Detailed information about the 100 most recent patent applications.
| Application Number | Title | Filing Date | Disposal Date | Disposition | Time (months) | Office Actions | Restrictions | Interview | Appeal |
|---|---|---|---|---|---|---|---|---|---|
| 19319633 | THRESHOLD-BASED ADAPTIVE ONTOLOGY AND KNOWLEDGE GRAPH MODIFICATION USING GENERATIVE ARTIFICIAL INTELLIGENCE | September 2025 | March 2026 | Allow | 6 | 0 | 0 | No | No |
| 19265958 | HYBRID LANGUAGE MODEL AND DETERMINISTIC PROCESSING FOR UNCERTAINTY ANALYSIS | July 2025 | December 2025 | Allow | 5 | 1 | 0 | Yes | No |
| 19196702 | ANOMALY DETECTION METHOD FOR MODEL OUTPUTS | May 2025 | February 2026 | Allow | 9 | 2 | 0 | Yes | No |
| 19062786 | HALLUCINATION MITIGATION THROUGH MODULAR MODEL ENSEMBLES | February 2025 | February 2026 | Allow | 11 | 2 | 0 | Yes | No |
| 19052538 | FAULT DETECTION METHOD FOR REFRIGERATION UNITS BASED ON IMPROVED DEEP LEARNING MODEL | February 2025 | August 2025 | Allow | 6 | 2 | 0 | No | No |
| 19019273 | INTEGRATED CIRCUIT TO IMPLEMENT SPIKING NEURAL NETWORK | January 2025 | January 2026 | Allow | 12 | 2 | 0 | No | No |
| 19000716 | SYSTEMS AND METHODS FOR PARALLEL EXPLORATION OF A HYPERPARAMETER SEARCH SPACE | December 2024 | November 2025 | Allow | 11 | 3 | 0 | Yes | No |
| 19000685 | SYSTEMS AND METHODS FOR PARALLEL EXPLORATION OF A HYPERPARAMETER SEARCH SPACE | December 2024 | July 2025 | Allow | 7 | 1 | 0 | Yes | No |
| 18923064 | ARTIFICIAL INTELLIGENCE TECHNIQUES UTILIZING A GENERATIVE RELATIONAL NETWORK | October 2024 | April 2025 | Allow | 6 | 2 | 0 | No | No |
| 18923458 | METHOD AND SYSTEM FOR LOCAL COMPRESSION OF ARTIFICIAL INTELLIGENCE MODEL | October 2024 | March 2025 | Allow | 5 | 1 | 0 | Yes | No |
| 18918265 | DISTRIBUTED NONLINEAR SUPPORT VECTOR MACHINES | October 2024 | March 2025 | Allow | 5 | 1 | 0 | Yes | No |
| 18823415 | RICH MEDIA PRESENTATION OF RECOMMENDATIONS IN GENERATIVE MEDIA | September 2024 | September 2025 | Allow | 12 | 2 | 0 | No | No |
| 18823403 | INFORMATION PROCESSING METHOD AND APPARATUS | September 2024 | July 2025 | Allow | 11 | 2 | 0 | No | No |
| 18798724 | SYSTEMS AND METHODS FOR INTERFACING WITH A SERVER OVER A NETWORK | August 2024 | October 2025 | Allow | 15 | 3 | 1 | Yes | No |
| 18833603 | Method, System, and Computer Program Product for Improving Machine Learning Models | July 2024 | March 2026 | Allow | 19 | 3 | 0 | Yes | No |
| 18770545 | Cross-Model Format Comparison | July 2024 | March 2026 | Allow | 20 | 3 | 0 | No | Yes |
| 18752163 | NEURAL NETWORK METHOD AND APPARATUS | June 2024 | December 2025 | Allow | 18 | 3 | 0 | Yes | No |
| 18739449 | System, Method, and Computer Program Product for Time-Based Ensemble Learning Using Supervised and Unsupervised Machine Learning Models | June 2024 | December 2025 | Allow | 18 | 2 | 0 | Yes | No |
| 18737595 | MESSAGE SUGGESTIONS | June 2024 | February 2026 | Allow | 20 | 2 | 0 | Yes | No |
| 18736250 | SYSTEM AND METHOD FOR DEVICE IDENTIFICATION AND UNIQUENESS | June 2024 | April 2025 | Allow | 10 | 1 | 0 | No | No |
| 18732052 | METHODS AND SYSTEMS FOR NEURAL ARCHITECTURE SEARCH | June 2024 | February 2026 | Allow | 21 | 3 | 0 | No | No |
| 18648707 | AUTOMATED SYSTEMS FOR MACHINE LEARNING MODEL DEVELOPMENT, ANALYSIS, AND REFINEMENT | April 2024 | October 2025 | Allow | 18 | 2 | 0 | Yes | No |
| 18632180 | HIGH-DENSITY NEUROMORPHIC COMPUTING ELEMENT | April 2024 | February 2025 | Allow | 10 | 1 | 0 | No | No |
| 18615189 | Computer-implemented or hardware-implemented method of entity identification, a computer program product and an apparatus for entity identification | March 2024 | October 2025 | Allow | 19 | 2 | 0 | No | No |
| 18431472 | Method, Device and Equipment for Selecting Key Geological Parameters of a To-Be-Prospected Block | February 2024 | October 2024 | Abandon | 8 | 2 | 0 | Yes | No |
| 18394205 | SYSTEMS AND METHODS FOR BLIND MULTIMODAL LEARNING | December 2023 | February 2025 | Allow | 14 | 1 | 0 | No | No |
| 18495902 | MACHINE LEARNING FOR NUTRIENT QUANTITY ESTIMATION IN SCORE-BASED DIETS AND METHODS OF USE THEREOF | October 2023 | April 2025 | Abandon | 18 | 1 | 0 | No | No |
| 18378649 | METHOD FOR GENERATING PROGRAMMABLE ACTIVATION FUNCTION AND APPARATUS USING THE SAME | October 2023 | March 2025 | Allow | 17 | 1 | 0 | No | No |
| 18461473 | FLEET AND ASSET MANAGEMENT FOR EDGE COMPUTING OF MACHINE LEARNING AND ARTIFICIAL INTELLIGENCE WORKLOADS DEPLOYED FROM CLOUD TO EDGE | September 2023 | June 2024 | Allow | 9 | 2 | 1 | Yes | No |
| 18360129 | ONLINE PROBABILISTIC INVERSE OPTIMIZATION SYSTEM, ONLINE PROBABILISTIC INVERSE OPTIMIZATION METHOD, AND ONLINE PROBABILISTIC INVERSE OPTIMIZATION PROGRAM | July 2023 | March 2026 | Allow | 31 | 3 | 0 | No | No |
| 18226858 | System, Method, and Computer Program Product for Implementing a Hybrid Deep Neural Network Model to Determine a Market Strategy | July 2023 | May 2024 | Allow | 10 | 1 | 0 | No | No |
| 18271231 | METHOD FOR ANALYZING OUTPUT OF NEURAL NETWORK, AND SYSTEM THEREFOR | July 2023 | March 2026 | Allow | 32 | 0 | 0 | No | No |
| 18209188 | TRADE PLATFORM WITH REINFORCEMENT LEARNING | June 2023 | August 2024 | Allow | 15 | 1 | 0 | No | No |
| 18207043 | PROCESSING MACHINE LEARNING ATTRIBUTES | June 2023 | April 2025 | Allow | 22 | 5 | 0 | Yes | No |
| 18329418 | TRANSPOSING NEURAL NETWORK MATRICES IN HARDWARE | June 2023 | September 2024 | Allow | 15 | 1 | 0 | No | No |
| 18324453 | DETERMINING JOURNALIST RISK OF A DATASET USING POPULATION EQUIVALENCE CLASS DISTRIBUTION ESTIMATION | May 2023 | April 2025 | Abandon | 23 | 3 | 0 | Yes | No |
| 18310658 | AUTOMATED SYSTEMS FOR MACHINE LEARNING MODEL DEVELOPMENT, ANALYSIS, AND REFINEMENT | May 2023 | October 2023 | Allow | 5 | 2 | 0 | Yes | No |
| 18309106 | PARTITIONING SENSOR BASED DATA TO GENERATE DRIVING PATTERN MAP | April 2023 | May 2024 | Allow | 12 | 1 | 0 | No | No |
| 18308784 | METHOD AND SYSTEM FOR DETERMINING CONVERTER TAPPING QUANTITY | April 2023 | January 2024 | Allow | 9 | 2 | 0 | No | No |
| 18303134 | NEURAL NETWORK FOR PROCESSING GRAPH DATA | April 2023 | May 2024 | Allow | 13 | 1 | 0 | No | No |
| 18133800 | SYSTEM AND METHOD FOR DEVICE IDENTIFICATION AND UNIQUENESS | April 2023 | March 2024 | Allow | 11 | 1 | 0 | No | No |
| 18175117 | SUPPORT MODEL AND ORCHESTRATION PROCEDURE FOR MANUAL LABELING PROCESSES | February 2023 | March 2026 | Allow | 36 | 1 | 0 | No | No |
| 18174343 | LEARNING APPARATUS AND METHOD | February 2023 | March 2026 | Allow | 36 | 1 | 0 | Yes | No |
| 18173005 | LEARNING APPARATUS AND METHOD | February 2023 | March 2026 | Allow | 37 | 1 | 0 | Yes | No |
| 18112827 | DATA OPTIMIZATION FOR HIGH BANDWIDTH (HBW) NVM AI INFERENCE SYSTEM | February 2023 | November 2025 | Allow | 32 | 0 | 0 | No | No |
| 18111471 | HIGH-DENSITY NEUROMORPHIC COMPUTING ELEMENT | February 2023 | January 2024 | Allow | 11 | 0 | 0 | Yes | No |
| 18019119 | ARTIFICIAL INTELLIGENCE FEEDBACK METHOD AND ARTIFICIAL INTELLIGENCE FEEDBACK SYSTEM | February 2023 | March 2026 | Allow | 37 | 1 | 0 | No | No |
| 18162204 | PROVISION OF COMPUTER RESOURCES BASED ON LOCATION HISTORY | January 2023 | April 2024 | Allow | 15 | 1 | 0 | No | No |
| 18103483 | EVALUATING MACHINE LEARNING MODEL PERFORMANCE BY LEVERAGING SYSTEM FAILURES | January 2023 | July 2023 | Allow | 6 | 1 | 0 | Yes | No |
| 18100455 | METHOD FOR REDUCING BIAS IN DEEP LEARNING CLASSIFIERS USING ENSEMBLES | January 2023 | January 2026 | Allow | 35 | 1 | 0 | No | No |
| 18154551 | COMPUTERIZED ASSISTANCE USING ARTIFICIAL INTELLIGENCE KNOWLEDGE BASE | January 2023 | April 2024 | Allow | 15 | 1 | 0 | Yes | No |
| 18153220 | REAL-TIME ENSEMBLE EVALUATION | January 2023 | January 2026 | Allow | 36 | 1 | 0 | Yes | No |
| 18014428 | INFERENCE DEVICE, INFERENCE METHOD, AND RECORDING MEDIUM | January 2023 | March 2026 | Allow | 38 | 1 | 0 | Yes | No |
| 18092256 | TELEOPERATION FOR TRAINING OF ROBOTS USING MACHINE LEARNING | December 2022 | November 2025 | Allow | 34 | 0 | 0 | No | No |
| 18087290 | GENERATING HIGH-QUALITY THREAT INTELLIGENCE FROM AGGREGATED THREAT REPORTS | December 2022 | January 2026 | Allow | 36 | 1 | 0 | No | No |
| 18081721 | Controller training based on historical data | December 2022 | March 2024 | Allow | 15 | 1 | 0 | Yes | No |
| 18080777 | BINARY NEURAL NETWORK MODEL TRAINING METHOD AND SYSTEM, AND IMAGE PROCESSING METHOD AND SYSTEM | December 2022 | February 2026 | Abandon | 38 | 1 | 1 | No | No |
| 18075784 | LEARNING HYPER-PARAMETER SCALING MODELS FOR UNSUPERVISED ANOMALY DETECTION | December 2022 | January 2026 | Allow | 37 | 1 | 0 | Yes | No |
| 18061697 | GRAPH CONVOLUTIONAL NETWORKS WITH MOTIF-BASED ATTENTION | December 2022 | September 2024 | Allow | 22 | 2 | 0 | Yes | No |
| 18008293 | Scalable Transfer Learning with Expert Models | December 2022 | January 2026 | Allow | 37 | 1 | 0 | Yes | No |
| 17981243 | SCALABLE SYSTEMS AND METHODS FOR CURATING USER EXPERIENCE TEST RESULTS | November 2022 | July 2023 | Allow | 9 | 2 | 0 | Yes | No |
| 17980706 | MULTIPLATE CONDUCTOR | November 2022 | July 2025 | Allow | 33 | 0 | 0 | No | No |
| 17981264 | Automatic Generation of Preferred Views for Personal Content Collections | November 2022 | July 2025 | Abandon | 32 | 2 | 0 | Yes | Yes |
| 17976679 | LEAN PARSING: A NATURAL LANGUAGE PROCESSING SYSTEM AND METHOD FOR PARSING DOMAIN-SPECIFIC LANGUAGES | October 2022 | March 2024 | Allow | 17 | 1 | 0 | Yes | No |
| 17975198 | CONTROL METHOD BASED ON ADAPTIVE NEURAL NETWORK MODEL FOR DISSOLVED OXYGEN OF AERATION SYSTEM | October 2022 | May 2023 | Allow | 7 | 2 | 0 | No | No |
| 17972510 | EXPLANATORY DROPOUT FOR MACHINE LEARNING MODELS | October 2022 | November 2025 | Allow | 36 | 1 | 0 | Yes | No |
| 18046906 | COMPUTER-IMPLEMENTED OR HARDWARE-IMPLEMENTED METHOD OF ENTITY IDENTIFICATION, A COMPUTER PROGRAM PRODUCT AND AN APPARATUS FOR ENTITY IDENTIFICATION | October 2022 | April 2023 | Allow | 6 | 1 | 0 | No | No |
| 17966323 | ARCHIVING FILES VIA SUPERDENSE CODING | October 2022 | October 2025 | Allow | 36 | 0 | 1 | No | No |
| 17938686 | SOLID-STATE DETECTOR CHARACTERIZATION BY MACHINE LEARNING-BASED PHYSICAL MODEL WITH REDUCED DEFECT LEVELS | October 2022 | November 2025 | Allow | 37 | 1 | 1 | No | No |
| 17953517 | METHOD FOR MULTI-TASK-BASED PREDICTING MASSIVEUSER LOADS BASED ON MULTI-CHANNEL CONVOLUTIONAL NEURAL NETWORK | September 2022 | June 2023 | Abandon | 9 | 1 | 0 | No | No |
| 17940798 | MACHINE LEARNING FOR NUTRIENT QUANTITY ESTIMATION IN SCORE-BASED DIETS AND METHODS OF USE THEREOF | September 2022 | June 2023 | Allow | 9 | 2 | 0 | Yes | No |
| 17939351 | SYSTEMS AND METHODS FOR BLIND MULTIMODAL LEARNING | September 2022 | August 2023 | Allow | 11 | 1 | 0 | No | No |
| 17821910 | TRANSFERRING INFORMATION THROUGH KNOWLEDGE GRAPH EMBEDDINGS | August 2022 | June 2025 | Allow | 34 | 0 | 0 | No | No |
| 17886499 | COMPUTER-READABLE RECORDING MEDIUM HAVING STORED THEREIN MACHINE LEARNING PROGRAM, METHOD FOR MACHINE LEARNING, AND INFORMATION PROCESSING APPARATUS | August 2022 | March 2026 | Allow | 43 | 2 | 0 | Yes | No |
| 17878242 | LEARNING DEVICE, INFORMATION PROCESSING APPARATUS, SUBSTRATE PROCESSING DEVICE, SUBSTRATE PROCESSING SYSTEM, LEARNING METHOD, RECIPE DETERMINATION METHOD AND NON-TRANSITORY COMPUTER-READABLE MEDIUM STORING LEARNING PROGRAM | August 2022 | July 2025 | Allow | 35 | 0 | 1 | No | No |
| 17795233 | NEURAL HASHING FOR SIMILARITY SEARCH | July 2022 | June 2023 | Allow | 10 | 1 | 0 | No | No |
| 17814782 | STICKIFICATION USING ANYWHERE PADDING TO ACCELERATE DATA MANIPULATION | July 2022 | September 2025 | Allow | 38 | 0 | 0 | No | No |
| 17871389 | Image Processing Model Training Method and Apparatus | July 2022 | December 2025 | Abandon | 41 | 1 | 0 | No | No |
| 17864885 | FINGERPRINTING DATA TO DETECT VARIANCES | July 2022 | March 2026 | Allow | 44 | 2 | 1 | Yes | No |
| 17791967 | LEARNING PROCESSING DEVICE AND LEARNING PROCESSING METHOD | July 2022 | March 2026 | Allow | 44 | 1 | 0 | No | No |
| 17807761 | UNCERTAINTY SCORING FOR NEURAL NETWORKS VIA STOCHASTIC WEIGHT PERTURBATIONS | June 2022 | February 2026 | Abandon | 44 | 2 | 0 | Yes | No |
| 17785429 | SYSTEM, METHOD, AND COMPUTER PROGRAM PRODUCT FOR TIME-BASED ENSEMBLE LEARNING USING SUPERVISED AND UNSUPERVISED MACHINE LEARNING MODELS | June 2022 | March 2024 | Allow | 21 | 2 | 0 | Yes | No |
| 17750568 | METHOD FOR GENERATING PROGRAMMABLE ACTIVATION FUNCTION AND APPARATUS USING THE SAME | May 2022 | July 2023 | Allow | 14 | 2 | 0 | No | No |
| 17750072 | COUPLED NETWORKS FOR PHYSICS-BASED MACHINE LEARNING | May 2022 | July 2025 | Allow | 38 | 0 | 0 | No | No |
| 17748891 | CAUSAL INFERENCE VIA NEUROEVOLUTIONARY SELECTION | May 2022 | November 2025 | Allow | 42 | 2 | 0 | Yes | No |
| 17744601 | NON-UNIFORM QUANTIZATION OF PRE-TRAINED DEEP NEURAL NETWORK | May 2022 | March 2023 | Allow | 10 | 1 | 0 | No | No |
| 17769707 | METHOD AND APPARATUS FOR PRUNING NEURAL NETWORKS | April 2022 | March 2026 | Allow | 47 | 2 | 0 | No | No |
| 17659015 | HARMONIC DENSELY CONNECTING METHOD OF BLOCK OF CONVOLUTIONAL NEURAL NETWORK MODEL AND SYSTEM THEREOF, AND NON-TRANSITORY TANGIBLE COMPUTER READABLE RECORDING MEDIUM | April 2022 | May 2025 | Allow | 37 | 0 | 0 | No | No |
| 17719314 | INTELLIGENT TOPIC SEGMENTATION WITHIN A COMMUNICATION SESSION | April 2022 | January 2025 | Allow | 33 | 2 | 0 | Yes | No |
| 17716945 | HIGH PERFORAMANCE MACHINE LEARNING INFERENCE FRAMEWORK FOR EDGE DEVICES | April 2022 | February 2023 | Allow | 11 | 1 | 0 | No | No |
| 17712876 | Localized Temporal Model Forecasting | April 2022 | April 2024 | Allow | 25 | 3 | 0 | Yes | No |
| 17710663 | SEMANTIC SCRIPT LANGUAGE PROCESSING | March 2022 | July 2025 | Allow | 40 | 2 | 0 | Yes | No |
| 17706586 | ASYNCHRONOUS NEURAL NETWORK TRAINING | March 2022 | May 2024 | Allow | 26 | 2 | 0 | No | No |
| 17763739 | Field Programmable Gate Array Architecture Optimized For Machine Learning Applications | March 2022 | January 2026 | Allow | 45 | 1 | 1 | Yes | No |
| 17703935 | MULTI-CHANNEL PROTEIN VOXELIZATION TO PREDICT VARIANT PATHOGENICITY USING DEEP CONVOLUTIONAL NEURAL NETWORKS | March 2022 | May 2025 | Allow | 38 | 1 | 0 | Yes | No |
| 17681480 | Processing Machine Learning Attributes | February 2022 | March 2023 | Allow | 12 | 1 | 0 | No | No |
| 17677556 | HYPERBOLIC FUNCTIONS FOR MACHINE LEARNING ACCELERATION | February 2022 | April 2023 | Allow | 13 | 1 | 0 | Yes | No |
| 17635405 | METHODS, APPARATUS AND MACHINE-READABLE MEDIA RELATING TO MACHINE-LEARNING IN A COMMUNICATION NETWORK | February 2022 | November 2025 | Allow | 45 | 1 | 1 | No | No |
| 17635210 | A COMPUTER-IMPLEMENTED OR HARDWARE-IMPLEMENTED METHOD OF ENTITY IDENTIFICATION, A COMPUTER PROGRAM PRODUCT AND AN APPARATUS FOR ENTITY IDENTIFICATION | February 2022 | July 2022 | Allow | 5 | 0 | 0 | No | No |
| 17591161 | CORRECTING CONTENT GENERATED BY DEEP LEARNING | February 2022 | October 2024 | Allow | 33 | 1 | 0 | Yes | No |
This analysis examines appeal outcomes and the strategic value of filing appeals for examiner LEE, TSU-CHANG.
With a 53.8% 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, 36.1% 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 LEE, TSU-CHANG works in Art Unit 2128 and has examined 228 patent applications in our dataset. With an allowance rate of 81.6%, this examiner has an above-average tendency to allow applications. Applications typically reach final disposition in approximately 44 months.
Examiner LEE, TSU-CHANG's allowance rate of 81.6% places them in the 53% percentile among all USPTO examiners. This examiner has an above-average tendency to allow applications.
On average, applications examined by LEE, TSU-CHANG receive 2.15 office actions before reaching final disposition. This places the examiner in the 58% percentile for office actions issued. This examiner issues a slightly above-average number of office actions.
The median time to disposition (half-life) for applications examined by LEE, TSU-CHANG is 44 months. This places the examiner in the 14% percentile for prosecution speed. Applications take longer to reach final disposition with this examiner compared to most others.
Conducting an examiner interview provides a +5.3% benefit to allowance rate for applications examined by LEE, TSU-CHANG. 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, 32.0% of applications are subsequently allowed. This success rate is in the 67% percentile among all examiners. Strategic Insight: RCEs show above-average effectiveness with this examiner. Consider whether your amendments or new arguments are strong enough to warrant an RCE versus filing a continuation.
This examiner enters after-final amendments leading to allowance in 17.3% of cases where such amendments are filed. This entry rate is in the 20% 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, 71.4% result in withdrawal of the rejection or reopening of prosecution. This success rate is in the 59% 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 63.9% of appeals filed. This is in the 44% percentile among all examiners. Of these withdrawals, 30.4% 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, 56.0% are granted (fully or in part). This grant rate is in the 58% 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 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 2.2% of allowed cases (in the 69% percentile). This examiner issues Quayle actions more often than average when claims are allowable but formal matters remain (MPEP § 714.14).
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.