Detailed information about the 100 most recent patent applications.
| Application Number | Title | Filing Date | Disposal Date | Disposition | Time (months) | Office Actions | Restrictions | Interview | Appeal |
|---|---|---|---|---|---|---|---|---|---|
| 18909783 | Mitigation for Prompt Injection in A.I. Models Capable of Accepting Text Input | October 2024 | May 2025 | Abandon | 7 | 1 | 0 | No | 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 |
| 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 |
| 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 |
| 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 |
| 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 |
| 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 |
| 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 |
| 17356935 | TRAINING NEURAL NETWORKS USING NORMALIZED TARGET OUTPUTS | June 2021 | May 2023 | Allow | 23 | 1 | 0 | No | 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 |
| 17306240 | Hierarchical Topic Machine Learning Operation | May 2021 | November 2022 | Allow | 19 | 1 | 0 | No | No |
| 17306237 | Cognitive Machine Learning Architecture | May 2021 | April 2023 | Allow | 24 | 3 | 0 | No | No |
| 17243750 | EXECUTING A NETWORK OF CHATBOTS USING A PARALLEL BOT APPROACH | April 2021 | November 2024 | Allow | 43 | 2 | 0 | No | No |
| 17284480 | DEEP NEURAL NETWORK HARDWARE ACCELERATOR BASED ON POWER EXPONENTIAL QUANTIZATION | April 2021 | August 2024 | Allow | 40 | 1 | 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 |
| 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 |
| 17167326 | PARAMETER UPDATE APPARATUS, CLASSIFICATION APPARATUS, RECORDING MEDIUM, AND PARAMETER UPDATE METHOD | February 2021 | February 2025 | Abandon | 49 | 2 | 0 | No | 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 |
| 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 |
| 17257895 | PREPARING SUPERPOSITIONS OF COMPUTATIONAL BASIS STATES ON A QUANTUM COMPUTER | January 2021 | December 2024 | Allow | 47 | 1 | 0 | No | No |
| 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 |
| 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 |
| 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 |
| 16991285 | MACHINE LEARNING DEVICE, RECEIVING DEVICE AND MACHINE LEARNING METHOD | August 2020 | June 2025 | Abandon | 58 | 3 | 0 | No | No |
| 16983065 | METHOD AND SYSTEM FOR DATA CLASSIFICATION TO GENERATE A SECOND ALIMENTARY PROVIDER | August 2020 | July 2022 | Allow | 23 | 5 | 0 | Yes | No |
| 16930356 | INFORMATION PROCESSING APPARATUS, METHOD OF PROCESSING INFORMATION, AND NON-TRANSITORY COMPUTER-READABLE STORAGE MEDIUM FOR STORING INFORMATION PROCESSING PROGRAM | July 2020 | August 2023 | Allow | 37 | 2 | 0 | No | No |
| 16924297 | APPARATUS AND METHOD WITH NEURAL NETWORK MODEL RECONFIGURATION | July 2020 | November 2024 | Allow | 52 | 4 | 0 | Yes | No |
| 16920807 | DIAGNOSTIC METHOD, LEARNING METHOD, LEARNING DEVICE, AND STORAGE MEDIUM STORING PROGRAM | July 2020 | December 2023 | Abandon | 41 | 2 | 0 | Yes | No |
| 16921471 | NEURAL NETWORK WEIGHT MATRIX ADJUSTING METHOD, WRITING CONTROL METHOD AND RELATED APPARATUS | July 2020 | January 2024 | Allow | 43 | 1 | 0 | Yes | No |
| 16885382 | ONE-SHOT LEARNING FOR NEURAL NETWORKS | May 2020 | August 2021 | Abandon | 15 | 2 | 0 | No | No |
| 16878364 | SYSTEMS AND METHODS FOR MODELING NOISE SEQUENCES AND CALIBRATING QUANTUM PROCESSORS | May 2020 | March 2024 | Allow | 45 | 1 | 0 | Yes | No |
| 16838971 | ULTRA PIPELINED ACCELERATOR FOR MACHINE LEARNING INFERENCE | April 2020 | April 2025 | Allow | 60 | 3 | 0 | No | No |
| 16837672 | Distributed Rule-Based Probabilistic Time-Series Classifier | April 2020 | February 2022 | Allow | 22 | 1 | 0 | No | No |
| 16826524 | Systems and Methods of Cross Layer Rescaling for Improved Quantization Performance | March 2020 | August 2024 | Allow | 53 | 4 | 0 | Yes | No |
| 16806324 | MODEL INTEGRATION METHOD AND DEVICE | March 2020 | June 2021 | Allow | 15 | 1 | 0 | Yes | No |
| 16783563 | ABNORMALITY DETECTION APPARATUS, ABNORMALITY DETECTION SYSTEM, AND ABNORMALITY DETECTION METHOD | February 2020 | July 2025 | Allow | 60 | 3 | 0 | Yes | No |
| 16781718 | COMPUTER-BASED SYSTEMS INCLUDING MACHINE LEARNING MODELS TRAINED ON DISTINCT DATASET TYPES AND METHODS OF USE THEREOF | February 2020 | October 2021 | Allow | 21 | 2 | 0 | Yes | No |
| 16779510 | MISMATCH DETECTION MODEL | January 2020 | June 2023 | Allow | 41 | 2 | 0 | Yes | No |
| 16713779 | VERIFICATION AND SYNTHESIS OF CYBER PHYSICAL SYSTEMS WITH MACHINE LEARNING AND CONSTRAINT-SOLVER-DRIVEN LEARNING | December 2019 | July 2024 | Allow | 55 | 5 | 0 | Yes | No |
| 16710296 | METHOD FOR ADJUSTING OUTPUT LEVEL OF MULTILAYER NEURAL NETWORK NEURON | December 2019 | November 2023 | Allow | 47 | 2 | 0 | No | No |
| 16619278 | ROBUST ANTI-ADVERSARIAL MACHINE LEARNING | December 2019 | May 2021 | Abandon | 18 | 2 | 0 | No | No |
| 16699616 | METHODS AND SYSTEMS FOR INFORMING FOOD ELEMENT DECISIONS IN THE ACQUISITION OF EDIBLE MATERIALS FROM ANY SOURCE | November 2019 | February 2023 | Allow | 38 | 8 | 0 | Yes | No |
| 16696628 | DETERMINING VARIABLE ATTRIBUTION BETWEEN INSTANCES OF DISCRETE SERIES MODELS | November 2019 | December 2020 | Allow | 12 | 2 | 0 | Yes | No |
| 16689065 | GENERATING OUTPUT DATA ITEMS USING TEMPLATE DATA ITEMS | November 2019 | July 2020 | Allow | 8 | 1 | 0 | Yes | No |
| 16686632 | SYSTEMS AND METHODS FOR SYNTHETIC DATA GENERATION USING A CLASSIFIER | November 2019 | July 2021 | Allow | 20 | 2 | 0 | Yes | Yes |
| 16580953 | METHODS FOR AUTOMATICALLY CONFIGURING PERFORMANCE EVALUATION SCHEMES FOR MACHINE LEARNING ALGORITHMS | September 2019 | April 2023 | Allow | 42 | 2 | 0 | Yes | No |
| 16559711 | SYSTEMS AND METHODS FOR CLASSIFYING DRIVER BEHAVIOR | September 2019 | December 2021 | Allow | 27 | 4 | 0 | Yes | No |
| 16551435 | INFORMATION PROCESSING DEVICE AND INFORMATION PROCESSING METHOD | August 2019 | March 2024 | Allow | 54 | 8 | 0 | Yes | No |
| 16537867 | VALIDATION OF MODELS AND DATA FOR COMPLIANCE WITH LAWS | August 2019 | May 2020 | Allow | 9 | 0 | 0 | Yes | No |
| 16536348 | METHOD OF AND SYSTEM FOR GENERATING A PREDICTION MODEL AND DETERMINING AN ACCURACY OF A PREDICTION MODEL | August 2019 | March 2022 | Allow | 32 | 1 | 0 | No | No |
| 16524341 | APPARATUS AND METHOD OF COMPRESSING NEURAL NETWORK | July 2019 | June 2024 | Allow | 59 | 6 | 0 | Yes | No |
| 16508115 | METHODS AND APPARATUS FOR SPIKING NEURAL NETWORK COMPUTING BASED ON A MULTI-LAYER KERNEL ARCHITECTURE | July 2019 | April 2023 | Abandon | 45 | 0 | 1 | No | No |
| 16504924 | SEQUENCE PROCESSING USING ONLINE ATTENTION | July 2019 | April 2021 | Allow | 21 | 4 | 0 | Yes | No |
| 16455473 | METHODS AND APPARATUS TO ANALYZE COMPUTER SYSTEM ATTACK MECHANISMS | June 2019 | April 2023 | Abandon | 46 | 2 | 0 | No | No |
| 16467576 | DICTIONARY LEARNING DEVICE, DICTIONARY LEARNING METHOD, DATA RECOGNITION METHOD, AND PROGRAM STORAGE MEDIUM | June 2019 | June 2023 | Abandon | 49 | 4 | 0 | Yes | No |
| 16466118 | FUZZY INPUT FOR AUTOENCODERS | June 2019 | July 2023 | Abandon | 49 | 4 | 0 | No | No |
| 16417133 | LEARNING DATA AUGMENTATION POLICIES | May 2019 | June 2020 | Allow | 13 | 2 | 0 | Yes | No |
| 16337154 | SECOND-ORDER OPTIMIZATION METHODS FOR AVOIDING SADDLE POINTS DURING THE TRAINING OF DEEP NEURAL NETWORKS | March 2019 | April 2024 | Abandon | 60 | 3 | 0 | No | No |
| 16364538 | ACCELERATING NEURON COMPUTATIONS IN ARTIFICIAL NEURAL NETWORKS BY SKIPPING BITS | March 2019 | November 2024 | Allow | 60 | 4 | 0 | Yes | No |
| 16295384 | METHOD AND APPARATUS FOR OPTIMIZING AND APPLYING MULTILAYER NEURAL NETWORK MODEL, AND STORAGE MEDIUM | March 2019 | April 2023 | Allow | 49 | 3 | 0 | Yes | No |
| 16331518 | TECHNIQUES FOR POLICY-CONTROLLED ANALYTIC DATA COLLECTION IN LARGE-SCALE SYSTEMS | March 2019 | January 2025 | Abandon | 60 | 5 | 0 | No | No |
| 16289627 | SYSTEMS AND METHODS FOR AN ATTRIBUTE GENERATOR TOOL WORKFLOW | February 2019 | September 2020 | Abandon | 19 | 1 | 0 | Yes | No |
| 16327679 | ASYCHRONOUS TRAINING OF MACHINE LEARNING MODEL | February 2019 | May 2024 | Allow | 60 | 5 | 0 | Yes | No |
| 16254493 | TERMINAL RULE OPERATION DEVICE AND METHOD | January 2019 | April 2024 | Abandon | 60 | 6 | 0 | Yes | No |
| 16180699 | INTELLIGENT RECOMMENDATION OF CONVENIENT EVENT OPPORTUNITIES | November 2018 | June 2022 | Allow | 44 | 3 | 0 | No | No |
| 16178853 | DISCRETIZED EMBEDDINGS OF PHYSIOLOGICAL WAVEFORMS | November 2018 | March 2023 | Abandon | 52 | 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, 14.3% of applications that filed an appeal were subsequently allowed. This appeal filing benefit rate is in the bottom 25% across the USPTO, indicating that filing appeals is less effective here than in most other areas.
⚠ 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 189 patent applications in our dataset. With an allowance rate of 73.5%, this examiner has a below-average tendency to allow applications. Applications typically reach final disposition in approximately 48 months.
Examiner MANG, VAN C's allowance rate of 73.5% places them in the 30% percentile among all USPTO examiners. This examiner has a below-average tendency to allow applications.
On average, applications examined by MANG, VAN C receive 2.82 office actions before reaching final disposition. This places the examiner in the 92% 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 48 months. This places the examiner in the 1% percentile for prosecution speed. Applications take longer to reach final disposition with this examiner compared to most others.
Conducting an examiner interview provides a +33.5% benefit to allowance rate for applications examined by MANG, VAN C. This interview benefit is in the 84% 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, 27.1% of applications are subsequently allowed. This success rate is in the 36% 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 10.0% of cases where such amendments are filed. This entry rate is in the 5% 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 75.0% of appeals filed. This is in the 59% percentile among all examiners. Of these withdrawals, 66.7% 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, 15.8% 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 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 0.0% of allowed cases (in the 9% 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.