Detailed information about the 100 most recent patent applications.
| Application Number | Title | Filing Date | Disposal Date | Disposition | Time (months) | Office Actions | Restrictions | Interview | Appeal |
|---|---|---|---|---|---|---|---|---|---|
| 18783409 | DATA MODIFICATION OPERATORS FOR REDUCING BIAS IN MACHINE LEARNING AND ARTIFICIAL INTELLIGENCE MODELS | July 2024 | November 2025 | Allow | 15 | 3 | 0 | Yes | No |
| 18651384 | AUGMENTING MACHINE LEARNING LANGUAGE MODELS USING SEARCH ENGINE RESULTS | April 2024 | February 2025 | Allow | 10 | 0 | 0 | Yes | No |
| 18605337 | Generative Artificial Intelligence Model Prompt Injection Classifier | March 2024 | January 2025 | Allow | 10 | 2 | 0 | Yes | No |
| 18385559 | AUTOMATED COMPREHENSION AND INTEREST-BASED OPTIMIZATION OF CONTENT | October 2023 | October 2024 | Allow | 12 | 1 | 0 | Yes | No |
| 18497924 | NEURAL PROGRAMMING | October 2023 | November 2024 | Allow | 13 | 1 | 0 | Yes | No |
| 18358823 | AUTOMATED DETECTION OF CONTENT GENERATED BY ARTIFICIAL INTELLIGENCE | July 2023 | January 2025 | Allow | 18 | 4 | 0 | Yes | No |
| 18259682 | Network Model Training Method and Apparatus, Electronic Apparatus and Computer-readable Storage Medium | June 2023 | February 2025 | Allow | 19 | 2 | 0 | Yes | No |
| 18268465 | System, Method, and Computer Program Product for Multi-Domain Ensemble Learning Based on Multivariate Time Sequence Data | June 2023 | May 2024 | Allow | 11 | 1 | 0 | Yes | No |
| 18300707 | METHODS AND APPARATUS FOR TIME-SERIES FORECASTING USING DEEP LEARNING MODELS OF A DEEP BELIEF NETWORK WITH QUANTUM COMPUTING | April 2023 | September 2025 | Allow | 29 | 4 | 1 | Yes | No |
| 18131831 | MODEL SELECTION IN ENSEMBLE LEARNING | April 2023 | February 2025 | Abandon | 23 | 4 | 0 | Yes | No |
| 18104210 | AUGMENTING MACHINE LEARNING LANGUAGE MODELS USING SEARCH ENGINE RESULTS | January 2023 | January 2024 | Allow | 12 | 2 | 0 | Yes | No |
| 18081242 | MACHINE LEARNING TECHNIQUES FOR VALIDATING AND MUTATING OUTPUTS FROM PREDICTIVE SYSTEMS | December 2022 | September 2025 | Allow | 33 | 0 | 0 | Yes | No |
| 18000845 | AUTOMATED ELECTRIC SUBMERSIBLE PUMP (ESP) FAILURE ANALYSIS | December 2022 | February 2026 | Allow | 38 | 1 | 0 | Yes | No |
| 17979486 | INTERACTIVE QUALITATIVE-QUANTITATIVE LIVE LABELING FOR DEEP LEARNING ARTIFICIAL INTELLIGENCE | November 2022 | September 2024 | Allow | 22 | 3 | 0 | Yes | No |
| 17899124 | PREDICTIVE FOG DATA CENTER MIGRATION | August 2022 | February 2026 | Allow | 41 | 1 | 0 | Yes | No |
| 17876451 | EVALUATING OUTPUT SEQUENCES USING AN AUTO-REGRESSIVE LANGUAGE MODEL NEURAL NETWORK | July 2022 | May 2024 | Allow | 21 | 3 | 0 | Yes | No |
| 17869780 | CROSS-HIERARCHICAL MACHINE LEARNING PREDICTION | July 2022 | August 2025 | Allow | 37 | 3 | 0 | Yes | Yes |
| 17649791 | Motion Compensation and Refinement in Recurrent Neural Networks | February 2022 | October 2025 | Abandon | 44 | 1 | 0 | No | No |
| 17362997 | HARDWARE AND NEURAL ARCHITECTURE CO-SEARCH | June 2021 | August 2022 | Allow | 13 | 3 | 0 | Yes | No |
| 17358891 | APPARATUSES, COMPUTER-IMPLEMENTED METHODS, AND COMPUTER PROGRAM PRODUCTS FOR IMPROVED GLOBAL QUBIT POSITIONING IN A QUANTUM COMPUTING ENVIRONMENT | June 2021 | November 2025 | Allow | 53 | 1 | 0 | Yes | No |
| 17293458 | TRAINING DEVICE, METHOD AND PROGRAM | May 2021 | October 2025 | Abandon | 53 | 1 | 0 | No | No |
| 17286982 | ELECTRONIC DEVICE AND CONTROL METHOD THEREFOR | April 2021 | November 2024 | Allow | 43 | 2 | 0 | Yes | No |
| 17280184 | CLASSIFICATION DEVICE, CLASSIFICATION METHOD, AND CLASSIFICATION PROGRAM | March 2021 | September 2025 | Abandon | 53 | 1 | 0 | No | No |
| 17275981 | METHOD FOR DETECTING ANOMALIES IN A DATA SET | March 2021 | October 2025 | Abandon | 56 | 4 | 0 | Yes | No |
| 17197677 | METHOD AND APPARATUS FOR QUANTIZING NEURAL NETWORK MODEL IN DEVICE | March 2021 | November 2025 | Abandon | 56 | 4 | 0 | Yes | No |
| 17196626 | SYSTEM AND METHOD FOR DETECTION OF IMPAIRMENT IN COGNITIVE FUNCTION | March 2021 | December 2024 | Allow | 45 | 1 | 0 | Yes | No |
| 17182053 | TRAINING TREE-BASED MACHINE-LEARNING MODELING ALGORITHMS FOR PREDICTING OUTPUTS AND GENERATING EXPLANATORY DATA | February 2021 | February 2025 | Allow | 48 | 1 | 0 | Yes | No |
| 17165226 | INTELLIGENT DATA CURATION | February 2021 | January 2022 | Allow | 12 | 2 | 0 | Yes | No |
| 17160606 | SYSTEMS AND METHODS FOR EVALUATING TEMPORAL DEPENDENCY TREES | January 2021 | September 2021 | Allow | 7 | 1 | 0 | Yes | No |
| 17127908 | MACHINE LEARNING MODEL FOR ANALYSIS OF INSTRUCTION SEQUENCES | December 2020 | August 2023 | Allow | 32 | 1 | 0 | Yes | No |
| 17116724 | METHOD, APPARATUS, AND SYSTEM FOR PROVIDING PLACE CATEGORY PREDICTION | December 2020 | November 2024 | Abandon | 47 | 2 | 0 | No | No |
| 17098007 | Systems and Methods for Out-of-Distribution Detection | November 2020 | November 2025 | Allow | 60 | 3 | 0 | Yes | No |
| 17078066 | BRAIN OPERATING SYSTEM INFRASTRUCTURE | October 2020 | October 2025 | Abandon | 59 | 2 | 0 | No | Yes |
| 17071482 | Computer System | October 2020 | December 2024 | Abandon | 50 | 2 | 0 | No | No |
| 17069794 | SYSTEMS AND METHODS FOR ARTIFICIAL INTELLIGENCE DISCOVERED CODES | October 2020 | September 2022 | Allow | 23 | 4 | 0 | Yes | No |
| 17067753 | METHOD OF JUDGING RELIABILITY OF DEEP LEARNING MACHINE | October 2020 | November 2022 | Abandon | 25 | 2 | 0 | Yes | No |
| 17046492 | DECISION-MAKING DEVICE, UNMANNED SYSTEM, DECISION-MAKING METHOD, AND PROGRAM | October 2020 | March 2026 | Allow | 60 | 4 | 0 | Yes | Yes |
| 17063606 | MODEL SELECTION FOR PRODUCTION SYSTEM VIA AUTOMATED ONLINE EXPERIMENTS | October 2020 | December 2024 | Allow | 51 | 2 | 0 | Yes | No |
| 17027241 | PERSON RE-IDENTIFICATION METHOD COMBINING REVERSE ATTENTION AND MULTI-SCALE DEEP SUPERVISION | September 2020 | March 2022 | Abandon | 18 | 2 | 0 | No | No |
| 17025759 | HYPER-PARAMETER TUNING METHOD FOR MACHINE LEARNING ALGORITHMS USING PATTERN RECOGNITION AND REDUCED SEARCH SPACE APPROACH | September 2020 | September 2024 | Abandon | 48 | 1 | 0 | Yes | No |
| 16980592 | Low Overhead Quantum Computation Using Lattice Surgery | September 2020 | June 2025 | Allow | 57 | 2 | 1 | Yes | No |
| 16988232 | SYSTEMS AND METHODS FOR EMBEDDING PROBLEMS INTO AN ANALOG PROCESSOR | August 2020 | September 2023 | Allow | 37 | 1 | 0 | Yes | No |
| 16968347 | EVALUATION DEVICE | August 2020 | March 2025 | Abandon | 55 | 3 | 0 | Yes | No |
| 16987808 | DISPLAYING CONTEXTUAL INFORMATION OF MEDIA | August 2020 | May 2025 | Allow | 57 | 4 | 0 | Yes | No |
| 16938402 | SUBGRAPH GUIDED KNOWLEDGE GRAPH QUESTION GENERATION | July 2020 | October 2025 | Abandon | 60 | 4 | 0 | Yes | No |
| 16928474 | ANOMALY DETECTION IN NETWORK TOPOLOGY | July 2020 | September 2024 | Allow | 50 | 2 | 0 | Yes | No |
| 16918484 | UNSUPERVISED CONCEPT DISCOVERY AND CROSS-MODAL RETRIEVAL IN TIME SERIES AND TEXT COMMENTS BASED ON CANONICAL CORRELATION ANALYSIS | July 2020 | March 2024 | Abandon | 45 | 2 | 0 | Yes | No |
| 16908499 | PREDICTIVE ANALYTIC METHOD FOR PATTERN AND TREND RECOGNITION IN DATASETS | June 2020 | July 2024 | Abandon | 49 | 1 | 0 | No | No |
| 16889695 | GBDT MODEL FEATURE INTERPRETATION METHOD AND APPARATUS | June 2020 | August 2021 | Allow | 14 | 2 | 0 | Yes | No |
| 16859811 | NEURAL PROGRAMMING | April 2020 | June 2023 | Allow | 37 | 2 | 0 | Yes | No |
| 16747076 | WINOGRAD TRANSFORM CONVOLUTION OPERATIONS FOR NEURAL NETWORKS | January 2020 | December 2024 | Abandon | 59 | 4 | 0 | Yes | No |
| 16632328 | METHODS, SYSTEMS AND NON-TRANSITORY COMPUTER READABLE MEDIA FOR AUTOMATED DESIGN OF MOLECULES WITH DESIRED PROPERTIES USING ARTIFICIAL INTELLIGENCE | January 2020 | June 2025 | Allow | 60 | 4 | 0 | Yes | No |
| 16744824 | DUAL CAPACITOR MIXED SIGNAL MUTIPLIER | January 2020 | August 2024 | Allow | 55 | 2 | 0 | Yes | No |
| 16744327 | UPDATE OF ATTENUATION COEFFICIENT FOR A MODEL CORRESPONDING TO TIME-SERIES INPUT DATA | January 2020 | May 2022 | Allow | 28 | 1 | 0 | Yes | No |
| 16741824 | MACHINE LEARNING MODEL VALIDATION AND AUTHENTICATION | January 2020 | June 2024 | Allow | 53 | 2 | 0 | Yes | No |
| 16731854 | Hierarchy Optimization Method for Machine Learning | December 2019 | October 2023 | Allow | 45 | 2 | 0 | Yes | No |
| 16570284 | DEEP NEURAL NETWORK BASED USER SEGMENTATION | September 2019 | January 2024 | Allow | 53 | 1 | 0 | Yes | No |
| 16527755 | LEARNING DEVICE AND LEARNING METHOD | July 2019 | May 2024 | Allow | 57 | 2 | 0 | Yes | No |
| 16525777 | RETRIEVAL OF FREQUENCY ASKED QUESTIONS USING ATTENTIVE MATCHING | July 2019 | May 2023 | Allow | 45 | 3 | 0 | Yes | No |
| 16515521 | METHOD FOR INTEGRATING PREDICTION RESULT | July 2019 | May 2022 | Abandon | 34 | 2 | 0 | Yes | No |
| 16457884 | DECISION TREE TRAINING USING A DATABASE SYSTEM | June 2019 | May 2025 | Allow | 60 | 8 | 0 | Yes | No |
| 16453380 | Dataset Dependent Low Rank Decomposition Of Neural Networks | June 2019 | October 2025 | Allow | 60 | 7 | 0 | Yes | No |
| 16417600 | PREDICTING THE DISASTER RECOVERY INVOCATION RESPONSE TIME | May 2019 | February 2023 | Allow | 45 | 4 | 0 | Yes | No |
| 16462196 | METHODS AND SYSTEMS FOR ENGINEERING COLLAGEN | May 2019 | December 2023 | Abandon | 55 | 5 | 0 | Yes | No |
| 16373913 | METHOD AND APPARATUS FOR STOCHASTIC INFERENCE BETWEEN MULTIPLE RANDOM VARIABLES VIA COMMON REPRESENTATION | April 2019 | November 2022 | Allow | 44 | 4 | 0 | Yes | No |
| 16374738 | METHODS AND DEVICES FOR OPTIMIZING MACHINE LEARNING MODEL COMPACTNESS AND ACCURACY THROUGH HARDWARE LATENCY HYSTERESIS EFFECT | April 2019 | January 2024 | Abandon | 57 | 2 | 0 | Yes | No |
| 16358701 | METHOD FOR EXECUTING ACTIVATION FUNCTION FOR DEEP LEARNING ALGORITHM, AND APPARATUS FOR EXECUTING SAID METHOD | March 2019 | December 2022 | Abandon | 45 | 2 | 0 | No | No |
| 16352587 | FAILED AND CENSORED INSTANCES BASED REMAINING USEFUL LIFE (RUL) ESTIMATION OF ENTITIES | March 2019 | December 2022 | Allow | 45 | 2 | 0 | Yes | No |
| 16299225 | DEEP FOREST MODEL DEVELOPMENT AND TRAINING | March 2019 | November 2023 | Allow | 57 | 4 | 0 | Yes | No |
| 16280292 | FAULT DIAGNOSIS APPARATUS AND MACHINE LEARNING DEVICE | February 2019 | November 2023 | Abandon | 57 | 5 | 0 | Yes | No |
| 16269639 | METHOD FOR ADJUSTING TREATMENT BY USING ADJUSTED CONTINUOUS VARIABLES AND METHOD AND APPARATUS FOR ADJUSTING TREATMENT BY ANALYZING CORRELATIONS USING THE SAME | February 2019 | February 2023 | Allow | 48 | 3 | 0 | Yes | No |
| 16266806 | AVOIDING DEAD ENDS IN REAL-TIME HEURISTIC SEARCH | February 2019 | May 2024 | Abandon | 60 | 3 | 0 | Yes | No |
| 16260726 | INCREMENTAL AI FIRMWARE UPDATES USING IN-DEVICE TRAINING AND PEER-TO-PEER UPDATES | January 2019 | March 2024 | Abandon | 60 | 5 | 0 | No | No |
| 16314374 | Binning for Nonlinear Modeling | December 2018 | April 2023 | Allow | 52 | 1 | 0 | Yes | No |
| 16226521 | HYBRID NEURAL NETWORK PRUNING | December 2018 | November 2024 | Allow | 60 | 6 | 0 | Yes | No |
| 16310851 | INFORMATION PROCESSING SYSTEM, INFORMATION PROCESSING METHOD, AND RECORDING MEDIUM | December 2018 | April 2023 | Abandon | 52 | 2 | 0 | Yes | No |
| 16216296 | UTILIZING RECURRENT NEURAL NETWORKS TO RECOGNIZE AND EXTRACT OPEN INTENT FROM TEXT INPUTS | December 2018 | January 2024 | Allow | 60 | 4 | 0 | Yes | No |
| 16203124 | METHODS OF PROCESSING AND GENERATING IMAGE DATA IN A CONNECTIONIST NETWORK | November 2018 | May 2022 | Allow | 42 | 1 | 0 | Yes | No |
| 16198853 | Automated Personalized Classification of Journey Data Captured by One or More Movement-Sensing Devices | November 2018 | October 2022 | Allow | 47 | 2 | 0 | Yes | No |
| 16139386 | METHOD AND SYSTEM FOR SERVICE AGENT ASSISTANCE OF ARTICLE RECOMMENDATIONS TO A CUSTOMER IN AN APP SESSION | September 2018 | September 2022 | Allow | 48 | 2 | 0 | Yes | No |
| 16085315 | METHOD FOR DYNAMICALLY SELECTING OPTIMAL MODEL BY THREE-LAYER ASSOCIATION FOR LARGE DATA VOLUME PREDICTION | September 2018 | February 2024 | Abandon | 60 | 4 | 0 | No | No |
| 16033460 | NEURAL NETWORK MODEL FOR PREDICTING USAGE IN A HYPER-CONVERGED INFRASTRUCTURE | July 2018 | May 2024 | Allow | 60 | 4 | 0 | Yes | Yes |
| 15921737 | FEATURE-BASED SELECTIVE CONTROL OF A NEURAL NETWORK | March 2018 | August 2022 | Abandon | 53 | 1 | 0 | No | No |
| 15905988 | OPTIMIZING COMBINED SENSORY DATA BY UTILITARIAN SELECTION OF A SUBGROUP OF AVAILABLE SENSORS | February 2018 | March 2025 | Allow | 60 | 6 | 0 | Yes | No |
| 15892890 | NEURAL NETWORK COMPRESSION | February 2018 | December 2023 | Allow | 60 | 3 | 0 | Yes | No |
| 15886109 | ESTABLISHING A LOGICAL CONNECTION BETWEEN AN INDIRECT UTTERANCE AND A TRANSACTION | February 2018 | February 2024 | Allow | 60 | 4 | 0 | Yes | No |
| 15843900 | MALWARE DETECTION AND CLASSIFICATION USING ARTIFICIAL NEURAL NETWORK | December 2017 | January 2025 | Abandon | 60 | 6 | 0 | Yes | No |
| 15833985 | LAYER-LEVEL QUANTIZATION IN NEURAL NETWORKS | December 2017 | August 2023 | Abandon | 60 | 5 | 0 | Yes | No |
| 15830067 | METHOD AND DEVICE FOR A COMPUTERIZED MECHANICAL DEVICE | December 2017 | May 2022 | Allow | 54 | 3 | 0 | Yes | No |
| 15830710 | DATA-DRIVEN GHOSTING USING DEEP IMITATION LEARNING | December 2017 | August 2024 | Allow | 60 | 6 | 0 | Yes | No |
| 15804197 | NEURAL NETWORK COOPERATION | November 2017 | November 2022 | Allow | 60 | 4 | 0 | Yes | No |
| 15715460 | ARTIFICIAL INTELLIGENCE DEVELOPMENT VIA USER-SELECTABLE/CONNECTABLE MODEL REPRESENTATIONS | September 2017 | March 2023 | Allow | 60 | 4 | 0 | Yes | No |
| 15671829 | Zero Coefficient Skipping Convolution Neural Network Engine | August 2017 | January 2022 | Abandon | 53 | 1 | 0 | No | No |
| 15620926 | Data Processing System with Machine Learning Engine to Provide Profile Generation and Event Control Functions | June 2017 | March 2022 | Abandon | 57 | 3 | 0 | Yes | No |
| 15620510 | METHODS AND SYSTEMS FOR DETERMINING PAST ACTIVITIES OF A USER AND TRIGGERING AN ACTION ACCORDINGLY | June 2017 | November 2021 | Abandon | 53 | 2 | 0 | Yes | No |
| 15620400 | AUTOMATED COMPREHENSION AND INTEREST-BASED OPTIMIZATION OF CONTENT | June 2017 | July 2023 | Allow | 60 | 5 | 0 | Yes | Yes |
| 15612777 | COMMUNICATION REPLY BOT | June 2017 | November 2023 | Abandon | 60 | 8 | 0 | Yes | No |
| 15591235 | ENTITY MODEL ESTABLISHMENT | May 2017 | August 2021 | Allow | 51 | 3 | 0 | Yes | No |
| 15476685 | COLLABORATIVE PERSONALIZATION VIA SIMULTANEOUS EMBEDDING OF USERS AND THEIR PREFERENCES | March 2017 | June 2022 | Allow | 60 | 6 | 0 | Yes | No |
| 15476291 | ENTITY RESOLUTION FOR THE INTERNET OF THINGS | March 2017 | December 2021 | Allow | 56 | 4 | 0 | Yes | No |
This analysis examines appeal outcomes and the strategic value of filing appeals for examiner KIM, SEHWAN.
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, 40.0% 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.
✓ Filing a Notice of Appeal is strategically valuable. The act of filing often prompts favorable reconsideration during the mandatory appeal conference.
Examiner KIM, SEHWAN works in Art Unit 2129 and has examined 95 patent applications in our dataset. With an allowance rate of 62.1%, this examiner allows applications at a lower rate than most examiners at the USPTO. Applications typically reach final disposition in approximately 55 months.
Examiner KIM, SEHWAN's allowance rate of 62.1% places them in the 22% percentile among all USPTO examiners. This examiner is less likely to allow applications than most examiners at the USPTO.
On average, applications examined by KIM, SEHWAN receive 3.33 office actions before reaching final disposition. This places the examiner in the 92% 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 KIM, SEHWAN is 55 months. This places the examiner in the 2% percentile for prosecution speed. Applications take longer to reach final disposition with this examiner compared to most others.
Conducting an examiner interview provides a +72.0% benefit to allowance rate for applications examined by KIM, SEHWAN. This interview benefit is in the 99% 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, 20.1% of applications are subsequently allowed. This success rate is in the 22% 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 3.3% of cases where such amendments are filed. This entry rate is in the 5% 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, 50.0% result in withdrawal of the rejection or reopening of prosecution. This success rate is in the 43% 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 100.0% of appeals filed. This is in the 89% percentile among all examiners. Of these withdrawals, 33.3% occur early in the appeal process (after Notice of Appeal but before Appeal Brief). Strategic Insight: This examiner frequently reconsiders rejections during the appeal process compared to other examiners. Per MPEP § 1207.01, all appeals must go through a mandatory appeal conference. Filing a Notice of Appeal may prompt favorable reconsideration even before you file an Appeal Brief.
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 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.