Sone-071 !!exclusive!!

| Step | Description | |------|-------------| | | Use a lightweight BERT‑based classifier (trained on 150k historic queries) to label intent: date_range , numeric_range , status , tag , custom_field . | | 2. Entity Detection | Run spaCy NER + custom regexes for amounts, dates (relative like “last month”, “Q1 2025”), IDs. | | 3. Filter Generation | Map intent+entities to filter JSON structures. | | 4. Scoring | Score each candidate with a logistic regression that factors: confidence from intent, entity match count, historical acceptance rate (per tenant). | | 5. Result Count Estimation | Issue a lightweight COUNT(*) query using the generated filter on the search index (cached for 30 s). | | 6. Feedback Loop | Store SUGGESTION_APPLIED or SUGGESTION_REJECTED events. Retrain the ranking model nightly. |

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Without a specific context, SONE-071 could refer to a variety of things such as a project code name, a scientific or medical study identifier, a product or model designation, or even a codename for a technology or initiative. For this report, let's consider it could be related to a scientific or technological endeavor. SONE-071