Detailed information about the 100 most recent patent applications.
| Application Number | Title | Filing Date | Disposal Date | Disposition | Time (months) | Office Actions | Restrictions | Interview | Appeal |
|---|---|---|---|---|---|---|---|---|---|
| 19040977 | TRAINING NODES OF A NEURAL NETWORK TO BE DECISIVE | January 2025 | June 2025 | Allow | 4 | 1 | 0 | No | No |
| 18385143 | SYSTEMS AND METHODS OF GENERATIVE MACHINE-LEARNING GUIDED BY MODAL CLASSIFICATION | October 2023 | March 2024 | Allow | 5 | 1 | 0 | Yes | No |
| 18491817 | CROSS-MEDIA CORRESPONDING KNOWLEDGE GENERATION METHOD AND APPARATUS | October 2023 | September 2024 | Allow | 11 | 2 | 0 | No | No |
| 18357680 | SCHEMA-BASED MACHINE LEARNING MODEL MONITORING | July 2023 | May 2025 | Allow | 22 | 4 | 0 | Yes | No |
| 18196855 | LOCATING A DECISION BOUNDARY FOR COMPLEX CLASSIFIER | May 2023 | December 2024 | Allow | 20 | 2 | 0 | No | Yes |
| 18300007 | LEARNING WITH MOMENT ESTIMATION USING DIFFERENT TIME CONSTANTS | April 2023 | April 2024 | Allow | 12 | 1 | 0 | No | No |
| 18031009 | Identification method of urban functional areas based on mixing degree of functions and integrated learning | April 2023 | May 2024 | Allow | 13 | 2 | 0 | Yes | No |
| 18105424 | DIAGNOSING AND TROUBLESHOOTING MAINTENANCE REPAIR REQUESTS USING AN ARTIFICIAL INTELLIGENCE-DRIVEN CHATBOT | February 2023 | June 2025 | Abandon | 28 | 3 | 0 | Yes | No |
| 18148225 | MACHINE LEARNING-BASED METHODS AND SYSTEMS FOR MODELING USER-SPECIFIC, ACTIVITY SPECIFIC ENGAGEMENT PREDICTING SCORES | December 2022 | February 2024 | Allow | 13 | 1 | 0 | No | No |
| 18058937 | AUTOMATED KNOWLEDGE BASE | November 2022 | January 2024 | Allow | 14 | 1 | 0 | No | No |
| 18055031 | BIAS REDUCTION IN MACHINE LEARNING MODEL TRAINING AND INFERENCE | November 2022 | January 2024 | Allow | 14 | 2 | 0 | Yes | No |
| 18049286 | CONTEXTUAL BANDITS-BASED ECOSYSTEM RECOMMENDER SYSTEM FOR SYNCHRONIZED PERSONALIZATION | October 2022 | June 2023 | Allow | 8 | 1 | 0 | Yes | No |
| 17967725 | Counterexample-Guided Update of a Motion Planner | October 2022 | December 2022 | Allow | 2 | 0 | 0 | No | No |
| 17896927 | DATA MANAGEMENT SUGGESTIONS FROM KNOWLEDGE GRAPH ACTIONS | August 2022 | November 2023 | Allow | 15 | 2 | 0 | Yes | No |
| 17904872 | MEASURING LOCAL CD UNIFORMITY USING SCATTEROMETRY AND MACHINE LEARNING | August 2022 | August 2024 | Allow | 23 | 3 | 0 | Yes | Yes |
| 17888920 | PREDICTIVE LEARNER RECOMMENDATION PLATFORM | August 2022 | January 2024 | Allow | 17 | 3 | 0 | No | No |
| 17815851 | GENERATING SYNTHETIC DATA EXAMPLES AS INTERPOLATION OF TWO DATA EXAMPLES THAT IS LINEAR IN THE SPACE OF RELATIVE SCORES | July 2022 | February 2023 | Allow | 7 | 1 | 0 | No | No |
| 17810778 | IMITATION LEARNING FOR MACHINE LEARNING SYSTEMS WITH SYNTHETIC DATA GENERATORS | July 2022 | November 2022 | Allow | 4 | 0 | 0 | No | No |
| 17840853 | EDUCATION LEARNING ENVIRONMENT AND METHODS FOR USING SAME | June 2022 | May 2024 | Allow | 23 | 4 | 0 | Yes | No |
| 17837444 | BIAS MITIGATING MACHINE LEARNING TRAINING SYSTEM | June 2022 | August 2022 | Allow | 2 | 0 | 0 | No | No |
| 17557298 | BIAS MITIGATING MACHINE LEARNING TRAINING SYSTEM | December 2021 | March 2022 | Allow | 3 | 0 | 0 | No | No |
| 17555535 | METHOD AND PLATFORM FOR PRE-TRAINED LANGUAGE MODEL AUTOMATIC COMPRESSION BASED ON MULTILEVEL KNOWLEDGE DISTILLATION | December 2021 | June 2022 | Allow | 6 | 0 | 0 | No | No |
| 17546085 | TEA IMPURITY DATA ANNOTATION METHOD BASED ON SUPERVISED MACHINE LEARNING | December 2021 | December 2023 | Abandon | 24 | 2 | 0 | No | No |
| 17508665 | SYSTEMS AND METHODS FOR TUNING HYPERPARAMETERS OF A MODEL AND ADVANCED CURTAILMENT OF A TRAINING OF THE MODEL | October 2021 | June 2025 | Allow | 44 | 0 | 0 | No | No |
| 17491435 | FEATURE PROCESSING METHOD AND APPARATUS FOR ARTIFICIAL INTELLIGENCE RECOMMENDATION MODEL, ELECTRONIC DEVICE, AND STORAGE MEDIUM | September 2021 | September 2024 | Allow | 35 | 2 | 0 | Yes | No |
| 17482057 | MACHINE LEARNING-BASED METHODS AND SYSTEMS FOR MODELING USER-SPECIFIC, ACTIVITY SPECIFIC ENGAGEMENT PREDICTING SCORES | September 2021 | August 2022 | Allow | 11 | 2 | 0 | Yes | No |
| 17440472 | AUTOMATED KNOWLEDGE BASE | September 2021 | August 2022 | Allow | 10 | 1 | 0 | Yes | No |
| 17459086 | Method to Train Model | August 2021 | March 2023 | Abandon | 19 | 3 | 0 | Yes | No |
| 17408450 | SYSTEM AND METHOD FOR INTELLIGENT SERVICE INTERMEDIATION | August 2021 | April 2024 | Allow | 32 | 6 | 0 | No | Yes |
| 17403668 | SYSTEM AND METHOD FOR BANK-BALANCED SPARSE ACTIVATION AND JOINT-ACTIVATION-WEIGHT-SPARSE TRAINING OF NEURAL NETWORKS | August 2021 | April 2022 | Allow | 8 | 1 | 0 | Yes | No |
| 17385054 | PREDICTIVE LEARNER SCORE | July 2021 | November 2023 | Allow | 27 | 5 | 0 | Yes | No |
| 17320876 | Real-Time Predictive Knowledge Pattern Machine | May 2021 | October 2021 | Allow | 5 | 3 | 0 | Yes | No |
| 17319282 | METHODS AND SYSTEMS FOR MONITORING DISTRIBUTED DATA-DRIVEN MODELS | May 2021 | November 2022 | Allow | 18 | 3 | 0 | Yes | No |
| 17315695 | TRAINING RECURRENT NEURAL NETWORKS TO GENERATE SEQUENCES | May 2021 | December 2023 | Allow | 31 | 0 | 0 | No | No |
| 17313108 | SYSTEM AND METHODS TO PROVIDE SEAMLESS INFORMATION EXCHANGE BETWEEN CERTIFIED AND UNCERTIFIED APPLICATIONS | May 2021 | March 2025 | Allow | 46 | 3 | 0 | Yes | No |
| 17227523 | MACHINE LEARNING FOR LOCATING INFORMATION IN KNOWLEDGE GRAPHS | April 2021 | March 2025 | Allow | 47 | 0 | 0 | Yes | No |
| 17227630 | SYSTEMS AND METHODS FOR SELECTIVELY COMMUNICATING TO A USER VIA A SYSTEM 1 OR SYSTEM 2 CHANNEL | April 2021 | June 2025 | Allow | 50 | 1 | 0 | Yes | No |
| 17225056 | COMPUTERIZED SYSTEMS AND METHODS FOR PRODUCT CATEGORIZATION USING ARTIFICIAL INTELLIGENCE | April 2021 | August 2024 | Allow | 40 | 2 | 0 | No | No |
| 17208399 | VARIABLE SENSITIVITY NODE | March 2021 | April 2025 | Allow | 49 | 1 | 0 | No | No |
| 17166483 | STRUCTURE-BASED, LIGAND ACTIVITY PREDICTION USING BINDING MODE PREDICTION INFORMATION | February 2021 | February 2025 | Allow | 49 | 1 | 0 | No | No |
| 17166493 | ARCHITECTURES FOR NATURAL LANGUAGE PROCESSING | February 2021 | July 2023 | Abandon | 29 | 1 | 0 | No | No |
| 17250506 | IDENTIFYING SALIENT FEATURES FOR GENERATIVE NETWORKS | January 2021 | October 2024 | Allow | 45 | 1 | 0 | Yes | No |
| 17160697 | SIDELINK-SUPPORTED FEDERATED LEARNING FOR TRAINING A MACHINE LEARNING COMPONENT | January 2021 | February 2025 | Allow | 49 | 1 | 0 | Yes | No |
| 17161210 | METHODS AND SYSTEMS FOR POWER MANAGEMENT IN A PATTERN RECOGNITION PROCESSING SYSTEM | January 2021 | November 2023 | Allow | 34 | 1 | 0 | No | No |
| 17160480 | OPTIMIZATION DEVICE, METHOD FOR CONTROLLING OPTIMIZATION DEVICE, AND COMPUTER-READABLE RECORDING MEDIUM RECORDING PROGRAM FOR CONTROLLING OPTIMIZATION DEVICE | January 2021 | January 2025 | Allow | 48 | 1 | 0 | No | No |
| 17261500 | INCENTIVE CONTROL FOR MULTI-AGENT SYSTEMS | January 2021 | April 2025 | Abandon | 51 | 1 | 0 | No | No |
| 17146556 | HYBRID VIBRATION-SOUND ACOUSTIC PROFILING USING A SIAMESE NETWORK TO DETECT LOOSE PARTS | January 2021 | June 2025 | Abandon | 53 | 2 | 0 | Yes | No |
| 17143152 | CONTEXTUALIZED HUMAN MACHINE SYSTEMS AND METHODS OF USE | January 2021 | October 2024 | Allow | 45 | 1 | 0 | No | No |
| 17132704 | Medical Fact Verification Method and Apparatus, Electronic Device, and Storage Medium | December 2020 | April 2025 | Abandon | 51 | 2 | 0 | No | No |
| 17130107 | ENTITY DISAMBIGUATION USING GRAPH NEURAL NETWORKS | December 2020 | August 2024 | Allow | 44 | 1 | 0 | Yes | No |
| 17129023 | MATRIX REPRESENTATION OF NEURAL NETWORKS | December 2020 | December 2024 | Abandon | 48 | 4 | 0 | Yes | No |
| 17128904 | QUANTITATIVE ANALYSIS METHOD AND APPARATUS FOR USER DECISION-MAKING BEHAVIOR | December 2020 | April 2025 | Abandon | 52 | 2 | 0 | No | No |
| 17115014 | METHODS AND SYSTEMS FOR EVALUATING AND IMPROVING THE CONTENT OF A KNOWLEDGE DATASTORE | December 2020 | March 2021 | Allow | 4 | 1 | 0 | No | No |
| 17115116 | Training a Neural Network Using Small Training Datasets | December 2020 | August 2023 | Abandon | 32 | 1 | 0 | No | No |
| 17107193 | Artificial Intelligence (AI) based Decision-Making Model for Orthodontic Diagnosis and Treatment Planning | November 2020 | July 2024 | Abandon | 43 | 1 | 0 | No | No |
| 17106858 | MACHINE LEARNING MODEL TRAINED USING FEATURES EXTRACTED FROM N-GRAMS OF MOUSE EVENT DATA | November 2020 | April 2024 | Allow | 41 | 2 | 0 | Yes | No |
| 17107760 | PREDICTIVE LEARNER SCORE | November 2020 | May 2021 | Allow | 5 | 1 | 0 | No | No |
| 17099338 | DATA TRAINING IN MULTI-SENSOR SETUPS | November 2020 | March 2023 | Allow | 28 | 1 | 0 | No | No |
| 17088146 | METHOD AND SYSTEM FOR GENERATING AN ALIMENTARY ELEMENT PREDICTION MACHINE-LEARNING MODEL | November 2020 | November 2022 | Allow | 24 | 5 | 0 | Yes | No |
| 17051458 | CREATION DEVICE, CREATION METHOD, AND PROGRAM | October 2020 | March 2025 | Abandon | 53 | 2 | 0 | No | No |
| 17079516 | Coastal Aquatic Conditions Reporting System Using A Learning Engine | October 2020 | April 2024 | Abandon | 42 | 1 | 0 | No | No |
| 17074201 | MACHINE LEARNING MODEL BIAS DETECTION AND MITIGATION | October 2020 | October 2024 | Allow | 48 | 3 | 0 | Yes | No |
| 17074364 | Systems and Methods for Training Generative Models Using Summary Statistics and Other Constraints | October 2020 | January 2024 | Allow | 39 | 1 | 0 | No | No |
| 17046014 | UNIFICATION OF MODELS HAVING RESPECTIVE TARGET CLASSES WITH DISTILLATION | October 2020 | May 2024 | Allow | 43 | 1 | 0 | Yes | No |
| 17065428 | MAPPABLE FILTER FOR NEURAL PROCESSOR CIRCUIT | October 2020 | June 2024 | Allow | 45 | 1 | 0 | Yes | No |
| 17062141 | DOCUMENT INITIATED INTELLIGENT WORKFLOW | October 2020 | March 2024 | Allow | 41 | 1 | 0 | Yes | No |
| 17035546 | CONTROLLING AGENTS OVER LONG TIME SCALES USING TEMPORAL VALUE TRANSPORT | September 2020 | May 2023 | Allow | 31 | 1 | 0 | No | No |
| 16948281 | MACHINE LEARNING BASED APPROACH TO DETECT WELL ANALOGUE | September 2020 | January 2024 | Allow | 41 | 1 | 0 | Yes | No |
| 16969868 | INFORMATION PROCESSING APPARATUS, METHOD, AND NON-TRANSITORY STORAGE MEDIUM | August 2020 | July 2024 | Abandon | 47 | 2 | 0 | Yes | No |
| 16968465 | SYSTEMS AND METHODS FOR TRAINING GENERATIVE MACHINE LEARNING MODELS | August 2020 | March 2024 | Abandon | 43 | 1 | 0 | No | No |
| 16945679 | OPERATION-BASED PARTITIONING OF A PARALLELIZABLE MACHINE LEARNING MODEL NETWORK ON ACCELERATOR HARDWARE | July 2020 | March 2024 | Abandon | 43 | 1 | 0 | No | No |
| 16942341 | GATEWAY FOR DISTRIBUTING AN ARTIFICIAL NEURAL NETWORK AMONG MULTIPLE PROCESSING NODES | July 2020 | September 2023 | Allow | 38 | 2 | 0 | No | No |
| 16938998 | TARGET VARIABLE DISTRIBUTION-BASED ACCEPTANCE OF MACHINE LEARNING TEST DATA SETS | July 2020 | March 2024 | Allow | 44 | 2 | 0 | Yes | No |
| 16934685 | Secure Training of Multi-Party Deep Neural Network | July 2020 | February 2023 | Allow | 31 | 1 | 0 | No | No |
| 16901608 | Imitation Training for Machine Learning Systems with Synthetic Data Generators | June 2020 | April 2022 | Allow | 22 | 3 | 0 | No | No |
| 16898890 | ELECTRONIC APPARATUS AND SERVER FOR REFINING ARTIFICIAL INTELLIGENCE MODEL, AND METHOD OF REFINING ARTIFICIAL INTELLIGENCE MODEL | June 2020 | May 2024 | Allow | 47 | 4 | 0 | Yes | No |
| 16894834 | SYSTEMS AND METHODS FOR PREPARING DATA FOR USE BY MACHINE LEARNING ALGORITHMS | June 2020 | July 2023 | Abandon | 37 | 1 | 0 | No | No |
| 16881172 | EXTENDING FINITE RANK DEEP KERNEL LEARNING TO FORECASTING OVER LONG TIME HORIZONS | May 2020 | February 2023 | Allow | 33 | 1 | 0 | Yes | No |
| 16881336 | PSEUDO-ROUNDING IN ARTIFICIAL NEURAL NETWORKS | May 2020 | January 2024 | Allow | 43 | 2 | 0 | Yes | No |
| 15930859 | AUTOMATED NEURAL NETWORK GENERATION USING FITNESS ESTIMATION | May 2020 | September 2020 | Allow | 4 | 0 | 0 | Yes | No |
| 15930968 | ESTIMATION OF LATENT WAITING AND SERVICE TIMES FROM INCOMPLETE EVENT LOGS | May 2020 | December 2020 | Allow | 7 | 1 | 0 | Yes | No |
| 16871473 | METHODS, ELECTRONIC DEVICES, AND COMPUTER STORAGE MEDIA FOR TESTING DEPTH LEARNING CHIP | May 2020 | February 2023 | Allow | 33 | 1 | 0 | Yes | No |
| 16849422 | SYSTEMS AND METHODS FOR TUNING HYPERPARAMETERS OF A MODEL AND ADVANCED CURTAILMENT OF A TRAINING OF THE MODEL | April 2020 | March 2021 | Allow | 11 | 1 | 0 | No | No |
| 16846825 | ELECTRONIC APPARATUS AND SERVER FOR REFINING ARTIFICIAL INTELLIGENCE MODEL, AND METHOD OF REFINING ARTIFICIAL INTELLIGENCE MODEL | April 2020 | August 2022 | Allow | 28 | 5 | 0 | Yes | No |
| 16827444 | GRADIENT COMPRESSION FOR DISTRIBUTED TRAINING | March 2020 | February 2025 | Allow | 59 | 6 | 0 | Yes | Yes |
| 16793536 | COMPUTERIZED SYSTEMS AND METHODS FOR PRODUCT CATEGORIZATION USING ARTIFICIAL INTELLIGENCE | February 2020 | January 2021 | Allow | 11 | 0 | 0 | Yes | No |
| 16787443 | LEARNING WITH MOMENT ESTIMATION USING DIFFERENT TIME CONSTANTS | February 2020 | January 2023 | Allow | 35 | 2 | 0 | No | No |
| 16784462 | DOCUMENT HANDLING | February 2020 | February 2024 | Abandon | 48 | 3 | 0 | Yes | No |
| 16738114 | HYPERPARAMETER DETERMINATION FOR A DIFFERENTIALLY PRIVATE FEDERATED LEARNING PROCESS | January 2020 | January 2024 | Allow | 48 | 3 | 0 | Yes | No |
| 16709598 | RAPID PREDICTIVE ANALYSIS OF VERY LARGE DATA SETS USING THE DISTRIBUTED COMPUTATIONAL GRAPH USING CONFIGURABLE ARRANGEMENT OF PROCESSING COMPONENTS | December 2019 | July 2022 | Allow | 31 | 5 | 0 | Yes | No |
| 16707464 | TRAINING RECURRENT NEURAL NETWORKS TO GENERATE SEQUENCES | December 2019 | January 2021 | Allow | 13 | 1 | 0 | No | No |
| 16670098 | PLAN RECOGNITION WITH UNRELIABLE OBSERVATIONS | October 2019 | August 2020 | Allow | 9 | 0 | 0 | Yes | No |
| 16601324 | CONTROLLING AGENTS OVER LONG TIME SCALES USING TEMPORAL VALUE TRANSPORT | October 2019 | May 2020 | Allow | 7 | 1 | 0 | No | No |
| 16589356 | ROTATING BODY MONITORING AND ALERT SYSTEM AND METHOD | October 2019 | March 2021 | Allow | 17 | 1 | 0 | No | No |
| 16481891 | INFORMATION PROCESSING DEVICE, METHOD, AND PROGRAM THAT USE DEEP LEARNING | July 2019 | July 2023 | Abandon | 48 | 2 | 0 | No | No |
| 16526360 | AUTOMATED NEURAL NETWORK GENERATION USING FITNESS ESTIMATION | July 2019 | March 2020 | Allow | 7 | 1 | 0 | Yes | No |
| 16416854 | OPTIMAL CONTENT IDENTIFICATION FOR LEARNING PATHS | May 2019 | May 2024 | Abandon | 60 | 4 | 0 | Yes | No |
| 16404141 | DEEP-LEARNING MODEL CATALOG CREATION | May 2019 | October 2022 | Allow | 42 | 1 | 0 | No | No |
| 16386371 | DYNAMIC PREFETCHING OF ONTOLOGIES BASED ON ML-BASED EXECUTION PATTERN RECOGNITION | April 2019 | April 2022 | Allow | 36 | 5 | 0 | Yes | No |
| 16362690 | COMPUTER-READABLE RECORDING MEDIUM, LEARNING METHOD, AND LEARNING DEVICE | March 2019 | April 2023 | Abandon | 49 | 2 | 0 | No | No |
This analysis examines appeal outcomes and the strategic value of filing appeals for examiner GARNER, CASEY R.
With a 66.7% 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, 62.5% of applications that filed an appeal were subsequently allowed. This appeal filing benefit rate is in the top 25% across the USPTO, indicating that filing appeals is particularly effective here. The act of filing often prompts favorable reconsideration during the mandatory appeal conference.
✓ 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 GARNER, CASEY R works in Art Unit 2123 and has examined 241 patent applications in our dataset. With an allowance rate of 71.4%, this examiner has a below-average tendency to allow applications. Applications typically reach final disposition in approximately 44 months.
Examiner GARNER, CASEY R's allowance rate of 71.4% places them in the 26% percentile among all USPTO examiners. This examiner has a below-average tendency to allow applications.
On average, applications examined by GARNER, CASEY R receive 2.42 office actions before reaching final disposition. This places the examiner in the 83% 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 GARNER, CASEY R is 44 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 +13.4% benefit to allowance rate for applications examined by GARNER, CASEY R. This interview benefit is in the 55% percentile among all examiners. Recommendation: Interviews provide an above-average benefit with this examiner and are worth considering.
When applicants file an RCE with this examiner, 24.6% of applications are subsequently allowed. This success rate is in the 27% 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 12.3% of cases where such amendments are filed. This entry rate is in the 7% 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 4% 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 57.1% of appeals filed. This is in the 24% percentile among all examiners. Of these withdrawals, 25.0% 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, 13.8% are granted (fully or in part). This grant rate is in the 7% 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 8% 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 1.2% of allowed cases (in the 58% 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.