Detailed information about the 100 most recent patent applications.
| Application Number | Title | Filing Date | Disposal Date | Disposition | Time (months) | Office Actions | Restrictions | Interview | Appeal |
|---|---|---|---|---|---|---|---|---|---|
| 17116479 | EXTRACTING EXPLANATIONS FROM SUPPORTING EVIDENCE | December 2020 | June 2025 | Allow | 54 | 1 | 0 | Yes | No |
| 17068142 | AI GUIDED SPECTRUM OPERATIONS | October 2020 | April 2024 | Abandon | 42 | 1 | 0 | No | No |
| 17041667 | INFORMATION PROCESSING APPARATUS, INFORMATION PROCESSING METHOD, AND PROGRAM | September 2020 | January 2025 | Abandon | 52 | 2 | 0 | No | No |
| 17020118 | CLOUD TASK SCHEDULING METHOD BASED ON PHAGOCYTOSIS-BASED HYBRID PARTICLE SWARM OPTIMIZATION AND GENETIC ALGORITHM | September 2020 | May 2025 | Abandon | 56 | 2 | 0 | No | No |
| 17008719 | INFORMATION PROCESSING APPARATUS, STORAGE MEDIUM AND INFORMATION PROCESSING METHOD | September 2020 | October 2024 | Abandon | 49 | 3 | 0 | No | No |
| 16989866 | COMPLEMENTARY EVIDENCE IDENTIFICATION IN NATURAL LANGUAGE INFERENCE | August 2020 | April 2025 | Allow | 56 | 3 | 0 | Yes | No |
| 16927655 | ACCOUNT PREDICTION USING MACHINE LEARNING | July 2020 | January 2025 | Allow | 54 | 4 | 0 | Yes | Yes |
| 16917963 | CONFIDENCE CLASSIFIERS FOR DIAGNOSTIC TRAINING DATA | July 2020 | February 2025 | Abandon | 56 | 2 | 0 | Yes | No |
| 16859789 | INCREASING SECURITY OF NEURAL NETWORKS BY DISCRETIZING NEURAL NETWORK INPUTS | April 2020 | March 2022 | Allow | 23 | 4 | 0 | Yes | Yes |
| 16812105 | DETERMINING COMPUTER-EXECUTED ENSEMBLE MODEL | March 2020 | August 2022 | Abandon | 29 | 4 | 0 | Yes | No |
| 16752240 | Methods and Systems for Nucleic Acid Variant Detection and Analysis | January 2020 | March 2021 | Abandon | 14 | 1 | 0 | No | No |
| 16697483 | Interpretable Supervised Anomaly Detection for Determining Reasons for Unsupervised Anomaly Decision | November 2019 | March 2025 | Allow | 60 | 7 | 0 | Yes | Yes |
| 16656761 | ELECTRONIC DEVICE AND METHOD FOR CONTROLLING THE ELECTRONIC DEVICE | October 2019 | May 2025 | Abandon | 60 | 8 | 0 | Yes | No |
| 16654584 | SYSTEM AND METHOD FOR PROVIDING CONTENT BASED ON KNOWLEDGE GRAPH | October 2019 | February 2025 | Allow | 60 | 5 | 0 | Yes | No |
| 16576835 | INFORMATION PROCESSING APPARATUS AND GENERATION METHOD OF TIMING PATH LEARNING MODEL | September 2019 | July 2023 | Abandon | 46 | 1 | 0 | No | No |
| 16566511 | SYSTEM AND METHOD FOR NEXT OBJECT PREDICTION FOR ICS FLOW USING ARTIFICIAL INTELLIGENCE/MACHINE LEARNING | September 2019 | January 2025 | Allow | 60 | 6 | 0 | Yes | No |
| 16527968 | DIAGNOSING & TRIAGING PERFORMANCE ISSUES IN LARGE-SCALE SERVICES | July 2019 | October 2024 | Abandon | 60 | 4 | 0 | Yes | No |
| 16457133 | APPARATUS AND METHODS FOR PROGRAM SYNTHESIS USING GENETIC ALGORITHMS | June 2019 | November 2024 | Abandon | 60 | 4 | 0 | Yes | No |
| 16428075 | METHOD AND SYSTEM FOR DATA COMMUNICATION | May 2019 | August 2024 | Abandon | 60 | 4 | 0 | Yes | No |
| 16386784 | ARTIFICIAL NEURAL NETWORK REGULARIZATION SYSTEM FOR A RECOGNITION DEVICE AND A MULTI-STAGE TRAINING METHOD ADAPTABLE THERETO | April 2019 | February 2023 | Abandon | 46 | 2 | 0 | No | No |
| 16236402 | REMOVING UNNECESSARY HISTORY FROM REINFORCEMENT LEARNING STATE | December 2018 | November 2023 | Allow | 58 | 5 | 0 | Yes | Yes |
| 16222706 | Machine Learning based Fixed-Time Optimal Path Generation | December 2018 | November 2024 | Abandon | 60 | 6 | 0 | Yes | No |
| 16306502 | DEVICE-BASED ANOMALY DETECTION USING RANDOM FOREST MODELS | November 2018 | April 2024 | Abandon | 60 | 4 | 0 | No | No |
| 16305565 | ENABLING SEMANTICS REASONING SERVICE IN M2M/IOT SERVICE LAYER | November 2018 | December 2023 | Abandon | 60 | 3 | 0 | Yes | No |
| 16201953 | SYSTEM AND METHOD FOR GENERATING AN AIRCRAFT FAULT PREDICTION CLASSIFIER | November 2018 | October 2023 | Allow | 58 | 5 | 0 | Yes | No |
| 16129154 | Predicting Non-Observable Parameters for Digital Components | September 2018 | March 2025 | Abandon | 60 | 6 | 0 | Yes | No |
| 16115153 | SYSTEM AND METHOD FOR FACILITATING MODEL-BASED CLASSIFICATION OF TRANSACTIONS | August 2018 | October 2023 | Allow | 60 | 3 | 0 | Yes | Yes |
| 16110419 | EFFICIENT CONFIGURATION SELECTION FOR AUTOMATED MACHINE LEARNING | August 2018 | October 2024 | Allow | 60 | 5 | 0 | Yes | No |
| 16018649 | SYSTEM AND METHOD FOR ABSTRACTING CHARACTERISTICS OF CYBER-PHYSICAL SYSTEMS | June 2018 | November 2023 | Abandon | 60 | 4 | 0 | No | No |
| 16000977 | METHOD OF AND SERVER FOR CONVERTING CATEGORICAL FEATURE VALUE INTO A NUMERIC REPRESENTATION THEREOF AND FOR GENERATING A SPLIT VALUE FOR THE CATEGORICAL FEATURE | June 2018 | March 2024 | Allow | 60 | 4 | 0 | No | No |
| 16000809 | METHOD OF AND SYSTEM FOR GENERATING PREDICTION QUALITY PARAMETER FOR A PREDICTION MODEL EXECUTED IN A MACHINE LEARNING ALGORITHM | June 2018 | March 2025 | Abandon | 60 | 6 | 0 | Yes | Yes |
| 15973244 | DETERMINING INCREASED VALUE BASED ON HOLDOUT IMPRESSIONS | May 2018 | September 2024 | Abandon | 60 | 4 | 0 | No | Yes |
| 15934523 | EXECUTING SUBLAYERS OF A FULLY-CONNECTED LAYER | March 2018 | May 2023 | Allow | 60 | 3 | 0 | Yes | No |
| 15907656 | COMPUTER IMPLEMENTED SYSTEM AND METHOD FOR GENERATING REMINDERS FOR UN-ACTIONED EMAILS | February 2018 | September 2023 | Abandon | 60 | 4 | 0 | Yes | No |
| 15882134 | MACHINE LEARNT MATCH RULES | January 2018 | August 2023 | Abandon | 60 | 4 | 0 | Yes | Yes |
| 15878302 | SYSTEM AND METHOD OF BAYES NET CONTENT GRAPH CONTENT RECOMMENDATION | January 2018 | March 2024 | Abandon | 60 | 4 | 0 | No | No |
| 15867169 | DETERMINING STRATEGIC DIGITAL CONTENT TRANSMISSION TIME UTILIZING RECURRENT NEURAL NETWORKS AND SURVIVAL ANALYSIS | January 2018 | March 2024 | Abandon | 60 | 3 | 0 | Yes | Yes |
| 15859578 | CONTENT RATING CLASSIFICATION WITH COGNITIVE COMPUTING SUPPORT | December 2017 | May 2023 | Abandon | 60 | 8 | 0 | Yes | No |
| 15816644 | Double Blind Machine Learning Insight Interface Apparatuses, Methods and Systems | November 2017 | September 2024 | Abandon | 60 | 6 | 0 | No | No |
| 15815528 | SYSTEM AND METHOD FOR FACILITATING COMPREHENSIVE CONTROL DATA FOR A DEVICE | November 2017 | April 2024 | Abandon | 60 | 6 | 0 | Yes | Yes |
| 15787863 | MACHINE LEARNING DEVICE AND MACHINING TIME PREDICTION DEVICE | October 2017 | September 2020 | Allow | 35 | 2 | 0 | Yes | No |
| 15786452 | SOFTWARE DEFINED NEURAL NETWORK LAYER PIPELINING | October 2017 | May 2024 | Allow | 60 | 6 | 0 | Yes | Yes |
| 15786514 | MINIMIZING MEMORY READS AND INCREASING PERFORMANCE OF A NEURAL NETWORK ENVIRONMENT USING A DIRECTED LINE BUFFER | October 2017 | August 2023 | Abandon | 60 | 5 | 0 | Yes | No |
| 15722196 | CREATING MACHINE LEARNING MODELS FROM STRUCTURED INTELLIGENCE DATABASES | October 2017 | August 2023 | Abandon | 60 | 6 | 0 | Yes | No |
| 15717889 | LIVE STYLE TRANSFER ON A MOBILE DEVICE | September 2017 | September 2021 | Allow | 48 | 2 | 0 | Yes | No |
| 15717858 | UNSUPERVISED MACHINE LEARNING MODELS IN HEALTHCARE EPISODE PREDICTION | September 2017 | February 2023 | Abandon | 60 | 4 | 0 | Yes | No |
| 15716201 | ARTIFICIAL INTELLIGENCE BASED SELF-ORGANIZING EVENT-ACTION MANAGEMENT SYSTEM FOR LARGE-SCALE NETWORKS | September 2017 | June 2023 | Allow | 60 | 5 | 0 | Yes | No |
| 15716047 | METHODS AND APPARATUS FOR TRAINING A NEURAL NETWORK | September 2017 | January 2023 | Abandon | 60 | 4 | 0 | Yes | No |
| 15689988 | CAPTURING KNOWLEDGE COVERAGE OF MACHINE LEARNING MODELS | August 2017 | December 2023 | Abandon | 60 | 6 | 0 | No | Yes |
| 15630604 | IMAGE CAPTIONING UTILIZING SEMANTIC TEXT MODELING AND ADVERSARIAL LEARNING | June 2017 | May 2021 | Allow | 47 | 2 | 0 | Yes | No |
| 15621067 | COGNITIVE FLOW PREDICTION | June 2017 | July 2023 | Abandon | 60 | 8 | 0 | No | No |
| 15620247 | NETWORK PATH PREDICTION AND SELECTION USING MACHINE LEARNING | June 2017 | December 2022 | Abandon | 60 | 6 | 0 | Yes | No |
| 15615005 | SYSTEM AND METHOD FOR FLEET DRIVER BIOMETRIC TRACKING | June 2017 | May 2022 | Allow | 59 | 4 | 0 | Yes | No |
| 15592846 | Methods And Systems For Providing Travel Recommendations | May 2017 | March 2021 | Abandon | 47 | 2 | 0 | No | No |
| 15592103 | IDENTIFICATION AND CLASSIFICATION OF TRAINING NEEDS FROM UNSTRUCTURED COMPUTER TEXT USING A NEURAL NETWORK | May 2017 | August 2021 | Allow | 51 | 3 | 0 | No | No |
| 15498006 | STATE DETERMINATION APPARATUS, STATE DETERMINATION METHOD, AND INTEGRATED CIRCUIT | April 2017 | October 2022 | Allow | 60 | 4 | 0 | Yes | No |
| 15497798 | Traffic Condition Forecasting Using Matrix Compression and Deep Neural Networks | April 2017 | August 2020 | Allow | 39 | 1 | 0 | Yes | No |
| 15482401 | DATA ANALYSIS SYSTEM, AND CONTROL METHOD, PROGRAM, AND RECORDING MEDIUM THEREFOR | April 2017 | July 2020 | Abandon | 40 | 1 | 0 | No | No |
| 15465842 | AUTOMATED FRAUD CLASSIFICATION USING MACHINE LEARNING | March 2017 | June 2021 | Allow | 51 | 3 | 0 | Yes | No |
| 15465679 | SYSTEM FOR QUERYING MODELS | March 2017 | March 2022 | Abandon | 60 | 6 | 0 | Yes | No |
| 15465412 | VIRTUAL ASSISTANT ESCALATION | March 2017 | November 2023 | Abandon | 60 | 10 | 0 | Yes | No |
| 15464925 | CONTENT RATING CLASSIFICATION WITH COGNITIVE COMPUTING SUPPORT | March 2017 | May 2023 | Abandon | 60 | 8 | 0 | Yes | No |
| 15464330 | METHOD AND ALGORITHM OF RECURSIVE DEEP LEARNING QUANTIZATION FOR WEIGHT BIT REDUCTION | March 2017 | March 2022 | Allow | 60 | 3 | 0 | No | No |
| 15456473 | CASCADED RANDOM DECISION TREES USING CLUSTERS | March 2017 | December 2022 | Abandon | 60 | 6 | 0 | Yes | No |
| 15444280 | SPATIAL EXCLUSIVITY BY VELOCITY FOR MOTION PROCESSING ANALYSIS | February 2017 | July 2021 | Allow | 53 | 7 | 0 | Yes | No |
| 15437588 | PREDICTION OF GENETIC TRAIT EXPRESSION USING DATA ANALYTICS | February 2017 | May 2020 | Abandon | 39 | 1 | 0 | No | No |
| 15505231 | CONVOLUTIONAL NEURAL NETWORK | February 2017 | April 2021 | Allow | 50 | 2 | 0 | Yes | No |
| 15436841 | TECHNOLOGIES FOR OPTIMIZED MACHINE LEARNING TRAINING | February 2017 | December 2020 | Allow | 46 | 2 | 0 | Yes | No |
| 15436825 | INTERACTIVE SEARCH ENGINE | February 2017 | May 2021 | Abandon | 51 | 4 | 0 | Yes | No |
| 15425670 | IDENTIFYING NOVEL INFORMATION | February 2017 | February 2020 | Allow | 36 | 1 | 0 | Yes | No |
| 15425978 | ENTITY DISAMBIGUATION | February 2017 | July 2023 | Allow | 60 | 7 | 0 | Yes | No |
| 15399081 | TRAINING A MACHINE LEARNING-BASED TRAFFIC ANALYZER USING A PROTOTYPE DATASET | January 2017 | October 2022 | Abandon | 60 | 6 | 0 | Yes | No |
| 15399714 | HARDWARE ACCELERATED MACHINE LEARNING | January 2017 | July 2021 | Allow | 54 | 3 | 0 | Yes | No |
| 15399722 | DEPLOYING LOCAL Q AND A SYSTEMS IN IoT DEVICES | January 2017 | August 2021 | Abandon | 55 | 4 | 0 | Yes | No |
| 15362744 | SOURCE CODE BUG PREDICTION | November 2016 | November 2022 | Abandon | 60 | 4 | 0 | Yes | Yes |
| 15357095 | METHOD AND SYSTEM FOR VERIFYING RULES OF A ROOT CAUSE ANALYSIS SYSTEM IN CLOUD ENVIRONMENT | November 2016 | June 2020 | Abandon | 42 | 2 | 0 | No | No |
| 15356662 | HIGH-RISK ROAD LOCATION PREDICTION | November 2016 | May 2021 | Abandon | 54 | 4 | 0 | Yes | No |
| 15355857 | METHOD AND SYSTEM FOR EXECUTING A CONTAINERIZED STATEFUL APPLICATION ON A STATELESS COMPUTING PLATFORM USING MACHINE LEARNING | November 2016 | March 2022 | Allow | 60 | 5 | 0 | Yes | No |
| 15354235 | METHODS AND SYSTEMS FOR IDENTIFYING GAPS IN PREDICTIVE MODEL ONTOLOGY | November 2016 | May 2021 | Abandon | 54 | 4 | 0 | No | No |
| 15353671 | METHOD AND APPARATUS FOR PREDICTING HEALTH DATA VALUE THROUGH GENERATION OF HEALTH DATA PATTERN | November 2016 | December 2019 | Abandon | 37 | 1 | 0 | No | No |
| 15346707 | MODEL ADAPTATION AND ONLINE LEARNING FOR UNSTABLE ENVIRONMENTS | November 2016 | June 2021 | Allow | 55 | 4 | 0 | No | Yes |
| 15334682 | ARTIFICIAL INTELLIGENCE CONTROLLER THAT PROCEDURALLY TAILORS ITSELF TO AN APPLICATION | October 2016 | September 2023 | Allow | 60 | 6 | 0 | Yes | Yes |
| 15334692 | SYSTEM FOR ITERATIVELY TRAINING AN ARTIFICIAL INTELLIGENCE USING CLOUD-BASED METRICS | October 2016 | May 2020 | Abandon | 42 | 2 | 0 | Yes | No |
| 15289052 | APPARATUS AND METHOD FOR SPATIAL PROCESSING OF CONCEPTS | October 2016 | June 2021 | Abandon | 56 | 4 | 0 | No | No |
| 15280960 | Suggesting Activities | September 2016 | July 2022 | Abandon | 60 | 4 | 0 | Yes | No |
| 15271324 | RECURRENT NEURAL NETWORK PROCESSING POOLING OPERATION | September 2016 | August 2021 | Allow | 59 | 5 | 0 | Yes | No |
| 15236215 | TIME SERIES FORECASTING TO DETERMINE RELATIVE CAUSAL IMPACT | August 2016 | September 2022 | Allow | 60 | 6 | 0 | Yes | No |
| 14548833 | SYSTEMS AND METHODS FOR DETERMINING ACTIVITY LEVEL AT A MERCHANT LOCATION BY LEVERAGING REAL-TIME TRANSACTION DATA | November 2014 | April 2022 | Abandon | 60 | 8 | 0 | Yes | Yes |
| 14539392 | Application Complexity Computation | November 2014 | June 2020 | Abandon | 60 | 3 | 0 | Yes | No |
This analysis examines appeal outcomes and the strategic value of filing appeals for examiner MULLINAX, CLINT LEE.
With a 33.3% reversal rate, the PTAB reverses the examiner's rejections in a meaningful percentage of cases. This reversal rate is above the USPTO average, indicating that appeals have better success 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, 31.2% 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 show good success rates. If you have a strong case on the merits, consider fully prosecuting the appeal to a Board decision.
⚠ Filing a Notice of Appeal shows limited benefit. Consider other strategies like interviews or amendments before appealing.
Examiner MULLINAX, CLINT LEE works in Art Unit 2123 and has examined 89 patent applications in our dataset. With an allowance rate of 39.3%, this examiner allows applications at a lower rate than most examiners at the USPTO. Applications typically reach final disposition in approximately 10000 months.
Examiner MULLINAX, CLINT LEE's allowance rate of 39.3% places them in the 8% percentile among all USPTO examiners. This examiner is less likely to allow applications than most examiners at the USPTO.
On average, applications examined by MULLINAX, CLINT LEE receive 4.12 office actions before reaching final disposition. This places the examiner in the 96% 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 MULLINAX, CLINT LEE is 10000 months. This places the examiner in the 0% percentile for prosecution speed. Applications take longer to reach final disposition with this examiner compared to most others.
Conducting an examiner interview provides a +32.4% benefit to allowance rate for applications examined by MULLINAX, CLINT LEE. This interview benefit is in the 79% 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, 11.2% 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 1.5% of cases where such amendments are filed. This entry rate is in the 5% 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, 50.0% result in withdrawal of the rejection or reopening of prosecution. This success rate is in the 44% 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 76.9% of appeals filed. This is in the 68% 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 shows above-average willingness to reconsider rejections during appeals. The mandatory appeal conference (MPEP § 1207.01) provides an opportunity for reconsideration.
When applicants file petitions regarding this examiner's actions, 28.6% are granted (fully or in part). This grant rate is in the 15% 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 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.