Adaptive stimulation device with integrated feedback mechanism and proxy measurement

The adaptive stimulation system addresses the challenge of inconsistent outcomes in existing devices by using multimodal sensors and optimization algorithms to autonomously adjust stimulation based on unconscious physiological responses, ensuring personalized and effective therapeutic and experiential outcomes.

WO2026097013A1 Publication Date: 2026-05-07HABERMAN SETH
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Patent Information

Application Number
PCT/US2025/053769
Authority / Receiving Office
WO · WO
Patent Type
Applications
Current Assignee / Owner
Priority Date
2024-11-01
Filing Date
2025-11-03
Publication Date
2026-05-07

AI Technical Summary

Technical Problem

Existing stimulation devices lack adaptive control mechanisms that can dynamically respond to individual physiological and affective states, relying on manual user adjustments and invasive neural interfaces, leading to inconsistent therapeutic and experiential outcomes.

Method used

A closed-loop adaptive stimulation system that uses multimodal physiological sensors to measure unconscious responses, integrating advanced optimization algorithms to autonomously adjust stimulation parameters based on heart rate variability, electrodermal activity, and other proxy signals, without requiring direct neural input.

Benefits of technology

The system provides continuous, objective feedback to optimize therapeutic and hedonic outcomes in real-time, reducing cognitive burden and achieving personalized and effective stimulation across diverse applications.

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Abstract

An adaptive stimulation system optimizes pain relief, pleasure enhancement, and healing through closed-loop physiological effectiveness monitoring. The system comprises a stimulation device, sensors measuring physiological responses indicative of stimulation effectiveness, and a processor implementing real-time adaptive control. The system measures unconscious physiological responses that users cannot voluntarily control and involuntary reactions to objectively assess whether stimulation achieves desired outcomes. Through proxy learning, system sensors correlate with sophisticated clinical measurements via machine learning, enabling clinical-grade optimization using accessible devices. The system continuously explores multidimensional parameter spaces to discover optimal stimulation patterns personalized to individual users, automatically adapting parameters based on measured physiological effectiveness rather than subjective user control or preset patterns. Applications include medical devices, wellness products, and intimate stimulation systems.
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