Technology areas: Computing & Software • Communications • Transportation, E-Commerce & Mechanical Systems
9 pending office actions • 1 client • 9 examiners • 9 art units • 0 of 9 (0%) have an AI response strategy ready • 30 patents granted in the last 365 days
This Intuit profile covers Transportation, E-Commerce & Mechanical Systems and shows 9 pending office actions. The associated career record includes 21180 applications, a resolved allowance rate of 84.39%, and an average of 3.7 office actions per allowance.
The broader figures indicate that a majority of resolved matters reached allowance and that allowed matters generally involved multiple examination exchanges. They provide context for the associated record but do not determine the outcome of the open Intuit matters.
For practitioners, the transportation and e-commerce focus calls for clear treatment of system functionality, user interaction, and mechanical features where applicable. The open docket should be managed by deadline and claim importance, with historical figures used for process review rather than matter-specific prediction.
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 | 2 (22%) |
| §101 + other | 5 (56%) |
| §103 only | 2 (22%) |
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 |
|---|---|---|---|
| PATEL, SHREYANS A | 1 | 88.4% | +7.9% |
| VIG, NARESH | 1 | 36.6% | +43.2% |
| BEKERMAN, MICHAEL | 1 | 32.9% | +30.2% |
| MANOHARAN, SHASHIDHAR SHANKAR | 1 | 75.0% | +40.0% |
| MUDRICK, TIMOTHY A | 1 | 83.7% | +14.5% |
| LI, LIANG Y | 1 | 62.0% | +68.3% |
| ZARRINEH, SHAHRIAR | 1 | 77.3% | +6.9% |
| LIU, CHIA-YI | 1 | 27.2% | +20.5% |
| KRINGEN, MICHELLE THERESE | 1 | 56.3% | +38.3% |
Cases in front of an examiner with an allow rate of 80%+ where the difficulty is Easy or Medium. The top 1 ordered by deadline are shown.
| App # | Title | Examiner | Due in |
|---|---|---|---|
| 18435975 | CONFIGURABLE API CONNECTOR | MUDRICK, TIMOTHY A | — |
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 |
|---|---|---|---|
| 18767899 | END-TO-END AUTOMATED LARGE LANGUAGE MODEL EVALUATION AND DEPLOYMENT | PATEL, SHREYANS A | 8d overdue |
| 18496813 | AUTOMATED RECOMMENDATIONS FOR HIERARCHICAL DATA STRUCTURES | LIU, CHIA-YI | 9d |
| 18674732 | COLLABORATIVE COMPONENTS FRAMEWORK FOR CONTENT-BASED RECOMMENDATIONS SYSTEM | VIG, NARESH | 34d |
| 18674736 | PERSONALIZED CAMPAIGN GENERATION THROUGH DEEP CUSTOMER LEARNING | BEKERMAN, MICHAEL | 62d |
| 18651668 | SYSTEMS AND METHODS FOR PERSONALIZED SUMMARIZATION TECHNIQUES USING RETRIEVAL AUGMENTED GENERATION | MANOHARAN, SHASHIDHAR SHANKAR | — |
| 18426226 | PRE-FILTERING FOR AN ARTIFICIAL INTELLEGENCE PIPELINE | LI, LIANG Y | — |
| 18450931 | SYSTEMS AND METHODS FOR GENERATING RECOMMENDATIONS USING CONTEXTUAL BANDIT MODELS WITH NON-LINEAR ORACLES | KRINGEN, MICHELLE THERESE | — |
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 7 ordered by deadline are shown.
| App # | Title | Examiner | Due in |
|---|---|---|---|
| 18496813 | AUTOMATED RECOMMENDATIONS FOR HIERARCHICAL DATA STRUCTURES | LIU, CHIA-YI | 9d |
| 18674732 | COLLABORATIVE COMPONENTS FRAMEWORK FOR CONTENT-BASED RECOMMENDATIONS SYSTEM | VIG, NARESH | 34d |
| 18674736 | PERSONALIZED CAMPAIGN GENERATION THROUGH DEEP CUSTOMER LEARNING | BEKERMAN, MICHAEL | 62d |
| 18651668 | SYSTEMS AND METHODS FOR PERSONALIZED SUMMARIZATION TECHNIQUES USING RETRIEVAL AUGMENTED GENERATION | MANOHARAN, SHASHIDHAR SHANKAR | — |
| 18435975 | CONFIGURABLE API CONNECTOR | MUDRICK, TIMOTHY A | — |
| 18426226 | PRE-FILTERING FOR AN ARTIFICIAL INTELLEGENCE PIPELINE | LI, LIANG Y | — |
| 18450931 | SYSTEMS AND METHODS FOR GENERATING RECOMMENDATIONS USING CONTEXTUAL BANDIT MODELS WITH NON-LINEAR ORACLES | KRINGEN, MICHELLE THERESE | — |
| Client (Assignee) | Pending OAs |
|---|---|
| Intuit | 9 |
| Art Unit | Apps |
|---|---|
| 2659 | 1 |
| 3622 | 1 |
| 3621 | 1 |
| 2655 | 1 |
| 2198 | 1 |
| 2143 | 1 |
| 2496 | 1 |
| 3692 | 1 |
| 3689 | 1 |
| App # | Title | Client | Examiner | Art Unit | Statutes | Status | Due in | AI | Filed |
|---|---|---|---|---|---|---|---|---|---|
| 18767899 | END-TO-END AUTOMATED LARGE LANGUAGE MODEL EVALUATION AND DEPLOYMENT | Intuit Inc. | PATEL, SHREYANS A | 2659 | §101§103 | Final Rejection | 8d overdue | Pending | Jul 09, 2024 |
| 18674732 | COLLABORATIVE COMPONENTS FRAMEWORK FOR CONTENT-BASED RECOMMENDATIONS SYSTEM | Intuit Inc. | VIG, NARESH | 3622 | §101§103 | Final Rejection | 34d | Pending | May 24, 2024 |
| 18674736 | PERSONALIZED CAMPAIGN GENERATION THROUGH DEEP CUSTOMER LEARNING | Intuit Inc. | BEKERMAN, MICHAEL | 3621 | §101 | Non-Final OA | 62d | Pending | May 24, 2024 |
| 18651668 | SYSTEMS AND METHODS FOR PERSONALIZED SUMMARIZATION TECHNIQUES USING RETRIEVAL AUGMENTED GENERATION | Intuit Inc. | MANOHARAN, SHASHIDHAR SHANKAR | 2655 | §101§103 | Final Rejection | — | Pending | Apr 30, 2024 |
| 18435975 | CONFIGURABLE API CONNECTOR | Intuit Inc. | MUDRICK, TIMOTHY A | 2198 | §103 | Final Rejection | — | Pending | Feb 07, 2024 |
| 18426226 | PRE-FILTERING FOR AN ARTIFICIAL INTELLEGENCE PIPELINE | Intuit Inc. | LI, LIANG Y | 2143 | §101§103 | Non-Final OA | — | Pending | Jan 29, 2024 |
| 18525333 | API SECURITY SENSITIVE FIELDS | Intuit Inc. | ZARRINEH, SHAHRIAR | 2496 | §103 | Non-Final OA | — | Pending | Nov 30, 2023 |
| 18496813 | AUTOMATED RECOMMENDATIONS FOR HIERARCHICAL DATA STRUCTURES | Intuit Inc. | LIU, CHIA-YI | 3692 | §101 | Final Rejection | 9d | Pending | Oct 27, 2023 |
| 18450931 | SYSTEMS AND METHODS FOR GENERATING RECOMMENDATIONS USING CONTEXTUAL BANDIT MODELS WITH NON-LINEAR ORACLES | Intuit Inc. | KRINGEN, MICHELLE THERESE | 3689 | §101§102§103 | Non-Final OA | — | Pending | Aug 16, 2023 |
IP Author helps law firms respond to office actions faster with AI-generated responses, examiner analytics, and prosecution intelligence.
Start Free Trial84.4% of resolved applications prosecuted by Dla Piper LLP US - Intuit were allowed, across 21,180 applications in USPTO records.
3.70 office actions per allowed application on average.
9 pending office actions as of October 2026, for 1 client, before 9 examiners in 9 art units.
