Detailed information about the 100 most recent patent applications.
| Application Number | Title | Filing Date | Disposal Date | Disposition | Time (months) | Office Actions | Restrictions | Interview | Appeal |
|---|---|---|---|---|---|---|---|---|---|
| 19073692 | PROJECTING DATA TRENDS USING CUSTOMIZED MODELING | March 2025 | April 2025 | Allow | 2 | 0 | 0 | Yes | No |
| 18892769 | PROJECTING DATA TRENDS USING CUSTOMIZED MODELING | September 2024 | December 2024 | Allow | 3 | 0 | 0 | No | No |
| 18738895 | PROVIDING ARTIFICAL INTELLIGENCE BASED MODEL TO NODE BASED ON REPRESENTATION OF TASK PERFORMED BY ARTIFICAL INTELLIGENCE BASED MODEL | June 2024 | October 2025 | Allow | 16 | 2 | 0 | Yes | No |
| 18516032 | DEVICE, METHOD, PROGRAM, AND SYSTEM FOR DETECTING UNIDENTIFIED WATER | November 2023 | March 2025 | Abandon | 15 | 1 | 0 | Yes | No |
| 18506090 | SYSTEM FOR TRAINING AND DEPLOYING GENERATIVE LANGUAGE MODEL FOR FORMULATING INSTRUCTIONS FOR FACILITY ASSET AND UPDATING DIFFERENT MAP TYPES OF FACILITY | November 2023 | March 2025 | Allow | 16 | 2 | 0 | Yes | No |
| 18088229 | RESHAPE AND BROADCAST OPTIMIZATIONS TO AVOID UNNECESSARY DATA MOVEMENT | December 2022 | March 2025 | Allow | 26 | 3 | 0 | No | No |
| 17978015 | FINDING SHORT COUNTERFACTUALS | October 2022 | November 2025 | Abandon | 36 | 6 | 0 | Yes | No |
| 17977961 | STACKED MACHINE LEARNING MODELS FOR TRANSACTION CATEGORIZATION | October 2022 | May 2025 | Abandon | 30 | 4 | 0 | Yes | No |
| 18046666 | DATABASE UTILIZING SPATIAL PROBABILITY MODELS FOR DATA COMPRESSION | October 2022 | January 2026 | Allow | 40 | 0 | 0 | Yes | No |
| 17681555 | METHOD FOR COMPREHENSIVE PERFORMANCE EVALUATION OF STUDENTS BASED ON DEEP LEARNING NETWORK | February 2022 | February 2026 | Allow | 47 | 1 | 0 | Yes | No |
| 17630461 | METHODS AND APPARATUS TO PROCESS A MACHINE LEARNING MODEL IN A WEB-BROWSER ENVIRONMENT | January 2022 | November 2025 | Abandon | 46 | 1 | 0 | No | No |
| 17438475 | DATA ANALYSIS DEVICE, DATA ANALYSIS METHOD, AND DATA ANALYSIS PROGRAM FOR EXTRACTING GROUPS OF IMPORTANT FEATURES FROM MULTIDIMENSIONAL DATA USING SPARSE GROUP LASSO | September 2021 | November 2025 | Abandon | 50 | 2 | 0 | No | No |
| 17468270 | INFORMATION PROCESSING APPARATUS, INFORMATION PROCESSING SYSTEM AND INFORMATION PROCESSING METHOD | September 2021 | March 2025 | Abandon | 43 | 1 | 0 | Yes | No |
| 17460698 | SYSTEM AND METHOD FOR TRAINING TEACHER MODEL BASED ON STRUCTURE OF STUDENT MODEL | August 2021 | February 2026 | Allow | 53 | 3 | 0 | Yes | No |
| 17433588 | DATA CONVERSION LEARNING DEVICE, DATA CONVERSION DEVICE, METHOD, AND PROGRAM | August 2021 | November 2025 | Abandon | 50 | 1 | 1 | No | No |
| 17315764 | COUNTERFACTUAL NEURAL NETWORK LEARNING FOR CONTEXTUAL ENHANCED EARNINGS CALL ANALYSIS | May 2021 | March 2025 | Allow | 46 | 2 | 0 | Yes | No |
| 17276577 | METHODS AND APPARATUSES FOR IMPLEMENTING ADAPTIVE MULTI-TRACE CARVING TO TRACK SIGNAL TRACES | March 2021 | September 2024 | Allow | 43 | 2 | 0 | Yes | No |
| 17182996 | METHOD FOR GENERATING ANOMALOUS DATA SIMILAR TO NORMAL DATA | February 2021 | November 2024 | Abandon | 45 | 6 | 0 | No | No |
| 17039069 | METHOD OF TRAINING A NEURAL NETWORK AND RELATED SYSTEM AND METHOD FOR CATEGORIZING AND RECOMMENDING ASSOCIATED CONTENT | September 2020 | November 2025 | Abandon | 60 | 3 | 0 | No | No |
| 17035879 | METHODS AND APPARATUS FOR MATRIX PROCESSING IN A CONVOLUTIONAL NEURAL NETWORK | September 2020 | February 2025 | Allow | 53 | 4 | 0 | Yes | No |
| 17019975 | LOAD FORECASTING FOR ENERGY CONSUMPTION DEVICES | September 2020 | November 2025 | Abandon | 60 | 5 | 0 | Yes | No |
| 17014721 | LEARNING DEVICE, LEARNING METHOD, AND COMPUTER PROGRAM GENERATING LEARNING DATA APPROPRIATE FOR GENERALIZATION PERFORMANCE OF NEURAL NETWORK USED FOR ESTIMATION | September 2020 | June 2025 | Abandon | 58 | 4 | 0 | Yes | No |
| 16936549 | MITIGATING REGRESSION TEST VARIABILITY | July 2020 | October 2024 | Allow | 51 | 2 | 0 | Yes | No |
| 16885918 | TRAINING ACTION SELECTION NEURAL NETWORKS USING OFF-POLICY ACTOR CRITIC REINFORCEMENT LEARNING AND STOCHASTIC DUELING NEURAL NETWORKS | May 2020 | August 2024 | Allow | 51 | 2 | 0 | Yes | No |
| 16576113 | USER-REALISTIC PATH SYNTHESIS VIA MULTI-TASK GENERATIVE ADVERSARIAL NETWORKS FOR CONTINUOUS PATH KEYBOARD INPUT | September 2019 | December 2024 | Abandon | 60 | 4 | 0 | Yes | No |
| 16201871 | REORGANIZABLE DATA PROCESSING ARRAY FOR NEURAL NETWORK COMPUTING | November 2018 | September 2024 | Allow | 60 | 5 | 0 | Yes | No |
| 16163914 | Automatic Genre Classification Determination of Web Content to which the Web Content Belongs Together with a Corresponding Genre Probability | October 2018 | April 2020 | Allow | 18 | 1 | 0 | Yes | No |
| 16004454 | PROCESSING CIRCUIT AND NEURAL NETWORK COMPUTATION METHOD THEREOF FOR STATICALLY CONFIGURING NEURAL NETWORK TASKS | June 2018 | November 2024 | Abandon | 60 | 6 | 0 | No | No |
| 15969132 | SYSTEM FOR GENERATING SYNTHETIC SENTIMENT USING MULTIPLE POINTS OF REFERENCE WITHIN A HIERARCHICAL HEAD NOUN STRUCTURE | May 2018 | October 2025 | Abandon | 60 | 10 | 0 | No | No |
| 15967482 | NEURAL HARDWARE ACCELERATOR FOR PARALLEL AND DISTRIBUTED TENSOR COMPUTATIONS | April 2018 | August 2024 | Allow | 60 | 6 | 0 | Yes | Yes |
| 15941985 | BLOCK TRANSFER OF NEURON OUTPUT VALUES THROUGH DATA MEMORY FOR NEUROSYNAPTIC PROCESSORS | March 2018 | November 2025 | Allow | 60 | 7 | 0 | Yes | No |
| 15884105 | FUNCTIONAL SYNTHESIS OF NETWORKS OF NEUROSYNAPTIC CORES ON NEUROMORPHIC SUBSTRATES | January 2018 | June 2025 | Allow | 60 | 6 | 0 | Yes | No |
| 15682454 | Creation, Use And Training Of Computer-Based Discovery Avatars | August 2017 | March 2025 | Allow | 60 | 6 | 0 | Yes | No |
| 15280027 | HIERARCHICAL SCALABLE NEUROMORPHIC SYNAPTRONIC SYSTEM FOR SYNAPTIC AND STRUCTURAL PLASTICITY | September 2016 | July 2018 | Allow | 22 | 1 | 0 | No | No |
| 15205077 | DYNAMIC THRESHOLD FILTERING FOR WATCHED QUESTIONS | July 2016 | September 2019 | Allow | 39 | 3 | 0 | No | No |
| 14744708 | Horizontal Decision Tree Learning from Very High Rate Data Streams With Horizontal Parallel Conflict Resolution | June 2015 | October 2019 | Allow | 52 | 3 | 0 | Yes | No |
| 14729454 | AUTOMATIC GENRE CLASSIFICATION DETERMINATION OF WEB CONTENT TO WHICH THE WEB CONTENT BELONGS TOGETHER WITH A CORRESPONDING GENRE PROBABILITY | June 2015 | June 2018 | Allow | 36 | 1 | 0 | Yes | No |
| 14239313 | METHOD FOR THE COMPUTER-ASSISTED MODELING OF A WIND POWER INSTALLATION OR A PHOTOVOLTAIC INSTALLATION WITH A FEED FORWARD NEURAL NETWORK | March 2014 | July 2018 | Allow | 52 | 4 | 0 | Yes | No |
| 13175603 | AUTOMATIC USER IDENTIFICATION FROM BUTTON PRESSES RECORDED IN A FEATURE VECTOR | July 2011 | September 2013 | Allow | 27 | 1 | 0 | No | No |
This analysis examines appeal outcomes and the strategic value of filing appeals for examiner BEJCEK II, ROBERT H.
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, 100.0% 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.
✓ Filing a Notice of Appeal is strategically valuable. The act of filing often prompts favorable reconsideration during the mandatory appeal conference.
Examiner BEJCEK II, ROBERT H works in Art Unit 2148 and has examined 28 patent applications in our dataset. With an allowance rate of 64.3%, this examiner allows applications at a lower rate than most examiners at the USPTO. Applications typically reach final disposition in approximately 52 months.
Examiner BEJCEK II, ROBERT H's allowance rate of 64.3% places them in the 25% percentile among all USPTO examiners. This examiner is less likely to allow applications than most examiners at the USPTO.
On average, applications examined by BEJCEK II, ROBERT H receive 3.61 office actions before reaching final disposition. This places the examiner in the 95% 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 BEJCEK II, ROBERT H is 52 months. This places the examiner in the 3% percentile for prosecution speed. Applications take longer to reach final disposition with this examiner compared to most others.
Conducting an examiner interview provides a +45.6% benefit to allowance rate for applications examined by BEJCEK II, ROBERT H. This interview benefit is in the 91% 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, 16.7% of applications are subsequently allowed. This success rate is in the 14% 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 6% 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 100.0% of appeals filed. This is in the 89% percentile among all examiners. Of these withdrawals, 100.0% 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, 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 11% 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 11% 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.