Detailed information about the 100 most recent patent applications.
| Application Number | Title | Filing Date | Disposal Date | Disposition | Time (months) | Office Actions | Restrictions | Interview | Appeal |
|---|---|---|---|---|---|---|---|---|---|
| 19270319 | Multilingual Healthcare System with Personalized Medical Assistant, Decision Support, and Closed-Loop Device Control | July 2025 | December 2025 | Allow | 5 | 1 | 0 | Yes | No |
| 18909783 | Mitigation for Prompt Injection in A.I. Models Capable of Accepting Text Input | October 2024 | May 2025 | Abandon | 7 | 1 | 0 | No | No |
| 18639784 | Machine Learning-Based Approach to Characterize Software Supply Chain Risk | April 2024 | February 2026 | Allow | 22 | 4 | 0 | Yes | No |
| 18639805 | Machine Learning Approach to Identify Software Supply Chain Vulnerabilities from Binary Code Similarity Detection | April 2024 | January 2026 | Allow | 21 | 5 | 0 | Yes | No |
| 18605700 | TRAINING NEURAL NETWORK SYSTEMS TO PERFORM MULTIPLE MACHINE LEARNING TASKS | March 2024 | March 2025 | Allow | 12 | 1 | 0 | No | No |
| 18584625 | LEARNING DATA AUGMENTATION POLICIES | February 2024 | February 2025 | Allow | 12 | 1 | 0 | Yes | No |
| 18394044 | DEVELOPER ACTIVITY MODELER ENGINE FOR A PLATFORM SIGNAL MODELER | December 2023 | September 2025 | Allow | 21 | 2 | 0 | No | No |
| 18387159 | MISMATCH DETECTION MODEL | November 2023 | December 2025 | Allow | 25 | 4 | 0 | Yes | No |
| 18368318 | SYSTEMS AND METHODS FOR SYNTHETIC DATA GENERATION USING A CLASSIFIER | September 2023 | March 2025 | Allow | 18 | 2 | 0 | Yes | No |
| 18241725 | Predictive system for semiconductor manufacturing using generative large language models | September 2023 | September 2024 | Allow | 13 | 2 | 0 | No | No |
| 18354595 | PRODUCT QUALITY PREDICTION METHOD BASED ON DUAL-CHANNEL INFORMATION COMPLEMENTARY FUSION STACKED AUTO-ENCODER | July 2023 | January 2025 | Allow | 18 | 1 | 0 | No | No |
| 18222353 | PRIVACY-AWARE MODELING USING GENERALIZED AND PARTITIONED MODELS | July 2023 | July 2025 | Abandon | 25 | 4 | 0 | Yes | No |
| 18341967 | SYSTEMS AND METHODS FOR PATIENT-SPECIFIC EPILEPTIC SEIZURE PREDICTION | June 2023 | September 2025 | Allow | 27 | 4 | 0 | Yes | No |
| 18212614 | MACHINE LEARNING PLATFORM FOR DYNAMIC MODEL SELECTION | June 2023 | December 2025 | Allow | 30 | 5 | 0 | Yes | No |
| 18326499 | GENERATING FORECASTED EMISSIONS VALUE MODIFICATIONS AND MONITORING FOR PHYSICAL EMISSIONS SOURCES UTILIZING MACHINE-LEARNING MODELS | May 2023 | July 2024 | Allow | 14 | 2 | 0 | Yes | No |
| 18199901 | TRAINING NEURAL NETWORK SYSTEMS TO PERFORM MULTIPLE MACHINE LEARNING TASKS | May 2023 | December 2023 | Allow | 7 | 1 | 0 | No | No |
| 18199363 | MULTI-MODAL DATA PREDICTION METHOD BASED ON CAUSAL MARKOV MODEL | May 2023 | August 2024 | Abandon | 15 | 2 | 0 | No | No |
| 18143512 | Mitigation for Prompt Injection in A.I. Models Capable of Accepting Text Input | May 2023 | September 2024 | Allow | 16 | 4 | 0 | Yes | No |
| 18141725 | APPARATUS AND A METHOD FOR HIGHER-ORDER GROWTH MODELING | May 2023 | August 2024 | Allow | 15 | 4 | 0 | Yes | No |
| 18102075 | EFFICIENT REAL TIME SERVING OF ENSEMBLE MODELS | January 2023 | February 2025 | Allow | 25 | 3 | 0 | Yes | No |
| 18015065 | WEIGHT PRECISION CONFIGURATION METHOD AND APPARATUS, COMPUTER DEVICE AND STORAGE MEDIUM | January 2023 | August 2023 | Allow | 7 | 1 | 0 | No | No |
| 18071261 | METHOD AND SYSTEM FOR EVOLVED SARSA REINFORCEMENT LEARNING FOR FLOW SHOP SCHEDULING | November 2022 | December 2025 | Allow | 36 | 1 | 0 | No | No |
| 17890570 | METHOD AND SYSTEM FOR ARTIFICIAL INTELLIGENCE LEARNING USING MESSAGING SERVICE AND METHOD AND SYSTEM FOR RELAYING ANSWER USING ARTIFICIAL INTELLIGENCE | August 2022 | March 2025 | Abandon | 31 | 4 | 0 | Yes | No |
| 17819777 | METHOD AND APPARATUS FOR TRAINING CLASSIFICATION MODEL AND DATA CLASSIFICATION | August 2022 | September 2025 | Allow | 37 | 1 | 0 | No | No |
| 17872937 | MODELING CHARACTERS THAT INTERACT WITH USERS AS PART OF A CHARACTER-AS-A-SERVICE IMPLEMENTATION | July 2022 | February 2026 | Allow | 43 | 6 | 0 | Yes | No |
| 17865966 | MEMRISTOR AND NEUROMORPHIC DEVICE | July 2022 | December 2025 | Allow | 41 | 1 | 0 | Yes | No |
| 17807943 | BACKEND QUANTUM AND CLASSICAL RUNTIMES VIA CLOUD | June 2022 | November 2025 | Allow | 41 | 2 | 0 | Yes | No |
| 17728060 | SYSTEMS AND METHODS FOR CLASSIFYING DRIVER BEHAVIOR | April 2022 | October 2025 | Allow | 42 | 5 | 0 | Yes | No |
| 17683287 | COMPUTER-BASED SYSTEMS INCLUDING MACHINE LEARNING MODELS TRAINED ON DISTINCT DATASET TYPES AND METHODS OF USE THEREOF | February 2022 | May 2024 | Allow | 27 | 2 | 0 | No | No |
| 17651388 | GENERATING FORECASTED EMISSIONS VALUE MODIFICATIONS AND MONITORING FOR PHYSICAL EMISSIONS SOURCES UTILIZING MACHINE-LEARNING MODELS | February 2022 | March 2023 | Allow | 13 | 2 | 0 | Yes | No |
| 17670368 | DEEP NEURAL NETWORK-BASED DECISION NETWORK | February 2022 | September 2024 | Allow | 32 | 0 | 0 | Yes | No |
| 17668280 | MACHINE LEARNING MODEL GENERATION AND UPDATING FOR MANUFACTURING EQUIPMENT | February 2022 | November 2025 | Allow | 45 | 2 | 0 | Yes | No |
| 17578826 | System and Method for Identification and Verification | January 2022 | June 2023 | Allow | 17 | 2 | 0 | Yes | No |
| 17572800 | INTELLIGENT AROMATIC SIMULATION OF FOOD RECIPE | January 2022 | October 2023 | Allow | 21 | 1 | 0 | No | No |
| 17624812 | METHOD AND SYSTEM USING AI CALL PREDICTION AND CACHE | January 2022 | October 2025 | Allow | 45 | 4 | 0 | Yes | No |
| 17618372 | INFORMATION LEARNING APPARATUS, INFORMATION PROCESSING APPARATUS, INFORMATION LEARNING METHOD, INFORMATION PROCESSING METHOD AND PROGRAM | December 2021 | August 2025 | Allow | 44 | 1 | 0 | No | No |
| 17521840 | DUAL-SPARSE NEURAL PROCESSING UNIT WITH MULTI-DIMENSIONAL ROUTING OF NON-ZERO VALUES | November 2021 | January 2026 | Allow | 51 | 0 | 0 | No | No |
| 17520482 | SYSTEMS AND METHODS FOR SYNTHETIC DATA GENERATION USING A CLASSIFIER | November 2021 | June 2023 | Allow | 19 | 1 | 0 | No | No |
| 17482200 | SYSTEM, METHOD, AND PROGRAM FOR PREDICTING INFORMATION | September 2021 | June 2025 | Allow | 45 | 3 | 0 | Yes | No |
| 17475174 | METHODS AND SYSTEMS FOR SIMULATING DYNAMICAL SYSTEMS VIA SYNAPTIC DESCENT IN ARTIFICIAL NEURAL NETWORKS | September 2021 | July 2025 | Allow | 46 | 1 | 0 | No | No |
| 17468276 | ARTIFICIAL INTELLIGENCE POWERED REDLINING OF TEXT DOCUMENTS | September 2021 | September 2025 | Abandon | 49 | 2 | 0 | No | No |
| 17467920 | SYSTEM FOR CONTEXTUAL AND POSITIONAL PARAMETERIZED RECORD BUILDING | September 2021 | July 2025 | Abandon | 46 | 1 | 0 | No | No |
| 17381064 | CORE DATA SERVICES PERFORMANCE ANNOTATIONS | July 2021 | October 2025 | Allow | 51 | 3 | 0 | Yes | No |
| 17379310 | TRAINABLE DIFFERENTIAL PRIVACY FOR MACHINE LEARNING | July 2021 | July 2025 | Allow | 48 | 2 | 0 | Yes | No |
| 17423088 | OBLIVIOUS BINARY NEURAL NETWORKS | July 2021 | January 2026 | Allow | 54 | 1 | 0 | No | No |
| 17356935 | TRAINING NEURAL NETWORKS USING NORMALIZED TARGET OUTPUTS | June 2021 | May 2023 | Allow | 23 | 1 | 0 | No | No |
| 17356145 | MULTI-INSTANCE LEARNING FRAMEWORK FOR ARTIFICIAL INTELLIGENCE (AI) HOUSEHOLD INFERENCE MODELS | June 2021 | January 2026 | Allow | 55 | 3 | 0 | Yes | No |
| 17353511 | METHOD OF TRAINING A NEURAL NETWORK TO REFLECT EMOTIONAL PERCEPTION AND RELATED SYSTEM AND METHOD FOR CATEGORIZING AND FINDING ASSOCIATED CONTENT | June 2021 | March 2022 | Allow | 25 | 1 | 0 | No | No |
| 17351775 | INTENT-BASED SCHEDULING VIA DIGITAL PERSONAL ASSISTANT | June 2021 | June 2025 | Allow | 48 | 3 | 0 | Yes | No |
| 17332893 | PARTIALLY LOCAL FEDERATED LEARNING | May 2021 | June 2025 | Allow | 48 | 2 | 0 | Yes | No |
| 17328028 | ABSTRACTION LIBRARY TO ENABLE SCALABLE DISTRIBUTED MACHINE LEARNING | May 2021 | August 2025 | Allow | 51 | 3 | 0 | Yes | No |
| 17313698 | SYSTEMS AND METHODS FOR RENDERING NEAR-REAL-TIME EMBEDDING MODELS FOR PERSONALIZED NEWS RECOMMENDATIONS | May 2021 | January 2026 | Allow | 57 | 6 | 0 | Yes | No |
| 17308723 | DATA PROCESSING DEVICE OF SPIKE NEURAL NETWORK AND DATA PROCESSING METHOD THEREOF | May 2021 | August 2025 | Allow | 52 | 3 | 0 | Yes | No |
| 17306237 | Cognitive Machine Learning Architecture | May 2021 | April 2023 | Allow | 24 | 3 | 0 | No | No |
| 17306240 | Hierarchical Topic Machine Learning Operation | May 2021 | November 2022 | Allow | 19 | 1 | 0 | No | No |
| 17244649 | PREDICTIVE PERFORMANCE ON SLICES VIA ACTIVE LEARNING | April 2021 | December 2025 | Abandon | 56 | 4 | 0 | Yes | No |
| 17243750 | EXECUTING A NETWORK OF CHATBOTS USING A PARALLEL BOT APPROACH | April 2021 | November 2024 | Allow | 43 | 2 | 0 | No | No |
| 17289154 | ANALYSIS DEVICE, MACHINE LEARNING DEVICE, ANALYSIS SYSTEM, ANALYSIS METHOD, AND RECORDING MEDIUM | April 2021 | March 2026 | Abandon | 58 | 2 | 0 | Yes | No |
| 17238698 | MACHINE LEARNING SYSTEM FOR ASSESSING HEART VALVES AND SURROUNDING CARDIOVASCULAR TRACTS | April 2021 | March 2026 | Allow | 59 | 2 | 0 | No | Yes |
| 17284480 | DEEP NEURAL NETWORK HARDWARE ACCELERATOR BASED ON POWER EXPONENTIAL QUANTIZATION | April 2021 | August 2024 | Allow | 40 | 1 | 0 | Yes | No |
| 17215021 | MACHINE LEARNING IN A CONTINUOUS INTEGRATION AND DEPLOYMENT ENVIRONMENT FOR COMPLIANCE AND SECURITY OF INFRASTRUCTURE AS CODE | March 2021 | March 2026 | Allow | 59 | 4 | 0 | Yes | No |
| 17216654 | Lossless Tiling in Convolution Networks - Read-Modify-Write in Backward Pass | March 2021 | September 2021 | Allow | 9 | 1 | 0 | Yes | No |
| 17279121 | ABNORMAL DRIVING STATE DETERMINATION DEVICE AND METHOD USING NEURAL NETWORK MODEL | March 2021 | July 2025 | Abandon | 52 | 2 | 0 | No | No |
| 17275931 | AI CREATION VERIFICATION DEVICE | March 2021 | December 2025 | Abandon | 57 | 5 | 0 | No | No |
| 17197579 | DETERMINING VARIABLE ATTRIBUTION BETWEEN INSTANCES OF DISCRETE SERIES MODELS | March 2021 | March 2025 | Abandon | 48 | 4 | 0 | No | No |
| 17195865 | ARITHMETIC APPARATUS AND ARITHMETIC METHOD | March 2021 | February 2025 | Allow | 47 | 2 | 0 | Yes | No |
| 17187230 | METHOD AND APPARATUS FOR MONITORING PHYSICAL ACTIVITY | February 2021 | May 2025 | Abandon | 50 | 3 | 0 | No | No |
| 17184880 | NETWORK OF TENSOR TIME SERIES | February 2021 | November 2025 | Allow | 56 | 3 | 0 | Yes | No |
| 17183323 | METHODS AND APPARATUS TO PERFORM DEEPFAKE DETECTION USING AUDIO AND VIDEO FEATURES | February 2021 | March 2026 | Abandon | 60 | 4 | 0 | Yes | No |
| 17167326 | PARAMETER UPDATE APPARATUS, CLASSIFICATION APPARATUS, RECORDING MEDIUM, AND PARAMETER UPDATE METHOD | February 2021 | February 2025 | Abandon | 49 | 2 | 0 | No | No |
| 17167281 | ARTIFICIAL INTELLIGENCE BASED CONFIGURE PRICE QUOTE RECOMMENDATION SYSTEM | February 2021 | October 2025 | Allow | 57 | 2 | 0 | Yes | No |
| 17165240 | GRAPH DATA REPRESENTATION SYSTEMS AND METHODS FOR ELIGIBILITY DETERMINATION AND/OR MONITORING | February 2021 | May 2025 | Allow | 51 | 3 | 0 | Yes | No |
| 17164691 | ATTENTION NEURAL NETWORKS WITH LOCALITY-SENSITIVE HASHING | February 2021 | January 2025 | Allow | 47 | 2 | 0 | Yes | No |
| 17163343 | TRAINING METHOD AND SYSTEM FOR DECISION TREE MODEL, STORAGE MEDIUM, AND PREDICTION METHOD | January 2021 | April 2025 | Allow | 51 | 1 | 0 | Yes | No |
| 17159618 | EVENT CORRELATION BASED ON PATTERN RECOGNITION AND MACHINE LEARNING | January 2021 | January 2026 | Abandon | 59 | 4 | 0 | Yes | No |
| 17154243 | FAST CONVERGING GRADIENT COMPRESSOR FOR FEDERATED LEARNING | January 2021 | April 2025 | Allow | 51 | 3 | 0 | Yes | No |
| 17152524 | RECRUITMENT PROCESS GRAPH BASED UNSUPERVISED ANOMALY DETECTION | January 2021 | May 2025 | Allow | 52 | 2 | 0 | Yes | No |
| 17152155 | INFORMATION PROCESSING DEVICE, INFORMATION PROCESSING METHOD, AND RECORDING MEDIUM | January 2021 | May 2025 | Allow | 52 | 4 | 0 | No | No |
| 17149602 | COMPUTING DOT PRODUCTS AT HARDWARE ACCELERATOR | January 2021 | September 2025 | Allow | 56 | 2 | 0 | No | No |
| 17257895 | PREPARING SUPERPOSITIONS OF COMPUTATIONAL BASIS STATES ON A QUANTUM COMPUTER | January 2021 | December 2024 | Allow | 47 | 1 | 0 | No | No |
| 17258151 | Elevator Maintenance Solution Leveraging IOT Data, Cloud-Based Predictive Analytics and Machine Learning | January 2021 | January 2026 | Abandon | 60 | 2 | 0 | Yes | No |
| 17137720 | INTEGRATED FEATURE ENGINEERING | December 2020 | March 2026 | Allow | 60 | 4 | 0 | Yes | Yes |
| 17133266 | VEHICLE-MOUNTED DEVICE AND METHOD FOR TRAINING OBJECT RECOGNITION MODEL | December 2020 | December 2024 | Abandon | 48 | 1 | 0 | No | No |
| 17128895 | SYSTEMS AND METHODS ASSOCIATED WITH MULTI DATA TYPE MULTI DATA SET ARTIFICIAL INTELLIGENCE PACKAGES, MACHINE LEARNING PACKAGES AND MATHEMATICAL SYSTEMS | December 2020 | November 2024 | Abandon | 46 | 0 | 1 | No | No |
| 17123528 | CONVOLUTIONAL NEURAL NETWORK WITH MULTIPLE OUTPUT FRAMES | December 2020 | February 2025 | Allow | 50 | 3 | 0 | No | No |
| 17110629 | GENERATING DATA BASED ON PRE-TRAINED MODELS USING GENERATIVE ADVERSARIAL MODELS | December 2020 | February 2025 | Allow | 51 | 3 | 0 | No | No |
| 15734940 | ON-LINE PREDICTION METHOD OF SURFACE ROUGHNESS OF PARTS BASED ON SDAE-DBN ALGORITHM | December 2020 | March 2024 | Allow | 39 | 1 | 0 | No | No |
| 17109809 | APPARATUS AND METHOD FOR RECOMMENDING FEDERATED LEARNING BASED ON TENDENCY ANALYSIS OF RECOGNITION MODEL AND METHOD FOR FEDERATED LEARNING IN USER TERMINAL | December 2020 | August 2024 | Abandon | 45 | 1 | 0 | No | No |
| 17106149 | Predictive Readiness and Accountability Management | November 2020 | May 2024 | Abandon | 42 | 1 | 0 | No | No |
| 16950821 | HIGH EFFICIENCY OPTICAL NEURAL NETWORK | November 2020 | December 2024 | Abandon | 49 | 1 | 0 | No | No |
| 17090116 | ARTIFICIAL CONTENT IDENTIFICATION USING SUBSET SCANNING OVER GENERATIVE MODEL ACTIVATIONS | November 2020 | December 2025 | Allow | 60 | 5 | 0 | Yes | No |
| 17053069 | ENERGY IDENTIFICATION METHOD FOR MICRO-ENERGY DEVICE BASED ON BP NEURAL NETWORK | November 2020 | December 2024 | Abandon | 49 | 2 | 0 | No | No |
| 17089631 | SOURCE-AGNOSTIC IMAGE PROCESSING | November 2020 | July 2024 | Allow | 45 | 2 | 0 | Yes | No |
| 17082148 | METHOD AND DEVICE FOR TRAINING GENERATIVE ADVERSARIAL NETWORK FOR CONVERTING BETWEEN HETEROGENEOUS DOMAIN DATA | October 2020 | June 2025 | Allow | 56 | 3 | 0 | No | No |
| 17061103 | LEARNING DATA AUGMENTATION POLICIES | October 2020 | December 2023 | Allow | 38 | 1 | 0 | Yes | No |
| 17023195 | FEDERATED LEARNING TECHNIQUE FOR APPLIED MACHINE LEARNING | September 2020 | September 2024 | Allow | 48 | 2 | 0 | No | No |
| 17020094 | VALIDATION OF MODELS AND DATA FOR COMPLIANCE WITH LAWS | September 2020 | December 2022 | Allow | 27 | 1 | 0 | No | No |
| 17003384 | TRAINING AND APPLICATION METHOD APPARATUS SYSTEM AND STROAGE MEDIUM OF NEURAL NETWORK MODEL | August 2020 | July 2025 | Allow | 59 | 4 | 0 | Yes | No |
| 16997532 | AUTOMATED TRAINING, RETRAINING AND RELEARNING APPLIED TO DATA ANALYTICS | August 2020 | June 2024 | Allow | 46 | 2 | 0 | Yes | No |
| 16993724 | SYSTEM, METHOD, AND COMPUTER PROGRAM FOR TRANSFORMER NEURAL NETWORKS | August 2020 | June 2024 | Allow | 46 | 1 | 0 | No | No |
This analysis examines appeal outcomes and the strategic value of filing appeals for examiner MANG, VAN C.
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, 22.2% 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 MANG, VAN C works in Art Unit 2126 and has examined 205 patent applications in our dataset. With an allowance rate of 72.2%, this examiner has a below-average tendency to allow applications. Applications typically reach final disposition in approximately 49 months.
Examiner MANG, VAN C's allowance rate of 72.2% places them in the 35% percentile among all USPTO examiners. This examiner has a below-average tendency to allow applications.
On average, applications examined by MANG, VAN C receive 2.95 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 MANG, VAN C is 49 months. This places the examiner in the 6% percentile for prosecution speed. Applications take longer to reach final disposition with this examiner compared to most others.
Conducting an examiner interview provides a +32.9% benefit to allowance rate for applications examined by MANG, VAN C. This interview benefit is in the 81% 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, 26.0% of applications are subsequently allowed. This success rate is in the 42% 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 9.2% of cases where such amendments are filed. This entry rate is in the 9% 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 83.3% of appeals filed. This is in the 76% 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 frequently reconsiders rejections during the appeal process compared to other examiners. Per MPEP § 1207.01, all appeals must go through a mandatory appeal conference. Filing a Notice of Appeal may prompt favorable reconsideration even before you file an Appeal Brief.
When applicants file petitions regarding this examiner's actions, 23.1% are granted (fully or in part). This grant rate is in the 12% 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 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.