AI-Based Syntactic Pattern Recognition of Sequences
Bibliographic record
Abstract
This patent concerns the traditional problem encountered in the syntactic Pattern Recognition (PR) of strings or sequences. The primary investigator 1 involved in this work is a Full Professor at Carleton University in Ottawa, Canada, and is a Fellow of the IEEE. The primary problem solved by the invention involves determining the string or sequence that is most similar to a sequence presented to the system. The search could be initiated by presenting, to the system, a noisy or inexact version of a string contained in memory-for example, at a web-site or in the library or database. The invention will yield the closest string/sequence by searching the dictionary of possible words using a newly invented AIbased strategy. The core of this invention is this search strategy, called the Clustered Beam Search. Experiments have been done to show the benefits of the CBS over the current state-ofthe-art, and the results demonstrate an unbelievably marked improvement (sometimes as high as 90%) for large libraries and databases. The solution provided by the invention would be applicable in numerous areas including: Inexact or proximity searching on the Internet, keyword-based search in libraries and databases, spelling correction, speech and character recognition (including optical character recognition), and the processing of biological sequences, for example, in human genome projects. These applications are briefly described below.
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.
How this classification was reachedexpand
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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".