Obviousness

To analyze the obviousness of US patent 10379539 under 35 U.S.C. § 103, we must follow the framework established by Graham v. John Deere Co. and reaffirmed by KSR Int'l Co. v. Teleflex Inc.. This framework involves:

1. Determining the scope and content of the prior art.
2. Ascertaining the differences between the claimed invention and the prior art.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence of nonobviousness (secondary considerations), if presented.

A rejection under § 103 requires an articulated reasoning with a rational underpinning to support the legal conclusion of obviousness, not just conclusory statements. The motivation to combine prior art references does not need to be explicitly stated in the references themselves; it can be reasoned from knowledge generally available to one of ordinary skill in the art, established scientific principles, or legal precedent.

1. Scope and Content of the Prior Art

The Google Patents page for US10379539 lists several "Prior art keywords" including "route," "robot," "pose," "points," and "poses." The patent itself also explicitly cites U.S. patent application Ser. No. 15/341,612, which is now U.S. Pat. No. 10,001,780, as priority art. The provided information does not list additional specific prior art patents or publications that were cited during the examination of US10379539. However, the background section of US10379539 discusses the challenges in robotic navigation, specifically that "Current robots may not be able to make real time adjustments to its planned path in response to these changes (e.g., blockages). In such situations, current robots may stop, collide into objects, and/or make sub-optimal adjustments to its route." This implies that prior art existed for:

• Robotic navigation and route determination: Robots could determine a route to travel, either by learning from a user demonstration or by planning based on an environment map.
• Obstacle avoidance: Robots had localized plans to navigate around obstacles detected by sensors, typically with basic commands like stopping, slowing down, or deviating left or right.

More broadly, the field of autonomous navigation and path planning for robots is well-established. For instance, prior art commonly includes:

• Global and local path planning: Global planning involves designing the overall path from start to destination using map information, while local planning performs short-term adjustments based on the current environmental state, often addressing dynamic obstacles and path smoothing.
• Sensor-based environment mapping: Robots use various sensors (e.g., sonar, LIDAR, radar, cameras) to collect data and create maps of their environment.
• Route planning algorithms: Algorithms like A*, D*, Dijkstra's, and Rapidly-exploring Random Tree (RRT) are used to identify optimal routes, often minimizing cost functions that include distance and time.
• Dynamic obstacle avoidance: Systems existed for evasive maneuvers in autonomous vehicles to avoid obstacles, checking for collisions and determining avoidance feasibility. Neural network approaches also improved a system's ability to adapt to dynamic environments for path planning and obstacle avoidance.
• Cost functions in route planning: Route planning often involves cost functions, which can include factors beyond just distance or speed, such as preferred or disfavored areas (e.g., dangerous zones). Optimization-based algorithms explicitly formulate objective functions for path smoothness, energy consumption, and safe obstacle-avoidance distances.

2. Differences Between the Claimed Invention and the Prior Art

The key distinguishing features of US10379539, particularly as outlined in independent claims 1, 9, and 17, are the dynamic adjustment of a planned route through a "force field" approach and interpolation:

• Route poses with footprints and internal points: The patent defines "route poses" which represent the robot's pose, size, and shape (footprint) along the route, and importantly, each route pose has a plurality of points disposed within it.
• Force calculation and repositioning: The core of the invention involves determining repulsive forces from detected environmental objects onto the plurality of points of each route pose and attractive forces from other route poses onto these points. The route poses are then repositioned (translated and/or rotated) in response to these forces. This is described as a "dynamic route planning" mechanism.
• Collision-free path generation via interpolation: After repositioning, interpolation is performed between the route poses to generate a collision-free path for the robot. The interpolation can generate additional "interpolation route poses" with similar footprints, ensuring the robot fits in the generated path.

Prior art generally describes global and local planning, and obstacle avoidance. However, the specific methodology of using "route poses" with internal points, calculating repulsive and attractive forces on these internal points to dynamically reposition the poses, and then interpolating between the repositioned poses to create a collision-free path, appears to be a distinction. While prior art dealt with avoiding obstacles and updating paths, it often did so with basic commands or through more computationally intensive optimization methods. The "force field" analogy and the dynamic repositioning of discrete "route poses" with footprints based on these forces, followed by interpolation, offers a more granular and potentially more efficient approach to real-time dynamic route planning.

3. Level of Ordinary Skill in the Pertinent Art

A person having ordinary skill in the art (POSITA) in the context of US10379539 would likely possess:

• A Bachelor's or Master's degree in robotics, computer science, electrical engineering, or a related field.
• Experience in autonomous navigation systems, path planning algorithms, sensor integration (LIDAR, cameras, etc.), and control systems for mobile robots.
• Familiarity with concepts like Simultaneous Localization and Mapping (SLAM), obstacle detection and avoidance, and real-time system adjustments.
• Understanding of computational geometry and physics-based modeling for robotic interactions with environments.

4. Objective Evidence of Nonobviousness (Secondary Considerations)

The provided patent text and search results do not explicitly detail any secondary considerations such as commercial success, long-felt but unresolved needs, or failure of others, which could rebut a prima facie case of obviousness. The background section does mention that "Current robots may not be able to make real time adjustments to its planned path in response to these changes (e.g., blockages). In such situations, current robots may stop, collide into objects, and/or make sub-optimal adjustments to its route. Accordingly, there is a need for improved systems and methods for autonomous navigation, including systems and methods for dynamic route planning." This statement suggests a recognized problem and a long-felt need for better dynamic route planning, which could be a factor in favor of non-obviousness if adequately supported by evidence.

Obviousness Analysis and Motivation to Combine

To establish obviousness, we need to identify prior art references that, when combined, would teach or suggest all elements of the claimed invention, and articulate a motivation for a POSITA to make such a combination.

Potential Combination 1: General Robotic Navigation + Dynamic Obstacle Avoidance + Potential Fields

• Prior Art Elements:

  • Reference A (General Robotic Navigation): Represents the common knowledge in the art regarding robot control, mapping (e.g., generating an environment map using sensor data), and initial route determination. This is broadly acknowledged in the background of US10379539 and supported by patents such as US10126136B2, which discusses route searching and guidance for autonomous vehicles, often minimizing a specified cost function.
  • Reference B (Dynamic Obstacle Avoidance/Local Planning): Represents prior art that enables robots to detect and react to dynamic obstacles in real-time. This is described in US10379539 as "localized plans in a small area around it (e.g., in the order of a few meters), where the robot can determine how it will navigate around obstacles detected by its sensors (typically with basic commands to turn when an object is detected)." Similarly, "Path Planning Trends for Autonomous Mobile Robot Navigation: A Review" discusses local planning for dynamic obstacles and real-time obstacle avoidance. A "Non-Optimization-Based Dynamic Path Planning for Autonomous Obstacle Avoidance" paper describes a two-layer approach where a path planner generates a reference trajectory, and collision checks are performed to avoid obstacles.
  • Reference C (Potential Fields/Force-based Planning): This concept, while not explicitly detailed in the provided snippets for the patent's own prior art, is a well-known technique in robotics for obstacle avoidance and goal seeking. Potential field methods define attractive forces towards a goal and repulsive forces from obstacles. The Dynamic Path Planning for Unmanned Autonomous Vehicles Based on CAS-UNet and Graph Neural Networks discusses optimization-based planning algorithms that formulate objective functions for path smoothness, energy consumption, and safe obstacle-avoidance distances, which inherently involves considering forces or costs related to proximity.

• Motivation to Combine: A POSITA, seeking to improve the real-time responsiveness and "naturalness" of robot navigation beyond basic stop/turn commands, would be motivated to integrate a more sophisticated dynamic obstacle avoidance mechanism into a general navigation system. The known concept of potential fields or force-based planning offers an intuitive way to model repulsive forces from obstacles and attractive forces along a desired path. Applying this concept to discrete "route poses" with defined "footprints" (representing the robot's physical presence) would be a logical step to ensure collision avoidance with respect to the robot's actual dimensions, rather than just a single point. Further, representing these "footprints" with "a plurality of points" allows for more accurate force calculations and finer-grained repositioning, especially for non-circular robot shapes or complex obstacle geometries. The interpolation step is a standard method to generate a continuous path from discrete waypoints or poses, ensuring smooth and executable motion for the robot. Therefore, once the "route poses" are adjusted, using interpolation to generate the final path would be an obvious choice for a POSITA.

• Mapping to Claims:

  • "create a map of the environment based at least in part on the collected data": Taught by Reference A (general robotic navigation and mapping).
  • "determine a route in the map in which the robot will travel": Taught by Reference A (initial path planning).
  • "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": Combining Reference B (local planning/obstacle avoidance considering robot's interaction with environment) with Reference C (force-based planning where "points" represent interaction areas) and common knowledge of representing robot geometry. The "footprint" concept to represent robot size and shape for collision avoidance is a standard design choice.
  • "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": Taught by Reference C (potential fields/force-based planning), applied to the individual points of the route pose as a direct application of existing principles for more precise collision avoidance. The attractive forces between route poses are also a known concept in path smoothing and maintaining path continuity, as seen in optimization-based planning focusing on path smoothness.
  • "reposition one or more route poses in response to the forces on each point of the one or more route poses": This is the direct consequence and application of the force determination from Reference C.
  • "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": Taught by Reference A and B, as interpolation is a common technique for generating continuous trajectories from discrete points in path planning. The "collision-free" aspect is a natural goal of any obstacle avoidance system, further refined by the force-based repositioning.

Potential Combination 2: AI/Machine Learning for Dynamic Environments + Route Planning with Cost Functions

• Prior Art Elements:

  • Reference D (AI/Machine Learning for Dynamic Environments): Represents advancements in using AI, such as neural networks, for autonomous navigation in dynamic environments and obstacle avoidance, enabling more "human-like trajectories" and adapting to complex scenarios.
  • Reference A (General Robotic Navigation): As above.
  • Reference B (Dynamic Obstacle Avoidance/Local Planning): As above.
  • Reference E (Route Planning with Customizable Cost Functions): Represents prior art that utilizes customizable cost functions in route planning, allowing for the inclusion of various criteria beyond just distance, such as avoiding dangerous zones or considering performance characteristics of vehicle sensors.

• Motivation to Combine: A POSITA working with autonomous robots in dynamic environments would be motivated to leverage the adaptive capabilities of AI/machine learning (Reference D) to enhance traditional route planning and obstacle avoidance (References A and B). Recognizing that simple collision avoidance might lead to "unnatural" or "sub-optimal" adjustments (as acknowledged in US10379539's background), a POSITA would integrate the principles of force-based modeling (implicit in cost functions for safe obstacle avoidance). The "forces" on the route poses in US10379539 can be viewed as an implementation of a dynamic cost function. A POSITA would see the benefit of defining these costs/forces at a granular level (on points within a robot's footprint) to achieve more nuanced and adaptive path adjustments. Using AI to learn or dynamically adjust the parameters of these "forces" based on environmental characteristics (distance, shape, material, color, as mentioned in US10379539) would be a logical extension of existing capabilities to improve navigation robustness and efficiency. The concept of "repositioning route poses" is effectively optimizing the path based on these dynamic cost/force functions, and interpolation (Reference A/B) would follow naturally.

Conclusion on Obviousness:

Based on the analysis, a strong argument for obviousness under 35 U.S.C. § 103 could be made by combining known principles of robotic navigation, dynamic obstacle avoidance, and potential field theory. The concept of using attractive and repulsive forces to guide robotic movement and avoid collisions is well-established in robotics. While US10379539 describes a specific implementation involving "route poses" with "footprints" and "plurality of points," and dynamically repositioning them, these elements appear to be logical applications and refinements of existing techniques. A POSITA, seeking to create more robust and adaptable autonomous navigation systems, would find sufficient motivation to combine these prior art elements. The stated problem of "sub-optimal adjustments" and the "need for improved systems" in the patent's background further suggest that the improvements offered by US10379539 address a known problem with known tools, albeit in a novel combination and implementation. Without specific "secondary considerations" (e.g., unexpected results, commercial success, etc.) to rebut obviousness, the claimed invention, particularly the independent claims, would likely be considered obvious to a person of ordinary skill in the art at the time of the invention.