Detailed information about the 100 most recent patent applications.
| Application Number | Title | Filing Date | Disposal Date | Disposition | Time (months) | Office Actions | Restrictions | Interview | Appeal |
|---|---|---|---|---|---|---|---|---|---|
| 19324906 | SYSTEMS AND METHODS OF SENSOR DATA FUSION | September 2025 | February 2026 | Allow | 5 | 1 | 0 | No | No |
| 19256894 | SYSTEMS AND METHODS OF SENSOR DATA FUSION | July 2025 | September 2025 | Allow | 2 | 1 | 0 | No | No |
| 19184626 | SYSTEMS AND METHODS OF SENSOR DATA FUSION | April 2025 | June 2025 | Allow | 2 | 1 | 0 | No | No |
| 19032776 | SYSTEM AND METHOD FOR EXPLAINING AND CONTESTING OUTCOMES OF GENERATIVE AI MODELS WITH DESIRED EXPLANATION PROPERTIES | January 2025 | September 2025 | Allow | 8 | 1 | 0 | Yes | No |
| 19027530 | METHOD AND DEVICE FOR TRAINING AND PREDICTING A CONJUNCTION PARAMETER FROM CONJUNCTION DATA MESSAGES | January 2025 | December 2025 | Allow | 11 | 2 | 0 | No | No |
| 18990145 | SYSTEMS AND METHODS OF SENSOR DATA FUSION | December 2024 | April 2025 | Allow | 4 | 1 | 0 | Yes | No |
| 18807491 | METHOD AND SYSTEM FOR IMPROVING MACHINE LEARNING OPERATION BY REDUCING MACHINE LEARNING BIAS | August 2024 | February 2025 | Allow | 6 | 1 | 0 | Yes | No |
| 18726756 | STROKE PREDICTION MULTI-ARCHITECTURE STACKED ENSEMBLE SUPERMODEL | July 2024 | March 2025 | Allow | 8 | 0 | 0 | No | No |
| 18675062 | Machine Learning Model Understanding As-A-Service | May 2024 | April 2025 | Allow | 10 | 1 | 0 | No | No |
| 18669419 | SYSTEMS AND METHODS FOR MACHINE LEARNING USING A NETWORK OF DECISION-MAKING NODES | May 2024 | March 2025 | Allow | 10 | 1 | 0 | No | No |
| 18654681 | BATCH SELECTION POLICIES FOR TRAINING MACHINE LEARNING MODELS USING ACTIVE LEARNING | May 2024 | January 2025 | Allow | 8 | 1 | 0 | Yes | No |
| 18442718 | SYSTEM AND METHOD FOR MIMICKING A NEURAL NETWORK WITHOUT ACCESS TO THE ORIGINAL TRAINING DATASET OR THE TARGET MODEL | February 2024 | February 2025 | Allow | 12 | 0 | 0 | No | No |
| 18514663 | DYNAMIC ARTIFICIAL INTELLIGENCE / MACHINE LEARNING MODEL UPDATE, OR RETRAIN AND UPDATE, IN DIGITAL PROCESSES AT RUNTIME | November 2023 | February 2025 | Allow | 15 | 1 | 0 | No | No |
| 18497031 | SYSTEM AND METHOD FOR REAL-TIME ARTIFICIAL INTELLIGENCE SITUATION DETERMINATION BASED ON DISTRIBUTED DEVICE EVENT DATA | October 2023 | April 2025 | Allow | 18 | 1 | 0 | No | No |
| 18367330 | COMPUTER-BASED SYSTEMS HAVING COMPUTER ENGINES AND DATA STRUCTURES CONFIGURED FOR MACHINE LEARNING DATA INSIGHT PREDICTION AND METHODS OF USE THEREOF | September 2023 | March 2026 | Allow | 30 | 2 | 0 | No | No |
| 18464935 | ROTATING DATA FOR NEURAL NETWORK COMPUTATIONS | September 2023 | September 2024 | Allow | 12 | 0 | 0 | No | No |
| 18450263 | CLASSIFYING USER BEHAVIOR AS ANOMALOUS | August 2023 | March 2025 | Abandon | 19 | 1 | 0 | No | No |
| 18448402 | Dynamic Subsystem Operational Sequencing to Concurrently Control and Distribute Supervised Learning Processor Training and Provide Predictive Responses to Input Data | August 2023 | August 2025 | Allow | 24 | 2 | 0 | No | No |
| 18272747 | SYSTEM AND METHOD FOR THE DISCOVERING EFFICIENT RANDOM NEURAL NETWORKS | July 2023 | September 2024 | Allow | 14 | 0 | 0 | No | No |
| 18336531 | HIERARCHICAL TOURNAMENT-BASED MACHINE LEARNING PREDICTIONS | June 2023 | December 2024 | Allow | 18 | 1 | 0 | No | No |
| 18325744 | REAL TIME CONTEXT DEPENDENT DEEP LEARNING | May 2023 | October 2024 | Allow | 16 | 1 | 0 | No | No |
| 18312797 | EMPIRICAL MODELING WITH GLOBALLY ENFORCED GENERAL CONSTRAINTS | May 2023 | July 2024 | Allow | 15 | 0 | 0 | No | No |
| 18136394 | CLASSIFICATION OF DANGEROUS GOODS VIA MACHINE LEARNING | April 2023 | August 2024 | Allow | 16 | 0 | 0 | No | No |
| 18132635 | MACHINE LEARNING BASED FUNCTION TESTING | April 2023 | September 2024 | Allow | 17 | 1 | 0 | No | No |
| 18116487 | PROSPECTIVE MEDIA CONTENT GENERATION USING NEURAL NETWORK MODELING | March 2023 | July 2024 | Allow | 16 | 1 | 0 | No | No |
| 18112582 | DEEP LEARNING FOR CREDIT CONTROLS | February 2023 | April 2024 | Allow | 14 | 2 | 0 | No | No |
| 18158166 | SYSTEMS AND METHODS FOR USING CONTRASTIVE PRE-TRAINING TO GENERATE TEXT AND CODE EMBEDDINGS | January 2023 | April 2024 | Allow | 15 | 2 | 0 | No | No |
| 18068408 | SCALABLE NEUTRAL ATOM BASED QUANTUM COMPUTING | December 2022 | June 2024 | Allow | 18 | 1 | 0 | Yes | No |
| 17992769 | SYSTEMS AND METHODS FOR REDUCING MANUFACTURING FAILURE RATES | November 2022 | December 2023 | Allow | 13 | 1 | 0 | Yes | No |
| 17984754 | APPARATUS AND METHOD FOR CREATING NON-FUNGIBLE TOKENS (NFTS) FOR FUTURE USER EXPERIENCES | November 2022 | March 2024 | Allow | 16 | 2 | 0 | Yes | No |
| 17979479 | HIERARCHICAL TOURNAMENT-BASED MACHINE LEARNING PREDICTIONS | November 2022 | March 2023 | Allow | 4 | 0 | 0 | No | No |
| 17966288 | METHOD AND SYSTEM FOR EXPLORING A PERSONAL INTEREST SPACE | October 2022 | January 2024 | Allow | 15 | 1 | 0 | No | No |
| 17887022 | METHOD AND APPARATUS FOR EVALUATING JOINT TRAINING MODEL | August 2022 | February 2025 | Abandon | 30 | 4 | 0 | Yes | No |
| 17870733 | CLASSIFYING USER BEHAVIOR AS ANOMALOUS | July 2022 | March 2023 | Allow | 8 | 0 | 0 | No | No |
| 17858070 | Dynamic Subsystem Operational Sequencing to Concurrently Control and Distribute Supervised Learning Processor Training and Provide Predictive Responses to Input Data | July 2022 | April 2023 | Allow | 9 | 0 | 0 | No | No |
| 17855323 | OBTAINING A GENERATED DATASET WITH A PREDETERMINED BIAS FOR EVALUATING ALGORITHMIC FAIRNESS OF A MACHINE LEARNING MODEL | June 2022 | April 2023 | Allow | 10 | 1 | 0 | Yes | No |
| 17804253 | Progressive Objective Addition in Multi-objective Heuristic Systems and Methods | May 2022 | August 2023 | Allow | 15 | 1 | 0 | No | No |
| 17663663 | ARTIFICIAL INTELLIGENT SYSTEMS AND METHODS FOR IDENTIFYING A DRUNK PASSENGER BY A CAR HAILING ORDER | May 2022 | December 2024 | Abandon | 31 | 1 | 0 | No | No |
| 17712380 | SELF-REGULATING POWER MANAGEMENT FOR A NEURAL NETWORK SYSTEM | April 2022 | April 2023 | Allow | 13 | 1 | 0 | No | No |
| 17711880 | ARTIFICIAL INTELLIGENCE-BASED USE CASE MODEL RECOMMENDATION METHODS AND SYSTEMS | April 2022 | March 2023 | Allow | 12 | 2 | 0 | Yes | No |
| 17689914 | Machine Learning-Based Media Content Placement | March 2022 | May 2025 | Allow | 38 | 1 | 0 | No | No |
| 17588704 | Cognitive Personas | January 2022 | June 2023 | Allow | 16 | 1 | 0 | No | No |
| 17551572 | NEURAL NETWORK METHOD AND APPARATUS | December 2021 | August 2025 | Allow | 44 | 5 | 0 | Yes | No |
| 17548070 | Data Drift Impact In A Machine Learning Model | December 2021 | December 2023 | Allow | 24 | 4 | 0 | Yes | No |
| 17520919 | ROTATING DATA FOR NEURAL NETWORK COMPUTATIONS | November 2021 | April 2023 | Allow | 18 | 0 | 0 | No | No |
| 17506294 | APPARATUS AND METHOD FOR FORECASTED PERFORMANCE LEVEL ADJUSTMENT AND MODIFICATION | October 2021 | January 2023 | Allow | 15 | 1 | 0 | No | No |
| 17495707 | PROCESSING AND RE-USING ASSISTED SUPPORT DATA TO INCREASE A SELF-SUPPORT KNOWLEDGE BASE | October 2021 | August 2023 | Allow | 22 | 1 | 0 | Yes | No |
| 17491466 | SYSTEM AND METHOD FOR REAL-TIME ARTIFICIAL INTELLIGENCE SITUATION DETERMINATION BASED ON DISTRIBUTED DEVICE EVENT DATA | September 2021 | July 2023 | Allow | 22 | 3 | 0 | Yes | No |
| 17488198 | MACHINE LEARNING FOR INTELLIGENT RADIOTHERAPY DATA ANALYTICS | September 2021 | November 2025 | Allow | 49 | 2 | 0 | Yes | No |
| 17481977 | METHOD AND COMPUTER PROGRAM PRODUCT FOR TRAINING A PAIRWISE CLASSIFIER FOR USE IN ENTITY RESOLUTION IN LARGE DATA SETS | September 2021 | June 2022 | Allow | 9 | 2 | 0 | No | No |
| 17479180 | Organizing Neural Networks | September 2021 | September 2023 | Allow | 24 | 1 | 0 | No | No |
| 17468360 | BEHAVIOR ANALYSIS USING DISTRIBUTED REPRESENTATIONS OF EVENT DATA | September 2021 | April 2024 | Allow | 31 | 2 | 0 | No | No |
| 17404153 | REAL TIME CONTEXT DEPENDENT DEEP LEARNING | August 2021 | February 2023 | Allow | 18 | 1 | 0 | No | No |
| 17402151 | MANAGEMENT METHOD OF MACHINE LEARNING MODEL FOR NETWORK DATA ANALYTICS FUNCTION DEVICE | August 2021 | May 2025 | Allow | 45 | 1 | 0 | No | No |
| 17399217 | PREDICTIVE CLASSIFICATION MODEL FOR AUTO-POPULATION OF TEXT BLOCK TEMPLATES INTO AN APPLICATION | August 2021 | April 2025 | Allow | 44 | 1 | 0 | Yes | No |
| 17429875 | MODEL LEARNING APPARATUS, LABEL ESTIMATION APPARATUS, METHOD AND PROGRAM THEREOF | August 2021 | November 2025 | Abandon | 51 | 2 | 0 | No | No |
| 17427173 | TASK-AWARE NEURAL NETWORK ARCHITECTURE SEARCH | July 2021 | November 2025 | Abandon | 51 | 2 | 0 | No | No |
| 17381141 | DECISION-MAKING UNDER SELECTIVE LABELS | July 2021 | December 2025 | Allow | 52 | 2 | 0 | Yes | No |
| 17371721 | TECHNOLOGY FOR ANALYZING SENSOR DATA TO DETECT CONFIGURATIONS OF VEHICLE OPERATION | July 2021 | September 2024 | Allow | 38 | 1 | 0 | No | No |
| 17367529 | PORTABLE DEVICE AND METHOD USING ACCELERATED NETWORK SEARCH ARCHITECTURE | July 2021 | February 2026 | Abandon | 55 | 2 | 0 | No | No |
| 17418799 | DOMAIN KNOWLEDGE DETERMINATION | June 2021 | January 2024 | Abandon | 30 | 1 | 0 | No | No |
| 17349957 | NEURAL NETWORK SYSTEM AND METHOD OF OPERATING THE SAME | June 2021 | October 2025 | Abandon | 51 | 2 | 0 | No | No |
| 17339063 | SYSTEMS AND METHODS FOR END-TO-END LEARNING OF OPTIMAL DRIVING POLICY | June 2021 | December 2025 | Allow | 54 | 3 | 0 | Yes | Yes |
| 17337080 | DUAL CYCLE TENSOR DROPOUT IN A NEURAL NETWORK | June 2021 | February 2022 | Allow | 8 | 1 | 0 | Yes | No |
| 17332606 | PROBABILISTIC INFERENCE IN MACHINE LEARNING USING A QUANTUM ORACLE | May 2021 | March 2025 | Allow | 45 | 1 | 0 | No | No |
| 17319599 | CONFIGURING AUTOSAVE TRIGGERS BASED ON VALUE METRICS | May 2021 | September 2024 | Allow | 40 | 0 | 0 | No | No |
| 17314529 | COURSE CONTENT DATA ANALYSIS AND PREDICTION | May 2021 | March 2026 | Allow | 58 | 2 | 0 | No | No |
| 17184371 | SYSTEMS AND METHODS FOR USE WITH RECURRENT NEURAL NETWORKS | February 2021 | May 2025 | Allow | 51 | 2 | 0 | Yes | No |
| 17183072 | OPTIMIZING MACHINE LEARNING MODELS WITH A DEVICE FARM | February 2021 | February 2022 | Allow | 11 | 2 | 0 | No | No |
| 17267916 | PROACTIVE DEFENSE OF UNTRUSTWORTHY MACHINE LEARNING SYSTEM | February 2021 | March 2025 | Allow | 49 | 2 | 0 | Yes | No |
| 17170343 | Distributed Adversarial Training for Robust Deep Neural Networks | February 2021 | October 2025 | Allow | 56 | 2 | 0 | Yes | No |
| 17169319 | INFORMATION DELIVERY PLATFORM | February 2021 | January 2023 | Abandon | 23 | 0 | 0 | No | No |
| 17150799 | Method and System for making Recommendation from Binary Data Using Neighbor-Score Matrix and Latent Factors | January 2021 | March 2025 | Abandon | 50 | 2 | 0 | Yes | Yes |
| 17142117 | Generation of Secure Synthetic Data Based On True-Source Datasets | January 2021 | November 2025 | Allow | 59 | 1 | 0 | Yes | No |
| 17136677 | METHOD AND SYSTEM FOR SELECTING A LEARNING MODEL FROM AMONG A PLURALITY OF LEARNING MODELS | December 2020 | March 2025 | Allow | 50 | 2 | 0 | Yes | No |
| 17134481 | MACHINE LEARNING OF RESPONSE SELECTION TO STRUCTURED DATA INPUT INCLUDING MOMENTUM CLASSIFICATION | December 2020 | October 2024 | Allow | 46 | 0 | 0 | No | No |
| 17133086 | METHOD, APPARATUS, AND COMPUTER PROGRAM PRODUCT FOR SURVEILLANCE OF ROAD ENVIRONMENTS VIA DEEP LEARNING | December 2020 | December 2024 | Allow | 48 | 2 | 0 | No | No |
| 17131044 | CALCULATING A SOLUTION FOR AN OBJECTIVE FUNCTION BASED ON TWO OBJECTIVE FUNCTIONS | December 2020 | February 2023 | Allow | 26 | 1 | 0 | No | No |
| 17128763 | COMPUTER-IMPLEMENTED METHODS AND SYSTEMS FOR COMPRESSING DEEP NEURAL NETWORK MODELS USING ALTERNATING DIRECTION METHOD OF MULTIPLIERS (ADMM) | December 2020 | March 2026 | Allow | 60 | 3 | 0 | Yes | No |
| 17115094 | SYSTEM, METHOD AND APPARATUS FOR INTELLIGENT CACHING | December 2020 | August 2025 | Allow | 56 | 4 | 1 | No | No |
| 17112396 | SELF IMPROVING ANNOTATION QUALITY ON DIVERSE AND MULTI-DISCIPLINARY CONTENT | December 2020 | October 2025 | Allow | 59 | 4 | 0 | Yes | No |
| 17106488 | NONLINEAR OPTIMIZATION SYSTEM | November 2020 | April 2021 | Allow | 5 | 1 | 0 | No | No |
| 17058809 | EXTRACTION OF PREDICTION RULE ASSOCIATED WITH INPUT DATA THAT INDICATES CONDITION SATISFIED BY THE INPUT DATA | November 2020 | April 2024 | Abandon | 41 | 2 | 0 | No | No |
| 17101184 | PROBABILISTIC DECISION MAKING SYSTEM AND METHODS OF USE | November 2020 | April 2024 | Abandon | 41 | 2 | 0 | No | No |
| 16951848 | ARTIFICIAL NEURAL NETWORK BYPASS COMPILER | November 2020 | October 2025 | Allow | 59 | 2 | 0 | Yes | No |
| 16951799 | METHODS, SYSTEMS, ARTICLES OF MANUFACTURE, AND APPARATUS TO GENERATE CODE SEMANTICS | November 2020 | March 2026 | Abandon | 60 | 4 | 0 | Yes | No |
| 17093917 | DISTRIBUTABLE EVENT PREDICTION AND MACHINE LEARNING RECOGNITION SYSTEM | November 2020 | March 2021 | Allow | 4 | 1 | 0 | Yes | No |
| 17093151 | APPARATUS, SYSTEMS, AND METHODS FOR GROUPING DATA RECORDS | November 2020 | January 2025 | Allow | 50 | 3 | 0 | No | No |
| 17092033 | DISCOVERY OF HARDWARE CHARACTERISTICS OF DEEP LEARNING ACCELERATORS FOR OPTIMIZATION VIA COMPILER | November 2020 | June 2024 | Allow | 43 | 3 | 0 | Yes | No |
| 17069688 | SYSTEMS AND METHODS FOR MACHINE LEARNING USING A NETWORK OF DECISION-MAKING NODES | October 2020 | February 2024 | Allow | 40 | 2 | 0 | Yes | No |
| 17067284 | INTERACTIVE AGENT AND CONTROL USING REINFORCEMENT LEARNING | October 2020 | May 2025 | Allow | 55 | 4 | 0 | Yes | No |
| 17063034 | DISTRIBUTING TENSOR COMPUTATIONS ACROSS COMPUTING DEVICES | October 2020 | November 2024 | Allow | 50 | 2 | 0 | Yes | No |
| 17041533 | ATTENTION FILTERING FOR MULTIPLE INSTANCE LEARNING | September 2020 | February 2025 | Allow | 52 | 1 | 0 | No | No |
| 17029333 | System and Method for Assessing an Operating Condition of an Asset | September 2020 | November 2024 | Allow | 50 | 1 | 0 | No | No |
| 16982441 | LOSS-ERROR-AWARE QUANTIZATION OF A LOW-BIT NEURAL NETWORK | September 2020 | May 2024 | Allow | 44 | 0 | 0 | No | No |
| 17002419 | Quantum Circuit Learning Device, Quantum Circuit Learning Method, and Recording Medium | August 2020 | September 2023 | Allow | 36 | 0 | 0 | No | No |
| 16998748 | SYSTEM-LEVEL CONTROL USING TREE-BASED REGRESSION WITH OUTLIER REMOVAL | August 2020 | September 2025 | Allow | 60 | 4 | 0 | Yes | No |
| 16944324 | System and Method for Ensemble Expert Diversification | July 2020 | October 2023 | Allow | 38 | 2 | 0 | No | No |
| 16942239 | TRANSLATION OF A QUANTUM DESIGN ACROSS MULTIPLE APPLICATIONS | July 2020 | June 2024 | Allow | 46 | 1 | 0 | Yes | No |
| 16941595 | SYSTEMS AND METHODS FOR MONITORING PERFORMANCE OF A MACHINE LEARNING MODEL EXTERNALLY TO THE MACHINE LEARNING MODEL | July 2020 | September 2024 | Allow | 49 | 1 | 0 | No | No |
This analysis examines appeal outcomes and the strategic value of filing appeals for examiner PELLETT, DANIEL T.
With a 28.6% reversal rate, the PTAB affirms the examiner's rejections in the vast majority 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, 37.8% of applications that filed an appeal were subsequently allowed. This appeal filing benefit rate is above the USPTO average, suggesting that filing an appeal can be an effective strategy for prompting reconsideration.
⚠ Appeals to PTAB face challenges. Ensure your case has strong merit before committing to full Board review.
✓ Filing a Notice of Appeal is strategically valuable. The act of filing often prompts favorable reconsideration during the mandatory appeal conference.
Examiner PELLETT, DANIEL T works in Art Unit 2121 and has examined 275 patent applications in our dataset. With an allowance rate of 81.5%, this examiner has an above-average tendency to allow applications. Applications typically reach final disposition in approximately 46 months.
Examiner PELLETT, DANIEL T's allowance rate of 81.5% places them in the 53% percentile among all USPTO examiners. This examiner has an above-average tendency to allow applications.
On average, applications examined by PELLETT, DANIEL T receive 2.56 office actions before reaching final disposition. This places the examiner in the 75% percentile for office actions issued. This examiner issues a slightly above-average number of office actions.
The median time to disposition (half-life) for applications examined by PELLETT, DANIEL T 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 +8.5% benefit to allowance rate for applications examined by PELLETT, DANIEL T. This interview benefit is in the 39% percentile among all examiners. Recommendation: Interviews provide a below-average benefit with this examiner.
When applicants file an RCE with this examiner, 27.6% of applications are subsequently allowed. This success rate is in the 48% percentile among all examiners. Strategic Insight: RCEs show below-average effectiveness with this examiner. Carefully evaluate whether an RCE or continuation is the better strategy.
This examiner enters after-final amendments leading to allowance in 13.8% of cases where such amendments are filed. This entry rate is in the 14% 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, 46.2% result in withdrawal of the rejection or reopening of prosecution. This success rate is in the 41% 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 62.2% of appeals filed. This is in the 40% percentile among all examiners. Of these withdrawals, 43.5% occur early in the appeal process (after Notice of Appeal but before Appeal Brief). Strategic Insight: This examiner shows below-average willingness to reconsider rejections during appeals. Be prepared to fully prosecute appeals if filed.
When applicants file petitions regarding this examiner's actions, 48.6% are granted (fully or in part). This grant rate is in the 44% 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 0.0% 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.