Detailed information about the 100 most recent patent applications.
| Application Number | Title | Filing Date | Disposal Date | Disposition | Time (months) | Office Actions | Restrictions | Interview | Appeal |
|---|---|---|---|---|---|---|---|---|---|
| 18753150 | LINEAR MACHINE LEARNING METHOD BASED ON DNA HYBRIDIZATION REACTION TECHNOLOGY | June 2024 | February 2026 | Abandon | 19 | 2 | 0 | No | No |
| 18292570 | Device Deployment Method for AI Model, System, and Storage Medium | January 2024 | January 2026 | Abandon | 24 | 3 | 0 | Yes | No |
| 17824946 | SYSTEM AND METHOD FOR FEATURE SELECTION RECOMMENDATION | May 2022 | January 2026 | Abandon | 43 | 1 | 0 | No | No |
| 17549273 | MACHINE LEARNING ARTIFICIAL INTELLIGENCE SYSTEM FOR PREDICTING POPULAR HOURS | December 2021 | April 2024 | Abandon | 28 | 2 | 0 | Yes | No |
| 17503794 | System for Finding Shortest Pathway between Neurons in Neuronal Linkage Pathways | October 2021 | October 2025 | Abandon | 48 | 1 | 0 | No | No |
| 17503205 | RADIO FREQUENCY ENVIRONMENT AWARENESS WITH EXPLAINABLE RESULTS | October 2021 | October 2025 | Abandon | 48 | 1 | 0 | No | No |
| 17289356 | INFORMATION PROCESSING APPARATUS AND INFORMATION PROCESSING METHOD | April 2021 | January 2026 | Allow | 56 | 2 | 0 | Yes | No |
| 17231269 | SYSTEM AND METHOD FOR IMPLEMENTING AN ARTIFICIALLY INTELLIGENT VIRTUAL ASSISTANT USING MACHINE LEARNING | April 2021 | June 2025 | Abandon | 50 | 1 | 0 | Yes | No |
| 17219723 | MACHINE LEARNING MODEL AGGREGATOR | March 2021 | December 2025 | Abandon | 56 | 2 | 0 | Yes | No |
| 17263093 | NEURAL NETWORK COMPRISING SPINTRONIC RESONATORS | January 2021 | June 2024 | Allow | 41 | 0 | 0 | Yes | No |
| 17134804 | MAPPING MACHINE LEARNING ACTIVATION DATA TO A REPRESENTATIVE VALUE PALETTE | December 2020 | February 2026 | Abandon | 60 | 4 | 0 | Yes | No |
| 17125062 | Randomization-Based Network of Domain Specific Rule Bases | December 2020 | March 2025 | Abandon | 51 | 2 | 0 | Yes | No |
| 17121149 | SEMI-SUPERVISED LEARNING OF TRAINING GRADIENTS VIA TASK GENERATION | December 2020 | November 2025 | Allow | 59 | 2 | 0 | Yes | No |
| 17116767 | TIME SERIES DATA PROCESSING DEVICE AND OPERATING METHOD THEREOF | December 2020 | October 2025 | Abandon | 59 | 3 | 0 | Yes | No |
| 17108438 | UTILIZING OBJECT ORIENTED PROGRAMMING TO VALIDATE MACHINE LEARNING CLASSIFIERS AND WORD EMBEDDINGS | December 2020 | March 2026 | Abandon | 60 | 4 | 0 | Yes | No |
| 17101796 | AUTOMATICALLY TRAINING AN ANALYTICAL MODEL | November 2020 | June 2025 | Abandon | 54 | 2 | 0 | No | No |
| 17101517 | AUTOMATED DATA INGESTION USING AN AUTOENCODER | November 2020 | July 2024 | Abandon | 44 | 2 | 0 | Yes | No |
| 16952941 | GENERATING HYPOTHESIS CANDIDATES ASSOCIATED WITH AN INCOMPLETE KNOWLEDGE GRAPH | November 2020 | December 2025 | Abandon | 60 | 3 | 0 | Yes | No |
| 17099584 | MACHINE LEARNING MODELS FOR EVALUATING DIFFERENCES BETWEEN GROUPS AND METHODS THEREOF | November 2020 | December 2025 | Abandon | 60 | 1 | 0 | Yes | No |
| 17015124 | INFORMATION PROCESSING APPARATUS, NON-TRANSITORY COMPUTER-READABLE STORAGE MEDIUM FOR STORING PROGRAM, AND INFORMATION PROCESSING METHOD | September 2020 | June 2024 | Abandon | 45 | 1 | 0 | No | No |
| 17001746 | DECOUPLING MEMORY AND COMPUTATION TO ENABLE PRIVACY ACROSS MULTIPLE KNOWLEDGE BASES OF USER DATA | August 2020 | December 2025 | Allow | 60 | 3 | 0 | Yes | No |
| 16994923 | AUTOMATED MODEL PIPELINE GENERATION WITH ENTITY MONITORING, INTERACTION, AND INTERVENTION | August 2020 | January 2026 | Abandon | 60 | 4 | 0 | Yes | No |
| 16936333 | PROCESSORS, DEVICES, SYSTEMS, AND METHODS FOR NEUROMORPHIC COMPUTING BASED ON MODULAR MACHINE LEARNING MODELS | July 2020 | July 2025 | Abandon | 60 | 3 | 0 | Yes | No |
| 16911187 | COUPLING OF RATIONAL AGENTS TO QUANTUM PROCESSES | June 2020 | February 2024 | Abandon | 44 | 0 | 1 | No | No |
| 16746866 | SYSTEM AND METHOD FOR TIME-DEPENDENT MACHINE LEARNING ARCHITECTURE | January 2020 | October 2024 | Allow | 57 | 3 | 0 | Yes | No |
| 16721379 | QUANTUM COMPUTATION FOR OPTIMIZATION IN EXCHANGE SYSTEMS | December 2019 | July 2024 | Abandon | 55 | 3 | 0 | Yes | No |
| 16714708 | ANALYZING RECURRENT STREAMS OF DIGITAL DATA TO DETECT AN ANOMALY | December 2019 | August 2024 | Abandon | 57 | 3 | 0 | Yes | No |
| 16690800 | MACHINE ASSISTED TROUBLESHOOTING OF A CUSTOMER SUPPORT ISSUE | November 2019 | January 2026 | Abandon | 60 | 2 | 0 | Yes | Yes |
| 16399121 | QUANTUM COMPUTATION FOR OPTIMIZATION IN EXCHANGE SYSTEMS | April 2019 | December 2019 | Allow | 7 | 1 | 0 | No | No |
| 16227767 | SYSTEM AND METHOD FOR CONTEXT BASED DEEP KNOWLEDGE TRACING | December 2018 | October 2025 | Abandon | 60 | 4 | 0 | Yes | No |
| 16151431 | SYSTEMS AND METHODS FOR DATA STREAM SIMULATION | October 2018 | July 2025 | Allow | 60 | 7 | 1 | Yes | Yes |
| 16132479 | LEARNING METHOD, LEARNING DEVICE WITH MULTI-FEEDING LAYERS AND TESTING METHOD, TESTING DEVICE USING THE SAME | September 2018 | October 2019 | Allow | 13 | 1 | 0 | Yes | No |
| 16117731 | INSTRUCTIONAL DESIGN TOOL | August 2018 | February 2024 | Allow | 60 | 5 | 0 | Yes | No |
| 15992143 | GRAPHICAL USER INTERFACE FEATURES FOR UPDATING A CONVERSATIONAL BOT | May 2018 | March 2025 | Abandon | 60 | 5 | 0 | Yes | Yes |
| 15959119 | MACHINE LEARNING ARTIFICIAL INTELLIGENCE SYSTEM FOR PREDICTING POPULAR HOURS | April 2018 | October 2019 | Allow | 18 | 1 | 0 | Yes | Yes |
| 15876723 | DISTRIBUTED DATA VARIABLE ANALYSIS AND HIERARCHICAL GROUPING SYSTEM | January 2018 | March 2019 | Abandon | 14 | 1 | 0 | Yes | No |
| 15876624 | DISTRIBUTED HIGH-CARDINALITY DATA TRANSFORMATION SYSTEM | January 2018 | March 2019 | Abandon | 14 | 1 | 0 | Yes | No |
| 15855950 | TOPIC CLASSIFICATION USING A JOINTLY TRAINED ARTIFICIAL NEURAL NETWORK | December 2017 | December 2023 | Abandon | 60 | 4 | 0 | Yes | No |
| 15843119 | AUTOMATIC SEEDING OF AN APPLICATION PROGRAMMING INTERFACE (API) INTO A CONVERSATIONAL INTERFACE | December 2017 | April 2024 | Abandon | 60 | 4 | 0 | Yes | Yes |
| 15693488 | COMPRESSION METHOD OF DEEP NEURAL NETWORKS | September 2017 | March 2019 | Abandon | 18 | 2 | 0 | Yes | No |
| 15502764 | METHODS AND SYSTEMS FOR BASE MAP AND INFERENCE MAPPING | February 2017 | January 2024 | Allow | 60 | 5 | 0 | Yes | No |
| 15339204 | ONTOLOGICAL SYSTEMS | October 2016 | August 2019 | Abandon | 33 | 3 | 0 | Yes | No |
| 15212974 | SYSTEMS, APPARATUSES, METHODS AND COMPUTER-READABLE MEDIUM FOR AUTOMATICALLY GENERATING PLAYLISTS BASED ON TASTE PROFILES | July 2016 | February 2024 | Abandon | 60 | 6 | 0 | Yes | No |
| 14977585 | Intelligent Personal Agent Platform and System and Methods for Using Same | December 2015 | October 2024 | Abandon | 60 | 8 | 0 | No | No |
| 14862212 | SCORING ATTRIBUTES IN DEEP QUESTION ANSWERING SYSTEMS BASED ON ALGORITHMIC SOURCE CODE INFLUENCES | September 2015 | June 2018 | Allow | 33 | 2 | 0 | Yes | No |
| 14854885 | LEARNING OF CLASSIFICATION MODEL | September 2015 | October 2019 | Allow | 49 | 2 | 0 | Yes | No |
| 14846289 | SCALABLE NEURAL HARDWARE FOR THE NOISY-OR MODEL OF BAYESIAN NETWORKS | September 2015 | September 2018 | Allow | 37 | 1 | 0 | No | No |
| 14845243 | CONVOLUTION MATRIX MULTIPLY WITH CALLBACK FOR DEEP TILING FOR DEEP CONVOLUTIONAL NEURAL NETWORKS | September 2015 | July 2019 | Abandon | 47 | 2 | 0 | Yes | No |
| 14845117 | VECTOR COMPUTATION UNIT IN A NEURAL NETWORK PROCESSOR | September 2015 | September 2018 | Allow | 36 | 1 | 0 | Yes | No |
| 14841722 | COMMUNICATING A NEURAL NETWORK FEATURE VECTOR (NNFV) TO A HOST AND RECEIVING BACK A SET OF WEIGHT VALUES FOR A NEURAL NETWORK | September 2015 | August 2024 | Allow | 60 | 7 | 0 | Yes | Yes |
| 14788178 | PROCEDURAL MODELING USING AUTOENCODER NEURAL NETWORKS | June 2015 | October 2019 | Allow | 51 | 1 | 0 | Yes | No |
| 14747187 | RAPID TRAFFIC PARAMETER ESTIMATION | June 2015 | August 2019 | Allow | 50 | 3 | 0 | Yes | No |
| 14746488 | COMMUNICATING POSTSYNAPTIC NEURON FIRES TO NEUROMORPHIC CORES | June 2015 | May 2019 | Allow | 46 | 3 | 0 | Yes | Yes |
| 14742861 | REDUCING GRAPHICAL TEXT ANALYSIS USING PHYSIOLOGICAL PRIORS | June 2015 | May 2019 | Allow | 46 | 3 | 0 | Yes | No |
| 14740863 | ARCHITECTURE AND METHODOLOGY FOR PERFORMING REAL-TIME AUTONOMOUS ANALYTICS OVER MULTIPLE ACTUAL AND VIRTUAL DEVICES | June 2015 | November 2018 | Abandon | 41 | 2 | 0 | Yes | No |
| 14726855 | Cross-Module Behavioral Validation | June 2015 | October 2018 | Abandon | 40 | 2 | 0 | Yes | No |
| 14723698 | MAPPING USER ACTIONS TO HISTORICAL PATHS TO DETERMINE A PREDICTED ENDPOINT | May 2015 | October 2024 | Abandon | 60 | 9 | 0 | Yes | Yes |
| 14710418 | STREAM PROCESSING WITH MULTIPLE CONNECTIONS BETWEEN LOCAL AND CENTRAL MODELERS | May 2015 | September 2019 | Allow | 52 | 2 | 0 | No | No |
| 14710333 | COMPUTE INTENSIVE STREAM PROCESSING WITH CONCEPT DRIFT DETECTION | May 2015 | May 2019 | Abandon | 48 | 2 | 0 | Yes | No |
| 14702203 | QUANTUM-ASSISTED TRAINING OF NEURAL NETWORKS | May 2015 | May 2019 | Allow | 48 | 2 | 1 | Yes | No |
| 14669203 | REDUCING GRAPHICAL TEXT ANALYSIS USING PHYSIOLOGICAL PRIORS | March 2015 | April 2019 | Allow | 49 | 3 | 0 | Yes | No |
| 14597652 | SYSTEM, METHOD, AND STORAGE MEDIUM FOR GENERATING HYPOTHESES IN DATA SETS | January 2015 | August 2019 | Allow | 55 | 3 | 0 | Yes | No |
| 14586043 | REGULARIZATION RELAXATION SCHEME | December 2014 | June 2019 | Allow | 54 | 4 | 0 | Yes | No |
| 14574861 | SCORING ATTRIBUTES IN DEEP QUESTION ANSWERING SYSTEMS BASED ON ALGORITHMIC SOURCE CODE INFLUENCES | December 2014 | June 2018 | Allow | 42 | 2 | 0 | Yes | No |
| 14247121 | METHOD AND DEVICE FOR CREATING A FUNCTION MODEL FOR A CONTROL UNIT OF AN ENGINE SYSTEM | April 2014 | March 2019 | Allow | 59 | 2 | 0 | Yes | Yes |
| 14171786 | SYSTEM AND METHOD FOR DECISION MAKING IN STRATEGIC ENVIRONMENTS | February 2014 | June 2017 | Abandon | 41 | 5 | 0 | Yes | No |
| 14164444 | DISTRIBUTED AND LEARNING MACHINE-BASED APPROACH TO GATHERING LOCALIZED NETWORK DYNAMICS | January 2014 | May 2019 | Allow | 60 | 5 | 0 | Yes | No |
| 14043498 | SDI (SDI FOR EPI-DEMICS) | October 2013 | June 2019 | Abandon | 60 | 5 | 0 | No | No |
| 13957319 | EFFICIENT DFA GENERATION FOR NON-MATCHING CHARACTERS AND CHARACTER CLASSES IN REGULAR EXPRESSIONS | August 2013 | October 2018 | Allow | 60 | 5 | 0 | No | No |
| 13947355 | SUGGESTING CONNECTIONS TO A USER BASED ON AN EXPECTED VALUE OF THE SUGGESTION TO THE SOCIAL NETWORKING SYSTEM | July 2013 | June 2019 | Allow | 60 | 4 | 0 | Yes | Yes |
| 13906220 | PREDICTING ACCURACY OF SUBMITTED DATA | May 2013 | October 2018 | Allow | 60 | 7 | 0 | Yes | Yes |
| 13834987 | RULES ENGINE AS A PLATFORM FOR MOBILE APPLICATIONS | March 2013 | October 2019 | Abandon | 60 | 7 | 0 | No | No |
| 13795087 | Machine Assisted Troubleshooting of a Customer Support Issue | March 2013 | December 2019 | Abandon | 60 | 6 | 0 | Yes | Yes |
| 13545257 | ACTION EXECUTION BASED ON USER MODIFIED HYPOTHESIS | July 2012 | October 2019 | Abandon | 60 | 4 | 0 | No | Yes |
| 13399887 | SYSTEM AND METHOD FOR DETECTING MEDICAL ANOMALIES USING A MOBILE COMMUNICATION DEVICE | February 2012 | May 2019 | Allow | 60 | 6 | 0 | Yes | Yes |
| 13369095 | METHODS AND APPARATUS FOR SPIKING NEURAL COMPUTATION | February 2012 | July 2019 | Abandon | 60 | 7 | 0 | Yes | No |
| 12998945 | ADAPTIVE IMPLICIT LEARNING FOR RECOMMENDER SYSTEM | June 2011 | February 2019 | Abandon | 60 | 8 | 0 | Yes | No |
| 13096784 | Suggesting Users for Interacting in Online Applications in a Social Networking Environment | April 2011 | December 2015 | Abandon | 56 | 3 | 0 | Yes | Yes |
| 12420641 | INCORPORATING REPRESENTATIONAL AUTHENTICITY INTO VIRTUAL WORLD INTERACTIONS | April 2009 | July 2012 | Allow | 39 | 1 | 0 | No | No |
| 12368047 | ASSOCIATIVE MEMORY LEARNING AGENT FOR ANALYSIS OF MANUFACTURING NON-CONFORMANCE APPLICATIONS | February 2009 | June 2019 | Allow | 60 | 8 | 0 | Yes | Yes |
| 12374759 | SYSTEM AND METHOD FOR NETWORK ASSOCIATION INFERENCE, VALIDATION AND PRUNING BASED ON INTEGRATED CONSTRAINTS FROM DIVERSE DATA | January 2009 | February 2012 | Allow | 37 | 1 | 0 | Yes | No |
| 12170508 | DETECTING ANOMALOUS PROCESS BEHAVIOR | July 2008 | December 2013 | Allow | 60 | 3 | 1 | Yes | No |
| 12042451 | PLATFORM FOR CAPTURING KNOWLEDGE | March 2008 | March 2020 | Abandon | 60 | 6 | 0 | Yes | Yes |
| 11870698 | METHOD AND APPARATUS FOR IMPROVED REWARD-BASED LEARNING USING NONLINEAR DIMENSIONALITY REDUCTION | October 2007 | June 2011 | Allow | 44 | 2 | 0 | Yes | No |
| 11856109 | Method and System for Object Detection Using Probabilistic Boosting Cascade Tree | September 2007 | December 2019 | Abandon | 60 | 7 | 0 | No | Yes |
| 11550583 | Method and System for Constructing a Classifier | October 2006 | November 2008 | Abandon | 25 | 7 | 0 | Yes | Yes |
This analysis examines appeal outcomes and the strategic value of filing appeals for examiner RIFKIN, BEN M.
With a 21.4% reversal rate, the PTAB affirms the examiner's rejections in the vast majority of cases. This reversal rate is below the USPTO average, indicating that appeals face more challenges here than typical.
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, 29.2% of applications that filed an appeal were subsequently allowed. This appeal filing benefit rate is below the USPTO average, suggesting that filing an appeal has limited effectiveness in prompting favorable reconsideration.
⚠ Appeals to PTAB face challenges. Ensure your case has strong merit before committing to full Board review.
⚠ Filing a Notice of Appeal shows limited benefit. Consider other strategies like interviews or amendments before appealing.
Examiner RIFKIN, BEN M works in Art Unit 2123 and has examined 83 patent applications in our dataset. With an allowance rate of 44.6%, this examiner allows applications at a lower rate than most examiners at the USPTO. Applications typically reach final disposition in approximately 56 months.
Examiner RIFKIN, BEN M's allowance rate of 44.6% places them in the 9% percentile among all USPTO examiners. This examiner is less likely to allow applications than most examiners at the USPTO.
On average, applications examined by RIFKIN, BEN M receive 3.34 office actions before reaching final disposition. This places the examiner in the 92% 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 RIFKIN, BEN M is 56 months. This places the examiner in the 2% percentile for prosecution speed. Applications take longer to reach final disposition with this examiner compared to most others.
Conducting an examiner interview provides a +13.7% benefit to allowance rate for applications examined by RIFKIN, BEN M. This interview benefit is in the 51% 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, 12.5% of applications are subsequently allowed. This success rate is in the 7% 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 13.0% of cases where such amendments are filed. This entry rate is in the 13% 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, 40.0% result in withdrawal of the rejection or reopening of prosecution. This success rate is in the 36% percentile among all examiners. Note: Pre-appeal conferences show below-average success with this examiner. Consider whether your arguments are strong enough to warrant a PAC request.
This examiner withdraws rejections or reopens prosecution in 46.2% of appeals filed. This is in the 12% percentile among all examiners. Of these withdrawals, 41.7% 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, 75.0% are granted (fully or in part). This grant rate is in the 80% percentile among all examiners. Strategic Note: Petitions are frequently granted regarding this examiner's actions compared to other examiners. Per MPEP § 1002.02(c), various examiner actions are petitionable to the Technology Center Director, including prematureness of final rejection, refusal to enter amendments, and requirement for information. If you believe an examiner action is improper, consider filing a petition.
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.