Detailed information about the 100 most recent patent applications.
| Application Number | Title | Filing Date | Disposal Date | Disposition | Time (months) | Office Actions | Restrictions | Interview | Appeal |
|---|---|---|---|---|---|---|---|---|---|
| 19078199 | WHOLE BRAIN EMULATION SYSTEM | March 2025 | September 2025 | Allow | 6 | 1 | 0 | Yes | No |
| 19013433 | METHOD FOR OPTIMIZING COMPUTING POWER OF NEURAL NETWORK MODULE, CHIP, ELECTRONIC DEVICE AND MEDIUM | January 2025 | March 2026 | Allow | 14 | 3 | 0 | No | No |
| 18959714 | VARIABLE CURVATURE BENDING ARC CONTROL METHOD FOR ROLL BENDING MACHINE | November 2024 | September 2025 | Allow | 10 | 3 | 0 | Yes | No |
| 18047397 | QUANTUM COMPUTING BASED KERNEL ALIGNMENT FOR A SUPPORT VECTOR MACHINE TASK | October 2022 | February 2026 | Allow | 40 | 1 | 0 | Yes | No |
| 17956638 | IDENTIFYING IDLE-CORES IN DATA CENTERS USING MACHINE-LEARNING (ML) | September 2022 | February 2026 | Allow | 40 | 1 | 0 | No | No |
| 17786650 | NEURAL NETWORK ACCELERATION CIRCUIT AND METHOD | June 2022 | January 2026 | Allow | 43 | 1 | 0 | Yes | No |
| 17805674 | Hierarchical Gradient Averaging For Enforcing Subject Level Privacy | June 2022 | September 2025 | Allow | 39 | 1 | 0 | No | No |
| 17664905 | COMPUTATIONAL STORAGE DEVICE FOR DEEP-LEARNING RECOMMENDATION SYSTEM AND METHOD OF OPERATING THE SAME | May 2022 | January 2026 | Allow | 44 | 2 | 0 | Yes | No |
| 17706298 | REDUCING CLASS IMBALANCE IN MACHINE-LEARNING TRAINING DATASET | March 2022 | October 2025 | Allow | 43 | 1 | 0 | No | No |
| 17761306 | Information Processing System, and Information Processing Program | March 2022 | March 2026 | Abandon | 48 | 2 | 0 | No | No |
| 17694063 | SCREENING FOR FLUCTUATING ENERGY RELAXATION TIMES | March 2022 | January 2026 | Allow | 47 | 1 | 0 | No | No |
| 17654400 | ELECTRONIC DEVICE AND CONVOLUTIONAL NEURAL NETWORK TRAINING METHOD | March 2022 | January 2026 | Abandon | 46 | 1 | 0 | No | No |
| 17638748 | NEURAL NETWORK PROCESSOR SYSTEM AND METHODS OF OPERATING AND FORMING THEREOF | February 2022 | January 2026 | Allow | 47 | 3 | 0 | No | No |
| 17559001 | OPTIMIZING ROUTE MODIFICATION USING QUANTUM GENERATED ROUTE REPOSITORY | December 2021 | September 2025 | Allow | 45 | 2 | 0 | Yes | No |
| 17391291 | TECHNIQUES FOR GENERATING MACHINE LEARNING TRAINED MODELS | August 2021 | March 2026 | Abandon | 55 | 3 | 1 | Yes | No |
| 15692889 | SELECTING OPERATING SYSTEMS BASED ON A COMPUTING DEVICE MODE | August 2017 | December 2019 | Allow | 28 | 2 | 0 | Yes | No |
| 15418969 | ARITHMETIC PROCESSING CIRCUIT AND INFORMATION PROCESSING APPARATUS | January 2017 | July 2020 | Allow | 41 | 2 | 0 | No | No |
| 15418452 | MULTI-AGENT PLAN RECOGNITION | January 2017 | July 2020 | Allow | 42 | 3 | 0 | Yes | No |
| 15416694 | CROP YIELD ESTIMATION USING AGRONOMIC NEURAL NETWORK | January 2017 | February 2020 | Allow | 37 | 1 | 0 | No | No |
| 15386393 | User Identification with Voiceprints on Online Social Networks | December 2016 | December 2019 | Allow | 35 | 1 | 0 | Yes | No |
| 15347052 | Performing Compliance Operations Using Cognitive Blockchains | November 2016 | March 2020 | Allow | 40 | 2 | 0 | No | No |
| 15223485 | MEASURING MUTUAL UNDERSTANDING IN HUMAN-COMPUTER CONVERSATION | July 2016 | May 2020 | Allow | 46 | 3 | 0 | Yes | No |
| 14135119 | Natural Language Processing With Adaptable Rules Based On User Inputs | December 2013 | March 2017 | Abandon | 39 | 6 | 0 | No | Yes |
This analysis examines appeal outcomes and the strategic value of filing appeals for examiner HICKS, AUSTIN JAMES.
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, 0.0% of applications that filed an appeal were subsequently allowed. This appeal filing benefit rate is in the bottom 25% across the USPTO, indicating that filing appeals is less effective here than in most other areas.
⚠ Filing a Notice of Appeal shows limited benefit. Consider other strategies like interviews or amendments before appealing.
Examiner HICKS, AUSTIN JAMES works in Art Unit 2142 and has examined 10 patent applications in our dataset. With an allowance rate of 80.0%, this examiner has a below-average tendency to allow applications. Applications typically reach final disposition in approximately 41 months.
Examiner HICKS, AUSTIN JAMES's allowance rate of 80.0% places them in the 49% percentile among all USPTO examiners. This examiner has a below-average tendency to allow applications.
On average, applications examined by HICKS, AUSTIN JAMES receive 2.50 office actions before reaching final disposition. This places the examiner in the 73% percentile for office actions issued. This examiner issues a slightly above-average number of office actions.
The median time to disposition (half-life) for applications examined by HICKS, AUSTIN JAMES is 41 months. This places the examiner in the 21% percentile for prosecution speed. Applications take longer to reach final disposition with this examiner compared to most others.
Conducting an examiner interview provides a +8.3% benefit to allowance rate for applications examined by HICKS, AUSTIN JAMES. This interview benefit is in the 39% percentile among all examiners. Recommendation: Interviews provide a below-average benefit with this examiner.
When applicants file an RCE with this examiner, 41.7% of applications are subsequently allowed. This success rate is in the 93% percentile among all examiners. Strategic Insight: RCEs are highly effective with this examiner compared to others. If you receive a final rejection, filing an RCE with substantive amendments or arguments has a strong likelihood of success.
This examiner enters after-final amendments leading to allowance in 28.6% of cases where such amendments are filed. This entry rate is in the 40% percentile among all examiners. Strategic Recommendation: This examiner shows below-average receptiveness to after-final amendments. You may need to file an RCE or appeal rather than relying on after-final amendment entry.
This examiner withdraws rejections or reopens prosecution in 100.0% of appeals filed. This is in the 89% percentile among all examiners. Of these withdrawals, 100.0% occur early in the appeal process (after Notice of Appeal but before Appeal Brief). Strategic Insight: This examiner frequently reconsiders rejections during the appeal process compared to other examiners. Per MPEP § 1207.01, all appeals must go through a mandatory appeal conference. Filing a Notice of Appeal may prompt favorable reconsideration even before you file an Appeal Brief.
When applicants file petitions regarding this examiner's actions, 100.0% are granted (fully or in part). This grant rate is in the 90% 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 10% 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.