Latent Navigation via Geodesic Trajectories and Symbolic Anchors
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Solution Overview
Problem
Existing deep learning approaches for spatiotemporal media processing struggle with navigating, interpreting, and generating content within high-dimensional latent spaces, lacking mechanisms for continuous zoom, rotation, and semantic traversal, and fail to maintain persistent cognitive states or integrate symbolic reasoning effectively.
Innovation Solution
A system integrating hierarchical and Lorentzian autoencoders with geodesic trajectory mapping, symbolic anchors, and generative augmentation, enabling continuous zoom, rotation, and semantic traversal through a strategy caching system that preserves geometric and temporal relationships, and supports intelligent navigation and synthesis.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If traditional autoencoders are used for spatiotemporal compression, then data compression is achieved, but the ability to navigate and generate content within latent spaces is limited
Solution Approach 1:
The system segments the latent space navigation task into discrete symbolic actions (zoom, rotation, translation, reconstruction) that can be composed and cached independently. Each symbolic action corresponds to a specific transformation operation in the latent hyperspace, allowing the system to build complex navigation strategies from basic atomic operations while maintaining compression efficiency.
Solution Approach 2:
The patent introduces symbolic anchors as intermediary elements that bridge the compressed latent representations and the navigation operations. These symbolic anchors serve as reference points in the latent space that enable meaningful navigation and interpretation of compressed content without requiring full decompression, thus maintaining compression efficiency while enabling versatile navigation.
2Adaptability or versatility
If hierarchical and Lorentzian autoencoders are used with geodesic trajectory mapping, then continuous zoom and rotation are enabled, but device complexity increases
Solution Approach 1:
The system changes the parameterization of the latent space by imposing geometric constraints (Lorentzian metric) and hierarchical structure on the autoencoder outputs. This parameter transformation enables continuous zoom and rotation operations through simple parameter adjustments in the latent space, avoiding the need for complex post-processing while maintaining geometric correctness.
Solution Approach 2:
The patent introduces dynamic navigation through geodesic trajectories in the latent space, allowing the system to adaptively traverse compressed representations based on user intent. The symbolic action cache dynamically stores and reuses navigation patterns, reducing computational complexity for repeated operations while maintaining the flexibility of continuous zoom and rotation.
3Productivity
If strategy caching system is implemented for navigation patterns, then productivity is improved, but memory requirements increase
Solution Approach 1:
The system extracts only the essential symbolic actions and their parameters from complete navigation trajectories for caching. Instead of storing entire navigation paths, it caches the atomic operations (zoom level changes, rotation angles, translation vectors) that can be recombined to reproduce the navigation effect, significantly reducing memory requirements while maintaining navigation efficiency.
Solution Approach 2:
The symbolic action cache stores partial navigation information sufficient for efficient reuse without complete trajectory details. The system caches enough information to reconstruct navigation patterns (symbolic action sequences with key parameters) rather than full high-dimensional trajectory data, achieving productivity improvement with minimal memory overhead.
Data Source
AI summary
A system and method for generation-augmented latent hyperspace navigation in spatiotemporal media using hierarchical and Lorentzian autoencoders. The system compresses media into latent representations while preserving geometric, temporal, and semantic relationships. A latent hyperspace manager organizes compressed data as geodesic trajectories, and a geodesic trajectory mapper computes navigation paths. Symbolic anchors provide persistent reference points, while spatiotemporal routing coordinates decisions across multiple scales. A strategy caching system preserves successful navigation patterns for reuse as procedural memory. A synthetic content generator including latent diffusion models, neural radiance fields, and context-aware refinement produces augmentation for continuous zoom, bidirectional traversal, and rotational reorientation. A user input interface and zoom controller enable interactive exploration and reconstruction, supporting applications in immersive media, visualization, and surveillance.