Detailed information about the 100 most recent patent applications.
| Application Number | Title | Filing Date | Disposal Date | Disposition | Time (months) | Office Actions | Restrictions | Interview | Appeal |
|---|---|---|---|---|---|---|---|---|---|
| 18793742 | RESOURCE CONSERVATION IN ARTIFICIAL INTELLIGENCE PIPELINE EXECUTION | August 2024 | November 2025 | Abandon | 16 | 2 | 0 | Yes | No |
| 18442254 | Stochastic Gradient Boosting For Deep Neural Networks | February 2024 | February 2026 | Allow | 24 | 1 | 0 | No | No |
| 18411709 | METHODS AND APPARATUSES FOR RESOURCE-OPTIMIZED FERMIONIC LOCAL SIMULATION ON QUANTUM COMPUTER FOR QUANTUM CHEMISTRY | January 2024 | October 2025 | Allow | 22 | 2 | 0 | Yes | No |
| 18148670 | METHODS AND APPARATUSES FOR RESOURCE-OPTIMIZED FERMIONIC LOCAL SIMULATION ON QUANTUM COMPUTER FOR QUANTUM CHEMISTRY | December 2022 | October 2025 | Allow | 34 | 3 | 0 | No | No |
| 17988900 | METHOD AND SYSTEM FOR TRAINING NEURAL NETWORK FOR GENERATING SEARCH STRING | November 2022 | March 2026 | Abandon | 40 | 1 | 0 | No | No |
| 17655803 | DESIGNING A FAIR MACHINE LEARNING MODEL THROUGH USER INTERACTION | March 2022 | February 2026 | Abandon | 46 | 2 | 0 | Yes | No |
| 17696603 | KNOWLEDGE-BASED VALIDATION OF EXTRACTED ENTITIES WITH CONFIDENCE CALIBRATION | March 2022 | September 2025 | Abandon | 42 | 1 | 0 | No | No |
| 17583830 | TOUCH-RELATED CONTAMINATION STATE DETERMINATIONS | January 2022 | November 2025 | Abandon | 46 | 1 | 0 | No | No |
| 17622448 | Classifying Data by Manipulating the Quantum States of Qubits | December 2021 | September 2025 | Allow | 45 | 1 | 0 | No | No |
| 17381853 | COMPUTER-READABLE RECORDING MEDIUM STORING INFORMATION PROCESSING PROGRAM, INFORMATION PROCESSING METHOD, AND INFORMATION PROCESSING DEVICE | July 2021 | March 2025 | Abandon | 44 | 1 | 0 | No | No |
| 17376256 | AIR QUALITY PREDICTION MODEL TRAINING METHOD, AIR QUALITY PREDICTION METHOD, ELECTRONIC DEVICE AND STORAGE MEDIUM | July 2021 | December 2025 | Abandon | 53 | 2 | 0 | No | No |
| 17356712 | INFORMATION PROCESSING APPARATUS, INFORMATION PROCESSING METHOD AND COMPUTER READABLE MEDIUM | June 2021 | August 2025 | Abandon | 50 | 2 | 0 | Yes | No |
| 17349175 | MODEL PARAMETER TRAINING METHOD, APPARATUS, AND DEVICE BASED ON FEDERATION LEARNING, AND MEDIUM | June 2021 | October 2025 | Abandon | 52 | 2 | 0 | No | No |
| 17206731 | LEARNING DATA PROCESSING TO IMPROVE QUALITY OF LEARNING DATA | March 2021 | July 2025 | Abandon | 52 | 2 | 0 | Yes | No |
| 17204209 | Method and Apparatus for Outputting Information, Device and Storage Medium | March 2021 | May 2025 | Abandon | 50 | 2 | 0 | No | No |
| 17196037 | QUANTUM CIRCUIT AND METHODS FOR USE THEREWITH | March 2021 | July 2025 | Allow | 52 | 2 | 0 | No | Yes |
| 17177813 | METHODS AND APPARATUSES FOR RESOURCE-OPTIMIZED FERMIONIC LOCAL SIMULATION ON QUANTUM COMPUTER FOR QUANTUM CHEMISTRY | February 2021 | November 2025 | Allow | 57 | 3 | 0 | No | No |
| 17159463 | FEATURE RANDOMIZATION FOR SECURING MACHINE LEARNING MODELS | January 2021 | July 2025 | Abandon | 54 | 2 | 0 | Yes | No |
| 17142137 | Risk-Reliability Framework for Evaluating Synthetic Data Models | January 2021 | August 2025 | Abandon | 55 | 3 | 0 | Yes | No |
| 17139790 | CONTENT TARGETING USING CONTENT CONTEXT AND USER PROPENSITY | December 2020 | March 2026 | Allow | 60 | 5 | 0 | Yes | No |
| 17120400 | HORIZON-BASED SMOOTHING OF FORECASTING MODEL | December 2020 | July 2025 | Abandon | 55 | 4 | 0 | Yes | No |
| 17080312 | APPARATUS AND METHOD FOR EVALUATING THE PERFORMANCE OF DEEP LEARNING MODELS | October 2020 | December 2024 | Abandon | 50 | 2 | 0 | No | No |
| 17070786 | EVENT DRIVEN CONFIGURABLE ARTIFICIAL INTELLIGENCE WORKFLOW | October 2020 | February 2026 | Abandon | 60 | 4 | 0 | Yes | No |
| 17030051 | NEURAL NETWORK WEIGHT DISTRIBUTION USING A TREE DIRECT-MEMORY ACCESS (DMA) BUS | September 2020 | June 2025 | Abandon | 57 | 3 | 0 | Yes | No |
| 16998515 | COMPUTER-IMPLEMENTED METHOD FOR TRAINING A MODEL, METHOD FOR CONTROLLING, ASSISTANCE AND CLASSIFICATION SYSTEM | August 2020 | November 2024 | Abandon | 51 | 2 | 0 | No | No |
| 16971482 | A LEARNING DEVICE AND LEARNING METHOD FOR LEARNING A CLASSIFIER USING A NEURAL NETWORK | August 2020 | December 2024 | Abandon | 52 | 2 | 0 | Yes | No |
| 16991088 | MACHINE LEARNING DEVICE, MACHINE LEARNING METHOD, AND MACHINE LEARNING PROGRAM | August 2020 | November 2025 | Abandon | 60 | 6 | 0 | Yes | No |
| 16986719 | MACHINE LEARNING SYSTEM USING A STOCHASTIC PROCESS AND METHOD | August 2020 | October 2025 | Abandon | 60 | 3 | 0 | No | No |
| 16965308 | METHOD AND SYSTEM FOR GENERATING HUMOROUS PERSONALITY INFORMATION FOR ROBOT BY USING KNOWLEDGE BASE | July 2020 | December 2025 | Abandon | 60 | 5 | 0 | No | No |
| 16928098 | AUTOMATIC GENERATION OF ODATA SERVICES FROM SKETCHES USING DEEP LEARNING | July 2020 | June 2025 | Abandon | 59 | 4 | 0 | Yes | No |
| 16912052 | DATA SAMPLE ANALYSIS IN A DATASET FOR A MACHINE LEARNING MODEL | June 2020 | March 2025 | Abandon | 57 | 6 | 0 | No | No |
| 16839896 | FEDERATED LEARNING OF MACHINE LEARNING MODEL FEATURES | April 2020 | January 2026 | Abandon | 60 | 7 | 0 | Yes | No |
| 16712329 | System and Method for Robust Optimization for Trajectory-Centric ModelBased Reinforcement Learning | December 2019 | December 2024 | Abandon | 60 | 4 | 0 | Yes | No |
| 16658914 | ELECTRONIC DEVICE AND METHOD FOR CONTROLLING ELECTRONIC DEVICE THEREOF | October 2019 | February 2025 | Abandon | 60 | 7 | 0 | Yes | No |
| 16550520 | Method, System, and Computer Program Product for Maintaining Model State | August 2019 | October 2025 | Abandon | 60 | 6 | 0 | Yes | No |
| 16524440 | SYSTEMS AND METHODS FOR OPTIMIZING MACHINE LEARNING MODELS BY SUMMARIZING LIST CHARACTERISTICS BASED ON MULTI-DIMENSIONAL FEATURE VECTORS | July 2019 | April 2025 | Allow | 60 | 4 | 0 | Yes | No |
| 16411090 | COMMUNICATING IN A FEDERATED LEARNING ENVIRONMENT | May 2019 | July 2025 | Abandon | 60 | 8 | 0 | Yes | No |
| 16237197 | NEURAL NETWORK ACTIVATION COMPRESSION WITH NARROW BLOCK FLOATING-POINT | December 2018 | June 2025 | Allow | 60 | 4 | 0 | Yes | Yes |
| 16223092 | LOSSY COMPRESSION OF NEURAL NETWORK ACTIVATION MAPS | December 2018 | February 2026 | Allow | 60 | 7 | 0 | Yes | No |
| 16009815 | REINFORCEMENT LEARNING EXPLORATION BY EXPLOITING PAST EXPERIENCES FOR CRITICAL EVENTS | June 2018 | December 2025 | Allow | 60 | 8 | 0 | Yes | Yes |
This analysis examines appeal outcomes and the strategic value of filing appeals for examiner FEITL, LEAH M.
With a 100.0% reversal rate, the PTAB has reversed the examiner's rejections more often than affirming them. This reversal rate is in the top 25% across the USPTO, indicating that appeals are more successful here than in most other areas.
Filing a Notice of Appeal can sometimes lead to allowance even before the appeal is fully briefed or decided by the PTAB. This occurs when the examiner or their supervisor reconsiders the rejection during the mandatory appeal conference (MPEP § 1207.01) after the appeal is filed.
In this dataset, 66.7% of applications that filed an appeal were subsequently allowed. This appeal filing benefit rate is in the top 25% across the USPTO, indicating that filing appeals is particularly effective here. The act of filing often prompts favorable reconsideration during the mandatory appeal conference.
✓ Appeals to PTAB show good success rates. If you have a strong case on the merits, consider fully prosecuting the appeal to a Board decision.
✓ Filing a Notice of Appeal is strategically valuable. The act of filing often prompts favorable reconsideration during the mandatory appeal conference.
Examiner FEITL, LEAH M works in Art Unit 2147 and has examined 32 patent applications in our dataset. With an allowance rate of 25.0%, this examiner allows applications at a lower rate than most examiners at the USPTO. Applications typically reach final disposition in approximately 57 months.
Examiner FEITL, LEAH M's allowance rate of 25.0% places them in the 3% percentile among all USPTO examiners. This examiner is less likely to allow applications than most examiners at the USPTO.
On average, applications examined by FEITL, LEAH M receive 3.84 office actions before reaching final disposition. This places the examiner in the 96% percentile for office actions issued. This examiner issues more office actions than most examiners, which may indicate thorough examination or difficulty in reaching agreement with applicants.
The median time to disposition (half-life) for applications examined by FEITL, LEAH M is 57 months. This places the examiner in the 1% percentile for prosecution speed. Applications take longer to reach final disposition with this examiner compared to most others.
Conducting an examiner interview provides a +0.0% benefit to allowance rate for applications examined by FEITL, LEAH M. This interview benefit is in the 13% percentile among all examiners. Note: Interviews show limited statistical benefit with this examiner compared to others, though they may still be valuable for clarifying issues.
When applicants file an RCE with this examiner, 6.8% of applications are subsequently allowed. This success rate is in the 3% percentile among all examiners. Strategic Insight: RCEs show lower effectiveness with this examiner compared to others. Consider whether a continuation application might be more strategic, especially if you need to add new matter or significantly broaden claims.
This examiner enters after-final amendments leading to allowance in 4.8% of cases where such amendments are filed. This entry rate is in the 5% percentile among all examiners. Strategic Recommendation: This examiner rarely enters after-final amendments compared to other examiners. You should generally plan to file an RCE or appeal rather than relying on after-final amendment entry. Per MPEP § 714.12, primary examiners have discretion in entering after-final amendments, and this examiner exercises that discretion conservatively.
When applicants request a pre-appeal conference (PAC) with this examiner, 0.0% result in withdrawal of the rejection or reopening of prosecution. This success rate is in the 6% percentile among all examiners. Note: Pre-appeal conferences show limited success with this examiner compared to others. While still worth considering, be prepared to proceed with a full appeal brief if the PAC does not result in favorable action.
This examiner withdraws rejections or reopens prosecution in 60.0% of appeals filed. This is in the 35% percentile among all examiners. Strategic Insight: This examiner shows below-average willingness to reconsider rejections during appeals. Be prepared to fully prosecute appeals if filed.
When applicants file petitions regarding this examiner's actions, 200.0% are granted (fully or in part). This grant rate is in the 98% percentile among all examiners. Strategic Note: Petitions are frequently granted regarding this examiner's actions compared to other examiners. Per MPEP § 1002.02(c), various examiner actions are petitionable to the Technology Center Director, including prematureness of final rejection, refusal to enter amendments, and requirement for information. If you believe an examiner action is improper, consider filing a petition.
Examiner's Amendments: This examiner makes examiner's amendments in 0.0% of allowed cases (in the 11% percentile). This examiner rarely makes examiner's amendments compared to other examiners. You should expect to make all necessary claim amendments yourself through formal amendment practice.
Quayle Actions: This examiner issues Ex Parte Quayle actions in 0.0% of allowed cases (in the 11% percentile). This examiner rarely issues Quayle actions compared to other examiners. Allowances typically come directly without a separate action for formal matters.
Based on the statistical analysis of this examiner's prosecution patterns, here are tailored strategic recommendations:
Not Legal Advice: The information provided in this report is for informational purposes only and does not constitute legal advice. You should consult with a qualified patent attorney or agent for advice specific to your situation.
No Guarantees: We do not provide any guarantees as to the accuracy, completeness, or timeliness of the statistics presented above. Patent prosecution statistics are derived from publicly available USPTO data and are subject to data quality limitations, processing errors, and changes in USPTO practices over time.
Limitation of Liability: Under no circumstances will IronCrow AI be liable for any outcome, decision, or action resulting from your reliance on the statistics, analysis, or recommendations presented in this report. Past prosecution patterns do not guarantee future results.
Use at Your Own Risk: While we strive to provide accurate and useful prosecution statistics, you should independently verify any information that is material to your prosecution strategy and use your professional judgment in all patent prosecution matters.