Detailed information about the 100 most recent patent applications.
| Application Number | Title | Filing Date | Disposal Date | Disposition | Time (months) | Office Actions | Restrictions | Interview | Appeal |
|---|---|---|---|---|---|---|---|---|---|
| 18898516 | COPILOT IMPLEMENTATION: TESTING CONFORMANCE WITH KNOWLEDGE DOMAIN | September 2024 | October 2025 | Abandon | 12 | 2 | 0 | Yes | No |
| 17978498 | Systems and Methods for Improved Adversarial Training of Machine-Learned Models | November 2022 | January 2026 | Abandon | 39 | 2 | 0 | Yes | No |
| 17639713 | Methods for Compressing a Neural Network | March 2022 | March 2026 | Allow | 48 | 2 | 0 | Yes | No |
| 17449287 | Processing Data Batches in a Multi-Layer Network | September 2021 | June 2025 | Allow | 45 | 1 | 0 | Yes | No |
| 17394575 | TRAINING IN NEURAL NETWORKS | August 2021 | July 2025 | Abandon | 48 | 1 | 0 | No | No |
| 17425684 | ESTIMATION APPARATUS, LEARNING APPARATUS, METHODS AND PROGRAMS FOR THE SAME | July 2021 | October 2025 | Abandon | 50 | 2 | 0 | No | No |
| 17374033 | METHOD AND SYSTEM FOR CAUSAL INFERENCE IN PRESENCE OF HIGH-DIMENSIONAL COVARIATES AND HIGH-CARDINALITY TREATMENTS | July 2021 | December 2025 | Abandon | 53 | 2 | 0 | No | No |
| 17373600 | CONVOLUTIONAL NEURAL NETWORK (CNN)-BASED ANOMALY DETECTION | July 2021 | March 2026 | Abandon | 56 | 2 | 0 | No | No |
| 17421128 | GRAPH SUMMARIZATION APPARATUS, GRAPH SUMMARIZATION METHOD AND PROGRAM | July 2021 | October 2025 | Abandon | 51 | 2 | 0 | No | No |
| 17368864 | METHOD FOR PERFORMING CLUSTERING ON POWER SYSTEM OPERATION MODES BASED ON SPARSE AUTOENCODER | July 2021 | December 2025 | Abandon | 53 | 2 | 0 | No | No |
| 17358725 | SENSOR COMPENSATION USING BACKPROPAGATION | June 2021 | December 2025 | Allow | 53 | 2 | 0 | Yes | No |
| 17350082 | SYSTEMS AND METHODS FOR AUTOMATIC PRODUCT USAGE MODEL TRAINING AND PREDICTION | June 2021 | October 2025 | Abandon | 52 | 2 | 0 | Yes | No |
| 17349157 | UTILIZING USAGE SIGNAL TO PROVIDE AN INTELLIGENT USER EXPERIENCE | June 2021 | December 2025 | Abandon | 54 | 2 | 0 | Yes | No |
| 17413373 | TRAINING METHOD AND APPARATUS, DIALOGUE PROCESSING METHOD AND SYSTEM, AND MEDIUM | June 2021 | March 2026 | Allow | 57 | 3 | 0 | Yes | No |
| 17299102 | DELTA-SIGMA MODULATION NEURONS FOR HIGH-PRECISION TRAINING OF MEMRISTIVE SYNAPSES IN DEEP NEURAL NETWORKS | June 2021 | January 2026 | Allow | 55 | 2 | 0 | Yes | No |
| 17281991 | MULTI-OBJECTIVE OPTIMIZATION METHOD AND SYSTEM FOR MASTER PRODUCTION PLAN OF CASTING PARALLEL WORKSHOPS | April 2021 | June 2025 | Abandon | 50 | 2 | 0 | No | No |
| 17282057 | ELECTRONIC DEVICE FOR REARRANGING KERNELS OF NEURAL NETWORK AND OPERATING METHOD THEREOF | April 2021 | March 2025 | Allow | 47 | 2 | 0 | No | No |
| 17214294 | AUTOMATED GENERATION AND INTEGRATION OF AN OPTIMIZED REGULAR EXPRESSION | March 2021 | May 2025 | Allow | 49 | 2 | 0 | Yes | No |
| 17212747 | Generalized Activations Function for Machine Learning | March 2021 | July 2025 | Abandon | 51 | 2 | 0 | Yes | No |
| 17186392 | Visual Analytics System to Assess, Understand, and Improve Deep Neural Networks | February 2021 | August 2025 | Allow | 54 | 3 | 0 | Yes | No |
| 17168369 | DATA PROCESSING BASED ON NEURAL NETWORK | February 2021 | February 2025 | Abandon | 48 | 1 | 0 | No | No |
| 17157319 | Activation Compression Method for Deep Learning Acceleration | January 2021 | June 2025 | Allow | 52 | 3 | 0 | Yes | No |
| 17156821 | ADAPTIVE FILTER BASED LEARNING MODEL FOR TIME SERIES SENSOR SIGNAL CLASSIFICATION ON EDGE DEVICES | January 2021 | September 2025 | Allow | 56 | 3 | 0 | Yes | No |
| 17155078 | System and Method with Federated Learning Model for Medical Research Applications | January 2021 | September 2024 | Abandon | 44 | 1 | 0 | No | No |
| 17129464 | METHOD, APPARATUS, AND SYSTEM FOR GENERATING ASYNCHRONOUS LEARNING RULES AND/ARCHITECTURES | December 2020 | November 2025 | Abandon | 59 | 4 | 0 | No | No |
| 17254002 | OPERATION METHOD AND APPARATUS FOR NETWORK LAYER IN DEEP NEURAL NETWORK | December 2020 | December 2024 | Allow | 48 | 2 | 0 | No | No |
| 17253005 | INFORMATION PROCESSING APPARATUS, INFORMATION PROCESSING METHOD, AND PROGRAM | December 2020 | July 2025 | Abandon | 55 | 3 | 0 | No | No |
| 17115839 | DETECTING ANOMALIES IN COMPUTER SYSTEMS BASED ON FORECASTED TIMESERIES | December 2020 | July 2025 | Abandon | 55 | 2 | 0 | No | No |
| 17061112 | SYSTEM AND METHOD TO PREDICT SUCCESS BASED ON ANALYSIS OF FAILURE | October 2020 | December 2024 | Abandon | 50 | 2 | 0 | No | No |
| 16992724 | STOCHASTIC DATA AUGMENTATION FOR MACHINE LEARNING | August 2020 | June 2024 | Abandon | 46 | 1 | 0 | No | No |
| 16944512 | Causal Reasoning and Counterfactual Probabilistic Programming Framework Using Approximate Inference | July 2020 | September 2024 | Abandon | 50 | 2 | 0 | No | No |
| 16891980 | MACHINE LEARNING MODEL SURETY | June 2020 | July 2025 | Abandon | 60 | 4 | 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 ALSHAHARI, SADIK AHMED works in Art Unit 2121 and has examined 29 patent applications in our dataset. With an allowance rate of 34.5%, this examiner allows applications at a lower rate than most examiners at the USPTO. Applications typically reach final disposition in approximately 52 months.
Examiner ALSHAHARI, SADIK AHMED's allowance rate of 34.5% places them in the 5% percentile among all USPTO examiners. This examiner is less likely to allow applications than most examiners at the USPTO.
On average, applications examined by ALSHAHARI, SADIK AHMED receive 2.14 office actions before reaching final disposition. This places the examiner in the 58% 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 ALSHAHARI, SADIK AHMED is 52 months. This places the examiner in the 3% percentile for prosecution speed. Applications take longer to reach final disposition with this examiner compared to most others.
Conducting an examiner interview provides a +54.9% benefit to allowance rate for applications examined by ALSHAHARI, SADIK AHMED. 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, 37.5% of applications are subsequently allowed. This success rate is in the 85% 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.
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 9% 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.