Detailed information about the 100 most recent patent applications.
| Application Number | Title | Filing Date | Disposal Date | Disposition | Time (months) | Office Actions | Restrictions | Interview | Appeal |
|---|---|---|---|---|---|---|---|---|---|
| 19193326 | REGENERATIVE MODEL-CONTINUOUS EVOLUTION SYSTEM | April 2025 | July 2025 | Allow | 2 | 0 | 0 | No | No |
| 19074465 | AUTONOMOUS DRIVING TEST METHOD BASED ON MULTI-AGENT SWARM ADVERSARIAL, DEVICE AND MEDIUM | March 2025 | May 2025 | Allow | 2 | 0 | 0 | No | No |
| 18954244 | DYNAMIC AUTOMATED CONTENT COMPLIANCE VERIFICATION | November 2024 | June 2025 | Allow | 7 | 1 | 0 | Yes | No |
| 18635410 | DISTRIBUTED GAUSSIAN PROCESS CLASSIFICATION COMPUTING SYSTEM | April 2024 | October 2024 | Allow | 6 | 1 | 0 | Yes | No |
| 18620299 | Regenerative Model - Continuous Evolution System | March 2024 | April 2025 | Allow | 12 | 2 | 0 | Yes | No |
| 18583837 | MULTI-THREAD DISTRIBUTED TRAINING OF A RECOMMENDER MODEL | February 2024 | July 2025 | Allow | 17 | 3 | 0 | Yes | No |
| 18235632 | GENERATING AND PROCESSING AUGMENTED COMPUTATIONAL GRAPHS | August 2023 | January 2026 | Allow | 29 | 2 | 0 | Yes | No |
| 18354589 | SYSTEMS AND METHODS FOR SLOT MACHINE GAME DEVELOPMENT UTILIZING GRAPHIC-BASED ARTIFICIAL INTELLIGENCE GAME DESIGN SYSTEMS | July 2023 | November 2025 | Allow | 28 | 3 | 0 | No | No |
| 18214301 | METHOD AND APPARATUS FOR GENERATING Q & A MODEL BY USING ADVERSARIAL LEARNING | June 2023 | April 2025 | Allow | 21 | 1 | 0 | No | No |
| 18204141 | BED WITH USER TRACKING FEATURES | May 2023 | October 2025 | Abandon | 28 | 1 | 0 | Yes | No |
| 18144445 | Systems and Methods Enabling Online One-Shot Learning and Generalization by Intelligent Systems of Task-Relevant Features and Transfer to a Cohort of Intelligent Systems | May 2023 | March 2025 | Allow | 23 | 1 | 0 | No | No |
| 18109811 | SYSTEM, METHOD, AND COMPUTER PROGRAM PRODUCT FOR THE PRODUCTION AND CONSUMPTION OF NATURAL INTELLIGENCE USING AN ARTIFICIAL BRAIN | February 2023 | March 2026 | Allow | 37 | 0 | 0 | No | No |
| 18089855 | QUANTUM FORMULATION INDEPENDENT SOLVER | December 2022 | October 2023 | Allow | 10 | 1 | 0 | No | No |
| 17973383 | GENERATING MACHINE LEARNING MODELS USING GENETIC DATA | October 2022 | November 2024 | Allow | 24 | 1 | 0 | No | No |
| 17971584 | COMPUTER NETWORK ARCHITECTURE WITH AUTOMATED CLAIMS COMPLETION, MACHINE LEARNING AND ARTIFICIAL INTELLIGENCE | October 2022 | April 2023 | Allow | 6 | 1 | 0 | No | No |
| 17937899 | SYSTEM AND METHOD FOR PREDICTING SUBJECT ENROLLMENT | October 2022 | July 2024 | Allow | 22 | 2 | 0 | Yes | No |
| 17940606 | SERVERS, METHODS AND SYSTEMS FOR FAIR AND SECURE VERTICAL FEDERATED LEARNING | September 2022 | March 2026 | Allow | 42 | 1 | 0 | No | No |
| 17816288 | AUTOMATED SELECTION OF PRINCIPAL COMPONENT ANALYSIS VARIANTS FOR LARGE-SCALE DATAASETS | July 2022 | December 2025 | Allow | 41 | 1 | 0 | Yes | No |
| 17872528 | NEURAL NETWORKS FOR SELECTING ACTIONS TO BE PERFORMED BY A ROBOTIC AGENT | July 2022 | January 2026 | Allow | 42 | 2 | 0 | Yes | No |
| 17727915 | DETERIORATION SUPPRESSION PROGRAM, DETERIORATION SUPPRESSION METHOD, AND NON-TRANSITORY COMPUTER-READABLE STORAGE MEDIUM | April 2022 | March 2026 | Abandon | 47 | 1 | 0 | No | No |
| 17715997 | Learning method for the determination of a level of a space-time trending physical quantity in the presence of physical obstacles in a chosen spacial zone | April 2022 | March 2026 | Abandon | 47 | 1 | 0 | No | No |
| 17712148 | METHOD FOR EXTREME CLASS IMBALANCE WITHIN FRAUD DETECTION | April 2022 | November 2025 | Allow | 44 | 1 | 0 | No | No |
| 17708063 | CREATION METHOD, STORAGE MEDIUM, AND INFORMATION PROCESSING APPARATUS | March 2022 | March 2026 | Abandon | 47 | 1 | 0 | No | No |
| 17703822 | FEATURE EFFECTIVENESS ASSESSMENT METHOD AND APPARATUS, ELECTRONIC DEVICE, AND STORAGE MEDIUM | March 2022 | December 2025 | Allow | 44 | 1 | 0 | Yes | No |
| 17637190 | COMPILER FOR NEURAL ACCELERATOR | February 2022 | November 2025 | Allow | 45 | 1 | 0 | Yes | No |
| 17674808 | DYNAMICALLY TUNING HYPERPARAMETERS DURING ML MODEL TRAINING | February 2022 | December 2025 | Allow | 45 | 1 | 0 | Yes | No |
| 17580574 | COLLABORATIVE DATA SCHEMA MANAGEMENT FOR FEDERATED LEARNING | January 2022 | December 2025 | Abandon | 46 | 1 | 0 | No | No |
| 17625320 | METHOD OF SEMI-SUPERVISED DATA COLLECTION AND MACHINE LEARNING LEVERAGING DISTRIBUTED COMPUTING DEVICES | January 2022 | August 2025 | Abandon | 44 | 1 | 0 | No | No |
| 17646889 | ADAPTIVE TRAINING COMPLETION TIME AND STATUS FOR MACHINE LEARNING MODELS | January 2022 | October 2025 | Allow | 45 | 1 | 0 | No | No |
| 17548516 | CLASSIFYING AND ORGANIZING DIGITAL CONTENT ITEMS AUTOMATICALLY UTILIZING CONTENT ITEM CLASSIFICATION MODELS | December 2021 | August 2025 | Allow | 44 | 6 | 0 | Yes | Yes |
| 17532140 | SYSTEM AND METHOD USING SEPARABLE TRANSFER LEARNING-BASED ARTIFICIAL NEURAL NETWORK | November 2021 | September 2025 | Allow | 46 | 2 | 0 | No | No |
| 17609946 | AUTOMATED METHOD AND SYSTEM FOR CATEGORISING AND DESCRIBING THIN SECTIONS OF ROCK SAMPLES OBTAINED FROM CARBONATE ROCKS | November 2021 | February 2026 | Allow | 51 | 2 | 0 | No | No |
| 17442091 | INFORMATION PROCESSING SYSTEM AND METHOD FOR CONTROLLING COMPONENT MOUNTER | September 2021 | February 2026 | Allow | 53 | 1 | 0 | No | No |
| 17473813 | METHOD AND APPARATUS FOR REDUCING COMPUTATIONAL COMPLEXITY OF CONVOLUTIONAL NEURAL NETWORKS | September 2021 | November 2024 | Allow | 38 | 1 | 0 | No | No |
| 17469580 | AGGREGATION MODEL REPLICATION AT A DISASTER RECOVERY SITE | September 2021 | October 2025 | Allow | 50 | 2 | 0 | Yes | No |
| 17402769 | MODEL OPTIMIZATION METHOD, ELECTRONIC DEVICE, AND COMPUTER PROGRAM PRODUCT | August 2021 | September 2025 | Allow | 49 | 1 | 0 | No | No |
| 17400319 | ELECTRIFIED VEHICLE WITH INDICATION OF ADEQUATE DRIVING RANGE BASED ON AUTOENCODER | August 2021 | June 2025 | Allow | 46 | 1 | 0 | No | No |
| 17389862 | KNOWLEDGE GRAPH ANALYTICS KERNELS IN HIGH PERFORMANCE COMPUTING | July 2021 | May 2025 | Allow | 45 | 1 | 0 | Yes | No |
| 17427467 | FRACTAL COGNITIVE COMPUTING NODE, COMPUTER-IMPLEMENTED METHOD FOR LEARNING PROCEDURES, COMPUTATIONAL COGNITION CLUSTER AND COMPUTATIONAL COGNITION ARCHITECTURE | July 2021 | December 2025 | Allow | 53 | 2 | 0 | No | No |
| 17386965 | OPTIMIZING A PROGNOSTIC-SURVEILLANCE SYSTEM TO ACHIEVE A USER-SELECTABLE FUNCTIONAL OBJECTIVE | July 2021 | August 2025 | Allow | 49 | 2 | 0 | Yes | No |
| 17423103 | A DEEP LEARNING MODEL FOR LEARNING PROGRAM EMBEDDINGS | July 2021 | September 2023 | Allow | 27 | 4 | 0 | Yes | No |
| 17369524 | SYSTEM AND METHODS FOR GENERATING OPTIMAL DATA PREDICTIONS IN REAL-TIME FOR TIME SERIES DATA SIGNALS | July 2021 | June 2025 | Allow | 47 | 2 | 0 | Yes | No |
| 17226416 | TRAINING DNN BY UPDATING AN ARRAY USING A CHOPPER | April 2021 | March 2025 | Allow | 47 | 2 | 0 | Yes | No |
| 17226369 | SYSTEM AND METHODS FOR GENERATING TASKS BASED ON AGENT PROFILES | April 2021 | November 2025 | Allow | 55 | 4 | 0 | Yes | No |
| 17210463 | DESIGN OF MACHINES WITH FEELING ANALOGUES | March 2021 | August 2024 | Allow | 41 | 2 | 0 | Yes | No |
| 17201781 | ARTIFICIAL INTELLIGENT AGENT REWARDING METHOD DETERMINED BY SOCIAL INTERACTION WITH INTELLIGENT OBSERVERS | March 2021 | May 2025 | Abandon | 50 | 2 | 0 | Yes | No |
| 17199618 | SYSTEM FOR PREDICTING OPTICAL PROPERTIES OF MOLECULES BASED ON MACHINE LEARNING AND METHOD THEREOF | March 2021 | September 2024 | Allow | 42 | 2 | 0 | No | No |
| 17183720 | ARITHMETIC PROCESSING DEVICE HAVING SHARED DRAM ON BUS | February 2021 | July 2025 | Abandon | 53 | 2 | 0 | No | No |
| 17270082 | ABDUCTION APPARATUS, ABDUCTION METHOD, AND COMPUTER-READABLE RECORDING MEDIUM | February 2021 | November 2023 | Allow | 33 | 1 | 0 | Yes | No |
| 17170414 | SYSTEMS AND METHODS FOR UTILIZING COMPRESSED CONVOLUTIONAL NEURAL NETWORKS TO PERFORM MEDIA CONTENT PROCESSING | February 2021 | November 2023 | Abandon | 34 | 1 | 0 | No | No |
| 17258924 | ENVIRONMENT MODELING AND ABSTRACTION OF NETWORK STATES FOR COGNITIVE FUNCTIONS | January 2021 | October 2024 | Abandon | 45 | 1 | 0 | No | No |
| 17144467 | ROBOTIC PROCESS AUTOMATION ARCHITECTURES AND PROCESSES FOR HOSTING, MONITORING, AND RETRAINING MACHINE LEARNING MODELS | January 2021 | May 2025 | Allow | 52 | 4 | 0 | Yes | No |
| 17143392 | ROBOTIC PROCESS AUTOMATION ARCHITECTURES AND PROCESSES FOR HOSTING, MONITORING, AND RETRAINING MACHINE LEARNING MODELS | January 2021 | April 2025 | Allow | 52 | 4 | 0 | Yes | No |
| 17132067 | METHOD FOR TRAINING VIRTUAL ANIMAL TO MOVE BASED ON CONTROL PARAMETERS | December 2020 | January 2024 | Abandon | 37 | 1 | 0 | No | No |
| 17113669 | MEMORY AUGMENTED GENERATIVE TEMPORAL MODELS | December 2020 | January 2024 | Allow | 37 | 1 | 0 | Yes | No |
| 17112966 | COMPRESSED RECURRENT NEURAL NETWORK MODELS | December 2020 | December 2023 | Allow | 36 | 2 | 0 | Yes | No |
| 15734779 | RELATIONSHIP ANALYSIS DEVICE, RELATIONSHIP ANALYSIS METHOD, AND RECORDING MEDIUM FOR ANALYZING RELATIONSHIP BETWEEN A PLURALITY OF TYPES OF DATA USING KERNEL MEAN LEARNING | December 2020 | January 2024 | Allow | 38 | 2 | 0 | Yes | No |
| 17107745 | NEURAL ARCHITECTURE SEARCH FOR DENSE IMAGE PREDICTION TASKS | November 2020 | November 2024 | Abandon | 48 | 3 | 0 | No | No |
| 17057882 | COMPRESSION METHOD AND SYSTEM FOR FREQUENT TRANSMISSION OF DEEP NEURAL NETWORK | November 2020 | September 2024 | Abandon | 46 | 1 | 0 | No | No |
| 17101019 | INITIALIZING OPTIMIZATION SOLVERS | November 2020 | December 2023 | Allow | 37 | 2 | 0 | Yes | No |
| 16952308 | METHODS AND SYSTEMS FOR REMOTE TRAINING OF A MACHINE LEARNING MODEL BASED UPON MODEL STABILITY | November 2020 | July 2025 | Abandon | 56 | 4 | 0 | No | No |
| 17097811 | Conditional Computation For Continual Learning | November 2020 | January 2025 | Allow | 51 | 3 | 0 | Yes | No |
| 17089116 | ANIMAL HEALTH DECISION SUPPORT SYSTEM AND METHODS | November 2020 | September 2023 | Allow | 35 | 2 | 0 | Yes | No |
| 17029506 | TRANSFER LEARNING WITH MACHINE LEARNING SYSTEMS | September 2020 | April 2024 | Allow | 43 | 2 | 0 | Yes | No |
| 17014126 | Management and Evaluation of Machine-Learned Models Based on Locally Logged Data | September 2020 | April 2024 | Abandon | 43 | 2 | 0 | Yes | No |
| 17004220 | SUPPORT SYSTEM FOR DESIGNING AN ARTIFICIAL INTELLIGENCE APPLICATION, EXECUTABLE ON DISTRIBUTED COMPUTING PLATFORMS | August 2020 | February 2025 | Abandon | 54 | 4 | 0 | No | No |
| 16975679 | LEARNING QUALITY ESTIMATION DEVICE, METHOD, AND PROGRAM | August 2020 | December 2024 | Allow | 51 | 2 | 0 | No | No |
| 16998420 | Bed with User Tracking Features | August 2020 | October 2023 | Abandon | 37 | 1 | 0 | No | No |
| 16990970 | HIERARCHICAL WEIGHT PREPROCESSING FOR NEURAL NETWORK ACCELERATOR | August 2020 | March 2025 | Allow | 55 | 3 | 0 | Yes | No |
| 16990965 | USING META-LEARNING TO OPTIMIZE AUTOMATIC SELECTION OF MACHINE LEARNING PIPELINES | August 2020 | November 2024 | Abandon | 51 | 2 | 0 | Yes | No |
| 16968606 | LEARNING DEVICE, LEARNING METHOD, AND PROGRAM THEREFOR FOR SHORTEN TIME IN GENERATING APPROPRIATE TEACHER DATA | August 2020 | June 2024 | Allow | 46 | 2 | 0 | No | No |
| 16988830 | ENVIRONMENT-BASED-THREAT ALERTING TO USER VIA MOBILE PHONE | August 2020 | February 2024 | Allow | 42 | 1 | 0 | No | No |
| 16968394 | METHOD AND SYSTEM FOR PREDICTING A PORT-STAY DURATION OF A VESSEL AT A PORT | August 2020 | September 2024 | Abandon | 49 | 2 | 0 | No | No |
| 16986963 | INTEGRATED CIRCUIT DEFECT DIAGNOSIS USING MACHINE LEARNING | August 2020 | November 2024 | Allow | 52 | 3 | 0 | No | No |
| 16935434 | METHOD TO PARAMETERIZE A 3D MODEL | July 2020 | March 2024 | Abandon | 44 | 1 | 0 | No | No |
| 16935921 | RUNTIME ENVIRONMENT DETERMINATION FOR SOFTWARE CONTAINERS | July 2020 | August 2024 | Allow | 49 | 2 | 0 | Yes | No |
| 16934574 | SYMBOLIC MODEL DISCOVERY BASED ON A COMBINATION OF NUMERICAL LEARNING METHODS AND REASONING | July 2020 | December 2023 | Abandon | 41 | 2 | 0 | Yes | No |
| 16933361 | UNIFIED FRAMEWORK FOR DYNAMIC CLUSTERING AND DISCRETE TIME EVENT PREDICTION | July 2020 | July 2024 | Allow | 48 | 3 | 0 | Yes | No |
| 16929975 | METHOD AND APPARATUS FOR REINFORCEMENT MACHINE LEARNING | July 2020 | January 2024 | Allow | 43 | 3 | 0 | No | No |
| 16926645 | SYSTEM AND METHOD FOR DIFFERENTIATED PRIVACY MANAGEMENT OF USER CONTENT | July 2020 | October 2025 | Abandon | 60 | 4 | 0 | Yes | No |
| 16925161 | DYNAMIC COMPUTATION RATES FOR DISTRIBUTED DEEP LEARNING | July 2020 | March 2024 | Allow | 44 | 2 | 0 | Yes | No |
| 16913082 | UPDATING WEIGHT VALUES IN A MACHINE LEARNING SYSTEM | June 2020 | March 2023 | Allow | 32 | 1 | 0 | No | No |
| 16904818 | DISTRIBUTABLE EVENT PREDICTION AND MACHINE LEARNING RECOGNITION SYSTEM | June 2020 | December 2020 | Allow | 6 | 1 | 0 | Yes | No |
| 16903980 | IMITATION TRAINING FOR MACHINE LEARNING NETWORKS | June 2020 | October 2021 | Abandon | 16 | 2 | 0 | Yes | No |
| 16898971 | PROCESSING COMPUTATIONAL GRAPHS | June 2020 | May 2023 | Allow | 35 | 4 | 0 | Yes | No |
| 16890018 | IDEA GENERATION SUPPORT DEVICE, IDEA GENERATION SUPPORT SYSTEM, AND RECORDING MEDIUM | June 2020 | September 2025 | Abandon | 60 | 4 | 0 | Yes | No |
| 16868627 | DIGITAL CONTENT VARIATIONS VIA EXTERNAL REACTION | May 2020 | January 2026 | Abandon | 60 | 6 | 0 | Yes | No |
| 16845398 | Cognitive Session Graphs Including Blockchains | April 2020 | April 2023 | Allow | 36 | 2 | 0 | Yes | No |
| 16811606 | SYSTEM AND METHODS FOR INTEGRATING ANIMAL HEALTH RECORDS | March 2020 | September 2023 | Allow | 43 | 4 | 0 | Yes | No |
| 16808106 | NEAR REAL-TIME DETECTION AND CLASSIFICATION OF MACHINE ANOMALIES USING MACHINE LEARNING AND ARTIFICIAL INTELLIGENCE | March 2020 | February 2023 | Abandon | 36 | 5 | 0 | Yes | No |
| 16776910 | Generating Solution Keyword Tag Clouds Based on Support Forum Post Analytics | January 2020 | February 2023 | Allow | 36 | 2 | 0 | No | Yes |
| 16724302 | LEARNING METHOD AND LEARNING DEVICE FOR INTEGRATING OBJECT DETECTION INFORMATION ACQUIRED THROUGH V2V COMMUNICATION FROM OTHER AUTONOMOUS VEHICLE WITH OBJECT DETECTION INFORMATION GENERATED BY PRESENT AUTONOMOUS VEHICLE, AND TESTING METHOD AND TESTING DEVICE USINGTHE SAME | December 2019 | June 2020 | Allow | 6 | 1 | 0 | Yes | No |
| 16699443 | METHOD AND APPARATUS FOR GENERATING Q & A MODEL BY USING ADVERSARIAL LEARNING | November 2019 | March 2023 | Allow | 39 | 1 | 0 | Yes | No |
| 16696514 | LEARNING DEVICE, LEARNING METHOD, AND COMPUTER-READABLE RECORDING MEDIUM FOR LEARNING RNN PARAMETER | November 2019 | July 2023 | Allow | 44 | 2 | 0 | No | No |
| 16690686 | Determining Data Representative of Bias Within a Model | November 2019 | January 2023 | Allow | 38 | 2 | 0 | Yes | No |
| 16683310 | COGNITIVE QUESTION ANSWERING PIPELINE CALIBRATING | November 2019 | March 2023 | Allow | 40 | 2 | 0 | No | No |
| 16672356 | SYSTEMS AND METHODS FOR KNOWLEDGE MANAGEMENT | November 2019 | January 2022 | Abandon | 27 | 1 | 0 | No | No |
| 16597914 | SYSTEM AND METHOD FOR DETERMINING QUALITY METRICS FOR A QUESTION SET | October 2019 | November 2022 | Abandon | 38 | 4 | 0 | Yes | No |
| 16577294 | SYSTEM, METHOD, AND COMPUTER PROGRAM PRODUCT FOR REDUCTION, OPTIMIZATION, SECURITY, AND ACCELERATION OF COMPUTER DATA TRANSMISSION USING NEURAL SYNCHRONIZATION | September 2019 | December 2024 | Allow | 60 | 4 | 0 | Yes | No |
| 16555233 | INFORMATION PROCESSING DEVICE AND METHOD FOR DETERMINING HOW TO ACCESS A PROCESSING CIRCUIT FROM ACQUIRED OPERATION STATISTICAL INFORMATION | August 2019 | June 2023 | Abandon | 46 | 2 | 0 | No | No |
This analysis examines appeal outcomes and the strategic value of filing appeals for examiner HINCKLEY, CHASE PAUL.
With a 63.6% reversal rate, the PTAB has reversed the examiner's rejections more often than affirming them. This reversal rate is in the top 25% across the USPTO, indicating that appeals are more successful here than in most other areas.
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, 61.5% 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 HINCKLEY, CHASE PAUL works in Art Unit 2124 and has examined 171 patent applications in our dataset. With an allowance rate of 68.4%, this examiner has a below-average tendency to allow applications. Applications typically reach final disposition in approximately 47 months.
Examiner HINCKLEY, CHASE PAUL's allowance rate of 68.4% places them in the 30% percentile among all USPTO examiners. This examiner has a below-average tendency to allow applications.
On average, applications examined by HINCKLEY, CHASE PAUL receive 2.80 office actions before reaching final disposition. This places the examiner in the 82% 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 HINCKLEY, CHASE PAUL is 47 months. This places the examiner in the 9% percentile for prosecution speed. Applications take longer to reach final disposition with this examiner compared to most others.
Conducting an examiner interview provides a +9.4% benefit to allowance rate for applications examined by HINCKLEY, CHASE PAUL. This interview benefit is in the 41% percentile among all examiners. Recommendation: Interviews provide a below-average benefit with this examiner.
When applicants file an RCE with this examiner, 26.1% of applications are subsequently allowed. This success rate is in the 42% percentile among all examiners. Strategic Insight: RCEs show below-average effectiveness with this examiner. Carefully evaluate whether an RCE or continuation is the better strategy.
This examiner enters after-final amendments leading to allowance in 22.1% of cases where such amendments are filed. This entry rate is in the 28% percentile among all examiners. Strategic Recommendation: This examiner shows below-average receptiveness to after-final amendments. You may need to file an RCE or appeal rather than relying on after-final amendment entry.
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 47.6% of appeals filed. This is in the 13% percentile among all examiners. Of these withdrawals, 10.0% occur early in the appeal process (after Notice of Appeal but before Appeal Brief). 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, 34.8% are granted (fully or in part). This grant rate is in the 22% 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.