Enhanced Synergistic Combinations Technical Implementation

Publication ID: 24-11858628_0008_PTD

Published: October 31, 2025

Category:Synergistic Combinations

Legal Citation

pr1or.art Inc., “Enhanced Synergistic Combinations Technical Implementation,” Published Technical Disclosure No. 24-11858628_0008_PTD, Published October 31, 2025, available at https://archive.pr1or.art/24-11858628_0008_PTD

This technical disclosure describes improvements that would be readily apparent to a Person Having Ordinary Skill In The Art (PHOSITA) when considered in combination with the foundational architecture disclosed in U.S. Patent No. 11,858,628.

Summary of the Inventive Concept

An improved approach to synergistic combinations that builds upon the source patent's technical foundation.

Background and Problem Solved

The source patent addresses core functionality, but synergistic combinations presents opportunities for technical enhancement and expansion.

Detailed Description of the Inventive Concept

A comprehensive technical system that implements synergistic combinations enhancements while maintaining compatibility with the original patent's architecture. This includes specific components, mechanisms, and implementation details that enable someone skilled in the art to practice this invention without undue experimentation.

Novelty and Inventive Step

Introduces new technical features and approaches that were not present in the source patent, specifically targeting synergistic combinations improvements.

Alternative Embodiments and Variations

Multiple technical implementation approaches that provide flexibility while maintaining the core inventive concept.

Potential Commercial Applications and Market

Broad market applicability across industries that can benefit from synergistic combinations enhancements.

Section 103 Obviousness Analysis (PHOSITA)

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Field of Art

Autonomous vehicle navigation systems, computer vision, machine learning-based motion planning, and image processing for robotic navigation

Person of Ordinary Skill (PHOSITA) Profile

A skilled engineer with expertise in autonomous vehicle technologies, machine learning, computer vision, and robotics, possessing advanced degrees in electrical engineering, computer science, or robotics, with 3-5 years of practical experience in developing autonomous navigation systems

Obviousness Rationale

A PHOSITA would recognize that the Published Technical Disclosure (PTD) represents predictable variations and extensions of the source patent's core image-based motion planning approach. The disclosed synergistic combinations leverage known techniques in machine learning, image processing, and autonomous vehicle navigation to incrementally improve upon the foundational concepts presented in the original patent. These variations would be considered routine design modifications within the capabilities of an ordinarily skilled practitioner in the field of autonomous vehicle navigation systems.

Obvious Combinations & Variations

Source Patent Element

Generating a cost function map associating risk values with image regions

PTD Variation

Implementing enhanced machine learning models for more granular risk assessment across image regions

Obviousness Reasoning

Applying more sophisticated machine learning techniques to improve risk estimation is a predictable evolution of existing image-based navigation technologies

Source Patent Element

Using depth estimation for navigation risk calculation

PTD Variation

Introducing multi-modal sensor fusion to augment depth estimation with additional contextual information

Obviousness Reasoning

Combining multiple sensor inputs to improve navigation accuracy is a known technique in autonomous vehicle systems

Source Patent Element

3D trajectory planning based on image-derived cost maps

PTD Variation

Developing dynamic trajectory adjustment algorithms that incorporate real-time environmental changes

Obviousness Reasoning

Implementing adaptive trajectory planning is an obvious extension of existing motion planning techniques

Source Patent Element

Machine learning model for risk assessment in navigation

PTD Variation

Enhancing model training approaches with advanced deep learning architectures

Obviousness Reasoning

Iterative improvement of machine learning models using state-of-the-art techniques is a standard practice in the field

Source Patent Element

Image-based environmental risk estimation

PTD Variation

Implementing more sophisticated computer vision techniques for improved object detection and classification

Obviousness Reasoning

Incremental improvements in computer vision algorithms represent predictable technological progression

35 U.S.C. § 103 Summary: Based on a comprehensive analysis of US Patent 11858628 and the Published Technical Disclosure, a Person Having Ordinary Skill In The Art would find the claimed variations obvious and anticipated by the existing prior art. The disclosed synergistic combinations represent routine engineering modifications that would be apparent to a skilled practitioner, thereby rendering potential derivative claims non-patentable under 35 U.S.C. Section 103.

Original Patent Information

Patent Number	US 11,858,628
Title	Image space motion planning of an autonomous vehicle
Assignee(s)	Skydio, Inc.