Detailed information about the 100 most recent patent applications.
| Application Number | Title | Filing Date | Disposal Date | Disposition | Time (months) | Office Actions | Restrictions | Interview | Appeal |
|---|---|---|---|---|---|---|---|---|---|
| 18919417 | SUPERVISORY NEURON FOR CONTINUOUSLY ADAPTIVE NEURAL NETWORK | October 2024 | February 2026 | Allow | 16 | 3 | 0 | No | No |
| 18743059 | SYSTEM AND METHOD FOR IN-MEMORY IMAGE PROCESSING | June 2024 | February 2026 | Allow | 20 | 3 | 0 | Yes | No |
| 18583459 | MACHINE LEARNING BASED FILE RANKING METHODS AND SYSTEMS | February 2024 | December 2025 | Allow | 22 | 1 | 0 | Yes | No |
| 17484898 | FEDERATED LEARNING ACCELERATORS AND RELATED METHODS | September 2021 | December 2025 | Abandon | 50 | 2 | 0 | No | No |
| 17462632 | AUTONOMOUS SELF-LEARNING SYSTEM | August 2021 | April 2025 | Abandon | 44 | 1 | 0 | No | No |
| 17245772 | CONTEXTUAL BANDIT MACHINE LEARNING SYSTEMS AND METHODS FOR CONTENT DELIVERY | April 2021 | November 2025 | Abandon | 55 | 4 | 0 | Yes | No |
| 17200606 | Method and Apparatus for Augmented Data Anomaly Detection | March 2021 | January 2026 | Abandon | 58 | 2 | 0 | No | No |
| 17185810 | DATA PROCESSING SYSTEM AND DATA PROCESSING METHOD | February 2021 | May 2025 | Abandon | 51 | 2 | 0 | No | No |
| 17249177 | System and Method for Automated Prediction of Event Probabilities with Model Based Filtering | February 2021 | January 2025 | Abandon | 46 | 1 | 0 | No | No |
| 17161123 | NEURAL NETWORK ACCELERATOR | January 2021 | February 2025 | Abandon | 49 | 2 | 0 | No | No |
| 17058428 | SYSTEM AND METHOD FOR COMPACT, FAST, AND ACCURATE LSTMS | November 2020 | February 2026 | Abandon | 60 | 6 | 0 | Yes | No |
| 17096425 | VOLUME PRESERVING ARTIFICIAL NEURAL NETWORK AND SYSTEM AND METHOD FOR BUILDING A VOLUME PRESERVING TRAINABLE ARTIFICIAL NEURAL NETWORK | November 2020 | November 2025 | Allow | 60 | 6 | 0 | Yes | No |
| 17015332 | METHOD AND APPARATUS FOR TRIMMING SENSOR OUTPUT USING A NEURAL NETWORK ENGINE | September 2020 | March 2025 | Allow | 54 | 4 | 0 | Yes | No |
| 17007910 | Data Analysis Device and Multi-Model Co-Decision-Making System and Method | August 2020 | December 2024 | Abandon | 52 | 2 | 0 | No | No |
| 17007667 | Systems and Methods for Predicting Instance Geometry | August 2020 | December 2025 | Abandon | 60 | 4 | 0 | Yes | No |
| 17005067 | Transformer-Based Neural Network including a Mask Attention Network | August 2020 | November 2024 | Allow | 51 | 3 | 0 | Yes | No |
| 16990315 | METHOD AND DEVICE FOR PROCESSING DATA OF A TECHNICAL SYSTEM | August 2020 | February 2025 | Abandon | 55 | 4 | 0 | No | No |
| 16969145 | MODEL LEARNING APPARATUS, MODEL LEARNING METHOD, AND PROGRAM | August 2020 | August 2025 | Abandon | 60 | 4 | 0 | Yes | No |
| 16987892 | METHODS AND APPARATUSES FOR TRAINING NEURAL NETWORKS | August 2020 | May 2025 | Abandon | 57 | 4 | 0 | Yes | No |
| 16985467 | CONVOLUTIONAL RECURRENT GENERATIVE ADVERSARIAL NETWORK FOR ANOMALY DETECTION | August 2020 | December 2025 | Abandon | 60 | 2 | 0 | Yes | Yes |
| 16942263 | CHAINED NEURAL ENGINE WRITE-BACK ARCHITECTURE | July 2020 | February 2025 | Allow | 55 | 4 | 0 | Yes | No |
| 16927018 | Object-Centric Learning with Slot Attention | July 2020 | January 2026 | Allow | 60 | 5 | 0 | Yes | Yes |
| 16914970 | ARTIFICIAL NEURAL NETWORK WITH SPARSE WEIGHTS | June 2020 | July 2025 | Abandon | 60 | 6 | 0 | No | No |
| 16829331 | USING VECTOR CLOCKS TO SIMPLIFY A DEPENDENCY GRAPH IN A NEURAL NETWORK ACCELERATOR | March 2020 | July 2024 | Allow | 52 | 3 | 0 | Yes | No |
| 16816453 | NEURAL NETWORK WEIGHT ENCODING | March 2020 | December 2024 | Allow | 58 | 4 | 0 | Yes | No |
| 16712836 | FLEXIBLE ARRAY DATA LOADING | December 2019 | March 2025 | Allow | 60 | 4 | 0 | Yes | Yes |
| 16558554 | NEURAL NETWORK CIRCUITRY HAVING FLOATING POINT FORMAT WITH ASYMMETRIC RANGE | September 2019 | September 2024 | Allow | 60 | 5 | 0 | Yes | No |
| 16552631 | NEURAL PROCESSING SYSTEM AND OPERATING METHOD THEREOF | August 2019 | January 2025 | Abandon | 60 | 6 | 0 | Yes | No |
| 16513208 | TRAINING OF MACHINE READING AND COMPREHENSION SYSTEMS | July 2019 | January 2025 | Allow | 60 | 5 | 0 | Yes | No |
| 16457851 | HARDWARE AGNOSTIC DEEP NEURAL NETWORK COMPILER | June 2019 | December 2025 | Abandon | 60 | 8 | 0 | Yes | No |
| 16249279 | NEURAL NETWORK METHOD AND APPARATUS | January 2019 | August 2025 | Allow | 60 | 7 | 0 | Yes | No |
| 16221295 | WEIGHT SKIPPING DEEP LEARNING ACCELERATOR | December 2018 | January 2025 | Abandon | 60 | 5 | 0 | No | No |
| 16303623 | INTERACTION NETWORKS | November 2018 | September 2025 | Abandon | 60 | 6 | 0 | Yes | No |
This analysis examines appeal outcomes and the strategic value of filing appeals for examiner GODO, MORIAM MOSUNMOLA.
With a 0.0% reversal rate, the PTAB affirms the examiner's rejections in the vast majority of cases. This reversal rate is in the bottom 25% across the USPTO, indicating that appeals face significant challenges here.
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, 33.3% of applications that filed an appeal were subsequently allowed. This appeal filing benefit rate is above the USPTO average, suggesting that filing an appeal can be an effective strategy for prompting reconsideration.
⚠ Appeals to PTAB face challenges. Ensure your case has strong merit before committing to full Board review.
✓ Filing a Notice of Appeal is strategically valuable. The act of filing often prompts favorable reconsideration during the mandatory appeal conference.
Examiner GODO, MORIAM MOSUNMOLA works in Art Unit 2148 and has examined 30 patent applications in our dataset. With an allowance rate of 36.7%, this examiner allows applications at a lower rate than most examiners at the USPTO. Applications typically reach final disposition in approximately 10000 months.
Examiner GODO, MORIAM MOSUNMOLA's allowance rate of 36.7% places them in the 6% percentile among all USPTO examiners. This examiner is less likely to allow applications than most examiners at the USPTO.
On average, applications examined by GODO, MORIAM MOSUNMOLA receive 4.03 office actions before reaching final disposition. This places the examiner in the 98% 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 GODO, MORIAM MOSUNMOLA is 10000 months. This places the examiner in the 0% percentile for prosecution speed. Applications take longer to reach final disposition with this examiner compared to most others.
Conducting an examiner interview provides a +55.0% benefit to allowance rate for applications examined by GODO, MORIAM MOSUNMOLA. This interview benefit is in the 95% 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, 8.5% of applications are subsequently allowed. This success rate is in the 4% 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 14.8% of cases where such amendments are filed. This entry rate is in the 16% 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, 100.0% result in withdrawal of the rejection or reopening of prosecution. This success rate is in the 71% percentile among all examiners. Strategic Recommendation: Pre-appeal conferences show above-average effectiveness with this examiner. If you have strong arguments, a PAC request may result in favorable reconsideration.
This examiner withdraws rejections or reopens prosecution in 66.7% of appeals filed. This is in the 48% percentile among all examiners. Of these withdrawals, 50.0% occur early in the appeal process (after Notice of Appeal but before Appeal Brief). 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, 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 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.