Detailed information about the 100 most recent patent applications.
| Application Number | Title | Filing Date | Disposal Date | Disposition | Time (months) | Office Actions | Restrictions | Interview | Appeal |
|---|---|---|---|---|---|---|---|---|---|
| 19011578 | SYSTEM AND METHOD FOR IMPROVING THE CLARITY OF OVERLAPPING OBJECTS | January 2025 | October 2025 | Allow | 9 | 1 | 0 | Yes | No |
| 18129270 | SYSTEM AND METHOD FOR IMPLEMENTING A TRUST DISCRETIONARY DISTRIBUTION TOOL | March 2023 | September 2025 | Allow | 30 | 1 | 0 | Yes | No |
| 18155553 | CLUSTERING AND DYNAMIC RE-CLUSTERING OF SIMILAR TEXTUAL DOCUMENTS | January 2023 | October 2025 | Allow | 33 | 4 | 0 | Yes | No |
| 18053094 | CLASSIFICATION OF ERRONEOUS CELL DATA | November 2022 | October 2025 | Allow | 35 | 2 | 0 | Yes | No |
| 18049503 | METHOD AND SYSTEM FOR FEDERATED LEARNING BASED IDENTIFICATION OF NON-MALICIOUS CLASSIFICATION MODELS | October 2022 | December 2025 | Allow | 37 | 1 | 0 | No | No |
| 17747576 | GENERATING SUMMARY DOCUMENTS FOR COMMUNICATIONS IN A COMMUNICATION PLATFORM | May 2022 | March 2025 | Abandon | 34 | 6 | 0 | Yes | No |
| 17706665 | PREDICTION-MODEL-BUILDING METHOD, STATE PREDICTION METHOD AND DEVICES THEREOF | March 2022 | February 2026 | Allow | 47 | 2 | 0 | Yes | No |
| 17701595 | TRAINING WEB-ELEMENT PREDICTORS USING NEGATIVE-EXAMPLE SAMPLING | March 2022 | March 2026 | Abandon | 48 | 1 | 0 | No | No |
| 17699489 | METHOD OF PREDICTING CHARACTERISTICS OF SEMICONDUCTOR DEVICE AND COMPUTING DEVICE PERFORMING THE SAME | March 2022 | October 2025 | Allow | 43 | 1 | 0 | Yes | No |
| 17692911 | MULTI-CHANNEL FEEDBACK ANALYTICS FOR PRESENTATION GENERATION | March 2022 | March 2025 | Abandon | 36 | 4 | 0 | Yes | No |
| 17652268 | AUTOMATING BIAS EVALUATION FOR MACHINE LEARNING PROJECTS | February 2022 | September 2025 | Allow | 43 | 1 | 0 | No | No |
| 17627092 | BIOLOGICALLY INSPIRED SLEEP ALGORITHM FOR ARTIFICIAL NEURAL NETWORKS | January 2022 | August 2025 | Allow | 43 | 2 | 0 | Yes | No |
| 17618045 | PREDICTION MODEL RE-LEARNING DEVICE, PREDICTION MODEL RE-LEARNING METHOD, AND PROGRAM RECORDING MEDIUM | December 2021 | February 2026 | Abandon | 50 | 2 | 0 | Yes | No |
| 17612528 | LINEAR NEURAL RECONSTRUCTION FOR DEEP NEURAL NETWORK COMPRESSION | November 2021 | December 2025 | Abandon | 49 | 1 | 0 | No | No |
| 17450353 | MODEL MANAGEMENT USING CONTAINERS | October 2021 | November 2025 | Allow | 49 | 2 | 0 | Yes | No |
| 17245892 | Kernelized Classifiers in Neural Networks | April 2021 | December 2025 | Allow | 56 | 3 | 0 | No | No |
| 17092013 | COMPILER CONFIGURABLE TO GENERATE INSTRUCTIONS EXECUTABLE BY DIFFERENT DEEP LEARNING ACCELERATORS FROM A DESCRIPTION OF AN ARTIFICIAL NEURAL NETWORK | November 2020 | October 2025 | Allow | 59 | 3 | 0 | Yes | No |
No appeal data available for this record. This may indicate that no appeals have been filed or decided for applications in this dataset.
Examiner PAULA, CESAR B works in Art Unit 2145 and has examined 5 patent applications in our dataset. With an allowance rate of 60.0%, this examiner allows applications at a lower rate than most examiners at the USPTO. Applications typically reach final disposition in approximately 50 months.
Examiner PAULA, CESAR B's allowance rate of 60.0% places them in the 20% percentile among all USPTO examiners. This examiner is less likely to allow applications than most examiners at the USPTO.
On average, applications examined by PAULA, CESAR B receive 2.20 office actions before reaching final disposition. This places the examiner in the 60% 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 PAULA, CESAR B is 50 months. This places the examiner in the 5% percentile for prosecution speed. Applications take longer to reach final disposition with this examiner compared to most others.
Conducting an examiner interview provides a +16.7% benefit to allowance rate for applications examined by PAULA, CESAR B. This interview benefit is in the 57% percentile among all examiners. Recommendation: Interviews provide an above-average benefit with this examiner and are worth considering.
When applicants file an RCE with this examiner, 50.0% of applications are subsequently allowed. This success rate is in the 97% 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 0.0% of cases where such amendments are filed. This entry rate is in the 0% 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.
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.