Detailed information about the 100 most recent patent applications.
| Application Number | Title | Filing Date | Disposal Date | Disposition | Time (months) | Office Actions | Restrictions | Interview | Appeal |
|---|---|---|---|---|---|---|---|---|---|
| 19325796 | HIERARCHICAL CASCADE ARCHITECTURE OF SEMANTIC FINGERPRINTING OPERATIONS FOR AGENT ROUTING | September 2025 | January 2026 | Allow | 4 | 0 | 0 | No | No |
| 19279103 | DYNAMIC ARTIFICIAL INTELLIGENCE AGENT ORCHESTRATION USING A LARGE LANGUAGE MODEL GATEWAY ROUTER | July 2025 | September 2025 | Allow | 2 | 0 | 0 | No | No |
| 19202824 | Method And System For AI-Based Generation Of Therapeutic Meal Plans | May 2025 | November 2025 | Allow | 6 | 2 | 0 | Yes | No |
| 19058730 | SYSTEMS AND METHODS FOR TRAINING MULTIMODAL SELF-SUPERVISED MODELS | February 2025 | July 2025 | Allow | 5 | 1 | 0 | Yes | No |
| 18797304 | Machine Learning Platform for Polygenic Models | August 2024 | August 2025 | Abandon | 12 | 2 | 0 | Yes | No |
| 18760545 | METHOD AND DEVICE FOR UROTHELIAL CARCINOMA DETECTION | July 2024 | October 2025 | Allow | 16 | 2 | 1 | Yes | Yes |
| 18745562 | DOMAIN-AWARE LARGE LANGUAGE MODEL GOVERNANCE | June 2024 | January 2026 | Allow | 19 | 4 | 0 | Yes | Yes |
| 18738927 | UTILIZING A CLINICAL-PHENOMICS CAUSAL DISCOVERY FRAMEWORK TO GENERATE CAUSAL DISCOVERY PREDICTIONS | June 2024 | August 2025 | Allow | 14 | 4 | 0 | Yes | No |
| 18672589 | Predicting Likelihood of Request Classifications Using Deep Learning | May 2024 | November 2024 | Allow | 6 | 1 | 0 | No | No |
| 18648250 | APPARATUS (AND/OR METHOD) OF TRAINING A MACHINE-LEARNING MODEL TO GENERATE DETERMINATIONS USING MISMATCHED-CHANNEL SIGNALS | April 2024 | June 2025 | Allow | 13 | 2 | 0 | Yes | No |
| 18641217 | SYSTEMS AND METHODS FOR TRANSFORMING ELECTROCARDIOGRAM IMAGES FOR USE IN ONE OR MORE MACHINE LEARNING MODELS | April 2024 | April 2025 | Allow | 12 | 2 | 0 | Yes | No |
| 18601269 | APPARATUS AND METHOD FOR TRAINING A TUNABLE DATA STRUCTURE TO PREDICT INTERNAL RIBOSOME ENTRY SITE (IRES) ACTIVITY | March 2024 | August 2024 | Allow | 5 | 1 | 0 | Yes | No |
| 18415684 | NEURAL PROCESSING UNIT AND METHOD OF OPERATION THEREOF | January 2024 | August 2025 | Allow | 19 | 4 | 0 | No | No |
| 18530909 | NEURAL PROCESSING UNIT BEING OPERATED ACCORDING TO DYNAMICALLY CALIBRATED PHASE OF CLOCK SIGNAL | December 2023 | June 2024 | Allow | 6 | 1 | 0 | No | No |
| 18525523 | METHODS AND SYSTEMS FOR IMPLEMENTING DYNAMIC-ACTION SYSTEMS IN REAL-TIME DATA STREAMS | November 2023 | December 2025 | Allow | 25 | 3 | 0 | Yes | No |
| 18510755 | COUNTER BASED RESISTIVE PROCESSING UNIT FOR PROGRAMMABLE AND RECONFIGURABLE ARTIFICIAL-NEURAL-NETWORKS | November 2023 | July 2025 | Allow | 20 | 1 | 0 | No | No |
| 18499621 | Personalized Model Training for Users Using Data Labels | November 2023 | August 2025 | Abandon | 22 | 3 | 0 | Yes | No |
| 18226091 | MULTI-VOLTAGE CONTACTORS, CONTROLS, AND RELATED METHODS | July 2023 | July 2025 | Allow | 24 | 1 | 1 | No | No |
| 18348052 | CONTEXTUAL BANDIT FOR MULTIPLE MACHINE LEARNING MODELS FOR CONTENT DELIVERY | July 2023 | November 2024 | Allow | 16 | 3 | 0 | Yes | No |
| 18333998 | ACTIVE LEARNING VIA A SAMPLE CONSISTENCY ASSESSMENT | June 2023 | September 2025 | Abandon | 27 | 1 | 0 | No | No |
| 18205441 | FOLDABLE DISPLAY APPARATUS AND METHOD OF MANUFACTURING THE SAME | June 2023 | June 2024 | Allow | 13 | 1 | 0 | No | No |
| 18320803 | SYSTEMS AND METHODS FOR IMPLEMENTING AN INTELLIGENT MACHINE LEARNING OPTIMIZATION PLATFORM FOR MULTIPLE TUNING CRITERIA | May 2023 | March 2026 | Allow | 34 | 2 | 0 | Yes | No |
| 18197093 | NEUROMORPHIC PROCESSOR AND OPERATING METHOD THEREOF | May 2023 | August 2025 | Allow | 27 | 1 | 1 | Yes | No |
| 18313072 | Processing Sensor Data with Multi-Model System on Resource-Constrained Device | May 2023 | March 2025 | Abandon | 23 | 2 | 0 | Yes | No |
| 18138425 | MANAGEMENT APPARATUS FOR MONITORING AND/OR CONTROLLING A FACILITY DEVICE | April 2023 | December 2023 | Allow | 7 | 1 | 0 | No | No |
| 18136832 | MACHINE LEARNING SYSTEMS FOR PROCESSING MULTI-MODAL PATIENT DATA | April 2023 | September 2025 | Abandon | 29 | 2 | 0 | Yes | No |
| 18110430 | SEMICONDUCTOR DEVICE INCLUDING TRANSISTOR AND LIGHT-EMITTING ELEMENT | February 2023 | March 2024 | Allow | 13 | 1 | 0 | No | No |
| 18167748 | WEATHER-DRIVEN MULTI-CATEGORY INFRASTRUCTURE IMPACT FORECASTING | February 2023 | November 2025 | Allow | 33 | 6 | 0 | No | No |
| 18155129 | NERVOUS SYSTEM EMULATOR ENGINE AND METHODS USING SAME | January 2023 | September 2024 | Allow | 20 | 0 | 0 | No | No |
| 18095724 | PORTABLE FLUID SENSORY DEVICE WITH LEARNING CAPABILITIES | January 2023 | January 2025 | Abandon | 24 | 2 | 1 | No | No |
| 18012938 | SYSTEM AND METHOD FOR ACCELERATING RNN NETWORK, AND STORAGE MEDIUM | December 2022 | August 2023 | Allow | 8 | 1 | 0 | No | No |
| 18083242 | SYSTEMS FOR FAST AND/OR EFFICIENT PROCESSING OF DECISION NETWORKS, AND RELATED METHODS AND APPARATUS | December 2022 | February 2026 | Allow | 38 | 2 | 0 | No | Yes |
| 17782054 | DEVICE COMPRISING AN ADAPTABLE AND ADDRESSABLE NEUROMORPHIC STRUCTURE | November 2022 | August 2025 | Allow | 38 | 0 | 0 | No | No |
| 17990167 | SPIKE NEURAL NETWORK CIRCUIT INCLUDING PROBABILISTIC OPERATOR | November 2022 | October 2025 | Allow | 35 | 1 | 0 | No | No |
| 17975837 | CONVOLUTIONAL NEURAL NETWORK (CNN) PROCESSING METHOD AND APPARATUS | October 2022 | December 2025 | Allow | 37 | 3 | 1 | Yes | No |
| 17971453 | Machine Learning Systems, Methods, Components, and Software for Recommending and Ordering Independent Medical Examinations | October 2022 | March 2025 | Abandon | 29 | 1 | 0 | No | No |
| 17960705 | MACHINE LEARNING SYSTEMS FOR PROCESSING MULTI-MODAL PATIENT DATA | October 2022 | March 2023 | Allow | 5 | 1 | 0 | Yes | No |
| 17958261 | TRANSFER-LEARNING FOR STRUCTURED DATA WITH REGARD TO JOURNEYS DEFINED BY SETS OF ACTIONS | September 2022 | January 2026 | Allow | 39 | 1 | 0 | Yes | No |
| 17912807 | SECOND TYPE COMPUTER ASSEMBLY LINE BALANCING OPTIMIZATION METHOD BASED ON MIGRATION GENETIC ALGORITHM | September 2022 | November 2025 | Allow | 38 | 0 | 0 | No | No |
| 17900779 | CHROMOSOME REPRESENTATION LEARNING IN EVOLUTIONARY OPTIMIZATION TO EXPLOIT THE STRUCTURE OF ALGORITHM CONFIGURATION | August 2022 | January 2026 | Allow | 41 | 1 | 0 | Yes | No |
| 17869708 | COMPUTER-DECISION SUPPORT FOR PREDICTING AND MANAGING NON-ADHEARANCE TO TREATMENT | July 2022 | September 2024 | Allow | 26 | 4 | 0 | Yes | No |
| 17864172 | SYSTEMS AND METHODS FOR TRAINING MATRIX-BASED DIFFERENTIABLE PROGRAMS | July 2022 | May 2025 | Abandon | 34 | 2 | 0 | Yes | No |
| 17862915 | METHODS AND SYSTEMS FOR GENERATING A SUPPLEMENT INSTRUCTION SET USING ARTIFICIAL INTELLIGENCE | July 2022 | March 2024 | Allow | 20 | 3 | 0 | Yes | No |
| 17812093 | SYSTEMS AND METHODS FOR PROVIDING MACHINE LEARNING MODEL EXPLAINABILITY INFORMATION | July 2022 | November 2025 | Abandon | 41 | 1 | 0 | No | No |
| 17810543 | RELATING COMPLEX DATA | July 2022 | April 2025 | Abandon | 33 | 5 | 0 | Yes | No |
| 17852874 | SYSTEMS AND METHODS FOR GENERATING REDUCED ORDER MODELS | June 2022 | February 2025 | Abandon | 31 | 4 | 0 | Yes | No |
| 17837624 | SYSTEM AND METHOD FOR TRAINING AND REFINING MACHINE LEARNING MODELS FOR INTENT CLASSIFICATION | June 2022 | December 2025 | Allow | 43 | 1 | 0 | No | No |
| 17805572 | GENERATING ARTIFICIAL INTELLIGENCE PLANS OF HIGH DIVERSITY | June 2022 | November 2025 | Allow | 42 | 1 | 0 | No | No |
| 17749435 | SYSTEMS AND METHODS FOR INTENT DISCOVERY AND PROCESS EXECUTION | May 2022 | February 2024 | Allow | 21 | 3 | 0 | Yes | No |
| 17746802 | TEMPORAL EXPLANATIONS OF MACHINE LEARNING MODEL OUTCOMES | May 2022 | January 2026 | Allow | 44 | 3 | 1 | No | No |
| 17722950 | SYSTEM AND METHOD FOR SOFTWARE PROGRAM GENERATION USING GENETIC PROGRAMMING | April 2022 | December 2025 | Abandon | 44 | 1 | 0 | No | No |
| 17720620 | DATA PROCESSING SYSTEM, OPERATING METHOD THEREOF, AND COMPUTING SYSTEM USING THE SAME | April 2022 | September 2025 | Allow | 41 | 0 | 1 | No | No |
| 17702632 | Enhancing Evolutionary Optimization in Uncertain Environments By Allocating Evaluations Via Multi-Armed Bandit Algorithms | March 2022 | January 2024 | Allow | 22 | 3 | 0 | No | No |
| 17753351 | SOLAR CELL GROUP MANUFACTURING DEVICE, SOLAR CELL GROUP, AND METHOD FOR MANUFACTURING SOLAR CELL GROUP | February 2022 | March 2025 | Abandon | 37 | 3 | 1 | Yes | No |
| 17681652 | INTEGRATING MACHINE LEARNING INTO CONTROL SYSTEMS FOR INDUSTRIAL FACILITIES | February 2022 | February 2023 | Allow | 12 | 0 | 0 | No | No |
| 17652236 | SMART TRAINING AND SMART DEPLOYMENT OF MACHINE LEARNING MODELS | February 2022 | November 2025 | Allow | 45 | 2 | 0 | Yes | No |
| 17678713 | SYSTEMS AND METHODS FOR PROVIDING MEDIA CONTENT RECOMMENDATIONS | February 2022 | June 2025 | Allow | 39 | 2 | 0 | Yes | No |
| 17587270 | Using Machine Learning to Predict Outcomes for Documents | January 2022 | December 2024 | Abandon | 34 | 4 | 0 | Yes | No |
| 17647375 | METHOD FOR AUTOMATED ENSEMBLE MACHINE LEARNING USING HYPERPARAMETER OPTIMIZATION | January 2022 | February 2026 | Abandon | 49 | 2 | 0 | No | No |
| 17563726 | APPARATUS AND METHOD FOR RECOMMENDING COLLABORATIVE FILTERING BASED ON LEARNABLE-TIME ORDINARY DIFFERENTIAL EQUATION | December 2021 | February 2026 | Allow | 49 | 2 | 0 | Yes | No |
| 17545795 | UNIVERSAL ARTIFICIAL INTELLIGENCE ENGINE FOR AUTONOMOUS COMPUTING DEVICES AND SOFTWARE APPLICATIONS | December 2021 | March 2025 | Allow | 39 | 4 | 0 | Yes | No |
| 17529654 | METHOD, APPARATUS AND SYSTEM FOR ESTIMATING CAUSALITY AMONG OBSERVED VARIABLES | November 2021 | October 2024 | Abandon | 34 | 4 | 0 | Yes | No |
| 17612330 | Method for Assessing a Function-Specific Robustness of a Neural Network | November 2021 | November 2025 | Abandon | 48 | 2 | 0 | No | No |
| 17519935 | ACCURATE AND INTERPRETABLE RULES FOR USER SEGMENTATION | November 2021 | August 2024 | Abandon | 33 | 4 | 0 | Yes | No |
| 17518629 | COUNTER BASED RESISTIVE PROCESSING UNIT FOR PROGRAMMABLE AND RECONFIGURABLE ARTIFICIAL-NEURAL-NETWORKS | November 2021 | October 2023 | Allow | 24 | 1 | 1 | No | No |
| 17518728 | SYSTEM AND METHOD OF ACCELERATING EXECUTION OF A NEURAL NETWORK | November 2021 | June 2023 | Allow | 20 | 1 | 0 | No | No |
| 17497682 | METHOD AND SYSTEM FOR ONTOLOGY DRIVEN DATA COLLECTION AND PROCESSING | October 2021 | October 2025 | Abandon | 48 | 7 | 0 | Yes | No |
| 17487787 | NEURAL NETWORK-BASED CONFIDENCE ASSESSMENT MODULE FOR HEALTHCARE CODING APPLICATIONS | September 2021 | March 2023 | Allow | 18 | 1 | 0 | No | No |
| 17485187 | Machine Learning Systems, Methods, Components, and Software for Recommending and Ordering Independent Medical Examinations | September 2021 | July 2022 | Allow | 10 | 1 | 1 | Yes | No |
| 17446719 | SYSTEMS AND METHODS FOR TRANSFER LEARNING OF NEURAL NETWORKS | September 2021 | May 2023 | Allow | 20 | 1 | 0 | Yes | No |
| 17389831 | MACHINE LEARNING BASED DEPOLARIZATION IDENTIFICATION AND ARRHYTHMIA LOCALIZATION VISUALIZATION | July 2021 | January 2022 | Allow | 5 | 1 | 0 | Yes | No |
| 17358213 | METHODS AND SYSTEMS FOR GENERATING A DATA STRUCTURE USING GRAPHICAL MODELS | June 2021 | February 2022 | Allow | 7 | 1 | 0 | Yes | No |
| 17359145 | DISCOVERING NOVEL FEATURES TO USE IN MACHINE LEARNING TECHNIQUES, SUCH AS MACHINE LEARNING TECHNIQUES FOR DIAGNOSING MEDICAL CONDITIONS | June 2021 | October 2024 | Allow | 40 | 3 | 0 | Yes | No |
| 17327603 | SYSTEMS AND METHODS FOR DETERMINING LIKELIHOOD OF INCIDENT OCCURRENCE | May 2021 | March 2023 | Allow | 22 | 4 | 0 | Yes | No |
| 17230958 | DEEP NEURAL NETWORK ACCELERATING METHOD USING RING TENSORS AND SYSTEM THEREOF, AND NON-TRANSITORY COMPUTER READABLE MEMORY | April 2021 | September 2025 | Allow | 53 | 3 | 0 | Yes | No |
| 17224116 | WEATHER-DRIVEN MULTI-CATEGORY INFRASTRUCTURE IMPACT FORECASTING | April 2021 | October 2022 | Allow | 19 | 0 | 0 | No | No |
| 17207664 | HYBRID MODEL AND ARCHITECTURE SEARCH FOR AUTOMATED MACHINE LEARNING SYSTEMS | March 2021 | February 2026 | Allow | 59 | 7 | 0 | Yes | No |
| 17205620 | EXPANDABLE NEUROMORPHIC CIRCUIT | March 2021 | August 2025 | Allow | 52 | 3 | 0 | Yes | No |
| 17196806 | APPARATUS AND METHOD TO MITIGATE PHASE FREQUENCY MODULATION DUE TO INDUCTIVE COUPLING | March 2021 | January 2024 | Allow | 34 | 1 | 1 | No | No |
| 17178736 | INSERTING ELEMENTS INTO ARTIFICIAL INTELLIGENCE CONTENT | February 2021 | April 2023 | Abandon | 26 | 1 | 0 | No | No |
| 17177632 | NEURAL NETWORK OPTIMIZATION MECHANISM | February 2021 | November 2023 | Abandon | 33 | 5 | 0 | Yes | No |
| 17172178 | CONTROL DEVICE FOR CONTROLLING MULTIPLE OPERATING CHARACTERISTICS OF AN ELECTRICAL LOAD | February 2021 | June 2023 | Allow | 28 | 2 | 0 | No | No |
| 17165714 | IDENTIFICATION OF A CHARACTERISTIC OF A PHYSICAL SYSTEM BASED ON COLLABORATIVE SENSOR NETWORKS | February 2021 | September 2025 | Allow | 56 | 5 | 1 | Yes | No |
| 17157796 | RESPONDING TO REMOTE MEDIA CLASSIFICATION QUERIES USING CLASSIFIER MODELS AND CONTEXT PARAMETERS | January 2021 | July 2025 | Allow | 54 | 7 | 0 | Yes | No |
| 17152705 | SYSTEMS AND METHODS FOR ARTIFICIAL INTELLIGENCE- AND MACHINE LEARNING-BASED EVALUATIONS AND EXPLANATIONS OF PROBLEMS | January 2021 | March 2025 | Allow | 50 | 4 | 1 | Yes | No |
| 17149854 | Computer-Implemented Systems and Methods of Analyzing Spatial, Temporal and Contextual Elements of Data for Predictive Decision-Making | January 2021 | March 2024 | Allow | 38 | 8 | 0 | Yes | No |
| 17260993 | RELATING COMPLEX DATA | January 2021 | February 2022 | Allow | 13 | 0 | 0 | No | No |
| 17148030 | CONVERSATION AGENT FOR COLLABORATIVE SEARCH ENGINE | January 2021 | February 2025 | Abandon | 49 | 5 | 0 | Yes | No |
| 17145124 | ADAPTIVE AND REUSABLE PROCESSING OF RETROACTIVE SEQUENCES FOR AUTOMATED PREDICTIONS | January 2021 | October 2023 | Abandon | 33 | 1 | 1 | No | No |
| 17141785 | AUTOMATICALLY DETECTING INVALID EVENTS IN A DISTRIBUTED COMPUTING ENVIRONMENT | January 2021 | April 2025 | Abandon | 52 | 6 | 0 | Yes | No |
| 17122428 | WEAK NEURAL ARCHITECTURE SEARCH (NAS) PREDICTOR | December 2020 | December 2025 | Allow | 60 | 4 | 0 | Yes | Yes |
| 17247439 | METHODS AND APPARATUS FOR USING ARTIFICIAL INTELLIGENCE ENTITIES TO PROVIDE INFORMATION TO AN END USER | December 2020 | January 2025 | Abandon | 49 | 5 | 0 | Yes | No |
| 17115395 | SYSTEMS AND METHODS FOR COOPERATIVE MACHINE LEARNING | December 2020 | July 2023 | Abandon | 31 | 2 | 0 | No | No |
| 17112503 | CONTROL TOWER AND ENTERPRISE MANAGEMENT PLATFORM WITH A MACHINE LEARNING/ARTIFICIAL INTELLIGENCE MANAGING SENSOR AND THE CAMERA FEEDS INTO DIGITAL TWIN | December 2020 | June 2024 | Allow | 42 | 1 | 1 | Yes | No |
| 17093978 | EXPLANATORY CONFUSION MATRICES FOR MACHINE LEARNING | November 2020 | July 2025 | Allow | 56 | 4 | 0 | Yes | No |
| 17076490 | FOLDABLE DISPLAY APPARATUS AND METHOD OF MANUFACTURING THE SAME | October 2020 | February 2023 | Allow | 28 | 1 | 0 | No | No |
| 16948888 | RECOMMENDING A DOCUMENT FOR A USER TO ACCESS | October 2020 | November 2024 | Abandon | 49 | 4 | 1 | Yes | No |
| 17038394 | INFERENTIAL ANALYSIS AND REPORTING OF CONTEXTUAL COMPLAINTS DATA | September 2020 | September 2024 | Allow | 48 | 4 | 0 | Yes | Yes |
| 17035005 | NEURAL NETWORK ACCELERATORS RESILIENT TO CONDUCTANCE DRIFT | September 2020 | December 2024 | Allow | 50 | 2 | 1 | Yes | No |
| 17035427 | INFORMATION ACQUISTION METHOD BASED ON OBJECT SIMILARITY RELATIONSHIP DEVICE AND STORAGE MEDIUM | September 2020 | February 2025 | Abandon | 52 | 2 | 1 | No | No |
This analysis examines appeal outcomes and the strategic value of filing appeals for examiner COLE, BRANDON S.
With a 62.5% 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, 60.0% 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 COLE, BRANDON S works in Art Unit 2128 and has examined 195 patent applications in our dataset. With an allowance rate of 75.4%, this examiner has a below-average tendency to allow applications. Applications typically reach final disposition in approximately 42 months.
Examiner COLE, BRANDON S's allowance rate of 75.4% places them in the 41% percentile among all USPTO examiners. This examiner has a below-average tendency to allow applications.
On average, applications examined by COLE, BRANDON S receive 2.59 office actions before reaching final disposition. This places the examiner in the 76% 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 COLE, BRANDON S is 42 months. This places the examiner in the 18% percentile for prosecution speed. Applications take longer to reach final disposition with this examiner compared to most others.
Conducting an examiner interview provides a -0.9% benefit to allowance rate for applications examined by COLE, BRANDON S. 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, 21.3% of applications are subsequently allowed. This success rate is in the 25% 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 19.1% of cases where such amendments are filed. This entry rate is in the 23% 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, 50.0% result in withdrawal of the rejection or reopening of prosecution. This success rate is in the 43% percentile among all examiners. Note: Pre-appeal conferences show below-average success with this examiner. Consider whether your arguments are strong enough to warrant a PAC request.
This examiner withdraws rejections or reopens prosecution in 65.2% of appeals filed. This is in the 46% percentile among all examiners. Of these withdrawals, 40.0% occur early in the appeal process (after Notice of Appeal but before Appeal Brief). Strategic Insight: This examiner shows below-average willingness to reconsider rejections during appeals. Be prepared to fully prosecute appeals if filed.
When applicants file petitions regarding this examiner's actions, 55.2% are granted (fully or in part). This grant rate is in the 57% percentile among all examiners. Strategic Note: Petitions show above-average success regarding this examiner's actions. Petitionable matters include restriction requirements (MPEP § 1002.02(c)(2)) and various procedural issues.
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.