Detailed information about the 100 most recent patent applications.
| Application Number | Title | Filing Date | Disposal Date | Disposition | Time (months) | Office Actions | Restrictions | Interview | Appeal |
|---|---|---|---|---|---|---|---|---|---|
| 18790709 | TRAINING AN AUTOENCODER WITH A CLASSIFIER | July 2024 | December 2024 | Allow | 4 | 0 | 0 | No | No |
| 18444906 | CUTOFF VALUE OPTIMIZATION FOR BIAS MITIGATING MACHINE LEARNING TRAINING SYSTEM WITH MULTI-CLASS TARGET | February 2024 | May 2024 | Allow | 3 | 0 | 0 | No | No |
| 18468011 | ADDING A SPLIT DETECTOR COMPOUND NODE TO A DEEP NEURAL NETWORK | September 2023 | April 2024 | Allow | 7 | 1 | 0 | No | No |
| 18336906 | NEURAL PROCESSOR WITH ACTIVATION COMPRESSION | June 2023 | September 2024 | Allow | 15 | 1 | 0 | No | No |
| 18208455 | BIAS MITIGATING MACHINE LEARNING TRAINING SYSTEM WITH MULTI-CLASS TARGET | June 2023 | October 2023 | Allow | 4 | 0 | 0 | No | No |
| 18309470 | AUTOMATIC CLASSIFICATION OF DATA SENSITIVITY THROUGH MACHINE LEARNING | April 2023 | July 2023 | Allow | 3 | 0 | 0 | No | No |
| 18140366 | UPDATE OF LOCAL FEATURES MODEL BASED ON CORRECTION TO ROBOT ACTION | April 2023 | March 2024 | Allow | 11 | 0 | 0 | No | No |
| 18191737 | NEURAL PROCESSING DEVICE AND METHOD FOR CONVERTING DATA THEREOF | March 2023 | September 2024 | Allow | 18 | 3 | 0 | No | No |
| 18096854 | SYSTEM AND METHOD FOR EVALUATING THE PERFORMANCE AND USAGE OF A QUESTION ANSWERING COGNITIVE COMPUTING TOOL | January 2023 | May 2024 | Allow | 16 | 1 | 0 | No | No |
| 18147313 | DEEP NEURAL NETWORK WITH COMPOUND NODE FUNCTIONING AS A DETECTOR AND REJECTER | December 2022 | June 2023 | Allow | 5 | 0 | 0 | No | No |
| 17982474 | COMPUTATION OF NEURAL NETWORK NODE BY NEURAL NETWORK INFERENCE CIRCUIT | November 2022 | August 2024 | Allow | 21 | 1 | 0 | No | No |
| 18051906 | BIAS MITIGATING MACHINE LEARNING TRAINING SYSTEM | November 2022 | June 2023 | Allow | 8 | 0 | 0 | No | No |
| 17977964 | PRIOR INJECTIONS FOR SEMI-LABELED SAMPLES | October 2022 | February 2024 | Allow | 15 | 1 | 0 | No | No |
| 18051308 | MACHINE LEARNING (ML) MODELING BY DNA COMPUTING | October 2022 | January 2024 | Allow | 14 | 0 | 0 | No | No |
| 17895762 | Entropy-Based Techniques for Creation of Well-Balanced Computer Based Reasoning Systems | August 2022 | January 2024 | Allow | 17 | 1 | 0 | No | No |
| 17821903 | NEURAL PROCESSING DEVICE | August 2022 | May 2023 | Allow | 8 | 1 | 0 | No | No |
| 17760023 | NETWORK ACCURACY QUANTIFICATION METHOD AND SYSTEM, DEVICE, ELECTRONIC DEVICE AND READABLE MEDIUM | August 2022 | August 2023 | Allow | 12 | 2 | 0 | No | No |
| 17853143 | APPARATUS AND METHOD FOR GENERATING A COMPILED ARTIFICIAL INTELLIGENCE (AI) MODEL | June 2022 | February 2024 | Allow | 19 | 5 | 0 | Yes | No |
| 17838722 | NEURAL CAPACITANCE: NEURAL NETWORK SELECTION VIA EDGE DYNAMICS | June 2022 | May 2025 | Allow | 35 | 0 | 0 | No | No |
| 17838240 | COMPILING ASYMMETRICALLY-QUANTIZED NEURAL NETWORK MODELS FOR DEEP LEARNING ACCELERATION | June 2022 | June 2025 | Allow | 36 | 0 | 0 | No | No |
| 17664898 | ASYNCHRONOUS AGENTS WITH LEARNING COACHES AND STRUCTURALLY MODIFYING DEEP NEURAL NETWORKS WITHOUT PERFORMANCE DEGRADATION | May 2022 | September 2022 | Allow | 4 | 0 | 0 | No | No |
| 17741614 | CLOUD BASED MACHINE LEARNING | May 2022 | September 2024 | Allow | 29 | 2 | 0 | No | No |
| 17707309 | SPARSITY HANDLING FOR MACHINE LEARNING MODEL FORECASTING | March 2022 | June 2022 | Allow | 3 | 0 | 0 | No | No |
| 17653006 | ASYNCHRONOUS AGENTS WITH LEARNING COACHES AND STRUCTURALLY MODIFYING DEEP NEURAL NETWORKS WITHOUT PERFORMANCE DEGRADATION | March 2022 | June 2022 | Allow | 3 | 0 | 0 | No | No |
| 17675617 | ANALOG SWITCHED-CAPACITOR NEURAL NETWORK | February 2022 | March 2024 | Allow | 25 | 1 | 0 | No | No |
| 17672163 | MODIFYING MACHINE LEARNING MODELS TO IMPROVE LOCALITY | February 2022 | October 2023 | Allow | 20 | 0 | 0 | No | No |
| 17589716 | SYSTEMS, METHODS, AND MEDIA FOR DECODING OBSERVED SPIKE COUNTS FOR SPIKING CELLS | January 2022 | April 2022 | Allow | 2 | 0 | 0 | No | No |
| 17572899 | INTEGRATED ARTIFICIAL NEURON DEVICE | January 2022 | December 2023 | Allow | 23 | 1 | 0 | No | No |
| 17570673 | CIRCUIT ARCHITECTURE WITH BIASED RANDOMIZATION | January 2022 | December 2023 | Allow | 23 | 1 | 0 | No | No |
| 17566972 | Pulse-Width Modulated Multiplier | December 2021 | March 2024 | Abandon | 26 | 1 | 0 | No | No |
| 17507376 | CAUSAL INFERENCE AND POLICY OPTIMIZATION SYSTEM BASED ON DEEP LEARNING MODELS | October 2021 | March 2022 | Allow | 5 | 0 | 0 | No | No |
| 17500900 | Predictor Generation Genetic Algorithm | October 2021 | March 2024 | Allow | 29 | 1 | 0 | No | No |
| 17497189 | VIRTUAL ASSISTANT CONFIGURED TO RECOMMENDED ACTIONS IN FURTHERANCE OF AN EXISTING CONVERSATION | October 2021 | April 2024 | Allow | 30 | 1 | 0 | Yes | No |
| 17441729 | METHOD AND APPARATUS FOR PREDICTING SUBSTRATE IMAGE | September 2021 | December 2024 | Allow | 39 | 0 | 0 | No | No |
| 17460861 | UPDATE OF LOCAL FEATURES MODEL BASED ON CORRECTION TO ROBOT ACTION | August 2021 | December 2022 | Allow | 16 | 0 | 0 | No | No |
| 17412350 | Systems and Methods of Applying Semantic Features for Machine Learning of Message Categories | August 2021 | April 2024 | Allow | 31 | 2 | 0 | No | No |
| 17378988 | METHODS OF CHEMICAL COMPUTATION | July 2021 | June 2023 | Allow | 23 | 1 | 0 | No | No |
| 17330349 | Genetic algorithm-based encoding of neural networks | May 2021 | March 2022 | Allow | 10 | 1 | 1 | Yes | No |
| 17292882 | AUTOMATICALLY GENERATING TRAINING DATA SETS FOR OBJECT RECOGNITION | May 2021 | November 2024 | Allow | 43 | 1 | 0 | No | No |
| 17306877 | METHOD AND APPARATUS FOR DISTRIBUTED AND COOPERATIVE COMPUTATION IN ARTIFICIAL NEURAL NETWORKS | May 2021 | February 2024 | Allow | 34 | 1 | 0 | No | No |
| 17225630 | Personalizing User Experience with Neural Fitted Q Iteration | April 2021 | July 2024 | Allow | 39 | 1 | 0 | Yes | No |
| 17250889 | CONVOLUTIONAL NEURAL NETWORK ACCELERATOR | March 2021 | September 2024 | Abandon | 42 | 1 | 0 | No | No |
| 17188837 | DETERMINING MODEL PARAMETERS USING SECRET SHARING | March 2021 | May 2021 | Allow | 3 | 1 | 0 | No | No |
| 17144058 | DATA MANAGEMENT SYSTEM, DATA MANAGEMENT METHOD, AND RECORDING MEDIUM HAVING RECORDED THEREON A DATA MANAGEMENT PROGRAM | January 2021 | September 2024 | Allow | 44 | 1 | 0 | No | No |
| 17131925 | Baum-Welch Accelerator | December 2020 | August 2024 | Allow | 44 | 1 | 0 | No | No |
| 17131424 | APPARATUS AND METHOD FOR A TENSOR PERMUTATION ENGINE | December 2020 | February 2023 | Allow | 26 | 1 | 0 | No | No |
| 17253013 | METHODS OF CHEMICAL COMPUTATION | December 2020 | April 2021 | Allow | 3 | 0 | 0 | No | No |
| 17115989 | INSTRUCTIONS AND LOGIC TO PERFORM FLOATING POINT AND INTEGER OPERATIONS FOR MACHINE LEARNING | December 2020 | July 2024 | Allow | 43 | 2 | 0 | Yes | No |
| 17101613 | SENSOR FUSION DEVICE FOR VEHICLE | November 2020 | August 2024 | Allow | 45 | 2 | 0 | No | No |
| 17098950 | PARAMETER SELECTION METHOD, COMPUTER-READABLE RECORDING MEDIUM RECORDING PARAMETER SELECTION PROGRAM, AND INFORMATION PROCESSING DEVICE | November 2020 | April 2024 | Abandon | 41 | 1 | 0 | No | No |
| 17081361 | SYSTEMS AND METHODS FOR PREDICTING PEST PRESSURE USING GEOSPATIAL FEATURES AND MACHINE LEARNING | October 2020 | August 2024 | Allow | 45 | 2 | 0 | No | No |
| 17044783 | CAUSALITY ESTIMATION OF TIME SERIES VIA SUPERVISED LEARNING | October 2020 | August 2024 | Allow | 47 | 2 | 0 | No | No |
| 17018907 | CONTENT BASED REMOTE DATA PACKET INTERVENTION | September 2020 | November 2022 | Allow | 26 | 0 | 0 | No | No |
| 16945415 | DATA FIELD EXTRACTION BY A DATA INTAKE AND QUERY SYSTEM | July 2020 | October 2024 | Allow | 50 | 1 | 0 | Yes | No |
| 16929168 | PULSE GENERATION FOR UPDATING CROSSBAR ARRAYS | July 2020 | June 2022 | Allow | 23 | 1 | 0 | Yes | No |
| 16929172 | SPARSE MODIFIABLE BIT LENGTH DETERMINSTIC PULSE GENERATION FOR UPDATING ANALOG CROSSBAR ARRAYS | July 2020 | September 2022 | Allow | 26 | 1 | 0 | No | No |
| 16913146 | CONTROLLING THE OPERATING SPEED OF STAGES OF AN ASYNCHRONOUS PIPELINE | June 2020 | July 2023 | Allow | 37 | 3 | 0 | No | No |
| 16900641 | SCALABLE NEUTRAL ATOM BASED QUANTUM COMPUTING | June 2020 | January 2024 | Allow | 43 | 5 | 0 | Yes | No |
| 16896925 | ACCIDENT-DATA-BASED VEHICLE FEATURE DETERMINATION | June 2020 | October 2020 | Allow | 4 | 0 | 0 | Yes | No |
| 16876995 | System and Method for Variable Lane Architecture | May 2020 | September 2020 | Allow | 4 | 0 | 0 | No | No |
| 16826364 | Quantum Optical Neural Networks | March 2020 | July 2023 | Allow | 39 | 1 | 0 | No | No |
| 16792031 | DETERMINING MODEL PARAMETERS USING SECRET SHARING | February 2020 | October 2020 | Allow | 8 | 0 | 0 | No | No |
| 16739341 | DETERMINING A PROBABILITY OF A RELATIONSHIP BETWEEN LAYERS OF GEOGRAPHIC INFORMATION SYSTEM DATA | January 2020 | November 2020 | Allow | 10 | 1 | 0 | No | No |
| 16626824 | DNA-BASED NEURAL NETWORK | December 2019 | September 2024 | Allow | 57 | 2 | 1 | No | No |
| 16717633 | WAVELET REPRESENTATION FOR ACCELERATED DEEP LEARNING | December 2019 | January 2024 | Abandon | 49 | 1 | 0 | No | No |
| 16714974 | COMPUTING DEVICE AND METHOD | December 2019 | March 2023 | Allow | 39 | 1 | 0 | No | No |
| 16710070 | SYSTEMS AND METHODS FOR SITUATION AWARENESS | December 2019 | December 2023 | Allow | 48 | 1 | 1 | No | No |
| 16703358 | VISUALIZATION TO SUPPORT EVENT MONITORING SYSTEM | December 2019 | April 2020 | Allow | 4 | 0 | 0 | No | No |
| 16703301 | EVENT MONITORING SYSTEM USING FREQUENCY SEGMENTS | December 2019 | April 2020 | Allow | 4 | 1 | 0 | No | No |
| 16618910 | ASYNCHRONOUS AGENTS WITH LEARNING COACHES AND STRUCTURALLY MODIFYING DEEP NEURAL NETWORKS WITHOUT PERFORMANCE DEGRADATION | December 2019 | November 2021 | Allow | 24 | 3 | 0 | Yes | No |
| 16699029 | FEATURE MAP AND WEIGHT SELECTION METHOD AND ACCELERATING DEVICE | November 2019 | February 2023 | Allow | 39 | 1 | 0 | No | No |
| 16697196 | NEURAL NETWORK CALCULATION APPARATUS AND METHOD | November 2019 | September 2022 | Allow | 34 | 1 | 0 | No | No |
| 16694911 | TYPE-2 FUZZY NEURAL NETWORK-BASED COOPERATIVE CONTROL METHOD FOR WASTEWATER TREATMENT PROCESS | November 2019 | October 2022 | Allow | 35 | 1 | 0 | No | No |
| 16668957 | NEURAL NETWORK INSTRUCTION STREAMING | October 2019 | June 2022 | Allow | 31 | 1 | 0 | Yes | No |
| 16654214 | ACCIDENT-DATA-BASED VEHICLE FEATURE DETERMINATION | October 2019 | March 2020 | Allow | 5 | 0 | 0 | No | No |
| 16590265 | SYSTEMS AND METHODS FOR ENERGY-EFFICIENT DATA PROCESSING | October 2019 | November 2022 | Allow | 37 | 2 | 0 | No | No |
| 16549608 | EVENT MONITORING SYSTEM | August 2019 | November 2019 | Allow | 3 | 0 | 0 | No | No |
| 16536510 | METHODS AND SYSTEMS FOR ENCODING AND PROCESSING SYMBOL STRUCTURES USING VECTOR-DERIVED TRANSFORMATION BINDING | August 2019 | September 2024 | Abandon | 60 | 3 | 0 | No | No |
| 16481016 | ACCELERATED DEEP LEARNING | July 2019 | November 2023 | Allow | 51 | 1 | 0 | No | No |
| 16509252 | SYSTEMS AND METHODS FOR PIPELINED PARALLELISM TO ACCELERATE DISTRIBUTED PROCESSING | July 2019 | December 2023 | Abandon | 53 | 4 | 0 | Yes | No |
| 16504311 | MACHINE LEARNING MODELS FOR PREDICTING TIME IN TRAFFIC | July 2019 | April 2022 | Allow | 33 | 1 | 0 | No | No |
| 16457172 | GUARDED STORAGE EVENT HANDLING DURING TRANSACTIONAL EXECUTION | June 2019 | October 2020 | Allow | 16 | 1 | 0 | Yes | No |
| 16441025 | APPARATUS AND METHOD FOR PERFORMING A FORWARD OPERATION OF ARTIFICIAL NEURAL NETWORKS | June 2019 | September 2020 | Allow | 15 | 2 | 0 | No | No |
| 16441019 | APPARATUS AND METHOD FOR EXECUTING REVERSAL TRAINING OF ARTIFICIAL NEURAL NETWORK | June 2019 | May 2020 | Allow | 11 | 1 | 0 | No | No |
| 16441885 | RESISTIVE CROSSBAR ARRAYS WITH REDUCED NUMBERS OF ELEMENTS | June 2019 | June 2022 | Allow | 36 | 1 | 0 | Yes | No |
| 16432402 | INSTRUCTIONS AND LOGIC TO PERFORM FLOATING-POINT AND INTEGER OPERATIONS FOR MACHINE LEARNING | June 2019 | June 2021 | Allow | 25 | 2 | 0 | No | No |
| 16345533 | MEMRISTIVE LEARNING FOR NEUROMORPHIC CIRCUITS | April 2019 | January 2022 | Allow | 33 | 0 | 0 | No | No |
| 16369310 | METHOD AND APPARATUS FOR REFINING AN AUTOMATED CODING MODEL | March 2019 | October 2022 | Allow | 42 | 2 | 0 | No | No |
| 16291471 | INFORMATION PROCESSING APPARATUS FOR CONVOLUTION OPERATIONS IN LAYERS OF CONVOLUTIONAL NEURAL NETWORK | March 2019 | June 2022 | Abandon | 39 | 1 | 0 | No | No |
| 16290117 | DERIVING A CONCORDANT SOFTWARE NEURAL NETWORK LAYER FROM A QUANTIZED FIRMWARE NEURAL NETWORK LAYER | March 2019 | October 2022 | Allow | 43 | 1 | 0 | Yes | No |
| 16270848 | Artificial Neurons Using Diffusive Memristor | February 2019 | September 2022 | Allow | 44 | 2 | 0 | No | No |
| 16271273 | CAPSULE VECTOR SPIN NEURON IMPLEMENTATION OF A CAPSULE NEURAL NETWORK PRIMITIVE | February 2019 | March 2024 | Abandon | 60 | 2 | 1 | No | No |
| 16268578 | SERIALIZED ELECTRO-OPTIC NEURAL NETWORK USING OPTICAL WEIGHTS ENCODING | February 2019 | May 2022 | Allow | 39 | 1 | 0 | Yes | No |
| 16258174 | SUPERCONDUCTING NEUROMORPHIC CORE | January 2019 | June 2021 | Allow | 29 | 0 | 0 | No | No |
| 16254848 | Tuning Local Conductances Of Molecular Networks: Applications To Artificial Neural Networks | January 2019 | March 2023 | Allow | 50 | 1 | 0 | No | No |
| 16254475 | MACHINE LEARNING BASED VIDEO COMPRESSION | January 2019 | August 2022 | Allow | 42 | 2 | 0 | No | Yes |
| 16240003 | METHOD AND DEVICE FOR VERIFYING A NEURON FUNCTION IN A NEURAL NETWORK | January 2019 | December 2022 | Allow | 47 | 1 | 0 | No | No |
| 16237439 | METHOD AND SYSTEM FOR SIMILARITY-BASED MULTI-LABEL LEARNING | December 2018 | January 2024 | Allow | 60 | 5 | 0 | Yes | No |
| 16230928 | NEURAL NETWORKS IMPLEMENTED WITH DSD CIRCUITS | December 2018 | February 2023 | Allow | 50 | 0 | 1 | No | No |
| 16222693 | METHOD AND ELECTRONIC DEVICE FOR CONVOLUTION CALCULATION IN NEURAL NETWORK | December 2018 | October 2022 | Allow | 45 | 1 | 0 | No | No |
This analysis examines appeal outcomes and the strategic value of filing appeals for examiner GEIB, BENJAMIN P.
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, 40.0% 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.
✓ Filing a Notice of Appeal is strategically valuable. The act of filing often prompts favorable reconsideration during the mandatory appeal conference.
Examiner GEIB, BENJAMIN P works in Art Unit 2123 and has examined 240 patent applications in our dataset. With an allowance rate of 92.1%, this examiner allows applications at a higher rate than most examiners at the USPTO. Applications typically reach final disposition in approximately 41 months.
Examiner GEIB, BENJAMIN P's allowance rate of 92.1% places them in the 77% percentile among all USPTO examiners. This examiner is more likely to allow applications than most examiners at the USPTO.
On average, applications examined by GEIB, BENJAMIN P receive 1.68 office actions before reaching final disposition. This places the examiner in the 47% 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 GEIB, BENJAMIN P is 41 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 +3.3% benefit to allowance rate for applications examined by GEIB, BENJAMIN P. This interview benefit is in the 23% percentile among all examiners. Note: Interviews show limited statistical benefit with this examiner compared to others, though they may still be valuable for clarifying issues.
When applicants file an RCE with this examiner, 32.5% of applications are subsequently allowed. This success rate is in the 61% percentile among all examiners. Strategic Insight: RCEs show above-average effectiveness with this examiner. Consider whether your amendments or new arguments are strong enough to warrant an RCE versus filing a continuation.
This examiner enters after-final amendments leading to allowance in 33.3% of cases where such amendments are filed. This entry rate is in the 41% percentile among all examiners. Strategic Recommendation: This examiner shows below-average receptiveness to after-final amendments. You may need to file an RCE or appeal rather than relying on after-final amendment entry.
When applicants request a pre-appeal conference (PAC) with this examiner, 100.0% result in withdrawal of the rejection or reopening of prosecution. This success rate is in the 69% percentile among all examiners. Strategic Recommendation: Pre-appeal conferences show above-average effectiveness with this examiner. If you have strong arguments, a PAC request may result in favorable reconsideration.
This examiner withdraws rejections or reopens prosecution in 100.0% of appeals filed. This is in the 87% percentile among all examiners. Of these withdrawals, 60.0% 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, 20.0% are granted (fully or in part). This grant rate is in the 11% 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 8% 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 0.9% of allowed cases (in the 55% percentile). This examiner issues Quayle actions more often than average when claims are allowable but formal matters remain (MPEP § 714.14).
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.