Detailed information about the 100 most recent patent applications.
| Application Number | Title | Filing Date | Disposal Date | Disposition | Time (months) | Office Actions | Restrictions | Interview | Appeal |
|---|---|---|---|---|---|---|---|---|---|
| 18898799 | Method for Constructing Boolean Algebra System of Ising Perceptual Computer and Ising Machine Programming Interface | September 2024 | October 2025 | Abandon | 12 | 1 | 0 | No | No |
| 17993108 | APPARATUS AND METHOD FOR GENERATING AN ACTIVITY ARTICLE | November 2022 | May 2025 | Abandon | 30 | 5 | 0 | Yes | No |
| 17545135 | FEDERATED LEARNING MECHANISM | December 2021 | February 2026 | Abandon | 50 | 2 | 0 | No | No |
| 17474413 | TELEOPERATION FOR TRAINING OF ROBOTS USING MACHINE LEARNING | September 2021 | July 2025 | Abandon | 46 | 1 | 0 | No | No |
| 17167890 | HIERARCHICAL MULTI-AGENT IMITATION LEARNING WITH CONTEXTUAL BANDITS | February 2021 | June 2025 | Abandon | 52 | 2 | 0 | Yes | No |
| 17259130 | Systems and Methods for Generative Models for Design | January 2021 | February 2025 | Abandon | 49 | 2 | 0 | No | No |
| 17115609 | MULTI-MODEL ANALYTICS ENGINE FOR ANALYZING REPORTS | December 2020 | July 2025 | Abandon | 55 | 4 | 0 | Yes | No |
| 17099861 | NODE SHARING FOR A RULE ENGINE CODED IN A COMPILED LANGUAGE | November 2020 | October 2023 | Allow | 35 | 1 | 0 | Yes | No |
| 17082396 | APPARATUS AND METHOD FOR LEARNING TEXT DETECTION MODEL | October 2020 | March 2025 | Abandon | 53 | 2 | 0 | No | No |
| 17081841 | QUANTIZED ARCHITECTURE SEARCH FOR MACHINE LEARNING MODELS | October 2020 | June 2025 | Abandon | 55 | 3 | 0 | No | No |
| 16978446 | Behaviour Models for Autonomous Vehicle Simulators | September 2020 | October 2024 | Abandon | 49 | 1 | 0 | Yes | No |
| 16969052 | DYNAMIC DISTRIBUTION ESTIMATION DEVICE, METHOD, AND PROGRAM | August 2020 | June 2025 | Abandon | 58 | 4 | 0 | Yes | No |
| 16938478 | AUTONOMOUS BEHAVIORS IN A MULTIAGENT ADVERSARIAL SCENE | July 2020 | March 2025 | Abandon | 56 | 3 | 0 | Yes | No |
| 16926763 | METHOD FOR PRELOADING APPLICATION, STORAGE MEDIUM, AND TERMINAL | July 2020 | April 2024 | Abandon | 45 | 1 | 0 | No | No |
| 16924077 | INFORMATION PROCESSING APPARATUS | July 2020 | October 2023 | Abandon | 39 | 1 | 0 | No | No |
| 16839966 | Selective Inference Generation with Distributed Machine-Learned Models | April 2020 | July 2024 | Abandon | 52 | 3 | 0 | No | No |
| 16797394 | PREDICTION MODELING IN SEQUENTIAL FLOW NETWORKS | February 2020 | April 2025 | Abandon | 60 | 4 | 0 | Yes | No |
| 16573597 | SYSTEMS AND TECHNIQUES FOR IDENTIFYING AND EXPLOITING RELATIONSHIPS BETWEEN MEDIA CONSUMPTION AND HEALTH | September 2019 | August 2020 | Abandon | 11 | 0 | 0 | No | No |
| 16466828 | DEFUZZIFICATION APPARATUS AND METHOD | June 2019 | May 2024 | Abandon | 59 | 3 | 0 | No | No |
| 16421850 | QUESTION ANSWERING SYSTEM, QUESTION ANSWERING PROCESSING METHOD, AND QUESTION ANSWERING INTEGRATED SYSTEM | May 2019 | October 2022 | Abandon | 41 | 2 | 0 | No | No |
| 16365475 | LOW LATENCY AND HIGH THROUGHPUT INFERENCE | March 2019 | December 2021 | Abandon | 33 | 1 | 0 | No | No |
| 16271064 | Systems and Methods for Distributed Generation of Decision Tree-Based Models | February 2019 | April 2022 | Abandon | 38 | 2 | 0 | Yes | No |
| 16225042 | AUTOMATIC ANNOTATION AND GENERATION OF DATA FOR SUPERVISED MACHINE LEARNING IN VEHICLE ADVANCED DRIVER ASSISTANCE SYSTEMS | December 2018 | July 2023 | Abandon | 55 | 2 | 0 | No | No |
| 16059100 | LEARNING SERVICE PROVIDING APPARATUS | August 2018 | September 2022 | Abandon | 49 | 1 | 0 | No | No |
| 16011651 | AUTOMATED FEATURE GENERATION, SELECTION AND HYPERPARAMETER TUNING FROM STRUCTURED DATA FOR SUPERVISED LEARNING PROBLEMS | June 2018 | January 2023 | Abandon | 55 | 4 | 0 | Yes | No |
| 15948805 | COMPUTER BASED REASONING AND ARTIFICIAL INTELLIGENCE SYSTEMS | April 2018 | September 2022 | Abandon | 54 | 2 | 0 | No | No |
| 15944905 | INTELLIGENT INCENTIVE DISTRIBUTION | April 2018 | September 2022 | Abandon | 54 | 2 | 0 | Yes | No |
| 15932289 | RULE-BASED SYSTEM AND METHOD TO BE USED IN THE SYSTEM | February 2018 | November 2021 | Abandon | 45 | 1 | 0 | No | No |
| 15855912 | EMBEDDED LEARNING FOR RESPONSE PREDICTION | December 2017 | September 2022 | Abandon | 57 | 1 | 0 | Yes | No |
| 15812568 | GENERATING A PREDICTIVE BEHAVIOR MODEL FOR PREDICTING USER BEHAVIOR USING UNSUPERVISED FEATURE LEARNING AND A RECURRENT NEURAL NETWORK | November 2017 | January 2021 | Allow | 38 | 0 | 0 | No | No |
| 15717495 | SOCIAL COLLABORATION IN PROBABILISTIC PREDICTION | September 2017 | December 2020 | Allow | 38 | 1 | 0 | No | No |
| 15628832 | CRIME RISK FORECASTING | June 2017 | May 2021 | Abandon | 46 | 0 | 0 | Yes | No |
| 15468034 | NETWORK-PROBABILITY RECOMMENDATION SYSTEM | March 2017 | May 2021 | Abandon | 49 | 0 | 0 | Yes | No |
| 15443635 | SYSTEMS, METHODS, AND COMPUTER-READABLE MEDIA FOR MAPPING NATURAL FRACTURE NETWORK IN SHALE | February 2017 | November 2020 | Abandon | 45 | 1 | 0 | No | No |
| 15406916 | METHOD AND SYSTEM FOR CLASSIFYING INPUT DATA ARRIVED ONE BY ONE IN TIME | January 2017 | August 2020 | Abandon | 43 | 1 | 0 | No | No |
| 15403958 | USER STATE PREDICTIONS FOR PRESENTING INFORMATION | January 2017 | August 2020 | Abandon | 43 | 0 | 0 | Yes | No |
| 15390915 | SYSTEM TO DETECT MACHINE-INITIATED EVENTS IN TIME SERIES DATA | December 2016 | January 2021 | Abandon | 48 | 2 | 0 | No | No |
| 15383759 | Method and Apparatus for Establishing and Using User Recommendation Model in Social Network | December 2016 | February 2024 | Abandon | 60 | 8 | 0 | Yes | No |
| 15316366 | METHOD AND APPARATUS FOR RECOMMENDATION BY APPLYING EFFICIENT ADAPTIVE MATRIX FACTORIZATION | December 2016 | February 2020 | Abandon | 39 | 1 | 0 | No | No |
| 15273505 | NEUROMORPHIC COMPUTING DEVICE, MEMORY DEVICE, SYSTEM, AND METHOD TO MAINTAIN A SPIKE HISTORY FOR NEURONS IN A NEUROMORPHIC COMPUTING ENVIRONMENT | September 2016 | October 2019 | Allow | 37 | 1 | 0 | No | No |
| 15208020 | VECTOR OPERATORS FOR DISTRIBUTIONAL ENTAILMENT | July 2016 | October 2019 | Abandon | 39 | 1 | 0 | No | No |
| 15134905 | SYSTEMS AND METHODS FOR FAILURE PREDICTION IN INDUSTRIAL ENVIRONMENTS | April 2016 | July 2019 | Abandon | 39 | 2 | 0 | No | Yes |
| 14879349 | System and Method for Extracting Table Data from Text Documents Using Machine Learning | October 2015 | June 2019 | Abandon | 44 | 1 | 0 | No | No |
| 14845236 | SEQUENTIAL IMAGE SAMPLING AND STORAGE OF FINE-TUNED FEATURES | September 2015 | June 2019 | Abandon | 45 | 2 | 0 | Yes | No |
| 14810544 | Production Control Support Apparatus and Production Control Support Method | July 2015 | May 2019 | Abandon | 45 | 1 | 0 | No | No |
This analysis examines appeal outcomes and the strategic value of filing appeals for examiner ZHEN, LI B.
With a 0.0% reversal rate, the PTAB affirms the examiner's rejections in the vast majority of cases. This reversal rate is in the bottom 25% across the USPTO, indicating that appeals face significant challenges here.
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, 0.0% 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 ZHEN, LI B works in Art Unit 2121 and has examined 43 patent applications in our dataset. With an allowance rate of 9.3%, this examiner allows applications at a lower rate than most examiners at the USPTO. Applications typically reach final disposition in approximately 46 months.
Examiner ZHEN, LI B's allowance rate of 9.3% places them in the 1% percentile among all USPTO examiners. This examiner is less likely to allow applications than most examiners at the USPTO.
On average, applications examined by ZHEN, LI B receive 1.79 office actions before reaching final disposition. This places the examiner in the 40% 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 ZHEN, LI B is 46 months. This places the examiner in the 11% percentile for prosecution speed. Applications take longer to reach final disposition with this examiner compared to most others.
Conducting an examiner interview provides a -4.9% benefit to allowance rate for applications examined by ZHEN, LI B. This interview benefit is in the 5% percentile among all examiners. Note: Interviews show limited statistical benefit with this examiner compared to others, though they may still be valuable for clarifying issues.
When applicants file an RCE with this examiner, 0.0% of applications are subsequently allowed. This success rate is in the 0% 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 0.0% of cases where such amendments are filed. This entry rate is in the 0% 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 5% 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 0.0% of appeals filed. This is in the 0% 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, 100.0% are granted (fully or in part). This grant rate is in the 90% 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.