Detailed information about the 100 most recent patent applications.
| Application Number | Title | Filing Date | Disposal Date | Disposition | Time (months) | Office Actions | Restrictions | Interview | Appeal |
|---|---|---|---|---|---|---|---|---|---|
| 18638513 | FRAMEWORK FOR CAUSAL LEARNING OF NEURAL NETWORKS | April 2024 | February 2025 | Abandon | 10 | 1 | 0 | No | No |
| 18222379 | FRAMEWORK FOR CAUSAL LEARNING OF NEURAL NETWORKS | July 2023 | July 2024 | Abandon | 12 | 1 | 0 | Yes | No |
| 17051252 | METHOD AND SYSTEM FOR TRAINING MACHINE LEARNING SYSTEM | October 2020 | August 2024 | Abandon | 46 | 1 | 0 | No | No |
| 16975628 | TARGETING MANY-BODY EIGENSTATES ON A QUANTUM COMPUTER | August 2020 | April 2024 | Allow | 44 | 1 | 0 | Yes | No |
| 16704407 | COMPUTER-BASED SYSTEMS HAVING COMPUTER ENGINES AND DATA STRUCTURES CONFIGURED FOR MACHINE LEARNING DATA INSIGHT PREDICTION AND METHODS OF USE THEREOF | December 2019 | June 2023 | Allow | 42 | 1 | 0 | No | No |
| 16688818 | Explainable Machine Learning Predictions | November 2019 | March 2024 | Allow | 52 | 5 | 0 | Yes | No |
| 16671302 | LEARNING DEVICE AND LEARNING METHOD | November 2019 | December 2022 | Allow | 37 | 2 | 0 | Yes | No |
| 16663615 | Augmented Intelligence Assurance as a Service | October 2019 | September 2024 | Abandon | 59 | 5 | 0 | No | No |
| 16663607 | Augmented Intelligence System Explainability Generation Engine | October 2019 | July 2024 | Abandon | 57 | 4 | 0 | No | Yes |
| 16579001 | METHOD AND SYSTEM FOR PREDICTING AND PREEMPTING PATCHING FAILURES | September 2019 | April 2024 | Allow | 55 | 6 | 0 | Yes | No |
| 16496365 | ARTIFICIAL INTELLIGENCE SERVER | September 2019 | November 2023 | Abandon | 50 | 4 | 0 | No | No |
| 16545708 | METHODS AND SYSTEMS FOR RELATING FEATURES WITH LABELS IN ELECTRONICS | August 2019 | May 2023 | Allow | 45 | 4 | 0 | Yes | Yes |
| 16535121 | SELF-AWARE SERVICE ASSURANCE IN A 5G TELCO NETWORK | August 2019 | April 2024 | Abandon | 57 | 5 | 0 | No | No |
| 16476410 | REDUCING ERRORS INTRODUCED BY MODEL UPDATES | July 2019 | November 2024 | Abandon | 60 | 4 | 0 | Yes | Yes |
| 16418232 | Curating Training Data For Incremental Re-Training Of A Predictive Model | May 2019 | April 2024 | Abandon | 58 | 5 | 0 | Yes | No |
| 16384588 | PREDICTING MACHINE LEARNING OR DEEP LEARNING MODEL TRAINING TIME | April 2019 | April 2022 | Allow | 36 | 2 | 0 | Yes | No |
| 16368894 | SYSTEM AND METHOD FOR IDENTIFYING MISCLASSIFICATIONS BY A NEURAL NETWORK | March 2019 | December 2022 | Abandon | 45 | 2 | 0 | No | No |
| 16361915 | MACHINE LEARNING-BASED ADJUSTMENTS IN VOLUME DIAGNOSIS PROCEDURES FOR DETERMINATION OF ROOT CAUSE DISTRIBUTIONS | March 2019 | March 2024 | Allow | 60 | 3 | 0 | Yes | No |
| 16286962 | RESILIENT MANAGEMENT OF RESOURCE UTILIZATION | February 2019 | September 2022 | Abandon | 43 | 1 | 0 | No | No |
| 16325571 | FLEXIBLE JOB-SHOP SCHEDULING METHOD BASED ON LIMITED STABLE MATCHING STRATEGY | February 2019 | February 2023 | Abandon | 48 | 2 | 0 | No | No |
| 16275061 | SYSTEM AND METHOD FOR EXTENDING MACHINE LEARNING TO EDGE DEVICES | February 2019 | July 2022 | Abandon | 41 | 1 | 0 | No | No |
| 16265142 | METHOD AND SYSTEM FOR APPLYING MACHINE LEARNING APPROACH TO ROUTING WEBPAGE TRAFFIC BASED ON VISITOR ATTRIBUTES | February 2019 | April 2023 | Abandon | 50 | 3 | 0 | No | No |
| 16253892 | DETERMINING FEATURE IMPACT WITHIN MACHINE LEARNING MODELS USING PROTOTYPES ACROSS ANALYTICAL SPACES | January 2019 | September 2022 | Allow | 44 | 3 | 0 | Yes | No |
| 16254033 | MACHINE LEARNING PIPELINE FAILURE PREDICTION | January 2019 | May 2023 | Abandon | 51 | 4 | 0 | Yes | No |
| 16233779 | EVENT DETECTION USING SENSOR DATA | December 2018 | October 2021 | Abandon | 34 | 1 | 0 | No | No |
| 16234286 | SYSTEMS AND METHODS FOR ALLOCATING ORDERS | December 2018 | December 2022 | Abandon | 48 | 1 | 0 | No | No |
| 16230602 | PROBABILISTIC MODELING SYSTEM AND METHOD | December 2018 | March 2022 | Abandon | 39 | 1 | 0 | No | No |
| 16230914 | METHOD AND APPARATUS FOR DESIGNING A POWER DISTRIBUTION NETWORK USING MACHINE LEARNING TECHNIQUES | December 2018 | April 2022 | Abandon | 40 | 2 | 0 | No | No |
| 16214703 | POST-HOC IMPROVEMENT OF INSTANCE-LEVEL AND GROUP-LEVEL PREDICTION METRICS | December 2018 | April 2023 | Allow | 52 | 6 | 0 | Yes | No |
| 16212643 | Splitting Neural Network Filters for Implementation by Neural Network Inference Circuit | December 2018 | September 2021 | Allow | 34 | 1 | 0 | No | No |
| 16206387 | ACTIVE LEARNING MODEL TRAINING FOR PAGE OPTIMIZATION | November 2018 | November 2022 | Abandon | 48 | 1 | 0 | No | No |
| 16180462 | PIPELINING TO IMPROVE NEURAL NETWORK INFERENCE ACCURACY | November 2018 | October 2022 | Abandon | 47 | 2 | 0 | No | No |
| 16168377 | Systems and Methods For Detecting Long Term Seasons | October 2018 | January 2024 | Allow | 60 | 5 | 0 | Yes | No |
| 16166039 | MINI-MACHINE LEARNING | October 2018 | May 2023 | Allow | 55 | 3 | 0 | Yes | No |
| 16093956 | Apparatus and methods for forward propagation in neural networks supporting discrete data | October 2018 | February 2022 | Abandon | 40 | 1 | 0 | No | No |
| 16156300 | SOLUTION SEARCH PROCESSING APPARATUS AND SOLUTION SEARCH PROCESSING METHOD | October 2018 | May 2022 | Abandon | 43 | 2 | 0 | No | No |
| 16124047 | PREDICTION CHARACTERIZATION FOR BLACK BOX MACHINE LEARNING MODELS | September 2018 | February 2022 | Allow | 41 | 1 | 0 | Yes | No |
| 16102828 | ABILITY-PROVIDING-DATA GENERATION APPARATUS | August 2018 | February 2024 | Abandon | 60 | 3 | 0 | No | Yes |
| 16071884 | COMPUTER SYSTEM AND CONTROL METHOD | July 2018 | March 2022 | Allow | 44 | 2 | 0 | Yes | No |
| 16027454 | METHOD AND SYSTEM FOR REDUCING COMMUNICATION FREQUENCY IN NEURAL NETWORK SYSTEMS | July 2018 | November 2021 | Abandon | 41 | 1 | 0 | No | No |
| 16013162 | METHOD AND APPARATUS FOR RECOGNIZING A LOW-QUALITY ARTICLE BASED ON ARTIFICIAL INTELLIGENCE, DEVICE AND MEDIUM | June 2018 | March 2023 | Allow | 57 | 5 | 0 | Yes | No |
| 15967508 | CAUSALITY FOR MACHINE LEARNING SYSTEMS | April 2018 | May 2024 | Abandon | 60 | 5 | 0 | No | No |
| 15950257 | ARTIFICIAL NEURAL NETWORK | April 2018 | March 2022 | Abandon | 48 | 2 | 0 | No | No |
| 15945888 | DATA DEPENDENT MODEL INITIALIZATION | April 2018 | September 2023 | Abandon | 60 | 4 | 0 | Yes | No |
| 15945321 | METHODS FOR CREATING AND ANALYZING DYNAMIC TRAIL NETWORKS | April 2018 | October 2021 | Abandon | 42 | 1 | 0 | No | No |
| 15914656 | SYSTEM AND METHOD FOR BUILDING STATISTICAL PREDICTIVE MODELS USING AUTOMATED INSIGHTS | March 2018 | July 2022 | Abandon | 52 | 2 | 0 | No | No |
| 15880690 | NEURAL NETWORK METHOD AND APPARATUS | January 2018 | October 2021 | Allow | 45 | 1 | 0 | No | No |
| 15874121 | Systems and Methods for Improved Adversarial Training of Machine-Learned Models | January 2018 | July 2022 | Allow | 54 | 3 | 0 | Yes | No |
| 15873673 | Dictionary Based Deduplication of Training Set Samples for Machine Learning Based Computer Threat Analysis | January 2018 | February 2022 | Allow | 49 | 3 | 0 | No | No |
| 15870070 | DYNAMIC GENERATION OF DATA SETS FOR TRAINING MACHINE-TRAINED NETWORK | January 2018 | January 2024 | Allow | 60 | 2 | 0 | Yes | Yes |
| 15868889 | ARTIFICIAL NEURAL NETWORK DEVICE AND OPERATION METHOD THEREOF | January 2018 | October 2022 | Abandon | 57 | 4 | 0 | No | No |
| 15855015 | BUILDING A BINARY NEURAL NETWORK ARCHITECTURE | December 2017 | June 2023 | Abandon | 60 | 6 | 0 | Yes | No |
| 15851142 | IDENTIFYING RELATIONSHIPS BETWEEN ENTITIES USING MACHINE LEARNING | December 2017 | March 2023 | Abandon | 60 | 2 | 0 | Yes | No |
| 15843949 | COMPUTERIZED HIGH-SPEED ANOMALY DETECTION | December 2017 | March 2023 | Allow | 60 | 4 | 0 | Yes | No |
| 15821660 | LOW-DIMENSIONAL NEURAL-NETWORK-BASED ENTITY REPRESENTATION | November 2017 | November 2023 | Allow | 60 | 6 | 0 | Yes | No |
| 15815899 | MACHINE LEARNING MODEL INTERPRETATION | November 2017 | February 2023 | Allow | 60 | 3 | 0 | Yes | No |
| 15811728 | SELF-CRITICAL SEQUENCE TRAINING OF MULTIMODAL SYSTEMS | November 2017 | August 2023 | Abandon | 60 | 7 | 0 | No | No |
| 15788795 | SYSTEM AND METHOD FOR GENERATING SQL SUPPORT FOR TREE ENSEMBLE CLASSIFIERS | October 2017 | November 2022 | Abandon | 60 | 4 | 0 | Yes | No |
This analysis examines appeal outcomes and the strategic value of filing appeals for examiner KWAN, WILLIAM WAI.
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, 20.0% 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 KWAN, WILLIAM WAI works in Art Unit 2121 and has examined 56 patent applications in our dataset. With an allowance rate of 41.1%, this examiner allows applications at a lower rate than most examiners at the USPTO. Applications typically reach final disposition in approximately 50 months.
Examiner KWAN, WILLIAM WAI's allowance rate of 41.1% places them in the 8% percentile among all USPTO examiners. This examiner is less likely to allow applications than most examiners at the USPTO.
On average, applications examined by KWAN, WILLIAM WAI 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 KWAN, WILLIAM WAI is 50 months. This places the examiner in the 5% percentile for prosecution speed. Applications take longer to reach final disposition with this examiner compared to most others.
Conducting an examiner interview provides a +59.7% benefit to allowance rate for applications examined by KWAN, WILLIAM WAI. This interview benefit is in the 97% 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, 14.6% of applications are subsequently allowed. This success rate is in the 10% percentile among all examiners. Strategic Insight: RCEs show lower effectiveness with this examiner compared to others. Consider whether a continuation application might be more strategic, especially if you need to add new matter or significantly broaden claims.
This examiner enters after-final amendments leading to allowance in 5.9% of cases where such amendments are filed. This entry rate is in the 6% 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 66.7% of appeals filed. This is in the 48% percentile among all examiners. Strategic Insight: This examiner shows below-average willingness to reconsider rejections during appeals. Be prepared to fully prosecute appeals if filed.
When applicants file petitions regarding this examiner's actions, 0.0% are granted (fully or in part). This grant rate is in the 1% 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.