Detailed information about the 100 most recent patent applications.
| Application Number | Title | Filing Date | Disposal Date | Disposition | Time (months) | Office Actions | Restrictions | Interview | Appeal |
|---|---|---|---|---|---|---|---|---|---|
| 19257909 | COMPUTATIONALLY EFFICIENT FRAMEWORK FOR SEQUENCE-TO-SEQUENCE MODELING AND REINFORCEMENT LEARNING WITH DEEP HISTORY | July 2025 | October 2025 | Allow | 4 | 1 | 0 | Yes | No |
| 19191984 | OPTIMIZING KEY VALUE CACHE FOR LARGE LANGUAGE MODEL INFERENCE | April 2025 | March 2026 | Allow | 10 | 2 | 0 | Yes | No |
| 18943185 | SYSTEM AND METHOD FOR PROCESSING EXPERIMENTAL DATA | November 2024 | April 2025 | Allow | 5 | 1 | 0 | Yes | No |
| 18775232 | PRE-TRAINED MACHINE LEARNING MODELS FOR REAL-TIME SPEECH FORM CONVERSION | July 2024 | December 2024 | Allow | 5 | 1 | 0 | No | No |
| 18665932 | SYSTEMS AND METHODS FOR TRAINING MACHINE LEARNING MODELS USING UNLABELED ELECTROCARDIOGRAM DATA | May 2024 | March 2025 | Allow | 10 | 1 | 0 | Yes | No |
| 18637313 | METHOD AND DEVICE FOR USER GROUPING AND RESOURCE ALLOCATION IN NOMA-MEC SYSTEM BASED | April 2024 | November 2024 | Allow | 7 | 1 | 0 | No | No |
| 18624774 | ARTIFICIAL INTELLIGENCE APPLICATION ASSISTANT | April 2024 | March 2025 | Allow | 11 | 2 | 0 | Yes | No |
| 18412698 | ARTIFICIAL INTELLIGENCE MODEL INFERENCE AND TUNING PEER-TO-PEER NETWORK SYSTEM | January 2024 | March 2025 | Allow | 14 | 2 | 0 | Yes | No |
| 18402361 | APPARATUS AND METHOD FOR GENERATING A SOLUTION | January 2024 | June 2024 | Allow | 6 | 1 | 0 | Yes | No |
| 18514467 | SYSTEM, NETWORK AND METHOD FOR SELECTIVE ACTIVATION OF A COMPUTING NETWORK INVOLVING SUPER-IMPOSABLE STOCHASTIC GRAPHS | November 2023 | April 2025 | Allow | 17 | 2 | 0 | Yes | No |
| 18506690 | SYSTEMS AND METHODS FOR PREPROCESSING TARGET DATA AND GENERATING PREDICTIONS USING A MACHINE LEARNING MODEL | November 2023 | April 2025 | Allow | 17 | 3 | 0 | Yes | No |
| 18361518 | Training Gradient Boosted Decision Trees With Progressive Maximum Depth For Parsimony And Interpretability | July 2023 | March 2025 | Allow | 20 | 4 | 0 | Yes | No |
| 17849391 | TOKEN SYNTHESIS FOR MACHINE LEARNING MODELS | June 2022 | August 2025 | Allow | 38 | 0 | 0 | No | No |
| 17840774 | CREATING METHOD OF A CLASSIFICATION MODEL ABOUT HARD DISK EFFICIENCY PROBLEM, ANALYSIS METHOD OF HARD DISK EFFICIENCY PROBLEM AND CLASSIFICATION MODEL CREATING SYSTEM ABOUT HARD DISK EFFICIENCY PROBLEM | June 2022 | September 2025 | Allow | 39 | 1 | 0 | No | No |
| 17727681 | CLASSIFYING AN ENTITY FOR FOLFOX TREATMENT | April 2022 | July 2024 | Allow | 27 | 3 | 0 | Yes | No |
| 17721311 | AUTOMATED POSITIVE TRAIN CONTROL EVENT DATA EXTRACTION AND ANALYSIS ENGINE FOR PERFORMING ROOT CAUSE ANALYSIS OF UNSTRUCTURED DATA | April 2022 | August 2023 | Allow | 16 | 3 | 0 | No | No |
| 17714563 | INFORMATION PROCESSING APPARATUS FOR IMPROVING ROBUSTNESS OF DEEP NEURAL NETWORK BY USING ADVERSARIAL TRAINING AND FORMAL METHOD | April 2022 | October 2025 | Allow | 43 | 2 | 0 | No | No |
| 17684752 | MODELS FOR PREDICTING RESISTANCE TRENDS | March 2022 | January 2026 | Allow | 46 | 2 | 0 | No | No |
| 17619934 | A DYNAMICALLY DISTORTED TIME WARPING DISTANCE MEASURE BETWEEN CONTINUOUS BOUNDED DISCRETE-TIME SERIES | December 2021 | January 2026 | Abandon | 49 | 1 | 0 | Yes | No |
| 17533384 | SAMPLING FROM A SET SPINS WITH CLAMPING | November 2021 | January 2026 | Allow | 50 | 2 | 0 | No | No |
| 17496553 | METHODS AND SYSTEMS FOR PROCESSING UNSTRUCTURED AND UNLABELLED DATA | October 2021 | March 2026 | Allow | 53 | 2 | 0 | Yes | No |
| 17599973 | SYSTEM AND METHOD FOR MODIFYING KNOWLEDGE GRAPH FOR PROVIDING SERVICE | September 2021 | March 2026 | Allow | 53 | 3 | 0 | Yes | No |
| 17438079 | LOW DISPLACEMENT RANK BASED DEEP NEURAL NETWORK COMPRESSION | September 2021 | January 2026 | Allow | 52 | 2 | 0 | No | No |
| 17433677 | Systems and Methods for Producing an Architecture of a Pyramid Layer | August 2021 | February 2026 | Allow | 53 | 2 | 0 | Yes | No |
| 17392690 | MODIFYING COMPUTATIONAL GRAPHS | August 2021 | November 2025 | Allow | 52 | 1 | 0 | Yes | No |
| 17379937 | MACHINE LEARNING TRACEBACK-ENABLED DECISION RATIONALES AS MODELS FOR EXPLAINABILITY | July 2021 | September 2025 | Allow | 50 | 2 | 0 | Yes | No |
| 17320098 | CONTROLLING OPERATION OF ACTOR AND LEARNER COMPUTING UNITS BASED ON A USAGE RATE OF A REPLAY MEMORY | May 2021 | February 2026 | Allow | 57 | 1 | 0 | Yes | No |
| 17302214 | PERSONAL PROTECTIVE EQUIPMENT SYSTEM HAVING ANALYTICS ENGINE WITH INTEGRATED MONITORING, ALERTING, AND PREDICTIVE SAFETY EVENT AVOIDANCE | April 2021 | August 2025 | Allow | 51 | 3 | 0 | No | No |
| 17222283 | Systems and methods for predictive coding analysis for electronic discovery | April 2021 | October 2025 | Allow | 55 | 2 | 0 | Yes | No |
| 17220445 | Systems and Methods for Predictive Coding Utilizing Confidence Levels | April 2021 | March 2026 | Allow | 59 | 3 | 0 | No | No |
| 17216362 | Adaptive Probabilistic Latent Semantic Analysis System For Automated Document Coding And Review In Electronic Discovery | March 2021 | December 2025 | Allow | 56 | 2 | 0 | Yes | No |
| 17202687 | Real-time Configurator Validation and Recommendation Engine | March 2021 | November 2025 | Abandon | 56 | 1 | 0 | No | No |
| 17164756 | ADAPTIVE LEARNING FOR IMAGE CLASSIFICATION | February 2021 | November 2025 | Allow | 57 | 2 | 0 | No | No |
| 17133075 | OPTIMIZING AI/ML MODEL TRAINING FOR INDIVIDUAL AUTONOMOUS AGENTS | December 2020 | June 2025 | Abandon | 54 | 2 | 0 | Yes | No |
| 17127287 | LEARNING DATA, SEMANTIC SPACE, AND GRAPH KNOWLEDGE MULTI-LAYERED KNOWLEDGE BASE SYSTEM AND PROCESSING METHOD THEREOF | December 2020 | August 2024 | Allow | 43 | 1 | 0 | No | No |
| 17247584 | ESTIMATED ONLINE HARD NEGATIVE MINING VIA PROBABILISTIC SELECTION AND SCORES HISTORY CONSIDERATION | December 2020 | February 2025 | Allow | 50 | 2 | 0 | Yes | No |
| 17112729 | SYSTEMS AND TECHNIQUES FOR PREDICTIVE DATA ANALYTICS | December 2020 | October 2025 | Abandon | 58 | 2 | 0 | No | No |
| 17110698 | MEMORY-AUGMENTED NEURAL NETWORK SYSTEM | December 2020 | August 2024 | Allow | 44 | 1 | 0 | Yes | No |
| 17107625 | MODEL CONSTRUCTION SYSTEM, MODEL CONSTRUCTION APPARATUS, AND MODEL CONSTRUCTION METHOD | November 2020 | June 2025 | Abandon | 55 | 1 | 0 | No | No |
| 17106892 | Hardware Implementation of a Neural Network | November 2020 | March 2024 | Allow | 40 | 1 | 0 | No | No |
| 17057722 | METHOD OF KNOWLEDGE SHARING AMONG DIALOGUE SYSTEMS, DIALOGUE METHOD AND DEVICE | November 2020 | July 2025 | Allow | 55 | 2 | 0 | Yes | No |
| 16950407 | METHOD AND SYSTEM FOR PREDICTING PROTEIN-PROTEIN INTERACTION BETWEEN HOST AND PATHOGEN | November 2020 | March 2024 | Allow | 40 | 1 | 0 | No | No |
| 16950570 | METHOD, DEVICE AND COMPUTER PROGRAM FOR PREDICTING A SUITABLE CONFIGURATION OF A MACHINE LEARNING SYSTEM FOR A TRAINING DATA SET | November 2020 | November 2025 | Allow | 60 | 2 | 0 | No | No |
| 17097249 | Obtaining Custom Artificial Neural Network Architectures | November 2020 | December 2023 | Allow | 37 | 1 | 0 | No | No |
| 17076834 | MODULAR DISTRIBUTED ARTIFICIAL NEURAL NETWORKS | October 2020 | November 2024 | Abandon | 49 | 1 | 0 | No | No |
| 17076836 | MODULAR DISTRIBUTED ARTIFICIAL NEURAL NETWORKS | October 2020 | November 2024 | Abandon | 49 | 1 | 0 | No | No |
| 17075735 | MODULAR DISTRIBUTED ARTIFICIAL NEURAL NETWORKS | October 2020 | November 2024 | Abandon | 48 | 1 | 0 | No | No |
| 17075733 | MODULAR DISTRIBUTED ARTIFICIAL NEURAL NETWORKS | October 2020 | October 2024 | Abandon | 47 | 1 | 0 | No | No |
| 17074661 | MODULAR DISTRIBUTED ARTIFICIAL NEURAL NETWORKS | October 2020 | September 2024 | Abandon | 47 | 1 | 0 | No | No |
| 17074667 | MODULAR DISTRIBUTED ARTIFICIAL NEURAL NETWORKS | October 2020 | October 2024 | Abandon | 47 | 1 | 0 | No | No |
| 17071078 | NEURAL NETWORK TRAINING METHOD AND APPARATUS, COMPUTER DEVICE, AND STORAGE MEDIUM | October 2020 | August 2025 | Allow | 58 | 3 | 0 | No | No |
| 17071519 | PARALLEL PROCESSING FOR SIGNAL GENERATION NEURAL NETWORKS | October 2020 | January 2024 | Allow | 39 | 1 | 0 | Yes | No |
| 17033037 | SYSTEM AND METHOD FOR AUTOMATICALLY RECOGNIZING VIRTUAL BALL SPORTS INFORMATION | September 2020 | December 2024 | Abandon | 51 | 3 | 0 | No | No |
| 17025952 | METHOD AND APPARATUS FOR GENERATING TEMPORAL KNOWLEDGE GRAPH, DEVICE, AND MEDIUM | September 2020 | August 2024 | Allow | 47 | 2 | 0 | No | No |
| 16981857 | PRODUCT DETECTION DEVICE, METHOD, AND PROGRAM | September 2020 | April 2025 | Abandon | 57 | 2 | 0 | No | No |
| 17018930 | CONVEX OPTIMIZED STOCHASTIC VECTOR SAMPLING BASED REPRESENTATION OF GROUND TRUTH | September 2020 | February 2024 | Allow | 41 | 1 | 0 | Yes | No |
| 16979874 | INFORMATION PROCESSING APPARATUS AND INFORMATION PROCESSING METHOD | September 2020 | May 2025 | Allow | 56 | 3 | 0 | No | No |
| 17015196 | MODIFYING COMPUTATIONAL GRAPHS | September 2020 | April 2021 | Allow | 7 | 1 | 0 | No | No |
| 17003073 | MERGED RECOMMENDATIONS OF REAL ESTATE LISTINGS | August 2020 | July 2025 | Allow | 59 | 3 | 0 | No | No |
| 17000503 | MULTIDIMENSIONAL HIERARCHY LEVEL RECOMMENDATION FOR FORECASTING MODELS | August 2020 | February 2025 | Allow | 53 | 4 | 0 | Yes | No |
| 16942625 | OLFACTORY PREDICTIONS USING NEURAL NETWORKS | July 2020 | September 2025 | Abandon | 60 | 3 | 0 | Yes | No |
| 16940713 | DATA TRANSFORMATION IN INGESTION PROCESSES VIA WORKFLOWS UTILIZING SEQUENCES OF ATOMIC FUNCTIONS | July 2020 | June 2024 | Allow | 46 | 1 | 0 | No | No |
| 16936742 | SYSTEMS AND METHODS EMPLOYING NEW EVOLUTION SCHEDULES IN AN ANALOG COMPUTER WITH APPLICATIONS TO DETERMINING ISOMORPHIC GRAPHS AND POST-PROCESSING SOLUTIONS | July 2020 | January 2024 | Allow | 42 | 1 | 0 | No | No |
| 16933994 | SATISFIABILITY PROBLEM SOLVER IMPLEMENTED ON SPIKING NEURAL NETWORK EMBEDDED SYSTEMS | July 2020 | September 2025 | Allow | 60 | 2 | 0 | No | No |
| 16928392 | COMPUTER-IMPLEMENTED BOND NETWORK SYSTEM FOR POSTHUMOUS PERSONA SIMULATION | July 2020 | February 2024 | Abandon | 43 | 4 | 0 | No | Yes |
| 16923795 | SUPERVISED MACHINE LEARNING FOR AUTOMATED ASSISTANTS | July 2020 | May 2023 | Allow | 34 | 5 | 0 | Yes | No |
| 16885414 | HIGH QUALITY PATTERN MINING MODEL AND METHOD BASED ON IMPROVED MULTI-OBJECTIVE EVOLUTIONARY ALGORITHM | May 2020 | June 2024 | Abandon | 48 | 2 | 0 | No | No |
| 16854352 | BATCH RENORMALIZATION LAYERS | April 2020 | October 2023 | Allow | 42 | 1 | 0 | No | No |
| 16813268 | MACHINE LEARNING MODEL TRAINING METHOD AND DEVICE, AND ELECTRONIC DEVICE | March 2020 | October 2020 | Allow | 7 | 0 | 0 | No | No |
| 16747103 | GENERIC QUANTIZATION OF ARTIFICIAL NEURAL NETWORKS | January 2020 | August 2024 | Allow | 54 | 1 | 0 | Yes | No |
| 16734811 | METHOD, APPARATUS, AND COMPUTER PROGRAM PRODUCT FOR DETERMINING A PROVIDER RETURN RATE | January 2020 | May 2025 | Abandon | 60 | 4 | 0 | Yes | No |
| 16725589 | RECOMMENDATION SYSTEM CONSTRUCTION METHOD AND APPARATUS | December 2019 | September 2020 | Allow | 8 | 1 | 0 | Yes | No |
| 16722831 | Systems for Second-Order Predictive Data Analytics, And Related Methods and Apparatus | December 2019 | October 2023 | Allow | 46 | 1 | 0 | No | No |
| 16709968 | APPARATUS AND METHODS FOR TRAINING IN CONVOLUTIONAL NEURAL NETWORKS | December 2019 | July 2025 | Allow | 60 | 3 | 0 | No | No |
| 16709961 | APPARATUS AND METHODS FOR FORWARD PROPAGATION IN CONVOLUTIONAL NEURAL NETWORKS | December 2019 | June 2025 | Allow | 60 | 3 | 0 | No | No |
| 16700988 | METHODS AND SYSTEM FOR MANAGING PREDICTIVE MODELS | December 2019 | July 2024 | Allow | 55 | 2 | 0 | Yes | No |
| 16682813 | SYSTEMS FOR TIME-SERIES PREDICTIVE DATA ANALYTICS, AND RELATED METHODS AND APPARATUS | November 2019 | May 2025 | Allow | 60 | 3 | 0 | No | No |
| 16582928 | TRAINING A MACHINE LEARNING MODEL USING A BATCH BASED ACTIVE LEARNING APPROACH | September 2019 | February 2023 | Allow | 41 | 2 | 0 | Yes | No |
| 16576832 | COMPUTER SYSTEM AND METHOD FOR MONITORING THE TECHNICAL STATE OF INDUSTRIAL PROCESS SYSTEMS | September 2019 | December 2020 | Allow | 15 | 1 | 0 | No | No |
| 16569181 | ANOMALY DETECTION FOR NON-STATIONARY DATA | September 2019 | February 2025 | Allow | 60 | 3 | 0 | Yes | No |
| 16564531 | GENERATING COMBINED FEATURE EMBEDDING FOR MINORITY CLASS UPSAMPLING IN TRAINING MACHINE LEARNING MODELS WITH IMBALANCED SAMPLES | September 2019 | December 2022 | Allow | 39 | 1 | 0 | Yes | No |
| 16563116 | MACHINE LEARNING ON A SERVO CONTROL DEVICE AND OUTPUTTING OF LEARNED PARAMETERS FROM THE MACHING LEARNING | September 2019 | November 2024 | Abandon | 60 | 6 | 0 | No | No |
| 16490915 | DEVICE, SYSTEM AND METHOD FOR ESTIMATING AN EXTERNAL OR INTRINSIC FACTOR RESULTING IN A STATE TRANSITION | September 2019 | October 2024 | Allow | 60 | 4 | 0 | No | No |
| 16553831 | HYPERPARAMETER AND NETWORK TOPOLOGY SELECTION IN NETWORK DEMAND FORECASTING | August 2019 | September 2023 | Allow | 48 | 1 | 0 | Yes | No |
| 16540883 | SYSTEMS AND METHODS FOR GENERATING AMPLIFIER GAIN MODELS USING ACTIVE LEARNING | August 2019 | October 2022 | Allow | 39 | 2 | 0 | Yes | No |
| 16532519 | Techniques for Cyber-Attack Event Log Fabrication | August 2019 | December 2023 | Allow | 53 | 4 | 0 | Yes | No |
| 16513720 | METHOD FOR TRAINING AND TESTING DATA EMBEDDING NETWORK TO GENERATE MARKED DATA BY INTEGRATING ORIGINAL DATA WITH MARK DATA, AND TRAINING DEVICE AND TESTING DEVICE USING THE SAME | July 2019 | July 2020 | Allow | 12 | 2 | 0 | Yes | No |
| 16512128 | IDENTIFYING ENTITIES USING A DEEP-LEARNING MODEL | July 2019 | January 2025 | Abandon | 60 | 2 | 0 | Yes | No |
| 16511743 | SYSTEM AND METHOD FOR GENERATING ULTIMATE REASON CODES FOR COMPUTER MODELS | July 2019 | April 2025 | Allow | 60 | 3 | 0 | Yes | No |
| 16511544 | SYSTEM AND METHOD FOR PERFORMING SMALL CHANNEL COUNT CONVOLUTIONS IN ENERGY-EFFICIENT INPUT OPERAND STATIONARY ACCELERATOR | July 2019 | February 2023 | Allow | 43 | 3 | 0 | Yes | No |
| 16459057 | BATCH RENORMALIZATION LAYERS | July 2019 | February 2020 | Allow | 7 | 1 | 0 | Yes | No |
| 16454881 | NEUROMORPHIC SYSTEM AND OPERATING METHOD THEREOF | June 2019 | September 2022 | Allow | 38 | 1 | 0 | No | No |
| 16453735 | NEURAL NETWORK ENGINE UTILIZING A SERIAL BUS | June 2019 | February 2024 | Allow | 55 | 2 | 0 | Yes | Yes |
| 16447924 | SYSTEMS AND TECHNIQUES FOR DETERMINING THE PREDICTIVE VALUE OF A FEATURE | June 2019 | October 2025 | Allow | 60 | 4 | 0 | No | No |
| 16443948 | Automatic labeling of telecommunication network data to train supervised machine learning | June 2019 | June 2024 | Allow | 60 | 5 | 0 | Yes | No |
| 16433819 | EXECUTING COMPUTATIONAL GRAPHS ON GRAPHICS PROCESSING UNITS | June 2019 | September 2024 | Allow | 60 | 3 | 0 | Yes | No |
| 16412783 | SYSTEM AND METHOD FOR SCALABLE, INTERACTIVE, COLLABORATIVE TOPIC IDENTIFICATION AND TRACKING | May 2019 | March 2022 | Allow | 34 | 2 | 0 | Yes | No |
| 16381200 | NEURAL PROCESSING DEVICE AND OPERATION METHOD THEREOF | April 2019 | April 2023 | Allow | 48 | 2 | 0 | Yes | No |
| 16360188 | ELECTRONIC APPARATUS AND CONTROL METHOD THEREOF | March 2019 | November 2023 | Allow | 56 | 4 | 0 | No | No |
| 16282098 | SYSTEM AND METHOD FOR SELF-HEALING IN DECENTRALIZED MODEL BUILDING FOR MACHINE LEARNING USING BLOCKCHAIN | February 2019 | September 2023 | Allow | 54 | 3 | 0 | Yes | Yes |
This analysis examines appeal outcomes and the strategic value of filing appeals for examiner SITIRICHE, LUIS A.
With a 38.5% 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, 38.9% 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 SITIRICHE, LUIS A works in Art Unit 2126 and has examined 199 patent applications in our dataset. With an allowance rate of 75.4%, this examiner has a below-average tendency to allow applications. Applications typically reach final disposition in approximately 54 months.
Examiner SITIRICHE, LUIS A's allowance rate of 75.4% places them in the 41% percentile among all USPTO examiners. This examiner has a below-average tendency to allow applications.
On average, applications examined by SITIRICHE, LUIS A receive 2.63 office actions before reaching final disposition. This places the examiner in the 77% 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 SITIRICHE, LUIS A is 54 months. This places the examiner in the 2% percentile for prosecution speed. Applications take longer to reach final disposition with this examiner compared to most others.
Conducting an examiner interview provides a +12.0% benefit to allowance rate for applications examined by SITIRICHE, LUIS A. This interview benefit is in the 48% percentile among all examiners. Recommendation: Interviews provide a below-average benefit with this examiner.
When applicants file an RCE with this examiner, 22.8% of applications are subsequently allowed. This success rate is in the 30% 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 32.4% of cases where such amendments are filed. This entry rate is in the 47% percentile among all examiners. Strategic Recommendation: This examiner shows below-average receptiveness to after-final amendments. You may need to file an RCE or appeal rather than relying on after-final amendment entry.
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 38.1% of appeals filed. This is in the 6% percentile among all examiners. Of these withdrawals, 12.5% 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, 16.7% are granted (fully or in part). This grant rate is in the 9% 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 9% 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 2.7% of allowed cases (in the 72% 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.