Semantic Inference for Peripheral Input Emulation in Software Control

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Solution Overview

Problem

Existing robotic systems lack the ability to perform optimized manipulation and augmentation based on semantic analysis, failing to effectively capture and present data from multiple devices or services in a manner that aligns with specific goals.

Innovation Solution

A semantic robotic device equipped with a processor, memory, and transceiver that captures and analyzes data to perform optimized manipulation and augmentation by emulating peripheral inputs, refactoring capabilities, and applying semantic factorization to achieve desired outcomes.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Productivity

If semantic analysis is applied to determine affirmative and non-affirmative circumstances, then data manipulation and augmentation are optimized, but device complexity increases

Engineering Contradiction:
Improvedata manipulation efficiencyVSAvoidsystem complexity
Core Design Contradiction:
ProductivityVSDevice complexity

Solution Approach 1:

The system segments data manipulation into distinct affirmative and non-affirmative circumstances through semantic analysis. The processor divides data processing tasks into separate pathways based on semantic evaluation, allowing optimized manipulation for each circumstance type while maintaining overall system organization.

Inventive Principle:
Principle #1Segmentation

Solution Approach 2:

The semantic analysis module acts as an intermediary between raw data capture and data manipulation operations. It evaluates data to determine affirmative or non-affirmative circumstances, then directs appropriate manipulation strategies, thereby optimizing productivity without requiring complete system redesign.

Inventive Principle:
Principle #24Intermediary (Mediator)

2Productivity

If semantic analysis is applied to determine affirmative and non-affirmative circumstances, then data manipulation and augmentation are optimized, but computational resources increase

Engineering Contradiction:
Improvedata manipulation efficiencyVSAvoidcomputational energy consumption
Core Design Contradiction:
ProductivityVSUse of energy by moving object

Solution Approach 1:

The system applies semantic analysis selectively to determine only the necessary affirmative or non-affirmative circumstances required for optimized manipulation. Rather than analyzing all data comprehensively, it performs partial analysis focused on key semantic factors that drive manipulation decisions, reducing overall computational energy consumption.

Inventive Principle:
Principle #16Partial or excessive action

3Extent of automation

If the device emulates peripheral input device inputs to launch and manipulate software applications, then automation capability improves, but device complexity increases

Engineering Contradiction:
Improvesoftware manipulation automationVSAvoidsystem architecture complexity
Core Design Contradiction:
Extent of automationVSDevice complexity

Solution Approach 1:

The system creates virtual copies of peripheral input device inputs through emulation. Rather than requiring physical peripheral devices, the processor generates and processes copies of input signals that mimic keyboard, mouse, or other peripheral inputs, enabling software manipulation automation without adding physical hardware complexity.

Inventive Principle:
Principle #26Copying

4Adaptability or versatility

If the device refactors capabilities and manipulation semantic routes based on further semantic inference, then adaptability improves, but processing time increases

Engineering Contradiction:
Improvecapability refactoring flexibilityVSAvoidprocessing time
Core Design Contradiction:
Adaptability or versatilityVSLoss of time

Solution Approach 1:

The system performs preliminary semantic analysis to pre-determine manipulation semantic routes and capability configurations. By anticipating required adaptations through initial semantic inference, the system prepares refactoring options in advance, reducing the time required for capability refactoring when actual manipulation tasks are executed.

Inventive Principle:
Principle #10Preliminary action

Data Source

PatentUS20260084315A1Semantic Inference Method
Publication Date: 2026.03.26 LUCOMM TECHNOLOGIES INC
  • US20260084315A1 patent drawing
  • US20260084315A1 patent drawing
  • US20260084315A1 patent drawing

AI summary

A method for performing semantic inference in a system having a processor, memory, and a transceiver arranged to receive a video signal from a target device includes storing a semantic goal, ingesting user guideline content, inferring capabilities semantics, and emulating peripheral input device signals which are communicated to a target device to launch and operate one or more software applications. Data is captured from the video signal and displayed on a user interface control, with the system performing semantic inference on the data and determining whether software application capabilities are factorized with respect to semantic goals, and further performing semantic inference on the data to emulate peripheral input device signals based on the determination.