Enhanced Systems for Generating Unique Non-Looping Sound Streams

Publication ID: 24-11857880_0006_PTD
Published: October 28, 2025
Category:Direct Improvements & Enhancements

Legal Citation

pr1or.art Inc., “Enhanced Systems for Generating Unique Non-Looping Sound Streams,” Published Technical Disclosure No. 24-11857880_0006_PTD, Published October 28, 2025, available at https://archive.pr1or.art/24-11857880_0006_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. 11,857,880.

Summary of the Inventive Concept

This inventive concept improves upon the original patent by leveraging machine learning algorithms, deep learning models, and real-time audio processing to generate unique non-looping sound streams from audio clips and audio tracks, providing a seamless and immersive listening experience.

Background and Problem Solved

The original patent's method for generating sound streams from audio clips and tracks has limitations in terms of audio segment selection and cross-fading techniques, leading to a less-than-optimal listening experience. This new inventive concept addresses these limitations by introducing advanced technologies to optimize the audio generation process.

Detailed Description of the Inventive Concept

The enhanced system utilizes machine learning algorithms to analyze user feedback and preference data, adapting the selection of audio segments and cross-fading techniques in real-time. Additionally, deep learning models are employed to select the most suitable audio segments from a plurality of audio source clips. The system also incorporates real-time audio processing and analysis to adapt to changes in the audio input, ensuring a dynamic and immersive audio experience.

Novelty and Inventive Step

The new claims introduce the use of machine learning algorithms, deep learning models, and real-time audio processing, which are not present in the original patent. These advancements provide a significant improvement in the audio generation process, resulting in a more seamless and immersive listening experience.

Alternative Embodiments and Variations

Alternative embodiments of the inventive concept could include the use of other AI-powered audio processing techniques, such as natural language processing or computer vision, to further enhance the audio generation process. Additionally, the system could be integrated with virtual or augmented reality platforms to provide a more immersive experience.

Potential Commercial Applications and Market

This inventive concept has significant commercial potential in the music and entertainment industries, particularly in the areas of relaxation and meditation, as well as in the development of immersive audio experiences for virtual and augmented reality applications.

CPC Classifications

SectionClassGroup
A A63 A63F13/54
G G06 G06F3/165

Field of Art

Audio signal processing, digital sound generation, and interactive media systems with a focus on adaptive sound stream creation and user experience design

Person of Ordinary Skill (PHOSITA) Profile

A skilled practitioner with expertise in digital signal processing, machine learning, audio engineering, and interactive media technologies, typically holding a master's or PhD in electrical engineering, computer science, or related field with 3-5 years of industry experience in audio technologies

Obviousness Rationale

A person having ordinary skill in the art would recognize that applying machine learning and real-time adaptive processing techniques to the existing audio stream generation method represents a predictable technological evolution. The source patent's foundational approach of mixing and cross-fading audio segments naturally invites algorithmic optimization through machine learning techniques. The proposed variations represent straightforward technological extensions using well-established machine learning approaches to enhance the core audio generation methodology.

Obvious Combinations & Variations

Source Patent Element
Method of selecting and cross-fading audio segments from multiple source clips
PTD Variation
Using deep learning models to select audio segments and optimize cross-fading techniques
Obviousness Reasoning
Machine learning for audio segment selection is a known technique in signal processing, representing a predictable application of AI to improve existing audio mixing methods
Source Patent Element
Audio track playback and mixing system
PTD Variation
Real-time audio processing that adapts to user feedback and environmental context
Obviousness Reasoning
Adaptive systems that respond to user input are standard design practice in interactive media, representing an obvious extension of existing audio generation technologies
Source Patent Element
Static audio stream generation method
PTD Variation
Dynamic sound stream generation using machine learning algorithms to continuously modify audio output
Obviousness Reasoning
Introducing adaptive algorithmic modifications to audio generation represents a predictable technological improvement using standard machine learning techniques
Source Patent Element
Audio segment selection process
PTD Variation
Incorporating user preference data and behavioral analysis to optimize audio segment selection
Obviousness Reasoning
Personalization through user data analysis is a well-established technique in interactive media design, representing an obvious enhancement to existing audio generation methods
Source Patent Element
Basic audio stream mixing approach
PTD Variation
Integration with virtual and augmented reality platforms for immersive audio experiences
Obviousness Reasoning
Cross-platform integration and immersive media design are standard evolutionary paths for audio technologies, representing a predictable technological progression
35 U.S.C. § 103 Summary: Based on the teachings of US Patent 11857880 and the disclosed technical variations, a person having ordinary skill in the art would find the proposed machine learning-enhanced audio stream generation methods obvious and lacking inventive step. The proposed system represents a straightforward technological evolution utilizing standard machine learning techniques to optimize existing audio generation methodologies, thereby rendering potential claims in this domain anticipated and non-patentable.

Original Patent Information

Patent NumberUS 11,857,880
TitleSystems for generating unique non-looping sound streams from audio clips and audio tracks
Assignee(s)SYNAPTICATS, INC.