Detailed information about the 100 most recent patent applications.
| Application Number | Title | Filing Date | Disposal Date | Disposition | Time (months) | Office Actions | Restrictions | Interview | Appeal |
|---|---|---|---|---|---|---|---|---|---|
| 18733296 | Dynamic Simulation Analytics | June 2024 | December 2024 | Allow | 7 | 1 | 0 | Yes | No |
| 18387799 | ARTIFICIAL INTELLIGENCE BASED TRANSACTIONS CONTEXTUALIZATION PLATFORM | November 2023 | April 2025 | Allow | 18 | 3 | 0 | Yes | No |
| 18199642 | CLIENT INTEREST PROFILES AND EMBEDDINGS FOR A RESEARCH ORGANIZATION | May 2023 | March 2024 | Allow | 10 | 2 | 0 | Yes | No |
| 18130517 | CONDENSED MEMORY NETWORKS | April 2023 | April 2025 | Abandon | 24 | 2 | 0 | Yes | No |
| 18030127 | SELF-ADAPTIVE MULTI-MODEL APPROACH IN REPRESENTATION FEATURE SPACE FOR PROPENSITY TO ACTION | April 2023 | June 2024 | Abandon | 15 | 2 | 0 | No | No |
| 18095426 | Method Of Data Selection And Anomaly Detection Based On Auto-Encoder Model | January 2023 | October 2024 | Abandon | 21 | 2 | 0 | No | No |
| 17850204 | SYSTEMS AND METHODS FOR FACILITATING RECOGNITION OF A DEVICE AND/OR AN INSTANCE OF AN APP INVOKED ON A DEVICE | June 2022 | September 2024 | Abandon | 27 | 1 | 0 | No | No |
| 17456405 | SYSTEM AND METHOD FOR DETERMINING A VEHICLE CLASSIFICATION FROM GPS TRACKS | November 2021 | April 2025 | Abandon | 41 | 3 | 0 | Yes | No |
| 17298496 | ADAPTIVE THERMAL DIFFUSIVITY | May 2021 | April 2025 | Allow | 47 | 3 | 1 | Yes | No |
| 17318635 | METHOD AND SYSTEM FOR FACILITATING CLASSIFICATION | May 2021 | March 2025 | Abandon | 46 | 1 | 0 | No | No |
| 17280034 | Predicted Variables in Programming | March 2021 | June 2025 | Abandon | 50 | 2 | 0 | Yes | No |
| 17158092 | NODE AGGREGATION WITH GRAPH NEURAL NETWORKS | January 2021 | February 2025 | Abandon | 49 | 2 | 0 | Yes | No |
| 17141895 | METHOD AND SYSTEM FOR MICROARCHITECTURE-AWARE PROGRAM SAMPLING | January 2021 | March 2025 | Allow | 50 | 2 | 0 | Yes | No |
| 17129367 | MULTIDIMENSIONAL DATA ANALYSIS FOR ISSUE PREDICTION | December 2020 | November 2024 | Allow | 47 | 2 | 0 | Yes | No |
| 17252218 | GENERATING A SELECTABLE SUGGESTION USING A PROVISIONAL MACHINE LEARNING MODEL WHEN USE OF A DEFAULT SUGGESTION MODEL IS INCONSEQUENTIAL | December 2020 | November 2024 | Allow | 47 | 1 | 0 | Yes | No |
| 17110330 | APPARATUSES AND METHODS FOR TRAINING A MACHINE LEARNING NETWORK FOR USE WITH A TIME-OF-FLIGHT CAMERA | December 2020 | January 2025 | Allow | 50 | 2 | 0 | Yes | No |
| 17092198 | MACHINE-LEARNING AND RULE-BASED SYSTEM AND METHOD FOR EVALUATING USER DATA AND GENERATING A STRATEGY TO ACHIEVE A DESIRED OUTCOME | November 2020 | May 2024 | Abandon | 42 | 1 | 0 | No | No |
| 16949477 | PERFORMANCE PREDICTION USING DYNAMIC MODEL CORRELATION | October 2020 | December 2024 | Allow | 50 | 2 | 0 | Yes | No |
| 16986506 | MACHINE LEARNING ACCELERATOR WITH DECISION TREE INTERCONNECTS | August 2020 | July 2024 | Allow | 48 | 2 | 0 | Yes | No |
| 16944698 | Artificial Neural Network Implementations | July 2020 | December 2023 | Allow | 41 | 2 | 0 | Yes | No |
| 16940944 | RULES AND MACHINE LEARNING TO PROVIDE REGULATORY COMPLIED FRAUD DETECTION SYSTEMS | July 2020 | November 2024 | Abandon | 51 | 4 | 0 | Yes | No |
| 16924213 | METHOD AND SYSTEM FOR GENERATING ROBUST SOLUTIONS TO OPTIMIZATION PROBLEMS USING MACHINE LEARNING | July 2020 | March 2024 | Abandon | 44 | 2 | 0 | No | No |
| 15931970 | SHARED SCRATCHPAD MEMORY WITH PARALLEL LOAD-STORE | May 2020 | October 2023 | Allow | 41 | 5 | 0 | Yes | No |
| 16863159 | ONBOARDING OF RETURN PATH DATA PROVIDERS FOR AUDIENCE MEASUREMENT | April 2020 | October 2023 | Allow | 41 | 3 | 0 | Yes | No |
| 16836528 | PRIVACY PRESERVING SYNTHETIC STRING GENERATION USING RECURRENT NEURAL NETWORKS | March 2020 | April 2024 | Allow | 48 | 2 | 0 | Yes | No |
| 16831060 | SPECULATIVE TRAINING USING PARTIAL GRADIENTS UPDATE | March 2020 | November 2023 | Allow | 44 | 1 | 0 | Yes | No |
| 16649751 | INFORMATION PROCESSING DEVICE | March 2020 | January 2023 | Abandon | 34 | 1 | 0 | No | No |
| 16824480 | ARTIFICIAL INTELLIGENCE SYSTEM PROVIDING AUTOMATED DISTRIBUTED TRAINING OF MACHINE LEARNING MODELS | March 2020 | October 2024 | Allow | 55 | 3 | 0 | Yes | No |
| 16593175 | System, Method, and Computer Program Product for Determining the Importance of a Feature of a Machine Learning Model | October 2019 | March 2025 | Abandon | 60 | 3 | 0 | Yes | Yes |
| 16560572 | DYNAMIC DRILLING DYSFUNCTION CODEX | September 2019 | April 2024 | Allow | 56 | 4 | 0 | Yes | No |
| 16552013 | EXPLANATIONS OF MACHINE LEARNING PREDICTIONS USING ANTI-MODELS | August 2019 | September 2023 | Abandon | 49 | 2 | 0 | Yes | No |
| 16545224 | PREDICTING A PERSONA CLASS BASED ON OVERLAP-AGNOSTIC MACHINE LEARNING MODELS FOR DISTRIBUTING PERSONA-BASED DIGITAL CONTENT | August 2019 | June 2024 | Abandon | 58 | 3 | 0 | Yes | No |
| 16544082 | METHOD AND DEVICE FOR DYNAMICALLY DETERMINING AN ARTIFICIAL INTELLIGENCE MODEL | August 2019 | December 2022 | Abandon | 40 | 1 | 0 | Yes | No |
| 16529059 | OPTIMIZATION OF NEURAL NETWORKS USING HARDWARE CALCULATION EFFICIENCY | August 2019 | August 2024 | Abandon | 60 | 4 | 0 | Yes | No |
| 16458148 | Scalable Predictive Analytic System | June 2019 | May 2025 | Abandon | 60 | 6 | 0 | Yes | No |
| 16454832 | ANOMALY DETECTION MODEL SELECTION AND VALIDITY FOR TIME SERIES DATA | June 2019 | April 2024 | Abandon | 57 | 5 | 0 | Yes | No |
| 16262772 | QUANTIZING NEURAL NETWORKS WITH BATCH NORMALIZATION | January 2019 | February 2024 | Allow | 60 | 2 | 0 | Yes | No |
| 16262223 | Systems and Methods for Intervention Optimization | January 2019 | November 2022 | Abandon | 46 | 3 | 0 | Yes | No |
| 16249340 | SYSTEM AND METHOD FOR IMPLEMENTING A CLIENT SENTIMENT ANALYSIS TOOL | January 2019 | March 2025 | Abandon | 60 | 6 | 0 | Yes | No |
| 16231847 | WALKER CAPABLE OF DETERMINING USE INTENT AND A METHOD OF OPERATING THE SAME | December 2018 | April 2024 | Abandon | 60 | 4 | 1 | Yes | No |
| 16230663 | PREDICTION OF RETURN PATH DATA QUALITY FOR AUDIENCE MEASUREMENT | December 2018 | October 2023 | Abandon | 57 | 3 | 0 | Yes | No |
| 16205565 | METHODS FOR SHARING MACHINE LEARNING BASED WEB SERVICE MODELS | November 2018 | February 2024 | Allow | 60 | 6 | 0 | Yes | No |
| 16092135 | LEARNING APPARATUS, LEARNING METHOD, AND RECORDING MEDIUM | October 2018 | December 2023 | Abandon | 60 | 4 | 0 | Yes | No |
| 16058017 | Minibatch Parallel Machine Learning System Design | August 2018 | October 2023 | Abandon | 60 | 2 | 0 | Yes | Yes |
| 15966363 | INFORMATION PROCESSING APPARATUS, METHOD AND NON-TRANSITORY COMPUTER-READABLE STORAGE MEDIUM | April 2018 | February 2023 | Abandon | 57 | 4 | 0 | No | No |
| 15862369 | SYSTEMS AND METHODS FOR HARDWARE-BASED POOLING | January 2018 | January 2025 | Allow | 60 | 5 | 0 | Yes | No |
| 15859552 | COGNITIVE SERVICE REQUEST CONSTRUCTION | December 2017 | June 2022 | Allow | 53 | 4 | 0 | Yes | No |
| 15811573 | EVENT IDENTIFICATION THROUGH MACHINE LEARNING | November 2017 | September 2023 | Allow | 60 | 4 | 0 | Yes | No |
| 15810160 | SYNAPSE ARRAY OF NEUROMORPHIC DEVICE INCLUDING SYNAPSES HAVING FERRO-ELECTRIC FIELD EFFECT TRANSISTORS AND OPERATION METHOD OF THE SAME | November 2017 | May 2022 | Allow | 54 | 1 | 0 | No | No |
| 15811074 | ANALOGY-BASED REASONING WITH MEMORY NETWORKS FOR FUTURE PREDICTION | November 2017 | January 2023 | Abandon | 60 | 2 | 0 | Yes | No |
| 15807618 | COGNITIVE SYSTEM TO ITERATIVELY EXPAND A KNOWLEDGE BASE | November 2017 | May 2023 | Abandon | 60 | 4 | 0 | Yes | No |
| 15707550 | CONDENSED MEMORY NETWORKS | September 2017 | December 2022 | Allow | 60 | 4 | 0 | Yes | No |
| 15549622 | CASCADED IDENTIFICATION IN BUILDING AUTOMATION | August 2017 | April 2025 | Abandon | 60 | 5 | 0 | No | Yes |
| 15648209 | SYSTEMS AND METHODS FOR PEST FORECASTING USING HISTORICAL PESTICIDE USAGE INFORMATION | July 2017 | June 2022 | Allow | 59 | 3 | 0 | No | No |
| 15646827 | ELECTRONIC SENSING SYSTEMS AND METHODS THEREOF | July 2017 | December 2021 | Allow | 53 | 3 | 0 | No | No |
| 15531212 | NEURAL NETWORK STRUCTURE AND A METHOD THERETO | May 2017 | May 2023 | Allow | 60 | 4 | 0 | Yes | Yes |
| 15606220 | NEURAL NETWORK APPARATUS AND CONTROL METHOD OF NEURAL NETWORK APPARATUS | May 2017 | April 2022 | Abandon | 59 | 4 | 0 | No | No |
| 15494530 | COGNITIVE SERVICE REQUEST CONSTRUCTION | April 2017 | June 2022 | Allow | 60 | 4 | 0 | Yes | No |
| 15518694 | SYSTEM AND METHOD FOR DETERMINING A VEHICLE CLASSIFICATION FROM GPS TRACKS | April 2017 | August 2021 | Allow | 52 | 4 | 0 | Yes | No |
| 15469149 | SELECTION SYSTEM FOR MACHINE LEARNING MODULE FOR DETERMINING TARGET METRICS FOR EVALUATION OF HEALTH CARE PROCEDURES AND PROVIDERS | March 2017 | April 2023 | Allow | 60 | 5 | 0 | Yes | No |
| 15467755 | TECHNOLOGIES FOR AUTO DISCOVER AND CONNECT TO A REST INTERFACE | March 2017 | August 2021 | Allow | 52 | 3 | 0 | No | No |
| 15449071 | ANALOG MULTIPLIER-ACCUMULATORS | March 2017 | December 2021 | Allow | 57 | 3 | 0 | Yes | No |
| 15447397 | SYSTEMS AND METHODS FOR MULTI-INSTANCE LEARNING-BASED CLASSIFICATION FOR STREAMING INPUTS | March 2017 | January 2024 | Allow | 60 | 7 | 0 | Yes | No |
| 15420971 | SET-CENTRIC SEMANTIC EMBEDDING | January 2017 | September 2021 | Allow | 55 | 6 | 0 | Yes | No |
| 15421424 | Deep Neural Network Model for Processing Data Through Multiple Linguistic Task Hierarchies | January 2017 | August 2021 | Allow | 54 | 2 | 1 | Yes | No |
| 15412243 | MISSING SENSOR VALUE ESTIMATION | January 2017 | November 2021 | Allow | 58 | 5 | 0 | Yes | No |
| 15406211 | DYNAMIC MULTISCALE ROUTING ON NETWORKS OF NEUROSYNAPTIC CORES | January 2017 | March 2022 | Allow | 60 | 3 | 0 | Yes | No |
| 15335050 | SMART SENSING: A SYSTEM AND A METHOD FOR DISTRIBUTED AND FAULT TOLERANT HIERARCHICAL AUTONOMOUS COGNITIVE INSTRUMENTATION | October 2016 | July 2024 | Allow | 60 | 9 | 0 | Yes | No |
| 15335341 | FEATURE SELECTION OF NEURAL ACTIVITY USING HIERARCHICAL CLUSTERING WITH STOCHASTIC SEARCH | October 2016 | February 2022 | Allow | 60 | 4 | 0 | Yes | No |
| 15334405 | METHOD AND APPARATUS FOR MACHINE LEARNING | October 2016 | October 2021 | Abandon | 60 | 4 | 0 | Yes | No |
| 15297894 | SYSTEMS AND METHODS FOR FACILITATING RECOGNITION OF A DEVICE AND/OR AN INSTANCE OF AN APP INVOKED ON A DEVICE | October 2016 | March 2022 | Allow | 60 | 5 | 0 | Yes | No |
| 15280463 | ARTIFICIAL NEURAL NETWORKS FOR HUMAN ACTIVITY RECOGNITION | September 2016 | April 2024 | Abandon | 60 | 7 | 0 | Yes | Yes |
| 15280126 | MACHINE LEARNING MODEL FOR PREDICTING STATE OF AN OBJECT REPRESENTING A POTENTIAL TRANSACTION | September 2016 | February 2022 | Abandon | 60 | 5 | 0 | Yes | No |
| 15278479 | ENSEMBLE MODEL POLICY GENERATION FOR PREDICTION SYSTEMS | September 2016 | July 2022 | Allow | 60 | 6 | 0 | No | No |
| 15267140 | EFFICIENT TRAINING OF NEURAL NETWORKS | September 2016 | September 2023 | Abandon | 60 | 8 | 0 | Yes | No |
This analysis examines appeal outcomes and the strategic value of filing appeals for examiner MENGISTU, TEWODROS E.
With a 33.3% reversal rate, the PTAB reverses the examiner's rejections in a meaningful percentage 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, 20.0% of applications that filed an appeal were subsequently allowed. This appeal filing benefit rate is in the bottom 25% across the USPTO, indicating that filing appeals is less effective here than in most other areas.
⚠ 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 MENGISTU, TEWODROS E works in Art Unit 2127 and has examined 74 patent applications in our dataset. With an allowance rate of 54.1%, this examiner allows applications at a lower rate than most examiners at the USPTO. Applications typically reach final disposition in approximately 56 months.
Examiner MENGISTU, TEWODROS E's allowance rate of 54.1% 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 MENGISTU, TEWODROS E receive 3.42 office actions before reaching final disposition. This places the examiner in the 99% 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 MENGISTU, TEWODROS E is 56 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 +26.0% benefit to allowance rate for applications examined by MENGISTU, TEWODROS E. This interview benefit is in the 76% 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, 20.6% of applications are subsequently allowed. This success rate is in the 15% 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 2.0% of cases where such amendments are filed. This entry rate is in the 1% 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, 0.0% result in withdrawal of the rejection or reopening of prosecution. This success rate is in the 4% percentile among all examiners. Note: Pre-appeal conferences show limited success with this examiner compared to others. While still worth considering, be prepared to proceed with a full appeal brief if the PAC does not result in favorable action.
This examiner withdraws rejections or reopens prosecution in 25.0% of appeals filed. This is in the 1% percentile among all examiners. 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, 22.2% are granted (fully or in part). This grant rate is in the 13% 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 0.0% of allowed cases (in the 9% 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.