Detailed information about the 100 most recent patent applications.
| Application Number | Title | Filing Date | Disposal Date | Disposition | Time (months) | Office Actions | Restrictions | Interview | Appeal |
|---|---|---|---|---|---|---|---|---|---|
| 18408716 | Schedule-Aware Tensor Distribution Module | January 2024 | January 2025 | Allow | 12 | 1 | 0 | Yes | No |
| 18508519 | NEUROMORPHIC METHOD AND APPARATUS WITH MULTI-BIT NEUROMORPHIC OPERATION | November 2023 | March 2025 | Allow | 16 | 1 | 0 | No | No |
| 18051510 | SYSTEM, SERVER AND METHOD FOR PREDICTING ADVERSE EVENTS | October 2022 | April 2024 | Allow | 18 | 1 | 1 | No | No |
| 17718612 | WEIGHT CONFIRMATION METHOD FOR AN ANALOG SYNAPTIC DEVICE OF AN ARTIFICIAL NEURAL NETWORK | April 2022 | August 2025 | Allow | 40 | 1 | 0 | No | No |
| 17706369 | DETECTION METHOD, COMPUTER-READABLE RECORDING MEDIUM STORING DETECTION PROGRAM, AND DETECTION DEVICE | March 2022 | August 2025 | Abandon | 41 | 1 | 0 | No | No |
| 17566885 | BUILDING AND EXECUTING DEEP LEARNING-BASED DATA PIPELINES | December 2021 | July 2025 | Allow | 43 | 1 | 0 | Yes | No |
| 17566877 | FIXED-POINT MULTIPLICATION FOR NETWORK QUANTIZATION | December 2021 | February 2026 | Allow | 49 | 1 | 0 | No | No |
| 17624231 | METHOD FOR PREDICTING AND CONTROLLING AWATER LEVEL OF A SERIES WATER CONVEYANCE CANAL ON A BASIS OF A FUZZY NEURAL NETWORK | December 2021 | March 2025 | Allow | 39 | 1 | 0 | No | No |
| 17535405 | ASCERTAINING AND/OR MITIGATING EXTENT OF EFFECTIVE RECONSTRUCTION, OF PREDICTIONS, FROM MODEL UPDATES TRANSMITTED IN FEDERATED LEARNING | November 2021 | February 2026 | Allow | 51 | 1 | 0 | No | No |
| 17492172 | PHYSICS AUGMENTED NEURAL NETWORKS CONFIGURED FOR OPERATING IN ENVIRONMENTS THAT MIX ORDER AND CHAOS | October 2021 | August 2025 | Allow | 47 | 1 | 0 | Yes | No |
| 17480292 | MODEL DEVELOPMENT TOOL TO TRAIN, EVALUATE AND PREDICT WITH DEEP LEARNING BY SELECTING FUNCTIONS | September 2021 | June 2025 | Allow | 44 | 2 | 0 | No | No |
| 17430901 | FEEDBACK MINING WITH DOMAIN-SPECIFIC MODELING | August 2021 | September 2025 | Abandon | 49 | 1 | 0 | No | No |
| 17388053 | MEMORY AND TRAINING METHOD FOR NEURAL NETWORK BASED ON MEMORY | July 2021 | August 2024 | Allow | 37 | 1 | 0 | Yes | No |
| 17304365 | DISTRIBUTING STRUCTURE RISK ASSESSMENT USING INFORMATION DISTRIBUTION STATIONS | June 2021 | October 2025 | Allow | 52 | 2 | 0 | Yes | No |
| 17326054 | Efficient Computation for Bayesian Optimization | May 2021 | May 2025 | Abandon | 48 | 2 | 0 | Yes | No |
| 17240710 | TIGHTLY COUPLED END-TO-END MULTI-SENSOR FUSION WITH INTEGRATED COMPENSATION | April 2021 | September 2024 | Allow | 40 | 1 | 0 | Yes | No |
| 17239857 | LOCALIZATION-BASED TEST GENERATION FOR INDIVIDUAL FAIRNESS TESTING OF ARTIFICIAL INTELLIGENCE MODELS | April 2021 | September 2024 | Allow | 41 | 2 | 0 | Yes | No |
| 17240108 | CLASSIFICATION MODEL CALIBRATION | April 2021 | March 2025 | Allow | 47 | 1 | 0 | No | No |
| 17240554 | PREDICTING PROPERTIES OF MATERIALS FROM PHYSICAL MATERIAL STRUCTURES | April 2021 | August 2024 | Allow | 40 | 1 | 0 | Yes | No |
| 17239892 | Z-FIRST REFERENCE NEURAL PROCESSING UNIT FOR MAPPING WINOGRAD CONVOLUTION AND A METHOD THEREOF | April 2021 | May 2025 | Allow | 49 | 2 | 0 | Yes | No |
| 17286330 | DESIGN AND OPTIMIZATION OF EDGE COMPUTING DISTRIBUTED NEURAL PROCESSOR FOR WEARABLE DEVICES | April 2021 | March 2025 | Allow | 47 | 1 | 0 | No | No |
| 17229894 | Method for Low Resource and Low Power Consuming Implementation of Nonlinear Activation Functions of Artificial Neural Networks | April 2021 | October 2024 | Abandon | 42 | 1 | 0 | No | No |
| 17229228 | FAST AND SCALABLE MULTI-TENANT SERVE POOL FOR CHATBOTS | April 2021 | August 2024 | Allow | 40 | 1 | 0 | Yes | No |
| 17216455 | AUTOMATIC FAILURE DIAGNOSIS AND CORRECTION IN MACHINE LEARNING MODELS | March 2021 | March 2025 | Allow | 48 | 2 | 0 | No | No |
| 17206217 | DIGITAL COMPETITION SELF-VALIDATION USING MACHINE LEARNING | March 2021 | September 2025 | Abandon | 54 | 2 | 0 | No | No |
| 17200994 | MANAGING USER MACHINE LEARNING (ML) MODELS | March 2021 | July 2025 | Allow | 52 | 2 | 0 | Yes | No |
| 17197099 | LEARNING METHOD AND INFORMATION PROCESSING APPARATUS | March 2021 | June 2025 | Abandon | 51 | 1 | 0 | No | No |
| 17163192 | SECURE SEARCH ENGINE UTILIZING A LEARNING ENGINE | January 2021 | October 2024 | Allow | 45 | 1 | 0 | Yes | No |
| 17139507 | UTILIZING MACHINE LEARNING MODELS TO CHARACTERIZE A RELATIONSHIP BETWEEN A USER AND AN ENTITY | December 2020 | December 2024 | Allow | 47 | 2 | 0 | Yes | No |
| 17124018 | USING GENERATIVE ADVERSARIAL NETWORKS TO CONSTRUCT REALISTIC COUNTERFACTUAL EXPLANATIONS FOR MACHINE LEARNING MODELS | December 2020 | September 2025 | Allow | 57 | 4 | 0 | Yes | Yes |
| 17121930 | DYNAMIC CONFIGURATION OF READOUT CIRCUITRY FOR DIFFERENT OPERATIONS IN ANALOG RESISTIVE CROSSBAR ARRAY | December 2020 | August 2024 | Allow | 44 | 1 | 0 | Yes | No |
| 17122807 | METHOD FOR PREDICTING VESSEL DENSITY IN A SURVEILLANCE AREA | December 2020 | September 2024 | Abandon | 45 | 2 | 0 | No | No |
| 17116727 | METHOD, APPARATUS, AND SYSTEM FOR PROVIDING A LOCATION REPRESENTATION FOR MACHINE LEARNING TASKS | December 2020 | May 2025 | Allow | 53 | 3 | 0 | Yes | No |
| 17115285 | AN EFFICIENT METHOD FOR VLSI IMPLEMENTATION OF USEFUL NEURAL NETWORK ACTIVATION FUNCTIONS | December 2020 | May 2025 | Allow | 53 | 3 | 0 | Yes | No |
| 17099762 | APPROXIMATE VALUE ITERATION WITH COMPLEX RETURNS BY BOUNDING | November 2020 | July 2024 | Allow | 44 | 1 | 0 | No | No |
| 17086218 | METHODS AND SYSTEMS FOR TRAINING MULTI-BIT SPIKING NEURAL NETWORKS FOR EFFICIENT IMPLEMENTATION ON DIGITAL HARDWARE | October 2020 | November 2024 | Allow | 48 | 1 | 0 | No | No |
| 17083186 | Hardware Implementations of Activation Functions in Neural Networks | October 2020 | July 2025 | Allow | 57 | 2 | 0 | Yes | Yes |
| 17081612 | DEEP NEURAL NETWORK HARDENER | October 2020 | July 2024 | Allow | 44 | 1 | 0 | Yes | No |
| 17064560 | AUTOMATED MODEL TRAINING DEVICE AND AUTOMATED MODEL TRAINING METHOD FOR TRAINING PIPELINE FOR DIFFERENT SPECTROMETERS | October 2020 | September 2024 | Abandon | 47 | 1 | 0 | No | No |
| 17063997 | TARGET TRACKING METHOD AND APPARATUS, MEDIUM, AND DEVICE | October 2020 | June 2024 | Allow | 45 | 2 | 0 | Yes | No |
| 16999257 | Field Programmable Neural Array | August 2020 | May 2024 | Abandon | 45 | 1 | 0 | No | No |
| 16987457 | RESERVOIR COMPUTER, RESERVOIR DESIGNING METHOD, AND NON-TRANSITORY COMPUTER-READABLE STORAGE MEDIUM FOR STORING RESERVOIR DESIGNING PROGRAM | August 2020 | August 2023 | Abandon | 36 | 1 | 0 | No | No |
| 16942892 | SYSTEMS AND METHODS FOR CORRIDOR INTENT PREDICTION | July 2020 | June 2023 | Allow | 35 | 1 | 0 | No | No |
| 16928708 | Control of Processing Node Operations | July 2020 | August 2023 | Allow | 38 | 2 | 0 | No | No |
| 16946925 | METHOD OF AUTOMATICALLY ASSIGNING A CLASSIFICATION | July 2020 | July 2024 | Abandon | 49 | 3 | 0 | Yes | No |
| 16922395 | MACHINE LEARNING DEVICE AND MACHINE LEARNING METHOD | July 2020 | August 2023 | Abandon | 37 | 2 | 0 | No | No |
| 16923004 | OPTIMIZING GLOBAL SPARSITY FOR NEURAL NETWORK | July 2020 | June 2024 | Allow | 47 | 2 | 0 | Yes | No |
| 16913054 | SYSTEM AND METHODS FOR FEATURE ENGINEERING BASED ON GRAPH LEARNING | June 2020 | December 2023 | Abandon | 42 | 2 | 0 | Yes | No |
| 16905769 | INPUT BATCHING WITH SERIAL DYNAMIC MEMORY ACCESS | June 2020 | September 2023 | Allow | 39 | 2 | 0 | Yes | No |
| 16709670 | SYSTEM AND METHOD FOR MACHINE-LEARNING | December 2019 | September 2022 | Allow | 34 | 1 | 0 | Yes | No |
| 16692848 | DATA FORMAT TRANSFORM METHOD TO IMPROVE AI ENGINE MAC UTILIZATION | November 2019 | August 2023 | Allow | 45 | 3 | 0 | Yes | No |
| 16684128 | EXECUTING REPLICATED NEURAL NETWORK LAYERS ON INFERENCE CIRCUIT | November 2019 | January 2024 | Allow | 50 | 1 | 0 | No | No |
| 16664668 | HETEROGENEOUS DEEP LEARNING ACCELERATOR | October 2019 | April 2024 | Allow | 54 | 2 | 0 | Yes | No |
| 16657263 | INTEGRATED NOISE GENERATION FOR ADVERSARIAL TRAINING | October 2019 | December 2022 | Allow | 38 | 1 | 0 | Yes | No |
| 16584994 | OPTIMIZATION DEVICE AND CONTROL METHOD OF OPTIMIZATION DEVICE | September 2019 | October 2022 | Allow | 37 | 1 | 0 | Yes | No |
| 16565884 | RESERVOIR ELEMENT AND NEUROMORPHIC ELEMENT | September 2019 | February 2023 | Allow | 41 | 1 | 0 | No | No |
| 16558585 | MACHINE LEARNING HARDWARE HAVING REDUCED PRECISION PARAMETER COMPONENTS FOR EFFICIENT PARAMETER UPDATE | September 2019 | September 2024 | Allow | 60 | 4 | 0 | Yes | No |
| 16556424 | NEUROMORPHIC METHOD AND APPARATUS WITH MULTI-BIT NEUROMORPHIC OPERATION | August 2019 | August 2023 | Allow | 47 | 1 | 0 | No | No |
| 16543645 | FEATURE MAP CACHING METHOD OF CONVOLUTIONAL NEURAL NETWORK AND SYSTEM THEREOF | August 2019 | August 2023 | Allow | 48 | 2 | 0 | No | No |
| 16522986 | MEMORY DEVICE AND OPERATION METHOD THEREOF | July 2019 | September 2023 | Allow | 50 | 2 | 0 | Yes | No |
| 16456707 | Schedule-Aware Tensor Distribution Module | June 2019 | October 2023 | Allow | 51 | 2 | 0 | No | No |
| 16448021 | TECHNOLOGIES FOR PERFORMING IN-MEMORY TRAINING DATA AUGMENTATION FOR ARTIFICIAL INTELLIGENCE | June 2019 | July 2023 | Abandon | 49 | 2 | 0 | No | No |
| 16417966 | DEEP NEURAL NETWORKS WITH INTERPRETABILITY | May 2019 | May 2023 | Allow | 48 | 1 | 0 | No | No |
| 16414260 | GRAPH NEURAL NETWORK FORCE FIELD COMPUTATIONAL ALGORITHMS FOR MOLECULAR DYNAMICS COMPUTER SIMULATIONS | May 2019 | July 2023 | Allow | 50 | 2 | 0 | Yes | No |
| 16407252 | USING COMPUTATIONAL COST AND INSTANTANEOUS LOAD ANALYSIS FOR INTELLIGENT DEPLOYMENT OF NEURAL NETWORKS ON MULTIPLE HARDWARE EXECUTORS | May 2019 | June 2023 | Allow | 49 | 2 | 0 | Yes | No |
| 16398710 | METHOD AND APPARATUS WITH NEURAL NETWORK PARAMETER QUANTIZATION | April 2019 | December 2023 | Allow | 55 | 3 | 0 | Yes | No |
| 16299634 | NEURAL NETWORK DEVICE | March 2019 | June 2022 | Allow | 40 | 1 | 0 | Yes | No |
| 16324214 | METHODS AND APPARATUS FOR SEMANTIC KNOWLEDGE TRANSFER | February 2019 | July 2023 | Abandon | 53 | 2 | 0 | No | No |
| 16260331 | GENERATION OF EXECUTABLE FILES CORRESPONDING TO NEURAL NETWORK MODELS | January 2019 | January 2023 | Allow | 47 | 1 | 0 | No | No |
| 16248543 | METHOD OF GENERATING TRAINING DATA FOR TRAINING A NEURAL NETWORK, METHOD OF TRAINING A NEURAL NETWORK AND USING NEURAL NETWORK FOR AUTONOMOUS OPERATIONS | January 2019 | February 2023 | Allow | 49 | 1 | 0 | Yes | No |
| 16196669 | CREATION OF SCOPE DEFINITIONS | November 2018 | April 2023 | Allow | 53 | 3 | 0 | Yes | No |
| 16194791 | DATA DRIVEN MIXED PRECISION LEARNING FOR NEURAL NETWORKS | November 2018 | November 2022 | Allow | 48 | 2 | 0 | Yes | No |
| 16164366 | MINIMIZATION OF COMPUTATIONAL DEMANDS IN MODEL AGNOSTIC CROSS-LINGUAL TRANSFER WITH NEURAL TASK REPRESENTATIONS AS WEAK SUPERVISION | October 2018 | September 2022 | Allow | 47 | 1 | 0 | Yes | No |
| 16131402 | CONSTRAINT-BASED DYNAMIC QUANTIZATION ADJUSTMENT FOR FIXED-POINT PROCESSING | September 2018 | December 2022 | Allow | 51 | 2 | 0 | Yes | No |
| 16128477 | SYSTEM, SERVER AND METHOD FOR PREDICTING ADVERSE EVENTS | September 2018 | October 2022 | Allow | 50 | 2 | 1 | No | No |
| 16117302 | MACHINE LEARNING INFERENCE ENGINE SCALABILITY | August 2018 | December 2023 | Allow | 60 | 3 | 0 | Yes | No |
| 16116029 | Computing Device for Multiple Activation Functions in Neural Networks | August 2018 | March 2023 | Abandon | 54 | 2 | 0 | No | No |
| 15979711 | MACHINE LEARNING DEVICE AND MACHINE LEARNING METHOD | May 2018 | March 2023 | Abandon | 58 | 2 | 0 | No | No |
| 15873609 | METHOD OF GENERATING TRAINING DATA FOR TRAINING A NEURAL NETWORK, METHOD OF TRAINING A NEURAL NETWORK AND USING NEURAL NETWORK FOR AUTONOMOUS OPERATIONS | January 2018 | December 2022 | Abandon | 58 | 1 | 0 | No | No |
This analysis examines appeal outcomes and the strategic value of filing appeals for examiner HALES, BRIAN J.
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 HALES, BRIAN J works in Art Unit 2125 and has examined 74 patent applications in our dataset. With an allowance rate of 77.0%, this examiner has a below-average tendency to allow applications. Applications typically reach final disposition in approximately 48 months.
Examiner HALES, BRIAN J's allowance rate of 77.0% places them in the 44% percentile among all USPTO examiners. This examiner has a below-average tendency to allow applications.
On average, applications examined by HALES, BRIAN J receive 1.68 office actions before reaching final disposition. This places the examiner in the 34% 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 HALES, BRIAN J is 48 months. This places the examiner in the 8% percentile for prosecution speed. Applications take longer to reach final disposition with this examiner compared to most others.
Conducting an examiner interview provides a +35.1% benefit to allowance rate for applications examined by HALES, BRIAN J. This interview benefit is in the 83% 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, 43.2% of applications are subsequently allowed. This success rate is in the 94% 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 45.5% of cases where such amendments are filed. This entry rate is in the 69% percentile among all examiners. Strategic Recommendation: This examiner shows above-average receptiveness to after-final amendments. If your amendments clearly overcome the rejections and do not raise new issues, consider filing after-final amendments before resorting to an RCE.
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, 50.0% 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, 66.7% are granted (fully or in part). This grant rate is in the 72% 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.