CAVE Automatic Virtual Environment Technology: A Patent Analysis
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Cave automatic virtual environment (CAVE) technology provides a highly immersive experience in virtual reality (VR) environments, transcending traditional boundaries of VR head-mounted devices. CAVE is applied to many fields, including education, construction, healthcare, and manufacturing. Despite its relevance, studies examining CAVE technology evolution and research directions are still lacking. To address this research gap, we analyzed patents using CAVE to understand the technology’s development and identify opportunities for future research, development, and innovation. Patent data were collected from the Lens database and analyzed using data mining techniques. An increasing number of CAVE patents were granted, reflecting significant growth and investments in this field. The results highlight emerging trends in the development of CAVE systems, emphasizing various technical configurations and innovative applications across a wide range of fields.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it