Published Technical Disclosure Archive

Enhanced New Applications & Use Cases Technical Implementation

Publication ID: 24-11858140_0007_PTD
Published: October 29, 2025
Category:New Applications & Use Cases

Legal Citation

pr1or.art Inc., “Enhanced New Applications & Use Cases Technical Implementation,” Published Technical Disclosure No. 24-11858140_0007_PTD, Published October 29, 2025, available at https://archive.pr1or.art/24-11858140_0007_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 new applications & use cases that builds upon the source patent's technical foundation.

Background and Problem Solved

The source patent addresses core functionality, but new applications & use cases presents opportunities for technical enhancement and expansion.

Detailed Description of the Inventive Concept

A comprehensive technical system that implements new applications & use cases 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 new applications & use cases 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 new applications & use cases 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 Automated Systems, specifically robotic learning systems with machine learning integration, focusing on adaptive control and work state modeling in industrial and collaborative robotic applications

Person of Ordinary Skill (PHOSITA) Profile

A robotics engineer with expertise in machine learning, robotic control systems, sensor integration, and adaptive algorithmic design, typically holding a master's or PhD in robotics, mechanical engineering, or computer science with 3-5 years of industrial robotics experience

Obviousness Rationale

A PHOSITA would recognize that the PTD's disclosed variations represent predictable extensions of the source patent's core machine learning and adaptive robotic control methodology. The technical improvements introduce incremental enhancements to work state modeling and sensor-based learning that naturally follow from the original patent's foundational approach. These variations demonstrate standard engineering problem-solving techniques applied to robotic system design.

Obvious Combinations & Variations

Source Patent Element
Robot system with state detection sensor and learning control unit
PTD Variation
Enhanced sensor integration with expanded state value detection and multi-dimensional work state modeling
Obviousness Reasoning
Known technique of expanding sensor capabilities to capture more granular system state information, representing a predictable optimization of existing robotic learning architectures
Source Patent Element
Additional learning unit calculating next work states
PTD Variation
Dynamic work state prediction with probabilistic modeling and expanded contextual parameter integration
Obviousness Reasoning
Applying advanced machine learning techniques to extend existing state transition modeling, representing a standard approach to improving adaptive robotic systems
Source Patent Element
Operator input and force-based learning mechanism
PTD Variation
Expanded human-robot interaction models with more sophisticated force feedback and collaborative learning protocols
Obviousness Reasoning
Implementing known human-robot collaboration techniques that represent a natural evolution of existing force-based learning methodologies
Source Patent Element
Robot system with switchable operation force modes
PTD Variation
Advanced operation mode selection with context-aware switching and enhanced decision algorithms
Obviousness Reasoning
Applying well-understood control system design principles to create more intelligent mode transition mechanisms
Source Patent Element
Machine learning based robot control system
PTD Variation
Generalized learning architecture supporting multiple application domains and expanded training protocols
Obviousness Reasoning
Demonstrating standard engineering approach of generalizing specialized robotic learning techniques for broader technological applicability
35 U.S.C. § 103 Summary: Based on the teachings of US Patent 11858140, a Person Having Ordinary Skill In The Art would find the technical variations disclosed herein to be obvious extensions of the prior art, representing predictable combinations of known robotic learning techniques that would be apparent to a skilled practitioner without requiring inventive insight beyond the existing technological state of the art.

Original Patent Information

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