Detailed information about the 100 most recent patent applications.
| Application Number | Title | Filing Date | Disposal Date | Disposition | Time (months) | Office Actions | Restrictions | Interview | Appeal |
|---|---|---|---|---|---|---|---|---|---|
| 18811340 | NETWORK FOR DETECTING EDGE CASES FOR USE IN TRAINING AUTONOMOUS VEHICLE CONTROL SYSTEMS | August 2024 | January 2026 | Allow | 17 | 2 | 0 | Yes | Yes |
| 18786758 | NEURAL NETWORK OPTIMIZATION DEVICE FOR EDGE DEVICE MEETING ON-DEMAND INSTRUCTION AND METHOD USING THE SAME | July 2024 | February 2026 | Allow | 19 | 2 | 0 | Yes | No |
| 18774585 | METHOD AND APPARATUS FOR TRAINING FEW-SHOT EVENT DETECTION MODEL BASED ON MULTILINGUAL PROMPT LEARNING | July 2024 | January 2025 | Allow | 6 | 1 | 0 | Yes | No |
| 18488984 | COUNT-TYPE QUALITY VARIABLE PREDICTION METHOD BASED ON VARIATIONAL BAYESIAN GAUSSIAN-POISSON MIXED REGRESSION MODEL | October 2023 | August 2025 | Abandon | 22 | 2 | 0 | Yes | No |
| 18180754 | TRAINING MACHINE LEARNING MODELS BY DETERMINING UPDATE RULES USING NEURAL NETWORKS | March 2023 | December 2024 | Allow | 21 | 2 | 0 | No | No |
| 18080407 | AUTOMATED GENERATION OF MACHINE LEARNING MODELS | December 2022 | January 2026 | Allow | 37 | 4 | 0 | Yes | Yes |
| 17344252 | SYSTEMS AND METHODS FOR MANAGING MACHINE LEARNING MODELS | June 2021 | April 2024 | Abandon | 35 | 6 | 0 | Yes | No |
| 17312364 | DETECTION LEARNING DEVICE, METHOD, AND PROGRAM | June 2021 | September 2025 | Abandon | 51 | 2 | 0 | No | No |
| 17337834 | Joint Architecture And Hyper-Parameter Search For Machine Learning Models | June 2021 | December 2025 | Abandon | 54 | 2 | 0 | Yes | No |
| 17223525 | PREDICTIVE MAINTENANCE USING VIBRATION ANALYSIS OF ROTATING MACHINES | April 2021 | February 2026 | Abandon | 59 | 4 | 0 | No | No |
| 17209302 | DIMENSION REDUCTION IN THE CONTEXT OF UNSUPERVISED LEARNING | March 2021 | July 2025 | Allow | 52 | 1 | 0 | Yes | No |
| 17196203 | METHOD FOR TRAINING A SINGLE NON-SYMMETRIC DECODER FOR LEARNING-BASED CODECS | March 2021 | April 2025 | Abandon | 50 | 2 | 0 | No | No |
| 17179365 | SYSTEM AND METHOD FOR WEATHER DEPENDENT MACHINE LEARNING ARCHITECTURE | February 2021 | May 2025 | Allow | 51 | 1 | 0 | Yes | No |
| 17174976 | End-To-End Graph Convolution Network | February 2021 | October 2025 | Abandon | 56 | 4 | 0 | Yes | No |
| 17133181 | METHODS AND APPARATUS TO IMPLEMENT A NEURAL NETWORK | December 2020 | February 2025 | Abandon | 50 | 2 | 0 | Yes | No |
| 17129998 | MULTI-LEVEL MULTI-OBJECTIVE AUTOMATED MACHINE LEARNING | December 2020 | May 2025 | Abandon | 52 | 4 | 0 | No | No |
| 17095669 | TIME-AFFECTING LINEAR PATHWAY-BASED ARTIFICIAL INTELLIGENCE SYSTEMS AND METHODS | November 2020 | January 2026 | Abandon | 60 | 3 | 0 | No | No |
| 17077416 | MODEL TRAINING AND SERVICE RECOMMENDATION | October 2020 | August 2024 | Abandon | 46 | 1 | 0 | No | No |
| 17074878 | SYSTEM AND METHOD FOR AUTOMATED OPTIMIZATION OF A NEURAL NETWORK MODEL | October 2020 | January 2026 | Allow | 60 | 4 | 0 | Yes | No |
| 17075128 | MACHINE LEARNING MODEL TRAINING METHOD FOR FACE RECOGNITION | October 2020 | April 2025 | Abandon | 54 | 3 | 0 | No | No |
| 17031930 | INCREMENTAL AND DECENTRALIZED MODEL PRUNING IN FEDERATED MACHINE LEARNING | September 2020 | October 2023 | Allow | 37 | 2 | 0 | Yes | No |
| 17029104 | INFORMATION PROCESSING SYSTEM, INFORMATION PROCESSING APPARATUS, AND INFORMATION PROCESSING PROGRAM | September 2020 | August 2024 | Abandon | 47 | 1 | 0 | No | No |
| 17025418 | Distilling from Ensembles to Improve Reproducibility of Neural Networks | September 2020 | June 2025 | Allow | 57 | 3 | 0 | Yes | Yes |
| 17018527 | SYSTEMS AND METHODS FOR UNSUPERVISED DETECTION OF ANOMALOUS CUSTOMER INTERACTIONS TO SECURE AND AUTHENTICATE A CUSTOMER SESSION | September 2020 | September 2025 | Abandon | 60 | 3 | 0 | Yes | No |
| 16977891 | INFORMATION PROCESSING APPARATUS, INFORMATION PROCESSING METHOD, AND PROGRAM FOR OBTAINING COMPLEMENTARY DATA | September 2020 | December 2024 | Abandon | 51 | 2 | 0 | Yes | No |
| 17001522 | ASSIGNING OWNERSHIP OF SYMPTOM OF RESPIRATORY DISEASE TO USER IN POPULATION OF USERS | August 2020 | September 2025 | Abandon | 60 | 2 | 1 | No | No |
| 16998680 | METHOD AND SYSTEM FOR DETERMINING POLICIES, RULES, AND AGENT CHARACTERISTICS, FOR AUTOMATING AGENTS, AND PROTECTION | August 2020 | March 2026 | Allow | 60 | 8 | 0 | No | No |
| 16997867 | Systems and Methods for Simulating Sense Data and Creating Perceptions | August 2020 | December 2023 | Abandon | 40 | 1 | 0 | No | No |
| 16993969 | SYSTEM AND METHODS FOR TRANSLATING ERROR MESSAGES | August 2020 | October 2024 | Abandon | 50 | 3 | 0 | Yes | No |
| 16942906 | DEVICE, METHOD, AND COMPUTER PROGRAM PRODUCT FOR EXECUTING INFERENCE USING INPUT SIGNAL | July 2020 | June 2025 | Allow | 59 | 4 | 0 | Yes | No |
| 16935538 | CLASSIFICATION OF A NON-MONETARY DONATION BASED ON MACHINE LEARNING | July 2020 | June 2025 | Allow | 59 | 4 | 0 | Yes | No |
| 16924326 | PRIORITIZING TASKS OF DOMAIN EXPERTS FOR MACHINE LEARNING MODEL TRAINING | July 2020 | April 2024 | Abandon | 45 | 3 | 0 | No | No |
| 16958876 | METHOD FOR TRAINING DATA OF DEEP LEARNING MODELS, STORAGE MEDIUM, AND ELECTRONIC DEVICE | June 2020 | May 2025 | Abandon | 58 | 4 | 0 | No | No |
| 16885927 | LEARNING SYSTEM, REHABILITATION SUPPORT SYSTEM, METHOD, PROGRAM, AND TRAINED MODEL | May 2020 | October 2024 | Abandon | 53 | 4 | 0 | Yes | No |
| 16766945 | TRAINING AN UNSUPERVISED MEMORY-BASED PREDICTION SYSTEM TO LEARN COMPRESSED REPRESENTATIONS OF AN ENVIRONMENT | May 2020 | July 2024 | Allow | 50 | 3 | 0 | Yes | No |
| 16551753 | LOW PRECISION AND COARSE-TO-FINE DYNAMIC FIXED-POINT QUANTIZATION DESIGN IN CONVOLUTION NEURAL NETWORK | August 2019 | April 2024 | Allow | 56 | 3 | 0 | No | No |
| 16436952 | Detecting Non-Anomalous and Anomalous Sequences of Computer-Executed Operations | June 2019 | May 2023 | Allow | 47 | 1 | 0 | No | No |
| 16426530 | Using Metamodeling for Fast and Accurate Hyperparameter Optimization of Machine Learning and Deep Learning Models | May 2019 | August 2023 | Allow | 51 | 2 | 0 | Yes | No |
| 16421606 | ITERATIVE NEURAL NETWORK TRAINING USING QUALITY ASSURANCE NEURAL NETWORK | May 2019 | March 2024 | Allow | 58 | 4 | 0 | Yes | No |
| 16413179 | GOAL-ORIENTED INTERACTIVE INSTRUCTIONAL SYSTEM BASED ON MACHINE LEARNING | May 2019 | August 2023 | Abandon | 51 | 1 | 0 | No | No |
| 16346313 | Convolutional Neural Network Compression Via Sparse Connection Patterns | April 2019 | June 2024 | Abandon | 60 | 2 | 0 | No | Yes |
| 16337981 | HARDWARE NEURAL NETWORK CONVERSION METHOD, COMPUTING DEVICE, COMPILING METHOD AND NEURAL NETWORK SOFTWARE AND HARDWARE COLLABORATION SYSTEM | March 2019 | October 2022 | Allow | 43 | 1 | 0 | Yes | No |
| 16356264 | MODEL TRAINING METHOD AND APPARATUS, AND DATA RECOGNITION METHOD BASED ON MODEL COMPRESSION | March 2019 | July 2023 | Abandon | 52 | 4 | 0 | Yes | No |
| 16274599 | Systems and Methods for Modification of Neural Networks Based on Estimated Edge Utility | February 2019 | March 2023 | Allow | 49 | 3 | 0 | Yes | No |
| 16259551 | METHOD AND DEVICE FOR DE-PREJUDICING ARTIFICIAL INTELLIGENCE BASED ANOMALY DETECTION | January 2019 | April 2022 | Allow | 38 | 1 | 0 | Yes | No |
| 16254925 | METHOD AND SYSTEM FOR PROCESSING NEURAL NETWORK MODEL USING PLURALITY OF ELECTRONIC DEVICES | January 2019 | May 2023 | Abandon | 52 | 5 | 0 | Yes | No |
| 16249854 | Systems and Methods for Modeling Probability Distributions | January 2019 | December 2022 | Allow | 47 | 3 | 0 | Yes | No |
| 16244248 | LSTM Training For Neural Network Based Course Of Action Selection | January 2019 | August 2022 | Abandon | 43 | 3 | 0 | Yes | No |
| 16241700 | NEURAL NETWORK-BASED TRANSLATION METHOD AND APPARATUS | January 2019 | November 2022 | Abandon | 47 | 2 | 0 | No | No |
| 16236085 | Training a Deep Learning System to Detect Engine Knock with Accuracy Associated with High Fidelity Knock Detection Sensors Despite Using Data from a Low Fidelity Knock Detection Sensor | December 2018 | August 2022 | Allow | 43 | 3 | 0 | Yes | No |
| 16216720 | Parallel Forward and Backward Propagation | December 2018 | March 2024 | Allow | 60 | 4 | 0 | Yes | Yes |
| 16213470 | AUTOMATED GENERATION OF MACHINE LEARNING MODELS | December 2018 | September 2022 | Allow | 45 | 3 | 0 | Yes | No |
| 16302592 | TRAINING MACHINE LEARNING MODELS BY DETERMINING UPDATE RULES USING RECURRENT NEURAL NETWORKS | November 2018 | November 2022 | Allow | 48 | 3 | 0 | Yes | No |
| 16189397 | Labeling Training Set Data | November 2018 | January 2023 | Abandon | 50 | 2 | 0 | Yes | No |
| 16189264 | METHOD, SYSTEM, AND COMPUTER PROGRAM PRODUCT TO EMPLOY A MULTI-LAYERED NEURAL NETWORK FOR CLASSIFICATION | November 2018 | August 2022 | Allow | 45 | 3 | 0 | Yes | No |
| 16176100 | DYNAMIC BOLTZMANN MACHINE FOR PREDICTING GENERAL DISTRIBUTIONS OF TIME SERIES DATASETS | October 2018 | September 2021 | Allow | 35 | 3 | 0 | Yes | No |
| 16173126 | Method, Apparatus and Computer Program for generating robust automatic learning systems and testing trained automatic learning systems | October 2018 | February 2023 | Allow | 51 | 1 | 0 | Yes | No |
| 16168415 | ARTIFICIAL INTELLIGENCE DEVICE AND METHOD FOR EXECUTING AN OPERATION BASED ON PREDICTED BIOMETRIC STATE OF A USER | October 2018 | May 2022 | Allow | 43 | 2 | 0 | Yes | No |
| 16167727 | Dynamic Batch Sizing for Inferencing of Deep Neural Networks in Resource-Constrained Environments | October 2018 | June 2023 | Abandon | 56 | 4 | 0 | Yes | No |
| 16147478 | TRAINING OF NEURAL NETWORKS BY INCLUDING IMPLEMENTATION COST AS AN OBJECTIVE | September 2018 | February 2023 | Abandon | 52 | 3 | 0 | Yes | No |
| 16137491 | COMPRESSION OF SPARSE DEEP CONVOLUTIONAL NETWORK WEIGHTS | September 2018 | November 2024 | Allow | 60 | 9 | 0 | Yes | No |
| 16117043 | METHOD, MEDIUM AND DEVICE FOR LEARNING USING INPUT VECTORS DERIVED FROM WORDS OF PHRASES | August 2018 | February 2023 | Abandon | 53 | 4 | 0 | No | No |
| 16058215 | BEHAVIORAL FINITE AUTOMATA AND NEURAL MODELS | August 2018 | April 2024 | Abandon | 60 | 4 | 0 | Yes | No |
| 16054358 | SYSTEM, METHOD AND APPARATUS FOR TRAINING NEURAL NETWORKS USING MULTIPLE DATASETS | August 2018 | December 2022 | Allow | 52 | 5 | 0 | Yes | No |
| 16023638 | OPTIMIZING INFERENCE FOR DEEP-LEARNING NEURAL NETWORKS IN A HETEROGENEOUS SYSTEM | June 2018 | May 2025 | Abandon | 60 | 9 | 0 | Yes | No |
| 16021654 | GENERATING SUPERVISED EMBEDDINGS USING UNSUPERVISED EMBEDDINGS | June 2018 | November 2021 | Abandon | 40 | 1 | 0 | No | No |
| 16004796 | CHARACTERIZING ACTIVITY IN A RECURRENT ARTIFICIAL NEURAL NETWORK | June 2018 | May 2025 | Allow | 60 | 8 | 0 | Yes | No |
| 16004243 | ARTIFICIAL NEURAL NETWORK TRAINING USING FLEXIBLE FLOATING POINT TENSORS | June 2018 | September 2024 | Allow | 60 | 6 | 0 | Yes | Yes |
| 15996968 | LEARNING AND GENERATION DEVICES AND METHODS FOR MACHINE LEARNING OF RELEVANCE IN DATA SETS | June 2018 | December 2023 | Abandon | 60 | 5 | 0 | No | No |
| 15989350 | ATTENTIVE NEURAL COLLABORATIVE FILTERING FOR MODELING IMPLICIT FEEDBACK | May 2018 | June 2024 | Abandon | 60 | 4 | 0 | No | Yes |
| 15973525 | IMPUTATION USING A NEURAL NETWORK | May 2018 | August 2023 | Abandon | 60 | 6 | 0 | Yes | No |
| 15933225 | SCHEDULING NEURAL NETWORK COMPUTATIONS BASED ON MEMORY CAPACITY | March 2018 | May 2022 | Allow | 50 | 3 | 0 | Yes | No |
| 15914229 | METHOD AND APPARATUS FOR LEARNING LOW-PRECISION NEURAL NETWORK THAT COMBINES WEIGHT QUANTIZATION AND ACTIVATION QUANTIZATION | March 2018 | October 2021 | Allow | 44 | 2 | 0 | Yes | No |
| 15898847 | GENERATIVE ADVERSARIAL NETWORKS IN PREDICTING SEQUENTIAL DATA | February 2018 | March 2023 | Abandon | 60 | 3 | 0 | Yes | No |
| 15898433 | OPTIMIZED ASYNCHRONOUS TRAINING OF NEURAL NETWORKS USING A DISTRIBUTED PARAMETER SERVER WITH EAGER UPDATES | February 2018 | December 2022 | Allow | 58 | 4 | 0 | Yes | No |
| 15895162 | MIXED SIGNAL CMOS RPU WITH DIGITAL WEIGHT STORAGE | February 2018 | January 2024 | Abandon | 60 | 4 | 0 | No | No |
| 15888812 | Neural Network with Embedded Filter Layer | February 2018 | October 2021 | Allow | 45 | 2 | 0 | Yes | No |
| 15886263 | BOOTSTRAPPING RECOMMENDATION SYSTEMS FROM PASSIVE DATA | February 2018 | March 2024 | Abandon | 60 | 3 | 0 | Yes | No |
| 15879351 | DYNAMIC WEBSITE CONTENT OPTIMIZATION | January 2018 | March 2025 | Allow | 60 | 10 | 0 | Yes | No |
| 15865294 | ZERO INJECTION FOR DISTRIBUTED DEEP LEARNING | January 2018 | June 2022 | Abandon | 53 | 1 | 0 | No | No |
| 15851723 | MODEL ENSEMBLE GENERATION | December 2017 | October 2023 | Abandon | 60 | 6 | 0 | Yes | No |
| 15833049 | Using Machine Learning System to Dynamically Process Events | December 2017 | May 2022 | Abandon | 53 | 4 | 0 | Yes | No |
| 15822454 | DECISION SUPPORT FOR EFFECTIVE LONG-TERM DRUG THERAPY | November 2017 | December 2023 | Abandon | 60 | 8 | 0 | Yes | No |
| 15820927 | SYSTEM FOR RECOGNIZING BEATING PATTERN AND METHOD FOR RECOGNIZING THE SAME | November 2017 | October 2022 | Abandon | 58 | 4 | 0 | Yes | No |
| 15817396 | OPTIMIZING A HIERARCHICAL RULE-BASED DECISION POLICY | November 2017 | May 2023 | Abandon | 60 | 6 | 0 | Yes | No |
| 15813363 | WATCHED HYPOTHESIS FOR DEEP QUESTION ANSWERING | November 2017 | January 2022 | Abandon | 50 | 4 | 0 | Yes | No |
This analysis examines appeal outcomes and the strategic value of filing appeals for examiner JABLON, ASHER H..
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, 40.0% 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 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 JABLON, ASHER H. works in Art Unit 2127 and has examined 80 patent applications in our dataset. With an allowance rate of 42.5%, this examiner allows applications at a lower rate than most examiners at the USPTO. Applications typically reach final disposition in approximately 52 months.
Examiner JABLON, ASHER H.'s allowance rate of 42.5% places them in the 8% percentile among all USPTO examiners. This examiner is less likely to allow applications than most examiners at the USPTO.
On average, applications examined by JABLON, ASHER H. receive 3.46 office actions before reaching final disposition. This places the examiner in the 94% 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 JABLON, ASHER H. 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 +42.9% benefit to allowance rate for applications examined by JABLON, ASHER H.. This interview benefit is in the 89% 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, 13.5% of applications are subsequently allowed. This success rate is in the 9% 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 3.0% of cases where such amendments are filed. This entry rate is in the 4% 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 37.5% of appeals filed. This is in the 6% 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, 40.0% are granted (fully or in part). This grant rate is in the 29% percentile among all examiners. Strategic Note: Petitions show below-average success regarding this examiner's actions. Ensure you have a strong procedural basis before filing.
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.