Detailed information about the 100 most recent patent applications.
| Application Number | Title | Filing Date | Disposal Date | Disposition | Time (months) | Office Actions | Restrictions | Interview | Appeal |
|---|---|---|---|---|---|---|---|---|---|
| 19239413 | PREDICTIVE MODELING FOR DEPENDENCY CONFIGURATION IN KNOWLEDGE-AUGMENTED NEURAL NETWORKS | June 2025 | November 2025 | Allow | 5 | 1 | 0 | No | No |
| 19226048 | IDENTIFYING AND REMEDIATING GAPS IN ARTIFICIAL INTELLIGENCE USE CASES USING A GENERATIVE ARTIFICIAL INTELLIGENCE MODEL | June 2025 | October 2025 | Allow | 5 | 1 | 0 | Yes | No |
| 19207355 | Validating Artificial Intelligence Model Outputs Using Hash Signatures and Chunk-Level Access Controls | May 2025 | June 2025 | Allow | 1 | 0 | 0 | Yes | No |
| 19015660 | VALIDATING VECTOR CONSTRAINTS OF OUTPUTS GENERATED BY MACHINE LEARNING MODELS | January 2025 | June 2025 | Allow | 5 | 1 | 0 | Yes | No |
| 19015646 | VALIDATING VECTOR CONSTRAINTS OF OUTPUTS GENERATED BY MACHINE LEARNING MODELS | January 2025 | June 2025 | Allow | 5 | 1 | 0 | Yes | No |
| 18983342 | VALIDATING AUTONOMOUS ARTIFICIAL INTELLIGENCE (AI) AGENTS USING GENERATIVE AI | December 2024 | July 2025 | Allow | 7 | 1 | 0 | Yes | No |
| 18935417 | MANAGING OPERATIONAL RESILIENCE OF SYSTEM ASSETS USING AN ARTIFICIAL INTELLIGENCE MODEL | November 2024 | November 2025 | Allow | 13 | 1 | 0 | Yes | No |
| 18905506 | DATA-DEPENDENT TRAINING FOR AUTOMATED KNOWLEDGE SYSTEM THAT COMPRISES A NEURAL NETWORK | October 2024 | March 2025 | Allow | 6 | 1 | 0 | No | No |
| 18889371 | Identifying and Remediating Gaps in Artificial Intelligence use Cases Using a Generative Artificial Intelligence Model | September 2024 | May 2025 | Allow | 8 | 1 | 0 | Yes | No |
| 18754513 | Exploratory Recommender Method and System | June 2024 | April 2025 | Allow | 9 | 2 | 0 | No | No |
| 18754575 | Probabilistically Tunable Conversational Method and System | June 2024 | April 2025 | Allow | 10 | 1 | 0 | No | No |
| 18754550 | Vector-Based Search Method and System | June 2024 | March 2025 | Allow | 9 | 1 | 0 | No | No |
| 18742404 | DATA-DEPENDENT NODE-TO-NODE KNOWLEDGE SHARING BY REGULARIZATION IN DEEP LEARNING | June 2024 | August 2024 | Allow | 2 | 1 | 0 | No | No |
| 18653858 | VALIDATING VECTOR CONSTRAINTS OF OUTPUTS GENERATED BY MACHINE LEARNING MODELS | May 2024 | November 2024 | Allow | 7 | 1 | 1 | Yes | No |
| 18622207 | MACHINE LEARNING TECHNIQUES FOR GENERATING PREDICTIONS BASED ON INCOMPLETE DATA | March 2024 | April 2025 | Allow | 12 | 2 | 0 | Yes | No |
| 18381022 | QUANTUM OPERATING SYSTEM UTILIZING MULTIPLE COMPILERS | October 2023 | June 2024 | Allow | 8 | 1 | 0 | No | No |
| 18278723 | QUANTUM CONVOLUTION OPERATOR | August 2023 | April 2024 | Allow | 8 | 1 | 0 | No | No |
| 18353698 | DATA-DEPENDENT NODE-TO-NODE KNOWLEDGE SHARING BY REGULARIZATION IN DEEP LEARNING | July 2023 | March 2024 | Allow | 8 | 1 | 0 | No | No |
| 18220110 | SYSTEM AND METHOD FOR SAMPLE EVALUATION TO MIMIC TARGET PROPERTIES | July 2023 | November 2023 | Allow | 4 | 1 | 0 | No | No |
| 18310405 | Method and Apparatus for Amplitude Estimation of Quantum Circuit, Storage Medium, and Electronic Apparatus | May 2023 | December 2023 | Allow | 7 | 1 | 0 | No | No |
| 18141296 | SYSTEMS AND METHODS FOR DATA STRUCTURE GENERATION BASED ON OUTLIER CLUSTERING | April 2023 | September 2023 | Allow | 4 | 1 | 0 | Yes | No |
| 18301660 | METHODS FOR DEVELOPMENT OF A MACHINE LEARNING SYSTEM THROUGH LAYERED GRADIENT BOOSTING | April 2023 | August 2023 | Allow | 4 | 0 | 0 | No | No |
| 18130257 | APPARATUS AND A METHOD FOR DIGITAL ASSET MAP GENERATION | April 2023 | November 2024 | Allow | 20 | 3 | 0 | Yes | No |
| 18124045 | CROP MONITORING SYSTEM AND METHOD THEREOF | March 2023 | August 2023 | Allow | 5 | 1 | 0 | No | No |
| 18123097 | SYSTEM, METHOD AND COMPUTER PROGRAM PRODUCT FOR INTERFACING SOFTWARE ENGINES | March 2023 | January 2024 | Allow | 10 | 2 | 0 | Yes | No |
| 18117986 | AUTOMATED FACTOR GENERATION FOR DECISION ENGINES | March 2023 | December 2023 | Allow | 10 | 2 | 0 | Yes | No |
| 18110830 | FIRST-QUANTIZATION BLOCK ENCODING FOR QUANTUM EMULATION | February 2023 | January 2026 | Allow | 35 | 0 | 0 | No | No |
| 18020014 | CONSTRUCTING AND PROGRAMMING DRIVER GRAPHS IN QUANTUM HARDWARE FOR NON-STOQUASTIC QUANTUM OPTIMIZATION ANNEALING PROCESSES | February 2023 | April 2024 | Allow | 14 | 2 | 0 | Yes | No |
| 18161312 | System and Method for Improving Generalization in Neural Networks Using Selective Reinitialization | January 2023 | March 2026 | Abandon | 38 | 1 | 0 | No | No |
| 18098898 | SYSTEM AND METHOD FOR SAMPLE EVALUATION TO MIMIC TARGET PROPERTIES | January 2023 | June 2023 | Allow | 5 | 1 | 0 | No | No |
| 18152102 | METHOD AND APPARATUS FOR INFORMATION REPRESENTATION, EXCHANGE, VALIDATION, AND UTILIZATION THROUGH DIGITAL CONSOLIDATION | January 2023 | February 2024 | Allow | 13 | 1 | 1 | Yes | No |
| 18003685 | INTEGRATED CIRCUIT WITH DYNAMIC FUSING OF NEURAL NETWORK BRANCH STRUCTURES BY TOPOLOGICAL SEQUENCING | December 2022 | December 2025 | Allow | 36 | 1 | 0 | No | No |
| 18146075 | USING CONSISTENCY METADATA FOR FILTERING OF MACHINE LEARNING DATA ACROSS JOBS | December 2022 | December 2025 | Allow | 36 | 0 | 0 | No | No |
| 18087357 | CLIFFORD NEURAL LAYERS FOR MULTIVECTOR SYSTEM MODELING | December 2022 | March 2026 | Allow | 39 | 2 | 0 | Yes | No |
| 18085926 | SYSTEM AND METHOD FOR DISTRIBUTING USER INTERFACE DEVICE CONFIGURATIONS | December 2022 | November 2025 | Allow | 34 | 0 | 0 | No | No |
| 18083082 | Temporally Sequenced Content Recommender Method and System | December 2022 | July 2025 | Allow | 31 | 2 | 0 | Yes | Yes |
| 18065393 | QUANTIZATION-AWARE TRAINING WITH NUMERICAL OVERFLOW AVOIDANCE FOR NEURAL NETWORKS | December 2022 | March 2026 | Allow | 39 | 1 | 0 | No | No |
| 18009341 | ENHANCED DYNAMIC RANDOM ACCESS MEMORY (EDRAM)-BASED COMPUTING-IN-MEMORY (CIM) CONVOLUTIONAL NEURAL NETWORK (CNN) ACCELERATOR | December 2022 | August 2023 | Allow | 9 | 0 | 0 | No | No |
| 18008808 | MACHINE LEARNING-BASED ANOMALY DETECTION USING A MULTI-LAYER PREDICTOR WITH HYBRID INPUTS | December 2022 | March 2026 | Allow | 39 | 1 | 0 | No | No |
| 18075521 | TRAINING MACHINE LEARNING MODELS TO PREDICT CHARACTERISTICS OF ADVERSE EVENTS USING INTERMITTENT DATA | December 2022 | February 2026 | Allow | 38 | 1 | 0 | Yes | No |
| 18074536 | SELF-SUPERVISED LEARNING OF A TASK WITH NORMALIZATION OF NUISANCE FROM A DIFFERENT TASK | December 2022 | December 2025 | Allow | 36 | 1 | 0 | Yes | No |
| 18057824 | SYSTEMS AND METHODS FOR CREATING AND SELECTING MODELS FOR PREDICTING MEDICAL CONDITIONS | November 2022 | October 2025 | Allow | 34 | 1 | 0 | Yes | No |
| 17987535 | SEMANTIC NETWORK FOR BIOACTIVE COMPUND DISCOVERY FROM SCIENTIFIC LITERATURE | November 2022 | March 2026 | Allow | 40 | 0 | 1 | No | No |
| 17983130 | Training Embedding Models Using a Stale Embedding Cache for Negative Sampling | November 2022 | December 2025 | Allow | 38 | 0 | 0 | No | No |
| 17995335 | MACHINE LEARNING FOR HIGH-ENERGY INTERACTIONS ANALYSIS | October 2022 | December 2025 | Allow | 38 | 1 | 0 | No | No |
| 17943176 | JOINTLY PRUNING AND QUANTIZING DEEP NEURAL NETWORKS | September 2022 | November 2025 | Allow | 39 | 1 | 0 | Yes | No |
| 17929604 | GRADIENT-BASED QUANTUM ASSISTED HAMILTONIAN LEARNING | September 2022 | March 2026 | Allow | 42 | 1 | 0 | No | No |
| 17889420 | LEARNING AND DEPLOYMENT OF ADAPTIVE WIRELESS COMMUNICATIONS | August 2022 | March 2026 | Allow | 42 | 1 | 0 | No | No |
| 17889186 | SYSTEMS AND METHODS FOR GENERATING A CHATBOT | August 2022 | March 2023 | Allow | 7 | 1 | 0 | Yes | No |
| 17888547 | METHOD FOR CAUSAL INFERENCE BASED ON COLLECTIVE MOVEMENTS OF ACTIVE GROUP | August 2022 | October 2023 | Abandon | 14 | 2 | 0 | No | No |
| 17886055 | SPECIALIZED FIXED FUNCTION HARDWARE FOR EFFICIENT CONVOLUTION | August 2022 | December 2025 | Allow | 41 | 1 | 0 | No | No |
| 17760398 | DATA-DEPENDENT NODE-TO-NODE KNOWLEDGE SHARING BY REGULARIZATION IN DEEP LEARNING | August 2022 | April 2023 | Allow | 8 | 1 | 0 | Yes | No |
| 17798038 | HIERARCHICAL NEUROMORPHIC SENSOR ARRAY WITH INTEGRATED LEARNING FOR PHYSICOCHEMICAL PROPERTY PREDICTION | August 2022 | February 2026 | Allow | 42 | 1 | 0 | No | No |
| 17878514 | ACCURACY OF MULTIVARIATE APPROACH FOR TIME-SERIES BASED FORECASTING | August 2022 | September 2025 | Allow | 38 | 1 | 0 | Yes | No |
| 17874573 | Systolic Array Processor for Neural Network Computation | July 2022 | October 2025 | Allow | 38 | 0 | 0 | No | No |
| 17793732 | NEURAL NETWORK ACCELERATING METHOD AND DEVICE WITH EFFICIENT USAGE OF TOTAL VIDEO MEMORY SIZE OF GPUS | July 2022 | March 2023 | Allow | 8 | 0 | 0 | No | No |
| 17859721 | SYSTEMS AND METHODS FOR PIPELINED HETEROGENEOUS DATAFLOW FOR ARTIFICIAL INTELLIGENCE ACCELERATORS | July 2022 | June 2025 | Allow | 35 | 0 | 0 | No | No |
| 17851890 | Performing a Cognitive Learning Operation via a Cognitive Learning Framework | June 2022 | January 2026 | Abandon | 43 | 1 | 0 | No | No |
| 17808314 | BLACK-BOX EXPLAINER FOR TIME SERIES FORECASTING | June 2022 | February 2026 | Allow | 44 | 2 | 0 | Yes | No |
| 17848372 | NPU FOR GENERATING FEATURE MAP BASED ON COEFFICIENTS AND METHOD THEREOF | June 2022 | November 2025 | Allow | 41 | 1 | 0 | No | No |
| 17843801 | SPECTRAL CLUSTERING OF GRAPHS ON FAULT TOLERANT AND NOISY QUANTUM DEVICES | June 2022 | January 2026 | Allow | 43 | 0 | 1 | No | No |
| 17842365 | GENERATING HYBRID QUANTUM-CLASSICAL NEURAL NETWORK ARCHITECTURES | June 2022 | October 2025 | Allow | 40 | 1 | 0 | Yes | No |
| 17806620 | Method of Universal Automated Verification of Vehicle Damage | June 2022 | January 2026 | Allow | 43 | 1 | 0 | No | No |
| 17835506 | SYSTEM AND METHOD FOR EMPLOYING MULTIPLE MODELS FOR FEDERATED MACHINE LEARNING | June 2022 | November 2025 | Allow | 41 | 2 | 0 | No | No |
| 17832776 | GRAPHICAL USER INTERFACE FOR AUTOMATED MACHINE LEARNING MODEL DEVELOPMENT WITH DATA HEALTH ASSESSMENT | June 2022 | August 2025 | Allow | 38 | 0 | 0 | No | No |
| 17826564 | SYSTEMS AND METHODS OF PROCESSING DIVERSE DATA SETS WITH A NEURAL NETWORK TO GENERATE SYNTHESIZED DATA SETS FOR PREDICTING A TARGET METRIC | May 2022 | November 2025 | Allow | 41 | 1 | 0 | No | No |
| 17745752 | VARIATIONAL ANALOG QUANTUM ORACLE LEARNING | May 2022 | August 2025 | Allow | 39 | 0 | 0 | No | No |
| 17732894 | PARSIMONIOUS INFERENCE ON CONVOLUTIONAL NEURAL NETWORKS | April 2022 | February 2026 | Allow | 45 | 2 | 0 | Yes | No |
| 17728590 | RISK ASSESSMENT WITH AUTOMATED ESCALATION OR APPROVAL | April 2022 | August 2022 | Allow | 4 | 0 | 0 | No | No |
| 17767247 | QUANTUM COMPUTING BASED HYBRID SOLUTION STRATEGIES FOR LARGE-SCALE DISCRETE-CONTINUOUS OPTIMIZATION PROBLEMS | April 2022 | May 2023 | Allow | 13 | 1 | 0 | Yes | No |
| 17639052 | SYSTEM AND METHOD FOR MACHINE LEARNING BASED PREDICTION OF SOCIAL MEDIA INFLUENCE OPERATIONS | February 2022 | January 2026 | Allow | 46 | 1 | 0 | No | No |
| 17676685 | OMNICHANNEL INTELLIGENT NEGOTIATION ASSISTANT | February 2022 | October 2022 | Allow | 8 | 2 | 0 | Yes | No |
| 17665370 | SYSTEMS AND METHODS FOR TRAINING NEURAL NETWORKS WITH SPARSE DATA | February 2022 | January 2026 | Allow | 47 | 1 | 0 | No | No |
| 17626927 | Threshold Adjustable Superconducting Logic Gate with Neural Circuit | January 2022 | August 2025 | Allow | 43 | 0 | 0 | No | No |
| 17623886 | CONVOLUTION ACCELERATION OPERATION METHOD AND APPARATUS, STORAGE MEDIUM AND TERMINAL DEVICE | December 2021 | April 2023 | Allow | 16 | 1 | 0 | No | No |
| 17559163 | GENERATING A NEURAL NETWORK MODEL USING REDUCIBLE BLOCKS | December 2021 | March 2026 | Allow | 50 | 1 | 1 | No | No |
| 17557282 | METHODS AND SYSTEMS TO IDENTIFY COLLABORATIVE COMMUNITIES FROM MULTIPLEX HEALTHCARE PROVIDERS | December 2021 | October 2025 | Allow | 46 | 1 | 0 | No | No |
| 17558285 | SPECIALIZED FIXED FUNCTION HARDWARE FOR EFFICIENT CONVOLUTION | December 2021 | June 2022 | Allow | 5 | 1 | 0 | No | No |
| 17643736 | GENERATING REPRESENTATIONS OF INPUT SEQUENCES USING NEURAL NETWORKS | December 2021 | March 2026 | Allow | 51 | 1 | 0 | No | No |
| 17541298 | TIME SERIES DEEP SURVIVAL ANALYSIS SYSTEM IN COMBINATION WITH ACTIVE LEARNING | December 2021 | August 2022 | Allow | 8 | 1 | 0 | No | No |
| 17541046 | METHOD AND APPARATUS FOR CERTIFICATION OF FACTS | December 2021 | September 2023 | Allow | 21 | 4 | 0 | Yes | No |
| 17537749 | SYSTEM AND METHOD FOR TEACHING COMPOSITIONALITY TO CONVOLUTIONAL NEURAL NETWORKS | November 2021 | January 2026 | Abandon | 50 | 1 | 0 | No | No |
| 17532774 | GENERATING AND MODIFYING ONTOLOGIES FOR MACHINE LEARNING MODELS | November 2021 | November 2025 | Allow | 48 | 2 | 0 | No | No |
| 17514107 | Partial Inference Framework For Sequential DNN Processing On Constrained Devices, And Acoustic Scene Classification Using Said Partial Inference Framework | October 2021 | May 2025 | Allow | 42 | 1 | 0 | No | No |
| 17499972 | NEURAL NETWORK TRAINING SYSTEM | October 2021 | May 2022 | Allow | 7 | 1 | 0 | No | No |
| 17498766 | NPU FOR GENERATING KERNEL OF ARTIFICIAL NEURAL NETWORK MODEL AND METHOD THEREOF | October 2021 | April 2022 | Allow | 6 | 1 | 1 | No | No |
| 17600883 | HYBRID QUANTUM-CLASSICAL COMPUTER SYSTEM FOR QUANTUM AUTOENCODER-BASED PROCESSING | October 2021 | August 2025 | Allow | 46 | 1 | 0 | No | No |
| 17480427 | SEARCH SYSTEM AND SEARCH METHOD | September 2021 | December 2025 | Abandon | 50 | 1 | 0 | No | No |
| 17477409 | Lossless Tiling in Convolution Networks - Section Boundaries | September 2021 | July 2025 | Allow | 46 | 1 | 0 | Yes | No |
| 17438908 | METHOD AND APPARATUS FOR UPDATING A NEURAL NETWORK | September 2021 | March 2025 | Allow | 42 | 1 | 0 | Yes | No |
| 17472483 | TRAINING DEVICE, ANALYSIS DEVICE, TRAINING METHOD, AND STORAGE MEDIUM FOR HUMAN OPERATION TIME-SERIES ANALYSIS | September 2021 | February 2026 | Allow | 53 | 2 | 0 | Yes | No |
| 17460689 | EARLY STOPPING OF ARTIFICIAL INTELLIGENCE MODEL TRAINING USING CONTROL LIMITS | August 2021 | September 2025 | Allow | 48 | 1 | 0 | Yes | No |
| 17410876 | DYNAMIC PROCESS MODEL OPTIMIZATION IN DOMAINS | August 2021 | October 2024 | Allow | 38 | 1 | 0 | No | No |
| 17445458 | SYSTEMS, METHODS, AND DEVICES FOR MEASURING SIMILARITY OF AND GENERATING RECOMMENDATIONS FOR UNIQUE ITEMS | August 2021 | September 2024 | Allow | 37 | 2 | 0 | Yes | No |
| 17397016 | SYSTEMS AND METHODS FOR MODELING MACHINE LEARNING AND DATA ANALYTICS | August 2021 | August 2024 | Abandon | 37 | 1 | 0 | No | No |
| 17394246 | GENERATION OF PROCESS MODELS IN DOMAINS WITH UNSTRUCTURED DATA | August 2021 | January 2024 | Allow | 30 | 1 | 0 | No | No |
| 17393392 | DYNAMICALLY TRAINED MODELS OF NAMED ENTITY RECOGNITION OVER UNSTRUCTURED DATA | August 2021 | March 2024 | Allow | 32 | 1 | 0 | No | No |
| 17443058 | SYSTEMS, METHODS, AND DEVICES FOR MEASURING SIMILARITY OF AND GENERATING RECOMMENDATIONS FOR UNIQUE ITEMS | July 2021 | February 2024 | Allow | 31 | 1 | 0 | No | No |
| 17373177 | SYSTEM AND METHOD FOR DOMAIN SPECIFIC NEURAL NETWORK PRUNING | July 2021 | March 2022 | Allow | 8 | 1 | 0 | Yes | No |
| 17372204 | ANOMALOUS REGION DETECTION WITH LOCAL NEURAL TRANSFORMATIONS | July 2021 | September 2025 | Allow | 50 | 1 | 0 | No | No |
This analysis examines appeal outcomes and the strategic value of filing appeals for examiner CHEN, ALAN S.
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, 14.3% 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.
⚠ 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 CHEN, ALAN S works in Art Unit 2125 and has examined 461 patent applications in our dataset. With an allowance rate of 90.5%, this examiner has an above-average tendency to allow applications. Applications typically reach final disposition in approximately 39 months.
Examiner CHEN, ALAN S's allowance rate of 90.5% places them in the 74% percentile among all USPTO examiners. This examiner has an above-average tendency to allow applications.
On average, applications examined by CHEN, ALAN S receive 1.37 office actions before reaching final disposition. This places the examiner in the 21% 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 CHEN, ALAN S is 39 months. This places the examiner in the 26% percentile for prosecution speed. Prosecution timelines are slightly slower than average with this examiner.
Conducting an examiner interview provides a +5.5% benefit to allowance rate for applications examined by CHEN, ALAN S. This interview benefit is in the 31% percentile among all examiners. Recommendation: Interviews provide a below-average benefit with this examiner.
When applicants file an RCE with this examiner, 36.8% of applications are subsequently allowed. This success rate is in the 84% percentile among all examiners. Strategic Insight: RCEs are highly effective with this examiner compared to others. If you receive a final rejection, filing an RCE with substantive amendments or arguments has a strong likelihood of success.
This examiner enters after-final amendments leading to allowance in 52.0% of cases where such amendments are filed. This entry rate is in the 78% percentile among all examiners. Strategic Recommendation: This examiner is highly receptive to after-final amendments compared to other examiners. Per MPEP § 714.12, after-final amendments may be entered "under justifiable circumstances." Consider filing after-final amendments with a clear showing of allowability rather than immediately filing an RCE, as this examiner frequently enters such amendments.
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 57.1% of appeals filed. This is in the 30% 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 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, 43.0% are granted (fully or in part). This grant rate is in the 35% 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 9.1% of allowed cases (in the 88% percentile). Per MPEP § 714.14, a Quayle action indicates that all claims are allowable but formal matters remain. This examiner frequently uses Quayle actions compared to other examiners, which is a positive indicator that once substantive issues are resolved, allowance follows quickly.
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.