Detailed information about the 100 most recent patent applications.
| Application Number | Title | Filing Date | Disposal Date | Disposition | Time (months) | Office Actions | Restrictions | Interview | Appeal |
|---|---|---|---|---|---|---|---|---|---|
| 18990590 | IDEOGRAPHIC CONTRASTIVE AUTOENCODER FOR LARGE LANGUAGE MODEL FINE-TUNING | December 2024 | November 2025 | Allow | 11 | 2 | 0 | Yes | No |
| 18852145 | GENERATION AND DISCRIMINATION TRAINING AS A VARIABLE RESOLUTION GAME | September 2024 | May 2025 | Allow | 8 | 2 | 0 | No | No |
| 18783592 | METHOD AND SYSTEM FOR PREDICTING RELEVANT NETWORK RELATIONSHIPS | July 2024 | February 2025 | Allow | 7 | 1 | 0 | Yes | No |
| 18777760 | METHOD AND SYSTEM FOR PREDICTING RELEVANT NETWORK RELATIONSHIPS | July 2024 | January 2025 | Allow | 6 | 1 | 0 | Yes | No |
| 18713631 | Entity Tag Association Prediction Method, Device, and Computer Readable Storage Medium | May 2024 | February 2025 | Allow | 9 | 1 | 0 | Yes | No |
| 18415034 | SYSTEMS AND METHODS FOR STATE CHANGE IMPLEMENTATION | January 2024 | February 2026 | Allow | 25 | 5 | 0 | Yes | No |
| 18397227 | METHOD AND APPARATUS FOR KNOWLEDGE GRAPH CONSTRUCTION, STORAGE MEDIUM, AND ELECTRONIC DEVICE | December 2023 | November 2024 | Allow | 10 | 2 | 0 | No | No |
| 18118110 | EARNING CODE CLASSIFICATION | March 2023 | July 2025 | Allow | 29 | 1 | 0 | Yes | No |
| 18178298 | TECHNIQUES FOR BUILDING A KNOWLEDGE GRAPH IN LIMITED KNOWLEDGE DOMAINS | March 2023 | February 2025 | Allow | 24 | 1 | 0 | Yes | No |
| 18163242 | METHOD OF PROVIDING INFORMATION ON NEURAL NETWORK MODEL AND ELECTRONIC APPARATUS FOR PERFORMING THE SAME | February 2023 | August 2024 | Abandon | 19 | 3 | 0 | No | No |
| 18012930 | METHOD AND DEVICE FOR SUBMITTING TRAINING TASK BY RATE LIMITING QUEUE | December 2022 | October 2023 | Abandon | 10 | 2 | 0 | Yes | No |
| 18145967 | SYSTEMS AND METHODS FOR DOMAIN ADAPTATION IN NEURAL NETWORKS | December 2022 | December 2025 | Abandon | 36 | 2 | 0 | Yes | No |
| 18072677 | GENERATING NEW DATA BASED ON CLASS-SPECIFIC UNCERTAINTY INFORMATION USING MACHINE LEARNING | November 2022 | September 2025 | Allow | 34 | 0 | 0 | No | No |
| 18050087 | CHARACTER LEVEL EMBEDDINGS FOR SPREADSHEET DATA EXTRACTION USING MACHINE LEARNING | October 2022 | June 2024 | Abandon | 20 | 3 | 0 | Yes | No |
| 17960943 | APPARATUS FOR MACHINE OPERATORMACHINE OPERATOR FEEDBACK CORRELATION | October 2022 | March 2025 | Allow | 30 | 4 | 0 | Yes | No |
| 17960744 | MACHINE LEARNING SYSTEMS FOR TRAINING ENCODER AND DECODER NEURAL NETWORKS | October 2022 | August 2023 | Allow | 11 | 1 | 0 | Yes | No |
| 17960755 | MACHINE LEARNING SYSTEMS FOR GENERATING MULTI-MODAL DATA ARCHETYPES | October 2022 | June 2023 | Allow | 8 | 1 | 0 | Yes | No |
| 17956120 | AUTOMATIC MACHINE LEARNING FEATURE BACKWARD STRIPPING | September 2022 | August 2025 | Allow | 34 | 0 | 0 | No | No |
| 17952620 | APPARATUS FOR ENHANCING LONGEVITY AND A METHOD FOR ITS USE | September 2022 | October 2024 | Abandon | 25 | 6 | 0 | Yes | No |
| 17820342 | DEEP LEARNING MODEL TRAINING SYSTEM | August 2022 | March 2023 | Allow | 7 | 1 | 0 | No | No |
| 17877836 | OVERCOMING DATA MISSINGNESS FOR IMPROVING PREDICTIONS | July 2022 | September 2024 | Allow | 25 | 4 | 0 | Yes | No |
| 17781827 | PHYSICS-GUIDED DEEP MULTIMODAL EMBEDDINGS FOR TASK-SPECIFIC DATA EXPLOITATION | June 2022 | May 2025 | Allow | 35 | 2 | 0 | Yes | Yes |
| 17763629 | PARAMETER ESTIMATION DEVICE, PARAMETER ESTIMATION METHOD, AND PARAMETER ESTIMATION PROGRAM | March 2022 | December 2025 | Abandon | 45 | 1 | 0 | No | No |
| 17655468 | Predictive Modeling of Aircraft Dynamics | March 2022 | February 2026 | Allow | 47 | 3 | 0 | Yes | No |
| 17696445 | DESIGN LEARNING: LEARNING DESIGN POLICIES BASED ON INTERACTIONS | March 2022 | November 2023 | Allow | 20 | 0 | 0 | No | No |
| 17636023 | TECHNIQUES TO TUNE SCALE PARAMETER FOR ACTIVATIONS IN BINARY NEURAL NETWORKS | February 2022 | February 2026 | Abandon | 48 | 2 | 0 | No | No |
| 17629572 | EVALUATION DEVICE FOR EVALUATING AN INPUT SIGNAL, AND CAMERA COMPRISING THE EVALUATION DEVICE | January 2022 | March 2026 | Abandon | 50 | 2 | 0 | Yes | No |
| 17617994 | ERROR DETECTION DEVICE, ERROR DETECTION METHOD, AND ERROR DETECTION PROGRAM | December 2021 | December 2025 | Allow | 48 | 1 | 0 | No | No |
| 17509582 | RULE GENERATION FOR MACHINE-LEARNING MODEL DISCRIMINATORY REGIONS | October 2021 | November 2025 | Allow | 48 | 2 | 0 | No | No |
| 17502503 | DETECTION OF CONTAINER INCIDENTS USING MACHINE LEARNING TECHNIQUES | October 2021 | April 2025 | Allow | 42 | 1 | 0 | Yes | No |
| 17485030 | FORM IN PLACE PERMANENT DRY DOCK | September 2021 | July 2023 | Abandon | 22 | 1 | 0 | No | No |
| 17473454 | COMPRESSION OF KERNEL DATA FOR NEURAL NETWORK OPERATIONS | September 2021 | March 2026 | Allow | 54 | 3 | 0 | Yes | No |
| 17461901 | SPLIT ARRAY ARCHITECTURE FOR ANALOG NEURAL MEMORY IN A DEEP LEARNING ARTIFICIAL NEURAL NETWORK | August 2021 | March 2025 | Allow | 42 | 1 | 0 | No | No |
| 17403149 | MACHINE LEARNING ENHANCED CLASSIFIER | August 2021 | December 2023 | Abandon | 28 | 3 | 0 | Yes | No |
| 17418007 | NEURAL NETWORK LEARNING DEVICE, METHOD, AND PROGRAM | June 2021 | February 2025 | Allow | 44 | 1 | 0 | No | No |
| 17356342 | SYSTEMS AND METHODS FOR USING FEDERATED LEARNING FOR TRAINING CENTRALIZED SEIZURE DETECTION AND PREDICTION MODELS ON DECENTRALIZED DATASETS | June 2021 | February 2025 | Allow | 44 | 1 | 0 | No | No |
| 17334518 | PARALLEL PROCESSING IN A SPIKING NEURAL NETWORK | May 2021 | April 2025 | Allow | 47 | 2 | 0 | No | No |
| 17204188 | Method for Configuring a Neural Network Model | March 2021 | October 2025 | Allow | 55 | 4 | 0 | Yes | No |
| 17191254 | DATA LABELING FOR SYNTHETIC DATA GENERATION | March 2021 | November 2024 | Allow | 45 | 1 | 0 | Yes | No |
| 17137670 | PREDICTING COMPONENT LIFESPAN INFORMATION BY PROCESSING USER INSTALL BASE DATA AND ENVIRONMENT-RELATED DATA USING MACHINE LEARNING TECHNIQUES | December 2020 | July 2025 | Allow | 54 | 3 | 0 | Yes | No |
| 17125626 | CONTEXT-AWARE AND STATELESS DEEP LEARNING AUTOTUNING FRAMEWORK | December 2020 | December 2024 | Allow | 48 | 2 | 0 | Yes | Yes |
| 17090032 | INFORMATION PROCESSING DEVICE AND METHOD, AND DEVICE FOR CLASSIFYING WITH MODEL | November 2020 | October 2024 | Abandon | 48 | 1 | 0 | No | No |
| 17080037 | VIRTUAL BUSINESS ASSISTANT AI ENGINE FOR MULTIPOINT COMMUNICATION | October 2020 | February 2025 | Abandon | 52 | 2 | 0 | No | No |
| 17079842 | METHOD AND APPARATUS FOR ANONYMIZING PERSONAL INFORMATION | October 2020 | December 2024 | Abandon | 50 | 3 | 0 | No | No |
| 17075963 | CONFIGURING A NEURAL NETWORK USING SMOOTHING SPLINES | October 2020 | July 2025 | Allow | 57 | 4 | 0 | Yes | No |
| 17060165 | PREDICTIVE MICROSERVICES ACTIVATION USING MACHINE LEARNING | October 2020 | September 2024 | Allow | 48 | 2 | 0 | Yes | No |
| 17033988 | ADDRESS INFORMATION FEATURE EXTRACTION METHOD BASED ON DEEP NEURAL NETWORK MODEL | September 2020 | December 2023 | Allow | 38 | 1 | 0 | No | No |
| 17033054 | NEURAL NETWORK DEVICE, OPERATION METHOD THEREOF, AND NEURAL NETWORK SYSTEM INCLUDING THE SAME | September 2020 | March 2025 | Allow | 54 | 5 | 0 | Yes | No |
| 17007193 | RESAMPLING EEG TRIAL DATA | August 2020 | December 2023 | Abandon | 40 | 1 | 0 | No | No |
| 17005763 | SYSTEMS AND METHODS FOR PARTIALLY SUPERVISED LEARNING WITH MOMENTUM PROTOTYPES | August 2020 | March 2024 | Allow | 43 | 1 | 0 | Yes | No |
| 17003673 | TRAINING ACTOR-CRITIC ALGORITHMS IN LABORATORY SETTINGS | August 2020 | May 2025 | Allow | 57 | 5 | 0 | No | No |
| 16971107 | Adversarial Probabilistic Regularization | August 2020 | May 2024 | Abandon | 45 | 2 | 0 | No | No |
| 16902496 | VIDEO FRAME INTERPOLATION METHOD, STORAGE MEDIUM AND TERMINAL | June 2020 | September 2021 | Abandon | 15 | 4 | 0 | No | No |
| 16767802 | Byzantine Tolerant Gradient Descent For Distributed Machine Learning With Adversaries | May 2020 | April 2024 | Abandon | 46 | 1 | 0 | No | No |
| 16876866 | SELECTING ACTION SLATES USING REINFORCEMENT LEARNING | May 2020 | December 2024 | Allow | 55 | 4 | 0 | Yes | Yes |
| 16762571 | LEARNING DEVICE, LEARNING METHOD, AND RECORDING MEDIUM | May 2020 | May 2024 | Abandon | 49 | 2 | 0 | No | No |
| 16612361 | NEURAL NETWORK PROCESSING METHOD, COMPUTER SYSTEM AND STORAGE MEDIUM | May 2020 | August 2025 | Allow | 60 | 4 | 0 | No | No |
| 16809570 | SYSTEM AND METHOD FOR RECOMMENDING DEMAND-SUPPLY AGENT COMBINATION PAIRS FOR TRANSACTIONS USING MACHINE LEARNING | March 2020 | July 2023 | Allow | 41 | 2 | 0 | Yes | No |
| 16796445 | Capability Indication Method, Route Setup Method, Mobile Terminal, and Network Device | February 2020 | January 2022 | Abandon | 23 | 2 | 0 | Yes | No |
| 16751897 | CONVOLUTIONAL NEURAL NETWORKS WITH ADJUSTABLE FEATURE RESOLUTIONS AT RUNTIME | January 2020 | May 2024 | Allow | 51 | 3 | 0 | Yes | No |
| 16738038 | NEURAL NETWORK DEVICE, NEURAL NETWORK SYSTEM, AND METHOD OF PROCESSING NEURAL NETWORK MODEL BY USING NEURAL NETWORK SYSTEM | January 2020 | September 2023 | Allow | 44 | 3 | 0 | Yes | No |
| 16719662 | LEARNING TASK COMPILING METHOD OF ARTIFICIAL INTELLIGENCE PROCESSOR AND RELATED PRODUCTS | December 2019 | December 2024 | Allow | 60 | 3 | 0 | No | No |
| 16675671 | CONTENT TYPE EMBEDDINGS | November 2019 | June 2024 | Allow | 55 | 4 | 0 | Yes | No |
| 16662090 | METHOD AND APPARATUS FOR ENHANCING EFFECTIVITY OF MACHINE LEARNING SOLUTIONS | October 2019 | May 2024 | Allow | 55 | 4 | 0 | Yes | No |
| 16659888 | NEURAL NETWORK MODEL DEPLOYMENT METHOD, PREDICTION METHOD AND RELATED DEVICE | October 2019 | March 2024 | Allow | 53 | 6 | 0 | Yes | No |
| 16593248 | METHOD AND SYSTEM FOR SEMI-SUPERVISED ANOMALY DETECTION WITH FEED-FORWARD NEURAL NETWORK FOR HIGH-DIMENSIONAL SENSOR DATA | October 2019 | May 2023 | Allow | 43 | 3 | 0 | Yes | No |
| 16592130 | ARTIFICIAL INTELLIGENCE HARDWARE WITH SYNAPTIC REUSE | October 2019 | December 2024 | Allow | 60 | 5 | 0 | Yes | No |
| 16550606 | MACHINE LEARNING DEVICE, CONTROL DEVICE, AND MACHINE LEARNING METHOD | August 2019 | September 2022 | Allow | 37 | 2 | 0 | No | Yes |
| 16542017 | TECHNIQUES FOR BUILDING A KNOWLEDGE GRAPH IN LIMITED KNOWLEDGE DOMAINS | August 2019 | November 2022 | Allow | 39 | 1 | 0 | Yes | No |
| 16537251 | ARTIFICIAL-INTELLIGENCE-AUGMENTED CLASSIFICATION SYSTEM AND METHOD FOR TENDER SEARCH AND ANALYSIS | August 2019 | May 2022 | Abandon | 33 | 1 | 0 | No | No |
| 16518356 | INFORMATION PROCESSING DEVICE, INFORMATION PROCESSING SYSTEMS, AND PROGRAMS | July 2019 | January 2024 | Abandon | 54 | 2 | 0 | No | No |
| 16518872 | PARSING UNLABELED COMPUTER SECURITY DATA LOGS | July 2019 | March 2022 | Allow | 32 | 1 | 0 | Yes | No |
| 16505617 | COMPONENT RELEASING METHOD, COMPONENT CREATION METHOD, AND GRAPHIC MACHINE LEARNING ALGORITHM PLATFORM | July 2019 | March 2026 | Abandon | 60 | 10 | 0 | Yes | No |
| 16456174 | Information Processing Apparatus and Information Processing Method | June 2019 | October 2022 | Abandon | 40 | 0 | 1 | Yes | No |
| 16453319 | CONTROL DATA CREATION DEVICE, COMPONENT CONTROL DEVICE, CONTROL DATA CREATION METHOD, COMPONENT CONTROL METHOD AND COMPUTER PROGRAM | June 2019 | October 2022 | Allow | 40 | 3 | 0 | Yes | No |
| 16445651 | Systems and Methods for Performing Knowledge Distillation | June 2019 | June 2023 | Allow | 48 | 4 | 0 | Yes | Yes |
| 16425657 | TECHNIQUES FOR USING MACHINE LEARNING FOR CONTROL AND PREDICTIVE MAINTENANCE OF BUILDINGS | May 2019 | July 2022 | Abandon | 38 | 1 | 0 | No | No |
| 16419509 | Channel Gating For Conditional Computation | May 2019 | December 2023 | Abandon | 55 | 4 | 0 | Yes | No |
| 16408609 | AUTOMATED REGRESSION DETECTION SYSTEM FOR ROBUST ENTERPRISE MACHINE LEARNING APPLICATIONS | May 2019 | March 2023 | Allow | 46 | 4 | 0 | Yes | No |
| 16398477 | USING MACHINE LEARNING TO DETECT SYSTEM CHANGES | April 2019 | February 2023 | Allow | 46 | 1 | 0 | No | No |
| 16377727 | FAIRNESS IMPROVEMENT THROUGH REINFORCEMENT LEARNING | April 2019 | December 2023 | Abandon | 57 | 6 | 0 | Yes | No |
| 16358220 | Earning Code Classification | March 2019 | December 2023 | Abandon | 57 | 4 | 0 | Yes | Yes |
| 16287224 | DISCOVERING AND RESOLVING TRAINING CONFLICTS IN MACHINE LEARNING SYSTEMS | February 2019 | January 2024 | Allow | 59 | 7 | 0 | Yes | No |
| 16284371 | PROGRAM SYNTHESIS USING ANNOTATIONS BASED ON ENUMERATION PATTERNS | February 2019 | July 2023 | Allow | 53 | 5 | 0 | Yes | No |
| 16281737 | METHOD OF PERFORMING LEARNING OF DEEP NEURAL NETWORK AND APPARATUS THEREOF | February 2019 | June 2023 | Allow | 52 | 3 | 0 | Yes | No |
| 16257965 | LEARNING DATA-AUGMENTATION FROM UNLABELED MEDIA | January 2019 | August 2024 | Abandon | 60 | 4 | 0 | Yes | No |
| 16255744 | DEEP LEARNING ACCELERATOR SYSTEM AND METHODS THEREOF | January 2019 | May 2025 | Abandon | 60 | 9 | 0 | Yes | No |
| 16248866 | Multi-Stage Machine Learning-Based Chain Diagnosis | January 2019 | February 2022 | Allow | 37 | 0 | 0 | No | No |
| 16317763 | CLASSIFYING IMAGES USING MACHINE LEARNING MODELS | January 2019 | September 2024 | Abandon | 60 | 4 | 0 | Yes | No |
| 16245463 | ADVERSARIAL INPUT IDENTIFICATION USING REDUCED PRECISION DEEP NEURAL NETWORKS | January 2019 | December 2023 | Abandon | 59 | 3 | 0 | Yes | Yes |
| 16239270 | IDENTIFICATION OF NON-DETERMINISTIC MODELS OF MULTIPLE DECISION MAKERS | January 2019 | October 2023 | Allow | 58 | 4 | 0 | No | No |
| 16176775 | SYSTEMS AND METHODS FOR DOMAIN ADAPTATION IN NEURAL NETWORKS | October 2018 | July 2023 | Abandon | 57 | 2 | 0 | No | No |
| 16145206 | MACHINE LEARNING SYSTEM, MACHINE LEARNING METHOD AND NON-TRANSITORY COMPUTER READABLE MEDIUM FOR OPERATING THE SAME | September 2018 | July 2023 | Abandon | 58 | 4 | 0 | No | No |
| 16046336 | CYBER ANOMALY DETECTION USING AN ARTIFICIAL NEURAL NETWORK | July 2018 | December 2021 | Allow | 41 | 1 | 0 | Yes | No |
| 16045033 | DEEP NEURAL NETWORK IMPLEMENTATION | July 2018 | October 2022 | Allow | 50 | 3 | 0 | Yes | No |
| 16042474 | ARTIFICIAL INTELLIGENCE FOR PROVIDING ENHANCED MICROBLOG MESSAGE INSERTION | July 2018 | January 2025 | Abandon | 60 | 6 | 0 | Yes | No |
| 16035122 | ORCHESTRATED SUPERVISION OF A COGNITIVE PIPELINE | July 2018 | February 2023 | Allow | 56 | 4 | 0 | Yes | No |
| 16031565 | ELECTRONIC APPARATUS AND CONTROL METHOD THEREOF | July 2018 | March 2024 | Abandon | 60 | 6 | 0 | Yes | Yes |
| 16011136 | SYSTEM AND METHOD FOR ANOMALY DETECTION VIA A MULTI-PREDICTION-MODEL ARCHITECTURE | June 2018 | December 2023 | Abandon | 60 | 3 | 0 | Yes | No |
| 15982635 | MACHINE LEARNING USING DYNAMIC MULTILAYER PERCEPTRONS | May 2018 | December 2021 | Abandon | 43 | 1 | 0 | No | No |
This analysis examines appeal outcomes and the strategic value of filing appeals for examiner RYLANDER, BART I.
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, 30.0% of applications that filed an appeal were subsequently allowed. This appeal filing benefit rate is below the USPTO average, suggesting that filing an appeal has limited effectiveness in prompting favorable reconsideration.
⚠ Appeals to PTAB face challenges. Ensure your case has strong merit before committing to full Board review.
⚠ Filing a Notice of Appeal shows limited benefit. Consider other strategies like interviews or amendments before appealing.
Examiner RYLANDER, BART I works in Art Unit 2124 and has examined 89 patent applications in our dataset. With an allowance rate of 59.6%, this examiner allows applications at a lower rate than most examiners at the USPTO. Applications typically reach final disposition in approximately 50 months.
Examiner RYLANDER, BART I's allowance rate of 59.6% places them in the 19% percentile among all USPTO examiners. This examiner is less likely to allow applications than most examiners at the USPTO.
On average, applications examined by RYLANDER, BART I receive 2.96 office actions before reaching final disposition. This places the examiner in the 85% 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 RYLANDER, BART I 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 +8.7% benefit to allowance rate for applications examined by RYLANDER, BART I. This interview benefit is in the 39% percentile among all examiners. Recommendation: Interviews provide a below-average benefit with this examiner.
When applicants file an RCE with this examiner, 20.5% of applications are subsequently allowed. This success rate is in the 22% 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 5.3% of cases where such amendments are filed. This entry rate is in the 6% 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, 66.7% result in withdrawal of the rejection or reopening of prosecution. This success rate is in the 53% 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 71.4% of appeals filed. This is in the 58% percentile among all examiners. Of these withdrawals, 60.0% 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, 42.1% are granted (fully or in part). This grant rate is in the 33% percentile among all examiners. Strategic Note: Petitions show below-average success regarding this examiner's actions. Ensure you have a strong procedural basis before filing.
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 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.