Detailed information about the 100 most recent patent applications.
| Application Number | Title | Filing Date | Disposal Date | Disposition | Time (months) | Office Actions | Restrictions | Interview | Appeal |
|---|---|---|---|---|---|---|---|---|---|
| 17056070 | ANOMALY DETECTION APPARATUS, METHOD, AND PROGRAM | November 2020 | February 2025 | Abandon | 51 | 2 | 0 | No | No |
| 17086057 | METHOD AND DEVICE FOR PROCESSING SENSOR DATA | October 2020 | October 2024 | Abandon | 47 | 3 | 0 | No | No |
| 17075618 | METHOD AND APPARATUS FOR TRAINING IMAGE CAPTION MODEL, AND STORAGE MEDIUM | October 2020 | May 2024 | Allow | 43 | 2 | 0 | Yes | No |
| 17042773 | DIGITAL WATERMARKING OF MACHINE LEARNING MODELS | September 2020 | November 2023 | Allow | 38 | 1 | 0 | No | No |
| 17032726 | METHOD AND APPARATUS FOR GENERATING INTERACTIVE SCENARIO, AND ELECTRONIC DEVICE | September 2020 | February 2025 | Abandon | 53 | 4 | 0 | No | No |
| 17029290 | QUANTUM ERROR MITIGATION USING HARDWARE-FRIENDLY PROBABILISTIC ERROR CORRECTION | September 2020 | May 2025 | Allow | 56 | 3 | 0 | Yes | No |
| 16948247 | BOOTSTRAPPING OF TEXT CLASSIFIERS | September 2020 | October 2024 | Abandon | 49 | 2 | 0 | No | No |
| 17010744 | SYSTEMS AND METHODS FOR INTELLIGENT DATA SHUFFLING FOR HIGH-PERFORMANCE DISTRIBUTED MACHINE LEARNING TRAINING | September 2020 | April 2024 | Allow | 43 | 3 | 0 | Yes | No |
| 17003967 | ADJUSTING A PRUNED NEURAL NETWORK | August 2020 | February 2025 | Abandon | 54 | 3 | 0 | Yes | No |
| 17002820 | MACHINE LEARNING MODEL COMPRESSION SYSTEM, PRUNING METHOD, AND COMPUTER PROGRAM PRODUCT | August 2020 | May 2025 | Abandon | 57 | 4 | 0 | Yes | No |
| 16989495 | INTELLIGENT QUESTION ANSWERING ON TABULAR CONTENT | August 2020 | April 2025 | Allow | 56 | 4 | 0 | Yes | No |
| 16940857 | FIXED, RANDOM, RECURRENT MATRICES FOR INCREASED DIMENSIONALITY IN NEURAL NETWORKS | July 2020 | October 2024 | Allow | 50 | 5 | 0 | Yes | No |
| 16933690 | ADAPTIVE NEURAL ARCHITECTURE SEARCH | July 2020 | June 2025 | Abandon | 59 | 5 | 0 | Yes | No |
| 16924015 | TRAINING NEURAL NETWORK CLASSIFIERS USING CLASSIFICATION METADATA FROM OTHER ML CLASSIFIERS | July 2020 | May 2025 | Abandon | 58 | 4 | 0 | Yes | No |
| 16759993 | GATED LINEAR NETWORKS | April 2020 | July 2023 | Allow | 39 | 1 | 0 | Yes | No |
| 16706550 | PARALLEL STREAMING APPARATUS AND METHOD FOR A FAULT TOLERANT QUANTUM COMPUTER | December 2019 | June 2025 | Abandon | 60 | 4 | 0 | Yes | No |
| 16683639 | ENSEMBLE APPROACH TO ALERTING TO MODEL DEGRADATION | November 2019 | May 2023 | Abandon | 42 | 1 | 0 | No | No |
| 16537752 | NEURAL NETWORK PROCESSING METHOD AND APPARATUS BASED ON NESTED BIT REPRESENTATION | August 2019 | May 2024 | Abandon | 58 | 5 | 0 | Yes | No |
| 16516229 | USING AND TRAINING CELLULAR NEURAL NETWORK INTEGRATED CIRCUIT HAVING MULTIPLE CONVOLUTION LAYERS OF DUPLICATE WEIGHTS IN PERFORMING ARTIFICIAL INTELLIGENCE TASKS | July 2019 | June 2022 | Abandon | 35 | 1 | 0 | No | No |
| 16477241 | SYSTEM AND METHOD FOR COGNITIVE ENGINEERING TECHNOLOGY FOR AUTOMATION AND CONTROL OF SYSTEMS | July 2019 | April 2024 | Abandon | 57 | 4 | 0 | No | No |
| 16507688 | ONLINE OPERATING MODE TRAJECTORY OPTIMIZATION FOR PRODUCTION PROCESSES | July 2019 | October 2023 | Allow | 52 | 6 | 0 | Yes | Yes |
| 16457392 | COMPUTATIONAL CREATIVITY BASED ON A TUNABLE CREATIVITY CONTROL FUNCTION OF A MODEL | June 2019 | May 2024 | Abandon | 59 | 6 | 0 | Yes | No |
| 16455457 | WIRELESS FEEDBACK CONTROL LOOPS WITH NEURAL NETWORKS TO PREDICT TARGET SYSTEM STATES | June 2019 | August 2022 | Allow | 38 | 2 | 0 | Yes | No |
| 16441106 | NEUROMORPHIC COMPUTING DEVICE | June 2019 | July 2023 | Abandon | 49 | 4 | 0 | Yes | No |
| 16415854 | Systems and Methods for Slate Optimization with Recurrent Neural Networks | May 2019 | June 2024 | Abandon | 60 | 4 | 0 | Yes | No |
| 16399945 | SYSTEM FOR DEEP LEARNING TRAINING USING EDGE DEVICES | April 2019 | February 2023 | Abandon | 46 | 2 | 0 | Yes | No |
| 16356928 | DIFFERENTIAL BIT WIDTH NEURAL ARCHITECTURE SEARCH | March 2019 | November 2022 | Allow | 44 | 2 | 0 | Yes | No |
| 16355622 | SYSTEMS AND METHODS FOR MUTUAL LEARNING FOR TOPIC DISCOVERY AND WORD EMBEDDING | March 2019 | September 2022 | Allow | 42 | 2 | 0 | Yes | No |
| 16299498 | Discriminative Cosine Embedding in Machine Learning | March 2019 | December 2022 | Allow | 45 | 3 | 0 | No | No |
| 16262947 | Device and Method of Training a Fully-Connected Neural Network | January 2019 | November 2023 | Abandon | 58 | 4 | 0 | Yes | No |
| 16247173 | Probabilistic Modeling System and Method | January 2019 | November 2021 | Abandon | 34 | 1 | 0 | No | No |
| 16247282 | Probabilistic Modeling System and Method | January 2019 | November 2021 | Abandon | 34 | 1 | 0 | No | No |
| 16241530 | WEIGHT SHIFTING FOR NEUROMORPHIC SYNAPSE ARRAY | January 2019 | August 2021 | Allow | 31 | 1 | 0 | Yes | No |
| 16236541 | SYSTEM AND METHOD FOR OUTLIER DETECTION USING A CASCADE OF NEURAL NETWORKS | December 2018 | March 2022 | Abandon | 39 | 1 | 0 | No | No |
| 16234184 | CONFIGURABLE NEURAL NETWORK ENGINE FOR CONVOLUTIONAL FILTER SIZES | December 2018 | August 2022 | Allow | 43 | 2 | 0 | Yes | No |
| 16212642 | Using Quinary Weights with Neural Network Inference Circuit Designed for Ternary Weights | December 2018 | March 2022 | Allow | 39 | 2 | 0 | Yes | Yes |
| 16209582 | DETERMINISTIC NEURAL NETWORKING INTEROPERABILITY | December 2018 | May 2023 | Abandon | 53 | 5 | 0 | No | No |
| 16181850 | NEURON CIRCUIT, SYSTEM, AND METHOD WITH SYNAPSE WEIGHT LEARNING | November 2018 | January 2022 | Allow | 39 | 3 | 0 | Yes | No |
| 16178133 | DEEP LEARNING SOFTWARE ENHANCED MICROELECTROMECHANICAL SYSTEMS (MEMS) BASED INERTIAL MEASUREMENT UNIT (IMU) | November 2018 | April 2025 | Abandon | 60 | 3 | 1 | No | Yes |
| 16178029 | SYSTEMS, METHODS, AND MEDIA FOR GATED RECURRENT NEURAL NETWORKS WITH REDUCED PARAMETER GATING SIGNALS AND/OR MEMORY-CELL UNITS | November 2018 | March 2025 | Abandon | 60 | 5 | 0 | Yes | No |
| 16175373 | Latent Space and Text-Based Generative Adversarial Networks (LATEXT-GANs) for Text Generation | October 2018 | February 2023 | Allow | 51 | 4 | 0 | Yes | No |
| 16153135 | ELECTRONIC APPARATUS AND CONTROL METHOD THEREOF | October 2018 | September 2022 | Allow | 48 | 4 | 0 | Yes | No |
| 16152227 | Hybrid Deep-Learning Action Prediction Architecture | October 2018 | February 2024 | Abandon | 60 | 5 | 0 | Yes | No |
| 16130058 | Adaptive Optimization with Improved Convergence | September 2018 | August 2022 | Allow | 48 | 1 | 0 | Yes | No |
| 16117558 | Farming Portfolio Optimization with Cascaded and Stacked Neural Models Incorporating Probabilistic Knowledge for a Defined Timeframe | August 2018 | February 2023 | Abandon | 54 | 4 | 0 | No | No |
| 16115868 | KNOWLEDGE TRANSFER BETWEEN RECURRENT NEURAL NETWORKS | August 2018 | November 2022 | Allow | 51 | 2 | 0 | Yes | No |
| 16035062 | SUGGESTING A RESPONSE TO A MESSAGE BY SELECTING A TEMPLATE USING A NEURAL NETWORK | July 2018 | September 2024 | Abandon | 60 | 5 | 0 | Yes | No |
| 16033796 | METHOD AND APPARATUS FOR GENERATING FIXED-POINT TYPE NEURAL NETWORK | July 2018 | February 2022 | Allow | 43 | 2 | 0 | Yes | No |
| 16020627 | DEVICES AND METHODS FOR INCREASING THE SPEED AND EFFICIENCY AT WHICH A COMPUTER IS CAPABLE OF MODELING A PLURALITY OF RANDOM WALKERS USING A DENSITY METHOD | June 2018 | April 2022 | Allow | 46 | 2 | 0 | Yes | No |
| 16020619 | DEVICES AND METHODS FOR INCREASING THE SPEED AND EFFICIENCY AT WHICH A COMPUTER IS CAPABLE OF MODELING A PLURALITY OF RANDOM WALKERS USING A PARTICLE METHOD | June 2018 | November 2021 | Allow | 41 | 1 | 0 | Yes | No |
| 16018784 | DEEP LEARNING MODEL SCHEDULING | June 2018 | August 2022 | Allow | 50 | 4 | 0 | Yes | No |
| 16066118 | METHODS, CONTROLLERS AND SYSTEMS FOR THE CONTROL OF DISTRIBUTION SYSTEMS USING A NEURAL NETWORK ARCHITECTURE | June 2018 | January 2022 | Allow | 43 | 1 | 0 | No | No |
| 16006211 | NEURAL NETWORK SYSTEM FOR RESHAPING A NEURAL NETWORK MODEL, APPLICATION PROCESSOR INCLUDING THE SAME, AND METHOD OF OPERATING THE SAME | June 2018 | May 2023 | Allow | 59 | 4 | 0 | Yes | No |
| 16060373 | SYSTEMS AND METHODS FOR GENERATIVE LEARNING | June 2018 | March 2022 | Abandon | 45 | 2 | 0 | No | No |
| 15984386 | Automated Dynamic Virtual Representation of Individual Attributes | May 2018 | April 2024 | Abandon | 60 | 4 | 0 | Yes | Yes |
| 15982615 | DYNAMIC DISCOVERY OF DEPENDENCIES AMONG TIME SERIES DATA USING NEURAL NETWORKS | May 2018 | May 2024 | Abandon | 60 | 5 | 0 | Yes | No |
| 15967327 | METHOD FOR AUTOMATING ACTIONS FOR AN ELECTRONIC DEVICE | April 2018 | December 2023 | Allow | 60 | 6 | 0 | Yes | No |
| 15958999 | MONITORING AND COMPARING FEATURES ACROSS ENVIRONMENTS | April 2018 | November 2021 | Abandon | 43 | 1 | 0 | No | No |
| 15954767 | METHODS AND ARRANGEMENTS TO MANAGE MEMORY IN CASCADED NEURAL NETWORKS | April 2018 | August 2022 | Allow | 52 | 3 | 0 | Yes | No |
| 15943773 | INTERPRETABLE BIO-MEDICAL LINK PREDICTION USING DEEP NEURAL REPRESENTATION | April 2018 | October 2023 | Abandon | 60 | 6 | 0 | Yes | No |
| 15941314 | DEEP NEURAL NETWORK ARCHITECTURE FOR SEARCH | March 2018 | October 2021 | Abandon | 42 | 1 | 0 | No | No |
| 15937460 | Automatically Detecting Frivolous Content in Data | March 2018 | November 2022 | Abandon | 55 | 2 | 0 | Yes | No |
| 15910412 | Fatigue Crack Growth Prediction | March 2018 | June 2023 | Abandon | 60 | 5 | 0 | Yes | No |
| 15909446 | NEURAL NETWORK DEVICE AND COMPUTING DEVICE | March 2018 | October 2023 | Abandon | 60 | 4 | 0 | Yes | No |
| 15888102 | HETEROGENEOUS PROCESSOR ARCHITECTURE FOR INTEGRATING CNN AND RNN INTO SINGLE HIGH-PERFORMANCE, LOW-POWER CHIP | February 2018 | January 2022 | Allow | 47 | 2 | 0 | No | No |
| 15886860 | Method for improving computations of correlation values between surface roughness and terrain parameters | February 2018 | April 2023 | Abandon | 60 | 2 | 0 | Yes | No |
| 15887321 | NETWORK COEFFICIENT COMPRESSION DEVICE, NETWORK COEFFICIENT COMPRESSION METHOD, AND COMPUTER PROGRAM PRODUCT | February 2018 | December 2021 | Abandon | 46 | 2 | 0 | No | No |
| 15881287 | METHOD AND APPARATUS FOR MULTI-DIMENSIONAL SEQUENCE PREDICTION | January 2018 | August 2021 | Abandon | 43 | 1 | 0 | No | No |
| 15854923 | Neural Array Having Multiple Layers Stacked Therein For Deep Belief Network And Method For Operating Neural Array | December 2017 | July 2022 | Allow | 54 | 3 | 0 | Yes | No |
This analysis examines appeal outcomes and the strategic value of filing appeals for examiner GERMICK, JOHNATHAN R.
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, 33.3% of applications that filed an appeal were subsequently allowed. This appeal filing benefit rate is above the USPTO average, suggesting that filing an appeal can be an effective strategy for prompting reconsideration.
⚠ 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 GERMICK, JOHNATHAN R works in Art Unit 2122 and has examined 69 patent applications in our dataset. With an allowance rate of 43.5%, this examiner allows applications at a lower rate than most examiners at the USPTO. Applications typically reach final disposition in approximately 50 months.
Examiner GERMICK, JOHNATHAN R's allowance rate of 43.5% places them in the 10% percentile among all USPTO examiners. This examiner is less likely to allow applications than most examiners at the USPTO.
On average, applications examined by GERMICK, JOHNATHAN R receive 3.01 office actions before reaching final disposition. This places the examiner in the 84% 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 GERMICK, JOHNATHAN R is 50 months. This places the examiner in the 7% percentile for prosecution speed. Applications take longer to reach final disposition with this examiner compared to most others.
Conducting an examiner interview provides a +37.1% benefit to allowance rate for applications examined by GERMICK, JOHNATHAN R. This interview benefit is in the 84% 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, 16.9% of applications are subsequently allowed. This success rate is in the 17% 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 7.0% of cases where such amendments are filed. This entry rate is in the 8% 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 50.0% of appeals filed. This is in the 16% 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 rarely withdraws rejections during the appeal process compared to other examiners. If you file an appeal, be prepared to fully prosecute it to a PTAB decision. Per MPEP § 1207, the examiner will prepare an Examiner's Answer maintaining the rejections.
When applicants file petitions regarding this examiner's actions, 66.7% are granted (fully or in part). This grant rate is in the 70% 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 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.