Detailed information about the 100 most recent patent applications.
| Application Number | Title | Filing Date | Disposal Date | Disposition | Time (months) | Office Actions | Restrictions | Interview | Appeal |
|---|---|---|---|---|---|---|---|---|---|
| 18957784 | SYSTEM AND METHOD FOR GENERATING A VISUAL REPRESENTATION OF AN EXECUTION SEQUENCE WITHIN A GRAPHICAL USER INTERFACE | November 2024 | January 2025 | Allow | 2 | 0 | 0 | Yes | No |
| 18677665 | Devices, Methods, and Graphical User Interfaces for Providing Haptic Feedback | May 2024 | January 2025 | Allow | 8 | 0 | 0 | No | No |
| 18609698 | AUGMENTED REALITY BEAUTY PRODUCT TUTORIALS | March 2024 | October 2024 | Allow | 7 | 0 | 0 | Yes | No |
| 18432228 | INTELLIGENT MANIPULATION OF DYNAMIC DECLARATIVE INTERFACES | February 2024 | February 2025 | Allow | 12 | 1 | 0 | Yes | No |
| 18407048 | CONFIGURABLE DEPLOYMENT OF DATA SCIENCE MODELS | January 2024 | August 2024 | Allow | 7 | 0 | 0 | Yes | No |
| 18402242 | A METHOD AND SYSTEM FOR DETERMINING PRODUCT SIMILARITY IN DIGITAL DOMAINS | January 2024 | August 2024 | Allow | 8 | 0 | 0 | Yes | No |
| 18497890 | SPECIAL LOCK MODE USER INTERFACE | October 2023 | August 2024 | Allow | 10 | 0 | 0 | Yes | No |
| 18485950 | TRAINING TEXT SUMMARIZATION NEURAL NETWORKS WITH AN EXTRACTED SEGMENTS PREDICTION OBJECTIVE | October 2023 | September 2024 | Allow | 11 | 1 | 0 | No | No |
| 18264253 | INFORMATION PROCESSING DEVICE AND INFORMATION PROCESSING PROGRAM | August 2023 | May 2025 | Allow | 21 | 0 | 0 | Yes | No |
| 18360867 | CONTROLLED SCREEN SHARING | July 2023 | April 2025 | Allow | 20 | 0 | 0 | Yes | No |
| 18222569 | SIMPLIFIED SHARING OF CONTENT AMONG COMPUTING DEVICES | July 2023 | July 2024 | Allow | 12 | 1 | 0 | Yes | No |
| 18352015 | Presenting Output To Indicate A Communication Attempt During A Communication Session | July 2023 | July 2024 | Allow | 13 | 1 | 0 | Yes | No |
| 18200241 | DISPLAY APPARATUS, USER TERMINAL, CONTROL METHOD, AND COMPUTER-READABLE MEDIUM | May 2023 | March 2025 | Allow | 22 | 1 | 0 | Yes | No |
| 18139708 | METHOD, COMPUTER PROGRAM PRODUCT, AND APPARATUS FOR PROVIDING AN ENERGY MAP | April 2023 | February 2025 | Allow | 22 | 1 | 0 | No | No |
| 18173011 | IMAGE FORMING APPARATUS AND INPUT DEVICE | February 2023 | March 2025 | Allow | 25 | 1 | 0 | No | No |
| 18106215 | PROMPTED TEXT-TO-IMAGE GENERATION | February 2023 | July 2024 | Allow | 18 | 2 | 0 | Yes | No |
| 18096441 | Monitoring and Management of Wearable Devices | January 2023 | November 2024 | Allow | 22 | 0 | 0 | No | No |
| 17751519 | Devices, Methods, and Graphical User Interfaces for Providing Haptic Feedback | May 2022 | December 2024 | Abandon | 30 | 2 | 0 | Yes | No |
| 17564706 | CLINICAL TRIAL MATCHING SYSTEM USING INFERRED BIOMARKER STATUS | December 2021 | October 2024 | Allow | 34 | 0 | 0 | Yes | No |
| 17522920 | BASE MUTATION DETECTION METHOD AND APPARATUS BASED ON SEQUENCING DATA, AND STORAGE MEDIUM | November 2021 | February 2025 | Allow | 39 | 0 | 0 | No | No |
| 17606050 | METHOD FOR SIMULATING STOCHASTIC OSCILLATION IN INDIVIDUAL-GRANULARITY LONG-DISTANCE EXPRESSWAY TRAFFIC FLOW USING QUANTUM HARMONIC OSCILLATOR | October 2021 | March 2025 | Allow | 41 | 0 | 0 | Yes | No |
| 17508544 | Quantifying User Experience | October 2021 | February 2025 | Allow | 40 | 0 | 0 | Yes | No |
| 17451270 | CROSS-TEMPORAL ENCODING MACHINE LEARNING MODELS | October 2021 | January 2025 | Allow | 39 | 0 | 0 | Yes | No |
| 17500645 | METHOD OF TRAINING ARTIFICIAL NEURAL NETWORK AND METHOD OF EVALUATING PRONUNCIATION USING THE METHOD | October 2021 | November 2024 | Allow | 37 | 0 | 0 | Yes | No |
| 17494055 | NEURO-SYMBOLIC REINFORCEMENT LEARNING WITH FIRST-ORDER LOGIC | October 2021 | January 2025 | Allow | 39 | 0 | 0 | No | No |
| 17489458 | SYSTEMS AND METHODS FOR USING A CONVOLUTIONAL NEURAL NETWORK TO DETECT CONTAMINATION | September 2021 | January 2025 | Allow | 39 | 0 | 0 | No | No |
| 17477771 | SYSTEM AND METHOD FOR LABEL GENERATION FOR TIMESERIES CLASSIFICATION | September 2021 | December 2024 | Allow | 39 | 0 | 0 | Yes | No |
| 17466156 | ADAPTIVE MODEL FOR VEHICLE PROCESSING OF IMAGES | September 2021 | November 2024 | Allow | 38 | 0 | 0 | Yes | No |
| 17402803 | METHOD, DEVICE, AND PROGRAM PRODUCT FOR PROCESSING SAMPLE DATA IN INTERNET OF THINGS ENVIRONMENT | August 2021 | November 2024 | Allow | 39 | 0 | 0 | Yes | No |
| 17397653 | SPARSITY-AWARE COMPUTE-IN-MEMORY | August 2021 | December 2024 | Allow | 41 | 0 | 0 | Yes | No |
| 17373896 | INFORMATION PROCESSING APPARATUS AND NON-TRANSITORY COMPUTER READABLE MEDIUM STORING PROGRAM | July 2021 | March 2025 | Abandon | 44 | 1 | 0 | No | No |
| 17359862 | MACHINE LEARNING MODEL SCENARIO-BASED TRAINING SYSTEM | June 2021 | October 2024 | Allow | 39 | 0 | 0 | Yes | No |
| 17304577 | METHOD AND SYSTEM FOR TRAINING MODEL TO PERFORM LINK PREDICTION IN KNOWLEDGE HYPERGRAPH | June 2021 | September 2024 | Allow | 39 | 0 | 0 | No | No |
| 17345730 | Learning Mahalanobis Distance Metrics from Data | June 2021 | March 2025 | Allow | 45 | 1 | 0 | No | No |
| 17322550 | INFORMATION PROCESSING APPARATUS AND NON-TRANSITORY COMPUTER READABLE MEDIUM | May 2021 | April 2025 | Abandon | 46 | 2 | 0 | No | No |
| 17233600 | DETERMINING COMPONENT CONTRIBUTIONS OF TIME-SERIES MODEL | April 2021 | July 2024 | Allow | 39 | 0 | 0 | Yes | No |
| 17180720 | ARITHMETIC PROCESSING DEVICE, INFORMATION PROCESSING APPARATUS, AND ARITHMETIC PROCESSING METHOD | February 2021 | March 2025 | Abandon | 49 | 1 | 0 | No | No |
| 17155896 | BRANCHING OPERATION FOR NEURAL PROCESSOR CIRCUIT | January 2021 | September 2024 | Allow | 44 | 1 | 0 | Yes | No |
| 17143769 | PIPELINED MACHINE LEARNING FRAMEWORKS | January 2021 | February 2025 | Allow | 49 | 2 | 0 | Yes | No |
| 17139800 | APPARATUS AND METHOD FOR BOUNDARY LEARNING OPTIMIZATION | December 2020 | July 2024 | Allow | 43 | 1 | 0 | No | No |
| 17105204 | COMPOSITIONS AND METHODS FOR CANCER DETECTION AND CLASSIFICATION USING NEURAL NETWORKS | November 2020 | December 2024 | Abandon | 48 | 1 | 0 | No | No |
| 16949958 | CONVERTING QUASI-AFFINE EXPRESSIONS TO MATRIX OPERATIONS | November 2020 | August 2024 | Allow | 45 | 2 | 0 | Yes | No |
| 17039447 | META-Q LEARNING | September 2020 | September 2024 | Allow | 48 | 3 | 0 | Yes | 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 NABI, REZA U works in Art Unit 2142 and has examined 25 patent applications in our dataset. With an allowance rate of 84.0%, this examiner has an above-average tendency to allow applications. Applications typically reach final disposition in approximately 40 months.
Examiner NABI, REZA U's allowance rate of 84.0% places them in the 59% percentile among all USPTO examiners. This examiner has an above-average tendency to allow applications.
On average, applications examined by NABI, REZA U receive 0.60 office actions before reaching final disposition. This places the examiner in the 3% 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 NABI, REZA U is 40 months. This places the examiner in the 23% percentile for prosecution speed. Applications take longer to reach final disposition with this examiner compared to most others.
Conducting an examiner interview provides a +40.0% benefit to allowance rate for applications examined by NABI, REZA U. This interview benefit is in the 87% 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, 50.0% of applications are subsequently allowed. This success rate is in the 97% percentile among all examiners. Strategic Insight: RCEs are highly effective with this examiner compared to others. If you receive a final rejection, filing an RCE with substantive amendments or arguments has a strong likelihood of success.
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 file petitions regarding this examiner's actions, 0.0% are granted (fully or in part). This grant rate is in the 1% 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 10% 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 11% 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.