Detailed information about the 100 most recent patent applications.
| Application Number | Title | Filing Date | Disposal Date | Disposition | Time (months) | Office Actions | Restrictions | Interview | Appeal |
|---|---|---|---|---|---|---|---|---|---|
| 19234079 | AGENT-BASED MODELER USING MULTIMODAL INPUT | June 2025 | December 2025 | Allow | 6 | 1 | 0 | Yes | No |
| 18804240 | TRAINING MODEL ALLOCATION METHOD, APPARATUS, COMPUTER DEVICE, AND STORAGE MEDIUM | August 2024 | September 2025 | Abandon | 13 | 2 | 0 | No | No |
| 18670279 | SYSTEM, NETWORK AND METHOD FOR SELECTIVE ACTIVATION OF A COMPUTING NETWORK | May 2024 | August 2024 | Allow | 2 | 0 | 0 | No | No |
| 18637794 | METHODS AND SYSTEMS FOR ENHANCED SENSOR ASSESSMENTS FOR PREDICTING SECONDARY ENDPOINTS | April 2024 | May 2025 | Allow | 13 | 2 | 0 | Yes | No |
| 18636196 | NEURAL PROCESSING UNIT THAT REUSES FEATURE MAPS AND ITS OPERATION METHOD | April 2024 | July 2025 | Allow | 15 | 3 | 0 | Yes | No |
| 18615984 | APPARATUS AND METHOD FOR LOCATION MONITORING | March 2024 | September 2024 | Allow | 6 | 1 | 0 | Yes | No |
| 18595104 | METHOD AND SYSTEM FOR USING A FORWARD CALIBRATION MODEL TO GENERATE CALIBRATED VALUES FOR AMBIENT PARAMETERS | March 2024 | June 2025 | Allow | 15 | 3 | 0 | Yes | No |
| 18439277 | System and method of providing correction assistance on machine learning workflow predictions | February 2024 | October 2025 | Allow | 20 | 2 | 0 | Yes | No |
| 18381054 | APPARATUS AND METHOD FOR DATA INGESTION FOR USER SPECIFIC OUTPUTS OF ONE OR MORE MACHINE LEARNING MODELS | October 2023 | June 2024 | Allow | 8 | 1 | 0 | Yes | No |
| 18310170 | SYSTEM AND METHOD FOR ADAPTIVE ORCHESTRATED PROCESS PARALLELIZATION | May 2023 | January 2026 | Allow | 33 | 0 | 0 | Yes | No |
| 18141360 | COMPUTER ASSISTED PROGRAMMING USING AUTOMATED NEXT NODE RECOMMENDER FOR COMPLEX DIRECTED ACYCLIC GRAPHS | April 2023 | February 2024 | Allow | 10 | 2 | 1 | Yes | No |
| 18185479 | MULTI-MODAL MACHINE LEARNING MEDICAL ASSESSMENT | March 2023 | January 2024 | Allow | 10 | 2 | 0 | Yes | No |
| 18123158 | SYSTEMS AND METHODS FOR GENERATING A PROBATIONARY AUTOMATED-DECISIONING WORKFLOW IN A MACHINE LEARNING-TASK ORIENTED DIGITAL THREAT OR DIGITAL ABUSE MITIGATION SYSTEM | March 2023 | November 2023 | Allow | 8 | 1 | 0 | No | No |
| 18119211 | METHOD AND SYSTEM FOR TRAINING A NEURAL NETWORK MODEL USING ADVERSARIAL LEARNING AND KNOWLEDGE DISTILLATION | March 2023 | January 2026 | Allow | 34 | 0 | 0 | No | No |
| 18163527 | METHOD OF COMPRESSING NEURAL NETWORK MODEL AND ELECTRONIC APPARATUS FOR PERFORMING THE SAME | February 2023 | May 2023 | Allow | 4 | 0 | 0 | No | No |
| 18018537 | QUANTUM COMPUTING DEVICE FOR DETERMINING A NETWORK PARAMETER | January 2023 | March 2026 | Allow | 37 | 1 | 0 | No | No |
| 18092635 | Method Of Training Object Prediction Models Using Ambiguous Labels | January 2023 | June 2024 | Allow | 17 | 3 | 0 | Yes | No |
| 18146346 | BUILDING PHOTOVOLTAIC DATA INTERPOLATION METHOD BASED ON WGAN AND WHALE OPTIMIZATION ALGORITHM | December 2022 | February 2026 | Allow | 38 | 1 | 0 | No | No |
| 18068636 | SYSTEM FOR ENGAGEMENT OF HUMAN AGENTS FOR DECISION-MAKING IN A DYNAMICALLY CHANGING ENVIRONMENT | December 2022 | November 2023 | Allow | 11 | 2 | 0 | No | No |
| 18077022 | METHOD AND SYSTEM FOR ON-THE-FLY OBJECT LABELING VIA CROSS MODALITY VALIDATION IN AUTONOMOUS DRIVING VEHICLES | December 2022 | March 2024 | Allow | 15 | 3 | 0 | Yes | No |
| 17968077 | APPARATUS AND METHOD FOR PREDICTING DOWNHOLE CONDITIONS | October 2022 | September 2024 | Allow | 23 | 6 | 0 | Yes | No |
| 17959439 | APPARATUS FOR BIAS ELIMINATED PERFORMANCE DETERMINATION | October 2022 | March 2024 | Allow | 17 | 3 | 0 | Yes | No |
| 17950028 | Graph Optimization Method and Apparatus for Neural Network Computation | September 2022 | January 2024 | Allow | 16 | 2 | 0 | No | Yes |
| 17792451 | DISTRIBUTED COMPUTER SYSTEM AND METHOD OF OPERATION THEREOF | July 2022 | July 2025 | Allow | 36 | 1 | 0 | No | No |
| 17788050 | SEMICONDUCTOR DEVICE | June 2022 | May 2025 | Allow | 35 | 0 | 0 | No | No |
| 17838581 | DISTRIBUTED MACHINE LEARNING DECENTRALIZED APPLICATION PLATFORM | June 2022 | November 2025 | Allow | 41 | 0 | 0 | No | No |
| 17837233 | SEMI-SUPERVISED METHOD AND APPARATUS FOR PUBLIC OPINION TEXT ANALYSIS | June 2022 | March 2024 | Abandon | 22 | 2 | 0 | Yes | No |
| 17838148 | SYSTEMS AND METHODS FOR CLINICAL DECISION SUPPORT FOR LIPID-LOWERING THERAPIES FOR CARDIOVASCULAR DISEASE | June 2022 | August 2023 | Allow | 14 | 1 | 1 | Yes | No |
| 17835820 | METHODS AND SYSTEMS FOR BUSINESS ASSURANCE DATA PROCESSING FOR AUTOMATED STANDARDS FRAMEWORK INTEGRATION | June 2022 | March 2026 | Abandon | 45 | 2 | 0 | No | No |
| 17826606 | SYSTEM AND METHOD FOR DESIGNING EFFICIENT SUPER RESOLUTION DEEP CONVOLUTIONAL NEURAL NETWORKS BY CASCADE NETWORK TRAINING, CASCADE NETWORK TRIMMING, AND DILATED CONVOLUTIONS | May 2022 | November 2025 | Allow | 42 | 1 | 0 | No | No |
| 17752203 | SYSTEM AND METHOD FOR AUTOMATED CATALOGING AND BUILDING MACHINE LEARNING MODELS | May 2022 | September 2025 | Allow | 40 | 0 | 0 | No | No |
| 17695180 | CORRELATING REQUEST AND RESPONSE DATA USING SUPERVISED LEARNING | March 2022 | March 2026 | Allow | 48 | 2 | 0 | No | No |
| 17694598 | SPECIAL PURPOSE NEURAL NETWORK TRAINING CHIP | March 2022 | September 2025 | Allow | 42 | 1 | 0 | No | No |
| 17621884 | DATA ANALYSIS APPARATUS, DATA ANALYSIS METHOD, AND DATA ANALYSIS PROGRAM | December 2021 | November 2025 | Abandon | 46 | 1 | 0 | No | No |
| 17558491 | SYSTEM AND METHOD FOR EVALUATING THE TRUE REACH OF SOCIAL MEDIA INFLUENCERS | December 2021 | November 2025 | Abandon | 46 | 0 | 1 | No | No |
| 17643635 | COLLABORATIVE DECISION MAKING IN DYNAMICALLY CHANGING ENVIRONMENT | December 2021 | October 2022 | Allow | 11 | 2 | 0 | Yes | No |
| 17615108 | COMPUTER-IMPLEMENTED BIDDING METHOD, COMPUTER EQUIPMENT AND STORAGE MEDIUM | November 2021 | September 2024 | Allow | 33 | 1 | 0 | Yes | No |
| 17529311 | METHOD FOR CONSTRUCTING AND TRAINING DECENTRALIZED MIGRATION DIAGRAM NEURAL NETWORK MODEL FOR PRODUCTION PROCESS | November 2021 | May 2022 | Allow | 5 | 1 | 0 | No | No |
| 17527802 | UNIQUE INPUT-RESPONSE RELATIONSHIPS FROM UNAMBIGUOUS PARAMETER SETS | November 2021 | August 2025 | Allow | 45 | 2 | 0 | Yes | No |
| 17524406 | AUTOMATIC SPATIAL REGRESSION SYSTEM | November 2021 | February 2022 | Allow | 3 | 0 | 0 | No | No |
| 17507188 | VIRTUALIZING EXTERNAL MEMORY AS LOCAL TO A MACHINE LEARNING ACCELERATOR | October 2021 | May 2025 | Allow | 43 | 1 | 0 | Yes | No |
| 17503585 | REAL-TIME ADAPTIVE OPERATIONS PERFORMANCE MANAGEMENT SYSTEM | October 2021 | March 2025 | Allow | 41 | 1 | 0 | No | No |
| 17500146 | EXPLANATION AND INTERPRETATION GENERATION SYSTEM | October 2021 | May 2022 | Allow | 7 | 1 | 0 | No | No |
| 17499845 | MAPPING BRAIN DATA TO BEHAVIOR | October 2021 | January 2025 | Allow | 40 | 5 | 0 | Yes | No |
| 17450473 | Artificial Intelligence System for Training a Classifier | October 2021 | September 2025 | Allow | 47 | 2 | 0 | No | No |
| 17493770 | OBJECT MODELING WITH ADVERSARIAL LEARNING | October 2021 | November 2025 | Allow | 50 | 3 | 0 | Yes | No |
| 17467096 | ATTENTION-BASED SEQUENCE TRANSDUCTION NEURAL NETWORKS | September 2021 | September 2024 | Allow | 37 | 0 | 0 | No | No |
| 17399360 | SYSTEMS AND METHODS USED TO ENHANCE ARTIFICIAL INTELLIGENCE SYSTEMS BY MITIGATING HARMFUL ARTIFICIAL INTELLIGENCE ACTIONS | August 2021 | July 2025 | Abandon | 47 | 1 | 1 | No | No |
| 17366919 | PER KERNEL KMEANS COMPRESSION FOR NEURAL NETWORKS | July 2021 | February 2026 | Allow | 55 | 4 | 0 | Yes | No |
| 17358766 | METHOD OF USING CLUSTERS TO TRAIN SUPERVISED ENTITY RESOLUTION IN BIG DATA | June 2021 | January 2025 | Allow | 43 | 1 | 0 | No | No |
| 17353541 | METHOD AND APPARATUS FOR CLUSTERING PRIVACY DATA OF PLURALITY OF PARTIES | June 2021 | October 2021 | Allow | 4 | 0 | 0 | Yes | No |
| 17345233 | Enhanced Computer Experience From Activity Prediction | June 2021 | March 2025 | Abandon | 45 | 1 | 0 | Yes | No |
| 17342070 | SYSTEMS AND METHODS USING ANGLE-BASED STOCHASTIC GRADIENT DESCENT | June 2021 | April 2022 | Allow | 10 | 2 | 0 | Yes | No |
| 17297152 | RELATIVISTIC QUANTUM COMPUTER / QUANTUM GRAVITY COMPUTER | May 2021 | June 2025 | Abandon | 48 | 1 | 1 | No | No |
| 17296796 | LEARNING DEVICE, LEARNING METHOD, AND LEARNING PROGRAM | May 2021 | October 2024 | Allow | 41 | 1 | 0 | No | No |
| 17329245 | SYNAPTIC WEIGHT TRANSFER BETWEEN CONDUCTANCE PAIRS WITH POLARITY INVERSION FOR REDUCING FIXED DEVICE ASYMMETRIES | May 2021 | January 2024 | Allow | 32 | 0 | 0 | No | No |
| 17317699 | KNOWLEDGE GRAPH GUIDED DATABASE COMPLETION AND CORRECTION SYSTEM AND METHODS | May 2021 | June 2025 | Abandon | 49 | 2 | 0 | Yes | No |
| 17239559 | CONCURRENT DATA PREDICTOR | April 2021 | January 2025 | Abandon | 45 | 1 | 0 | No | No |
| 17238870 | METHODS AND SYSTEMS FOR TENSOR NETWORK CONTRACTION BASED ON HYPERGRAPH DECOMPOSITION AND PARAMETER OPTIMIZATION | April 2021 | September 2025 | Allow | 53 | 3 | 0 | No | No |
| 17220006 | METHOD FOR TRANSFER LEARNING IN CLUSTERING | April 2021 | September 2025 | Abandon | 53 | 2 | 0 | No | No |
| 17217071 | SYSTEM AND METHOD TO DETECT SYMPTOMS OF IMPENDING CLIMATE CONTROL FAILURES OF TRANSPORT CLIMATE CONTROL SYSTEMS | March 2021 | August 2025 | Allow | 53 | 3 | 0 | Yes | No |
| 17216496 | METHOD AND SYSTEM FOR CONSTRAINT BASED HYPERPARAMETER TUNING | March 2021 | April 2025 | Allow | 49 | 2 | 0 | Yes | No |
| 17279473 | PRODUCT-SUM CALCULATION UNIT, NEUROMORPHIC DEVICE, AND PRODUCT-SUM CALCULATION METHOD | March 2021 | December 2024 | Allow | 45 | 2 | 0 | Yes | No |
| 17198841 | AUTOMATED AND ADAPTIVE DESIGN AND TRAINING OF NEURAL NETWORKS | March 2021 | January 2022 | Allow | 10 | 2 | 0 | Yes | No |
| 17196558 | METHOD OF USING CLUSTERS TO TRAIN SUPERVISED ENTITY RESOLUTION IN BIG DATA | March 2021 | May 2021 | Allow | 2 | 0 | 0 | No | No |
| 17195734 | SYSTEM AND METHOD FOR SELECTING A CANDIDATE TRANSFER APPARATUS | March 2021 | August 2025 | Allow | 54 | 4 | 0 | Yes | No |
| 17271801 | SCALED COMPUTE FABRIC FOR ACCELERATED DEEP LEARNING | February 2021 | January 2022 | Allow | 11 | 0 | 1 | No | No |
| 17187374 | IMPORTANCE ANALYSIS APPARATUS, METHOD, AND NON-TRANSITORY COMPUTER READABLE MEDIUM | February 2021 | April 2025 | Allow | 49 | 3 | 0 | Yes | No |
| 17187432 | SYSTEM TO INVOKE UPDATE OF MACHINE LEARNING MODELS ON EDGE COMPUTERS | February 2021 | August 2024 | Allow | 41 | 1 | 0 | Yes | No |
| 17180418 | METHOD AND SYSTEM FOR DETERMINING BEHAVIORAL PATTERNS OF USER GROUPS BASED ON INTERACTIONS WITH OTHER USER GROUPS USING MACHINE LEARNING | February 2021 | August 2025 | Allow | 54 | 3 | 0 | Yes | No |
| 17177694 | Universal Attention-Based Reinforcement Learning Model for Control Systems | February 2021 | April 2021 | Allow | 2 | 0 | 0 | No | No |
| 17169825 | MACHINE LEARNING BASED EXTRACTION OF PARTITION OBJECTS FROM ELECTRONIC DOCUMENTS | February 2021 | April 2025 | Allow | 50 | 2 | 0 | Yes | No |
| 17248710 | METHODS AND SYSTEMS FOR DETERMINING AND DISPLAYING PEDIGREES | February 2021 | April 2024 | Allow | 38 | 2 | 0 | Yes | No |
| 17164262 | METHOD AND SYSTEM FOR AN END-TO-END ARTIFICIAL INTELLIGENCE WORKFLOW | February 2021 | October 2024 | Abandon | 44 | 2 | 0 | No | No |
| 17161525 | SYSTEMS AND METHODS FOR IDENTIFYING SYNTHETIC IDENTITIES | January 2021 | September 2025 | Abandon | 56 | 2 | 0 | No | No |
| 17159982 | SEARCH ACCELERATION FOR ARTIFICIAL INTELLIGENCE | January 2021 | December 2023 | Allow | 35 | 0 | 0 | No | No |
| 17142941 | DEVICE AND COMPUTER-IMPLEMENTED METHOD FOR DATA-EFFICIENT ACTIVE MACHINE LEARNING | January 2021 | September 2025 | Allow | 57 | 3 | 0 | No | No |
| 17128219 | SYSTEMS AND METHODS FOR REDUCING MEMORY REQUIREMENTS IN NEURAL NETWORKS | December 2020 | December 2024 | Allow | 48 | 3 | 0 | Yes | No |
| 17122475 | ADAPTIVELY SYNCHRONIZING LEARNING OF MULTIPLE LEARNING MODELS | December 2020 | January 2025 | Allow | 49 | 2 | 0 | Yes | No |
| 17120974 | DEEP LEARNING OF ENTITY RESOLUTION RULES | December 2020 | February 2025 | Allow | 50 | 2 | 0 | Yes | No |
| 17120025 | Establishing a Trained Machine Learning Classifier in a Blockchain Network | December 2020 | April 2024 | Allow | 40 | 1 | 1 | No | No |
| 17117464 | ALGORITHMIC TRADING | December 2020 | October 2024 | Abandon | 46 | 3 | 0 | No | No |
| 17118460 | DETERMINING INTENT BASED ON USER INTERACTION DATA | December 2020 | February 2024 | Abandon | 38 | 1 | 0 | No | No |
| 17116321 | AUTOMATIC CREATION OF DIFFICULT ANNOTATED DATA LEVERAGING CUES | December 2020 | June 2024 | Allow | 42 | 3 | 0 | Yes | No |
| 17115362 | System and method of suggesting machine learning workflows through machine learning | December 2020 | June 2025 | Abandon | 55 | 2 | 0 | Yes | No |
| 17110085 | SYSTEM AND METHOD FOR AUTOMATED GENERATION OF OPTIMUM THRESHOLDS FOR POST PROCESSING OF MACHINE LEARNING MODELS IN CASE OF IMBALANCED CLASSIFICATION | December 2020 | October 2024 | Abandon | 46 | 1 | 0 | No | No |
| 16950145 | DEEP LEARNING MODEL TRAINING SYSTEM | November 2020 | January 2021 | Allow | 2 | 0 | 0 | No | No |
| 17098408 | FEATURE SUPERPOSITION PREDICTOR | November 2020 | April 2021 | Allow | 5 | 1 | 0 | Yes | No |
| 17096567 | VALIDATION OF GAMING SIMULATION FOR AI TRAINING BASED ON REAL WORLD ACTIVITIES | November 2020 | August 2025 | Abandon | 57 | 4 | 0 | Yes | No |
| 17091917 | SYSTEMS AND METHODS OF AUTONOMOUS LINE FLOW CONTROL IN ELECTRIC POWER SYSTEMS | November 2020 | July 2024 | Abandon | 45 | 1 | 0 | No | No |
| 17072653 | SYSTEM AND METHOD FOR SELECTING A CANDIDATE TRANSFER APPARATUS | October 2020 | December 2020 | Allow | 2 | 0 | 0 | No | No |
| 17063923 | METHOD FOR DEVELOPING MACHINE-LEARNING BASED TOOL | October 2020 | May 2024 | Allow | 44 | 1 | 1 | No | No |
| 17030176 | NEURAL NETWORK PROCESSING | September 2020 | September 2023 | Allow | 35 | 1 | 0 | No | No |
| 17019098 | OPTIMIZING NEURAL NETWORKS FOR RISK ASSESSMENT | September 2020 | December 2020 | Allow | 3 | 0 | 0 | No | No |
| 16948311 | METHODS AND SYSTEMS FOR DETERMINING AND DISPLAYING PEDIGREES | September 2020 | December 2021 | Allow | 15 | 1 | 1 | Yes | No |
| 17013136 | AUTOMATICALLY RECOMMENDING AN EXISTING MACHINE LEARNING PROJECT AS ADAPTABLE FOR USE IN A NEW MACHINE LEARNING PROJECT | September 2020 | March 2024 | Abandon | 43 | 1 | 0 | Yes | No |
| 17012494 | COLLABORATIVE DEEP LEARNING METHODS AND COLLABORATIVE DEEP LEARNING APPARATUSES | September 2020 | January 2024 | Allow | 40 | 1 | 0 | No | No |
| 17007739 | SYSTEM AND METHOD FOR DESIGNING EFFICIENT SUPER RESOLUTION DEEP CONVOLUTIONAL NEURAL NETWORKS BY CASCADE NETWORK TRAINING, CASCADE NETWORK TRIMMING, AND DILATED CONVOLUTIONS | August 2020 | September 2023 | Allow | 37 | 1 | 0 | No | No |
| 16998583 | TRAINING MACHINE LEARNING MODELS FOR AUTOMATED COMPOSITION GENERATION | August 2020 | January 2025 | Abandon | 53 | 2 | 0 | No | No |
| 16995655 | MULTI-TASK NEURAL NETWORKS WITH TASK-SPECIFIC PATHS | August 2020 | June 2023 | Allow | 34 | 0 | 0 | No | No |
This analysis examines appeal outcomes and the strategic value of filing appeals for examiner VINCENT, DAVID ROBERT.
With a 0.0% reversal rate, the PTAB affirms the examiner's rejections in the vast majority of cases. This reversal rate is in the bottom 25% across the USPTO, indicating that appeals face significant challenges here.
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, 30.8% of applications that filed an appeal were subsequently allowed. This appeal filing benefit rate is below the USPTO average, suggesting that filing an appeal has limited effectiveness in prompting favorable reconsideration.
⚠ Appeals to PTAB face challenges. Ensure your case has strong merit before committing to full Board review.
⚠ Filing a Notice of Appeal shows limited benefit. Consider other strategies like interviews or amendments before appealing.
Examiner VINCENT, DAVID ROBERT works in Art Unit 2123 and has examined 418 patent applications in our dataset. With an allowance rate of 83.7%, this examiner has an above-average tendency to allow applications. Applications typically reach final disposition in approximately 41 months.
Examiner VINCENT, DAVID ROBERT's allowance rate of 83.7% places them in the 58% percentile among all USPTO examiners. This examiner has an above-average tendency to allow applications.
On average, applications examined by VINCENT, DAVID ROBERT receive 1.47 office actions before reaching final disposition. This places the examiner in the 24% percentile for office actions issued. This examiner issues significantly fewer office actions than most examiners.
The median time to disposition (half-life) for applications examined by VINCENT, DAVID ROBERT is 41 months. This places the examiner in the 21% percentile for prosecution speed. Applications take longer to reach final disposition with this examiner compared to most others.
Conducting an examiner interview provides a +1.7% benefit to allowance rate for applications examined by VINCENT, DAVID ROBERT. This interview benefit is in the 21% 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, 33.8% of applications are subsequently allowed. This success rate is in the 74% percentile among all examiners. Strategic Insight: RCEs show above-average effectiveness with this examiner. Consider whether your amendments or new arguments are strong enough to warrant an RCE versus filing a continuation.
This examiner enters after-final amendments leading to allowance in 20.8% of cases where such amendments are filed. This entry rate is in the 26% 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, 40.0% result in withdrawal of the rejection or reopening of prosecution. This success rate is in the 36% 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 61.5% of appeals filed. This is in the 39% percentile among all examiners. Of these withdrawals, 25.0% 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, 41.2% are granted (fully or in part). This grant rate is in the 32% 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 3.6% of allowed cases (in the 82% percentile). Per MPEP § 1302.04, examiner's amendments are used to place applications in condition for allowance when only minor changes are needed. This examiner frequently uses this tool compared to other examiners, indicating a cooperative approach to getting applications allowed. Strategic Insight: If you are close to allowance but minor claim amendments are needed, this examiner may be willing to make an examiner's amendment rather than requiring another round of prosecution.
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.