Detailed information about the 100 most recent patent applications.
| Application Number | Title | Filing Date | Disposal Date | Disposition | Time (months) | Office Actions | Restrictions | Interview | Appeal |
|---|---|---|---|---|---|---|---|---|---|
| 19172987 | SYSTEM AND METHOD FOR AUTOMATIC EVALUATION OF ARTIFICIAL INTELLIGENCE MODELS | April 2025 | October 2025 | Allow | 7 | 1 | 0 | Yes | No |
| 19063966 | ARTIFICIAL INTELLIGENCE-BASED MODELING OF MOLECULAR SYSTEMS GUIDED BY QUANTUM MECHANICAL DATA | February 2025 | November 2025 | Abandon | 9 | 1 | 0 | No | No |
| 19056627 | FLEXIBLE ENTITY RESOLUTION NETWORKS | February 2025 | December 2025 | Allow | 10 | 1 | 0 | No | No |
| 18908380 | DATA PROCESSING | October 2024 | June 2025 | Allow | 8 | 1 | 0 | Yes | No |
| 18896663 | DATA GENERATION AND RETRAINING TECHNIQUES FOR FINE-TUNING OF EMBEDDING MODELS FOR EFFICIENT DATA RETRIEVAL | September 2024 | September 2025 | Allow | 11 | 2 | 0 | Yes | No |
| 18845939 | Method, System, and Computer Program Product for Use of Reinforcement Learning to Increase Machine Learning Model Label Accuracy | September 2024 | February 2026 | Allow | 17 | 0 | 0 | No | No |
| 18750394 | SYSTEM AND METHOD FOR INTERVENTIONS IN ARTIFICIAL INTELLIGENCE MODELS | June 2024 | September 2025 | Allow | 15 | 2 | 0 | Yes | No |
| 18739736 | Systems And Methods For Preprocessing Data For Audio Analysis | June 2024 | February 2026 | Abandon | 20 | 3 | 0 | No | No |
| 18596992 | METHOD FOR AN EXPLAINABLE AUTOENCODER AND AN EXPLAINABLE GENERATIVE ADVERSARIAL NETWORK | March 2024 | June 2025 | Allow | 15 | 1 | 0 | No | No |
| 18442037 | ROOT CAUSE DISCOVERY ENGINE | February 2024 | June 2025 | Allow | 16 | 1 | 0 | Yes | No |
| 18413872 | Increasing Accuracy and Resolution of Weather Forecasts Using Deep Generative Models | January 2024 | November 2024 | Allow | 10 | 1 | 0 | No | No |
| 18388859 | Method for Constructing Multimodality-Based Medical Large Model, and Related Device Thereof | November 2023 | June 2024 | Allow | 7 | 1 | 0 | No | No |
| 18482693 | Method For Predicting The Areas Of Information Needed To Be Collected | October 2023 | November 2024 | Allow | 13 | 2 | 0 | Yes | No |
| 18457708 | MULTI-DOMAIN JOINT SEMANTIC FRAME PARSING | August 2023 | May 2025 | Allow | 21 | 1 | 0 | Yes | No |
| 18351117 | Shared Processing with Deep Neural Networks | July 2023 | January 2025 | Allow | 18 | 2 | 0 | Yes | No |
| 18319570 | AREA AND POWER EFFICIENT IMPLEMENTATION OF RESISTIVE PROCESSING UNITS USING COMPLEMENTARY METAL OXIDE SEMICONDUCTOR TECHNOLOGY | May 2023 | May 2024 | Allow | 11 | 1 | 0 | No | No |
| 18180097 | ROOT CAUSE DISCOVERY ENGINE | March 2023 | October 2025 | Allow | 32 | 4 | 0 | Yes | Yes |
| 18103416 | TRAINING NEURAL NETWORKS USING A PRIORITIZED EXPERIENCE MEMORY | January 2023 | May 2024 | Allow | 15 | 2 | 0 | Yes | No |
| 18097892 | DATA TRANSMISSION BETWEEN TWO SYSTEMS TO IMPROVE OUTCOME PREDICTIONS | January 2023 | March 2024 | Abandon | 14 | 1 | 0 | No | No |
| 18094375 | METHOD FOR PERFORMING DEEP SIMILARITY MODELLING ON CLIENT DATA TO DERIVE BEHAVIORAL ATTRIBUTES AT AN ENTITY LEVEL | January 2023 | August 2024 | Abandon | 20 | 2 | 0 | Yes | No |
| 17982448 | Training Sparse Networks With Discrete Weight Values | November 2022 | August 2024 | Allow | 21 | 0 | 0 | Yes | No |
| 18051419 | CONTINUAL LEARNING TECHNIQUES FOR TRAINING MODELS | October 2022 | March 2026 | Allow | 40 | 1 | 0 | Yes | No |
| 17888565 | SYSTEM, METHOD AND COMPUTER PROGRAM PRODUCT FOR DETECTING POLICY VIOLATIONS | August 2022 | March 2024 | Abandon | 19 | 4 | 0 | No | No |
| 17829227 | TRAINING LARGE-SCALE VISION TRANSFORMER NEURAL NETWORKS | May 2022 | January 2026 | Allow | 44 | 0 | 0 | Yes | No |
| 17745103 | ANOMALY SCORE NORMALISATION BASED ON EXTREME VALUE THEORY | May 2022 | January 2026 | Allow | 44 | 1 | 0 | Yes | No |
| 17605404 | DATA PROCESSING METHOD, ELECTRONIC DEVICE AND COMPUTER-READABLE MEDIUM | April 2022 | March 2024 | Allow | 28 | 3 | 0 | Yes | No |
| 17655820 | GUIDED FEEDBACK LOOP FOR AUTOMATED INFORMATION CATEGORIZATION | March 2022 | October 2025 | Allow | 42 | 2 | 0 | Yes | No |
| 17652822 | IDENTIFYING AND CORRECTING VULNERABILITIES IN MACHINE LEARNING MODELS | February 2022 | December 2025 | Allow | 46 | 1 | 0 | No | No |
| 17676560 | Increasing Accuracy and Resolution of Weather Forecasts Using Deep Generative Models | February 2022 | September 2023 | Allow | 19 | 2 | 0 | No | No |
| 17674859 | METHOD AND PLATFORM FOR META-KNOWLEDGE FINE-TUNING BASED ON DOMAIN-INVARIANT FEATURES | February 2022 | February 2023 | Allow | 12 | 1 | 0 | No | No |
| 17672713 | NEURAL NETWORK TRAINING METHOD FOR MEMRISTOR MEMORY FOR MEMRISTOR ERRORS | February 2022 | June 2022 | Allow | 4 | 0 | 0 | Yes | No |
| 17672627 | THE ACCURACY OF LOW-BITWIDTH NEURAL NETWORKS BY REGULARIZING THE HIGHER-ORDER MOMENTS OF WEIGHTS AND HIDDEN STATES | February 2022 | January 2026 | Allow | 47 | 1 | 0 | No | No |
| 17592072 | SYSTEMS AND METHODS FOR QUANTIFYING DATA LEAKAGE FROM A SPLIT LAYER | February 2022 | October 2024 | Allow | 33 | 4 | 0 | Yes | No |
| 17588726 | Apparatus and Method of Implementing Batch-Mode Active Learning for Technology-Assisted Review of Documents | January 2022 | May 2024 | Abandon | 27 | 1 | 0 | No | No |
| 17535472 | ADAPTIVE TRAINING OF NEURAL NETWORK MODELS AT MODEL DEPLOYMENT DESTINATIONS | November 2021 | December 2022 | Allow | 13 | 0 | 0 | No | No |
| 17528942 | METHOD, ELECTRONIC DEVICE, AND COMPUTER PROGRAM PRODUCT FOR MANAGING INFERENCE PROCESS | November 2021 | February 2026 | Allow | 51 | 2 | 0 | No | No |
| 17527726 | METHOD FOR AN EXPLAINABLE AUTOENCODER AND AN EXPLAINABLE GENERATIVE ADVERSARIAL NETWORK | November 2021 | November 2023 | Allow | 24 | 2 | 1 | No | No |
| 17523310 | ROOT CAUSE DISCOVERY ENGINE | November 2021 | June 2025 | Allow | 43 | 5 | 0 | Yes | Yes |
| 17521597 | SYSTEMS AND METHODS FOR SCRAPING URLS BASED ON VIEWPORT VIEWS | November 2021 | October 2023 | Abandon | 23 | 2 | 0 | Yes | No |
| 17488166 | TRAINING NEURAL NETWORKS USING TRANSFER LEARNING | September 2021 | July 2025 | Allow | 46 | 1 | 0 | No | No |
| 17481297 | ROOT CAUSE DISCOVERY ENGINE | September 2021 | November 2023 | Allow | 26 | 2 | 0 | Yes | No |
| 17480056 | MACHINE LEARNING MODELING TO PREDICT HEURISTIC PARAMETERS FOR RADIATION THERAPY TREATMENT PLANNING | September 2021 | March 2026 | Allow | 54 | 2 | 0 | Yes | No |
| 17401154 | METHOD AND APPARATUS FOR OPTIMIZING QUANTIZATION MODEL, ELECTRONIC DEVICE, AND COMPUTER STORAGE MEDIUM | August 2021 | August 2024 | Allow | 36 | 2 | 0 | Yes | No |
| 17396396 | SYSTEMS AND METHODS FOR PROVIDING RESULTS BASED ON NODAL INTERRELATIONSHIPS AND UPDATING NODAL INTERRELATIONSHIP STRENGTHS BASED ON FEEDBACK REGARDING THE RESULTS | August 2021 | April 2025 | Allow | 44 | 1 | 0 | No | No |
| 17391432 | METHOD AND SYSTEM FOR BALANCED-WEIGHT SPARSE CONVOLUTION PROCESSING | August 2021 | March 2023 | Allow | 20 | 4 | 0 | Yes | No |
| 17387135 | ANALYTIC SYSTEM FOR INTERACTIVE GRAPHICAL MODEL SELECTION BASED ON WAVELET COEFFICIENTS | July 2021 | January 2022 | Allow | 6 | 0 | 0 | No | No |
| 17351194 | APPARATUS AND METHOD FOR DISTRIBUTED MODEL TRAINING, DEVICE, AND COMPUTER READABLE STORAGE MEDIUM | June 2021 | March 2026 | Allow | 57 | 3 | 0 | No | No |
| 17221217 | OPTIMIZING AUTOMATED MODELING ALGORITHMS FOR RISK ASSESSMENT AND GENERATION OF EXPLANATORY DATA | April 2021 | September 2021 | Allow | 5 | 1 | 0 | Yes | No |
| 17215205 | Providing the basis for ethical AI through explanations by coupling non-interpretable and interpretable systems | March 2021 | January 2022 | Allow | 9 | 1 | 0 | Yes | No |
| 17279490 | CLASSIFICATION DEVICE, CLASSIFICATION METHOD, PROGRAM, AND INFORMATION RECORDING MEDIUM | March 2021 | March 2026 | Allow | 59 | 2 | 0 | No | No |
| 17249761 | SYMMETRY-BASED QUANTUM COMPUTATIONAL CHEMISTRY | March 2021 | February 2025 | Abandon | 47 | 2 | 0 | No | No |
| 17192787 | Anomaly and Causation Detection in Computing Environments Using Counterfactual Processing | March 2021 | November 2025 | Abandon | 56 | 5 | 0 | Yes | No |
| 17250758 | AGENT SYSTEM FOR CONTENT RECOMMENDATIONS | March 2021 | November 2025 | Allow | 56 | 2 | 0 | Yes | No |
| 17166158 | SYSTEM AND METHOD HAVING THE ARTIFICIAL INTELLIGENCE (AI) ALGORITHM OF K-NEAREST NEIGHBORS (K-NN) | February 2021 | December 2025 | Allow | 58 | 2 | 0 | No | No |
| 17161944 | ADAPTIVE SELF-ADVERSARIAL NEGATIVE SAMPLING FOR GRAPH NEURAL NETWORK TRAINING | January 2021 | August 2025 | Allow | 54 | 2 | 0 | Yes | No |
| 17161548 | DATA TRANSMISSION BETWEEN TWO SYSTEMS TO IMPROVE OUTCOME PREDICTIONS | January 2021 | October 2022 | Allow | 21 | 0 | 0 | Yes | No |
| 17150767 | METHOD AND SYSTEM FOR GENERATING VARIABLE TRAINING DATA FOR ARTIFICIAL INTELLIGENCE SYSTEMS | January 2021 | August 2025 | Allow | 55 | 2 | 0 | Yes | No |
| 17137758 | System and Method of Clustering Machine Learning Flows | December 2020 | July 2025 | Allow | 55 | 2 | 0 | Yes | No |
| 17254669 | COMPUTATIONAL PROCESSING SYSTEM, SENSOR SYSTEM, COMPUTATIONAL PROCESSING METHOD, AND PROGRAM | December 2020 | December 2023 | Abandon | 36 | 1 | 0 | No | No |
| 17128290 | MERGE OPERATIONS FOR DARTS | December 2020 | September 2024 | Allow | 45 | 2 | 0 | Yes | No |
| 17121796 | REINFORCEMENT LEARNING FOR TESTING SUITE GENERATION | December 2020 | August 2025 | Allow | 56 | 3 | 0 | Yes | No |
| 17247280 | UTILIZING MACHINE LEARNING TO PROACTIVELY SCALE CLOUD INSTANCES IN A CLOUD COMPUTING ENVIRONMENT | December 2020 | June 2025 | Allow | 54 | 1 | 0 | Yes | No |
| 17114408 | COMBINATORIAL BLACK BOX OPTIMIZATION WITH EXPERT ADVICE | December 2020 | August 2024 | Allow | 44 | 1 | 0 | No | No |
| 17110600 | TECHNOLOGIES FOR INFERRING A PATIENT CONDITION USING MACHINE LEARNING | December 2020 | October 2025 | Allow | 58 | 4 | 0 | Yes | No |
| 17107046 | NEURAL NETWORK PRUNING METHOD AND SYSTEM VIA LAYERWISE ANALYSIS | November 2020 | March 2025 | Allow | 51 | 2 | 0 | No | No |
| 17085634 | Systems and Methods for Segregating Machine Learned Models for Distributed Processing | October 2020 | February 2026 | Allow | 60 | 3 | 0 | Yes | Yes |
| 17071025 | COMPUTER SYSTEM, LEARNING METHOD, AND PROGRAM | October 2020 | August 2024 | Abandon | 46 | 1 | 0 | No | No |
| 17046963 | GRAPH NEURAL NETWORKS REPRESENTING PHYSICAL SYSTEMS | October 2020 | August 2025 | Allow | 58 | 4 | 0 | Yes | No |
| 17062473 | Computer Operations and Architecture for Artificial Intelligence Networks and Wave Form Transistor | October 2020 | March 2025 | Abandon | 53 | 1 | 1 | No | No |
| 17020496 | FEDERATED LEARNING SYSTEM AND METHOD FOR DETECTING FINANCIAL CRIME BEHAVIOR ACROSS PARTICIPATING ENTITIES | September 2020 | May 2024 | Allow | 44 | 6 | 0 | No | Yes |
| 17011872 | SYSTEM AND METHOD FOR TAG-DIRECTED DEEP-LEARNING-BASED FEATURES FOR PREDICTING EVENTS AND MAKING DETERMINATIONS | September 2020 | July 2025 | Allow | 59 | 3 | 0 | Yes | No |
| 17004822 | EEG SIGNAL GENERATION NETWORK, METHOD AND STORAGE MEDIUM | August 2020 | June 2024 | Abandon | 45 | 1 | 0 | No | No |
| 16993305 | PREDICTING CUSTOMER INTERACTION OUTCOMES | August 2020 | March 2026 | Abandon | 60 | 6 | 0 | Yes | No |
| 16990091 | GRAPH PROCESSING METHOD AND SYSTEM | August 2020 | March 2024 | Abandon | 43 | 1 | 0 | No | No |
| 16930017 | METHOD AND SYSTEM FOR LEARNING PERTURBATION SETS IN MACHINE LEARNING | July 2020 | July 2025 | Abandon | 60 | 2 | 0 | No | Yes |
| 16926350 | Personalized compounding of therapeutic components and tracking of their influence on a measured parameter using a complex interaction model | July 2020 | February 2025 | Abandon | 55 | 2 | 0 | No | No |
| 16961368 | INFORMATION PROCESSING APPARATUS, INFORMATION PROCESSING METHOD, AND PROGRAM | July 2020 | August 2024 | Abandon | 49 | 2 | 0 | No | No |
| 16960448 | MODEL TRAINING APPARATUS, MODEL TRAINING METHOD, AND PROGRAM FOR RETRAINING ANOMALY DETECTION MODEL | July 2020 | January 2026 | Allow | 60 | 5 | 0 | Yes | No |
| 16866365 | TRAINING NEURAL NETWORKS USING A PRIORITIZED EXPERIENCE MEMORY | May 2020 | September 2022 | Allow | 28 | 2 | 0 | Yes | No |
| 16792021 | TIME-SERIES FAULT DETECTION, FAULT CLASSIFICATION, AND TRANSITION ANALYSIS USING A K-NEAREST-NEIGHBOR AND LOGISTIC REGRESSION APPROACH | February 2020 | June 2024 | Allow | 52 | 4 | 0 | Yes | No |
| 16778587 | DISTRIBUTED HYPERPARAMETER TUNING AND LOAD BALANCING FOR MATHEMATICAL MODELS | January 2020 | December 2023 | Allow | 46 | 2 | 0 | Yes | No |
| 16740088 | Apparatus and Method of Implementing Enhanced Batch-Mode Active Learning for Technology-Assisted Review of Documents | January 2020 | February 2022 | Allow | 25 | 1 | 0 | No | No |
| 16740044 | Apparatus and Method of Implementing Batch-Mode Active Learning for Technology-Assisted Review of Documents | January 2020 | September 2021 | Allow | 20 | 0 | 0 | Yes | No |
| 16700771 | OPTIMIZING AUTOMATED MODELING ALGORITHMS FOR RISK ASSESSMENT AND GENERATION OF EXPLANATORY DATA | December 2019 | April 2023 | Allow | 40 | 3 | 0 | Yes | No |
| 16699049 | PROCESSING METHOD AND ACCELERATING DEVICE | November 2019 | September 2025 | Allow | 60 | 3 | 0 | Yes | No |
| 16699055 | PROCESSING METHOD AND ACCELERATING DEVICE | November 2019 | September 2025 | Allow | 60 | 3 | 0 | Yes | No |
| 16699027 | PROCESSING METHOD AND ACCELERATING DEVICE | November 2019 | May 2025 | Allow | 60 | 2 | 0 | Yes | No |
| 16696919 | TIME AND ACCURACY ESTIMATE-BASED SELECTION OF MACHINE-LEARNING PREDICTIVE MODELS | November 2019 | October 2023 | Abandon | 47 | 1 | 0 | No | No |
| 16685045 | NEUROMORPHIC DEVICE WITH CROSSBAR ARRAY STRUCTURE STORING BOTH WEIGHTS AND NEURONAL STATES OF NEURAL NETWORKS | November 2019 | May 2023 | Allow | 42 | 1 | 0 | No | No |
| 16685478 | SYSTEM AND METHOD FOR A CONVOLUTIONAL NEURAL NETWORK FOR MULTI-LABEL CLASSIFICATION WITH PARTIAL ANNOTATIONS | November 2019 | February 2024 | Allow | 51 | 2 | 0 | No | No |
| 16601880 | INFORMATION OUTPUT SYSTEM, INFORMATION OUTPUT METHOD, AND RECORDING MEDIUM | October 2019 | April 2023 | Abandon | 42 | 2 | 0 | No | No |
| 16588931 | BIDIRECTIONAL NETWORK ON A DATA-FLOW CENTRIC PROCESSOR | September 2019 | April 2025 | Allow | 60 | 4 | 0 | Yes | Yes |
| 16587937 | Method and System for Material Screening | September 2019 | October 2024 | Abandon | 60 | 4 | 0 | No | No |
| 16570263 | SYSTEM, METHOD AND COMPUTER PROGRAM PRODUCT FOR DETECTING POLICY VIOLATIONS | September 2019 | July 2022 | Allow | 34 | 7 | 0 | No | No |
| 16451205 | NON-VOLATILE MEMORY-BASED COMPACT MIXED-SIGNAL MULTIPLY-ACCUMULATE ENGINE | June 2019 | September 2023 | Allow | 51 | 3 | 0 | Yes | No |
| 16431393 | Machine Learning Model Training Method And Apparatus | June 2019 | May 2024 | Allow | 59 | 3 | 0 | Yes | No |
| 16426763 | DETECTION OF OPERATION TENDENCY BASED ON ANOMALY DETECTION | May 2019 | July 2024 | Allow | 60 | 3 | 0 | Yes | No |
| 16243129 | DEVICE DISCOVERY AND CLASSIFICATION FROM ENCRYPTED NETWORK TRAFFIC | January 2019 | June 2022 | Allow | 42 | 3 | 0 | Yes | No |
| 16315223 | INFORMATION OUTPUT SYSTEM, INFORMATION OUTPUT METHOD, AND RECORDING MEDIUM | January 2019 | April 2023 | Abandon | 51 | 2 | 0 | Yes | No |
| 16238847 | MACHINE LEARNING SYSTEM UTILIZING MAGNETIZATION SUSCEPTIBILITY ADJUSTMENTS | January 2019 | December 2022 | Allow | 47 | 2 | 0 | Yes | No |
This analysis examines appeal outcomes and the strategic value of filing appeals for examiner KASSIM, IMAD MUTEE.
With a 0.0% reversal rate, the PTAB affirms the examiner's rejections in the vast majority of cases. This reversal rate is in the bottom 25% across the USPTO, indicating that appeals face significant challenges here.
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, 44.4% 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 face challenges. Ensure your case has strong merit before committing to full Board review.
✓ Filing a Notice of Appeal is strategically valuable. The act of filing often prompts favorable reconsideration during the mandatory appeal conference.
Examiner KASSIM, IMAD MUTEE works in Art Unit 2129 and has examined 93 patent applications in our dataset. With an allowance rate of 74.2%, this examiner has a below-average tendency to allow applications. Applications typically reach final disposition in approximately 53 months.
Examiner KASSIM, IMAD MUTEE's allowance rate of 74.2% places them in the 39% percentile among all USPTO examiners. This examiner has a below-average tendency to allow applications.
On average, applications examined by KASSIM, IMAD MUTEE receive 2.75 office actions before reaching final disposition. This places the examiner in the 81% 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 KASSIM, IMAD MUTEE is 53 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 +30.4% benefit to allowance rate for applications examined by KASSIM, IMAD MUTEE. This interview benefit is in the 78% percentile among all examiners. Recommendation: Interviews are highly effective with this examiner and should be strongly considered as a prosecution strategy. Per MPEP § 713.10, interviews are available at any time before the Notice of Allowance is mailed or jurisdiction transfers to the PTAB.
When applicants file an RCE with this examiner, 26.7% of applications are subsequently allowed. This success rate is in the 45% 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 13.6% of cases where such amendments are filed. This entry rate is in the 14% 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 77.8% of appeals filed. This is in the 69% 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 shows above-average willingness to reconsider rejections during appeals. The mandatory appeal conference (MPEP § 1207.01) provides an opportunity for reconsideration.
When applicants file petitions regarding this examiner's actions, 14.3% are granted (fully or in part). This grant rate is in the 8% 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 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.