Prosecution Insights
Prosecution Insights
Technology areas: Communications • Transportation, E-Commerce & Mechanical Systems
9 pending office actions • 6 art units • 9 examiners • 0 of 9 (0%) have an AI response strategy ready • 3 patents granted in the last 365 days
Qualtrics LLC is managing 9 pending office actions within the Transportation, E-Commerce & Mechanical Systems technology area. These actions are distributed among 9 distinct examiners, meaning no pending actions are currently assigned to the same individual. HARMON, COURTNEY N is the busiest examiner, though they only account for 1 pending office action.
The portfolio's activity is spread across 6 distinct art units, indicating a wide technical range within the designated technology area. With the current volume of pending office actions and 9 distinct examiners, the prosecution strategy is highly decentralized. Practitioners must navigate different examiner perspectives across the 6 distinct art units.
This lack of examiner overlap means that insights gained from 1 office action may not directly apply to others. The presence of the distinct art units suggests that while the technology area is Transportation, E-Commerce & Mechanical Systems, the 9 pending office actions involve several different specialized classifications. Managing these actions requires addressing the specific requirements of the different examiners, including HARMON, COURTNEY N.
Based on the USPTO statutory response window for each pending office action. 1 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 only | 2 (22%) |
| §101 + other | 4 (44%) |
| §103 only | 2 (22%) |
| Double-patenting + other | 1 (11%) |
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 |
|---|---|---|---|
| HARMON, COURTNEY N | 1 | 63.0% | +8.5% |
| DIVELBISS, MATTHEW H | 1 | 24.0% | +25.2% |
| GODBOLD, DOUGLAS | 1 | 83.3% | +10.6% |
| WALTON, CHESIREE A | 1 | 31.0% | +29.0% |
| FEACHER, LORENA R | 1 | 28.5% | +32.1% |
| KHATTAR, RAJESH | 1 | 36.7% | +35.2% |
| GARCIA-GUERRA, DARLENE | 1 | 23.3% | +34.3% |
| PASHA, ATHAR N | 1 | 90.5% | +15.2% |
| BYRD, UCHE SOWANDE | 1 | 22.6% | +26.7% |
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 |
|---|---|---|---|
| 18940609 | ORCHESTRATING MACHINE LEARNING MODELS TO CREATE SAFE, ROBUST, PERSONALIZED, BRAND EXPERIENCES BETWEEN USERS AND AN ARTIFICIAL INTELLIGENCE AGENT | GODBOLD, DOUGLAS | — |
| 18438230 | GENERATING COMMUNICATION SUMMARIES USING ARTIFICIAL INTELLIGENCE MODELS, SUMMARY TEMPLATES, AND ENRICHED TRANSCRIPTS | PASHA, ATHAR N | — |
Multi-statute / §101-driven matters, or cases in front of an examiner with an allow rate under 30%. The top 6 ordered by deadline are shown.
| App # | Title | Examiner | Due in |
|---|---|---|---|
| 18929363 | DETERMINING AND APPLYING ATTRIBUTE DEFINITIONS TO DIGITAL SURVEY DATA TO GENERATE SURVEY ANALYSES | WALTON, CHESIREE A | 3d overdue |
| 19205455 | UTILIZING A KNOWLEDGE GRAPH TO IMPLEMENT A DIGITAL SURVEY SYSTEM | DIVELBISS, MATTHEW H | — |
| 18906980 | UTILIZING LARGE LANGUAGE MODELS TO GENERATE OBFUSCATED SUMMARIES OF EMPLOYEE FEEDBACK DATA AND MODIFICATION SUGGESTIONS BASED ON THE EMPLOYEE FEEDBACK DATA | FEACHER, LORENA R | — |
| 18824067 | DETERMINING FRAUDULENT SURVEY RESPONSES TO DIGITAL SURVEYS USING RULE-BASED MODELS AND MACHINE-LEARNING MODELS | KHATTAR, RAJESH | — |
| 18824151 | GENERATING CUSTOMIZED FOLLOW-UP SURVEY INQUIRIES BASED ON RESPONSE QUALITY IN REAL TIME UTILIZING A MULTIMODAL MODEL | GARCIA-GUERRA, DARLENE | — |
| 17929596 | GENERATING AND PROVIDING AN ACCOUNT PRIORITIZATION SCORE BY INTEGRATING EXPERIENCE DATA AND ORGANIZATION DATA | BYRD, UCHE SOWANDE | — |
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 |
|---|---|---|---|
| 18929363 | DETERMINING AND APPLYING ATTRIBUTE DEFINITIONS TO DIGITAL SURVEY DATA TO GENERATE SURVEY ANALYSES | WALTON, CHESIREE A | 3d overdue |
| 19205455 | UTILIZING A KNOWLEDGE GRAPH TO IMPLEMENT A DIGITAL SURVEY SYSTEM | DIVELBISS, MATTHEW H | — |
| 18940609 | ORCHESTRATING MACHINE LEARNING MODELS TO CREATE SAFE, ROBUST, PERSONALIZED, BRAND EXPERIENCES BETWEEN USERS AND AN ARTIFICIAL INTELLIGENCE AGENT | GODBOLD, DOUGLAS | — |
| 18906980 | UTILIZING LARGE LANGUAGE MODELS TO GENERATE OBFUSCATED SUMMARIES OF EMPLOYEE FEEDBACK DATA AND MODIFICATION SUGGESTIONS BASED ON THE EMPLOYEE FEEDBACK DATA | FEACHER, LORENA R | — |
| 18824067 | DETERMINING FRAUDULENT SURVEY RESPONSES TO DIGITAL SURVEYS USING RULE-BASED MODELS AND MACHINE-LEARNING MODELS | KHATTAR, RAJESH | — |
| 18824151 | GENERATING CUSTOMIZED FOLLOW-UP SURVEY INQUIRIES BASED ON RESPONSE QUALITY IN REAL TIME UTILIZING A MULTIMODAL MODEL | GARCIA-GUERRA, DARLENE | — |
| 18438230 | GENERATING COMMUNICATION SUMMARIES USING ARTIFICIAL INTELLIGENCE MODELS, SUMMARY TEMPLATES, AND ENRICHED TRANSCRIPTS | PASHA, ATHAR N | — |
| 17929596 | GENERATING AND PROVIDING AN ACCOUNT PRIORITIZATION SCORE BY INTEGRATING EXPERIENCE DATA AND ORGANIZATION DATA | BYRD, UCHE SOWANDE | — |
| Art Unit | Apps |
|---|---|
| 3624 | 2 |
| 3625 | 2 |
| 2159 | 1 |
| 2655 | 1 |
| 3684 | 1 |
| 2657 | 1 |
| App # | Title | Examiner | Art Unit | Statutes | Status | Due in | AI | Filed |
|---|---|---|---|---|---|---|---|---|
| 19236735 | GENERATING RESPONSES TO REAL-TIME USER EVENTS UTILIZING USER PROFILE ATTRIBUTES AND A USER'S JOURNEY STATE OF AN EXPERIENCE JOURNEY | HARMON, COURTNEY N | 2159 | §112DP | Final Rejection | — | Pending | Jun 12, 2025 |
| 19205455 | UTILIZING A KNOWLEDGE GRAPH TO IMPLEMENT A DIGITAL SURVEY SYSTEM | DIVELBISS, MATTHEW H | — | §101§103 | Non-Final OA | — | Pending | May 12, 2025 |
| 18940609 | ORCHESTRATING MACHINE LEARNING MODELS TO CREATE SAFE, ROBUST, PERSONALIZED, BRAND EXPERIENCES BETWEEN USERS AND AN ARTIFICIAL INTELLIGENCE AGENT | GODBOLD, DOUGLAS | 2655 | §103 | Final Rejection | — | Pending | Nov 07, 2024 |
| 18929363 | DETERMINING AND APPLYING ATTRIBUTE DEFINITIONS TO DIGITAL SURVEY DATA TO GENERATE SURVEY ANALYSES | WALTON, CHESIREE A | 3624 | §101§103 | Final Rejection | 3d overdue | Pending | Oct 28, 2024 |
| 18906980 | UTILIZING LARGE LANGUAGE MODELS TO GENERATE OBFUSCATED SUMMARIES OF EMPLOYEE FEEDBACK DATA AND MODIFICATION SUGGESTIONS BASED ON THE EMPLOYEE FEEDBACK DATA | FEACHER, LORENA R | 3625 | §101§103§112 | Final Rejection | — | Pending | Oct 04, 2024 |
| 18824067 | DETERMINING FRAUDULENT SURVEY RESPONSES TO DIGITAL SURVEYS USING RULE-BASED MODELS AND MACHINE-LEARNING MODELS | KHATTAR, RAJESH | 3684 | §101 | Final Rejection | — | Pending | Sep 04, 2024 |
| 18824151 | GENERATING CUSTOMIZED FOLLOW-UP SURVEY INQUIRIES BASED ON RESPONSE QUALITY IN REAL TIME UTILIZING A MULTIMODAL MODEL | GARCIA-GUERRA, DARLENE | 3625 | §101§103 | Final Rejection | — | Pending | Sep 04, 2024 |
| 18438230 | GENERATING COMMUNICATION SUMMARIES USING ARTIFICIAL INTELLIGENCE MODELS, SUMMARY TEMPLATES, AND ENRICHED TRANSCRIPTS | PASHA, ATHAR N | 2657 | §103 | Non-Final OA | — | Pending | Feb 09, 2024 |
| 17929596 | GENERATING AND PROVIDING AN ACCOUNT PRIORITIZATION SCORE BY INTEGRATING EXPERIENCE DATA AND ORGANIZATION DATA | BYRD, UCHE SOWANDE | 3624 | §101 | Non-Final OA | — | Pending | Sep 02, 2022 |
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