Detailed information about the 100 most recent patent applications.
| Application Number | Title | Filing Date | Disposal Date | Disposition | Time (months) | Office Actions | Restrictions | Interview | Appeal |
|---|---|---|---|---|---|---|---|---|---|
| 17107621 | Neural Networks with Relational Memory | November 2020 | April 2023 | Allow | 28 | 1 | 0 | Yes | No |
| 16951181 | Systems and Methods for Identifying, Tracking, and Managing a Plurality of Social Network Users Having Predefined Characteristics | November 2020 | February 2023 | Allow | 27 | 1 | 1 | No | No |
| 17028799 | MACHINE LEARNING WITH DATA SHARING FOR CLINICAL RESEARCH DATA ACROSS MULTIPLE STUDIES AND TRIALS | September 2020 | January 2022 | Allow | 16 | 2 | 0 | Yes | No |
| 16889853 | SECURE CONVOLUTIONAL NEURAL NETWORKS (CNN) ACCELERATOR | June 2020 | August 2023 | Allow | 38 | 1 | 0 | Yes | No |
| 16646071 | ROBUST AUTO-ASSOCIATIVE MEMORY WITH RECURRENT NEURAL NETWORK | March 2020 | February 2022 | Abandon | 23 | 3 | 0 | Yes | No |
| 16751169 | LEARNING NON-DIFFERENTIABLE WEIGHTS OF NEURAL NETWORKS USING EVOLUTIONARY STRATEGIES | January 2020 | January 2023 | Allow | 36 | 2 | 0 | Yes | No |
| 16733367 | Affective Response-based User Authentication | January 2020 | October 2023 | Abandon | 46 | 3 | 0 | No | Yes |
| 16714597 | SYSTEMS AND METHODS FOR A DEVICE FOR STEERING ACOUSTIC STIMULATION USING MACHINE LEARNING | December 2019 | October 2023 | Abandon | 46 | 1 | 0 | No | No |
| 16620177 | COMPANION ANALYSIS NETWORK IN DEEP LEARNING | December 2019 | September 2022 | Allow | 33 | 2 | 0 | Yes | No |
| 16619516 | JOINT OPTIMIZATION OF ENSEMBLES IN DEEP LEARNING | December 2019 | October 2021 | Allow | 23 | 2 | 0 | Yes | No |
| 16601505 | BLACK-BOX OPTIMIZATION USING NEURAL NETWORKS | October 2019 | February 2022 | Allow | 28 | 4 | 0 | Yes | No |
| 16591926 | Machine Discovery of Aberrant Operating States | October 2019 | February 2022 | Abandon | 28 | 4 | 0 | No | No |
| 16583714 | DATA-DRIVEN ACTIVITY PREDICTION | September 2019 | September 2022 | Allow | 35 | 3 | 0 | Yes | No |
| 16576927 | BAYESIAN NONPARAMETRIC LEARNING OF NEURAL NETWORKS | September 2019 | February 2023 | Allow | 41 | 0 | 0 | No | No |
| 16541275 | QUANTIZATION METHOD AND DEVICE FOR WEIGHTS OF BATCH NORMALIZATION LAYER | August 2019 | May 2022 | Allow | 33 | 1 | 0 | Yes | No |
| 16523026 | RANDOMIZATION OF CASE-BASED KNOWLEDGE TO RULE-BASED KNOWLEDGE | July 2019 | August 2023 | Abandon | 49 | 2 | 0 | No | No |
| 16413730 | MACHINE LEARNING USING INFORMED PSEUDOLABELS | May 2019 | January 2023 | Allow | 44 | 2 | 0 | Yes | No |
| 16392391 | DATA PROCESSING USING A NEURAL NETWORK SYSTEM | April 2019 | February 2023 | Allow | 46 | 3 | 0 | Yes | No |
| 16362691 | MACHINE LEARNING FOR GENERATING AN INTEGRATED FORMAT DATA RECORD | March 2019 | June 2023 | Allow | 50 | 2 | 0 | Yes | No |
| 16282748 | NEURAL NETWORK METHOD AND APPARATUS WITH PARAMETER QUANTIZATION | February 2019 | March 2023 | Allow | 48 | 4 | 0 | Yes | No |
| 16265252 | Optimizing Neural Networks | February 2019 | June 2022 | Allow | 41 | 2 | 0 | Yes | No |
| 16260165 | METHOD AND APPARATUS FOR PROVIDING EFFICIENT TESTING OF SYSTEMS BY USING ARTIFICIAL INTELLIGENCE TOOLS | January 2019 | June 2022 | Allow | 41 | 4 | 0 | Yes | No |
| 16210584 | DE-CONFLICTING DATA LABELING IN REAL TIME DEEP LEARNING SYSTEMS | December 2018 | January 2023 | Allow | 50 | 3 | 0 | Yes | No |
| 16202577 | COMPUTER-READABLE RECORDING MEDIUM, DETERMINATION METHOD, AND DETERMINATION APPARATUS FOR CLASSIFYING TIME SERIES DATA | November 2018 | January 2023 | Allow | 49 | 3 | 0 | No | No |
| 16198519 | MACHINE LEARNING BASED DATABASE ANOMALY PREDICTION | November 2018 | July 2023 | Allow | 56 | 3 | 0 | Yes | No |
| 16191542 | TRAINING FIRST AND SECOND NEURAL NETWORK MODELS | November 2018 | January 2023 | Allow | 50 | 2 | 0 | No | No |
| 16176419 | Continual Neural Network Learning Via Explicit Structure Learning | October 2018 | December 2022 | Allow | 50 | 3 | 0 | Yes | No |
| 16173534 | RELATION EXTRACTION FROM TEXT USING MACHINE LEARNING | October 2018 | January 2023 | Allow | 51 | 1 | 0 | Yes | No |
| 16133833 | LEARNING PROGRAM, LEARNING APPARATUS, AND LEARNING METHOD | September 2018 | August 2022 | Allow | 47 | 3 | 0 | Yes | No |
| 16085612 | SYSTEM, METHOD AND COMPUTER PROGRAM PRODUCT FOR DATA ANALYSIS | September 2018 | March 2022 | Abandon | 42 | 5 | 0 | Yes | No |
| 16054709 | VIRTUAL/AUGMENTED REALITY DATA EVALUATION | August 2018 | February 2023 | Allow | 55 | 2 | 0 | Yes | No |
| 16044108 | Method for Meta-Level Continual Learning | July 2018 | March 2023 | Allow | 56 | 2 | 0 | No | No |
| 16007884 | SYSTEM AND METHOD FOR IMPLEMENTING NEURAL NETWORKS IN INTEGRATED CIRCUITS | June 2018 | December 2022 | Allow | 54 | 3 | 0 | Yes | No |
| 15953639 | PERFORMANCE MANAGER TO AUTONOMOUSLY EVALUATE REPLACEMENT ALGORITHMS | April 2018 | May 2022 | Allow | 49 | 3 | 0 | Yes | No |
| 15911048 | METHOD AND SYSTEM FOR FINDING A SOLUTION TO A PROVIDED PROBLEM BY SELECTING A WINNER IN EVOLUTIONARY OPTIMIZATION OF A GENETIC ALGORITHM | March 2018 | June 2022 | Allow | 51 | 4 | 0 | Yes | No |
| 15902686 | USER CUSTOMIZED PRIVATE LABEL PREDICTION | February 2018 | July 2023 | Allow | 60 | 3 | 0 | Yes | No |
| 15900826 | COMPUTER SYSTEM AND COMPUTATION METHOD USING RECURRENT NEURAL NETWORK TO PROCESS TIME-SERIES DATA | February 2018 | June 2023 | Abandon | 60 | 2 | 0 | No | No |
| 15855702 | SYSTEM AND METHOD FOR HIERARCHICAL DEEP SEMI-SUPERVISED EMBEDDINGS FOR DYNAMIC TARGETED ANOMALY DETECTION | December 2017 | October 2023 | Allow | 60 | 3 | 0 | Yes | No |
| 15559079 | A Data-driven Innovation Decision Support System, and Method | September 2017 | March 2022 | Abandon | 54 | 3 | 0 | Yes | No |
| 15699860 | CHECKPOINTING DISK CONFIGURATION USING MACHINE LEARNING | September 2017 | March 2022 | Abandon | 55 | 2 | 0 | No | No |
| 15667287 | PREDICTIVE NEURAL NETWORK WITH SENTIMENT DATA | August 2017 | November 2021 | Allow | 52 | 1 | 0 | Yes | No |
| 15664765 | ANALYSIS OF INTERACTIONS WITH DATA OBJECTS STORED BY A NETWORK-BASED STORAGE SERVICE | July 2017 | June 2022 | Allow | 58 | 3 | 0 | Yes | No |
| 15607555 | METHOD AND APPARATUS FOR TRAINING A MACHINE LEARNING ALGORITHM (MLA) FOR GENERATING A CONTENT RECOMMENDATION IN A RECOMMENDATION SYSTEM AND METHOD AND APPARATUS FOR GENERATING THE RECOMMENDED CONTENT USING THE MLA | May 2017 | July 2022 | Abandon | 60 | 4 | 1 | Yes | No |
| 15599058 | ASYNCHRONOUS NEURAL NETWORK TRAINING | May 2017 | November 2021 | Allow | 54 | 2 | 0 | Yes | No |
| 15527784 | SEQUENTIAL DATA ANALYSIS APPARATUS AND PROGRAM | May 2017 | September 2022 | Allow | 60 | 5 | 0 | Yes | No |
| 15494971 | DYNAMIC DISTRIBUTED TRAINING OF MACHINE LEARNING MODELS | April 2017 | May 2023 | Allow | 60 | 6 | 0 | Yes | No |
| 15494826 | NEURAL NETWORK TRAINING MECHANISM | April 2017 | October 2022 | Allow | 60 | 4 | 0 | Yes | No |
| 15322236 | METHOD AND SYSTEM FOR PROVIDING TYPE INFORMATION AND EVALUATION INFORMATION, USING DATA COLLECTED FROM USER TERMINAL | April 2017 | November 2023 | Abandon | 60 | 8 | 0 | Yes | No |
| 15463901 | METHOD AND SYSTEM FOR EXTRACTING RELEVANT ENTITIES FROM A TEXT CORPUS | March 2017 | November 2021 | Allow | 56 | 3 | 0 | No | No |
| 15431606 | Intelligent Autonomous Feature Extraction System Using Two Hardware Spiking Neutral Networks with Spike Timing Dependent Plasticity | February 2017 | July 2021 | Allow | 53 | 2 | 0 | Yes | No |
| 15429654 | Data Processing System with Machine Learning Engine to Provide System Disruption Detection and Predictive Impact and Mitigation Functions | February 2017 | February 2022 | Abandon | 60 | 4 | 0 | Yes | No |
| 15412510 | AUTOMATIC GENERATION AND TRANSMISSION OF A STATUS OF A USER AND/OR PREDICTED DURATION OF THE STATUS | January 2017 | March 2022 | Allow | 60 | 4 | 0 | Yes | Yes |
| 15408407 | NEURAL NETWORK CONNECTION REDUCTION | January 2017 | August 2021 | Allow | 54 | 4 | 0 | Yes | No |
| 15406557 | PROCESSING AND GENERATING SETS USING RECURRENT NEURAL NETWORKS | January 2017 | October 2021 | Allow | 57 | 4 | 1 | Yes | Yes |
| 15405816 | MESSAGE CHOICE MODEL TRAINER | January 2017 | March 2022 | Abandon | 60 | 6 | 0 | Yes | No |
| 15378001 | MACHINE LEARNING METHOD AND APPARATUS BASED ON WEAKLY SUPERVISED LEARNING | December 2016 | October 2021 | Allow | 58 | 5 | 0 | Yes | No |
| 15375050 | LABEL INFERENCE IN A SOCIAL NETWORK | December 2016 | April 2022 | Abandon | 60 | 3 | 0 | Yes | No |
| 15299145 | SYSTEMS AND METHODS FOR BUILDING AND UTILIZING ARTIFICIAL INTELLIGENCE THAT MODELS HUMAN MEMORY | October 2016 | November 2021 | Allow | 60 | 5 | 0 | Yes | No |
| 15236648 | MIXTURE MODEL APPROACH FOR NETWORK FORECASTING | August 2016 | January 2022 | Abandon | 60 | 5 | 0 | Yes | No |
| 15222325 | System and Method to Facilitate Welding Software as a Service | July 2016 | December 2021 | Allow | 60 | 5 | 0 | Yes | No |
| 15043292 | IDENTIFYING A THUMBNAIL IMAGE TO REPRESENT A VIDEO | February 2016 | December 2021 | Allow | 60 | 8 | 0 | Yes | Yes |
This analysis examines appeal outcomes and the strategic value of filing appeals for examiner HAEDI, SELENE.
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, 50.0% 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 HAEDI, SELENE works in Art Unit 2128 and has examined 61 patent applications in our dataset. With an allowance rate of 75.4%, this examiner has a below-average tendency to allow applications. Applications typically reach final disposition in approximately 51 months.
Examiner HAEDI, SELENE's allowance rate of 75.4% places them in the 43% percentile among all USPTO examiners. This examiner has a below-average tendency to allow applications.
On average, applications examined by HAEDI, SELENE receive 3.11 office actions before reaching final disposition. This places the examiner in the 86% 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 HAEDI, SELENE is 51 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 +31.6% benefit to allowance rate for applications examined by HAEDI, SELENE. This interview benefit is in the 78% 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, 24.6% of applications are subsequently allowed. This success rate is in the 39% 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 18.0% of cases where such amendments are filed. This entry rate is in the 22% 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 75.0% of appeals filed. This is in the 64% 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, 36.4% are granted (fully or in part). This grant rate is in the 23% 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.