Detailed information about the 100 most recent patent applications.
| Application Number | Title | Filing Date | Disposal Date | Disposition | Time (months) | Office Actions | Restrictions | Interview | Appeal |
|---|---|---|---|---|---|---|---|---|---|
| 19087299 | DATA PROCESSING METHOD, ELECTRONIC DEVICE, AND STORAGE MEDIUM | March 2025 | October 2025 | Allow | 7 | 1 | 0 | No | No |
| 18763705 | SYSTEMS AND METHODS FOR DEVELOPING A KNOWLEDGE BASE COMPRISED OF MULTI-MODAL DATA FROM MYRIAD SOURCES | July 2024 | January 2025 | Allow | 6 | 1 | 0 | No | No |
| 18609844 | APPARATUS AND METHOD FOR LOCAL OPTIMIZATION USING UNSUPERVISED LEARNING | March 2024 | March 2025 | Allow | 12 | 3 | 0 | Yes | No |
| 18420423 | SYSTEMS, METHODS, AND COMPUTER READABLE MEDIA FOR VISUALIZATION OF SEMANTIC INFORMATION AND INFERENCE OF TEMPORAL SIGNALS INDICATING SALIENT ASSOCIATIONS BETWEEN LIFE SCIENCE ENTITIES | January 2024 | April 2025 | Abandon | 14 | 1 | 0 | Yes | No |
| 18419807 | SYSTEM AND METHOD FOR ESTABLISHING CONTEXTUAL LINKS BETWEEN DATA IN A CONSTRUCTION ENVIRONMENT | January 2024 | December 2024 | Allow | 11 | 1 | 0 | Yes | No |
| 18406073 | Rejecting Biased Data Using A Machine Learning Model | January 2024 | April 2025 | Abandon | 15 | 1 | 0 | No | No |
| 18466751 | PREDICTING LIKELIHOODS OF CONDITIONS BEING SATISFIED USING NEURAL NETWORKS | September 2023 | April 2024 | Allow | 7 | 0 | 0 | No | No |
| 18353121 | LONG-TERM STREAMFLOW FORECAST METHOD AND SYSTEM BASED ON PROCESS-DATA SYNERGIC DRIVE | July 2023 | November 2023 | Allow | 4 | 0 | 0 | No | No |
| 18350675 | PERFORMANCE METRICS OF RUN-TIME PREDICTIONS OF AUTOMATED MACHINE LEARNING (AUTOML) MODELS RUN ON ACTUAL HARDWARE PROCESSORS | July 2023 | October 2023 | Allow | 3 | 0 | 0 | No | No |
| 18122338 | INTEGRATED HOSPITAL LOGISTICS MANAGEMENT SYSTEM USING AI TECHNOLOGY, AND INTEGRATED HOSPITAL LOGISTICS MANAGEMENT METHOD USING SAME | March 2023 | July 2023 | Allow | 4 | 0 | 0 | No | No |
| 18184011 | FEDERATED MACHINE LEARNING IN ADAPTIVE TRAINING SYSTEMS | March 2023 | September 2023 | Allow | 6 | 0 | 0 | No | No |
| 18116303 | INTERACTIVE ELECTRONIC DEVICE FOR PERFORMING FUNCTIONS OF PROVIDING RESPONSES TO QUESTIONS FROM USERS AND REAL-TIME CONVERSATION WITH THE USERS USING MODELS LEARNED BY DEEP LEARNING TECHNIQUE AND OPERATING METHOD THEREOF | March 2023 | March 2024 | Abandon | 12 | 2 | 0 | No | No |
| 18095421 | Dynamically Updatable Rules Engine, and Corresponding Systems and Methods of Use | January 2023 | March 2025 | Allow | 26 | 1 | 0 | Yes | No |
| 18150495 | TRUSTED DECISION SUPPORT SYSTEM AND METHOD | January 2023 | October 2024 | Abandon | 21 | 1 | 0 | No | No |
| 17978025 | Systems and Methods for Neural Networks Allocating Capital | October 2022 | June 2025 | Abandon | 32 | 1 | 0 | No | No |
| 18046301 | RUNTIME RECONFIGURABLE NEURAL NETWORK PROCESSOR CORE | October 2022 | March 2024 | Allow | 18 | 1 | 0 | No | No |
| 17940768 | METHOD AND SYSTEM FOR INFERRING DEVICE FINGERPRINT | September 2022 | October 2025 | Allow | 37 | 0 | 0 | No | No |
| 17897421 | Parallel Development and Deployment for Machine Learning Models | August 2022 | May 2024 | Allow | 21 | 1 | 0 | No | No |
| 17842917 | KNOWLEDGE INFERENCE ENGINE SYSTEM AND METHOD OF IMPLEMENTATION | June 2022 | September 2022 | Allow | 3 | 0 | 0 | No | No |
| 17730518 | VIDEO AUGMENTATION APPARATUS AND A METHOD FOR ITS USE | April 2022 | August 2023 | Allow | 16 | 2 | 0 | Yes | No |
| 17633968 | INFORMATION PROCESSING DEVICE AND ELECTRONIC APPARATUS EQUIPPED WITH SAME | February 2022 | February 2026 | Abandon | 48 | 1 | 0 | No | No |
| 17649510 | Rejecting Biased Data Using A Machine Learning Model | January 2022 | April 2025 | Abandon | 39 | 3 | 0 | No | No |
| 17537280 | Artificial Intelligence Guided Research and Development | November 2021 | August 2025 | Abandon | 45 | 2 | 0 | Yes | No |
| 17519156 | OPTIMIZED PREDICTION OF TREE ENSEMBLE | November 2021 | March 2026 | Allow | 52 | 3 | 0 | Yes | No |
| 17491625 | MINING TEXTUAL FEEDBACK | October 2021 | March 2024 | Allow | 30 | 1 | 0 | No | No |
| 17488827 | IMPLICIT VECTOR CONCATENATION WITHIN 2D MESH ROUTING | September 2021 | September 2025 | Allow | 47 | 1 | 0 | Yes | No |
| 17483160 | FIND MODEL SENSITIVITY USING PAYLOAD DATA | September 2021 | March 2026 | Abandon | 54 | 2 | 0 | Yes | No |
| 17479858 | TECHNIQUES TO MANAGE TRAINING OR TRAINED MODELS FOR DEEP LEARNING APPLICATIONS | September 2021 | March 2025 | Abandon | 41 | 2 | 0 | No | No |
| 17477423 | ENVIRONMENT FACTOR CONTROL DEVICE AND TRAINING METHOD THEREOF | September 2021 | September 2023 | Allow | 24 | 2 | 0 | Yes | No |
| 17460674 | KNOWLEDGE GRAPH WEIGHTING DURING CHATBOT SESSIONS | August 2021 | February 2024 | Allow | 29 | 1 | 0 | No | No |
| 17461288 | PROCESSOR SYSTEM AND METHOD FOR INCREASING DATA-TRANSFER BANDWIDTH DURING EXECUTION OF A SCHEDULED PARALLEL PROCESS | August 2021 | March 2026 | Abandon | 55 | 2 | 0 | No | No |
| 17372391 | WEBINTERFACE GENERATION AND TESTING USING ARTIFICIAL NEURAL NETWORKS | July 2021 | March 2024 | Allow | 33 | 2 | 0 | No | No |
| 17369757 | SYSTEMS, METHODS, AND COMPUTER READABLE MEDIA FOR VISUALIZATION OF SEMANTIC INFORMATION AND INFERENCE OF TEMPORAL SIGNALS INDICATING SALIENT ASSOCIATIONS BETWEEN LIFE SCIENCE ENTITIES | July 2021 | October 2023 | Allow | 27 | 1 | 0 | Yes | No |
| 17368941 | DISTRIBUTED CLASSIFICATION COMPUTING SYSTEM | July 2021 | September 2021 | Allow | 2 | 0 | 0 | No | No |
| 17368043 | Computer-Implemented, User-Controlled Method of Automatically Organizing, Storing, and Sharing Personal Information | July 2021 | September 2022 | Abandon | 14 | 2 | 0 | Yes | Yes |
| 17360155 | Integrated Optical Neuromorphic Computing Apparatus | June 2021 | January 2026 | Allow | 54 | 2 | 0 | Yes | No |
| 17361007 | MODEL REUSE-BASED MODEL PREDICTION | June 2021 | November 2022 | Abandon | 16 | 2 | 0 | Yes | No |
| 17355806 | Cognitive Machine Learning System for Mixed Mode Aviation | June 2021 | June 2025 | Abandon | 48 | 1 | 0 | No | No |
| 17351018 | METHODS FOR CONVERTING HIERARCHICAL DATA | June 2021 | April 2025 | Allow | 46 | 2 | 0 | Yes | No |
| 17312639 | WORK SET SELECTING DEVICE, WORK SET SELECTION METHOD, AND WORK SET SELECTION PROGRAM | June 2021 | August 2025 | Abandon | 51 | 2 | 0 | No | No |
| 17342540 | AIDS FOR PORTING PREDICTIVE MODELS ACROSS TENANTS AND HANDLING IMPACT OF SOURCE CHANGES ON PREDICTIVE MODELS | June 2021 | December 2023 | Allow | 30 | 1 | 0 | No | No |
| 17338661 | PREDICTION METHOD FOR SYSTEM ERRORS | June 2021 | June 2025 | Abandon | 49 | 2 | 0 | No | No |
| 17332696 | UNSUPERVISED LEARNING METHOD FOR GENERAL INVERSE PROBLEM AND APPARATUS THEREFOR | May 2021 | October 2024 | Allow | 40 | 1 | 0 | No | No |
| 17323364 | LEVERAGING EXPLANATIONS FOR TRAINING OF AN AI SYSTEM | May 2021 | March 2026 | Abandon | 58 | 2 | 0 | Yes | Yes |
| 17302848 | NEURAL NETWORK PROCESSOR | May 2021 | February 2024 | Allow | 33 | 1 | 0 | No | No |
| 17316009 | SYSTEM FOR DUAL-FILTERING FOR LEARNING SYSTEMS TO PREVENT ADVERSARIAL ATTACKS | May 2021 | December 2024 | Abandon | 43 | 2 | 0 | No | No |
| 17313504 | USING MACHINE LEARNING-BASED SEED HARVEST MOISTURE PREDICTIONS TO IMPROVE A COMPUTER-ASSISTED AGRICULTURAL FARM OPERATION | May 2021 | February 2024 | Allow | 33 | 1 | 0 | No | No |
| 17307724 | DEVICE AND METHOD FOR OPERATING A TEST STAND | May 2021 | August 2025 | Abandon | 52 | 2 | 0 | No | No |
| 17242623 | MACHINE LEARNING ASSISTED DISCOVERY OF OPERATING SYSTEM-PROVIDED FEATURES | April 2021 | March 2026 | Abandon | 59 | 3 | 0 | No | No |
| 17240631 | APPLICATION OF LOCAL INTERPRETABLE MODEL-AGNOSTIC EXPLANATIONS ON DECISION SERVICES | April 2021 | March 2026 | Abandon | 59 | 4 | 0 | Yes | No |
| 17288595 | ANALYSIS DEVICE, MACHINE LEARNING DEVICE, ANALYSIS SYSTEM, ANALYSIS METHOD, AND RECORDING MEDIUM | April 2021 | December 2025 | Abandon | 55 | 2 | 0 | No | No |
| 17229606 | DEVICE FOR PROCESSING TIME SERIES DATA HAVING IRREGULAR TIME INTERVAL AND OPERATING METHOD THEREOF | April 2021 | March 2025 | Allow | 47 | 2 | 0 | No | No |
| 17217907 | CATEGORIZING TRANSACTION RECORDS | March 2021 | August 2025 | Abandon | 53 | 2 | 0 | Yes | No |
| 17215372 | COMPUTE-IN-MEMORY DEVICES, SYSTEMS AND METHODS OF OPERATION THEREOF | March 2021 | October 2024 | Allow | 43 | 1 | 0 | No | No |
| 17281174 | METHOD, MACHINE-READABLE MEDIUM AND SYSTEM TO PARAMETERIZE SEMANTIC CONCEPTS IN A MULTI-DIMENSIONAL VECTOR SPACE AND TO PERFORM CLASSIFICATION, PREDICTIVE, AND OTHER MACHINE LEARNING AND AI ALGORITHMS THEREON | March 2021 | January 2025 | Abandon | 45 | 1 | 0 | No | No |
| 17216156 | ARTIFICIAL-INTELLIGENCE ARCHITECTURE FOR DETECTING DOCUMENT MANIPULATION | March 2021 | March 2024 | Allow | 36 | 0 | 0 | No | No |
| 17208861 | TECHNIQUES TO MANAGE TRAINING OR TRAINED MODELS FOR DEEP LEARNING APPLICATIONS | March 2021 | June 2021 | Allow | 3 | 0 | 0 | No | No |
| 17201907 | SYSTEM AND METHOD FOR TRAINING MACHINE LEARNING APPLICATIONS | March 2021 | September 2024 | Allow | 42 | 1 | 0 | No | No |
| 17191179 | MEMBERSHIP LEAKAGE QUANTIFICATION TO VERIFY DATA REMOVAL | March 2021 | June 2024 | Allow | 40 | 0 | 0 | No | No |
| 17185883 | SYSTEMS AND METHODS TO IDENTIFY PERSONS AND/OR IDENTIFY AND QUANTIFY PAIN, FATIGUE, MOOD, AND INTENT WITH PROTECTION OF PRIVACY | February 2021 | May 2024 | Allow | 39 | 1 | 0 | No | No |
| 17183889 | Identifying Portions of Electronic Communication Documents Using Machine Vision | February 2021 | March 2024 | Allow | 37 | 1 | 0 | No | No |
| 17270000 | DETERMINING STATES OF AN APPARATUS USING SUPPORT VECTOR MACHINES | February 2021 | September 2021 | Allow | 7 | 0 | 0 | No | No |
| 17175635 | Apparatus And Product Of Manufacture For Generating A Probability Value For An Event | February 2021 | August 2023 | Allow | 30 | 1 | 0 | No | No |
| 17263114 | SYNAPTIC CHAIN COMPRISING SPINTRONIC RESONATORS BASED ON THE SPIN DIODE EFFECT, AND NEURAL NETWORK COMPRISING SUCH A SYNAPTIC CHAIN | January 2021 | February 2025 | Allow | 49 | 2 | 0 | No | No |
| 17149627 | NOVELTY DETECTOR | January 2021 | August 2024 | Allow | 43 | 1 | 0 | No | No |
| 17141394 | ON-CHIP TRAINING NEUROMORPHIC ARCHITECTURE | January 2021 | June 2024 | Allow | 41 | 1 | 0 | No | No |
| 17122822 | VEHICLE PREDICTION MODULE EVALUATION AND TRAINING | December 2020 | May 2024 | Allow | 41 | 2 | 0 | Yes | No |
| 17113847 | PHYSICAL REALIZATIONS OF A UNIVERSAL ADIABATIC QUANTUM COMPUTER | December 2020 | July 2023 | Allow | 31 | 0 | 0 | No | No |
| 17105131 | GENERATING A DOMAIN-SPECIFIC KNOWLEDGE GRAPH FROM UNSTRUCTURED COMPUTER TEXT | November 2020 | October 2025 | Allow | 58 | 2 | 0 | No | No |
| 17091325 | Quantum Computer System and Method for Partial Differential Equation-Constrained Optimization | November 2020 | December 2024 | Abandon | 49 | 1 | 0 | No | No |
| 17091088 | Analyzing Behavior in Light of Social Time | November 2020 | June 2023 | Allow | 31 | 1 | 0 | No | No |
| 17087901 | METHOD AND ARRANGEMENT FOR RESETTING QUBITS | November 2020 | August 2024 | Allow | 46 | 1 | 0 | No | No |
| 17077204 | APPARATUS AND METHOD FOR TRAINING REINFORCEMENT LEARNING MODEL FOR USE IN COMBINATORIAL OPTIMIZATION | October 2020 | October 2024 | Allow | 48 | 1 | 0 | No | No |
| 17027615 | Webinterface Presentation Using Artificial Neural Networks | September 2020 | June 2023 | Allow | 33 | 1 | 0 | No | No |
| 17025643 | TECHNIQUES TO MANAGE TRAINING OR TRAINED MODELS FOR DEEP LEARNING APPLICATIONS | September 2020 | January 2021 | Allow | 4 | 2 | 0 | No | No |
| 16975410 | SENSING DEVICE, SENSING METHOD, AND SENSING PROGRAM | August 2020 | March 2024 | Allow | 43 | 1 | 0 | No | No |
| 17000048 | JOINT REPRESENTATION LEARNING FROM IMAGES AND TEXT | August 2020 | December 2023 | Allow | 40 | 1 | 0 | Yes | No |
| 16947606 | MACHINE-LEARNING BASED SOLVER OF COUPLED-PARTIAL DIFFERENTIAL EQUATIONS | August 2020 | September 2023 | Allow | 37 | 0 | 0 | No | No |
| 16940131 | PREDICTING LIKELIHOODS OF CONDITIONS BEING SATISFIED USING RECURRENT NEURAL NETWORKS | July 2020 | June 2023 | Allow | 34 | 1 | 0 | No | No |
| 16938804 | Client Management Tool System and Method | July 2020 | June 2023 | Allow | 35 | 1 | 0 | No | No |
| 16963094 | PREDICTION OF HEALTHCARE OUTCOMES AND RECOMMENDATION OF INTERVENTIONS USING DEEP LEARNING | July 2020 | October 2023 | Allow | 39 | 0 | 0 | No | No |
| 16925813 | ARCHITECTURES, SYSTEMS AND METHODS FOR PROGRAM DEFINED STATE SYSTEM | July 2020 | February 2021 | Allow | 7 | 1 | 0 | No | No |
| 16881791 | PREDICTIVE MODEL IMPORTATION | May 2020 | December 2023 | Allow | 43 | 2 | 0 | No | No |
| 16874493 | TRUSTED DECISION SUPPORT SYSTEM AND METHOD | May 2020 | June 2023 | Abandon | 37 | 1 | 0 | No | No |
| 15930900 | DATA-ANALYSIS-BASED, NOISY LABELED AND UNLABELED DATAPOINT DETECTION AND RECTIFICATION FOR MACHINE-LEARNING | May 2020 | November 2023 | Allow | 42 | 1 | 0 | Yes | No |
| 16819532 | INFORMATION PROCESSING SYSTEM, INFERENCE METHOD, ATTACK DETECTION METHOD, INFERENCE EXECUTION PROGRAM AND ATTACK DETECTION PROGRAM | March 2020 | September 2023 | Allow | 42 | 1 | 0 | No | No |
| 16805542 | Advanced Systems and Methods for Allocating Capital to Trading Strategies for Big Data Trading in Financial Markets | February 2020 | July 2022 | Allow | 29 | 1 | 0 | No | No |
| 16746692 | SYSTEMS AND METHODS FOR CORRECTING ERROR IN A FIRST CLASSIFIER BY EVALUATING CLASSIFIER OUTPUT IN PARALLEL | January 2020 | March 2024 | Allow | 50 | 1 | 0 | No | No |
| 16618931 | DATA SPLITTING BY GRADIENT DIRECTION FOR NEURAL NETWORKS | December 2019 | February 2021 | Allow | 14 | 1 | 0 | No | No |
| 16672243 | NORMALIZING TEXT ATTRIBUTES FOR MACHINE LEARNING MODELS | November 2019 | October 2023 | Allow | 48 | 4 | 0 | No | No |
| 16667673 | METHOD FOR PREDICTING A PERSISTENCE OVER TIME OF ENTRIES OF A KNOWLEDGE BASE | October 2019 | June 2023 | Allow | 44 | 1 | 0 | No | No |
| 16662100 | INFORMATION PROCESSING APPARATUS, INFORMATION PROCESSING METHOD AND PROGRAM | October 2019 | June 2023 | Abandon | 44 | 2 | 0 | Yes | No |
| 16661860 | USING MACHINE LEARNING-BASED SEED HARVEST MOISTURE PREDICTIONS TO IMPROVE A COMPUTER-ASSISTED AGRICULTURAL FARM OPERATION | October 2019 | January 2021 | Allow | 15 | 0 | 0 | No | No |
| 16597477 | Parallel Development and Deployment for Machine Learning Models | October 2019 | June 2022 | Allow | 32 | 1 | 0 | No | No |
| 16596134 | Architectures, Systems and Methods for Program Defined State System | October 2019 | March 2020 | Allow | 5 | 0 | 0 | No | No |
| 16593321 | COMPUTING CONVOLUTIONS USING A NEURAL NETWORK PROCESSOR | October 2019 | November 2022 | Allow | 38 | 2 | 0 | No | No |
| 16591995 | SYSTEMS AND METHODS FOR DATA VERIFICATION | October 2019 | December 2021 | Allow | 26 | 2 | 0 | No | No |
| 16589823 | Cognitive Agents for Use Within a Cognitive Environment | October 2019 | January 2024 | Allow | 52 | 4 | 0 | No | No |
| 16589030 | MACHINE LEARNING USING SEMANTIC CONCEPTS REPRESENTED WITH TEMPORAL AND SPATIAL DATA | September 2019 | March 2024 | Abandon | 53 | 2 | 0 | No | No |
| 16581095 | METHOD, SYSTEM AND PROGRAM PRODUCT FOR PERCEIVING AND COMPUTING EMOTIONS | September 2019 | September 2025 | Allow | 60 | 5 | 0 | Yes | No |
This analysis examines appeal outcomes and the strategic value of filing appeals for examiner STARKS, WILBERT L.
With a 33.3% 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, 39.6% 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 STARKS, WILBERT L works in Art Unit 2122 and has examined 680 patent applications in our dataset. With an allowance rate of 83.5%, this examiner has an above-average tendency to allow applications. Applications typically reach final disposition in approximately 36 months.
Examiner STARKS, WILBERT L's allowance rate of 83.5% places them in the 58% percentile among all USPTO examiners. This examiner has an above-average tendency to allow applications.
On average, applications examined by STARKS, WILBERT L receive 1.39 office actions before reaching final disposition. This places the examiner in the 21% percentile for office actions issued. This examiner issues significantly fewer office actions than most examiners.
The median time to disposition (half-life) for applications examined by STARKS, WILBERT L is 36 months. This places the examiner in the 35% percentile for prosecution speed. Prosecution timelines are slightly slower than average with this examiner.
Conducting an examiner interview provides a -12.2% benefit to allowance rate for applications examined by STARKS, WILBERT L. This interview benefit is in the 3% percentile among all examiners. Note: Interviews show limited statistical benefit with this examiner compared to others, though they may still be valuable for clarifying issues.
When applicants file an RCE with this examiner, 36.1% of applications are subsequently allowed. This success rate is in the 82% percentile among all examiners. Strategic Insight: RCEs are highly effective with this examiner compared to others. If you receive a final rejection, filing an RCE with substantive amendments or arguments has a strong likelihood of success.
This examiner enters after-final amendments leading to allowance in 31.8% of cases where such amendments are filed. This entry rate is in the 46% 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, 82.4% result in withdrawal of the rejection or reopening of prosecution. This success rate is in the 65% 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 70.7% of appeals filed. This is in the 58% percentile among all examiners. Of these withdrawals, 34.5% 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, 38.1% are granted (fully or in part). This grant rate is in the 27% 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 0.7% of allowed cases (in the 64% percentile). This examiner makes examiner's amendments more often than average to place applications in condition for allowance (MPEP § 1302.04).
Quayle Actions: This examiner issues Ex Parte Quayle actions in 0.7% of allowed cases (in the 57% 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.