Detailed information about the 100 most recent patent applications.
| Application Number | Title | Filing Date | Disposal Date | Disposition | Time (months) | Office Actions | Restrictions | Interview | Appeal |
|---|---|---|---|---|---|---|---|---|---|
| 18963469 | ITERATIVE ATTENTION-BASED NEURAL NETWORK TRAINING AND PROCESSING | November 2024 | April 2025 | Allow | 5 | 1 | 0 | No | No |
| 18963473 | ITERATIVE ATTENTION-BASED NEURAL NETWORK TRAINING AND PROCESSING | November 2024 | February 2025 | Allow | 2 | 0 | 0 | Yes | No |
| 18963462 | ITERATIVE ATTENTION-BASED NEURAL NETWORK TRAINING AND PROCESSING | November 2024 | April 2025 | Allow | 4 | 1 | 0 | No | No |
| 18963465 | ITERATIVE ATTENTION-BASED NEURAL NETWORK TRAINING AND PROCESSING | November 2024 | April 2025 | Allow | 5 | 1 | 0 | No | No |
| 18810460 | Iterative Attention-based Neural Network Training and Processing | August 2024 | January 2025 | Allow | 5 | 1 | 0 | Yes | No |
| 18810464 | Iterative Attention-based Neural Network Training and Processing | August 2024 | January 2025 | Allow | 5 | 1 | 0 | No | No |
| 18810465 | Iterative Attention-based Neural Network Training and Processing | August 2024 | January 2025 | Allow | 5 | 1 | 0 | No | No |
| 18792157 | SYSTEMS AND METHODS OF LARGE LANGUAGE MODEL DRIVEN ORCHESTRATION OF TASK-SPECIFIC MACHINE LEARNING SOFTWARE AGENTS | August 2024 | January 2025 | Allow | 6 | 1 | 0 | Yes | No |
| 18599120 | PROGRAMMING METHOD OF AN ACTIVATION FUNCTION AND AN ACTIVATION FUNCTION PROGRAMMING UNIT | March 2024 | October 2024 | Allow | 7 | 2 | 0 | Yes | No |
| 18242723 | STYLIZING INPUT IMAGES | September 2023 | February 2025 | Allow | 18 | 2 | 0 | Yes | No |
| 18347088 | SYSTEMS AND METHODS FOR TIME SERIES ANALYSIS USING ATTENTION MODELS | July 2023 | March 2025 | Allow | 20 | 1 | 0 | Yes | No |
| 18345445 | ACCELERATING NEURAL NETWORKS WITH ONE SHOT SKIP LAYER PRUNING | June 2023 | June 2025 | Allow | 23 | 2 | 0 | No | No |
| 18137398 | NEURAL ARCHITECTURE SEARCH FOR CONVOLUTIONAL NEURAL NETWORKS | April 2023 | April 2025 | Allow | 23 | 2 | 0 | No | No |
| 18304294 | AUTOMATIC ACTIONS BASED ON CONTEXTUAL REPLIES | April 2023 | February 2025 | Allow | 22 | 2 | 0 | Yes | No |
| 18101612 | Iterative Attention-based Neural Network Training and Processing | January 2023 | July 2024 | Allow | 18 | 0 | 0 | Yes | No |
| 18147471 | METHODS FOR PREDICTING LIKELIHOOD OF SUCCESSFUL EXPERIMENTAL SYNTHESIS OF COMPUTER-GENERATED MATERIALS BY COMBINING NETWORK ANALYSIS AND MACHINE LEARNING | December 2022 | October 2024 | Allow | 21 | 1 | 0 | Yes | No |
| 17940726 | COGNITIVE RULE ENGINE | September 2022 | January 2025 | Allow | 29 | 2 | 0 | No | No |
| 17883283 | NEURAL NETWORK PROCESSING WITH CHAINED INSTRUCTIONS | August 2022 | November 2024 | Allow | 27 | 2 | 0 | No | No |
| 17850531 | MULTIPLE INPUT NEURAL NETWORKS FOR DETECTING FRAUD | June 2022 | August 2024 | Allow | 26 | 1 | 0 | No | No |
| 17649912 | MACHINE LEARNING DEPLOYMENT PLATFORM | February 2022 | May 2025 | Allow | 39 | 1 | 0 | Yes | No |
| 17449871 | Deployment and Management of Energy Efficient Deep Neural Network Models on Edge Inference Computing Devices | October 2021 | November 2024 | Allow | 37 | 1 | 0 | Yes | No |
| 17475964 | ELECTRONIC CALCULATOR FOR THE IMPLEMENTATION OF AN ARTIFICIAL NEURAL NETWORK, WITH CALCULATION BLOCKS OF SEVERAL TYPES | September 2021 | April 2025 | Allow | 43 | 1 | 0 | No | No |
| 17466845 | METHODS, SYSTEMS, AND MEDIA FOR ROBUST CLASSIFICATION USING ACTIVE LEARNING AND DOMAIN KNOWLEDGE | September 2021 | April 2025 | Allow | 43 | 1 | 0 | No | No |
| 17461590 | SYNAPTIC CIRCUIT AND NEURAL NETWORKING APPARATUS | August 2021 | March 2025 | Allow | 42 | 1 | 0 | No | No |
| 17461440 | MEMORY DEVICE AND NEURAL NETWORK APPARATUS | August 2021 | September 2024 | Allow | 37 | 0 | 0 | No | No |
| 17431533 | DEEP CAUSAL LEARNING FOR CONTINUOUS TESTING, DIAGNOSIS, AND OPTIMIZATION | August 2021 | December 2024 | Allow | 40 | 2 | 0 | Yes | Yes |
| 17357323 | COOLING HIGH MOTIONAL STATES IN ION TRAP QUANTUM COMPUTERS | June 2021 | December 2024 | Allow | 41 | 1 | 0 | No | No |
| 17316114 | SYSTEM AND METHOD FOR PROCESSING BETWEEN A PLURALITY OF QUANTUM CONTROLLERS | May 2021 | October 2024 | Allow | 41 | 1 | 0 | No | No |
| 17290408 | SYSTEM AND METHODS FOR AN ARTIFICIAL INTELLIGENCE (AI) BASED APPROACH FOR PREDICTIVE MEDICATION ADHERENCE INDEX (MAI) | April 2021 | January 2025 | Abandon | 45 | 1 | 0 | No | No |
| 17281588 | SURGICAL SUPPORT SYSTEM, DATA PROCESSING APPARATUS AND METHOD | March 2021 | July 2024 | Allow | 40 | 1 | 0 | No | No |
| 17187151 | DEEP LEARNING-BASED CHANNEL BUFFER COMPRESSION | February 2021 | August 2024 | Allow | 41 | 1 | 0 | Yes | No |
| 17115941 | EXTRACTED MODEL ADVERSARIES FOR IMPROVED BLACK BOX ATTACKS | December 2020 | December 2024 | Allow | 48 | 0 | 0 | Yes | No |
| 17114825 | Continuous Integration and Automated Testing of Machine Learning Models | December 2020 | October 2024 | Allow | 47 | 2 | 0 | No | No |
| 15262582 | KILLING ASYMMETRIC RESISTIVE PROCESSING UNITS FOR NEURAL NETWORK TRAINING | September 2016 | March 2017 | Allow | 6 | 1 | 0 | No | No |
| 15159879 | MIGRATING A LEGACY SYSTEM BY INFERRING CONTEXT-SENSITIVE BUSINESS RULES FROM LEGACY SOURCE CODE | May 2016 | February 2018 | Allow | 21 | 2 | 0 | Yes | No |
| 14979658 | METHOD FOR HYBRID SOLAR TRACKING, AND APPARATUS FOR HYBRID SOLAR TRACKING AND PHOTOVOLTAIC BLIND SYSTEM USING SAME | December 2015 | March 2017 | Allow | 15 | 1 | 0 | Yes | No |
| 14635316 | DECIDING AN OPTIMAL ACTION IN CONSIDERATION OF RISK | March 2015 | May 2016 | Allow | 15 | 1 | 0 | No | No |
| 14466917 | APPARATUS AND METHODS FOR RATE-MODULATED PLASTICITY IN A NEURON NETWORK | August 2014 | July 2016 | Allow | 23 | 2 | 0 | Yes | No |
| 14050577 | AUTOMATICALLY DERIVING CONTEXT WHEN EXTRACTING A BUSINESS RULE | October 2013 | April 2016 | Allow | 31 | 2 | 0 | Yes | No |
| 14028396 | LEARNING-BASED DATA DECONTEXTUALIZATION | September 2013 | January 2016 | Allow | 28 | 1 | 0 | Yes | No |
| 13973513 | DATA BASED TRUTH MAINTENANCE | August 2013 | November 2015 | Allow | 27 | 0 | 0 | No | No |
| 13969135 | EFFICIENT RULE EXECUTION IN DECISION SERVICES | August 2013 | June 2017 | Allow | 46 | 3 | 0 | No | Yes |
| 13843666 | JABBA-TYPE CONTEXTUAL TAGGER | March 2013 | August 2016 | Allow | 41 | 2 | 0 | No | No |
| 13795165 | EARLY GENERATION OF INDIVIDUALS TO ACCELERATE GENETIC ALGORITHMS | March 2013 | September 2015 | Allow | 31 | 2 | 0 | No | No |
| 13760639 | NATURAL LANGUAGE QUESTION EXPANSION AND EXTRACTION | February 2013 | August 2016 | Allow | 42 | 2 | 0 | No | Yes |
| 13753152 | NEURISTOR-BASED RESERVOIR COMPUTING DEVICES | January 2013 | June 2015 | Allow | 29 | 1 | 0 | No | No |
| 13589407 | USING CYCLIC MARKOV DECISION PROCESS TO DETERMINE OPTIMUM POLICY | August 2012 | May 2015 | Allow | 33 | 2 | 0 | Yes | No |
| 13586385 | USING CYCLIC MARKOV DECISION PROCESS TO DETERMINE OPTIMUM POLICY | August 2012 | May 2015 | Allow | 33 | 2 | 0 | Yes | No |
| 13570680 | HYPOTHESIS-DRIVEN, REAL-TIME ANALYSIS OF PHYSIOLOGICAL DATA STREAMS USING TEXTUAL REPRESENTATIONS | August 2012 | November 2015 | Allow | 40 | 3 | 0 | Yes | No |
| 13371513 | DECIDING AN OPTIMAL ACTION IN CONSIDERATION OF RISK | February 2012 | October 2014 | Allow | 32 | 2 | 0 | No | No |
| 13368994 | METHODS AND APPARATUS FOR SPIKING NEURAL COMPUTATION | February 2012 | April 2015 | Allow | 38 | 3 | 0 | No | No |
| 13344938 | NO ENROLLMENT PROXIMITY TARGET DETECTION ON MOBILE DEVICES | January 2012 | August 2016 | Allow | 55 | 5 | 0 | Yes | No |
This analysis examines appeal outcomes and the strategic value of filing appeals for examiner NILSSON, ERIC.
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, 66.7% 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 NILSSON, ERIC works in Art Unit 2151 and has examined 43 patent applications in our dataset. With an allowance rate of 97.7%, this examiner allows applications at a higher rate than most examiners at the USPTO. Applications typically reach final disposition in approximately 33 months.
Examiner NILSSON, ERIC's allowance rate of 97.7% places them in the 93% percentile among all USPTO examiners. This examiner is more likely to allow applications than most examiners at the USPTO.
On average, applications examined by NILSSON, ERIC receive 1.53 office actions before reaching final disposition. This places the examiner in the 38% percentile for office actions issued. This examiner issues fewer office actions than average, which may indicate efficient prosecution or a more lenient examination style.
The median time to disposition (half-life) for applications examined by NILSSON, ERIC is 33 months. This places the examiner in the 27% percentile for prosecution speed. Prosecution timelines are slightly slower than average with this examiner.
Conducting an examiner interview provides a +4.2% benefit to allowance rate for applications examined by NILSSON, ERIC. This interview benefit is in the 27% percentile among all examiners. Recommendation: Interviews provide a below-average benefit with this examiner.
When applicants file an RCE with this examiner, 38.1% of applications are subsequently allowed. This success rate is in the 85% percentile among all examiners. Strategic Insight: RCEs are highly effective with this examiner compared to others. If you receive a final rejection, filing an RCE with substantive amendments or arguments has a strong likelihood of success.
This examiner enters after-final amendments leading to allowance in 68.4% of cases where such amendments are filed. This entry rate is in the 89% percentile among all examiners. Strategic Recommendation: This examiner is highly receptive to after-final amendments compared to other examiners. Per MPEP § 714.12, after-final amendments may be entered "under justifiable circumstances." Consider filing after-final amendments with a clear showing of allowability rather than immediately filing an RCE, as this examiner frequently enters such amendments.
This examiner withdraws rejections or reopens prosecution in 100.0% of appeals filed. This is in the 88% percentile among all examiners. 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, 50.0% are granted (fully or in part). This grant rate is in the 60% percentile among all examiners. Strategic Note: Petitions show above-average success regarding this examiner's actions. Petitionable matters include restriction requirements (MPEP § 1002.02(c)(2)) and various procedural issues.
Examiner's Amendments: This examiner makes examiner's amendments in 0.0% of allowed cases (in the 10% 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.