Detailed information about the 100 most recent patent applications.
| Application Number | Title | Filing Date | Disposal Date | Disposition | Time (months) | Office Actions | Restrictions | Interview | Appeal |
|---|---|---|---|---|---|---|---|---|---|
| 19035231 | METHOD FOR CONVERTING NEURAL NETWORK | January 2025 | November 2025 | Allow | 10 | 2 | 0 | Yes | No |
| 19023430 | UNMANNED AERIAL VEHICLE IDENTIFICATION METHOD BASED ON BLIND SOURCE SEPARATION AND DEEP LEARNING | January 2025 | April 2025 | Allow | 3 | 0 | 0 | No | No |
| 18900506 | RELATIVE MARGIN FOR CONTRASTIVE LEARNING | September 2024 | December 2024 | Allow | 3 | 0 | 0 | Yes | No |
| 18604152 | Context Driven Routine Prediction Assistance | March 2024 | September 2025 | Allow | 19 | 1 | 0 | Yes | No |
| 18418424 | CORRELATIONS BETWEEN WORKLOAD CHARACTERISTICS AND ELAPSED TIMES | January 2024 | March 2025 | Allow | 14 | 0 | 0 | No | No |
| 18394019 | GENERATIVE MACHINE LEARNING SYSTEMS FOR GENERATING STRUCTURAL INFORMATION REGARDING CHEMICAL COMPOUND | December 2023 | June 2025 | Allow | 18 | 2 | 0 | Yes | No |
| 18520192 | EXECUTING A GENETIC ALGORITHM ON A LOW-POWER CONTROLLER | November 2023 | April 2025 | Allow | 16 | 2 | 0 | Yes | No |
| 18519123 | SYSTEMS, APPARATUSES AND METHODS FOR SENSING FETAL ACTIVITY | November 2023 | March 2025 | Abandon | 16 | 1 | 0 | No | No |
| 18385226 | System and Methods for Achieving Orthogonal Control of Non-Orthogonal Qubit Parameters | October 2023 | August 2024 | Allow | 10 | 0 | 0 | Yes | No |
| 18375006 | SYSTEMS AND METHODS FOR AUTOREGRESSIVE RECURRENT NEURAL NETWORKS FOR IDENTIFYING ACTIONABLE VITAL ALERTS | September 2023 | July 2024 | Allow | 10 | 2 | 0 | Yes | No |
| 18244585 | MACHINE LEARNING NETWORKS, ARCHITECTURES AND TECHNIQUES FOR DETERMINING OR PREDICTING DEMAND METRICS IN ONE OR MORE CHANNELS | September 2023 | March 2025 | Allow | 18 | 2 | 0 | No | No |
| 18357554 | SYSTEM AND METHOD FOR EFFICIENT EVOLUTION OF DEEP CONVOLUTIONAL NEURAL NETWORKS USING FILTER-WISE RECOMBINATION AND PROPAGATED MUTATIONS | July 2023 | August 2024 | Allow | 13 | 0 | 0 | No | No |
| 18349089 | NEURAL NETWORKS WITH SWITCH LAYERS | July 2023 | October 2023 | Allow | 3 | 0 | 0 | No | No |
| 18348217 | CUSTOMIZED PREDICTIVE ANALYTICAL MODEL TRAINING | July 2023 | April 2025 | Allow | 21 | 1 | 0 | No | No |
| 18333437 | DEVICES AND METHODS FOR FORMING OPTICAL TRAPS FOR SCALABLE TRAPPED ATOM COMPUTING | June 2023 | October 2023 | Allow | 4 | 1 | 0 | Yes | No |
| 18265715 | SYNAPTIC WEIGHT TRAINING METHOD, TARGET IDENTIFICATION METHOD, ELECTRONIC DEVICE AND MEDIUM | June 2023 | February 2024 | Allow | 8 | 1 | 0 | No | No |
| 18205434 | SENSE-PLUS-COMPUTE QUANTUM-STATE CARRIERS | June 2023 | December 2024 | Allow | 18 | 1 | 0 | No | No |
| 18314628 | NETWORK MANAGEMENT BASED ON MODELING OF CASCADING EFFECT OF FAILURE | May 2023 | August 2025 | Allow | 27 | 2 | 0 | No | No |
| 18135259 | LEARNING AND DEPLOYING COMPRESSION OF RADIO SIGNALS | April 2023 | January 2025 | Allow | 21 | 1 | 0 | Yes | No |
| 18295153 | COMPUTER-BASED SYSTEMS CONFIGURED FOR DETECTING AND SPLITTING DATA TYPES IN A DATA FILE AND METHODS OF USE THEREOF | April 2023 | September 2024 | Allow | 18 | 0 | 0 | No | No |
| 18190874 | METHOD AND DEVICE FOR STUDENT TRAINING NETWORKS WITH TEACHER NETWORKS | March 2023 | March 2025 | Allow | 24 | 1 | 0 | No | No |
| 18114852 | COMPUTERIZED ENGINES AND GRAPHICAL USER INTERFACES FOR CUSTOMIZING AND VALIDATING FORECASTING MODELS | February 2023 | June 2023 | Allow | 3 | 0 | 0 | No | No |
| 18174394 | AUGMENTED RECURRENT NEURAL NETWORK WITH EXTERNAL MEMORY | February 2023 | June 2024 | Allow | 16 | 0 | 0 | No | No |
| 18171078 | SYSTEMS AND METHODS FOR AUTOREGRESSIVE RECURRENT NEURAL NETWORKS FOR IDENTIFYING ACTIONABLE VITAL ALERTS | February 2023 | July 2023 | Allow | 4 | 0 | 0 | Yes | No |
| 18108040 | MACHINE LEARNING NETWORKS, ARCHITECTURES AND TECHNIQUES FOR DETERMINING OR PREDICTING DEMAND METRICS IN ONE OR MORE CHANNELS | February 2023 | July 2023 | Allow | 5 | 1 | 0 | No | No |
| 18102402 | APPARATUS AND METHODS FOR GAUSSIAN BOSON SAMPLING | January 2023 | January 2024 | Allow | 12 | 0 | 0 | No | No |
| 18159136 | ANOMALY SCORE ADJUSTMENT ACROSS ANOMALY GENERATORS | January 2023 | July 2025 | Abandon | 30 | 1 | 0 | No | No |
| 18158182 | METHOD AND COMPUTING SYSTEM FOR MODELLING A PRIMATE BRAIN | January 2023 | September 2024 | Allow | 20 | 0 | 0 | No | No |
| 18092704 | METHOD AND SYSTEM FOR PREDICTING A GEOGRAPHIC LOCATION OF A NETWORK ENTITY | January 2023 | July 2024 | Allow | 18 | 0 | 0 | No | No |
| 18004013 | Methods, Electronic Devices, and Computer-Readable Media for Training, and Processing Data Through, a Spiking Neuron Network | December 2022 | November 2023 | Allow | 11 | 2 | 0 | No | No |
| 18013598 | QUANTUM DATA ERASURE METHOD, SYSTEM AND DEVICE, AND READABLE STORAGE MEDIUM | December 2022 | July 2023 | Allow | 7 | 1 | 0 | No | No |
| 18012297 | Method And Apparatus For Generating Weather Data Based On Machine Learning | December 2022 | September 2023 | Allow | 9 | 1 | 0 | Yes | No |
| 18145273 | SYSTEM AND METHOD FOR GENERATING TRAINING DATA FOR MACHINE LEARNING CLASSIFIER | December 2022 | December 2024 | Allow | 24 | 2 | 0 | No | No |
| 18069637 | METHOD FOR MANAGING TRAINING DATA | December 2022 | December 2024 | Abandon | 23 | 2 | 0 | No | No |
| 18080986 | QUANTUM TOMOGRAPHY AND PHOTON SOURCE OPTIMIZATION | December 2022 | August 2024 | Allow | 20 | 1 | 0 | Yes | No |
| 18008500 | HYPERPARAMETER ADJUSTMENT DEVICE, NON-TRANSITORY RECORDING MEDIUM IN WHICH HYPERPARAMETER ADJUSTMENT PROGRAM IS RECORDED, AND HYPERPARAMETER ADJUSTMENT PROGRAM | December 2022 | September 2024 | Allow | 22 | 1 | 0 | No | No |
| 18070442 | INTELLIGENT CONTROL WITH HIERARCHICAL STACKED NEURAL NETWORKS | November 2022 | June 2024 | Allow | 18 | 0 | 0 | No | No |
| 17990183 | TEMPORAL PROCESSING SCHEME AND SENSORIMOTOR INFORMATION PROCESSING | November 2022 | June 2024 | Allow | 19 | 2 | 0 | Yes | No |
| 17978491 | REINFORCEMENT LEARNING TECHNIQUES FOR NETWORK-BASED TRANSFER LEARNING | November 2022 | March 2023 | Allow | 5 | 1 | 0 | No | No |
| 17967147 | SYSTEM AND METHOD FOR BUILDING PREDICTIVE MODEL FOR SYNTHESIZING DATA | October 2022 | February 2024 | Allow | 16 | 1 | 0 | No | No |
| 17938131 | GENERATING AND MANAGING DEEP TENSOR NEURAL NETWORKS | October 2022 | January 2024 | Allow | 15 | 1 | 0 | Yes | No |
| 17894798 | Training network with discrete weight values | August 2022 | February 2025 | Allow | 29 | 2 | 0 | Yes | No |
| 17801283 | TURBULENCE FIELD UPDATE METHOD AND APPARATUS, AND RELATED DEVICE THEREOF | August 2022 | June 2023 | Allow | 10 | 1 | 0 | Yes | No |
| 17890843 | SEQUENCE-BASED ANOMALY DETECTION WITH HIERARCHICAL SPIKING NEURAL NETWORKS | August 2022 | September 2025 | Allow | 37 | 0 | 0 | No | No |
| 17859347 | DEVICE FOR OPTICALLY TRANSMITTING AND RECEIVING IMAGES | July 2022 | February 2025 | Allow | 32 | 2 | 0 | No | No |
| 17805730 | GROUP OF NEURAL NETWORKS ENSURING INTEGRITY | June 2022 | November 2023 | Allow | 17 | 1 | 0 | No | No |
| 17804621 | MACHINE LEARNING TECHNIQUE WITH TARGETED FEATURE SETS FOR CATEGORICAL ANOMALY DETECTION | May 2022 | April 2023 | Allow | 10 | 1 | 0 | No | No |
| 17658462 | SIMPLIFICATION OF SPIKING NEURAL NETWORK MODELS | April 2022 | January 2024 | Allow | 21 | 1 | 0 | No | No |
| 17709704 | FEATURE SEGMENTATION-BASED ENSEMBLE LEARNING FOR CLASSIFICATION AND REGRESSION | March 2022 | October 2025 | Allow | 42 | 1 | 0 | No | No |
| 17710454 | METHOD AND SYSTEM FOR GENERATING CONVERSATION SUMMARY | March 2022 | April 2023 | Allow | 12 | 2 | 0 | No | No |
| 17672543 | REPRESENTING A NEURAL NETWORK UTILIZING PATHS WITHIN THE NETWORK TO IMPROVE A PERFORMANCE OF THE NEURAL NETWORK | February 2022 | March 2024 | Allow | 25 | 2 | 0 | No | No |
| 17649993 | CORRELATIONS BETWEEN WORKLOAD CHARACTERISTICS AND ELAPSED TIMES | February 2022 | November 2023 | Allow | 21 | 2 | 1 | No | No |
| 17620451 | Small and Fast Video Processing Networks via Neural Architecture Search | December 2021 | June 2025 | Allow | 42 | 1 | 0 | No | No |
| 17545819 | AUTOENCODER-BASED INFORMATION CONTENT PRESERVING DATA ANONYMIZATION SYSTEM | December 2021 | March 2024 | Allow | 28 | 2 | 0 | Yes | No |
| 17456038 | GROUP OF NEURAL NETWORKS ENSURING INTEGRITY | November 2021 | April 2022 | Allow | 5 | 1 | 0 | Yes | No |
| 17613042 | PARAMETER ESTIMATION DEVICE, PARAMETER ESTIMATION METHOD, AND PARAMETER ESTIMATION PROGRAM | November 2021 | October 2025 | Abandon | 47 | 1 | 0 | Yes | No |
| 17512086 | APPARATUS AND METHODS FOR GAUSSIAN BOSON SAMPLING | October 2021 | October 2022 | Allow | 12 | 2 | 0 | No | No |
| 17510517 | Computational Analysis to Predict Molecular Recognition Space of Monoclonal Antibodies Through Random-Sequence Peptide Arrays | October 2021 | November 2023 | Allow | 24 | 1 | 0 | Yes | No |
| 17508344 | RANDOMIZED QUANTUM ALGORITHM FOR STATISTICAL PHASE ESTIMATION | October 2021 | October 2025 | Allow | 47 | 2 | 0 | Yes | No |
| 17505202 | QUANTUM COMPUTING WITH KERNEL METHODS FOR MACHINE LEARNING | October 2021 | January 2025 | Allow | 39 | 0 | 0 | Yes | No |
| 17503743 | ADAPTIVE PATH PLANNING METHOD BASED ON NEUTRAL NETWORKS TRAINED BY THE EVOLUTIONAL ALGORITHMS | October 2021 | March 2025 | Allow | 41 | 1 | 0 | No | No |
| 17500912 | SYSTEM AND METHOD FOR PERFORMING FAST COMPUTATIONS USING QUANTUM COUNTING AND PSEUDO-RANDOM SETS | October 2021 | June 2023 | Allow | 20 | 0 | 0 | Yes | No |
| 17450527 | OPEN SET CLASSIFICATION BASED ON HETEROGENOUS MODEL ENSEMBLE IN MULTISENSOR ENVIRONMENTS | October 2021 | September 2025 | Allow | 47 | 2 | 0 | Yes | No |
| 17486666 | MACHINE LEARNING FRAMEWORK FOR PERSONALIZED CLOTHING COMPATIBILITY | September 2021 | November 2024 | Allow | 37 | 0 | 0 | Yes | No |
| 17478805 | SYSTEM, METHOD, AND COMPUTER PROGRAM FOR AUTOMATICALLY CLASSIFYING USER ACCOUNTS IN A COMPUTER NETWORK USING KEYS FROM AN IDENTITY MANAGEMENT SYSTEM | September 2021 | April 2024 | Allow | 31 | 2 | 0 | Yes | No |
| 17477080 | SYSTEMS AND PROCESSES FOR OPERATING AND TRAINING A TEXT-BASED CHATBOT | September 2021 | February 2024 | Allow | 29 | 1 | 1 | No | No |
| 17474037 | COMPUTATION WITH ADJUSTABLE RESONANT OPTICAL METAMATERIALS | September 2021 | September 2023 | Allow | 24 | 1 | 0 | No | No |
| 17437244 | PARAMETER TUNING APPARATUS, PARAMETER TUNING METHOD, COMPUTER PROGRAM AND RECORDING MEDIUM | September 2021 | August 2025 | Abandon | 47 | 1 | 0 | No | No |
| 17466755 | TWO-VANE PUMP AND DESIGN METHOD OF TWO-VANE PUMP FOR WASTEWATER USING MACHINE LEARNING | September 2021 | January 2024 | Allow | 29 | 2 | 2 | No | No |
| 17464566 | AUTOMATIC GENERATION OF ATTRIBUTE SETS FOR COUNTERFACTUAL EXPLANATIONS | September 2021 | August 2025 | Allow | 47 | 2 | 0 | Yes | No |
| 17407621 | FORECASTING WITH DEEP STATE SPACE MODELS | August 2021 | March 2025 | Allow | 43 | 1 | 0 | No | No |
| 17384050 | APPARATUS AND METHOD FOR NEURAL ARCHITECTURE SEARCHING WITH TARGET DATA ADAPTION | July 2021 | May 2025 | Allow | 46 | 2 | 0 | Yes | No |
| 17376195 | METHOD AND APPARATUS FOR IMAGE RECOGNITION USING DUAL-SUBNETWORK ARCHITECTURE | July 2021 | July 2025 | Allow | 48 | 3 | 0 | Yes | No |
| 17350991 | CUSTOMIZED PREDICTIVE ANALYTICAL MODEL TRAINING | June 2021 | May 2023 | Allow | 23 | 0 | 0 | No | No |
| 17351101 | QUANTUM TOMOGRAPHY AND PHOTON SOURCE OPTIMIZATION | June 2021 | September 2022 | Allow | 15 | 2 | 0 | Yes | No |
| 17311840 | MATERIAL PHASE DETECTION IN ADDITIVE MANUFACTURING | June 2021 | January 2024 | Allow | 31 | 1 | 1 | No | No |
| 17332906 | GENETIC ALGORITHM WITH DETERMINISTIC LOGIC | May 2021 | August 2023 | Allow | 26 | 1 | 0 | No | No |
| 17332464 | METHOD AND APPARATUS OF INCREASING KNOWLEDGE BASED ON UNCERTAINTY IN NEURAL NETWORKS | May 2021 | September 2024 | Allow | 40 | 1 | 0 | No | No |
| 17329074 | MACHINE LEARNING MODELS IN LOCATION BASED EPISODE PREDICTION | May 2021 | April 2023 | Allow | 23 | 0 | 0 | No | No |
| 17291753 | METHOD OF SETTING ARTIFICIAL INTELLIGENCE EXECUTION MODEL AND ARTIFICIAL INTELLIGENCE EXECUTION ACCELERATION SYSTEM FOR ARTIFICIAL INTELLIGENCE EXECUTION ACCELERATION | May 2021 | December 2021 | Allow | 7 | 0 | 0 | Yes | No |
| 17306003 | Generative Model for Inverse Design of Materials, Devices, and Structures | May 2021 | December 2024 | Allow | 43 | 1 | 0 | Yes | No |
| 17234469 | SYSTEMS AND METHODS FOR ACHIEVING ORTHOGONAL CONTROL OF NON-ORTHOGONAL QUBIT PARAMETERS | April 2021 | July 2023 | Allow | 27 | 1 | 0 | No | No |
| 17233832 | TECHNIQUES FOR AUTOMATICALLY AND OBJECTIVELY IDENTIFYING INTENSE RESPONSES AND UPDATING DECISIONS RELATED TO INPUT/OUTPUT DEVICES ACCORDINGLY | April 2021 | June 2025 | Abandon | 49 | 1 | 0 | No | No |
| 17232690 | CLOUD WORKLOAD MANAGEMENT USING WORKLOAD PROFILES | April 2021 | December 2024 | Allow | 44 | 2 | 0 | No | No |
| 17283166 | INFORMATION PROCESSING APPARATUS FOR CONTROLLING FLIGHT OF AN AERIAL VEHICLE WITH A GENERATED LEARNING MODEL | April 2021 | September 2024 | Allow | 41 | 1 | 0 | No | No |
| 17209508 | ANOMALY DETECTION USING A NON-MIRRORED DIMENSIONAL-REDUCTION MODEL | March 2021 | February 2024 | Allow | 34 | 7 | 0 | Yes | No |
| 17209432 | ANOMALY DETECTION USING A DIMENSIONAL-REDUCTION MODEL | March 2021 | April 2022 | Allow | 13 | 3 | 0 | Yes | No |
| 17204202 | SYSTEM AND METHOD FOR IMPLEMENTING AN ASSESSMENT TOOL FOR CONVERTING A REGULATION INTO A SERIES OF QUESTIONS | March 2021 | July 2023 | Allow | 28 | 1 | 0 | No | No |
| 17197361 | UNIFORM ARTIFICIAL INTELLIGENCE MODEL CONVERSION | March 2021 | July 2024 | Allow | 40 | 1 | 0 | Yes | No |
| 17197535 | INTERPRETABLE MODEL CHANGES | March 2021 | February 2025 | Allow | 47 | 1 | 0 | Yes | No |
| 17187638 | MACHINE LEARNING THROUGH MULTIPLE LAYERS OF NOVEL MACHINE TRAINED PROCESSING NODES | February 2021 | August 2023 | Allow | 30 | 1 | 0 | No | No |
| 17183951 | METHOD AND DEVICE FOR SUPPORTING MANEUVER PLANNING FOR AN AUTOMATED DRIVING VEHICLE OR A ROBOT | February 2021 | October 2024 | Allow | 43 | 1 | 0 | Yes | No |
| 17178942 | COMPUTATION WITH OPTICAL METAMATERIALS | February 2021 | July 2021 | Allow | 5 | 1 | 0 | Yes | No |
| 17141237 | OPTIMIZATION APPARATUS AND OPTIMIZATION METHOD | January 2021 | October 2024 | Allow | 46 | 3 | 0 | Yes | No |
| 17136847 | METHOD FOR PERFORMING ADJUSTABLE CONTINUAL LEARNING ON DEEP NEURAL NETWORK MODEL BY USING SELECTIVE DEEP GENERATIVE REPLAY MODULE AND DEVICE USING THE SAME | December 2020 | May 2021 | Allow | 4 | 1 | 0 | No | No |
| 17124389 | Evolutionary Imitation Learning | December 2020 | March 2024 | Allow | 39 | 0 | 0 | Yes | No |
| 17119725 | MODELING OF INFORMATION TECHNOLOGY FAILURES OF ENTERPRISE COMPUTING SYSTEMS | December 2020 | August 2024 | Allow | 44 | 2 | 0 | Yes | No |
| 16973138 | INFORMATION PROCESSING DEVICE AND INFORMATION PROCESSING METHOD | December 2020 | November 2024 | Allow | 47 | 3 | 0 | No | No |
| 17113467 | Hybrid Decision Making Automation | December 2020 | October 2024 | Allow | 47 | 2 | 0 | No | No |
| 17109550 | LEARNING UNPAIRED MULTIMODAL FEATURE MATCHING FOR SEMI-SUPERVISED LEARNING | December 2020 | October 2024 | Allow | 47 | 2 | 0 | Yes | No |
This analysis examines appeal outcomes and the strategic value of filing appeals for examiner CHANG, LI WU.
With a 20.0% reversal rate, the PTAB affirms the examiner's rejections in the vast majority of cases. This reversal rate is below the USPTO average, indicating that appeals face more challenges 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, 47.4% 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 face challenges. Ensure your case has strong merit before committing to full Board review.
✓ Filing a Notice of Appeal is strategically valuable. The act of filing often prompts favorable reconsideration during the mandatory appeal conference.
Examiner CHANG, LI WU works in Art Unit 2124 and has examined 388 patent applications in our dataset. With an allowance rate of 88.7%, this examiner has an above-average tendency to allow applications. Applications typically reach final disposition in approximately 41 months.
Examiner CHANG, LI WU's allowance rate of 88.7% places them in the 70% percentile among all USPTO examiners. This examiner has an above-average tendency to allow applications.
On average, applications examined by CHANG, LI WU receive 2.14 office actions before reaching final disposition. This places the examiner in the 58% percentile for office actions issued. This examiner issues a slightly above-average number of office actions.
The median time to disposition (half-life) for applications examined by CHANG, LI WU is 41 months. This places the examiner in the 21% percentile for prosecution speed. Applications take longer to reach final disposition with this examiner compared to most others.
Conducting an examiner interview provides a +5.4% benefit to allowance rate for applications examined by CHANG, LI WU. This interview benefit is in the 31% percentile among all examiners. Recommendation: Interviews provide a below-average benefit with this examiner.
When applicants file an RCE with this examiner, 33.3% of applications are subsequently allowed. This success rate is in the 71% percentile among all examiners. Strategic Insight: RCEs show above-average effectiveness with this examiner. Consider whether your amendments or new arguments are strong enough to warrant an RCE versus filing a continuation.
This examiner enters after-final amendments leading to allowance in 25.9% of cases where such amendments are filed. This entry rate is in the 36% 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, 133.3% result in withdrawal of the rejection or reopening of prosecution. This success rate is in the 85% percentile among all examiners. Strategic Recommendation: Pre-appeal conferences are highly effective with this examiner compared to others. Before filing a full appeal brief, strongly consider requesting a PAC. The PAC provides an opportunity for the examiner and supervisory personnel to reconsider the rejection before the case proceeds to the PTAB.
This examiner withdraws rejections or reopens prosecution in 70.6% of appeals filed. This is in the 57% percentile among all examiners. Of these withdrawals, 33.3% 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, 40.5% are granted (fully or in part). This grant rate is in the 31% 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 3.1% of allowed cases (in the 80% percentile). Per MPEP § 1302.04, examiner's amendments are used to place applications in condition for allowance when only minor changes are needed. This examiner frequently uses this tool compared to other examiners, indicating a cooperative approach to getting applications allowed. Strategic Insight: If you are close to allowance but minor claim amendments are needed, this examiner may be willing to make an examiner's amendment rather than requiring another round of prosecution.
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.