as a force-directed graph approach to dynamic path planning, where "route poses" are nodes subject to repulsive forces from obstacles and attractive (cohesive) forces from adjacent nodes in the planned path.

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Defensive Disclosure Based on US Patent 10,001,780

Publication Date: 2026-05-13
Subject: Derivative Methods and Systems for Force-Directed Dynamic Navigation

Derivative Set 1: Based on Core Claim 1 (System Claim)

The following disclosures expand upon the system described in US Patent 10,001,780, which comprises sensors and a controller for force-based dynamic route planning.

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Variation 1.1: Material & Component Substitution

• Title: Autonomous Navigation System with Magneto-Rheological (MR) Fluid-Based Haptic Boundary Sensing.

• Enabling Description: This variation replaces conventional optical or acoustic sensors (LIDAR, Sonar) with a distributed network of low-power, piezoelectric pressure sensors coupled with a microfluidic system containing a magneto-rheological (MR) fluid. The robot's chassis is surrounded by a flexible, tactile bumper filled with this MR fluid. When the robot approaches an object, a low-level magnetic field is projected. The reflection and distortion of this field, detected by Hall effect sensors, causes a change in the MR fluid's viscosity, which in turn alters the pressure readings from the piezoelectric sensors. The controller interprets these pressure differentials as repulsive force vectors. The "footprint" of each route pose is modeled not as a rigid shape but as a deformable mesh, with the attractive forces between pose points calculated using a spring-mass model based on Hooke's Law, where the spring constant is dynamically adjusted based on vehicle speed. This provides a computationally efficient, non-optical method for sensing and reacting to proximal obstacles.

• Diagram:

  graph TD
      subgraph Robot Chassis
          A[Controller]
          B[MR Fluid Reservoir]
          C[Piezoelectric Sensor Array]
          D[Hall Effect Sensors]
          E[Electromagnet Emitters]
      end
  
      subgraph Environment
          F[Obstacle]
      end
  
      A --> B
      A --> E
      C --> A
      D --> A
      E -- Magnetic Field --> F
      F -- Field Distortion --> D
      F -- Proximity Pressure --> C
  
      subgraph Force Calculation
          A -- Pressure Data --> G{Compute Repulsive Force}
          A -- Pose Data --> H{Compute Attractive Force (Hooke's Law)}
          G --> I[Reposition Pose]
          H --> I
      end
  

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Variation 1.2: Operational Parameter Expansion

• Title: Nanoscale Route Planning for Targeted Drug Delivery via Force-Field Directed Microrobots.

• Enabling Description: This disclosure applies the force-directed navigation method to a swarm of nanoscale robots (nanobots) operating within a biological vascular system. The "map" is a 3D model of a capillary network generated from real-time ultrasound micro-tomography. Each nanobot is a "route pose." Repulsive forces are not from physical objects but are generated by chemical gradients (chemotaxis) of specific cellular biomarkers indicating unhealthy tissue. The nanobots' sensors are functionalized surface proteins that bind to these biomarkers, creating a "repulsive" signal. Attractive forces are generated by inter-swarm communication using localized, low-intensity acoustic pulses, ensuring the swarm maintains cohesion. The controller, an external magnetic guidance system, calculates the aggregate force vectors on the swarm and repositions it by modulating the magnetic field, steering the nanobots toward a target (e.g., a tumor) while avoiding healthy tissue regions that exert repulsive chemical forces.

• Diagram:

  sequenceDiagram
      participant Ext_Controller as External Controller
      participant Mag_Field as Magnetic Guidance System
      participant Nanobot_Swarm as Swarm (Poses)
      participant Bio_Env as Biological Environment
      participant Target_Cell as Target Cell
      participant Healthy_Cell as Healthy Cell
  
      Ext_Controller->>Bio_Env: Generate 3D map via Ultrasound
      Healthy_Cell-->>Nanobot_Swarm: Exert Repulsive Chemical Gradient
      Nanobot_Swarm->>Nanobot_Swarm: Maintain cohesion via Acoustic Pulses (Attractive Force)
      Nanobot_Swarm->>Ext_Controller: Report aggregate force vectors
      Ext_Controller->>Mag_Field: Calculate new guidance parameters
      Mag_Field->>Nanobot_Swarm: Reposition Swarm via modulated magnetic field
      Nanobot_Swarm->>Target_Cell: Interpolate path and navigate towards target
  

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Variation 1.3: Cross-Domain Application (Aerospace)

• Title: Dynamic Trajectory Planning for Satellite Debris Avoidance in Low Earth Orbit (LEO).

• Enabling Description: The system is applied to a satellite or spacecraft in LEO. The "environment" is a 4D map (3D space + time) of known orbital debris objects, updated continuously from ground-based radar tracking data (e.g., from the Space Surveillance Network). The satellite's planned trajectory is a series of "route poses" in spacetime. Each piece of debris is a source of a repulsive force, calculated based on its predicted proximity and relative velocity. The magnitude of the repulsive force is inversely proportional to the predicted time-to-closest-approach and proportional to the debris's kinetic energy. Attractive forces maintain the trajectory's orbital mechanics constraints, pulling poses toward the optimal orbital path to conserve fuel. The controller, an onboard flight computer, constantly recalculates the force equilibrium and, when a net force exceeds a threshold, commands micro-thruster burns to reposition the satellite onto the newly interpolated, collision-free trajectory.

• Diagram:

  stateDiagram-v2
      [*] --> Nominal_Orbit
      Nominal_Orbit --> Debris_Detected: Debris enters threat threshold
      Debris_Detected --> Force_Calculation: Compute F_repulsive(debris) & F_attractive(orbit)
      Force_Calculation --> Trajectory_Recalculation: Net Force > Threshold
      Trajectory_Recalculation --> Thruster_Burn: Interpolate new path
      Thruster_Burn --> Corrected_Orbit: Satellite repositioned
      Corrected_Orbit --> Nominal_Orbit: Threat cleared
      Debris_Detected --> Nominal_Orbit: Debris no longer a threat
  

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Variation 1.4: Cross-Domain Application (AgTech)

• Title: Precision Pollination Drone Swarm Navigation in Complex Orchard Environments.

• Enabling Description: A swarm of small autonomous drones is tasked with pollinating fruit trees. The "map" is a 3D point cloud of an orchard generated by a master drone using LIDAR. Individual blossoms identified via hyperspectral imaging are "targets" that exert an attractive force, while branches, leaves, and other drones are sources of repulsive forces. Each drone's planned path is a series of route poses. The attractive force from a blossom is proportional to a "pollination-priority" score (based on age and viability). The repulsive force from foliage is constant. The cohesive (attractive) force between drones is modeled to maintain a minimum separation distance to avoid air-wake turbulence. The onboard controller on each drone computes these forces and dynamically plans its path from blossom to blossom, maximizing pollination efficiency while ensuring no collisions.

• Diagram:

  classDiagram
    class Drone {
      +ID: int
      +position: Vector3D
      +plannedPath: list[RoutePose]
      +controller: Controller
      +pollinate()
    }
    class RoutePose {
      +position: Vector3D
      +orientation: Quaternion
      +footprint: Polygon
    }
    class Controller {
      +map: PointCloud
      +updateForces()
      +replanPath()
    }
    class ForceSource {
      +position: Vector3D
      +calculateForce(pose: RoutePose): Vector3D
    }
    class Blossom {
      <<Attractive>>
      +pollinationPriority: float
    }
    class Obstacle {
      <<Repulsive>>
      +type: string
    }
    class FellowDrone {
      <<Repulsive>>
    }
  
    Drone "1" -- "1" Controller
    Drone "1" -- "N" RoutePose
    Controller "1" -- "1" PointCloud
    ForceSource <|-- Blossom
    ForceSource <|-- Obstacle
    ForceSource <|-- FellowDrone
  

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Variation 1.5: Integration with Emerging Tech (AI/IoT)

• Title: AI-Modulated Force-Field Navigation with Real-Time IoT Hazard Data.

• Enabling Description: The core force-based navigation system is enhanced by an AI model (a trained neural network) that dynamically adjusts the parameters of the force functions. The robot is integrated into an IoT ecosystem. For example, in a smart warehouse, IoT sensors on shelves detect spills (repulsive force magnitude increases), report high-traffic zones from other devices (repulsive force field is widened), or signal freshly cleaned floors from other robots (attractive force is applied to guide the robot to uncleaned areas). The AI model is trained on historical data of navigation events (e.g., near-misses, battery consumption, task completion time) to predict optimal force parameters. For instance, it learns to reduce the magnitude of attractive forces (allowing for wider deviations) in cluttered areas to find safer paths, even if they are less direct, and increase them in open areas to optimize for speed.

• Diagram:

  flowchart LR
      subgraph IoT_Ecosystem
          A[Spill Sensor]
          B[Traffic Monitor]
          C[Other Robots]
      end
      subgraph Robot_System
          D[Onboard Controller]
          E[Force Function Module]
          F[AI Model (NN)]
      end
      G[Route Poses]
  
      A -- Spill Data --> F
      B -- Traffic Data --> F
      C -- Area Coverage Data --> F
      F -- Modulated Parameters (e.g., repulsion_gain, attraction_k) --> E
      E -- Forces --> G
      G -- Repositioned Poses --> D
      D --> G
  

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Variation 1.6: The "Inverse" or Failure Mode

• Title: Graceful Degradation Navigation via Potential Field Minimization.

• Enabling Description: In the event of a primary sensor (e.g., LIDAR) failure, the system enters a "low-power" or "safe" mode. In this mode, it relies solely on low-resolution, short-range proximity sensors (e.g., infrared) and odometry. The complex, multi-point "route pose" is simplified to a single point. The navigation algorithm switches from a dynamic force-balancing calculation to a simple potential field method. All objects detected by the proximity sensors generate a high-repulsion potential field, while the direction toward the last known "home" or "safe" location (stored in memory) generates a constant, weak attractive potential. The robot's motion is governed by gradient descent, always moving in the direction that minimizes its potential energy. This ensures the robot will attempt to retreat to a safe location while avoiding immediate collisions, without the computational overhead of the full force-directed interpolation model. The attractive force to the original path is set to zero, prioritizing safety over task completion.

• Diagram:

  stateDiagram-v2
      state "Full Operation Mode" as FullOp {
          direction LR
          state "Sensor Fusion" as SF
          state "Force-Directed Planning" as FDP
          state "Path Interpolation" as PI
          [*] --> SF
          SF --> FDP
          FDP --> PI
          PI --> SF
      }
  
      state "Safe Mode (Potential Field)" as SafeMode {
          direction LR
          state "Proximity Sensing" as PS
          state "Gradient Descent Navigation" as GDN
          [*] --> PS
          PS --> GDN
          GDN --> PS
      }
  
      FullOp --> SafeMode: Primary Sensor Failure
      SafeMode --> FullOp: Sensor Recovered
      SafeMode --> Docking_Attempt: Path to Home Found
      SafeMode --> Halted: No Safe Path / Collision Imminent
  

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Derivative Set 2: Based on Core Claim 9 (Method Claim)

The following disclosures expand upon the method of dynamic navigation described in US Patent 10,001,780.

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Variation 2.1: Material & Component Substitution

• Title: Quantum Annealing Method for Global Optimization of Force-Based Route Pose Placement.

• Enabling Description: This method replaces the iterative, greedy repositioning of route poses with a global optimization approach using a quantum annealer (or a simulated annealer on classical hardware). The state of all route poses along a path segment is encoded into a single QUBO (Quadratic Unconstrained Binary Optimization) problem. The objective function to be minimized is the total "energy" of the system, where the energy is the sum of all potential energies from repulsive forces (from obstacles) and attractive forces (from path cohesion). The quantum annealer finds the ground state of this QUBO problem, which corresponds to the globally optimal placement of all route poses simultaneously, avoiding local minima that an iterative approach might fall into. This is particularly effective for navigating through highly complex and constrained spaces (e.g., a maze-like environment) where a local adjustment could trap the robot.

• Diagram:

  graph TD
      A[Define Path with N Poses] --> B{Formulate QUBO};
      B -- Pose Positions & Forces --> C[Quantum Annealer];
      D[Map & Obstacle Data] --> B;
      C -- Solved Ground State --> E{Decode Optimal Pose Positions};
      E --> F[Generate Final Path via Interpolation];
  

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Combination Prior Art Scenarios

The following disclosures combine the teachings of US Patent 10,001,780 with existing open-source standards to produce obvious implementations.

1. Combination with ROS (Robot Operating System): The force-directed planning method is implemented as a ROS global_planner plugin. It subscribes to a costmap_2d topic, treating all cells with a lethal or inscribed obstacle cost as sources of repulsive force. It publishes a nav_msgs/Path message containing the interpolated, collision-free path. The attractive/repulsive force parameters (e.g., gains, thresholds) are exposed as dynamic reconfigure parameters, allowing them to be tuned in real-time. The "route poses" are implemented as a custom message type, force_planner/RoutePoseArray. This combination makes the patented method a modular, plug-and-play component within the standard ROS navigation stack, rendering it an obvious integration for anyone skilled in the art of robotics and ROS.

2. Combination with PX4 Autopilot: For drone navigation, the method is integrated into the PX4 flight control software. The algorithm runs as a module on the companion computer, receiving obstacle data from a connected sensor (e.g., an Intel RealSense camera) via the MAVLink protocol. The repulsive and attractive forces are calculated, and the resulting interpolated path is converted into a series of MAVLink TRAJECTORY_REPRESENTATION_WAYPOINTS messages. These messages are sent to the PX4 flight controller, which then executes the trajectory. This represents a straightforward application of the method to a widely used open-source drone autopilot system.

3. Combination with Blender (Physics Engine): The dynamic route planning method is implemented as a Python script within the open-source 3D modeling software, Blender. A 3D model of an environment is created or imported. The robot's path is represented as a "Curve" object, and the route poses are "Hooks" attached to the curve's control points. Obstacles in the scene are configured as "Collision" objects in Blender's physics engine, which generate a repulsive force field. The curve's control points (the poses) are linked by "Spring" physics constraints, providing the attractive force. Running the physics simulation (bpy.ops.ptcache.bake_all()) causes the curve to deform and settle into a collision-free path, which can then be exported as a series of coordinates. This demonstrates that the core method is an obvious application of principles already present in standard 3D physics simulation engines.