Detailed information about the 100 most recent patent applications.
| Application Number | Title | Filing Date | Disposal Date | Disposition | Time (months) | Office Actions | Restrictions | Interview | Appeal |
|---|---|---|---|---|---|---|---|---|---|
| 17136054 | META COOPERATIVE TRAINING PARADIGMS | December 2020 | March 2025 | Allow | 50 | 2 | 0 | Yes | No |
| 17111069 | DEVICE AND METHOD FOR PROCESSING DIGITAL DATA | December 2020 | February 2025 | Abandon | 51 | 2 | 0 | No | No |
| 17108426 | DYNAMIC CLASSIFICATION ENGINE SELECTION USING RULES AND ENVIRONMENTAL DATA METRICS | December 2020 | March 2025 | Abandon | 52 | 2 | 0 | No | No |
| 17105651 | SELF-ORGANIZING MAP LEARNING DEVICE AND METHOD, NON-TRANSITORY COMPUTER READABLE MEDIUM STORING SELF-ORGANIZING MAP LEARNING PROGRAM AND STATE DETERMINATION DEVICE | November 2020 | June 2025 | Abandon | 54 | 2 | 1 | No | No |
| 16952970 | SYSTEMS AND METHODS FOR PERFORMING A COMPUTER-IMPLEMENTED PRIOR ART SEARCH AND NOVEL MARKUSH LANDSCAPE | November 2020 | June 2025 | Abandon | 54 | 2 | 0 | Yes | No |
| 17090134 | DEEP SIMULATION NETWORKS | November 2020 | June 2025 | Abandon | 55 | 3 | 0 | No | No |
| 17064561 | BLOCK-BASED INFERENCE METHOD FOR MEMORY-EFFICIENT CONVOLUTIONAL NEURAL NETWORK IMPLEMENTATION AND SYSTEM THEREOF | October 2020 | November 2024 | Allow | 49 | 3 | 0 | No | No |
| 17039178 | HARDWARE-OPTIMIZED NEURAL ARCHITECTURE SEARCH | September 2020 | June 2024 | Allow | 44 | 2 | 0 | Yes | No |
| 17024062 | LEARNING METHOD OF NEURAL NETWORK MODEL FOR LANGUAGE GENERATION AND APPARATUS FOR PERFORMING THE LEARNING METHOD | September 2020 | October 2024 | Abandon | 49 | 2 | 0 | No | No |
| 17022895 | METHOD AND APPARATUS FOR PROCESSING SENSOR DATA USING A CONVOLUTIONAL NEURAL NETWORK | September 2020 | June 2024 | Abandon | 45 | 2 | 0 | No | No |
| 16975949 | UNSUPERVISED NEURAL NETWORK TRAINING USING LEARNED OPTIMIZERS | August 2020 | February 2025 | Abandon | 53 | 2 | 0 | No | No |
| 17002960 | MONITORING COMPUTING SYSTEM STATUS BY IMPLEMENTING A DEEP UNSUPERVISED BINARY CODING NETWORK | August 2020 | May 2024 | Abandon | 44 | 1 | 0 | No | No |
| 16927300 | COOPERATIVE USE OF A GENETIC ALGORITHM AND AN OPTIMIZATION TRAINER FOR AUTOENCODER GENERATION | July 2020 | January 2025 | Abandon | 54 | 2 | 0 | No | No |
| 16922544 | DEVICE AND METHOD FOR OPERATING A NEURAL NETWORK | July 2020 | August 2024 | Abandon | 50 | 2 | 0 | Yes | No |
| 16880147 | OUT-OF-DISTRIBUTION (OOD) DETECTION BY PERTURBATION | May 2020 | March 2025 | Abandon | 58 | 4 | 0 | Yes | No |
| 16844335 | TERMINAL DEVICE AND METHOD FOR ESTIMATING FIREFIGHTING DATA | April 2020 | August 2023 | Allow | 41 | 1 | 0 | No | No |
| 16815960 | Active Federated Learning for Assistant Systems | March 2020 | July 2024 | Abandon | 52 | 2 | 0 | No | No |
| 16792006 | SCORING FOR SEARCH RETRIEVAL AND RANKING ALIGNMENT | February 2020 | March 2025 | Abandon | 60 | 4 | 0 | Yes | No |
| 16601356 | MEMORY COMPONENT WITH INTERNAL LOGIC TO PERFORM A MACHINE LEARNING OPERATION | October 2019 | December 2024 | Abandon | 60 | 4 | 0 | Yes | No |
| 16509098 | SYSTEMS, METHODS, AND DEVICES FOR EARLY-EXIT FROM CONVOLUTION | July 2019 | January 2024 | Abandon | 54 | 2 | 0 | Yes | No |
| 16424166 | Automated Scaling Of Resources Based On Long Short-Term Memory Recurrent Neural Networks And Attention Mechanisms | May 2019 | December 2024 | Abandon | 60 | 4 | 0 | Yes | No |
| 16039056 | Neural Network Processing Method, Apparatus, Device and Computer Readable Storage Media | July 2018 | December 2023 | Abandon | 60 | 2 | 0 | No | 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 RAMESH, TIRUMALE K works in Art Unit 2121 and has examined 22 patent applications in our dataset. With an allowance rate of 18.2%, this examiner allows applications at a lower rate than most examiners at the USPTO. Applications typically reach final disposition in approximately 53 months.
Examiner RAMESH, TIRUMALE K's allowance rate of 18.2% 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 RAMESH, TIRUMALE K receive 2.36 office actions before reaching final disposition. This places the examiner in the 63% 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 RAMESH, TIRUMALE K is 53 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 +6.8% benefit to allowance rate for applications examined by RAMESH, TIRUMALE K. This interview benefit is in the 35% percentile among all examiners. Recommendation: Interviews provide a below-average benefit with this examiner.
When applicants file an RCE with this examiner, 14.3% of applications are subsequently allowed. This success rate is in the 12% 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 12.5% of cases where such amendments are filed. This entry rate is in the 14% 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 8% 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.