Published Technical Disclosure Archive

Enhanced Future Evolutions & Paradigm Shifts Technical Implementation

Publication ID: 24-11858140_0005_PTD
Published: October 29, 2025
Category:Future Evolutions & Paradigm Shifts

Legal Citation

pr1or.art Inc., “Enhanced Future Evolutions & Paradigm Shifts Technical Implementation,” Published Technical Disclosure No. 24-11858140_0005_PTD, Published October 29, 2025, available at https://archive.pr1or.art/24-11858140_0005_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. 11,858,140.

Summary of the Inventive Concept

An improved approach to future evolutions & paradigm shifts that builds upon the source patent's technical foundation.

Background and Problem Solved

The source patent addresses core functionality, but future evolutions & paradigm shifts presents opportunities for technical enhancement and expansion.

Detailed Description of the Inventive Concept

A comprehensive technical system that implements future evolutions & paradigm shifts 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 future evolutions & paradigm shifts 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 future evolutions & paradigm shifts enhancements.

CPC Classifications

SectionClassGroup
B B25 B25J9/163
B B25 B25J9/1697
B B25 B25J9/1602
B B25 B25J9/1679
B B25 B25J13/08

Field of Art

Robotics and Machine Learning Systems, specifically robotic assembly and adaptive learning technologies with focus on human-robot interaction and dynamic work state modeling

Person of Ordinary Skill (PHOSITA) Profile

A robotics engineer with expertise in machine learning, robotic control systems, and adaptive automation, holding a master's or PhD in robotics or mechatronics, with 3-5 years industry experience in developing intelligent robotic systems

Obviousness Rationale

A PHOSITA would recognize that the PTD's approach of enhancing robotic learning and adaptability represents a natural evolutionary progression from the source patent's foundational machine learning and work state modeling techniques. The disclosed variations leverage known machine learning paradigms and robotic control strategies to extend the original patent's capabilities in predictable ways. These enhancements would be considered routine optimization and incremental innovation within the field of adaptive robotic systems.

Obvious Combinations & Variations

Source Patent Element
Robot system with state detection sensor and learning control unit for work progression
PTD Variation
Enhanced future evolutions system with expanded state detection and adaptive learning mechanisms
Obviousness Reasoning
Predictable extension of existing machine learning techniques, representing a known approach to improving robotic system flexibility through enhanced sensor integration and dynamic model updating
Source Patent Element
Operator input for defining work states and updating robotic model
PTD Variation
Expanded human-robot interaction paradigm with more granular input mechanisms and contextual learning
Obviousness Reasoning
Obvious design choice to increase system adaptability by providing more sophisticated interaction models, utilizing known human-machine collaboration techniques
Source Patent Element
Calculation of next work states based on current state values
PTD Variation
Advanced predictive modeling approach for anticipating complex multi-stage robotic workflows
Obviousness Reasoning
Routine application of machine learning principles to extend predictive capabilities, representing a natural progression of existing state-tracking methodologies
Source Patent Element
Switching between operator and calculated operation forces
PTD Variation
Dynamic control regime with more nuanced transition mechanisms between human and autonomous operation
Obviousness Reasoning
Obvious implementation of known human-robot interaction design principles, representing a straightforward technical improvement
Source Patent Element
Machine learning model for robotic work progression
PTD Variation
Enhanced learning architecture supporting more complex, multi-dimensional state space exploration
Obviousness Reasoning
Predictable evolution of machine learning techniques, applying standard algorithmic expansion strategies to increase system intelligence
35 U.S.C. § 103 Summary: Based on the teachings of US Patent 11858140 and the technical variations disclosed herein, a person having ordinary skill in the art would find the claimed innovations obvious and lacking inventive distinctiveness. The presented technical disclosure demonstrates that the claimed variations represent routine engineering optimization and predictable extensions of existing robotic learning and control methodologies, thereby rendering such claims unpatentable under 35 U.S.C. Section 103.

Original Patent Information

Patent NumberUS 11,858,140
TitleRobot system and supplemental learning method
Assignee(s)KAWASAKI JUKOGYO KABUSHIKI KAISHA