Detailed information about the 100 most recent patent applications.
| Application Number | Title | Filing Date | Disposal Date | Disposition | Time (months) | Office Actions | Restrictions | Interview | Appeal |
|---|---|---|---|---|---|---|---|---|---|
| 18401718 | METHOD FOR EVALUATING AN ARTIFICIAL NEURAL NETWORK MODEL PERFORMANCE AND SYSTEM USING THE SAME | January 2024 | May 2024 | Allow | 5 | 2 | 0 | Yes | No |
| 18482731 | SYSTEMS AND METHODS OF LARGE LANGUAGE MODEL DRIVEN ORCHESTRATION OF TASK-SPECIFIC MACHINE LEARNING SOFTWARE AGENTS | October 2023 | April 2024 | Allow | 6 | 1 | 0 | No | No |
| 18223717 | ANOMALY DETECTION USING RPCA AND ICA | July 2023 | December 2023 | Allow | 4 | 1 | 0 | Yes | No |
| 18348327 | BEARING FAULT DIAGNOSIS METHOD BASED ON FUZZY BROAD LEARNING MODEL | July 2023 | January 2024 | Allow | 6 | 0 | 0 | No | No |
| 18325029 | Low-Power and Compact Neuron Circuit Implementing ReLU Activation Function | May 2023 | October 2023 | Allow | 5 | 1 | 0 | No | No |
| 18196466 | ARCHITECTURES, SYSTEMS AND METHODS FOR PROGRAM DEFINED TRANSACTION SYSTEM AND DECENTRALIZED CRYPTOCURRENCY SYSTEMS | May 2023 | August 2023 | Allow | 3 | 0 | 0 | No | No |
| 18312081 | SYSTEMS AND METHODS FOR DETECTING CRYPTOCURRENCY WALLET ARTIFACTS IN A FILE SYSTEM | May 2023 | July 2023 | Allow | 2 | 0 | 0 | No | No |
| 18129711 | DATA DIAGNOSIS METHOD, A COMPUTER PROGRAM STORING THE DATA DIAGNOSIS METHOD, AND COMPUTING DEVICE FOR PERFORMING DATA DIAGNOSIS METHOD | March 2023 | October 2023 | Allow | 6 | 1 | 0 | No | No |
| 18172757 | CONTROLLING DISTRIBUTION OF TRAINING DATA TO MEMBERS OF AN ENSEMBLE | February 2023 | April 2023 | Allow | 2 | 0 | 0 | No | No |
| 17947222 | ARCHITECTURES, SYSTEMS AND METHODS FOR PROGRAM DEFINED TRANSACTION SYSTEM AND DECENTRALIZED CRYPTOCURRENCY SYSTEMS | September 2022 | December 2022 | Allow | 3 | 0 | 0 | Yes | No |
| 17898109 | DATA CLINIC METHOD, COMPUTER PROGRAM STORING DATA CLINIC METHOD, AND COMPUTING DEVICE FOR PERFORMING DATA CLINIC METHOD | August 2022 | April 2023 | Allow | 8 | 1 | 0 | No | No |
| 17884149 | REPRESENTING GRAPH EDGES USING NEURAL NETWORKS | August 2022 | September 2024 | Allow | 25 | 1 | 0 | Yes | No |
| 17880534 | DIAGNOSING SOURCES OF NOISE IN AN EVALUATION | August 2022 | April 2024 | Allow | 21 | 1 | 0 | No | No |
| 17866645 | INTELLIGENT RECOGNITION AND ALERT METHODS AND SYSTEMS | July 2022 | February 2023 | Allow | 7 | 1 | 0 | Yes | No |
| 17867126 | EARLY WARNING AND EVENT PREDICTING SYSTEMS AND METHODS FOR PREDICTING FUTURE EVENTS | July 2022 | November 2022 | Allow | 4 | 1 | 0 | Yes | No |
| 17742269 | METHOD AND APPARATUS FOR ENERGY-AWARE DEEP NEURAL NETWORK COMPRESSION | May 2022 | September 2023 | Allow | 16 | 1 | 0 | No | No |
| 17733594 | REINFORCEMENT LEARNING USING BASELINE AND POLICY NEURAL NETWORKS | April 2022 | February 2024 | Allow | 21 | 0 | 0 | No | No |
| 17654194 | CONTROLLING DISTRIBUTION OF TRAINING DATA TO MEMBERS OF AN ENSEMBLE | March 2022 | November 2022 | Allow | 8 | 2 | 0 | No | No |
| 17654187 | KNOWLEDGE SHARING FOR MACHINE LEARNING SYSTEMS | March 2022 | November 2022 | Allow | 8 | 1 | 0 | No | No |
| 17687362 | SYSTEMS AND METHODS IMPLEMENTING AN INTELLIGENT MACHINE LEARNING TUNING SYSTEM PROVIDING MULTIPLE TUNED HYPERPARAMETER SOLUTIONS | March 2022 | January 2024 | Allow | 22 | 2 | 0 | Yes | No |
| 17671980 | INTELLIGENT RECOGNITION AND ALERT METHODS AND SYSTEMS | February 2022 | August 2022 | Allow | 6 | 1 | 0 | No | No |
| 17564071 | METHOD FOR AUTOMATICALLY COMPRESSING MULTITASK-ORIENTED PRE-TRAINED LANGUAGE MODEL AND PLATFORM THEREOF | December 2021 | September 2022 | Allow | 8 | 1 | 0 | No | No |
| 17555493 | SEMISUPERVISED AUTOENCODER FOR SENTIMENT ANALYSIS | December 2021 | August 2023 | Allow | 20 | 1 | 0 | No | No |
| 17549558 | UTILIZING MACHINE LEARNING AND COMPOSITE UTILITY SCORES FROM MULTIPLE EVENT CATEGORIES TO IMPROVE DIGITAL CONTENT DISTRIBUTION | December 2021 | December 2023 | Abandon | 24 | 1 | 0 | No | No |
| 17541186 | SELECTING REINFORCEMENT LEARNING ACTIONS USING A LOW-LEVEL CONTROLLER | December 2021 | September 2023 | Allow | 21 | 1 | 0 | No | No |
| 17510633 | Hardware Implementation of Convolutional Layer of Deep Neural Network | October 2021 | August 2023 | Allow | 22 | 1 | 0 | No | No |
| 17384519 | IDENTIFYING LEADING INDICATORS FOR TARGET EVENT PREDICTION | July 2021 | October 2023 | Allow | 27 | 2 | 0 | Yes | No |
| 17370434 | EARLY PATTERN DETECTION IN DATA FOR IMPROVED ENTERPRISE OPERATIONS | July 2021 | October 2023 | Allow | 27 | 2 | 0 | Yes | No |
| 17339988 | AUTOMATICALLY GENERATING ASSERTIONS AND INSIGHTS | June 2021 | July 2024 | Abandon | 38 | 2 | 0 | No | No |
| 17292493 | TECHNICAL KNOWLEDGE PREDICTION APPARATUS, METHOD, AND PROGRAM | May 2021 | July 2024 | Allow | 38 | 1 | 0 | No | No |
| 17242691 | COMPILER-BASED METHOD FOR FAST CNN PRUNING VIA COMPOSABILITY | April 2021 | October 2024 | Abandon | 42 | 1 | 0 | No | No |
| 17232455 | COMPOSITE MACHINE LEARNING SYSTEM FOR LABEL PREDICTION AND TRAINING DATA COLLECTION | April 2021 | July 2023 | Allow | 27 | 1 | 0 | Yes | No |
| 17281602 | Identification of Copied ML Model | March 2021 | June 2024 | Allow | 39 | 1 | 0 | No | No |
| 17281180 | DATA REPRESENTATIONS AND ARCHITECTURES, SYSTEMS, AND METHODS FOR MULTI-SENSORY FUSION, COMPUTING, AND CROSS-DOMAIN GENERALIZATION | March 2021 | October 2024 | Abandon | 42 | 1 | 0 | No | No |
| 17249293 | INFORMATION PROCESSING SYSTEM | February 2021 | July 2023 | Allow | 28 | 0 | 0 | No | No |
| 17175487 | GRADIENT PRUNING FOR EFFICIENT TRAINING OF MACHINE LEARNING MODELS | February 2021 | August 2024 | Allow | 42 | 2 | 0 | Yes | No |
| 17173380 | COGNITIVE AUTOMATION FOR NETWORKING, SECURITY, IoT, AND COLLABORATION | February 2021 | January 2024 | Allow | 36 | 2 | 0 | Yes | No |
| 17267467 | SYSTEMS AND METHODS FOR PROVIDING FLEXIBLE, MULTI-CAPACITY MODELS FOR USE OF DEEP NEURAL NETWORKS IN MOBILE DEVICES | February 2021 | February 2024 | Allow | 36 | 1 | 0 | No | No |
| 17170316 | ASYNCHRONOUS DEEP REINFORCEMENT LEARNING | February 2021 | July 2023 | Allow | 29 | 1 | 0 | Yes | No |
| 17157257 | DATA COLLECTION AGENT TRAINED FOR TELEMETRY DATA COLLECTION | January 2021 | May 2023 | Allow | 28 | 1 | 0 | No | No |
| 17157071 | TIME-PRESERVING EMBEDDINGS | January 2021 | August 2023 | Allow | 31 | 1 | 0 | Yes | No |
| 17155686 | COMPUTING APPARATUS, COMPUTING METHOD, STORAGE MEDIUM, AND TABLE GENERATING APPARATUS | January 2021 | November 2023 | Allow | 33 | 1 | 0 | No | No |
| 17155209 | SUPPLEMENTING ARTIFICIAL INTELLIGENCE (AI) / MACHINE LEARNING (ML) MODELS VIA ACTION CENTER, AI/ML MODEL RETRAINING HARDWARE CONTROL, AND AI/ML MODEL SETTINGS MANAGEMENT | January 2021 | August 2024 | Allow | 43 | 1 | 0 | No | No |
| 17154892 | NON-TRANSITORY COMPUTER-READABLE STORAGE MEDIUM, PROCESSING INFORMATION METHOD, AND INFORMATION PROCESSING APPARATUS | January 2021 | October 2024 | Abandon | 45 | 1 | 0 | No | No |
| 17153399 | ARTIFICIAL INTELLIGENCE (AI) ARCHITECTURE WITH SMART, AUTOMATED TRIGGERS OF INCOMING AND OUTGOING ACTIONS AND USAGE | January 2021 | July 2022 | Allow | 18 | 1 | 1 | No | No |
| 17136925 | SYSTEM PERFORMANCE OPTIMIZATION | December 2020 | April 2024 | Allow | 40 | 1 | 0 | Yes | No |
| 17128554 | Allocating Processing Resources To Concurrently-Executing Neural Networks | December 2020 | November 2023 | Allow | 35 | 1 | 0 | No | No |
| 17124791 | METHOD AND APPARATUS WITH NEURAL NETWORK OPERATION PROCESSING | December 2020 | July 2023 | Allow | 31 | 1 | 0 | No | No |
| 17114957 | ADVERSARIAL SEMI-SUPERVISED ONE-SHOT LEARNING | December 2020 | November 2023 | Allow | 36 | 0 | 0 | No | No |
| 17115464 | CONTROLLED TEXT GENERATION WITH SUPERVISED REPRESENTATION DISENTANGLEMENT AND MUTUAL INFORMATION MINIMIZATION | December 2020 | April 2024 | Allow | 40 | 1 | 0 | Yes | No |
| 17112711 | AUTOMATED AND CUSTOMIZED POST-PRODUCTION RELEASE REVIEW OF A MODEL | December 2020 | March 2023 | Allow | 27 | 1 | 0 | No | No |
| 17111053 | MULTIMODE RESONATORS FOR RESONATOR INDUCED PHASE GATES | December 2020 | February 2024 | Allow | 39 | 1 | 0 | Yes | No |
| 17104137 | SELF-PLAY TO IMPROVE TASK-ORIENTED DIALOG SYSTEMS AND METHODS | November 2020 | February 2024 | Allow | 39 | 1 | 0 | No | No |
| 17094598 | ERROR AWARE MODULE REDUNDANCY FOR MACHINE LEARNING | November 2020 | February 2024 | Allow | 39 | 2 | 0 | Yes | No |
| 17047426 | SELECTING A DISPLAY WITH MACHINE LEARNING | October 2020 | April 2024 | Allow | 42 | 2 | 0 | No | No |
| 17035967 | AUTOMATED KNOWLEDGE INFUSION FOR ROBUST AND TRANSFERABLE MACHINE LEARNING | September 2020 | May 2024 | Allow | 43 | 2 | 0 | No | No |
| 17030156 | Input Encoding for Classifier Generalization | September 2020 | January 2024 | Allow | 39 | 1 | 0 | Yes | No |
| 17027843 | Computer System and Method for Supporting Model Selection | September 2020 | July 2024 | Abandon | 46 | 2 | 0 | No | No |
| 16944762 | ACTIVE SURVEILLANCE AND LEARNING FOR MACHINE LEARNING MODEL AUTHORING AND DEPLOYMENT | July 2020 | January 2024 | Allow | 41 | 1 | 0 | Yes | No |
| 16942821 | COMPUTER-READABLE RECORDING MEDIUM RECORDING LEARNING PROGRAM AND LEARNING METHOD | July 2020 | March 2023 | Allow | 31 | 1 | 0 | No | No |
| 16936057 | VALUE OVER REPLACEMENT FEATURE (VORF) BASED DETERMINATION OF FEATURE IMPORTANCE IN MACHINE LEARNING | July 2020 | January 2024 | Abandon | 42 | 1 | 0 | No | No |
| 16925453 | AUTOMATIC RECOGNITION OF ENTITIES RELATED TO CLOUD INCIDENTS | July 2020 | October 2023 | Allow | 39 | 1 | 0 | Yes | No |
| 16924048 | UNSUPERVISED ANOMALY DETECTION BY SELF-PREDICTION | July 2020 | October 2023 | Allow | 40 | 1 | 0 | Yes | No |
| 16900658 | LOW RESOURCE COMPUTATIONAL BLOCK FOR A TRAINED NEURAL NETWORK | June 2020 | February 2024 | Allow | 45 | 1 | 0 | No | No |
| 16899243 | AI PARAMETER CONFIGURATION METHOD AND APPARATUS FOR RACING AI MODEL, AI PARAMETER CONFIGURATION DEVICE, AND STORAGE MEDIUM | June 2020 | December 2023 | Allow | 43 | 1 | 0 | Yes | No |
| 16896614 | CONSTRUCTING AND UTILIZING A KNOWLEDGE GRAPH FOR INFORMATION TECHNOLOGY INFRASTRUCTURE | June 2020 | July 2023 | Abandon | 37 | 1 | 0 | No | No |
| 16883673 | TUNABLE BIAS REDUCTION PIPELINE | May 2020 | January 2023 | Allow | 32 | 0 | 0 | Yes | No |
| 16864516 | RADIO SIGNAL IDENTIFICATION, IDENTIFICATION SYSTEM LEARNING, AND IDENTIFIER DEPLOYMENT | May 2020 | May 2023 | Allow | 37 | 1 | 0 | Yes | No |
| 16844399 | System and Method for Deploying and Versioning Machine Learning Models | April 2020 | September 2023 | Allow | 41 | 0 | 0 | Yes | No |
| 16796442 | DATA PROCESSING APPARATUS CONFIGURED TO EXECUTE HIERARCHICAL CALCULATION PROCESSING AND METHOD THEREOF | February 2020 | May 2023 | Allow | 39 | 1 | 0 | No | No |
| 16633893 | INFORMATION PROCESSING DEVICE, INFORMATION PROCESSING METHOD, AND RECORDING MEDIUM | January 2020 | December 2023 | Allow | 46 | 1 | 0 | Yes | No |
| 16748985 | SYSTEMS AND METHODS FOR TIME SERIES ANALYSIS USING ATTENTION MODELS | January 2020 | March 2023 | Allow | 38 | 1 | 1 | Yes | No |
| 16723201 | METHOD FOR ANALYZING A SIMULATION OF THE EXECUTION OF A QUANTUM CIRCUIT | December 2019 | December 2023 | Allow | 48 | 1 | 0 | No | No |
| 16714130 | AUTOMATIC ACTIONS BASED ON CONTEXTUAL REPLIES | December 2019 | January 2023 | Allow | 38 | 1 | 0 | Yes | No |
| 16697646 | METHOD AND APPARATUS FOR RE-CONFIGURING NEURAL NETWORK | November 2019 | March 2023 | Abandon | 40 | 1 | 0 | No | No |
| 16687076 | INFORMATION PROCESSING APPARATUS, INFORMATION PROCESSING METHOD, AND NON-TRANSITORY COMPUTER-READABLE STORAGE MEDIUM FOR STORING PROGRAM | November 2019 | December 2022 | Allow | 37 | 1 | 0 | No | No |
| 16681391 | STYLIZING INPUT IMAGES | November 2019 | May 2023 | Allow | 42 | 2 | 0 | Yes | No |
| 16677851 | SOLUTION FOR MACHINE LEARNING SYSTEM | November 2019 | November 2022 | Allow | 36 | 1 | 0 | Yes | No |
| 16674801 | NEURAL ARCHITECTURE SEARCH FOR CONVOLUTIONAL NEURAL NETWORKS | November 2019 | January 2023 | Allow | 38 | 1 | 0 | Yes | No |
| 16665882 | METHOD AND APPARATUS FOR GENERATING NEURAL NETWORK | October 2019 | December 2022 | Allow | 38 | 1 | 0 | No | No |
| 16660908 | Integrated Neural Network and Semantic System | October 2019 | January 2023 | Allow | 38 | 2 | 0 | No | No |
| 16592069 | BAYESIAN CAUSAL RELATIONSHIP NETWORK MODELS FOR HEALTHCARE DIAGNOSIS AND TREATMENT BASED ON PATIENT DATA | October 2019 | March 2023 | Allow | 41 | 1 | 0 | Yes | No |
| 16590209 | Data Mining Technique With Distributed Novelty Search | October 2019 | October 2022 | Allow | 36 | 1 | 0 | No | No |
| 16586035 | NEURAL NETWORK MODEL GENERATION AND DISTRIBUTION WITH CLIENT FEEDBACK | September 2019 | October 2022 | Allow | 37 | 1 | 0 | Yes | No |
| 16542051 | OPERATION METHOD | August 2019 | October 2022 | Allow | 38 | 1 | 0 | No | No |
| 16542033 | OPERATION METHOD | August 2019 | October 2022 | Allow | 38 | 1 | 0 | No | No |
| 16506827 | MACHINE LEARNING WITH DISTRIBUTED TRAINING | July 2019 | August 2022 | Allow | 37 | 1 | 0 | Yes | No |
| 16505590 | TECHNIQUES FOR VISUALIZING THE OPERATION OF NEURAL NETWORKS USING SAMPLES OF TRAINING DATA | July 2019 | December 2022 | Allow | 41 | 3 | 0 | No | No |
| 16457550 | ACCELERATING NEURAL NETWORKS WITH ONE SHOT SKIP LAYER PRUNING | June 2019 | February 2023 | Allow | 43 | 2 | 0 | No | No |
| 16442447 | PLATFORM FOR CONCURRENT EXECUTION OF GPU OPERATIONS | June 2019 | February 2023 | Allow | 44 | 2 | 0 | Yes | No |
| 16424127 | Behavior Analysis and Visualization for a Computer Infrastructure | May 2019 | January 2023 | Allow | 44 | 1 | 0 | Yes | No |
| 16371521 | Method for Cognitive Information Processing | April 2019 | January 2023 | Allow | 46 | 2 | 0 | No | No |
| 16357086 | ARCHITECTURE FOR IMPLEMENTING AN IMPROVED NETWORK | March 2019 | January 2023 | Abandon | 46 | 2 | 0 | No | No |
| 16271217 | DEVELOPMENT ENVIRONMENT FOR COGNITIVE INFORMATION PROCESSING SYSTEM | February 2019 | April 2023 | Allow | 50 | 3 | 0 | No | No |
| 16271195 | Cognitive Information Processing System Environment | February 2019 | January 2023 | Allow | 48 | 3 | 0 | No | No |
| 16314457 | SYSTEM AND METHOD FOR ESTIMATING TRAVEL TIME AND DISTANCE | December 2018 | August 2022 | Allow | 44 | 2 | 0 | Yes | No |
| 16004232 | METHODS FOR PREDICTING LIKELIHOOD OF SUCCESSFUL EXPERIMENTAL SYNTHESIS OF COMPUTER-GENERATED MATERIALS BY COMBINING NETWORK ANALYSIS AND MACHINE LEARNING | June 2018 | September 2022 | Allow | 52 | 1 | 0 | No | No |
| 15971884 | LOSS-SCALING FOR DEEP NEURAL NETWORK TRAINING WITH REDUCED PRECISION | May 2018 | July 2023 | Allow | 60 | 2 | 0 | Yes | No |
| 15753979 | COMPUTER SYSTEM AND DATA CLASSIFICATION METHOD | February 2018 | February 2023 | Abandon | 60 | 2 | 0 | No | No |
| 15856755 | METHODS AND APPARATUS FOR TRAINING A NEURAL NETWORK | December 2017 | July 2022 | Allow | 54 | 3 | 0 | No | No |
No appeal data available for this record. This may indicate that no appeals have been filed or decided for applications in this dataset.
Examiner NILSSON, ERIC works in Art Unit 2198 and has examined 85 patent applications in our dataset. With an allowance rate of 85.9%, this examiner has an above-average tendency to allow applications. Applications typically reach final disposition in approximately 39 months.
Examiner NILSSON, ERIC's allowance rate of 85.9% places them in the 63% percentile among all USPTO examiners. This examiner has an above-average tendency to allow applications.
On average, applications examined by NILSSON, ERIC receive 1.41 office actions before reaching final disposition. This places the examiner in the 22% percentile for office actions issued. This examiner issues significantly fewer office actions than most examiners.
The median time to disposition (half-life) for applications examined by NILSSON, ERIC is 39 months. This places the examiner in the 26% percentile for prosecution speed. Prosecution timelines are slightly slower than average with this examiner.
Conducting an examiner interview provides a +19.1% benefit to allowance rate for applications examined by NILSSON, ERIC. This interview benefit is in the 62% percentile among all examiners. Recommendation: Interviews provide an above-average benefit with this examiner and are worth considering.
When applicants file an RCE with this examiner, 29.3% of applications are subsequently allowed. This success rate is in the 55% 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 68.8% of cases where such amendments are filed. This entry rate is in the 91% 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.
When applicants file petitions regarding this examiner's actions, 47.1% are granted (fully or in part). This grant rate is in the 42% percentile among all examiners. Strategic Note: Petitions show below-average success regarding this examiner's actions. Ensure you have a strong procedural basis before filing.
Examiner's Amendments: This examiner makes examiner's amendments in 0.0% of allowed cases (in the 13% 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 16% 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.