Detailed information about the 100 most recent patent applications.
| Application Number | Title | Filing Date | Disposal Date | Disposition | Time (months) | Office Actions | Restrictions | Interview | Appeal |
|---|---|---|---|---|---|---|---|---|---|
| 19290471 | SYSTEMS AND METHODS FOR COGNITIVE INFERENCING FOR LARGE LANGUAGE MODELS | August 2025 | October 2025 | Allow | 2 | 0 | 0 | No | No |
| 18680987 | TECHNIQUES FOR SERVICE EXECUTION AND MONITORING FOR RUN-TIME SERVICE COMPOSITION | May 2024 | April 2025 | Allow | 10 | 1 | 0 | No | No |
| 18649545 | METHOD OF CONTROLLING FOR UNDESIRED FACTORS IN MACHINE LEARNING MODELS | April 2024 | June 2025 | Allow | 13 | 1 | 0 | No | No |
| 18426325 | ACTIONABLE SUGGESTIONS FOR ACTIVITIES | January 2024 | July 2025 | Allow | 17 | 1 | 0 | Yes | No |
| 18407329 | TRAINING A DOCUMENT CLASSIFICATION NEURAL NETWORK | January 2024 | January 2025 | Allow | 13 | 1 | 0 | No | No |
| 18502231 | SYSTEMS AND METHODS FOR AUTONOMOUSLY EXECUTING COMPUTER PROGRAMS | November 2023 | April 2025 | Allow | 17 | 1 | 0 | No | No |
| 18486060 | AUGMENTING ATTENTION-BASED NEURAL NETWORKS TO SELECTIVELY ATTEND TO PAST INPUTS | October 2023 | June 2025 | Allow | 20 | 1 | 0 | No | No |
| 18378033 | PROACTIVE VIRTUAL ASSISTANT | October 2023 | April 2025 | Allow | 18 | 1 | 0 | Yes | No |
| 18083673 | NON-VOLATILE MEMORY-BASED ACTIVATION FUNCTION | December 2022 | March 2026 | Allow | 39 | 1 | 0 | Yes | No |
| 17958476 | INFORMATION PROCESSING SYSTEM HAVING AN INFORMATION PROCESSING DEVICE AND MULTIPLE PARTIAL RESERVOIRS THAT ARE TRAINED, INFORMATION PROCESSING DEVICE THAT TRAINS MULTIPLE PARTIAL RESERVOIRS, AND NON-TRANSITORY COMPUTER READABLE MEMORY MEDIUM THAT STORES INFORMATION PROCESSING PROGRAM FOR TRAINING MULTIPLE PARTIAL RESERVOIRS | October 2022 | January 2026 | Allow | 39 | 1 | 0 | No | No |
| 17948108 | MACHINE LEARNING USING GRADIENT ESTIMATE DETERMINED USING IMPROVED PERTURBATIONS | September 2022 | November 2022 | Allow | 2 | 0 | 0 | Yes | No |
| 17887183 | METHOD AND APPARATUS WITH QUANTIZED LOOK UP TABLE FOR NEURAL NETWORK OPERATION | August 2022 | December 2025 | Allow | 40 | 1 | 0 | Yes | No |
| 17877063 | AGENT TRAINING METHOD, APPARATUS, AND COMPUTER-READABLE STORAGE MEDIUM | July 2022 | January 2026 | Allow | 41 | 1 | 0 | Yes | No |
| 17860439 | METHODS AND HARDWARE FOR INTER-LAYER DATA FORMAT CONVERSION IN NEURAL NETWORKS | July 2022 | November 2025 | Allow | 40 | 1 | 0 | No | No |
| 17847886 | NOTIFICATION MANAGEMENT AND CHANNEL SELECTION | June 2022 | September 2025 | Allow | 39 | 1 | 0 | Yes | No |
| 17806032 | SYSTEMS AND METHODS FOR DETERMINING EXPLAINABILITY OF MACHINE PREDICTED DECISIONS | June 2022 | October 2025 | Allow | 40 | 1 | 0 | No | No |
| 17829021 | SPIKE NEURAL NETWORK CIRCUIT | May 2022 | July 2025 | Allow | 37 | 0 | 0 | No | No |
| 17825868 | EFFICIENT LOOK-UP TABLE BASED FUNCTIONS FOR ARTIFICIAL INTELLIGENCE (AI) ACCELERATOR | May 2022 | January 2026 | Allow | 43 | 2 | 0 | Yes | No |
| 17736956 | IMPLEMENTING MONOTONIC CONSTRAINED NEURAL NETWORK LAYERS USING COMPLEMENTARY ACTIVATION FUNCTIONS | May 2022 | October 2022 | Allow | 5 | 1 | 0 | Yes | No |
| 17720316 | NEURAL PROCESSING UNIT CAPABLE OF REUSING DATA AND METHOD THEREOF | April 2022 | September 2022 | Allow | 5 | 1 | 0 | No | No |
| 17719932 | QUANTUM ALGORITHM AND DESIGN FOR A QUANTUM CIRCUIT ARCHITECTURE TO SIMULATE INTERACTING FERMIONS | April 2022 | February 2026 | Abandon | 46 | 1 | 0 | No | No |
| 17702686 | Developmental Network Model of Conscious Learning in Biological Brains | March 2022 | December 2025 | Abandon | 45 | 1 | 0 | No | No |
| 17652123 | INFORMATION PROCESSING DEVICE, INFORMATION PROCESSING METHOD, AND COMPUTER PROGRAM PRODUCT FOR ESTIMATING PARAMETER OF MODEL FOR A SPECIFIED TIME PARAMETER | February 2022 | August 2025 | Allow | 42 | 1 | 0 | No | No |
| 17675817 | HIERARCHICAL NEURAL NETWORK BASED IMPLEMENTATION FOR PREDICTING OUT OF STOCK PRODUCTS | February 2022 | August 2025 | Allow | 41 | 1 | 0 | Yes | No |
| 17651495 | ELECTRONIC DEVICE, METHOD, PROGRAM, AND SYSTEM FOR IDENTIFIER-INFORMATION INFERENCE USING IMAGE RECOGNITION MODEL | February 2022 | July 2025 | Allow | 41 | 1 | 0 | No | No |
| 17665279 | SPARSE AND DIFFERENTIABLE MIXTURE OF EXPERTS NEURAL NETWORKS | February 2022 | August 2025 | Allow | 43 | 1 | 0 | Yes | No |
| 17581767 | METACOGNITIVE SEDENION-VALUED NEURAL NETWORKS | January 2022 | September 2025 | Allow | 44 | 2 | 0 | Yes | No |
| 17566281 | OPERATIONAL NEURAL NETWORKS AND SELF-ORGANIZED OPERATIONAL NEURAL NETWORKS WITH GENERATIVE NEURONS | December 2021 | March 2026 | Allow | 50 | 2 | 0 | No | No |
| 17623753 | FIRING NEURAL NETWORK COMPUTING SYSTEM AND METHOD FOR BRAIN-LIKE INTELLIGENCE AND COGNITIVE COMPUTING | December 2021 | August 2025 | Allow | 44 | 1 | 0 | Yes | No |
| 17562808 | AUTOMATED MACHINE LEARNING MODEL EXPLANATION GENERATION | December 2021 | July 2025 | Allow | 43 | 1 | 0 | No | No |
| 17558327 | COMPRESSING IMAGE-TO-IMAGE MODELS | December 2021 | July 2025 | Allow | 43 | 1 | 0 | No | No |
| 17547107 | NEURAL NETWORKS BASED ON HYBRIDIZED SYNAPTIC CONNECTIVITY GRAPHS | December 2021 | October 2025 | Abandon | 46 | 1 | 0 | No | No |
| 17537156 | MACHINE LEARNING MODEL TRAINING USING AN ANALOG PROCESSOR | November 2021 | June 2025 | Allow | 42 | 1 | 0 | No | No |
| 17607648 | SYSTEM FOR SEQUENCING AND PLANNING | October 2021 | August 2025 | Abandon | 46 | 1 | 0 | No | No |
| 17511448 | TRAINING NEURAL NETWORKS WITH LABEL DIFFERENTIAL PRIVACY | October 2021 | July 2025 | Allow | 45 | 1 | 0 | No | No |
| 17509024 | AUTOMATIC PRODUCT DESCRIPTION GENERATION | October 2021 | April 2025 | Allow | 41 | 1 | 0 | Yes | No |
| 17504657 | METHODS, APPARATUSES AND COMPUTER PROGRAM PRODUCTS FOR PREDICTING MEASUREMENT DEVICE PERFORMANCE | October 2021 | February 2025 | Allow | 40 | 0 | 0 | No | No |
| 17503770 | ARTIFICIAL INTELLIGENCE ACCELERATORS | October 2021 | March 2025 | Allow | 41 | 1 | 0 | No | No |
| 17496002 | OBJECT RECOGNITION AND BEHAVIORAL ANALYSIS USING SEMANTIC REASONING | October 2021 | September 2025 | Allow | 47 | 2 | 0 | Yes | No |
| 17492777 | METHOD FOR DETERMINING SAFETY-CRITICAL OUTPUT VALUES BY WAY OF A DATA ANALYSIS DEVICE FOR A TECHNICAL ENTITY | October 2021 | June 2025 | Allow | 45 | 1 | 0 | Yes | No |
| 17599808 | STATE ESTIMATION DEVICE, STATE ESTIMATION PROGRAM, ESTIMATION MODEL, AND STATE ESTIMATION METHOD | September 2021 | March 2025 | Allow | 41 | 1 | 0 | No | No |
| 17449139 | METHOD FOR ASCERTAINING AN OUTPUT SIGNAL WITH THE AID OF A MACHINE LEARNING SYSTEM | September 2021 | August 2025 | Allow | 47 | 2 | 0 | No | No |
| 17479257 | CONVOLUTION SIZE PREDICTION TO REDUCE CALCULATIONS | September 2021 | January 2025 | Allow | 40 | 1 | 0 | No | No |
| 17470368 | Model Management System for Improving Training Data Through Machine Learning Deployment | September 2021 | February 2025 | Allow | 41 | 1 | 0 | No | No |
| 17435167 | TECHNIQUES FOR QUANTUM MEMORY ADDRESSING AND RELATED SYSTEMS AND METHODS | August 2021 | June 2025 | Allow | 46 | 2 | 0 | Yes | No |
| 17403069 | AUTOMATED GENERATION OF PREDICTIVE INSIGHTS CLASSIFYING USER ACTIVITY | August 2021 | July 2025 | Allow | 47 | 3 | 0 | Yes | No |
| 17401413 | DATA-BASED ESTIMATION OF OPERATING BEHAVIOR OF AN MR DEVICE | August 2021 | June 2025 | Allow | 46 | 2 | 0 | No | No |
| 17391684 | DETERMINING TRAFFIC VIOLATION HOTSPOTS | August 2021 | May 2025 | Allow | 45 | 2 | 0 | No | No |
| 17421693 | DATA ANALYSIS DEVICE, METHOD, AND PROGRAM | July 2021 | September 2025 | Abandon | 50 | 2 | 0 | No | No |
| 17357614 | SYSTEM AND METHOD FOR MITIGATING GENERALIZATION LOSS IN DEEP NEURAL NETWORK FOR TIME SERIES CLASSIFICATION | June 2021 | May 2025 | Allow | 47 | 1 | 0 | No | No |
| 17349280 | METHOD FOR TRAINING CLASSIFICATION MODEL, CLASSIFICATION METHOD, APPARATUS AND DEVICE | June 2021 | March 2025 | Allow | 45 | 1 | 0 | Yes | No |
| 17345603 | SYSTEM AND METHOD TO ALTER AN IMAGE | June 2021 | January 2025 | Allow | 44 | 1 | 0 | No | No |
| 17314318 | SYSTEMS AND METHODS FOR TREATING AND ESTIMATING PROGRESSION OF CHRONIC KIDNEY DISEASE | May 2021 | July 2025 | Allow | 50 | 2 | 1 | Yes | No |
| 17306934 | AUDIO PROCESSING WITH NEURAL NETWORKS | May 2021 | January 2025 | Allow | 45 | 2 | 0 | Yes | No |
| 17228974 | PREDICTING TRAJECTORY INTERSECTION BY ANOTHER ROAD USER | April 2021 | December 2022 | Allow | 20 | 0 | 0 | No | No |
| 17198963 | SYSTEM AND METHOD TO CAPTURE DATA TRANSITIONS AND SELECT OPTIMAL DATA INTENSIVE MACHINE LEARNING MODEL | March 2021 | January 2025 | Allow | 46 | 2 | 0 | Yes | No |
| 17144196 | PROVIDING AMBIENT INFORMATION BASED ON LEARNED USER CONTEXT AND INTERACTION, AND ASSOCIATED SYSTEMS AND DEVICES | January 2021 | September 2024 | Allow | 44 | 1 | 0 | Yes | No |
| 17083768 | ANOMALY DETECTION AND RESOLUTION | October 2020 | February 2025 | Allow | 52 | 3 | 0 | Yes | No |
| 17065369 | ARTIFICIAL INTELLIGENCE-BASED INFORMATION MANAGEMENT SYSTEM PERFORMANCE METRIC PREDICTION | October 2020 | October 2025 | Allow | 60 | 6 | 0 | Yes | No |
| 16893041 | METHOD OF CONTROLLING FOR UNDESIRED FACTORS IN MACHINE LEARNING MODELS | June 2020 | July 2022 | Allow | 25 | 1 | 0 | No | No |
| 15930728 | SYSTEMS AND METHODS OF PARALLEL AND DISTRIBUTED PROCESSING OF DATASETS FOR MODEL APPROXIMATION | May 2020 | March 2022 | Allow | 22 | 0 | 0 | No | No |
| 16796663 | ARTIFICIALLY INTELLIGENT SYSTEMS, DEVICES, AND METHODS FOR LEARNING AND/OR USING AN AVATAR'S CIRCUMSTANCES FOR AUTONOMOUS AVATAR OPERATION | February 2020 | August 2022 | Allow | 29 | 1 | 0 | Yes | No |
| 16779371 | RISK-BASED MACHINE LEARNING CLASSIFIER | January 2020 | January 2022 | Allow | 23 | 0 | 0 | Yes | No |
| 16739311 | METHOD AND APPARATUS FOR RECOGNIZING INTENTION, AND NON-TRANSITORY COMPUTER-READABLE RECORDING MEDIUM | January 2020 | September 2022 | Allow | 32 | 1 | 0 | Yes | No |
| 16735453 | TRAINING A DOCUMENT CLASSIFICATION NEURAL NETWORK | January 2020 | August 2021 | Allow | 19 | 1 | 0 | No | No |
| 16702348 | FEEDBACK MECHANISMS IN SEQUENCE LEARNING SYSTEMS WITH TEMPORAL PROCESSING CAPABILITY | December 2019 | August 2021 | Allow | 20 | 1 | 0 | No | No |
| 16672253 | NEUROMORPHIC SYSTEM FOR PERFORMING SUPERVISED LEARNING USING ERROR BACKPROPAGATION | November 2019 | September 2022 | Allow | 35 | 0 | 0 | Yes | No |
| 16654460 | MACHINE LEARNING BASED METHODOLOGY FOR SIGNAL WAVEFORM, EYE DIAGRAM, AND BIT ERROR RATE (BER) BATHTUB PREDICTION | October 2019 | November 2022 | Allow | 37 | 1 | 0 | Yes | No |
| 16578863 | REINFORCEMENT LEARNING WITH A STOCHASTIC ACTION SET | September 2019 | December 2022 | Allow | 39 | 0 | 0 | Yes | No |
| 16553465 | PERFORMANCE PREDICTION FROM COMMUNICATION DATA | August 2019 | November 2022 | Allow | 38 | 1 | 0 | No | No |
| 16437838 | APPARATUS AND METHOD FOR UTILIZING A PARAMETER GENOME CHARACTERIZING NEURAL NETWORK CONNECTIONS AS A BUILDING BLOCK TO CONSTRUCT A NEURAL NETWORK WITH FEEDFORWARD AND FEEDBACK PATHS | June 2019 | July 2022 | Allow | 38 | 1 | 0 | No | No |
| 16427559 | Noise and Signal Management for RPU Array | May 2019 | March 2022 | Allow | 34 | 0 | 0 | No | No |
| 16397283 | Data Sparsity Monitoring During Neural Network Training | April 2019 | September 2022 | Allow | 40 | 1 | 0 | No | No |
| 16396583 | NEURAL NETWORK ERROR CONTOUR GENERATION CIRCUIT | April 2019 | November 2022 | Allow | 42 | 1 | 1 | Yes | No |
| 16345657 | INTELLIGENT ANALYSIS SYSTEM USING MAGNETIC FLUX LEAKAGE DATA IN PIPELINE INNER INSPECTION | April 2019 | July 2022 | Allow | 39 | 3 | 0 | Yes | No |
| 16394227 | PREDICTING AND VISUALIZING OUTCOMES USING A TIME-AWARE RECURRENT NEURAL NETWORK | April 2019 | June 2022 | Allow | 37 | 1 | 0 | Yes | No |
| 16391871 | CLUSTERING METHOD, CLASSIFICATION METHOD, CLUSTERING APPARATUS, AND CLASSIFICATION APPARATUS | April 2019 | July 2022 | Abandon | 39 | 2 | 0 | No | No |
| 16377983 | SPIKE NEURAL NETWORK CIRCUIT INCLUDING RADIATION SOURCE | April 2019 | April 2022 | Allow | 36 | 1 | 0 | No | No |
| 16371230 | Complex-Valued Neural Networks | April 2019 | May 2022 | Abandon | 37 | 1 | 0 | No | No |
| 16293230 | NEUROMORPHIC COMPUTING IN DYNAMIC RANDOM ACCESS MEMORY | March 2019 | January 2022 | Allow | 34 | 1 | 0 | Yes | No |
| 16281904 | METHOD AND SYSTEM FOR PROVIDING EXPLANATION OF PREDICTION GENERATED BY AN ARTIFICIAL NEURAL NETWORK MODEL | February 2019 | January 2022 | Allow | 35 | 1 | 0 | No | No |
| 16325321 | GENETIC PROGRAMMING FOR PARTIAL LAYERS OF A DEEP LEARNING MODEL | February 2019 | June 2022 | Allow | 40 | 1 | 0 | No | No |
| 16264704 | METHOD AND ELECTRONIC APPARATUS FOR ADJUSTING A NEURAL NETWORK | February 2019 | May 2022 | Allow | 39 | 1 | 0 | No | No |
| 16263734 | PROVIDING INSIGHTS ABOUT A DYNAMIC MACHINE LEARNING MODEL | January 2019 | September 2022 | Allow | 43 | 3 | 0 | Yes | No |
| 16260898 | RECURRENT NEURAL NETWORKS WITH DIAGONAL AND PROGRAMMING FLUCTUATION TO FIND ENERGY GLOBAL MINIMA | January 2019 | October 2022 | Allow | 44 | 2 | 0 | Yes | No |
| 16254533 | SYSTEMS AND METHODS FOR DECISION MODELLING OF A TEMPORAL PATH | January 2019 | June 2022 | Allow | 41 | 1 | 0 | Yes | No |
| 16252336 | Progressive Modification of Neural Networks | January 2019 | January 2022 | Allow | 36 | 0 | 0 | No | No |
| 16243477 | MIXED-PRECISION DEEP-LEARNING WITH MULTI-MEMRISTIVE DEVICES | January 2019 | September 2021 | Allow | 32 | 0 | 0 | Yes | No |
| 16315991 | NEURAL NETWORK INCLUDING MEMORY ELEMENTS IMPLEMENTED AT NODES | January 2019 | December 2021 | Allow | 36 | 1 | 0 | No | No |
| 16213697 | MULTIPLEXING NEURAL NETWORK ARCHITECTURE | December 2018 | February 2023 | Abandon | 50 | 4 | 0 | No | No |
| 16210203 | MULTI-VARIABLES PROCESSING NEURONS AND UNSUPERVISED MULTI-TIMESCALE LEARNING FOR SPIKING NEURAL NETWORKS | December 2018 | April 2022 | Allow | 40 | 2 | 0 | Yes | No |
| 16206910 | PREDICTING DEEP LEARNING SCALING | November 2018 | October 2022 | Allow | 47 | 2 | 0 | Yes | No |
| 16200140 | METHOD AND APPARATUS FOR GENERATING TARGET NEURAL NETWORK STRUCTURE, ELECTRONIC DEVICE, AND STORAGE MEDIUM | November 2018 | November 2021 | Allow | 36 | 1 | 0 | Yes | No |
| 16198321 | SYSTEMS AND METHODS FOR OPTIMIZATION OF A DATA MODEL NETWORK ARCHITECTURE FOR TARGET DEPLOYMENT | November 2018 | October 2022 | Allow | 47 | 2 | 0 | Yes | No |
| 16196515 | STDP-BASED LEARNING METHOD FOR A NETWORK HAVING DUAL ACCUMULATOR NEURONS | November 2018 | November 2021 | Allow | 35 | 1 | 0 | No | No |
| 16189903 | SYSTEMS AND METHODS FOR DETERMINING AN ARTIFICIAL INTELLIGENCE MODEL IN A COMMUNICATION SYSTEM | November 2018 | April 2022 | Allow | 41 | 1 | 0 | Yes | No |
| 16177888 | Trust Rating Metric for Future Event Prediction of an Outcome | November 2018 | December 2021 | Allow | 37 | 2 | 0 | Yes | No |
| 16170045 | EMOTION IDENTIFYING SYSTEM, SYSTEM AND COMPUTER-READABLE MEDIUM | October 2018 | July 2022 | Abandon | 45 | 1 | 0 | No | No |
| 16169676 | SEMICONDUCTOR CHANNEL BASED NEUROMORPHIC SYNAPSE DEVICE INCLUDING TRAP-RICH LAYER | October 2018 | November 2021 | Allow | 37 | 1 | 0 | No | No |
| 16156994 | PROGRESSIVE MODIFICATION OF GENERATIVE ADVERSARIAL NEURAL NETWORKS | October 2018 | December 2021 | Allow | 38 | 1 | 0 | Yes | No |
This analysis examines appeal outcomes and the strategic value of filing appeals for examiner SCHNEE, HAL W.
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, 50.0% 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.
✓ Filing a Notice of Appeal is strategically valuable. The act of filing often prompts favorable reconsideration during the mandatory appeal conference.
Examiner SCHNEE, HAL W works in Art Unit 2129 and has examined 112 patent applications in our dataset. With an allowance rate of 88.4%, this examiner has an above-average tendency to allow applications. Applications typically reach final disposition in approximately 43 months.
Examiner SCHNEE, HAL W's allowance rate of 88.4% places them in the 69% percentile among all USPTO examiners. This examiner has an above-average tendency to allow applications.
On average, applications examined by SCHNEE, HAL W receive 1.55 office actions before reaching final disposition. This places the examiner in the 29% percentile for office actions issued. This examiner issues fewer office actions than average, which may indicate efficient prosecution or a more lenient examination style.
The median time to disposition (half-life) for applications examined by SCHNEE, HAL W is 43 months. This places the examiner in the 16% percentile for prosecution speed. Applications take longer to reach final disposition with this examiner compared to most others.
Conducting an examiner interview provides a +20.9% benefit to allowance rate for applications examined by SCHNEE, HAL W. 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, 34.3% of applications are subsequently allowed. This success rate is in the 76% 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 40.0% of cases where such amendments are filed. This entry rate is in the 61% percentile among all examiners. Strategic Recommendation: This examiner shows above-average receptiveness to after-final amendments. If your amendments clearly overcome the rejections and do not raise new issues, consider filing after-final amendments before resorting to an RCE.
When applicants request a pre-appeal conference (PAC) with this examiner, 200.0% result in withdrawal of the rejection or reopening of prosecution. This success rate is in the 94% percentile among all examiners. Strategic Recommendation: Pre-appeal conferences are highly effective with this examiner compared to others. Before filing a full appeal brief, strongly consider requesting a PAC. The PAC provides an opportunity for the examiner and supervisory personnel to reconsider the rejection before the case proceeds to the PTAB.
This examiner withdraws rejections or reopens prosecution in 100.0% of appeals filed. This is in the 89% percentile among all examiners. Of these withdrawals, 100.0% occur early in the appeal process (after Notice of Appeal but before Appeal Brief). Strategic Insight: This examiner frequently reconsiders rejections during the appeal process compared to other examiners. Per MPEP § 1207.01, all appeals must go through a mandatory appeal conference. Filing a Notice of Appeal may prompt favorable reconsideration even before you file an Appeal Brief.
When applicants file petitions regarding this examiner's actions, 50.0% are granted (fully or in part). This grant rate is in the 46% 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.