Detailed information about the 100 most recent patent applications.
| Application Number | Title | Filing Date | Disposal Date | Disposition | Time (months) | Office Actions | Restrictions | Interview | Appeal |
|---|---|---|---|---|---|---|---|---|---|
| 17127698 | SELF-OPTIMIZING LABELING PLATFORM | December 2020 | June 2025 | Abandon | 54 | 2 | 0 | No | No |
| 17079681 | BERT-BASED MACHINE-LEARNING TOOL FOR PREDICTING EMOTIONAL RESPONSE TO TEXT | October 2020 | June 2025 | Abandon | 56 | 3 | 0 | Yes | No |
| 17072757 | AUTOMATED SYNCHRONIZATION OF CLONE DIRECTED ACYCLIC GRAPHS | October 2020 | January 2025 | Allow | 51 | 3 | 0 | Yes | No |
| 17033474 | Dynamically Selecting Neural Networks for Detecting Predetermined Features | September 2020 | March 2024 | Abandon | 41 | 1 | 0 | No | No |
| 17022925 | Entity Extraction and Relationship Definition Using Machine Learning | September 2020 | March 2024 | Abandon | 42 | 2 | 0 | Yes | No |
| 17018555 | SELF-TRAINING TECHNIQUE FOR GENERATING NEURAL NETWORK MODELS | September 2020 | September 2024 | Abandon | 49 | 3 | 0 | No | No |
| 16987246 | CONTROLLING AGENTS USING REINFORCEMENT LEARNING WITH MIXED-INTEGER PROGRAMMING | August 2020 | December 2024 | Abandon | 52 | 3 | 0 | Yes | No |
| 16986556 | METHOD FOR RECOGNIZING AN ADVERSARIAL DISTURBANCE IN INPUT DATA OF A NEURAL NETWORK | August 2020 | May 2024 | Allow | 45 | 3 | 0 | Yes | No |
| 16943957 | TRAINING NEURAL NETWORKS USING LEARNED ADAPTIVE LEARNING RATES | July 2020 | September 2024 | Abandon | 49 | 3 | 0 | No | No |
| 15931362 | METHOD AND APPARATUS WITH NEURAL NETWORK DATA QUANTIZING | May 2020 | June 2024 | Allow | 49 | 3 | 0 | Yes | No |
| 16831971 | METHOD AND SYSTEM FOR CREATING SYNTHETIC UNSTRUCTURED FREE-TEXT MEDICAL DATA FOR TRAINING MACHINE LEARNING MODELS | March 2020 | November 2024 | Abandon | 56 | 4 | 0 | No | No |
| 16823562 | MODEL CREATION SUPPORTING METHOD AND MODEL CREATION SUPPORTING SYSTEM | March 2020 | November 2024 | Abandon | 56 | 3 | 0 | No | No |
| 16815358 | TIME SERIES ANALYSIS USING A SHAPELET LEARNING METHOD WITH AREA UNDER THE CURVE | March 2020 | April 2025 | Allow | 60 | 4 | 0 | No | No |
| 16743130 | AUTOMATED ANALYTICAL MODEL RETRAINING WITH A KNOWLEDGE GRAPH | January 2020 | March 2024 | Allow | 50 | 4 | 0 | Yes | No |
| 16717017 | VIRTUAL DATA SCIENTIST WITH PRESCRIPTIVE ANALYTICS | December 2019 | May 2025 | Abandon | 60 | 5 | 0 | Yes | No |
| 16696061 | DEVICE AND METHOD FOR TRAINING NEURAL NETWORK | November 2019 | December 2023 | Abandon | 49 | 3 | 0 | No | No |
| 16694921 | TIME SERIES DATA PROCESSING DEVICE AND OPERATING METHOD THEREOF | November 2019 | September 2024 | Abandon | 57 | 2 | 0 | No | Yes |
| 16693025 | NEURAL NETWORK TRAINING USING DETECTION PROCESSING FOR MULTIPLE RECOGNITION TASKS | November 2019 | January 2025 | Abandon | 60 | 6 | 0 | Yes | No |
| 16613212 | Knowledge Transfer Between Different Deep Learning Architectures | November 2019 | April 2025 | Allow | 60 | 7 | 0 | Yes | Yes |
| 16670690 | DEEP-LEARNING MODEL CREATION RECOMMENDATIONS | October 2019 | January 2025 | Abandon | 60 | 6 | 0 | Yes | No |
| 16586223 | TRAINING NEURAL NETWORKS TO GENERATE STRUCTURED EMBEDDINGS | September 2019 | July 2022 | Allow | 33 | 2 | 0 | Yes | No |
| 16566375 | COMPUTER-READABLE RECODING MEDIUM, LEARNING METHOD, PREDICTION METHOD, LEARNING APPARATUS, AND PREDICTION APPARATUS | September 2019 | March 2024 | Abandon | 54 | 4 | 0 | No | No |
| 16542403 | END-TO-END STRUCTURE-AWARE CONVOLUTIONAL NETWORKS FOR KNOWLEDGE BASE COMPLETION | August 2019 | June 2023 | Abandon | 46 | 3 | 0 | No | No |
| 16534856 | PERFORMANCE OF NEURAL NETWORKS USING LEARNED SPECIALIZED TRANSFORMATION FUNCTIONS | August 2019 | March 2025 | Abandon | 60 | 5 | 0 | Yes | No |
| 16481672 | ANOMALY DETECTION METHOD USING AN AUTOENCODER LEARNING FROM DATA ITEMS COLLECTED BY MEASURING DEVICES | July 2019 | May 2025 | Allow | 60 | 7 | 0 | Yes | No |
| 16430243 | GENERALIZED NONLINEAR MIXED EFFECT MODELS VIA GAUSSIAN PROCESSES | June 2019 | February 2023 | Abandon | 45 | 4 | 0 | Yes | No |
| 16422380 | POPULATION DIVERSITY BASED LEARNING IN ADVERSARIAL AND RAPID CHANGING ENVIRONMENTS | May 2019 | April 2023 | Abandon | 46 | 4 | 0 | Yes | No |
| 16421290 | SYSTEMS AND METHODS FOR HYBRID CONTENT PROVISIONING WITH DUAL RECOMMENDATION ENGINES | May 2019 | January 2025 | Abandon | 60 | 3 | 0 | No | No |
| 16407290 | Sensor-Action Fusion System for Optimising Sensor Measurement Collection from Multiple Sensors | May 2019 | March 2022 | Abandon | 34 | 1 | 0 | No | No |
| 16404733 | AUTOMATED TRAINING DATA EXTRACTION METHOD FOR DYNAMIC MODELS FOR AUTONOMOUS DRIVING VEHICLES | May 2019 | March 2023 | Allow | 46 | 3 | 0 | Yes | No |
| 16384738 | ADDRESSING A LOSS-METRIC MISMATCH WITH ADAPTIVE LOSS ALIGNMENT | April 2019 | April 2022 | Abandon | 36 | 2 | 0 | Yes | No |
| 16380537 | COMPUTER-READABLE RECORDING MEDIUM, MACHINE LEARNING METHOD, AND MACHINE LEARNING APPARATUS | April 2019 | October 2023 | Abandon | 54 | 4 | 0 | No | No |
| 16363891 | CONVERSATIONAL TURN ANALYSIS NEURAL NETWORKS | March 2019 | October 2022 | Abandon | 42 | 2 | 0 | No | No |
| 16355185 | EFFICIENT MACHINE LEARNING MODEL ARCHITECTURE SELECTION | March 2019 | September 2023 | Allow | 55 | 6 | 0 | No | No |
| 16296897 | Quantifying Vulnerabilities of Deep Learning Computing Systems to Adversarial Perturbations | March 2019 | November 2021 | Allow | 32 | 1 | 0 | Yes | No |
| 16293252 | RESOLVING OPAQUENESS OF COMPLEX MACHINE LEARNING APPLICATIONS | March 2019 | October 2024 | Allow | 60 | 5 | 0 | Yes | No |
| 16289531 | REINFORCEMENT LEARNING WITH SCHEDULED AUXILIARY CONTROL | February 2019 | September 2023 | Allow | 55 | 4 | 0 | Yes | No |
| 16278413 | AUTOMATIC DETECTION OF LABELING ERRORS | February 2019 | January 2022 | Abandon | 35 | 1 | 0 | No | No |
| 16325348 | LEARNING DEVICE, SIGNAL PROCESSING DEVICE, AND LEARNING METHOD | February 2019 | November 2022 | Allow | 45 | 3 | 0 | Yes | No |
| 16268071 | IMPLEMENTING A COMPUTER SYSTEM TASK INVOLVING NONSTATIONARY STREAMING TIME-SERIES DATA BASED ON A BIAS-VARIANCE-BASED ADAPTIVE LEARNING RATE | February 2019 | October 2022 | Abandon | 45 | 4 | 0 | Yes | No |
| 16263930 | INTERACTIVE REINFORCEMENT LEARNING WITH DYNAMIC REUSE OF PRIOR KNOWLEDGE | January 2019 | December 2021 | Allow | 34 | 2 | 0 | No | No |
| 16254037 | System and Method for Predicting Fine-Grained Adversarial Multi-Agent Motion | January 2019 | December 2022 | Allow | 47 | 3 | 0 | Yes | No |
| 16253366 | SYSTEM AND METHOD FOR CONTEXT-BASED TRAINING OF A MACHINE LEARNING MODEL | January 2019 | August 2022 | Allow | 43 | 3 | 0 | No | No |
| 16248670 | ASYNCHRONOUS EARLY STOPPING IN HYPERPARAMETER METAOPTIMIZATION FOR A NEURAL NETWORK | January 2019 | September 2023 | Abandon | 56 | 2 | 0 | Yes | Yes |
| 16246581 | LEARNING METHOD, LEARNING DEVICE, AND COMPUTER-READABLE RECORDING MEDIUM | January 2019 | June 2023 | Abandon | 53 | 5 | 0 | No | No |
| 16242999 | SCHEDULING HETEROGENEOUS EXECUTION ON HETEROGENEOUS HARDWARE | January 2019 | May 2023 | Allow | 52 | 3 | 0 | Yes | Yes |
| 16234783 | DOMAIN KNOWLEDGE INJECTION INTO SEMI-CROWDSOURCED UNSTRUCTURED DATA SUMMARIZATION FOR DIAGNOSIS AND REPAIR | December 2018 | November 2023 | Abandon | 58 | 2 | 1 | No | No |
| 16235467 | NETWORK EMBEDDING METHOD | December 2018 | August 2024 | Abandon | 60 | 6 | 0 | Yes | No |
| 16230909 | MULTIMODAL MACHINE LEARNING SELECTOR | December 2018 | December 2024 | Abandon | 60 | 6 | 0 | No | No |
| 16216138 | CREATING OPTIMIZED MACHINE-LEARNING MODELS FOR IMPROVING THE ACCURACY OF THE NEURAL NETWORK MODEL | December 2018 | May 2024 | Abandon | 60 | 8 | 0 | Yes | No |
| 16214598 | METHOD AND SYSTEM FOR TRANSFER LEARNING TO RANDOM TARGET DATASET AND MODEL STRUCTURE BASED ON META LEARNING | December 2018 | October 2021 | Abandon | 34 | 1 | 0 | No | No |
| 16210785 | COLLABORATIVE DEEP LEARNING MODEL AUTHORING TOOL | December 2018 | November 2024 | Abandon | 60 | 6 | 0 | Yes | Yes |
| 16204549 | MULTIFUNCTION PERCEPTRONS IN MACHINE LEARNING ENVIRONMENTS | November 2018 | May 2024 | Abandon | 60 | 6 | 0 | No | No |
| 16198642 | FRAMEWORK FOR PROVIDING RECOMMENDATIONS FOR MIGRATION OF A DATABASE TO A CLOUD COMPUTING SYSTEM | November 2018 | June 2022 | Allow | 43 | 2 | 0 | Yes | No |
| 16303256 | MODEL-FREE CONTROL FOR REINFORCEMENT LEARNING AGENTS | November 2018 | June 2025 | Abandon | 60 | 6 | 0 | Yes | No |
| 16188123 | Reinforcement Learning for Concurrent Actions | November 2018 | July 2022 | Allow | 44 | 3 | 0 | Yes | No |
| 16156114 | TECHNIQUES FOR IMPROVING DOWNSTREAM UTILITY IN MAKING FOLLOW RECOMMENDATIONS | October 2018 | May 2023 | Abandon | 55 | 5 | 0 | Yes | No |
| 16152953 | ARTIFICIAL NEURAL NETWORK WITH CONTEXT PATHWAY | October 2018 | November 2022 | Abandon | 49 | 2 | 0 | No | No |
| 16147939 | LOCAL LEARNING SYSTEM IN ARTIFICIAL INTELLIGENCE DEVICE | October 2018 | April 2023 | Abandon | 54 | 3 | 0 | No | No |
| 16145287 | METHOD FOR PROTECTING A MACHINE LEARNING ENSEMBLE FROM COPYING | September 2018 | June 2022 | Allow | 45 | 3 | 0 | Yes | Yes |
| 16131150 | COMMUNICATION EFFICIENT MACHINE LEARNING OF DATA ACROSS MULTIPLE SITES | September 2018 | January 2023 | Allow | 52 | 1 | 0 | No | No |
| 16110124 | INFORMATION PROCESSING APPARATUS AND METHOD OF CONTROLLING INFORMATION PROCESSING APPARATUS | August 2018 | August 2022 | Allow | 48 | 3 | 0 | Yes | No |
| 16051792 | REMOTE USAGE OF MACHINE LEARNED LAYERS BY A SECOND MACHINE LEARNING CONSTRUCT | August 2018 | July 2022 | Abandon | 48 | 1 | 0 | No | No |
| 16029389 | DATA PROCESSING SYSTEM, METHOD, AND DEVICE | July 2018 | November 2023 | Abandon | 60 | 5 | 0 | No | No |
| 16024434 | MACHINE LEARNING ANALYSIS OF INCREMENTAL EVENT CAUSALITY TOWARDS A TARGET OUTCOME | June 2018 | September 2022 | Allow | 50 | 3 | 0 | Yes | No |
| 16022317 | ARTIFICIAL INTELLIGENCE ASSISTED CONTENT AUTHORING FOR AUTOMATED AGENTS | June 2018 | April 2024 | Abandon | 60 | 5 | 0 | Yes | Yes |
| 16014503 | Job Merging for Machine and Deep Learning Hyperparameter Tuning | June 2018 | July 2022 | Allow | 48 | 4 | 0 | No | No |
| 15982478 | SYSTEM AND METHOD FOR AUTOMATIC BUILDING OF LEARNING MACHINES USING LEARNING MACHINES | May 2018 | January 2022 | Abandon | 44 | 1 | 0 | No | No |
| 15959040 | MODEL INTERPRETATION | April 2018 | January 2023 | Allow | 57 | 3 | 0 | Yes | No |
| 15928049 | INFERRING DIGITAL TWINS FROM CAPTURED DATA | March 2018 | February 2023 | Allow | 59 | 3 | 0 | Yes | No |
| 15909372 | CLASSIFICATION OF SOURCE DATA BY NEURAL NETWORK PROCESSING | March 2018 | May 2025 | Abandon | 60 | 10 | 0 | Yes | No |
| 15909442 | CLASSIFICATION OF SOURCE DATA BY NEURAL NETWORK PROCESSING | March 2018 | November 2023 | Abandon | 60 | 7 | 0 | Yes | No |
| 15899599 | LEARNING DEVICE, INFORMATION PROCESSING DEVICE, LEARNING METHOD, AND COMPUTER PROGRAM PRODUCT | February 2018 | November 2023 | Abandon | 60 | 4 | 0 | Yes | No |
| 15892475 | DETECTING DATASET POISONING ATTACKS INDEPENDENT OF A LEARNING ALGORITHM | February 2018 | July 2021 | Allow | 41 | 1 | 0 | Yes | No |
| 15891739 | SYSTEMS AND METHODS FOR PREDICTION OF OCCUPANCY IN BUILDINGS | February 2018 | February 2023 | Abandon | 60 | 4 | 0 | Yes | No |
| 15885727 | HIERARCHICAL AND INTERPRETABLE SKILL ACQUISITION IN MULTI-TASK REINFORCEMENT LEARNING | January 2018 | September 2022 | Allow | 56 | 1 | 0 | No | No |
| 15878543 | Configurable Convolution Neural Network Processor | January 2018 | May 2024 | Abandon | 60 | 5 | 0 | Yes | No |
| 15878965 | SYNAPSE MEMORY | January 2018 | September 2022 | Allow | 56 | 2 | 0 | Yes | No |
| 15876025 | SIGNIFICANT EVENTS IDENTIFIER FOR OUTLIER ROOT CAUSE INVESTIGATION | January 2018 | July 2023 | Abandon | 60 | 4 | 0 | No | No |
| 15845509 | COLLECTIVE DECISION MAKING BY CONSENSUS IN COGNITIVE ENVIRONMENTS | December 2017 | February 2023 | Abandon | 60 | 6 | 0 | Yes | No |
| 15844442 | Deep Neural Network Hardening Framework | December 2017 | May 2022 | Allow | 53 | 4 | 0 | Yes | No |
| 15841094 | ARCHITECTURES FOR TRAINING NEURAL NETWORKS USING BIOLOGICAL SEQUENCES, CONSERVATION, AND MOLECULAR PHENOTYPES | December 2017 | July 2024 | Abandon | 60 | 6 | 0 | Yes | Yes |
This analysis examines appeal outcomes and the strategic value of filing appeals for examiner HOANG, MICHAEL H.
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, 11.1% of applications that filed an appeal were subsequently allowed. This appeal filing benefit rate is in the bottom 25% across the USPTO, indicating that filing appeals is less effective here than in most other areas.
⚠ Filing a Notice of Appeal shows limited benefit. Consider other strategies like interviews or amendments before appealing.
Examiner HOANG, MICHAEL H works in Art Unit 2122 and has examined 82 patent applications in our dataset. With an allowance rate of 37.8%, this examiner allows applications at a lower rate than most examiners at the USPTO. Applications typically reach final disposition in approximately 54 months.
Examiner HOANG, MICHAEL H's allowance rate of 37.8% places them in the 7% percentile among all USPTO examiners. This examiner is less likely to allow applications than most examiners at the USPTO.
On average, applications examined by HOANG, MICHAEL H receive 3.63 office actions before reaching final disposition. This places the examiner in the 93% 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 HOANG, MICHAEL H is 54 months. This places the examiner in the 4% percentile for prosecution speed. Applications take longer to reach final disposition with this examiner compared to most others.
Conducting an examiner interview provides a +27.8% benefit to allowance rate for applications examined by HOANG, MICHAEL H. This interview benefit is in the 74% percentile among all examiners. Recommendation: Interviews provide an above-average benefit with this examiner and are worth considering.
When applicants file an RCE with this examiner, 10.8% of applications are subsequently allowed. This success rate is in the 7% 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 16.1% of cases where such amendments are filed. This entry rate is in the 19% 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, 66.7% result in withdrawal of the rejection or reopening of prosecution. This success rate is in the 54% 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 100.0% of appeals filed. This is in the 88% 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 frequently reconsiders rejections during the appeal process compared to other examiners. Per MPEP § 1207.01, all appeals must go through a mandatory appeal conference. Filing a Notice of Appeal may prompt favorable reconsideration even before you file an Appeal Brief.
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 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.