Detailed information about the 100 most recent patent applications.
| Application Number | Title | Filing Date | Disposal Date | Disposition | Time (months) | Office Actions | Restrictions | Interview | Appeal |
|---|---|---|---|---|---|---|---|---|---|
| 18642614 | ENHANCED VALIDITY MODELING USING MACHINE-LEARNING TECHNIQUES | April 2024 | October 2025 | Allow | 17 | 1 | 0 | No | No |
| 18582425 | CASCADING COMMAND SET ENGINEERING | February 2024 | January 2026 | Abandon | 23 | 3 | 0 | Yes | No |
| 18497893 | PRIVATE ARTIFICIAL INTELLIGENCE (AI) MODEL OF A USER FOR USE BY AN AUTONOMOUS PERSONAL COMPANION | October 2023 | April 2025 | Allow | 18 | 1 | 0 | Yes | No |
| 18334949 | ARTIFICIAL INTELLIGENCE BASED PROBLEM DESCRIPTIONS | June 2023 | September 2025 | Abandon | 28 | 2 | 0 | Yes | No |
| 18132929 | HARDWARE IMPLEMENTATION OF A CONVOLUTIONAL NEURAL NETWORK | April 2023 | November 2023 | Allow | 7 | 1 | 0 | No | No |
| 18116170 | STRUCTURE-BASED DEEP GENERATIVE MODEL FOR BINDING SITE DESCRIPTORS EXTRACTION AND DE NOVO MOLECULAR GENERATION | March 2023 | March 2025 | Allow | 25 | 4 | 0 | Yes | No |
| 18022985 | METHOD AND SYSTEM FOR CONSTRUCTING NEURAL NETWORK ARCHITECTURE SEARCH FRAMEWORK, DEVICE, AND MEDIUM | February 2023 | November 2024 | Abandon | 21 | 2 | 0 | No | No |
| 18070195 | GENERATING, USING A MACHINE LEARNING MODEL, REQUEST AGNOSTIC INTERACTION SCORES FOR ELECTRONIC COMMUNICATIONS, AND UTILIZATION OF SAME | November 2022 | June 2025 | Allow | 30 | 2 | 0 | Yes | No |
| 17990242 | MACHINE LEARNING-BASED SYSTEMS AND METHODS FOR IDENTIFYING AND RESOLVING CONTENT ANOMALIES IN A TARGET DIGITAL ARTIFACT | November 2022 | July 2024 | Abandon | 20 | 2 | 1 | No | No |
| 17986532 | APPARATUS AND METHOD FOR DETERMINING DESIGN PLAN COMPLIANCE USING MACHINE LEARNING | November 2022 | March 2024 | Allow | 16 | 3 | 0 | Yes | No |
| 17973344 | AUTOMATIC IDENTIFICATION OF LESSONS-LEARNED INCIDENT RECORDS | October 2022 | January 2025 | Allow | 27 | 5 | 0 | Yes | No |
| 18049106 | SYSTEM FOR DEEP LEARNING USING KNOWLEDGE GRAPHS | October 2022 | January 2025 | Allow | 26 | 2 | 0 | Yes | No |
| 17970509 | DEEP LEARNING-BASED SPLICE SITE CLASSIFICATION | October 2022 | January 2026 | Allow | 38 | 2 | 0 | Yes | No |
| 17962348 | Activation Functions for Deep Neural Networks | October 2022 | April 2025 | Allow | 30 | 3 | 0 | No | No |
| 17955763 | MACHINE LEARNING DRIVEN EXPERIMENTAL DESIGN FOR FOOD TECHNOLOGY | September 2022 | July 2024 | Abandon | 22 | 1 | 0 | No | No |
| 17901629 | IDENTIFYING TARGET REGIONS IN A COGNITIVE RESERVOIR SYSTEM | September 2022 | June 2025 | Allow | 34 | 2 | 0 | Yes | Yes |
| 17799933 | PURE INTEGER QUANTIZATION METHOD FOR LIGHTWEIGHT NEURAL NETWORK (LNN) | August 2022 | December 2023 | Allow | 16 | 2 | 0 | Yes | No |
| 17814684 | DRIFT DETECTION IN STATIC PROCESSES | July 2022 | June 2023 | Allow | 11 | 2 | 0 | Yes | No |
| 17848007 | APPARATUS AND METHOD FOR PROCESSING CONVOLUTION OPERATION OF NEURAL NETWORK | June 2022 | December 2024 | Allow | 30 | 2 | 0 | Yes | No |
| 17806382 | SYSTEMS AND METHODS FOR TRAINING AND EXECUTING A NEURAL NETWORK FOR COLLABORATIVE MONITORING OF RESOURCE USAGE | June 2022 | November 2025 | Abandon | 42 | 4 | 0 | Yes | No |
| 17753727 | SELECTIVE TRAINING OF DEEP LEARNING MODULES | March 2022 | September 2025 | Allow | 42 | 4 | 0 | No | Yes |
| 17591897 | METHODS OF TRAINING VARIATIONAL AUTOENCODERS TO RECOGNIZE ANOMALOUS DATA IN DISTRIBUTED SYSTEMS | February 2022 | March 2026 | Allow | 49 | 1 | 0 | Yes | No |
| 17592230 | 3-BRANCH DEEP NEURAL NETWORK | February 2022 | January 2026 | Allow | 47 | 1 | 0 | No | No |
| 17648894 | METHOD AND SYSTEM FOR EFFICIENT LEARNING ON LARGE MULTIPLEX NETWORKS | January 2022 | September 2025 | Allow | 44 | 1 | 0 | No | No |
| 17597223 | DEEP NEURAL NETWORK BASED ON FLASH ANALOG FLASH COMPUTING ARRAY | December 2021 | January 2026 | Allow | 48 | 2 | 0 | No | No |
| 17644692 | Automatic Control Group Generation | December 2021 | September 2025 | Allow | 45 | 1 | 0 | Yes | No |
| 17550280 | TRAINING DATA QUALITY FOR SPAM CLASSIFICATION | December 2021 | March 2024 | Abandon | 27 | 1 | 0 | No | No |
| 17521499 | METHOD OF DEEP LEARINING-BASED EXAMINATION OF A SEMICONDUCTOR SPECIMEN AND SYSTEM THEREOF | November 2021 | August 2024 | Allow | 34 | 2 | 0 | Yes | No |
| 17499930 | RESERVOIR COMPUTER | October 2021 | September 2025 | Allow | 47 | 1 | 0 | No | No |
| 17493064 | SYSTEMS AND METHODS FOR INITIATING AN UPDATED USER AMELIORATIVE PLAN | October 2021 | February 2026 | Allow | 52 | 2 | 0 | Yes | No |
| 17491581 | COMPUTER-READABLE RECORDING MEDIUM STORING OPTIMIZATION PROGRAM, OPTIMIZATION METHOD, AND INFORMATION PROCESSING APPARATUS | October 2021 | December 2025 | Abandon | 51 | 1 | 0 | No | No |
| 17488141 | BOOSTING DEEP REINFORCEMENT LEARNING PERFORMANCE BY COMBINING OFF-LINE DATA AND SIMULATORS | September 2021 | October 2025 | Allow | 48 | 1 | 0 | No | No |
| 17437871 | CONVOLUTIONAL NEURAL NETWORK DETERMINATION FOUNDATION EXTRACTION METHOD AND DEVICE | September 2021 | April 2025 | Abandon | 43 | 1 | 0 | No | No |
| 17351425 | SYSTEM AND ARCHITECTURE NEURAL NETWORK ACCELERATOR INCLUDING FILTER CIRCUIT | June 2021 | January 2026 | Allow | 55 | 1 | 1 | No | No |
| 17346842 | TRACE-BASED NEUROMORPHIC ARCHITECTURE FOR ADVANCED LEARNING | June 2021 | July 2025 | Abandon | 49 | 2 | 0 | Yes | No |
| 17335819 | GENERATING INPUT DATA FOR A MACHINE LEARNING MODEL | June 2021 | December 2024 | Allow | 43 | 1 | 0 | Yes | No |
| 17307950 | MACHINE LEARNING DRIVEN EXPERIMENTAL DESIGN FOR FOOD TECHNOLOGY | May 2021 | September 2022 | Allow | 16 | 3 | 0 | Yes | No |
| 17234980 | DYNAMIC VIDEO CONTENT OPTIMIZATION | April 2021 | April 2024 | Abandon | 36 | 1 | 0 | No | No |
| 17286854 | ROBUST LEARNING DEVICE, ROBUST LEARNING METHOD, AND ROBUST LEARNING PROGRAM | April 2021 | May 2025 | Abandon | 49 | 2 | 0 | No | No |
| 17209341 | using a prediction model to manage retraing of a trainable model | March 2021 | April 2025 | Abandon | 49 | 2 | 0 | No | No |
| 17200023 | PREDICTING GEOSPATIAL MEASURES | March 2021 | September 2025 | Allow | 54 | 3 | 0 | Yes | No |
| 17199976 | NEURAL NETWORK OPTIMIZATION | March 2021 | September 2024 | Abandon | 43 | 1 | 0 | No | No |
| 17200097 | GENERATION AND APPLICATION OF LOCATION EMBEDDINGS | March 2021 | December 2025 | Allow | 58 | 4 | 0 | Yes | No |
| 17200003 | DEVICE AND METHOD FOR RANDOM WALK SIMULATION | March 2021 | June 2025 | Allow | 51 | 3 | 0 | Yes | Yes |
| 17183716 | SYSTEM, METHOD, AND RECORDING MEDIUM FOR PREDICTING COGNITIVE STATES OF A SENDER OF AN ELECTRONIC MESSAGE | February 2021 | November 2025 | Abandon | 57 | 6 | 0 | Yes | No |
| 17149430 | TRAINING METHOD FOR A GENERATOR NEURAL NETWORK IMPOSING DATA EQUIVARIANCES | January 2021 | June 2025 | Allow | 53 | 2 | 0 | No | Yes |
| 17145804 | SELECTIVE BIT INVERSION IN STORAGE OPERATIONS FOR MACHINE LEARNING | January 2021 | March 2025 | Allow | 50 | 2 | 0 | Yes | No |
| 17145000 | METHOD FOR BUILDING A HEART RHYTHM CLASSIFICATION MODEL | January 2021 | August 2024 | Abandon | 43 | 1 | 0 | No | No |
| 17131500 | SELECTING ACTIONS FROM LARGE DISCRETE ACTION SETS USING REINFORCEMENT LEARNING | December 2020 | October 2023 | Allow | 34 | 1 | 0 | No | No |
| 17129148 | APPARATUSES AND METHODS FOR WEIGHT GRADIENT COMPUTATION IN NEUTRAL NETWORK | December 2020 | May 2025 | Abandon | 52 | 2 | 0 | No | No |
| 17107763 | CONTENT MANAGEMENT SYSTEM FOR TRAINED MACHINE LEARNING MODELS | November 2020 | June 2025 | Allow | 55 | 3 | 0 | No | No |
| 16949556 | METHODS AND APPARATUS FOR IMPROVING SIGNAL-TO-NOISE PERFORMANCE IN QUANTUM COMPUTATION | November 2020 | October 2024 | Abandon | 48 | 1 | 0 | No | No |
| 17052178 | NEURAL NETWORK PROCESSING ELEMENT OF ACCELERATOR TILE | October 2020 | January 2025 | Allow | 50 | 2 | 0 | Yes | No |
| 17063709 | SYSTEMS AND METHODS FOR GUIDED USER ACTIONS | October 2020 | July 2025 | Abandon | 58 | 4 | 0 | Yes | No |
| 17062556 | INSTRUCTION LENGTH DECODING | October 2020 | November 2024 | Allow | 49 | 3 | 0 | Yes | No |
| 17061187 | MACHINE VISION PARSING OF THREE-DIMENSIONAL ENVIRONMENTS EMPLOYING NEURAL NETWORKS | October 2020 | January 2025 | Allow | 52 | 2 | 0 | No | No |
| 16982781 | LEARNING APPARATUS, LEARNING METHOD, AND COMPUTER-READABLE RECORDING MEDIUM | September 2020 | March 2025 | Abandon | 53 | 2 | 0 | Yes | No |
| 16982798 | LEARNING DEVICE, LEARNING METHOD, AND LEARNING PROGRAM | September 2020 | July 2024 | Abandon | 46 | 1 | 0 | Yes | No |
| 17021923 | SYSTEM AND METHODS FOR PROCESSING SPATIAL DATA | September 2020 | December 2024 | Allow | 51 | 3 | 0 | Yes | No |
| 17013106 | DATA ANALYSIS SYSTEM USING ARTIFICIAL INTELLIGENCE | September 2020 | April 2024 | Abandon | 43 | 1 | 0 | No | No |
| 17009713 | Neural Network Methods for Defining System Topology | September 2020 | November 2025 | Allow | 60 | 3 | 0 | Yes | No |
| 17000612 | ARTIFICIAL NEURAL NETWORK CONFIGURATION AND DEPLOYMENT | August 2020 | June 2024 | Allow | 46 | 2 | 0 | Yes | No |
| 16999615 | TECHNIQUES FOR BIMODAL LEARNING IN A FINANCIAL CONTEXT | August 2020 | September 2025 | Allow | 60 | 4 | 0 | Yes | No |
| 16944251 | TRAINING A MACHINE LEARNING ALGORITHM TO PREDICT WHEN COMPUTING DEVICES MAY HAVE ISSUES | July 2020 | October 2024 | Allow | 50 | 4 | 0 | No | No |
| 16939950 | ENHANCED VALIDITY MODELING USING MACHINE-LEARNING TECHNIQUES | July 2020 | December 2023 | Allow | 41 | 0 | 0 | Yes | No |
| 16923551 | METHOD AND APPARATUS FOR ARTIFICIAL INTELLIGENCE MODEL PERSONALIZATION | July 2020 | December 2023 | Allow | 41 | 3 | 0 | Yes | No |
| 16892746 | ADAPTIVE STOCHASTIC LEARNING STATE COMPRESSION FOR FEDERATED LEARNING IN INFRASTRUCTURE DOMAINS | June 2020 | September 2025 | Abandon | 60 | 5 | 0 | Yes | No |
| 16869139 | COMPUTATION METHOD AND RELATED PRODUCTS OF RECURRENT NEURAL NETWORK | May 2020 | September 2023 | Abandon | 41 | 2 | 0 | No | No |
| 16856112 | NEURAL NETWORK METHOD AND APPARATUS | April 2020 | August 2025 | Abandon | 60 | 5 | 0 | Yes | No |
| 16849126 | SYSTEMS AND METHODS FOR TRAINING AND EXECUTING A NEURAL NETWORK FOR COLLABORATIVE MONITORING OF RESOURCE USAGE | April 2020 | February 2022 | Allow | 22 | 0 | 0 | Yes | No |
| 16839976 | TRAINING OF MODEL FOR PROCESSING SEQUENCE DATA | April 2020 | April 2024 | Allow | 49 | 3 | 0 | Yes | No |
| 16829205 | DEVICE AND METHOD FOR COMPRESSING MACHINE LEARNING MODEL | March 2020 | October 2024 | Abandon | 54 | 4 | 0 | No | No |
| 16699051 | WEIGHT QUANTIZATION IN NEURAL NETWORKS | November 2019 | October 2025 | Allow | 60 | 6 | 0 | No | No |
| 16668369 | METHODS AND SYSTEMS FOR PROVIDING DYNAMIC CONSTITUTIONAL GUIDANCE | October 2019 | July 2021 | Allow | 20 | 3 | 0 | No | No |
| 16588990 | CODED COMPUTATION STRATEGIES FOR DISTRIBUTED MATRIX-MATRIX AND MATRIX-VECTOR PRODUCTS | September 2019 | January 2026 | Abandon | 60 | 5 | 0 | No | No |
| 16576706 | APPARATUS FOR GENERATING TEMPERATURE PREDICTION MODEL AND METHOD FOR PROVIDING SIMULATION ENVIRONMENT | September 2019 | September 2023 | Abandon | 48 | 4 | 0 | No | No |
| 16562253 | MACHINE LEARNING INFORMED CONTROL SYSTEMS FOR EXTRUSION PRINTING PROCESSES | September 2019 | November 2023 | Allow | 50 | 2 | 0 | Yes | No |
| 16556937 | ARTIFICIAL NEURAL NETWORK WITH TRAINABLE ACTIVATION FUNCTIONS AND FRACTIONAL DERIVATIVE VALUES | August 2019 | March 2023 | Allow | 43 | 2 | 0 | Yes | No |
| 16524224 | RECURRENT NEURAL NETWORKS HAVING A PROBABILISTIC STATE COMPONENT AND STATE MACHINES EXTRACTED FROM THE RECURRENT NEURAL NETWORKS | July 2019 | March 2023 | Allow | 43 | 2 | 0 | No | No |
| 16524191 | MULTI-TRIPLET EXTRACTION METHOD BASED ON ENTITY-RELATION JOINT EXTRACTION MODEL | July 2019 | December 2024 | Abandon | 60 | 4 | 0 | No | No |
| 16521680 | OPTIMIZATION APPARATUS AND OPTIMIZATION APPARATUS CONTROL METHOD | July 2019 | December 2022 | Allow | 41 | 0 | 0 | No | No |
| 16506828 | METHOD AND SYSTEM FOR GENERATION OF HYBRID LEARNING TECHNIQUES | July 2019 | November 2024 | Abandon | 60 | 4 | 0 | No | No |
| 16505747 | MACHINE LEARNING SYSTEM AND BOLTZMANN MACHINE CALCULATION METHOD | July 2019 | March 2023 | Abandon | 44 | 2 | 0 | No | No |
| 16476261 | SPARSE PROCESSING IN NEURAL NETWORK PROCESSORS | July 2019 | January 2024 | Allow | 54 | 1 | 0 | No | No |
| 16455329 | NEURAL NETWORK LAYER-BY-LAYER DEBUGGING | June 2019 | December 2021 | Allow | 30 | 1 | 0 | Yes | No |
| 16448355 | METHOD FOR RANDOM SAMPLED CONVOLUTIONS WITH LOW COST ENHANCED EXPRESSIVE POWER | June 2019 | November 2023 | Abandon | 53 | 4 | 0 | No | No |
| 16416142 | METHOD AND APPARATUS FOR NEURAL NETWORK PRUNING | May 2019 | September 2024 | Abandon | 60 | 6 | 0 | Yes | No |
| 16391398 | Cognitive System Virtual Corpus Training and Utilization | April 2019 | March 2023 | Allow | 47 | 2 | 0 | No | No |
| 16351712 | COMPRESSION OF DEEP NEURAL NETWORKS | March 2019 | February 2024 | Allow | 60 | 3 | 0 | Yes | No |
| 16298022 | SYSTEM AND METHOD FOR EFFICIENT UTILIZATION OF MULTIPLIERS IN NEURAL-NETWORK COMPUTATIONS | March 2019 | November 2022 | Abandon | 44 | 1 | 0 | No | No |
| 16297412 | RESOURCE-EFFICIENT NEURAL ARCHITECTS | March 2019 | April 2023 | Allow | 49 | 2 | 0 | Yes | No |
| 16291263 | Optimizing Hierarchical Classification with Adaptive Node Collapses | March 2019 | March 2023 | Allow | 48 | 1 | 0 | No | No |
| 16278626 | MACHINE LEARNING SYSTEM AND METHOD FOR COPING WITH POTENTIAL OUTLIERS AND PERFECT LEARNING IN CONCEPT-DRIFTING ENVIRONMENT | February 2019 | March 2023 | Abandon | 49 | 1 | 0 | No | No |
| 16274426 | QUALITY MONITORING AND HIDDEN QUANTIZATION IN ARTIFICIAL NEURAL NETWORK COMPUTATIONS | February 2019 | February 2023 | Allow | 48 | 3 | 0 | Yes | No |
| 16268414 | CONTINUAL REINFORCEMENT LEARNING WITH A MULTI-TASK AGENT | February 2019 | August 2024 | Allow | 60 | 4 | 0 | Yes | No |
| 16266165 | OPTIMIZATION SYSTEM, OPTIMIZATION APPARATUS, AND OPTIMIZATION SYSTEM CONTROL METHOD FOR SOLVING OPTIMIZATION PROBLEMS BY A STOCHASTIC SEARCH | February 2019 | March 2023 | Allow | 50 | 3 | 0 | No | No |
| 16264863 | HELLINGER DISTANCE FOR MEASURING ACCURACIES OF MEAN AND STANDARD DEVIATION PREDICTION OF DYNAMIC BOLTZMANN MACHINE | February 2019 | June 2022 | Allow | 41 | 1 | 0 | Yes | No |
| 16262620 | SYSTEMS AND METHODS FOR CLASSIFICATION USING STRUCTURED AND UNSTRUCTURED ATTRIBUTES | January 2019 | December 2022 | Allow | 46 | 3 | 0 | No | No |
| 16259244 | BUILDING NEURAL NETWORKS FOR RESOURCE ALLOCATION FOR ITERATIVE WORKLOADS USING REINFORCEMENT LEARNING | January 2019 | May 2022 | Allow | 39 | 2 | 0 | Yes | No |
| 16247237 | Apparatus and Methods for Vector Based Transcendental Functions | January 2019 | January 2023 | Abandon | 60 | 6 | 0 | No | No |
This analysis examines appeal outcomes and the strategic value of filing appeals for examiner GIROUX, GEORGE.
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, 64.3% 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 GIROUX, GEORGE works in Art Unit 2128 and has examined 151 patent applications in our dataset. With an allowance rate of 70.2%, this examiner has a below-average tendency to allow applications. Applications typically reach final disposition in approximately 50 months.
Examiner GIROUX, GEORGE's allowance rate of 70.2% places them in the 32% percentile among all USPTO examiners. This examiner has a below-average tendency to allow applications.
On average, applications examined by GIROUX, GEORGE receive 2.83 office actions before reaching final disposition. This places the examiner in the 83% 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 GIROUX, GEORGE is 50 months. This places the examiner in the 5% percentile for prosecution speed. Applications take longer to reach final disposition with this examiner compared to most others.
Conducting an examiner interview provides a +22.7% benefit to allowance rate for applications examined by GIROUX, GEORGE. This interview benefit is in the 68% 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, 25.5% of applications are subsequently allowed. This success rate is in the 40% percentile among all examiners. Strategic Insight: RCEs show below-average effectiveness with this examiner. Carefully evaluate whether an RCE or continuation is the better strategy.
This examiner enters after-final amendments leading to allowance in 18.0% of cases where such amendments are filed. This entry rate is in the 21% 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, 0.0% result in withdrawal of the rejection or reopening of prosecution. This success rate is in the 5% percentile among all examiners. Note: Pre-appeal conferences show limited success with this examiner compared to others. While still worth considering, be prepared to proceed with a full appeal brief if the PAC does not result in favorable action.
This examiner withdraws rejections or reopens prosecution in 60.0% of appeals filed. This is in the 35% percentile among all examiners. Of these withdrawals, 16.7% 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, 66.7% are granted (fully or in part). This grant rate is in the 72% 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 2.0% of allowed cases (in the 74% percentile). This examiner makes examiner's amendments more often than average to place applications in condition for allowance (MPEP § 1302.04).
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.