Detailed information about the 100 most recent patent applications.
| Application Number | Title | Filing Date | Disposal Date | Disposition | Time (months) | Office Actions | Restrictions | Interview | Appeal |
|---|---|---|---|---|---|---|---|---|---|
| 18690017 | MODEL GRADIENT UPDATE METHOD AND DEVICE | March 2024 | April 2025 | Allow | 13 | 1 | 0 | No | No |
| 18015976 | Systems, Methods, and Computer Program Products for Generating Node Embeddings | January 2023 | September 2024 | Allow | 20 | 3 | 0 | Yes | No |
| 18082388 | SYSTEM AND METHOD FOR MONITORING AND IMPROVING CONVERSATIONAL ALIGNMENT TO DEVELOP AN ALLIANCE BETWEEN AN ARTIFICIAL INTELLIGENCE (AI) CHATBOT AND A USER | December 2022 | February 2026 | Abandon | 38 | 5 | 0 | Yes | No |
| 17827626 | NEURAL ARCHITECTURE SEARCH WITH WEIGHT SHARING | May 2022 | June 2023 | Allow | 13 | 0 | 0 | Yes | No |
| 17673431 | SYSTEM AND METHOD FOR ECONOMIC VIRTUOUS CYCLE SIMULATION BASED ON ARTIFICIAL INTELLIGENCE TWIN | February 2022 | January 2026 | Abandon | 47 | 1 | 0 | No | No |
| 17493743 | ONLINE HYPERPARAMETER TUNING IN DISTRIBUTED MACHINE LEARNING | October 2021 | March 2026 | Allow | 54 | 1 | 1 | Yes | No |
| 17440479 | NEURAL NETWORK OPERATION MODULE AND METHOD | September 2021 | August 2025 | Allow | 47 | 1 | 0 | No | No |
| 17438370 | MODEL ACCEPTANCE DETERMINATION SUPPORT SYSTEM AND MODEL ACCEPTANCE DETERMINATION SUPPORT METHOD | September 2021 | January 2026 | Abandon | 52 | 1 | 0 | No | No |
| 17435698 | System for Predicting User Drop-Out Rate and Tracking User Knowledge Based on Artificial Intelligence Learning and Method Therefor | September 2021 | June 2025 | Allow | 45 | 1 | 0 | No | No |
| 17376631 | MULTI-CLASS CLASSIFICATION USING A DUAL MODEL | July 2021 | August 2025 | Allow | 49 | 2 | 0 | Yes | No |
| 17366315 | METHOD OF BUILDING AND OPERATING DECODING STATUS AND PREDICTION SYSTEM | July 2021 | August 2025 | Allow | 49 | 2 | 0 | No | No |
| 17294872 | VERIFICATION OF ELECTRONIC IDENTITY COMPONENTS | May 2021 | January 2025 | Allow | 44 | 6 | 0 | Yes | No |
| 17210391 | NEURAL ARCHITECTURE SEARCH WITH WEIGHT SHARING | March 2021 | January 2022 | Allow | 10 | 2 | 0 | No | No |
| 17124106 | INFORMATION PROCESSING APPARATUS, INFORMATION PROCESSING METHOD, AND STORAGE MEDIUM | December 2020 | September 2025 | Allow | 57 | 2 | 0 | No | No |
| 17091837 | INTERFACE NEURAL NETWORK | November 2020 | November 2025 | Allow | 60 | 3 | 0 | Yes | No |
| 17089645 | SYSTEM AND METHOD FOR FACILITATING A MACHINE LEARNING MODEL REBUILD | November 2020 | October 2025 | Allow | 59 | 2 | 0 | Yes | Yes |
| 17083839 | METHOD OF TRAINING A MACHINE LEARNING SYSTEM FOR AN OBJECT RECOGNITION DEVICE | October 2020 | February 2026 | Allow | 60 | 4 | 0 | No | No |
| 17071135 | LONG-SHORT FIELD MEMORY NETWORKS | October 2020 | December 2025 | Abandon | 60 | 4 | 0 | Yes | No |
| 17019766 | Loss Function Optimization Using Taylor Series Expansion | September 2020 | January 2025 | Allow | 52 | 3 | 0 | No | No |
| 17016640 | QUERY-BASED MOLECULE OPTIMIZATION AND APPLICATIONS TO FUNCTIONAL MOLECULE DISCOVERY | September 2020 | January 2025 | Allow | 52 | 3 | 0 | No | No |
| 17002771 | PARTIALLY-OBSERVED SEQUENTIAL VARIATIONAL AUTO ENCODER | August 2020 | November 2025 | Abandon | 60 | 4 | 0 | Yes | No |
| 16983223 | SYSTEM AND METHOD FOR PROVIDING SUPERVISION OF PERSONS USING TAGS WITH VISUAL IDENTIFICATION CODES USING ARTIFICIAL INTELLIGENCE METHODS TO PREVENT FRAUD. | August 2020 | June 2024 | Abandon | 46 | 1 | 0 | No | No |
| 16935178 | CONTENT COLD-START MACHINE LEARNING SYSTEM | July 2020 | January 2026 | Abandon | 60 | 5 | 0 | Yes | No |
| 16902960 | EXPENSE FRAUD DETECTION | June 2020 | October 2025 | Abandon | 60 | 4 | 0 | Yes | No |
| 16895667 | SYSTEM AND METHOD FOR CORRECTING BIAS IN OUTPUTS | June 2020 | October 2024 | Abandon | 52 | 3 | 0 | No | No |
| 16644243 | A PROBABILISTIC DATA CLASSIFIER SYSTEM AND METHOD THEREOF | March 2020 | September 2024 | Abandon | 55 | 6 | 0 | Yes | Yes |
| 16799227 | CONTROL OF HYPERPARAMETER TUNING BASED ON MACHINE LEARNING | February 2020 | June 2023 | Allow | 40 | 5 | 0 | Yes | No |
| 16746941 | Method of Training Artificial Neural Network Using Sparse Connectivity Learning | January 2020 | January 2024 | Abandon | 48 | 2 | 0 | No | No |
| 16730635 | METHOD AND APPARATUS OF GENERATING QUESTION-ANSWER LEARNING MODEL THROUGH REINFORCEMENT LEARNING | December 2019 | July 2025 | Abandon | 60 | 5 | 0 | Yes | No |
| 16584623 | HUMAN-UNDERSTANDABLE MACHINE INTELLIGENCE | September 2019 | September 2024 | Abandon | 60 | 2 | 0 | Yes | No |
| 16494842 | LEARNING UNIFIED EMBEDDING | September 2019 | December 2025 | Allow | 60 | 4 | 0 | Yes | Yes |
| 16551246 | CONSTRUCTION SEQUENCING OPTIMIZATION | August 2019 | August 2024 | Abandon | 59 | 4 | 0 | No | No |
| 16545181 | NEURAL NETWORK METHOD, SYSTEM, AND COMPUTER PROGRAM PRODUCT WITH INFERENCE-TIME BITWIDTH FLEXIBILITY | August 2019 | May 2024 | Abandon | 57 | 3 | 1 | Yes | No |
| 16450480 | ADAPTIVE MEDICAL IMAGING DEVICE CONFIGURATION USING ARTIFICIAL INTELLIGENCE | June 2019 | December 2024 | Abandon | 60 | 6 | 0 | Yes | No |
| 16442298 | SYSTEM AND METHOD FOR DATA AUGMENTATION FOR TRACE DATASET | June 2019 | October 2023 | Allow | 52 | 2 | 0 | Yes | Yes |
| 16393104 | COMPUTER-READABLE RECORDING MEDIUM HAVING STORED THEREIN TRAINING PROGRAM, TRAINING METHOD, AND INFORMATION PROCESSING APPARATUS | April 2019 | January 2022 | Allow | 33 | 2 | 0 | No | No |
| 16279323 | MACHINE LEARNING ENGINEERING THROUGH HYBRID KNOWLEDGE REPRESENTATION | February 2019 | March 2023 | Allow | 49 | 2 | 0 | Yes | No |
| 16319040 | TRAINING MACHINE LEARNING MODELS ON MULTIPLE MACHINE LEARNING TASKS | January 2019 | December 2025 | Abandon | 60 | 4 | 0 | Yes | Yes |
| 16232134 | RECONFIGURATION OF EMBEDDED SERVICES ON DEVICES USING DEVICE FUNCTIONALITY INFORMATION | December 2018 | February 2025 | Abandon | 60 | 6 | 0 | No | No |
| 16216853 | METHOD AND SYSTEM FOR FACILITATING COMBINING CATEGORICAL AND NUMERICAL VARIABLES IN MACHINE LEARNING | December 2018 | December 2021 | Abandon | 36 | 1 | 0 | No | No |
| 16177892 | Machine Learning Based Capacity Management Automated System | November 2018 | June 2022 | Abandon | 44 | 2 | 0 | Yes | No |
| 16165013 | AUTOMATED SOFTWARE SELECTION USING A VECTOR-TRAINED DEEP LEARNING MODEL | October 2018 | April 2022 | Allow | 42 | 1 | 0 | Yes | No |
| 16107557 | AUTOMATED EARLY ANOMALY DETECTION IN A CONTINUOUS LEARNING MODEL | August 2018 | February 2026 | Abandon | 60 | 6 | 0 | Yes | Yes |
| 15964586 | INTEGRATING DEEP LEARNING INTO GENERALIZED ADDITIVE MIXED-EFFECT (GAME) FRAMEWORKS | April 2018 | December 2022 | Abandon | 56 | 1 | 0 | No | No |
| 15945924 | Tracking Potentially Lost Items Without Beacon Tags | April 2018 | July 2023 | Abandon | 60 | 4 | 0 | Yes | Yes |
| 15937486 | NEUROMORPHIC ACCELERATOR MULTITASKING | March 2018 | February 2022 | Allow | 47 | 2 | 0 | No | No |
| 15928053 | PREDICTING USING DIGITAL TWINS | March 2018 | October 2021 | Abandon | 43 | 1 | 0 | No | No |
| 15912544 | CONFIGURABLE NEURAL NETWORK PROCESSOR FOR MACHINE LEARNING WORKLOADS | March 2018 | February 2022 | Abandon | 47 | 2 | 1 | No | No |
| 15882258 | APPARATUS AND METHOD FOR PROTECTING A DIGITAL RIGHT OF MODEL DATA LEARNED FROM ARTIFICIAL INTELLIGENCE FOR SMART BROADCASTING CONTENTS | January 2018 | December 2023 | Abandon | 60 | 4 | 0 | No | No |
| 15788322 | METHOD AND SYSTEM FOR SYNTHESIS OF AN OPPORTUNITY FOR A COGNITIVE DECISION-MAKING PROCESS | October 2017 | January 2022 | Abandon | 51 | 2 | 0 | Yes | No |
| 15785685 | NEURAL NETWORK PROCESSING SYSTEM HAVING MULTIPLE PROCESSORS AND A NEURAL NETWORK ACCELERATOR | October 2017 | September 2021 | Allow | 47 | 2 | 0 | Yes | No |
| 15786102 | HOST-DIRECTED MULTI-LAYER NEURAL NETWORK PROCESSING VIA PER-LAYER WORK REQUESTS | October 2017 | April 2022 | Allow | 54 | 4 | 0 | Yes | Yes |
| 15703149 | OBSERVATION HUB DEVICE AND METHOD | September 2017 | November 2021 | Abandon | 51 | 2 | 0 | Yes | No |
| 15554985 | INFERENCE DEVICE AND INFERENCE METHOD | August 2017 | June 2022 | Abandon | 57 | 4 | 0 | No | No |
| 15617498 | MACHINE LEARNING ANOMALY DETECTION | June 2017 | October 2021 | Allow | 52 | 4 | 0 | Yes | No |
| 15616655 | Method of Adding Classes to Classifier | June 2017 | May 2025 | Abandon | 60 | 8 | 0 | Yes | Yes |
This analysis examines appeal outcomes and the strategic value of filing appeals for examiner TRIEU, EM N.
With a 40.0% reversal rate, the PTAB reverses the examiner's rejections in a meaningful percentage of cases. This reversal rate is above the USPTO average, indicating that appeals have better success here than typical.
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, 50.0% 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.
✓ Appeals to PTAB show good success rates. If you have a strong case on the merits, consider fully prosecuting the appeal to a Board decision.
✓ Filing a Notice of Appeal is strategically valuable. The act of filing often prompts favorable reconsideration during the mandatory appeal conference.
Examiner TRIEU, EM N works in Art Unit 2128 and has examined 51 patent applications in our dataset. With an allowance rate of 45.1%, this examiner allows applications at a lower rate than most examiners at the USPTO. Applications typically reach final disposition in approximately 52 months.
Examiner TRIEU, EM N's allowance rate of 45.1% places them in the 9% percentile among all USPTO examiners. This examiner is less likely to allow applications than most examiners at the USPTO.
On average, applications examined by TRIEU, EM N receive 3.08 office actions before reaching final disposition. This places the examiner in the 88% 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 TRIEU, EM N 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 -0.6% benefit to allowance rate for applications examined by TRIEU, EM N. This interview benefit is in the 11% percentile among all examiners. Note: Interviews show limited statistical benefit with this examiner compared to others, though they may still be valuable for clarifying issues.
When applicants file an RCE with this examiner, 14.6% of applications are subsequently allowed. This success rate is in the 10% 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 9.7% of cases where such amendments are filed. This entry rate is in the 10% 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 44.4% of appeals filed. This is in the 10% percentile among all examiners. Strategic Insight: This examiner rarely withdraws rejections during the appeal process compared to other examiners. If you file an appeal, be prepared to fully prosecute it to a PTAB decision. Per MPEP § 1207, the examiner will prepare an Examiner's Answer maintaining the rejections.
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.