Audio Generation Machine for Entity Representation
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Solution Overview
Problem
Current systems lack an efficient method to generate and represent audio that identifies and highlights entities within network-based systems, such as users or products, in a way that is engaging and shareable.
Innovation Solution
An audio generation machine within a network-based system uses identifiers to create audio pieces that represent entities, incorporating musical styles and instrument voices based on classification codes and user preferences, allowing for the generation and sharing of audio avatars that identify or reference entities.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If traditional text or image representations are used to identify entities in network-based systems, then entity identification is achieved, but user engagement and shareability are limited
Solution Approach 1:
The patent replaces traditional text/image-based entity representations with audio-based representations. This substitution transforms the mechanical/visual interaction model into an auditory one, enabling new forms of user engagement through music generation, audio sharing, and sonic entity identification that are more engaging and shareable across social platforms
Solution Approach 2:
The system changes the fundamental parameter of entity representation from visual/textual to auditory. By encoding entity identifiers into audio signals and generating unique musical pieces for each entity, the system transforms how users perceive and interact with entities, thereby increasing engagement and shareability through novel sensory channels
2Adaptability or versatility
If custom audio representations are created for each entity, then entity identification and user engagement improve, but system complexity and resource requirements increase
Solution Approach 1:
The patent implements a universal audio generation system that can represent any entity type (users, products, services) through a common musical framework. The system uses classification codes and universal musical schemas to generate audio representations across diverse entity categories, reducing the need for entity-specific complex processing while maintaining high adaptability
Solution Approach 2:
The system generates audio representations by copying and transforming entity identifier data into musical parameters. Rather than creating entirely unique complex audio content for each entity, the system uses algorithmic composition to map identifier characteristics to musical properties, efficiently producing distinctive audio representations through systematic transformation
3Manufacturing precision
If detailed classification codes and musical schemas are used to generate audio pieces, then audio representation quality improves, but processing time and computational resources increase
Solution Approach 1:
The system performs preliminary classification and schema assignment when entities are first created or registered in the network-based system. By pre-establishing classification codes and associated musical schemas, the system avoids complex real-time analysis during audio generation, significantly reducing processing time while maintaining high representation accuracy
Solution Approach 2:
The audio generation process is segmented into distinct stages: identifier extraction, classification code determination, musical schema selection, and audio synthesis. This segmentation allows each stage to be optimized independently, with pre-computed classification data feeding into efficient audio synthesis routines, reducing overall computational burden and generation time
Data Source
AI summary
Within a network-based system, an entity may be identified by an identifier of the entity. An audio generation machine may be configured to generate an audio piece that represents the entity, and the audio generation machine may generate the audio piece based on the identifier of the entity. Hence, the audio piece generated by the audio generation machine may be representative of the entity, and playback of the audio piece may identify the entity, reference the entity, highlight the entity, suggest the entity, or otherwise call the entity to mind (e.g., for one or more listeners of the audio piece). Thus, the generated audio piece may function as an audio-based avatar of the entity (e.g., a representative of the entity within a virtual world). Furthermore, the audio piece may be shared (e.g., in a social networking context or a social shopping context).