Detailed information about the 100 most recent patent applications.
| Application Number | Title | Filing Date | Disposal Date | Disposition | Time (months) | Office Actions | Restrictions | Interview | Appeal |
|---|---|---|---|---|---|---|---|---|---|
| 18863245 | VCSEL-based Coherent Scalable Deep Learning | November 2024 | April 2025 | Allow | 5 | 1 | 0 | Yes | No |
| 18590879 | Defect Prediction Operation | February 2024 | February 2025 | Allow | 12 | 1 | 0 | No | No |
| 18479775 | PROGRESSIVE NEURAL NETWORKS | October 2023 | June 2025 | Allow | 21 | 2 | 0 | Yes | No |
| 18352631 | SYSTEMS AND METHODS FOR ASSESSING AND MITIGATING PERSONAL HEALTH HAZARDS IN AN INDOOR ENVIRONMENT FOR A PLURALITY OF OCCUPANTS | July 2023 | June 2025 | Abandon | 23 | 3 | 0 | Yes | No |
| 18324349 | THING MACHINE | May 2023 | September 2024 | Abandon | 16 | 1 | 0 | No | No |
| 18140636 | AGENT-BASED TRAINING OF ARTIFICIAL INTELLIGENCE CHARACTER MODELS | April 2023 | February 2025 | Allow | 22 | 6 | 0 | Yes | No |
| 18034287 | METHOD FOR IMPLEMENTING ADAPTIVE STOCHASTIC SPIKING NEURON BASED ON FERROELECTRIC FIELD EFFECT TRANSISTOR | April 2023 | August 2023 | Allow | 4 | 0 | 0 | No | No |
| 18192744 | SYSTEM AND METHOD FOR ENHANCED DISTRIBUTION OF DATA TO COMPUTE NODES | March 2023 | March 2024 | Allow | 12 | 1 | 0 | Yes | No |
| 18119450 | WIND POWER PREDICTION METHOD AND SYSTEM BASED ON DEEP DETERMINISTIC POLICY GRADIENT ALGORITHM | March 2023 | August 2023 | Allow | 5 | 1 | 0 | No | No |
| 18084948 | METHOD AND APPARATUS FOR GENERATING FIXED-POINT QUANTIZED NEURAL NETWORK | December 2022 | March 2025 | Allow | 27 | 4 | 0 | Yes | No |
| 18072969 | MEMORY OPTIMIZATION METHOD AND DEVICE ORIENTED TO NEURAL NETWORK COMPUTING | December 2022 | October 2024 | Abandon | 22 | 2 | 0 | Yes | Yes |
| 17919312 | LARGE DEEP LEARNING MODEL TRAINING METHOD AND SYSTEM, DEVICE AND MEDIUM | October 2022 | September 2024 | Abandon | 23 | 4 | 0 | Yes | No |
| 17800172 | METHOD AND APPARATUS FOR CONVERTING NUMERICAL VALUES INTO SPIKES, ELECTRONIC DEVICE AND STORAGE MEDIUM | August 2022 | July 2023 | Allow | 11 | 1 | 0 | No | No |
| 17789392 | CLASSIFICATION MODEL TRAINING METHOD, SYSTEM, ELECTRONIC DEVICE AND STRORAGE MEDIUM | June 2022 | July 2023 | Allow | 13 | 2 | 0 | No | No |
| 17850826 | System and Methods for Customizing Neural Networks | June 2022 | April 2024 | Abandon | 22 | 2 | 0 | No | No |
| 17839010 | METHODS AND APPARATUS FOR DISTRIBUTED TRAINING OF A NEURAL NETWORK | June 2022 | December 2023 | Allow | 18 | 1 | 0 | Yes | No |
| 17714570 | AI-BASED INPUT OUTPUT EXPANSION ADAPTER FOR A TELEMATICS DEVICE AND METHODS FOR UPDATING AN AI MODEL THEREON | April 2022 | March 2023 | Allow | 12 | 2 | 0 | No | No |
| 17587658 | THING MACHINE | January 2022 | April 2023 | Allow | 15 | 0 | 0 | No | No |
| 17626453 | MACHINE LEARNING FOR SPLICE IMPROVEMENT | January 2022 | May 2024 | Allow | 29 | 2 | 0 | Yes | Yes |
| 17572487 | CONVOLUTIONAL NEURAL NETWORK TUNING SYSTEMS AND METHODS | January 2022 | December 2024 | Abandon | 35 | 4 | 0 | Yes | No |
| 17563379 | HARDWARE ACCELERATOR TEMPLATE AND DESIGN FRAMEWORK FOR IMPLEMENTING RECURRENT NEURAL NETWORKS | December 2021 | September 2024 | Abandon | 33 | 4 | 0 | Yes | No |
| 17531337 | CLINICAL DECISION SUPPORT SYSTEM USING PHENOTYPIC FEATURES | November 2021 | February 2025 | Allow | 39 | 3 | 0 | Yes | No |
| 17506521 | MACHINE LEARNING TECHNIQUES FOR ENVIRONMENTAL DISCOVERY, ENVIRONMENTAL VALIDATION, AND AUTOMATED KNOWLEDGE REPOSITORY GENERATION | October 2021 | November 2024 | Allow | 37 | 8 | 0 | Yes | No |
| 17506619 | Convolutional Self-encoding Fault Monitoring Method Based on Batch Imaging | October 2021 | March 2023 | Abandon | 17 | 2 | 0 | No | No |
| 17482197 | DEVICE FOR ASSESSING AND MANAGING A HEALTH IMPACT OF AN INDOOR ENVIRONMENT AT A SITE LOCATION | September 2021 | May 2022 | Allow | 8 | 1 | 0 | Yes | No |
| 17468702 | MODEL DEPLOYMENT METHOD, MODEL DEPLOYMENT DEVICE AND TERMINAL EQUIPMENT | September 2021 | March 2022 | Allow | 7 | 1 | 0 | No | No |
| 17372267 | AUTOMATIC DISCOVERY OF AUTOMATED DIGITAL SYSTEMS THROUGH LINK SALIENCE | July 2021 | December 2023 | Allow | 29 | 5 | 0 | Yes | No |
| 17366368 | SYSTEMS AND METHODS FOR ADJUSTING OPERATIONS OF AN INDUSTRIAL AUTOMATION SYSTEM BASED ON MULTIPLE DATA SOURCES | July 2021 | August 2023 | Allow | 25 | 1 | 0 | Yes | No |
| 17365145 | Defect Prediction Operation | July 2021 | October 2023 | Allow | 27 | 2 | 0 | No | No |
| 17337397 | MONITORING WEB APPLICATION BEHAVIOR FROM A BROWSER USING A DOCUMENT OBJECT MODEL | June 2021 | June 2023 | Abandon | 24 | 1 | 0 | No | No |
| 17291014 | TUNING OF AXIS CONTROL OF MULTI-AXIS MACHINES | May 2021 | February 2023 | Allow | 21 | 4 | 0 | Yes | No |
| 17225238 | AUTOMATICALLY GENERATING RULES FOR EVENT DETECTION SYSTEMS | April 2021 | January 2022 | Allow | 9 | 2 | 0 | Yes | No |
| 17208834 | AUTOMATIC RULE LEARNING IN SHARED RESOURCE SOLUTION DESIGN | March 2021 | February 2023 | Allow | 23 | 1 | 0 | No | No |
| 17201542 | PROGRESSIVE NEURAL NETWORKS | March 2021 | May 2023 | Allow | 26 | 1 | 0 | Yes | No |
| 17137746 | TECHNIQUES FOR DYNAMIC MACHINE LEARNING INTEGRATION | December 2020 | September 2022 | Allow | 21 | 4 | 0 | Yes | No |
| 17136409 | Ship Motion Prediction Method Based on Long Short-Term Memory Network and Gaussian Process Regression | December 2020 | April 2025 | Abandon | 51 | 1 | 0 | No | No |
| 17116117 | KNOWLEDGE DISTILLATION USING DEEP CLUSTERING | December 2020 | February 2025 | Allow | 50 | 4 | 0 | Yes | No |
| 17085555 | COVARIATE PROCESSING WITH NEURAL NETWORK EXECUTION BLOCKS | October 2020 | April 2025 | Allow | 54 | 2 | 0 | Yes | No |
| 17080433 | Processing Core with Meta Data Actuated Conditional Graph Execution | October 2020 | March 2024 | Allow | 41 | 3 | 0 | No | No |
| 17060338 | SYSTEMS AND METHODS FOR IMPLEMENTING OPERATIONAL TRANSFORMATIONS FOR RESTRICTED COMPUTATIONS OF A MIXED-SIGNAL INTEGRATED CIRCUIT | October 2020 | January 2021 | Allow | 3 | 1 | 0 | Yes | No |
| 17039055 | MACHINE LEARNING FOR MISSION SYSTEM | September 2020 | May 2025 | Abandon | 56 | 2 | 0 | No | No |
| 17034338 | DYNAMIC MINIBATCH SIZES | September 2020 | June 2024 | Allow | 45 | 1 | 0 | No | No |
| 16978412 | MEANING INFERENCE SYSTEM, METHOD, AND PROGRAM | September 2020 | April 2024 | Abandon | 43 | 1 | 0 | Yes | No |
| 17006641 | PROGRAM PREDICTOR | August 2020 | August 2023 | Allow | 35 | 2 | 0 | Yes | No |
| 16971996 | PREDICTION SYSTEM, PREDICTION METHOD, AND PROGRAM | August 2020 | February 2022 | Abandon | 18 | 3 | 0 | Yes | No |
| 16964435 | NEURAL NETWORK AND ITS INFORMATION PROCESSING METHOD, INFORMATION PROCESSING SYSTEM | July 2020 | October 2024 | Allow | 51 | 1 | 0 | Yes | No |
| 16935500 | METHODS AND SYSTEMS WITH CONVOLUTIONAL NEURAL NETWORK (CNN) PERFORMANCE | July 2020 | April 2025 | Allow | 57 | 3 | 0 | Yes | No |
| 16934369 | ARTIFICIAL INTELLIGENCE / MACHINE LEARNING MODEL DRIFT DETECTION AND CORRECTION FOR ROBOTIC PROCESS AUTOMATION | July 2020 | November 2024 | Allow | 52 | 2 | 0 | Yes | Yes |
| 16928657 | FULLY-PRINTED ALL-SOLID-STATE ORGANIC FLEXIBLE ARTIFICIAL SYNAPSE FOR NEUROMORPHIC COMPUTING | July 2020 | March 2025 | Abandon | 56 | 3 | 0 | No | No |
| 16879555 | DYNAMICALLY CONFIGURABLE MICROSERVICE MODEL FOR DATA ANALYSIS USING SENSORS | May 2020 | January 2021 | Allow | 8 | 1 | 0 | Yes | No |
| 16763676 | METHOD, CONTROLLER, AND COMPUTER PROGRAM PRODUCT FOR REDUCING OSCILLATIONS IN A TECHNICAL SYSTEM | May 2020 | January 2024 | Allow | 44 | 8 | 0 | Yes | Yes |
| 16865539 | LOAD BALANCING FOR MEMORY CHANNEL CONTROLLERS | May 2020 | August 2021 | Allow | 16 | 3 | 0 | Yes | No |
| 16852338 | SYNCHRONIZATION OF PROCESSING ELEMENTS THAT EXECUTE STATICALLY SCHEDULED INSTRUCTIONS IN A MACHINE LEARNING ACCELERATOR | April 2020 | March 2025 | Allow | 59 | 3 | 0 | Yes | No |
| 16848525 | ALL OPTICAL NEURAL NETWORK | April 2020 | December 2024 | Allow | 56 | 3 | 0 | Yes | No |
| 16824025 | MACHINE LEARNING SYSTEM | March 2020 | January 2021 | Allow | 9 | 1 | 0 | Yes | No |
| 16823193 | TRAINING ARTIFICIAL NEURAL NETWORKS WITH CONSTRAINTS | March 2020 | May 2025 | Allow | 60 | 5 | 0 | Yes | No |
| 16808222 | TRANSISTORLESS ALL-MEMRISTOR NEUROMORPHIC CIRCUITS FOR IN-MEMORY COMPUTING | March 2020 | March 2024 | Abandon | 49 | 1 | 0 | No | No |
| 16786218 | SMART HEATER SYSTEM | February 2020 | September 2022 | Allow | 31 | 3 | 0 | Yes | Yes |
| 16699593 | CONSERVATIVE CLASS PRELOADING FOR REAL TIME JAVA EXECUTION | November 2019 | February 2024 | Allow | 51 | 3 | 0 | No | No |
| 16688889 | DATA LAYOUT CONSCIOUS PROCESSING IN MEMORY ARCHITECTURE FOR EXECUTING NEURAL NETWORK MODEL | November 2019 | September 2024 | Allow | 58 | 3 | 0 | Yes | No |
| 16669471 | NEURON CIRCUIT AND OPERATING METHOD THEREOF | October 2019 | January 2024 | Allow | 50 | 1 | 0 | Yes | No |
| 16579765 | OSCILLATOR BASED NEURAL NETWORK APPARATUS | September 2019 | February 2024 | Allow | 53 | 2 | 0 | No | No |
| 16569556 | DEPLOYMENT AND MANAGEMENT PLATFORM FOR MODEL EXECUTION ENGINE CONTAINERS | September 2019 | September 2020 | Allow | 12 | 0 | 0 | No | No |
| 16561760 | SYSTEM FOR MANAGING CALCULATION PROCESSING GRAPH OF ARTIFICIAL NEURAL NETWORK AND METHOD OF MANAGING CALCULATION PROCESSING GRAPH BY USING THE SAME | September 2019 | October 2023 | Allow | 50 | 2 | 0 | Yes | No |
| 16557449 | REAL-TIME RENDERING BASED ON EFFICIENT DEVICE AND SERVER PROCESSING OF CONTENT UPDATES | August 2019 | February 2022 | Allow | 30 | 3 | 0 | Yes | No |
| 16554984 | COMPUTING CIRCUITRY | August 2019 | May 2023 | Allow | 45 | 2 | 0 | No | No |
| 16550290 | CONTROLLING PERFORMANCE OF DEPLOYED DEEP LEARNING MODELS ON RESOURCE CONSTRAINED EDGE DEVICE VIA PREDICTIVE MODELS | August 2019 | December 2024 | Allow | 60 | 6 | 0 | Yes | No |
| 16547699 | AUTOMATIC TESTING OF WEB PAGES USING AN ARTIFICIAL INTELLIGENCE ENGINE | August 2019 | April 2023 | Abandon | 44 | 1 | 0 | No | No |
| 16481261 | INFORMATION PROCESSING APPARATUS FOR CONFIGURING A LAYER OF A NEURAL NETWORK | July 2019 | March 2025 | Abandon | 60 | 3 | 0 | No | No |
| 16520654 | NEURAL NETWORK COMPUTATION DEVICE AND METHOD | July 2019 | May 2021 | Allow | 22 | 2 | 0 | No | No |
| 16520041 | NEURAL NETWORK PROCESSOR AND NEURAL NETWORK COMPUTATION METHOD | July 2019 | August 2022 | Allow | 37 | 5 | 0 | No | No |
| 16519994 | CODE USAGE MAP | July 2019 | February 2021 | Allow | 19 | 1 | 0 | No | No |
| 16478458 | Self-adaptive threshold neuron information processing method, self-adaptive leakage value neuron information processing method, system, computer device and readable storage medium | July 2019 | October 2022 | Allow | 39 | 1 | 0 | No | No |
| 16477422 | NEURAL NETWORK INFORMATION RECEIVING METHOD, SENDING METHOD, SYSTEM, APPARATUS AND READABLE STORAGE MEDIUM | July 2019 | July 2023 | Allow | 48 | 1 | 1 | Yes | No |
| 16508123 | METHODS AND APPARATUS FOR SPIKING NEURAL NETWORK COMPUTING BASED ON THRESHOLD ACCUMULATION | July 2019 | February 2023 | Abandon | 43 | 1 | 0 | No | No |
| 16456954 | Self-Trained Analog Artificial Neural Network Circuits | June 2019 | September 2023 | Abandon | 51 | 4 | 0 | Yes | No |
| 16446692 | METHODS AND SYSTEMS FOR ISOLATING SOFTWARE COMPONENTS | June 2019 | March 2021 | Abandon | 21 | 1 | 0 | No | No |
| 16465854 | MODULATION DEVICE AND METHOD, ARTIFICIAL SYNAPSE COMPRISING SAID MODULATION DEVICE, SHORT TERM PLASTICITY METHOD IN AN ARTIFICIAL NEURAL NETWORK COMPRISING SAID ARTIFICIAL SYNAPSE | May 2019 | October 2022 | Allow | 40 | 2 | 0 | Yes | No |
| 16417627 | UPDATING SOFTWARE COMPONENTS THROUGH ONLINE STORES | May 2019 | November 2019 | Allow | 6 | 1 | 0 | Yes | No |
| 16411590 | SYSTEM FOR AUTOMATIC, SIMULTANEOUS FEATURE SELECTION AND HYPERPARAMETER TUNING FOR A MACHINE LEARNING MODEL | May 2019 | February 2020 | Allow | 9 | 1 | 0 | Yes | No |
| 16405831 | USING MACHINE LEARNING TO PREDICT USER PROFILE AFFINITY BASED ON BEHAVIORAL DATA ANALYTICS | May 2019 | June 2020 | Allow | 14 | 1 | 0 | Yes | No |
| 16399676 | UNIFIED COGNITION FOR A VIRTUAL PERSONAL COGNITIVE ASSISTANT WHEN COGNITION IS EMBODIED ACROSS MULTIPLE EMBODIED COGNITION OBJECT INSTANCES | April 2019 | June 2022 | Allow | 38 | 3 | 0 | Yes | No |
| 16394136 | ANONYMITY ASSESSMENT SYSTEM | April 2019 | December 2020 | Allow | 19 | 1 | 0 | Yes | No |
| 16297034 | MACHINE MODELING SYSTEM AND METHOD | March 2019 | February 2024 | Abandon | 59 | 6 | 0 | Yes | No |
| 16294886 | PROGRAMMING MODEL FOR A BAYESIAN NEUROMORPHIC COMPILER | March 2019 | November 2021 | Allow | 33 | 1 | 0 | No | No |
| 16283711 | POWER CONVERSION IN NEURAL NETWORKS | February 2019 | September 2020 | Allow | 19 | 2 | 0 | Yes | No |
| 16267294 | PRIVATE APPLICATION DISTRIBUTION MECHANISMS AND ARCHITECTURES | February 2019 | January 2023 | Abandon | 48 | 6 | 0 | Yes | No |
| 16262807 | COMPILING MODELS FOR DEDICATED HARDWARE | January 2019 | February 2024 | Allow | 60 | 5 | 0 | Yes | No |
| 16194611 | SAVING BATTERY LIFE WITH INFERRED LOCATION | November 2018 | January 2024 | Abandon | 60 | 4 | 0 | Yes | No |
| 16164671 | SYSTEMS AND METHODS FOR CUSTOMIZING NEURAL NETWORKS | October 2018 | March 2022 | Allow | 41 | 1 | 0 | Yes | No |
| 16153991 | PROCESSING CORE WITH METADATA ACTUATED CONDITIONAL GRAPH EXECUTION | October 2018 | May 2021 | Allow | 31 | 1 | 0 | No | No |
| 16152348 | NEURAL NETWORK OPTIMIZATION | October 2018 | August 2023 | Allow | 58 | 5 | 0 | Yes | No |
| 16140269 | LOW SPIKE COUNT RING BUFFER MECHANISM ON NEUROMORPHIC HARDWARE | September 2018 | September 2022 | Allow | 48 | 2 | 0 | Yes | No |
| 16135563 | METHOD AND SYSTEM FOR VISUAL DATA MAPPING AND CODE GENERATION TO SUPPORT DATA INTEGRATION | September 2018 | February 2021 | Abandon | 29 | 2 | 0 | No | No |
| 16134935 | SPIKING NEURAL NETWORK-BASED NEUROMORPHIC SYSTEM | September 2018 | June 2022 | Abandon | 44 | 2 | 0 | No | No |
| 16127416 | TRIGGERED OPERATIONS TO IMPROVE ALLREDUCE OVERLAP | September 2018 | January 2023 | Allow | 52 | 3 | 0 | Yes | No |
| 16127170 | DATA SELECTION CIRCUIT | September 2018 | August 2023 | Allow | 60 | 4 | 0 | Yes | No |
| 16051788 | METHOD AND APPARATUS FOR GENERATING FIXED-POINT QUANTIZED NEURAL NETWORK | August 2018 | September 2022 | Allow | 49 | 2 | 0 | Yes | No |
| 16039155 | Systems and Methods for Overshoot Compensation | July 2018 | November 2022 | Abandon | 52 | 2 | 0 | No | No |
| 16034344 | DATA-DRIVEN AUTOMATIC CODE REVIEW | July 2018 | February 2023 | Allow | 55 | 2 | 0 | Yes | No |
This analysis examines appeal outcomes and the strategic value of filing appeals for examiner RUTTEN, JAMES D.
With a 42.9% 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, 42.9% 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 RUTTEN, JAMES D works in Art Unit 2121 and has examined 214 patent applications in our dataset. With an allowance rate of 72.9%, this examiner has a below-average tendency to allow applications. Applications typically reach final disposition in approximately 44 months.
Examiner RUTTEN, JAMES D's allowance rate of 72.9% places them in the 29% percentile among all USPTO examiners. This examiner has a below-average tendency to allow applications.
On average, applications examined by RUTTEN, JAMES D receive 2.92 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 RUTTEN, JAMES D is 44 months. This places the examiner in the 3% percentile for prosecution speed. Applications take longer to reach final disposition with this examiner compared to most others.
Conducting an examiner interview provides a +23.6% benefit to allowance rate for applications examined by RUTTEN, JAMES D. This interview benefit is in the 73% 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, 22.4% of applications are subsequently allowed. This success rate is in the 19% 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 10.9% of cases where such amendments are filed. This entry rate is in the 5% 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 4% 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 50.0% of appeals filed. This is in the 11% percentile among all examiners. Of these withdrawals, 14.3% 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, 24.4% are granted (fully or in part). This grant rate is in the 15% percentile among all examiners. Strategic Note: Petitions are rarely granted regarding this examiner's actions compared to other examiners. Ensure you have a strong procedural basis before filing a petition, as the Technology Center Director typically upholds this examiner's decisions.
Examiner's Amendments: This examiner makes examiner's amendments in 0.0% of allowed cases (in the 8% 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 9% 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.