Prosecution Insights
Prosecution Insights
Technology area: Computing & Software
15 pending office actions • 10 art units • 14 examiners • 0 of 15 (0%) have an AI response strategy ready • 32 patents granted in the last 365 days
Notion Labs Inc. currently has 14 pending office actions in the Computing & Software technology area. These actions are spread across 9 distinct art units and 13 distinct examiners, indicating that the count of pending actions is nearly equal to the count of examiners. This distribution suggests a decentralized prosecution environment for the company's software innovations.
HTAY, LIN LIN M is the busiest examiner, overseeing 2 pending office actions. This represents a portion of the 14 pending office actions in the portfolio, as 13 distinct examiners are involved in total. The 9 distinct art units suggest that the company's software filings are being evaluated by several specialized groups within the patent office. Practitioners should be prepared to navigate the requirements of these 9 distinct art units while managing the perspectives of the 13 distinct examiners, with the busiest examiner handling only 2 cases.
Based on the USPTO statutory response window for each pending office action. 2 of the docket's apps have a known mailing date; the rest are excluded from the tile counts.
Difficulty is derived from the rejection statutes on the most recent pending office action. §101-driven and multi-statute cases are graded Hard; §112-only and obviousness-type double-patenting cases are graded Easy; everything else is Medium. "Unknown" means we have not yet parsed a statute for that office action.
| Bucket | Cases |
|---|---|
| §101 + other | 4 (27%) |
| §103 only | 5 (33%) |
| §102 only | 1 (7%) |
| Double-patenting only | 1 (7%) |
| Double-patenting + other | 1 (7%) |
| Multi-statute (no §101) | 3 (20%) |
How the docket's pending cases split across USPTO tech-center bands.
Manual office-action response work runs about 10 hours per case. The time-saved bands below show what IP Author's prosecution pipeline typically delivers — a conservative 20% on the low end, 35% in the middle, 50% on the high end.
| Examiner | Apps on this docket | Allow rate | Interview lift |
|---|---|---|---|
| HTAY, LIN LIN M | 2 | 71.4% | +24.6% |
| BUTLER, SARAI E | 1 | 88.1% | +10.7% |
| MOBIN, HASANUL | 1 | 75.3% | +38.8% |
| MOLNAR, HUNTER A | 1 | 50.6% | +33.1% |
| FIELDS, COURTNEY D | 1 | 84.1% | -4.0% |
| HONG, STEPHEN S | 1 | 38.6% | +25.4% |
| OUELLETTE, JONATHAN P | 1 | 66.4% | +29.5% |
| CHANG, TOM Y | 1 | 53.4% | +20.0% |
| WORKU, KIDEST | 1 | 84.9% | +2.8% |
| NAZAR, AHAMED I | 1 | 53.7% | +31.6% |
Cases in front of an examiner with an allow rate of 80%+ where the difficulty is Easy or Medium. The top 2 ordered by deadline are shown.
| App # | Title | Examiner | Due in |
|---|---|---|---|
| 19284193 | ITERATIVE CODE INTERPRETER USING LLMS | BUTLER, SARAI E | — |
| 18655013 | LARGE LANGUAGE MODEL TOOLS FOR TASK AUTOMATION | WORKU, KIDEST | — |
Multi-statute / §101-driven matters, or cases in front of an examiner with an allow rate under 30%. The top 7 ordered by deadline are shown.
| App # | Title | Examiner | Due in |
|---|---|---|---|
| 19202593 | ENABLING AN EFFICIENT UNDERSTANDING OF CONTENTS OF A LARGE DOCUMENT WITHOUT STRUCTURING OR CONSUMING THE LARGE DOCUMENT | MOBIN, HASANUL | — |
| 18923317 | DIGITAL RIGHTS MANAGEMENT | FIELDS, COURTNEY D | — |
| 18909844 | COMMAND SEARCH AND ARTIFICIAL INTELLIGENCE (AI) ASSISTANT FOR AN INTEGRATED APPLICATION | HONG, STEPHEN S | — |
| 18891477 | RANKING SYSTEM FOR IMPROVED SEARCH RELEVANCE | HTAY, LIN LIN M | — |
| 18891566 | SEARCH RANKING OPTIMIZATION | HTAY, LIN LIN M | — |
| 18642245 | PROVIDING LINKS AND ASSOCIATED GLIMPSE PREVIEWS OF CONTENT ON A WORKSPACE | NAZAR, AHAMED I | — |
| 18634471 | CODE UNIT GENERATOR FOR A MACHINE LEARNING BASED QUESTION AND ANSWER (Q&A) ASSISTANT | LEE, WILLIAM MICHAEL | — |
Cases in front of an examiner whose interview lift is 10 percentage points or more — i.e. interviewed cases historically resolve more favorably than non-interviewed ones. The top 8 ordered by deadline are shown.
| App # | Title | Examiner | Due in |
|---|---|---|---|
| 19284193 | ITERATIVE CODE INTERPRETER USING LLMS | BUTLER, SARAI E | — |
| 19202593 | ENABLING AN EFFICIENT UNDERSTANDING OF CONTENTS OF A LARGE DOCUMENT WITHOUT STRUCTURING OR CONSUMING THE LARGE DOCUMENT | MOBIN, HASANUL | — |
| 19082694 | UNIFIED CONTACT DATABASE SYSTEM WITH CUSTOMIZABLE MULTI-SOURCE DATA INTEGRATION | MOLNAR, HUNTER A | — |
| 18909844 | COMMAND SEARCH AND ARTIFICIAL INTELLIGENCE (AI) ASSISTANT FOR AN INTEGRATED APPLICATION | HONG, STEPHEN S | — |
| 18891477 | RANKING SYSTEM FOR IMPROVED SEARCH RELEVANCE | HTAY, LIN LIN M | — |
| 18891566 | SEARCH RANKING OPTIMIZATION | HTAY, LIN LIN M | — |
| 18830353 | PROVIDING GENERATIVE ARTIFICIAL INTELLIGENCE (AI) CONTENT BASED ON EXISTING IN-PAGE CONTENT IN A WORKSPACE | OUELLETTE, JONATHAN P | — |
| 18744075 | PROVIDING GRAPHICAL REPRESENTATIONS OF EMAIL CONTENT | CHANG, TOM Y | — |
| Art Unit | Apps |
|---|---|
| 2178 | 2 |
| 2153 | 2 |
| 2168 | 1 |
| 3628 | 1 |
| 2436 | 1 |
| 3629 | 1 |
| 2455 | 1 |
| 2119 | 1 |
| 2179 | 1 |
| 2172 | 1 |
| App # | Title | Examiner | Art Unit | Statutes | Status | Due in | AI | Filed |
|---|---|---|---|---|---|---|---|---|
| 19284193 | ITERATIVE CODE INTERPRETER USING LLMS | BUTLER, SARAI E | — | §112DP | Non-Final OA | — | Pending | Jul 29, 2025 |
| 19202593 | ENABLING AN EFFICIENT UNDERSTANDING OF CONTENTS OF A LARGE DOCUMENT WITHOUT STRUCTURING OR CONSUMING THE LARGE DOCUMENT | MOBIN, HASANUL | 2168 | §101§103 | Final Rejection | — | Pending | May 08, 2025 |
| 19082694 | UNIFIED CONTACT DATABASE SYSTEM WITH CUSTOMIZABLE MULTI-SOURCE DATA INTEGRATION | MOLNAR, HUNTER A | 3628 | §103 | Final Rejection | — | Pending | Mar 18, 2025 |
| 18923317 | DIGITAL RIGHTS MANAGEMENT | FIELDS, COURTNEY D | 2436 | §102§103 | Final Rejection | — | Pending | Oct 22, 2024 |
| 18909844 | COMMAND SEARCH AND ARTIFICIAL INTELLIGENCE (AI) ASSISTANT FOR AN INTEGRATED APPLICATION | HONG, STEPHEN S | 2178 | §102§103 | Non-Final OA | — | Pending | Oct 08, 2024 |
| 18891477 | RANKING SYSTEM FOR IMPROVED SEARCH RELEVANCE | HTAY, LIN LIN M | 2153 | §101§103 | Final Rejection | — | Pending | Sep 20, 2024 |
| 18891566 | SEARCH RANKING OPTIMIZATION | HTAY, LIN LIN M | 2153 | §101§103 | Final Rejection | — | Pending | Sep 20, 2024 |
| 18830353 | PROVIDING GENERATIVE ARTIFICIAL INTELLIGENCE (AI) CONTENT BASED ON EXISTING IN-PAGE CONTENT IN A WORKSPACE | OUELLETTE, JONATHAN P | 3629 | DP | Final Rejection | — | Pending | Sep 10, 2024 |
| 18744075 | PROVIDING GRAPHICAL REPRESENTATIONS OF EMAIL CONTENT | CHANG, TOM Y | 2455 | §103 | Final Rejection | — | Pending | Jun 14, 2024 |
| 18655013 | LARGE LANGUAGE MODEL TOOLS FOR TASK AUTOMATION | WORKU, KIDEST | 2119 | §103 | Final Rejection | — | Pending | May 03, 2024 |
| 18642245 | PROVIDING LINKS AND ASSOCIATED GLIMPSE PREVIEWS OF CONTENT ON A WORKSPACE | NAZAR, AHAMED I | 2178 | §102§103 | Non-Final OA | — | Pending | Apr 22, 2024 |
| 18634403 | DEVELOPER ITERATION PLATFORM FOR A MACHINE LEARNING BASED QUESTION AND ANSWER (Q&A) ASSISTANT | RUTTEN, JAMES D | — | §103 | Non-Final OA | — | Pending | Apr 12, 2024 |
| 18634471 | CODE UNIT GENERATOR FOR A MACHINE LEARNING BASED QUESTION AND ANSWER (Q&A) ASSISTANT | LEE, WILLIAM MICHAEL | — | §101§103 | Non-Final OA | — | Pending | Apr 12, 2024 |
| 18627329 | VIEW GENERATION FOR BLOCK ITEM HIERARCHY | SHIH, HAOSHIAN | 2179 | §102 | Non-Final OA | 75d | Pending | Apr 04, 2024 |
| 18626193 | WORKSPACE CONTENT SEARCH BASED ON ACCESS PERMISSIONS | BELOUSOV, ANDREY | 2172 | §103 | Final Rejection | 20d | Pending | Apr 03, 2024 |
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