Detailed information about the 100 most recent patent applications.
| Application Number | Title | Filing Date | Disposal Date | Disposition | Time (months) | Office Actions | Restrictions | Interview | Appeal |
|---|---|---|---|---|---|---|---|---|---|
| 18911251 | SYSTEM | October 2024 | October 2025 | Abandon | 12 | 2 | 0 | No | No |
| 18582517 | SYSTEM, METHOD AND COMPUTER PROGRAM PRODUCT FOR DETECTING BYZANTINE NODES IN A CONSENSUS-BASED DISTRIBUTED SYSTEM | February 2024 | June 2025 | Abandon | 15 | 2 | 0 | Yes | No |
| 18415376 | Using Hierarchical Representations for Neural Network Architecture Searching | January 2024 | January 2026 | Allow | 24 | 1 | 0 | Yes | No |
| 18414718 | APPARATUS AND METHODS FOR MULTIPLE STAGE PROCESS MODELING | January 2024 | December 2025 | Allow | 23 | 5 | 0 | Yes | No |
| 18033069 | METHOD AND SYSTEM FOR ACCELERATING EQUILIBRIUM COMPUTATION ON SPARSE RECURRENT NEURAL NETWORK | April 2023 | April 2024 | Allow | 12 | 0 | 0 | No | No |
| 18247845 | AUDIOVISUAL SECONDARY HAPTIC SIGNAL RECONSTRUCTION METHOD BASED ON CLOUD-EDGE COLLABORATION | April 2023 | December 2023 | Allow | 8 | 1 | 0 | No | No |
| 18104273 | MACHINE LEARNING MODEL ARCHITECTURE FOR COMBINING NETWORK DATA AND SEQUENTIAL DATA | January 2023 | December 2024 | Abandon | 22 | 4 | 0 | No | No |
| 18089028 | LEARNING MODE FOR CONTEXT IDENTIFICATION | December 2022 | October 2024 | Abandon | 22 | 2 | 0 | No | No |
| 18052528 | METHOD AND SYSTEM FOR SELECTING MACHINE LEARNING MODEL BASED ON DATA DISTRIBUTION | November 2022 | November 2025 | Abandon | 36 | 6 | 0 | Yes | No |
| 17872910 | APPARATUS FOR ATTRIBUTE PATH GENERATION | July 2022 | May 2024 | Allow | 22 | 3 | 0 | Yes | No |
| 17852182 | ASSESSMENT OF USER PRICE SENSITIVITY | June 2022 | September 2024 | Allow | 26 | 3 | 0 | Yes | No |
| 17840745 | AUTOMATED MACHINE LEARNING TEST SYSTEM | June 2022 | November 2023 | Allow | 17 | 5 | 0 | Yes | No |
| 17740454 | COLLECTIVE CONFIGURATION OF SYSTEMS USING TRUSTWORTHY GRAPH ARTIFICIAL INTELLIGENCE WITH UNCERTAINTY PROPAGATION | May 2022 | March 2026 | Abandon | 46 | 1 | 0 | No | No |
| 17763334 | REMAINING LIFE PREDICTION SYSTEM, REMAINING LIFE PREDICTION DEVICE, AND REMAINING LIFE PREDICTION PROGRAM | March 2022 | March 2026 | Abandon | 47 | 2 | 0 | No | No |
| 17680175 | METHOD, APPARATUS, AND COMPUTER PROGRAM FOR CREATING STANDARDIZED BRAINWAVE IMAGE FOR TRAINING ARTIFICIAL INTELLIGENCE MODEL | February 2022 | March 2026 | Allow | 48 | 2 | 0 | No | No |
| 17650724 | GENERATING ACTION RECOMMENDATIONS FOR MODIFYING PHYSICAL EMISSION SOURCES BASED ON MANY SIMULATIONS OF DIFFERENT SCENARIOS UTILIZING A MODIFIED GRADIENT DESCENT MODEL | February 2022 | September 2024 | Allow | 31 | 5 | 0 | Yes | No |
| 17592829 | SYSTEMS AND METHODS FOR ENABLING THE TRAINING OF SEQUENTIAL MODELS USING A BLIND LEARNING APPROACH APPLIED TO A SPLIT LEARNING | February 2022 | October 2023 | Abandon | 21 | 3 | 0 | No | No |
| 17551994 | SYSTEM IMPLEMENTING ENCODER-DECODER NEURAL NETWORK ADAPTED TO PREDICTION IN BEHAVIORAL AND/OR PHYSIOLOGICAL CONTEXTS | December 2021 | August 2025 | Abandon | 44 | 1 | 0 | No | No |
| 17456581 | DEEP REINFORCEMENT LEARNING-BASED INTELLIGENT JOB BATCHING METHOD AND APPARATUS, AND ELECTRONIC DEVICE | November 2021 | September 2025 | Abandon | 46 | 1 | 0 | No | No |
| 17523607 | AUTOMATED MACHINE LEARNING TEST SYSTEM | November 2021 | August 2023 | Allow | 21 | 4 | 0 | Yes | Yes |
| 17494220 | SYSTEMS AND METHODS FOR DETECTING PREJUDICE BIAS IN MACHINE-LEARNING MODELS | October 2021 | December 2024 | Allow | 38 | 6 | 0 | Yes | No |
| 17299679 | LEARNING APPARATUS, ESTIMATION APPARATUS, PARAMETER CALCULATION METHOD AND PROGRAM | June 2021 | September 2025 | Abandon | 52 | 2 | 0 | No | No |
| 17223138 | LEARNING APPARATUS, COMMUNICATION SYSTEM, AND LEARNING METHOD | April 2021 | January 2026 | Abandon | 57 | 3 | 0 | Yes | No |
| 17223445 | MICROSERVICE COMPOSITIONS | April 2021 | January 2026 | Allow | 58 | 3 | 0 | Yes | No |
| 17223533 | SYSTEMS AND METHODS FOR OPTIMIZING WEARABLE ITEM SELECTION IN ELECTRONIC CLOTHING TRANSACTIONS PLATFORM | April 2021 | July 2025 | Abandon | 51 | 2 | 0 | Yes | No |
| 17223680 | METHOD AND SERVER FOR TRAINING MACHINE LEARNING ALGORITHM FOR RANKING OBJECTS | April 2021 | February 2025 | Allow | 47 | 2 | 0 | No | No |
| 17215613 | WEB ELEMENTS-BASED VIRTUAL ASSISTANT FOR DISTRIBUTED APPLICATIONS | March 2021 | December 2024 | Abandon | 44 | 1 | 0 | No | No |
| 17189943 | OPTIMIZATION APPARATUS, OPTIMIZATION METHOD, AND NON-TRANSITORY COMPUTER-READABLE STORAGE MEDIUM | March 2021 | January 2026 | Allow | 58 | 3 | 0 | No | No |
| 17165444 | INTERPRETABLE HIERARCHICAL CLUSTERING | February 2021 | October 2025 | Allow | 56 | 3 | 0 | Yes | No |
| 17163018 | QUANTILE HURDLE MODELING SYSTEMS AND METHODS FOR SPARSE TIME SERIES PREDICTION APPLICATIONS | January 2021 | November 2024 | Abandon | 45 | 1 | 0 | No | No |
| 17139869 | ANOMALY DETECTION, DATA PREDICTION, AND GENERATION OF HUMAN-INTERPRETABLE EXPLANATIONS OF ANOMALIES | December 2020 | November 2025 | Abandon | 59 | 3 | 0 | Yes | No |
| 17131680 | ENVIRONMENT SPECIFIC MODEL DELIVERY | December 2020 | June 2025 | Abandon | 54 | 2 | 0 | No | No |
| 17122385 | COPING WITH FEATURE ERROR SUPPRESSION: A MECHANISM TO HANDLE THE CONCEPT DRIFT | December 2020 | January 2026 | Allow | 60 | 4 | 0 | Yes | No |
| 17122401 | AUTOMATICALLY CHANGE ANOMALY DETECTION THRESHOLD BASED ON PROBABILISTIC DISTRIBUTION OF ANOMALY SCORES | December 2020 | February 2026 | Allow | 60 | 4 | 0 | Yes | No |
| 17119592 | MIXUP DATA AUGMENTATION FOR KNOWLEDGE DISTILLATION FRAMEWORK | December 2020 | August 2025 | Allow | 56 | 3 | 0 | Yes | No |
| 17114184 | HIERARCHICAL CNN-TRANSFORMER BASED MACHINE LEARNING | December 2020 | February 2026 | Abandon | 60 | 3 | 0 | No | No |
| 16951898 | ROUTING SPIKE MESSAGES IN SPIKING NEURAL NETWORKS | November 2020 | March 2026 | Allow | 60 | 4 | 0 | No | No |
| 16951849 | SYSTEMS AND METHODS FOR GENERATING HASH TREES AND USING NEURAL NETWORKS TO PROCESS THE SAME | November 2020 | June 2024 | Allow | 43 | 6 | 0 | Yes | No |
| 17099387 | SELECTING CONTENT ITEMS USING REINFORCEMENT LEARNING | November 2020 | January 2026 | Abandon | 60 | 4 | 0 | Yes | No |
| 17097603 | METHOD FOR CONTROLLING A ROBOT AND ROBOT CONTROLLER | November 2020 | January 2025 | Abandon | 50 | 2 | 0 | No | No |
| 17086864 | NEURAL NETWORK OPTIMIZATION METHOD, AND NEURAL NETWORK OPTIMIZATION DEVICE | November 2020 | July 2024 | Abandon | 45 | 2 | 0 | Yes | No |
| 17077568 | Document Information Extraction Without Additional Annotations | October 2020 | June 2025 | Allow | 56 | 3 | 0 | Yes | No |
| 17072709 | AUTOMATED SETUP AND COMMUNICATION COORDINATION FOR TRAINING AND UTILIZING MASSIVELY PARALLEL NEURAL NETWORKS | October 2020 | October 2025 | Allow | 60 | 4 | 0 | Yes | No |
| 17068691 | SUBROUTINE NEURAL NETWORKS | October 2020 | November 2025 | Allow | 60 | 4 | 0 | Yes | No |
| 17039505 | SYSTEM AND METHOD FOR USING ARTIFICIAL INTELLIGENCE TO DETERMINE A PROBABILITY OF OCCURRENCE OF A SUBSEQUENT INCIDENT AND PERFORMING A PREVENTATIVE ACTION | September 2020 | October 2021 | Abandon | 13 | 2 | 0 | Yes | No |
| 17014270 | ANOMALY DETECTION DEVICE, ANOMALY DETECTION METHOD, AND ANOMALY DETECTION PROGRAM | September 2020 | March 2026 | Abandon | 60 | 4 | 0 | No | No |
| 17013258 | METHODS AND APPARATUS FOR HARDWARE-AWARE MACHINE LEARNING MODEL TRAINING | September 2020 | November 2025 | Abandon | 60 | 4 | 0 | Yes | No |
| 17007438 | MACHINE-LEARNING PREDICTION OR SUGGESTION BASED ON OBJECT IDENTIFICATION | August 2020 | July 2025 | Allow | 59 | 4 | 0 | Yes | No |
| 16997839 | SYNTHETIC DATA GENERATION USING BAYESIAN MODELS AND MACHINE LEARNING TECHNIQUES | August 2020 | November 2025 | Abandon | 60 | 4 | 0 | No | No |
| 16923196 | CONTINUAL LEARNING USING CROSS CONNECTIONS | July 2020 | March 2025 | Allow | 57 | 4 | 0 | Yes | No |
| 15931605 | POWER ELECTRONIC CIRCUIT TROUBLESHOOT METHOD BASED ON BEETLE ANTENNAE OPTIMIZED DEEP BELIEF NETWORK ALGORITHM | May 2020 | December 2023 | Abandon | 43 | 1 | 0 | No | No |
| 16759567 | Using Hierarchical Representations for Neural Network Architecture Searching | April 2020 | September 2023 | Allow | 41 | 2 | 0 | Yes | No |
| 16753580 | MACHINE LEARNING SYSTEM | April 2020 | May 2023 | Abandon | 37 | 1 | 0 | No | No |
| 16823413 | OPTIMIZATION SYSTEM AND CONTROL METHOD FOR OPTIMIZATION SYSTEM | March 2020 | June 2024 | Abandon | 51 | 4 | 0 | No | No |
| 16820829 | Flexible Parameter Sharing for Multi-Task Learning | March 2020 | October 2023 | Allow | 42 | 2 | 0 | Yes | No |
| 16780975 | LEARNING METHOD, STORAGE MEDIUM, AND LEARNING APPARATUS | February 2020 | April 2024 | Abandon | 51 | 4 | 0 | No | No |
| 16744020 | Neuron-Based Computational Machine | January 2020 | January 2023 | Abandon | 36 | 4 | 0 | Yes | Yes |
| 16744037 | SYSTEMS, APPARATUS, METHODS, AND ARCHITECTURE FOR PRECISION HETEROGENEITY IN ACCELERATING NEURAL NETWORKS FOR INFERENCE AND TRAINING | January 2020 | October 2021 | Abandon | 21 | 2 | 0 | Yes | No |
| 16609130 | BUILDING ENSEMBLES FOR DEEP LEARNING BY PARALLEL DATA SPLITTING | October 2019 | October 2021 | Allow | 24 | 1 | 0 | Yes | No |
| 16596439 | Multi-Mode Planar Engine For Neural Processor | October 2019 | September 2024 | Allow | 60 | 4 | 0 | Yes | No |
| 16584711 | DIRECT COMPUTATION WITH COMPRESSED WEIGHT IN TRAINING DEEP NEURAL NETWORK | September 2019 | June 2024 | Abandon | 57 | 3 | 0 | Yes | No |
| 16558175 | DISCRETE LEARNING STRUCTURE | September 2019 | April 2025 | Abandon | 60 | 6 | 0 | No | Yes |
| 16550909 | ARTIFICIAL INTELLIGENCE BASED EXTRAPOLATION MODEL FOR OUTAGES IN LIVE STREAM DATA | August 2019 | March 2024 | Abandon | 55 | 2 | 0 | Yes | No |
| 16548560 | AUTONOMOUS SYSTEM INCLUDING A CONTINUALLY LEARNING WORLD MODEL AND RELATED METHODS | August 2019 | July 2022 | Abandon | 35 | 5 | 0 | Yes | No |
| 16511482 | DEEP LEARNING SYSTEM | July 2019 | December 2024 | Allow | 60 | 5 | 0 | Yes | No |
| 16476319 | PATTERN RECOGNITION APPARATUS, METHOD, AND PROGRAM | July 2019 | March 2022 | Allow | 33 | 3 | 0 | No | No |
| 16474029 | METHODS AND APPARATUS TO PERFORM TENSOR OPERATIONS | June 2019 | February 2026 | Allow | 60 | 6 | 0 | Yes | No |
| 16442004 | MULTI TASK ORIENTED RECOMMENDATION SYSTEM FOR BENCHMARK IMPROVEMENT | June 2019 | June 2025 | Abandon | 60 | 4 | 0 | No | Yes |
| 16428016 | PREDICTIVE MAINTENANCE SYSTEM FOR EQUIPMENT WITH SPARSE SENSOR MEASUREMENTS | May 2019 | November 2023 | Abandon | 53 | 4 | 0 | Yes | No |
| 16391007 | DEEP NEURAL NETWORK | April 2019 | July 2022 | Allow | 39 | 2 | 0 | No | No |
| 16380566 | ADVERSARIAL TRAINING FOR EVENT SEQUENCE ANALYSIS | April 2019 | September 2022 | Allow | 42 | 1 | 0 | Yes | No |
| 16376209 | COMPLETE PROCESS TRACE PREDICTION USING MULTIMODAL ATTRIBUTES | April 2019 | March 2024 | Abandon | 59 | 5 | 0 | Yes | No |
| 16371107 | MULTI-MODEL BASED ACCOUNT/PRODUCT SEQUENCE RECOMMENDER | March 2019 | November 2024 | Allow | 60 | 2 | 0 | Yes | Yes |
| 16364583 | RECORDING MEDIUM WITH MACHINE LEARNING PROGRAM RECORDED THEREIN, MACHINE LEARNING METHOD, AND INFORMATION PROCESSING APPARATUS | March 2019 | March 2024 | Abandon | 60 | 4 | 0 | No | No |
| 16355867 | SOLDERING PROCESS PARAMETER SUGGESTION METHOD AND SYSTEM THEREOF | March 2019 | November 2022 | Allow | 44 | 2 | 0 | Yes | No |
| 16352666 | HANDLING CATEGORICAL FIELD VALUES IN MACHINE LEARNING APPLICATIONS | March 2019 | June 2022 | Abandon | 39 | 4 | 0 | Yes | No |
| 16286638 | LEARNING METHOD, LEARNING DEVICE, AND COMPUTER-READABLE RECORDING MEDIUM | February 2019 | April 2024 | Abandon | 60 | 4 | 0 | No | No |
| 16273127 | AUTOMATIC RECOMMENDATION OF PREDICTOR VARIABLE VALUES FOR IMPROVING PREDICTIVE OUTCOMES | February 2019 | September 2022 | Allow | 43 | 2 | 0 | No | No |
| 16253350 | Cognitive Mechanism for Social Engineering Communication Identification and Response | January 2019 | September 2024 | Abandon | 60 | 6 | 0 | Yes | No |
| 16247297 | FACILITATING ONLINE RESOURCE ACCESS WITH BIAS CORRECTED TRAINING DATA GENERATED FOR FAIRNESS-AWARE PREDICTIVE MODELS | January 2019 | October 2023 | Abandon | 57 | 3 | 0 | Yes | Yes |
| 16240832 | OPTIMIZATION APPARATUS AND CONTROL METHOD THEREOF | January 2019 | October 2022 | Allow | 45 | 3 | 0 | Yes | No |
| 16230740 | NEURAL NETWORK PROCESSOR | December 2018 | April 2024 | Abandon | 60 | 4 | 0 | Yes | No |
| 16217332 | MODEL BUILDING DEVICE AND LOADING DISAGGREGATION SYSTEM | December 2018 | October 2023 | Allow | 58 | 3 | 0 | Yes | No |
| 16203263 | Multi-task Equidistant Embedding | November 2018 | September 2024 | Allow | 60 | 4 | 0 | Yes | No |
| 16303101 | MEMORY-EFFICIENT BACKPROPAGATION THROUGH TIME | November 2018 | October 2021 | Allow | 35 | 0 | 0 | Yes | No |
| 16179051 | METHOD AND SYSTEM FOR PREDICTING THE PROBABILITY OF REGULATORY COMPLIANCE APPROVAL | November 2018 | November 2024 | Allow | 60 | 5 | 0 | No | No |
| 16157455 | ONLINE LEARNING FOR DYNAMIC BOLTZMANN MACHINES WITH HIDDEN UNITS | October 2018 | March 2024 | Allow | 60 | 5 | 0 | Yes | No |
| 16138684 | HYPERPARAMETER TUNING USING VISUAL ANALYTICS IN A DATA SCIENCE PLATFORM | September 2018 | October 2024 | Allow | 60 | 4 | 0 | Yes | No |
| 16057035 | DEEP LEARNING MODEL FOR CLOUD BASED TECHNICAL SUPPORT AUTOMATION | August 2018 | August 2024 | Allow | 60 | 6 | 0 | No | No |
| 16033259 | NATURAL LANGUAGE PROCESSING WITH KNN | July 2018 | March 2025 | Allow | 60 | 6 | 0 | Yes | No |
| 16004757 | ENCODING AND DECODING INFORMATION AND ARTIFICIAL NEURAL NETWORKS | June 2018 | October 2023 | Allow | 60 | 4 | 0 | No | No |
| 16004635 | CHARACTERIZING ACTIVITY IN A RECURRENT ARTIFICIAL NEURAL NETWORK | June 2018 | June 2025 | Abandon | 60 | 4 | 0 | Yes | Yes |
| 15994144 | COMPUTER SYSTEM PREDICTION MACHINE LEARNING MODELS | May 2018 | August 2024 | Allow | 60 | 6 | 0 | Yes | No |
| 15984390 | Dynamic and Intuitive Aggregation of a Training Dataset | May 2018 | March 2024 | Allow | 60 | 2 | 0 | No | Yes |
| 15982622 | IDENTIFYING TRANSFER MODELS FOR MACHINE LEARNING TASKS | May 2018 | February 2025 | Allow | 60 | 5 | 0 | Yes | No |
| 15971850 | HARDWARE ACCELERATED NEURAL NETWORK SUBGRAPHS | May 2018 | June 2023 | Abandon | 60 | 5 | 0 | Yes | No |
| 15925417 | Systems and Methods for Online Annotation of Source Data using Skill Estimation | March 2018 | May 2022 | Allow | 50 | 3 | 0 | Yes | No |
| 15921369 | USING NATURAL LANGUAGE PROCESSING AND DEEP LEARNING FOR MAPPING ANY SCHEMA DATA TO A HIERARCHICAL STANDARD DATA MODEL (XDM) | March 2018 | October 2023 | Abandon | 60 | 5 | 0 | Yes | No |
| 15895182 | TEXT PREPARATION APPARATUS | February 2018 | March 2022 | Allow | 49 | 2 | 0 | Yes | No |
| 15849633 | DYNAMIC HARDWARE SELECTION FOR EXPERTS IN MIXTURE-OF-EXPERTS MODEL | December 2017 | September 2023 | Allow | 60 | 5 | 0 | Yes | No |
This analysis examines appeal outcomes and the strategic value of filing appeals for examiner STANDKE, ADAM C.
With a 50.0% reversal rate, the PTAB reverses the examiner's rejections in a meaningful percentage of cases. This reversal rate is above the USPTO average, indicating that appeals have better success here than typical.
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, 33.3% of applications that filed an appeal were subsequently allowed. This appeal filing benefit rate is above the USPTO average, suggesting that filing an appeal can be an effective strategy for prompting reconsideration.
✓ 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 STANDKE, ADAM C works in Art Unit 2129 and has examined 100 patent applications in our dataset. With an allowance rate of 48.0%, this examiner allows applications at a lower rate than most examiners at the USPTO. Applications typically reach final disposition in approximately 58 months.
Examiner STANDKE, ADAM C's allowance rate of 48.0% places them in the 11% percentile among all USPTO examiners. This examiner is less likely to allow applications than most examiners at the USPTO.
On average, applications examined by STANDKE, ADAM C receive 3.54 office actions before reaching final disposition. This places the examiner in the 94% 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 STANDKE, ADAM C is 58 months. This places the examiner in the 1% percentile for prosecution speed. Applications take longer to reach final disposition with this examiner compared to most others.
Conducting an examiner interview provides a +21.0% benefit to allowance rate for applications examined by STANDKE, ADAM C. This interview benefit is in the 65% 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, 18.1% of applications are subsequently allowed. This success rate is in the 17% 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 7.8% of cases where such amendments are filed. This entry rate is in the 8% 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, 80.0% result in withdrawal of the rejection or reopening of prosecution. This success rate is in the 62% 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 45.5% of appeals filed. This is in the 11% 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 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, 40.0% are granted (fully or in part). This grant rate is in the 29% 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 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.