Technology area: Computing & Software
21 pending office actions • 1 client • 21 examiners • 18 art units • 0 of 21 (0%) have an AI response strategy ready
Patterson + Sheridan, LLP - Intuit Inc. has 21 pending office actions in Computing & Software and 1016 career applications. The active docket is limited relative to the reported application base, with prosecution focused on software and computing subject matter.
The resolved allowance rate is 93.44%, and the average is 4.49 office actions per allowance. The allowance rate indicates frequent successful resolution, while the action average shows that allowed matters generally required several examination exchanges. This combination gives practitioners a favorable outcome measure alongside a meaningful response burden.
Based on the USPTO statutory response window for each pending office action. 4 of the docket's apps have a known mailing date; the rest are excluded from the tile counts.
Every pending office action with a known statutory deadline, placed on a days-until-due axis. Dots left of Today are overdue; the further right, the more runway. Cases that share a deadline window stack vertically. 4 of the docket's apps have a known mailing date.
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 only | 3 (14%) |
| §101 + other | 9 (43%) |
| §103 only | 5 (24%) |
| Multi-statute (no §101) | 4 (19%) |
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 |
|---|---|---|---|
| HALE, BROOKS T | 1 | 52.7% | +33.2% |
| JEUDY, JOSNEL | 1 | 83.9% | -16.0% |
| PULLIAS, JESSE SCOTT | 1 | 82.6% | +12.4% |
| FUELLING, MICHAEL | 1 | 44.1% | +31.1% |
| GAY, SONIA L | 1 | 82.4% | +11.4% |
| SAX, STEVEN PAUL | 1 | 69.0% | +45.9% |
| SMITH, BRIAN M | 1 | 52.5% | +36.9% |
| HASTY, NICHOLAS | 1 | 51.6% | +31.8% |
| MAUNI, HUMAIRA ZAHIN | 1 | 46.7% | +38.1% |
| VIRREIRA, ROLANDO PATRICK | 1 | — | — |
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 |
|---|---|---|---|
| 18789839 | MACHINE LEARNING BASED APPROACH FOR AUTOMATICALLY IDENTIFYING AND EXTRACTING TRANSACTIONS FROM WEBPAGES | PULLIAS, JESSE SCOTT | 28d |
| 18884841 | DETECTING SENSITIVE INFORMATION IN RECORDS USING CONTEXT AND DECOYS | JEUDY, JOSNEL | — |
Multi-statute / §101-driven matters, or cases in front of an examiner with an allow rate under 30%. The top 8 ordered by deadline are shown.
| App # | Title | Examiner | Due in |
|---|---|---|---|
| 18752964 | EVALUATING CONTEXT-SPECIFIC CONTENT GENERATED BY A GENERATIVE ARTIFICIAL INTELLIGENCE MODEL | GAY, SONIA L | 28d |
| 18789869 | DYNAMIC AUTOMATED RECOMMENDER SYSTEM WITH TUNABLE RECOMMENDATION DISTRIBUTIONS | FUELLING, MICHAEL | 42d |
| 18960567 | STRUCTURED PROMPT FRAMEWORK FOR MACHINE LEARNING MODEL OUPUT GENERATION | HALE, BROOKS T | — |
| 18734855 | ROBUST MULTI-HEAD REGRESSION METRICS FOR MACHINE LEARNING | SAX, STEVEN PAUL | — |
| 18680823 | TRAINING PREDICTIVE MODELS BASED ON REWARD SIGNALS | SMITH, BRIAN M | — |
| 18671148 | MACHINE LEARNING BASED APPROACH FOR TARGETED ITEM RECOMMENDATIONS BASED ON LATENT RELATIONSHIPS AMONG USER FEATURES AND ITEMS OF DIFFERENT TYPES | HASTY, NICHOLAS | — |
| 18661925 | AUTOMATED GENERATION OF A DATASET OF QUESTION-ANSWER PAIRS FOR DOMAIN-SPECIFIC HALLUCINATION TESTING OF GENERATIVE LANGUAGE PROCESSING MACHINE LEARNING MODELS | MAUNI, HUMAIRA ZAHIN | — |
| 18657190 | AUTOMATIC LABEL GENERATION WITH CONFIDENCE SCORES FOR TRAINING A MACHINE LEARNING MODEL TO PERFORM LINE ITEM EXTRACTION | VIRREIRA, ROLANDO PATRICK | — |
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 |
|---|---|---|---|
| 18789839 | MACHINE LEARNING BASED APPROACH FOR AUTOMATICALLY IDENTIFYING AND EXTRACTING TRANSACTIONS FROM WEBPAGES | PULLIAS, JESSE SCOTT | 28d |
| 18752964 | EVALUATING CONTEXT-SPECIFIC CONTENT GENERATED BY A GENERATIVE ARTIFICIAL INTELLIGENCE MODEL | GAY, SONIA L | 28d |
| 18789869 | DYNAMIC AUTOMATED RECOMMENDER SYSTEM WITH TUNABLE RECOMMENDATION DISTRIBUTIONS | FUELLING, MICHAEL | 42d |
| 18960567 | STRUCTURED PROMPT FRAMEWORK FOR MACHINE LEARNING MODEL OUPUT GENERATION | HALE, BROOKS T | — |
| 18734855 | ROBUST MULTI-HEAD REGRESSION METRICS FOR MACHINE LEARNING | SAX, STEVEN PAUL | — |
| 18680823 | TRAINING PREDICTIVE MODELS BASED ON REWARD SIGNALS | SMITH, BRIAN M | — |
| 18671148 | MACHINE LEARNING BASED APPROACH FOR TARGETED ITEM RECOMMENDATIONS BASED ON LATENT RELATIONSHIPS AMONG USER FEATURES AND ITEMS OF DIFFERENT TYPES | HASTY, NICHOLAS | — |
| 18661925 | AUTOMATED GENERATION OF A DATASET OF QUESTION-ANSWER PAIRS FOR DOMAIN-SPECIFIC HALLUCINATION TESTING OF GENERATIVE LANGUAGE PROCESSING MACHINE LEARNING MODELS | MAUNI, HUMAIRA ZAHIN | — |
| Client (Assignee) | Pending OAs |
|---|---|
| Intuit | 21 |
| Art Unit | Apps |
|---|---|
| 2124 | 3 |
| 2141 | 2 |
| 2166 | 1 |
| 2438 | 1 |
| 2655 | 1 |
| 3992 | 1 |
| 2657 | 1 |
| 2146 | 1 |
| 2122 | 1 |
| 2143 | 1 |
| App # | Title | Client | Examiner | Art Unit | Statutes | Status | Due in | AI | Filed |
|---|---|---|---|---|---|---|---|---|---|
| 18960567 | STRUCTURED PROMPT FRAMEWORK FOR MACHINE LEARNING MODEL OUPUT GENERATION | Intuit Inc. | HALE, BROOKS T | 2166 | §101 | Non-Final OA | — | Pending | Nov 26, 2024 |
| 18884841 | DETECTING SENSITIVE INFORMATION IN RECORDS USING CONTEXT AND DECOYS | Intuit Inc. | JEUDY, JOSNEL | 2438 | §103Other | Non-Final OA | — | Pending | Sep 13, 2024 |
| 18789839 | MACHINE LEARNING BASED APPROACH FOR AUTOMATICALLY IDENTIFYING AND EXTRACTING TRANSACTIONS FROM WEBPAGES | Intuit Inc. | PULLIAS, JESSE SCOTT | 2655 | §103 | Non-Final OA | 28d | Pending | Jul 31, 2024 |
| 18789869 | DYNAMIC AUTOMATED RECOMMENDER SYSTEM WITH TUNABLE RECOMMENDATION DISTRIBUTIONS | Intuit Inc. | FUELLING, MICHAEL | 3992 | §101§103 | Final Rejection | 42d | Pending | Jul 31, 2024 |
| 18752964 | EVALUATING CONTEXT-SPECIFIC CONTENT GENERATED BY A GENERATIVE ARTIFICIAL INTELLIGENCE MODEL | Intuit Inc. | GAY, SONIA L | 2657 | §102§103 | Final Rejection | 28d | Pending | Jun 25, 2024 |
| 18734855 | ROBUST MULTI-HEAD REGRESSION METRICS FOR MACHINE LEARNING | Intuit Inc. | SAX, STEVEN PAUL | 2146 | §101§103 | Non-Final OA | — | Pending | Jun 05, 2024 |
| 18680823 | TRAINING PREDICTIVE MODELS BASED ON REWARD SIGNALS | Intuit Inc. | SMITH, BRIAN M | 2122 | §101§102§103DP | Non-Final OA | — | Pending | May 31, 2024 |
| 18671148 | MACHINE LEARNING BASED APPROACH FOR TARGETED ITEM RECOMMENDATIONS BASED ON LATENT RELATIONSHIPS AMONG USER FEATURES AND ITEMS OF DIFFERENT TYPES | Intuit Inc. | HASTY, NICHOLAS | 2141 | §103§112 | Non-Final OA | — | Pending | May 22, 2024 |
| 18661925 | AUTOMATED GENERATION OF A DATASET OF QUESTION-ANSWER PAIRS FOR DOMAIN-SPECIFIC HALLUCINATION TESTING OF GENERATIVE LANGUAGE PROCESSING MACHINE LEARNING MODELS | Intuit Inc. | MAUNI, HUMAIRA ZAHIN | 2141 | §101§103 | Non-Final OA | — | Pending | May 13, 2024 |
| 18657190 | AUTOMATIC LABEL GENERATION WITH CONFIDENCE SCORES FOR TRAINING A MACHINE LEARNING MODEL TO PERFORM LINE ITEM EXTRACTION | Intuit Inc. | VIRREIRA, ROLANDO PATRICK | 2143 | §103§112 | Non-Final OA | — | Pending | May 07, 2024 |
| 18650651 | BENCHMARK CREATOR - AN ARTIFICIAL INTELLIGENCE-BASED APPROACH TO EVALUATING THE KNOWLEDGE OF A LANGUAGE MODEL FOR A DATASET | Intuit Inc. | ANDERSON, SCOTT C | 3694 | §101§103§112 | Non-Final OA | — | Pending | Apr 30, 2024 |
| 18651460 | MACHINE LEARNING BASED APPROACH FOR AUTOMATICALLY RECOMMENDING CONTEXT-SPECIFIC NAVIGATION OPTIONS WITHIN A USER INTERFACE | Intuit Inc. | NILSSON, ERIC | 2151 | §102§103 | Non-Final OA | — | Pending | Apr 30, 2024 |
| 18622276 | DECLARATIVE QUERY SCHEMA-BASED APPLICATION PROGRAMMING INTERFACE | Intuit Inc. | NGO, THANH | 2198 | §103 | Non-Final OA | — | Pending | Mar 29, 2024 |
| 18621368 | GENERATIVE ARTIFICIAL INTELLIGENCE BASED STATEFUL ADVICE SYSTEM | Intuit Inc. | MAC, GARY | 2127 | §101§103 | Non-Final OA | — | Pending | Mar 29, 2024 |
| 18612138 | AUTOMATED OPTIMIZATION OF EXTRACTION-BASED CATEGORIZATION PROCESSES | Intuit Inc. | NAULT, VICTOR ADELARD | 2124 | §101§103 | Non-Final OA | — | Pending | Mar 21, 2024 |
| 18421688 | SINGULARLY ADAPTIVE DIGITAL CONTENT GENERATION | Intuit Inc. | HUANG, YAO D | 2124 | §101§102§103§112 | Non-Final OA | — | Pending | Jan 24, 2024 |
| 18391919 | DECISION MODEL COMPRESSION FOR EFFICIENT PROCESSING AND STORAGE OF MULTI-CONDITION WORKFLOWS | Intuit Inc. | CHEIN, ALLEN C | 3627 | §101 | Final Rejection | — | Pending | Dec 21, 2023 |
| 18390359 | RE-TRAINING A MACHINE LEARNING MODEL IN REAL-TIME USING A FAST ALGORITHM | Intuit Inc. | RYLANDER, BART I | 2124 | §101§103 | Non-Final OA | — | Pending | Dec 20, 2023 |
| 18543798 | DATA SOURCE MAPPER FOR ENHANCED DATA RETRIEVAL | Intuit Inc. | CHEUNG, HUBERT G | 2152 | §103 | Non-Final OA | 20d | Pending | Dec 18, 2023 |
| 18480308 | DYNAMICALLY TARGETED NETWORK INVITATIONS | Intuit Inc. | WEBB III, JAMES L | 3624 | §101 | Non-Final OA | — | Pending | Oct 03, 2023 |
| 18067461 | Visual Question Answering for Discrete Document Field Extraction | Intuit Inc. | LEY, SALLY THI | 2147 | §103 | Non-Final OA | — | Pending | Dec 16, 2022 |
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Start Free Trial93.4% of resolved applications prosecuted by Patterson + Sheridan, LLP - Intuit Inc. were allowed, across 1,016 applications in USPTO records.
4.49 office actions per allowed application on average.
21 pending office actions as of October 2026, for 1 client, before 21 examiners in 18 art units.
