Detailed information about the 100 most recent patent applications.
| Application Number | Title | Filing Date | Disposal Date | Disposition | Time (months) | Office Actions | Restrictions | Interview | Appeal |
|---|---|---|---|---|---|---|---|---|---|
| 18871459 | RESOURCE ALLOCATION METHOD AND APPARATUS AND ARTIFICIAL INTELLIGENCE TRAINING SYSTEM | December 2024 | June 2025 | Allow | 6 | 1 | 0 | No | No |
| 18859208 | RESOURCE ALLOCATION METHOD AND APPARATUS FOR PERIPHERAL, SERVER, AND STORAGE MEDIUM | October 2024 | February 2025 | Allow | 3 | 0 | 0 | No | No |
| 18752865 | SYSTEM AND METHOD FOR MANAGING COMPUTERIZED NODES IN COMPUTER CLUSTER ENVIRONMENTS | June 2024 | November 2024 | Allow | 5 | 1 | 0 | No | No |
| 18749020 | METHOD OF RESOURCE ADJUSTMENT FOR SERVICE CLUSTER, ELECTRONIC DEVICE AND STORAGE MEDIUM | June 2024 | August 2024 | Allow | 2 | 0 | 0 | No | No |
| 18737810 | DATABASE-BASED DATA PROCESSING METHOD, DEVICE, MEDIUM AND ELECTRONIC APPARATUS | June 2024 | August 2024 | Allow | 2 | 0 | 0 | No | No |
| 18734363 | INTERACTIONS WITH A GENERATIVE RESPONSE ENGINE DURING A LONG RUNNING TASK | June 2024 | February 2025 | Allow | 9 | 2 | 0 | Yes | No |
| 18656174 | SHELF LABEL COMMUNICATION METHOD BASED ON SYNCHRONOUS NETWORK, SHELF LABEL SYSTEM AND COMPUTER DEVICE | May 2024 | July 2024 | Allow | 2 | 0 | 0 | No | No |
| 18649616 | VIRTUAL MACHINE PROVISIONING AND DIRECTORY SERVICE MANAGEMENT | April 2024 | February 2025 | Allow | 10 | 1 | 0 | No | No |
| 18642668 | CLIENT-CONFIGURABLE RETENTION PERIODS FOR MACHINE LEARNING SERVICE-MANAGED RESOURCES | April 2024 | November 2024 | Allow | 7 | 0 | 0 | No | No |
| 18700181 | MEMORY MANAGEMENT METHOD FOR DEVICE, MEMORY MANAGEMENT DEVICE, AND COMPUTING SYSTEM | April 2024 | July 2024 | Allow | 4 | 0 | 0 | No | No |
| 18608244 | MEMORY ALLOCATION FOR 3-D GRAPHICS RENDERING | March 2024 | May 2025 | Allow | 14 | 2 | 0 | No | No |
| 18594526 | Autonomous Warehouse-Scale Computers | March 2024 | March 2025 | Allow | 12 | 2 | 0 | Yes | No |
| 18582373 | Virtual Non-Uniform Memory Access (NUMA) Locality Table for NUMA Systems | February 2024 | February 2025 | Allow | 12 | 1 | 0 | No | No |
| 18442603 | SYSTEM AND METHOD FOR MAINTAINING DEPENDENCIES IN A PARALLEL PROCESS | February 2024 | September 2024 | Allow | 7 | 0 | 0 | No | No |
| 18437392 | MEDICAL IMAGING DISTRIBUTION SYSTEM AND DEVICE | February 2024 | September 2024 | Allow | 8 | 0 | 0 | No | No |
| 18404705 | HARDWARE ACCELERATION FOR FUNCTION PROCESSING | January 2024 | December 2024 | Allow | 12 | 1 | 1 | Yes | No |
| 18404715 | HARDWARE ACCELERATION FOR FUNCTION PROCESSING | January 2024 | August 2024 | Allow | 7 | 0 | 0 | No | No |
| 18542227 | MULTI-REGION DEPLOYMENT OF JOBS IN A FEDERATED CLOUD INFRASTRUCTURE | December 2023 | February 2025 | Allow | 14 | 2 | 0 | No | No |
| 18517896 | Resource Manager Integration in Cloud Computing Environments | November 2023 | August 2024 | Allow | 9 | 1 | 0 | No | No |
| 18516072 | Hardware Accelerator Service Discovery | November 2023 | November 2024 | Allow | 12 | 2 | 0 | No | No |
| 18512465 | Optimizing Distributed and Parallelized Batch Data Processing | November 2023 | September 2024 | Allow | 10 | 1 | 0 | No | No |
| 18320646 | ELECTRONIC APPARATUS AND CONTROLLING METHOD THEREOF | May 2023 | October 2024 | Allow | 17 | 3 | 0 | Yes | No |
| 18110547 | COMPUTING PLATFORM FOR SIMULATING AN INDUSTRIAL SYSTEM AND METHOD OF MANAGING THE SIMULATION | February 2023 | June 2025 | Allow | 28 | 0 | 0 | No | No |
| 18100812 | WORKLOAD PLACEMENT RESPONSIVE TO FAULT | January 2023 | June 2025 | Allow | 29 | 0 | 0 | No | No |
| 18052890 | Compact NUMA-aware Locks | November 2022 | November 2024 | Allow | 24 | 0 | 0 | No | No |
| 17976969 | HARDWARE-ACCELERATED COROUTINES FOR LINKED DATA STRUCTURES | October 2022 | April 2025 | Allow | 30 | 0 | 0 | No | No |
| 18045128 | VMID AS A GPU TASK CONTAINER FOR VIRTUALIZATION | October 2022 | July 2024 | Allow | 21 | 0 | 1 | No | No |
| 17897435 | INFORMATION INFRASTRUCTURE MANAGEMENT METHOD, MANAGEMENT SERVER, AND NON-TRANSITORY COMPUTER-READABLE RECORDING MEDIUM FOR MANAGING A COMPUTING MACHINE HAVING A COMPUTATIONAL RESOURCE FOR EXECUTING A WORKFLOW AND A STORAGE RESOURCE COMMUNICABLY COUPLED TO THE COMPUTING MACHINE | August 2022 | May 2025 | Allow | 33 | 0 | 0 | No | No |
| 17875786 | LCS SDXI RESOURCE OWNERSHIP SYSTEM | July 2022 | March 2025 | Allow | 31 | 0 | 0 | No | No |
| 17814890 | VEHICLE CONTROL MODULE ALLOCATION | July 2022 | June 2025 | Allow | 35 | 1 | 0 | Yes | No |
| 17862979 | MEMORY DISAGGREGATION IN A MULTI-NODE ENVIRONMENT | July 2022 | June 2025 | Allow | 35 | 0 | 0 | No | No |
| 17854592 | Just-In-Time Re-Partitioning of Feature Maps for Efficient Balancing of Compute Core Workloads | June 2022 | March 2025 | Allow | 33 | 0 | 0 | No | No |
| 17782066 | A Multi-Tenant Real-Time Process Controller for Edge Cloud Environments | June 2022 | May 2025 | Allow | 36 | 2 | 1 | No | No |
| 17780427 | MANAGING PROVENANCE INFORMATION FOR DATA PROCESSING PIPELINES | May 2022 | June 2025 | Allow | 36 | 2 | 0 | No | No |
| 17752299 | Compute Platform Recommendations for New Workloads in a Distributed Computing Environment | May 2022 | January 2025 | Allow | 32 | 2 | 0 | Yes | No |
| 17750717 | TRANSMITTING CONFIGURATION DATA BETWEEN CONTAINERS RUNNING ON DIFFERENT COMPUTER ARCHITECTURES VIA A SIDECAR CONTAINER | May 2022 | October 2024 | Allow | 29 | 0 | 0 | No | No |
| 17715530 | DATA PROCESSING USING A HETEROGENEOUS MEMORY POOL | April 2022 | April 2025 | Allow | 36 | 0 | 0 | No | No |
| 17765919 | MANAGEMENT METHOD FOR CDN FUNCTION VIRTUALIZATION, ELECTRONIC DEVICE, AND COMPUTER READABLE MEDIUM | April 2022 | February 2025 | Allow | 34 | 0 | 1 | No | No |
| 17677588 | DISTRIBUTED AI PLATFORM MESH FOR MACHINE LEARNING WORKFLOWS | February 2022 | January 2025 | Allow | 35 | 1 | 0 | No | No |
| 17635772 | EXPLICIT SCHEDULING OF ON-CHIP OPERATIONS | February 2022 | July 2025 | Allow | 40 | 2 | 1 | Yes | No |
| 17591459 | Virtual Machine Hot Migration Method, Apparatus, Electronic Device, and Computer Storage Medium | February 2022 | December 2024 | Allow | 34 | 1 | 0 | No | No |
| 17577842 | QUANTUM COMPUTER SYSTEM SCHEDULING AND PARAMETERIZATION BASED ON ERROR CORRECTION HISTORY | January 2022 | December 2024 | Allow | 35 | 1 | 0 | Yes | No |
| 17647878 | SYSTEM AND METHOD FOR PROVIDING CONTINUOUS OPTIMIZATION WORKFLOW | January 2022 | August 2024 | Allow | 31 | 1 | 0 | Yes | No |
| 17624252 | DIKW RESOURCE TRANSFER METHOD AND DEVICE FOR PURPOSE-ORIENTED CALCULATION AND INFERENCE | December 2021 | October 2024 | Allow | 33 | 1 | 0 | No | No |
| 17564498 | IOT EDGE SCHEDULER MODULE AND SYSTEM | December 2021 | July 2024 | Allow | 31 | 1 | 0 | No | No |
| 17564474 | CACHE BLOCKING FOR DISPATCHES | December 2021 | August 2024 | Allow | 32 | 0 | 1 | No | No |
| 17561433 | VIRTUALIZATION OF INTERPROCESSOR INTERRUPTS | December 2021 | November 2024 | Allow | 35 | 1 | 0 | No | No |
| 17560025 | METHODS AND APPARATUS TO CONDITIONALLY ACTIVATE A BIG CORE IN A COMPUTING SYSTEM | December 2021 | September 2024 | Allow | 33 | 0 | 0 | No | No |
| 17559848 | Adaptive service metering on ports in a link aggregation group | December 2021 | January 2025 | Allow | 37 | 2 | 0 | No | No |
| 17619335 | ASSIGNMENT CONTROL DEVICE, ASSIGNMENT CONTROL METHOD, AND ASSIGNMENT CONTROL PROGRAM | December 2021 | August 2024 | Allow | 32 | 1 | 0 | Yes | No |
| 17643741 | ON-DEMAND CLOUD ROBOTS FOR ROBOTIC PROCESS AUTOMATION | December 2021 | August 2024 | Allow | 32 | 1 | 0 | No | No |
| 17545859 | SYSTEMS AND METHODS FOR PROCESS RESTORATION SUBSEQUENT TO AN OPERATING SYSTEM CRASH | December 2021 | October 2024 | Allow | 34 | 1 | 0 | No | No |
| 17535046 | METHOD FOR MONITORING A TASK AND AN APPARATUS IMPLEMENTING THE SAME METHOD | November 2021 | January 2025 | Allow | 38 | 2 | 0 | Yes | No |
| 17530681 | MIGRATION BETWEEN CPU CORES | November 2021 | December 2024 | Allow | 37 | 2 | 0 | Yes | No |
| 17520086 | SYSTEM AND METHOD FOR ASYNCHRONOUS BACKEND PROCESSING OF EXPENSIVE COMMAND LINE INTERFACE COMMANDS | November 2021 | August 2024 | Allow | 34 | 1 | 1 | No | No |
| 17515117 | DATA PROCESSING PIPELINE HORIZONTAL SCALING | October 2021 | February 2025 | Allow | 40 | 1 | 0 | Yes | No |
| 17510575 | INFORMATION HANDLING SYSTEMS AND METHODS TO PROVIDE WORKLOAD REMEDIATION BASED ON WORKLOAD PERFORMANCE METRICS AND CONTEXTUAL INFORMATION | October 2021 | August 2024 | Allow | 33 | 1 | 0 | No | No |
| 17451709 | COORDINATED MICROSERVICES | October 2021 | April 2025 | Allow | 42 | 1 | 0 | No | No |
| 17502891 | Systems and Methods for Associating Modules in a Software Defined Control System for Industrial Process Plants | October 2021 | February 2025 | Allow | 40 | 2 | 0 | No | No |
| 17495900 | MIGRATING VIRTUAL MACHINES IN CLUSTER MEMORY SYSTEMS | October 2021 | September 2024 | Allow | 35 | 1 | 1 | No | No |
| 17494618 | METHODS AND APPARATUS TO EXPOSE CLOUD INFRASTRUCTURE RESOURCES TO TENANTS IN A MULTI-TENANT SOFTWARE SYSTEM | October 2021 | August 2024 | Allow | 34 | 1 | 0 | No | No |
| 17474963 | PREEMPTIVE SCHEDULING FOR SERVERLESS HIGH PERFORMANCE COMPUTING | September 2021 | December 2024 | Allow | 39 | 2 | 1 | Yes | No |
| 17468067 | TECHNOLOGIES TO OFFLOAD WORKLOAD EXECUTION | September 2021 | February 2025 | Allow | 41 | 1 | 0 | Yes | No |
| 17405979 | RANKING COMPUTING RESOURCES | August 2021 | November 2024 | Allow | 39 | 2 | 1 | Yes | No |
| 16281724 | MIGRATION CONTROL APPARATUS AND MIGRATION CONTROL METHOD | February 2019 | May 2020 | Allow | 15 | 0 | 0 | No | No |
| 16273274 | THREAD INTERRUPT OFFLOAD RE-PRIORITIZATION | February 2019 | October 2019 | Allow | 8 | 1 | 0 | No | No |
| 16209663 | SYSTEM AND METHOD FOR HANDLING DEPENDENCIES IN DYNAMIC THREAD SPAWNING FOR A MULTI-THREADING PROCESSOR | December 2018 | August 2019 | Allow | 8 | 0 | 0 | No | No |
| 16109844 | METHOD FOR DEPLOYING TASK TO NODE BASED ON EXECUTION COMPLETION POINT, TASK DEPLOYMENT APPARATUS AND STORAGE MEDIUM | August 2018 | June 2020 | Allow | 22 | 1 | 0 | No | No |
| 16045547 | METHODS AND APPARATUS TO ADJUST ENERGY REQUIREMENTS IN A DATA CENTER | July 2018 | May 2020 | Allow | 21 | 1 | 0 | No | No |
| 16035118 | DEVICE, METHOD, AND MEDIUM FOR EXECUTING A COMPUTING PROCESS WITHIN AN ACCESSED MONITORING REGION | July 2018 | January 2020 | Allow | 18 | 0 | 1 | No | No |
| 15970841 | ALLOCATING COMPUTING RESOURCES IN AN ONLINE SYSTEM | May 2018 | March 2020 | Allow | 23 | 0 | 1 | No | No |
| 15945908 | WORKLOAD MANAGEMENT WITH DATA ACCESS AWARENESS USING AN ORDERED LIST OF HOSTS IN A COMPUTING CLUSTER | April 2018 | October 2019 | Allow | 19 | 0 | 0 | No | No |
| 15946220 | COMPARE POINT DETECTION IN MULTI-THREADED COMPUTING ENVIRONMENTS | April 2018 | May 2019 | Allow | 14 | 1 | 0 | No | No |
| 15805267 | INDEPENDENT MAPPING OF THREADS | November 2017 | May 2019 | Allow | 19 | 0 | 0 | No | No |
| 15805522 | COLLECTING PERFORMANCE METRICS FROM JAVA VIRTUAL MACHINES | November 2017 | July 2018 | Allow | 8 | 1 | 0 | No | No |
| 15787472 | APPARATUS AND METHOD FOR MULTITENANCY IN CLOUD ENVIRONMENTS FOR PROCESSING LARGE DATASETS | October 2017 | February 2020 | Allow | 28 | 1 | 0 | No | No |
| 15695490 | STOPPING CENTRAL PROCESSING UNITS FOR DATA COLLECTION BASED ON EVENT CATEGORIES OF EVENTS | September 2017 | July 2019 | Allow | 23 | 1 | 0 | No | No |
| 15492829 | VIRTUAL MACHINE MANAGEMENT | April 2017 | May 2019 | Allow | 25 | 2 | 0 | No | No |
| 15471143 | JOB SCHEDULER FOR REMOTE MAINTENANCE OF SERVERS AND WORKSTATIONS | March 2017 | April 2018 | Allow | 13 | 2 | 0 | No | No |
| 15500510 | IDENTIFYING MULTIPLE RESOURCES TO PERFORM A SERVICE REQUEST | January 2017 | September 2018 | Allow | 19 | 1 | 0 | No | No |
| 15383738 | DYNAMIC RUNTIME TASK MANAGEMENT | December 2016 | May 2018 | Allow | 17 | 0 | 0 | No | No |
| 15300103 | VIRTUALIZED RESOURCE MANAGEMENT NODE AND VIRTUAL MIGRATION METHOD FOR SEAMLESS VIRTUAL MACHINE INTEGRATION | September 2016 | August 2018 | Allow | 23 | 2 | 0 | No | No |
| 15258278 | SCALING PAST THE JAVA VIRTUAL MACHINE THREAD LIMIT | September 2016 | December 2016 | Allow | 3 | 0 | 0 | No | No |
| 15258326 | SCALING PAST THE JAVA VIRTUAL MACHINE THREAD LIMIT | September 2016 | December 2016 | Allow | 3 | 0 | 0 | No | No |
| 15158546 | AUTOMATED WORKLOAD ANALYSIS AND SIMULATION PROCESS | May 2016 | March 2018 | Allow | 22 | 1 | 0 | No | No |
| 15087079 | Joint Network and Task Scheduling | March 2016 | December 2017 | Allow | 20 | 1 | 0 | No | No |
| 15067625 | COLLECTING PERFORMANCE METRICS FROM JAVA VIRTUAL MACHINES | March 2016 | July 2017 | Allow | 17 | 1 | 0 | No | No |
| 15068369 | FLEXIBLE BINDING OF TASKS TO TARGET RESOURCES | March 2016 | September 2017 | Allow | 18 | 1 | 0 | No | No |
| 15057213 | DYNAMIC AGGRESSIVENESS FOR OPTIMIZING PLACEMENT OF VIRTUAL MACHINES IN A COMPUTING ENVIRONMENT | March 2016 | November 2016 | Allow | 8 | 2 | 0 | No | No |
| 15052168 | SYSTEM AND METHOD FOR ASSISTING VIRTUAL MACHINE INSTANTIATION AND MIGRATION | February 2016 | March 2017 | Allow | 13 | 3 | 0 | No | No |
| 15051499 | JOB SCHEDULER FOR REMOTE MAINTENANCE OF SERVERS AND WORKSTATIONS | February 2016 | November 2016 | Allow | 9 | 3 | 0 | No | No |
| 14967639 | MIGRATING VIRTUAL ASSET | December 2015 | January 2017 | Allow | 13 | 1 | 0 | No | No |
| 14949325 | REVERTING TIGHTLY COUPLED THREADS IN AN OVER-SCHEDULED SYSTEM | November 2015 | November 2017 | Allow | 24 | 1 | 1 | No | No |
| 14872284 | DYNAMIC AGGRESSIVENESS FOR OPTIMIZING PLACEMENT OF VIRTUAL MACHINES IN A COMPUTING ENVIRONMENT | October 2015 | October 2016 | Allow | 13 | 2 | 0 | No | No |
| 14852737 | Efficient Scheduling of Multi-Versioned Tasks | September 2015 | July 2017 | Allow | 22 | 1 | 0 | No | No |
| 14841891 | REDEPLOYABLE RESOURCE FORECASTING | September 2015 | February 2017 | Allow | 17 | 0 | 0 | No | No |
| 14834472 | MIGRATING VIRTUAL ASSET | August 2015 | January 2017 | Allow | 17 | 1 | 0 | No | No |
| 14823335 | AUTOMATED CLOUD WORKLOAD MANAGEMENT IN A MAP-REDUCE ENVIRONMENT | August 2015 | November 2015 | Allow | 3 | 0 | 0 | No | No |
| 14812752 | SYSTEM AND METHOD FOR ASSISTING VIRTUAL MACHINE INSTANTIATION AND MIGRATION | July 2015 | December 2015 | Allow | 5 | 1 | 0 | No | No |
| 14810547 | TAGGING VIRTUAL MACHINE INSTANCES BASED ON COMMANDS | July 2015 | April 2016 | Allow | 8 | 1 | 0 | No | No |
This analysis examines appeal outcomes and the strategic value of filing appeals for examiner KESSLER, GREGORY AARON.
With a 100.0% reversal rate, the PTAB has reversed the examiner's rejections more often than affirming them. This reversal rate is in the top 25% across the USPTO, indicating that appeals are more successful here than in most other areas.
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, 80.0% of applications that filed an appeal were subsequently allowed. This appeal filing benefit rate is in the top 25% across the USPTO, indicating that filing appeals is particularly effective here. The act of filing often prompts favorable reconsideration during the mandatory appeal conference.
✓ 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 is strategically valuable. The act of filing often prompts favorable reconsideration during the mandatory appeal conference.
Examiner KESSLER, GREGORY AARON works in Art Unit 2197 and has examined 172 patent applications in our dataset. With an allowance rate of 99.4%, this examiner allows applications at a higher rate than most examiners at the USPTO. Applications typically reach final disposition in approximately 31 months.
Examiner KESSLER, GREGORY AARON's allowance rate of 99.4% places them in the 97% percentile among all USPTO examiners. This examiner is more likely to allow applications than most examiners at the USPTO.
On average, applications examined by KESSLER, GREGORY AARON receive 1.48 office actions before reaching final disposition. This places the examiner in the 35% 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 KESSLER, GREGORY AARON is 31 months. This places the examiner in the 35% percentile for prosecution speed. Prosecution timelines are slightly slower than average with this examiner.
Conducting an examiner interview provides a +0.7% benefit to allowance rate for applications examined by KESSLER, GREGORY AARON. This interview benefit is in the 14% 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, 40.7% of applications are subsequently allowed. This success rate is in the 91% 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 36.2% of cases where such amendments are filed. This entry rate is in the 47% percentile among all examiners. Strategic Recommendation: This examiner shows below-average receptiveness to after-final amendments. You may need to file an RCE or appeal rather than relying on after-final amendment entry.
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 6% 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 83.3% of appeals filed. This is in the 73% percentile among all examiners. 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, 40.0% are granted (fully or in part). This grant rate is in the 39% percentile among all examiners. Strategic Note: Petitions show below-average success regarding this examiner's actions. Ensure you have a strong procedural basis before filing.
Examiner's Amendments: This examiner makes examiner's amendments in 0.6% of allowed cases (in the 61% percentile). This examiner makes examiner's amendments more often than average to place applications in condition for allowance (MPEP § 1302.04).
Quayle Actions: This examiner issues Ex Parte Quayle actions in 3.5% of allowed cases (in the 74% percentile). This examiner issues Quayle actions more often than average when claims are allowable but formal matters remain (MPEP § 714.14).
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.