Machine Learning Control Signals for Sexual Stimulation Devices

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Solution Overview

Problem

Current control systems for sexual stimulation devices are rudimentary and primarily limited to pre-programmed routines, lacking the ability for automated customization based on user data and usage history.

Innovation Solution

A system and method utilizing machine learning algorithms to analyze user data and generate automated control signals for sexual stimulation devices, allowing for personalized and evolving stimulation patterns over time.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Adaptability or versatility

If pre-programmed routines are used, then device complexity is reduced, but adaptability to user preferences deteriorates

Engineering Contradiction:

Improveadaptability to user preferencesVSAvoidcontrol system complexity

Core Design Contradiction:

Adaptability or versatilityVSDevice complexity

Solution Approach 1:

The system performs self-service by automatically analyzing user feedback and usage data to generate customized control signals without requiring manual programming. The machine learning algorithm processes user responses and evolves stimulation patterns autonomously, allowing the device to adapt to user preferences while maintaining relatively simple control hardware.

Inventive Principle:

Principle #25Self-service

Solution Approach 2:

The patent replaces manual programming mechanisms with automated machine learning algorithms. Instead of requiring users to manually configure stimulation parameters, the system uses computational algorithms that process usage data and generate optimized control signals, substituting mechanical programming operations with automated intelligence.

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

2Adaptability or versatility

If manual programming is used, then adaptability to user preferences is improved, but ease of operation deteriorates

Engineering Contradiction:

Improvecustomization capabilityVSAvoidease of device operation

Core Design Contradiction:

Adaptability or versatilityVSEase of operation

Solution Approach 1:

The system eliminates the need for manual programming by performing self-service customization. It automatically analyzes user feedback and usage patterns to generate personalized control signals, making the device easy to operate while maintaining high adaptability to individual user preferences.

Inventive Principle:

Principle #25Self-service

Solution Approach 2:

The patent implements feedback mechanisms where user responses and usage data are continuously processed by machine learning algorithms to refine and improve stimulation patterns over time. This automated feedback loop enables continuous customization without requiring manual reprogramming, enhancing both ease of operation and adaptability.

Inventive Principle:

Principle #23Feedback

3Ease of operation

If automated machine learning algorithms are used, then ease of operation is improved, but device complexity increases

Engineering Contradiction:

Improveease of device operationVSAvoidcontrol system complexity

Core Design Contradiction:

Ease of operationVSDevice complexity

Solution Approach 1:

The patent replaces complex manual programming operations with automated machine learning algorithms, improving ease of operation. The system substitutes user programming efforts with computational processing, allowing simple user interaction while handling complexity through automated algorithms.

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

Solution Approach 2:

The machine learning algorithm serves as an intermediary between simple user input and complex stimulation control. It processes user feedback and usage data, translating minimal user input into sophisticated customized control signals, thereby maintaining ease of operation while managing system complexity through intelligent mediation.

Inventive Principle:

Principle #24Intermediary (Mediator)

4Productivity

If pre-programmed routines are used, then device complexity is reduced, but productivity in terms of personalized stimulation generation is worsened

Engineering Contradiction:

Improvepersonalized stimulation generation rateVSAvoidcontrol system complexity

Core Design Contradiction:

ProductivityVSDevice complexity

Solution Approach 1:

The system generates personalized stimulation autonomously through self-service machine learning processing. It continuously analyzes usage data and feedback to create customized control signals without external programming, significantly improving productivity in personalized stimulation generation while the computational complexity is managed through efficient algorithm design.

Inventive Principle:

Principle #25Self-service

Data Source

PatentUS20240050306A1Automated generation of control signals for sexual stimulation devices

Publication Date: 2024.02.15 SLOAN BRIAN

AI summary

A system and method for automated generation of control signals for sexual stimulation devices from usage history and other data. The system and method involve analyzing historical usage and other data for a user for a device or devices, processing the data through machine learning algorithms, and generating new or recombined patterns of stimulation based on the outputs from the machine learning algorithms. The resulting automated control signals represent partially or fully customized stimulation for a given user which evolve over time as the user continues to use the device or devices.