Detailed information about the 100 most recent patent applications.
| Application Number | Title | Filing Date | Disposal Date | Disposition | Time (months) | Office Actions | Restrictions | Interview | Appeal |
|---|---|---|---|---|---|---|---|---|---|
| 19204644 | METHOD AND DEVICE FOR TRAINING NEURAL NETWORK MODEL | May 2025 | February 2026 | Allow | 9 | 1 | 0 | No | No |
| 17590930 | METHOD FOR DETERMINING CLASS OF DATA TO BE DETERMINED USING MACHINE LEARNING MODEL, INFORMATION PROCESSING DEVICE, AND COMPUTER PROGRAM | February 2022 | March 2026 | Allow | 49 | 3 | 0 | No | No |
| 17557599 | DETERMINING PERFORMANCE CHANGE WITHIN A DATASET WITH AN APPLIED CONDITION USING MACHINE LEARNING MODELS | December 2021 | January 2026 | Allow | 48 | 2 | 0 | Yes | No |
| 17495214 | Method, System, and Computer Program Product for Knowledge Graph Based Embedding, Explainability, and/or Multi-Task Learning | October 2021 | October 2025 | Allow | 49 | 2 | 0 | Yes | No |
| 17477493 | Distributed Fault Detection | September 2021 | March 2026 | Allow | 54 | 3 | 0 | Yes | No |
| 17410400 | LABELING AN UNLABELED DATASET | August 2021 | July 2025 | Abandon | 46 | 1 | 0 | No | No |
| 17402100 | MACHINE LEARNING TECHNIQUES FOR EFFICIENT DATA PATTERN RECOGNITION ACROSS STRUCTURED DATA OBJECTS | August 2021 | September 2025 | Allow | 49 | 2 | 0 | Yes | No |
| 17402454 | INTELLIGENT VALIDATION OF NETWORK-BASED SERVICES VIA A LEARNING PROXY | August 2021 | December 2025 | Abandon | 52 | 2 | 0 | Yes | No |
| 17393795 | SERVER, CONTROL DEVICE FOR VEHICLE, AND MACHINE LEARNING SYSTEM FOR VEHICLE | August 2021 | October 2025 | Allow | 50 | 3 | 0 | Yes | No |
| 17368302 | Number Format Selection in Recurrent Neural Networks | July 2021 | September 2025 | Allow | 50 | 2 | 0 | Yes | No |
| 17353931 | RELIABLE INFERENCE OF A MACHINE LEARNING MODEL | June 2021 | January 2026 | Allow | 54 | 3 | 0 | Yes | No |
| 17351719 | METHODS AND SYSTEMS FOR GENERATING AN UNCERTAINTY SCORE FOR AN OUTPUT OF A GRADIENT BOOSTED DECISION TREE MODEL | June 2021 | September 2025 | Allow | 51 | 3 | 0 | Yes | No |
| 17321044 | TECHNOLOGIES FOR SCALING DEEP LEARNING TRAINING | May 2021 | April 2025 | Abandon | 47 | 2 | 0 | Yes | No |
| 17317052 | SEMANTIC REASONING FOR TABULAR QUESTION ANSWERING | May 2021 | October 2025 | Allow | 53 | 3 | 0 | Yes | No |
| 17313555 | Generating Knowledge Graphs From Conversational Data | May 2021 | September 2025 | Allow | 52 | 4 | 0 | No | No |
| 17221305 | TRAINING NEURAL NETWORKS REPRESENTED AS COMPUTATIONAL GRAPHS | April 2021 | March 2026 | Allow | 59 | 4 | 0 | Yes | No |
| 17218308 | SAFE OVERRIDE OF AI-BASED DECISIONS | March 2021 | July 2025 | Abandon | 51 | 2 | 0 | No | No |
| 17211910 | NEURAL NETWORK SECURITY | March 2021 | January 2026 | Abandon | 58 | 3 | 0 | Yes | No |
| 17194970 | METHOD AND SYSTEM FOR TRANSFER LEARNING BASED OBJECT DETECTION | March 2021 | March 2025 | Allow | 48 | 2 | 0 | Yes | No |
| 17194366 | SYSTEM AND METHOD FOR TRAINING RECOMMENDATION POLICIES | March 2021 | August 2024 | Allow | 41 | 1 | 0 | Yes | No |
| 17249028 | INTELLIGENT DISTANCE PROMPTING | February 2021 | November 2025 | Allow | 57 | 4 | 0 | Yes | No |
| 17163396 | FLEXIBLE EMBEDDING SYSTEMS AND METHODS FOR REAL-TIME COMPARISONS | January 2021 | February 2026 | Allow | 60 | 5 | 0 | Yes | No |
| 17163383 | COMPOSITE EMBEDDING SYSTEMS AND METHODS FOR MULTI-LEVEL GRANULARITY SIMILARITY RELEVANCE SCORING | January 2021 | February 2026 | Allow | 60 | 6 | 0 | Yes | Yes |
| 17157832 | EVENT PREDICTION BASED ON MULTIMODAL LEARNING | January 2021 | February 2025 | Allow | 48 | 3 | 0 | Yes | No |
| 17262974 | TRAINING METHOD AND SYSTEM OF NEURAL NETWORK MODEL AND PREDICTION METHOD AND SYSTEM | January 2021 | November 2024 | Allow | 46 | 2 | 0 | No | No |
| 17157270 | ESTIMATING USEFUL LIFE | January 2021 | February 2025 | Abandon | 49 | 2 | 0 | Yes | No |
| 17155452 | REINFORCED TEXT REPRESENTATION LEARNING | January 2021 | January 2025 | Abandon | 48 | 2 | 0 | Yes | No |
| 17142896 | METHODS AND SYSTEMS FOR DYNAMICALLY SELECTING ALTERNATIVE CONTENT BASED ON REAL-TIME EVENTS DURING DEVICE SESSIONS USING CROSS-CHANNEL, TIME-BOUND DEEP REINFORCEMENT MACHINE LEARNING | January 2021 | August 2025 | Allow | 55 | 2 | 0 | Yes | No |
| 17134430 | LOCALIZATION OF MACHINE LEARNING MODELS TRAINED WITH GLOBAL DATA | December 2020 | March 2025 | Abandon | 50 | 2 | 0 | Yes | No |
| 17129393 | METHOD FOR GENERATING LABELED DATA, IN PARTICULAR FOR TRAINING A NEURAL NETWORK, BY IMPROVING INITIAL LABELS | December 2020 | December 2025 | Abandon | 60 | 4 | 0 | No | No |
| 17111123 | Systems and method for selecting a classification of input data from multiple classification systems | December 2020 | April 2025 | Abandon | 52 | 2 | 0 | No | No |
This analysis examines appeal outcomes and the strategic value of filing appeals for examiner SPRAUL III, VINCENT ANTON.
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, 100.0% 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.
✓ Filing a Notice of Appeal is strategically valuable. The act of filing often prompts favorable reconsideration during the mandatory appeal conference.
Examiner SPRAUL III, VINCENT ANTON works in Art Unit 2129 and has examined 29 patent applications in our dataset. With an allowance rate of 65.5%, this examiner has a below-average tendency to allow applications. Applications typically reach final disposition in approximately 51 months.
Examiner SPRAUL III, VINCENT ANTON's allowance rate of 65.5% places them in the 26% percentile among all USPTO examiners. This examiner has a below-average tendency to allow applications.
On average, applications examined by SPRAUL III, VINCENT ANTON receive 2.69 office actions before reaching final disposition. This places the examiner in the 79% 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 SPRAUL III, VINCENT ANTON is 51 months. This places the examiner in the 4% percentile for prosecution speed. Applications take longer to reach final disposition with this examiner compared to most others.
Conducting an examiner interview provides a +40.6% benefit to allowance rate for applications examined by SPRAUL III, VINCENT ANTON. This interview benefit is in the 88% percentile among all examiners. Recommendation: Interviews are highly effective with this examiner and should be strongly considered as a prosecution strategy. Per MPEP § 713.10, interviews are available at any time before the Notice of Allowance is mailed or jurisdiction transfers to the PTAB.
When applicants file an RCE with this examiner, 32.6% of applications are subsequently allowed. This success rate is in the 69% percentile among all examiners. Strategic Insight: RCEs show above-average effectiveness with this examiner. Consider whether your amendments or new arguments are strong enough to warrant an RCE versus filing a continuation.
This examiner enters after-final amendments leading to allowance in 12.5% of cases where such amendments are filed. This entry rate is in the 13% 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 5% 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 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, 0.0% are granted (fully or in part). This grant rate is in the 1% percentile among all examiners. Strategic Note: Petitions are rarely granted regarding this examiner's actions compared to other examiners. Ensure you have a strong procedural basis before filing a petition, as the Technology Center Director typically upholds this examiner's decisions.
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 10% 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.