Detailed information about the 100 most recent patent applications.
| Application Number | Title | Filing Date | Disposal Date | Disposition | Time (months) | Office Actions | Restrictions | Interview | Appeal |
|---|---|---|---|---|---|---|---|---|---|
| 19115468 | Method, System, and Computer Program Product for Universal Depth Graph Neural Networks | March 2025 | November 2025 | Allow | 7 | 1 | 0 | Yes | No |
| 19054662 | BIOLOGICAL NEURAL NETWORK SYSTEM AND METHODS | February 2025 | March 2026 | Allow | 13 | 2 | 0 | No | No |
| 18811034 | SYSTEM AND METHOD FOR AUTOMATED CONSOLIDATION AND DISTRIBUTION OF STRUCTURED DATA | August 2024 | March 2026 | Allow | 19 | 3 | 0 | Yes | No |
| 18765014 | SYSTEMS, METHODS, AND GRAPHICAL USER INTERFACES FOR MITIGATING BIAS IN A MACHINE LEARNING-BASED DECISIONING MODEL | July 2024 | August 2025 | Allow | 13 | 2 | 0 | Yes | No |
| 18509585 | SYSTEMS, METHODS, AND STORAGE MEDIA FOR TRAINING A MACHINE LEARNING MODEL | November 2023 | August 2025 | Allow | 21 | 5 | 0 | Yes | No |
| 18312692 | MACHINE LEARNING TECHNIQUE FOR AUTOMATIC MODELING OF MULTIPLE-VALUED OUTPUTS | May 2023 | January 2025 | Allow | 21 | 0 | 0 | Yes | No |
| 18064708 | MONITORING CONSTRUCTION OF A STRUCTURE | December 2022 | December 2023 | Abandon | 12 | 1 | 0 | No | No |
| 18070100 | METHODS AND APPARATUS TO PERFORM MULTI-LEVEL HIERARCHICAL DEMOGRAPHIC CLASSIFICATION | November 2022 | July 2025 | Abandon | 32 | 4 | 0 | Yes | No |
| 18054446 | MIXTURE-OF-EXPERTS LAYER WITH SWITCHABLE PARALLEL MODES | November 2022 | March 2026 | Allow | 40 | 1 | 0 | Yes | No |
| 17888230 | ADVERSARIAL TRAINING OF NEURAL NETWORKS | August 2022 | January 2023 | Allow | 5 | 0 | 0 | No | No |
| 17748173 | Pattern Identification in Time-Series Social Media Data, and Output-Dynamics Engineering for a Dynamic System Having One or More Multi-Scale Time-Series Data Sets | May 2022 | March 2025 | Abandon | 34 | 4 | 0 | No | No |
| 17493228 | Systems And Methods For Performing Automatic Label Smoothing Of Augmented Training Data | October 2021 | September 2025 | Abandon | 48 | 2 | 0 | Yes | No |
| 17489530 | SYSTEMS, METHODS, AND STORAGE MEDIA FOR TRAINING A MACHINE LEARNING MODEL | September 2021 | October 2025 | Abandon | 49 | 3 | 0 | Yes | Yes |
| 17432253 | BESPOKE DETECTION MODEL | August 2021 | July 2025 | Abandon | 47 | 4 | 0 | Yes | No |
| 17392299 | Method and System of Performing Diagnostic Flowchart | August 2021 | February 2025 | Allow | 43 | 2 | 0 | Yes | Yes |
| 17330160 | Temporal Topic Machine Learning Operation | May 2021 | May 2023 | Allow | 24 | 1 | 0 | No | No |
| 17316503 | SYSTEMS AND METHODS FOR IMAGE OR VIDEO PERFORMANCE HEAT MAP GENERATION | May 2021 | March 2025 | Allow | 46 | 2 | 0 | Yes | No |
| 17279834 | DATA PROCESSING APPARATUS, DATA PROCESSING METHOD, AND PROGRAM | March 2021 | September 2025 | Abandon | 53 | 2 | 0 | Yes | No |
| 17206787 | COMPUTER SYSTEM AND CONTRIBUTION CALCULATION METHOD | March 2021 | July 2025 | Abandon | 52 | 2 | 0 | No | No |
| 17276767 | DATA PROCESSING APPARATUS, DATA PROCESSING METHOD, AND PROGRAM | March 2021 | November 2025 | Abandon | 56 | 3 | 0 | Yes | No |
| 17275459 | DEEP REINFORCEMENT LEARNING-BASED TECHNIQUES FOR END TO END ROBOT NAVIGATION | March 2021 | March 2026 | Allow | 60 | 2 | 0 | Yes | Yes |
| 17195835 | METHOD AND SYSTEM FOR LEARNING NEURAL NETWORK AND DEVICE | March 2021 | January 2025 | Allow | 46 | 2 | 0 | Yes | No |
| 17184750 | FOD Mitigation System and Method | February 2021 | April 2024 | Abandon | 38 | 1 | 0 | No | No |
| 17180976 | NEURAL NETWORKS FOR HANDLING VARIABLE-DIMENSIONAL TIME SERIES DATA | February 2021 | August 2024 | Allow | 42 | 2 | 0 | No | No |
| 17181101 | INFERENCE APPARATUS, METHOD, NON-TRANSITORY COMPUTER READABLE MEDIUM AND LEARNING APPARATUS | February 2021 | March 2026 | Abandon | 60 | 4 | 0 | Yes | No |
| 17180057 | Embedded Multi-Attribute Machine Learning For Storage Devices | February 2021 | July 2025 | Allow | 52 | 3 | 0 | Yes | No |
| 17153453 | NEUROMETRIC AUTHENTICATION SYSTEM | January 2021 | October 2025 | Allow | 56 | 4 | 0 | Yes | No |
| 17147362 | ADVERSARIAL LEARNING OF PRIVACY PRESERVING REPRESENTATIONS | January 2021 | February 2026 | Allow | 60 | 3 | 0 | No | No |
| 17139601 | AUGMENTED GAMMA BELIEF NETWORK OPERATION | December 2020 | April 2023 | Allow | 28 | 1 | 0 | No | No |
| 17139125 | ANALOG CIRCUITS FOR IMPLEMENTING BRAIN EMULATION NEURAL NETWORKS | December 2020 | August 2024 | Abandon | 44 | 1 | 0 | No | No |
| 17119288 | RECURRENT NEURAL NETWORK ARCHITECTURES BASED ON SYNAPTIC CONNECTIVITY GRAPHS | December 2020 | March 2024 | Abandon | 39 | 1 | 0 | No | No |
| 17118631 | METHOD AND COMPUTER IMPLEMENTED SYSTEM FOR GENERATING LAYOUT PLAN USING NEURAL NETWORK | December 2020 | August 2024 | Abandon | 45 | 1 | 0 | No | No |
| 17109107 | NEURAL NETWORK CORRECTION FOR LASER CURRENT DRIVER | December 2020 | November 2023 | Abandon | 36 | 1 | 0 | No | No |
| 17105552 | SIMULATED DEEP LEARNING METHOD BASED ON SDL MODEL | November 2020 | March 2025 | Abandon | 52 | 4 | 0 | No | No |
| 17104616 | SYSTEMS AND METHODS FOR AUTOMATIC MODEL GENERATION | November 2020 | December 2025 | Abandon | 60 | 4 | 0 | Yes | No |
| 17098049 | PHASE SELECTIVE CONVOLUTION WITH DYNAMIC WEIGHT SELECTION | November 2020 | August 2025 | Allow | 58 | 5 | 0 | Yes | No |
| 17080656 | JOINT MANY-TASK NEURAL NETWORK MODEL FOR MULTIPLE NATURAL LANGUAGE PROCESSING (NLP) TASKS | October 2020 | January 2023 | Allow | 27 | 1 | 0 | Yes | No |
| 17073547 | MULTI-AGENT COORDINATION METHOD AND APPARATUS | October 2020 | December 2023 | Allow | 38 | 1 | 0 | No | No |
| 17044607 | END-TO-END LEARNING IN COMMUNICATION SYSTEMS | October 2020 | December 2024 | Abandon | 50 | 2 | 0 | No | No |
| 17035795 | Software Defined Redundant Allocation Safety Mechanism In An Artificial Neural Network Processor | September 2020 | January 2025 | Allow | 52 | 5 | 0 | Yes | No |
| 16995073 | DEVICE AND METHOD FOR GENERATING A COUNTERFACTUAL DATA SAMPLE FOR A NEURAL NETWORK | August 2020 | March 2024 | Abandon | 43 | 2 | 0 | No | No |
| 16944845 | Automatic Transmission Method | July 2020 | August 2024 | Abandon | 49 | 2 | 0 | No | No |
| 16961073 | METHOD FOR GPU MEMORY MANAGEMENT FOR DEEP NEURAL NETWORK AND COMPUTING DEVICE FOR PERFORMING SAME | July 2020 | September 2025 | Abandon | 60 | 4 | 0 | No | No |
| 16961121 | PARAMETER CALCULATING DEVICE, PARAMETER CALCULATING METHOD, AND RECORDING MEDIUM HAVING PARAMETER CALCULATING PROGRAM RECORDED THEREON | July 2020 | January 2025 | Abandon | 54 | 2 | 0 | Yes | No |
| 16923270 | MACHINE LEARNING SYSTEMS FOR PREDICTING UNENROLLMENT IN CLAIMS PROCESSING | July 2020 | July 2025 | Allow | 60 | 6 | 0 | Yes | No |
| 16946790 | DATA ENLARGEMENT FOR BIG DATA ANALYTICS AND SYSTEM IDENTIFICATION | July 2020 | January 2026 | Abandon | 60 | 6 | 0 | Yes | No |
| 16770928 | RESIDUAL BINARY NEURAL NETWORK | June 2020 | October 2024 | Abandon | 53 | 4 | 0 | Yes | No |
| 16875041 | SYSTEMS AND METHODS FOR PREDICTING PAIN LEVEL | May 2020 | August 2022 | Allow | 27 | 5 | 0 | No | No |
| 16871537 | PREDICTING OPTICAL FIBER MANUFACTURING PERFORMANCE USING NEURAL NETWORK | May 2020 | March 2024 | Allow | 46 | 2 | 0 | No | No |
| 16865249 | Artificial Intelligence Techniques for Improving Efficiency | May 2020 | February 2022 | Allow | 22 | 3 | 0 | Yes | No |
| 16860830 | NEURAL NETWORK COMPUTING METHOD AND SYSTEM INCLUDING THE SAME | April 2020 | November 2025 | Abandon | 60 | 6 | 0 | Yes | No |
| 16786462 | NEURAL NETWORK DEVICE AND METHOD OF QUANTIZING PARAMETERS OF NEURAL NETWORK | February 2020 | March 2024 | Allow | 49 | 2 | 0 | Yes | No |
| 16697838 | GUIDED ROW INSERTION | November 2019 | January 2025 | Allow | 60 | 7 | 0 | Yes | No |
| 16692257 | Adversarial Training of Neural Networks | November 2019 | April 2022 | Allow | 29 | 1 | 0 | No | No |
| 16683634 | SYSTEMS AND METHODS FOR ALERTING TO MODEL DEGRADATION BASED ON SURVIVAL ANALYSIS | November 2019 | January 2025 | Abandon | 60 | 4 | 0 | Yes | No |
| 16677076 | ARTIFICIAL INTELLIGENCE FOR REFRIGERATION | November 2019 | March 2025 | Abandon | 60 | 6 | 0 | No | No |
| 16676229 | RADIO FREQUENCY BAND SEGMENTATION, SIGNAL DETECTION AND LABELLING USING MACHINE LEARNING | November 2019 | June 2025 | Allow | 60 | 5 | 0 | Yes | No |
| 16561896 | TRANSFER LEARNING WITH AUGMENTED NEURAL NETWORKS | September 2019 | September 2025 | Allow | 60 | 4 | 0 | Yes | Yes |
| 16552678 | AUTOMATIC GENERATION OF COMPUTING ARTIFACTS FOR DATA ANALYSIS | August 2019 | February 2026 | Abandon | 60 | 4 | 0 | Yes | Yes |
| 16536926 | System And Method For Heterogeneous Relational Kernel Learning | August 2019 | February 2022 | Allow | 30 | 3 | 0 | Yes | No |
| 16455334 | NEURAL NETWORK ACCELERATOR WITH RECONFIGURABLE MEMORY | June 2019 | August 2024 | Allow | 60 | 3 | 0 | Yes | No |
| 16394644 | SYSTEM AND METHOD FOR TRAINING A NEURAL NETWORK SYSTEM | April 2019 | May 2021 | Allow | 25 | 3 | 0 | Yes | No |
| 16373745 | MIXED-SIGNAL NEURONS FOR NEUROMORPHIC COMPUTING AND METHOD THEREOF | April 2019 | September 2024 | Allow | 60 | 4 | 0 | Yes | Yes |
| 16299104 | Artificial Intelligence Devices For Keywords Detection | March 2019 | February 2023 | Abandon | 47 | 1 | 0 | No | No |
| 16297449 | SPARSE ASSOCIATIVE MEMORY FOR IDENTIFICATION OF OBJECTS | March 2019 | September 2022 | Abandon | 43 | 5 | 0 | No | No |
| 16328182 | METHOD AND APPARATUS FOR REDUCING THE PARAMETER DENSITY OF A DEEP NEURAL NETWORK (DNN) | February 2019 | August 2023 | Allow | 54 | 4 | 0 | No | No |
| 16283021 | CONVOLUTIONAL NEURAL NETWORK OPTIMIZATION MECHANISM | February 2019 | March 2023 | Allow | 49 | 6 | 0 | Yes | No |
| 16236298 | SYSTEMS, METHODS, AND STORAGE MEDIA FOR TRAINING A MACHINE LEARNING MODEL | December 2018 | February 2022 | Allow | 37 | 5 | 0 | Yes | No |
| 16224145 | SYSTEMS FOR INTRODUCING MEMRISTOR RANDOM TELEGRAPH NOISE IN HOPFIELD NEURAL NETWORKS | December 2018 | October 2022 | Allow | 46 | 1 | 0 | No | No |
| 16223055 | System and Method for Training Artificial Neural Networks | December 2018 | August 2024 | Allow | 60 | 3 | 0 | Yes | No |
| 16211098 | Video Content Valuation Prediction Using A Prediction Network | December 2018 | February 2025 | Abandon | 60 | 6 | 0 | Yes | No |
| 16169840 | GRADIENT NORMALIZATION SYSTEMS AND METHODS FOR ADAPTIVE LOSS BALANCING IN DEEP MULTITASK NETWORKS | October 2018 | October 2022 | Allow | 48 | 3 | 0 | Yes | No |
| 15998632 | SYSTEM AND METHOD FOR GENERATING TIME-SPECTRAL DIAGRAMS IN AN INTEGRATED CIRCUIT SOLUTION | August 2018 | November 2022 | Abandon | 51 | 1 | 0 | No | No |
| 15780453 | Automated Decision Analysis by Model Operational Characteristic Curves | May 2018 | April 2022 | Allow | 47 | 4 | 0 | Yes | No |
| 15934091 | IN-FLIGHT SCALING OF MACHINE LEARNING TRAINING JOBS | March 2018 | December 2024 | Allow | 60 | 8 | 0 | Yes | No |
| 15761386 | COMPUTER SYSTEM INCORPORATING AN ADAPTIVE MODEL AND METHODS FOR TRAINING THE ADAPTIVE MODEL | March 2018 | September 2022 | Abandon | 54 | 0 | 1 | No | No |
| 15908420 | METHOD AND APPARATUS FOR MACHINE LEARNING | February 2018 | November 2020 | Allow | 33 | 3 | 0 | Yes | No |
| 15752469 | CO-CLUSTERING SYSTEM, METHOD AND PROGRAM | February 2018 | August 2021 | Abandon | 42 | 4 | 0 | No | No |
| 15869987 | NEURAL NETWORK TRAINING USING GENERATED RANDOM UNIT VECTOR | January 2018 | December 2021 | Allow | 47 | 2 | 0 | No | No |
| 15826430 | MACHINE LEARNING TECHNIQUE FOR AUTOMATIC MODELING OF MULTIPLE-VALUED OUTPUTS | November 2017 | April 2023 | Allow | 60 | 4 | 0 | Yes | Yes |
| 15820974 | COGNITIVE COMMUNICATION ASSISTANT SERVICES | November 2017 | December 2024 | Allow | 60 | 6 | 0 | Yes | No |
| 15812608 | CONVOLUTIONAL NEURAL NETWORK ON ANALOG NEURAL NETWORK CHIP | November 2017 | May 2022 | Abandon | 54 | 4 | 0 | Yes | No |
| 15800465 | LEARNING OF POLICY FOR SELECTION OF ASSOCIATIVE TOPIC IN DIALOG SYSTEM | November 2017 | December 2022 | Allow | 60 | 6 | 0 | Yes | No |
| 15720982 | INNER PRODUCT CONVOLUTIONAL NEURAL NETWORK ACCELERATOR | September 2017 | June 2024 | Allow | 60 | 5 | 0 | Yes | No |
| 15707830 | COGNITIVE MODELING APPARATUS INCLUDING MULTIPLE KNOWLEDGE NODE AND SUPERVISORY NODE DEVICES | September 2017 | March 2024 | Allow | 60 | 4 | 0 | No | No |
| 15550244 | AUTOMATED ACQUISITION OF A LOGICAL DEDUCTION PATH IN A MIVAR KNOWLEDGE BASE | August 2017 | March 2021 | Abandon | 43 | 1 | 0 | No | No |
| 15550302 | LEARNING FROM DISTRIBUTED DATA | August 2017 | October 2021 | Allow | 50 | 2 | 0 | Yes | No |
| 15548887 | DATA ANALYSIS SYSTEM, DATA ANALYSIS METHOD, AND DATA ANALYSIS PROGRAM | August 2017 | September 2022 | Abandon | 60 | 4 | 0 | No | No |
| 15650236 | SYSTEM AND METHOD FOR IDENTIFYING AND PROVIDING PERSONALIZED SELF-HELP CONTENT WITH ARTIFICIAL INTELLIGENCE IN A CUSTOMER SELF-HELP SYSTEM | July 2017 | July 2021 | Abandon | 48 | 2 | 0 | No | No |
| 15649348 | SYSTEM AND METHOD FOR DETECTING HOMOGLYPH ATTACKS WITH A SIAMESE CONVOLUTIONAL NEURAL NETWORK | July 2017 | November 2021 | Abandon | 52 | 3 | 0 | Yes | No |
| 15649492 | SYSTEMS AND METHODS FOR NEURAL EMBEDDING TRANSLATION | July 2017 | December 2022 | Allow | 60 | 5 | 0 | Yes | No |
| 15647543 | COOPERATIVE NEURAL NETWORK REINFORCEMENT LEARNING | July 2017 | April 2021 | Abandon | 45 | 2 | 0 | Yes | No |
| 15639997 | IN-MEMORY SPIKING NEURAL NETWORKS FOR MEMORY ARRAY ARCHITECTURES | June 2017 | February 2022 | Allow | 55 | 3 | 0 | Yes | No |
| 15626849 | ANSWERING QUESTIONS BASED ON SEMANTIC DISTANCES BETWEEN SUBJECTS | June 2017 | February 2022 | Allow | 55 | 4 | 0 | Yes | Yes |
| 15626362 | COGNITIVE COMMUNICATION ASSISTANT SERVICES | June 2017 | April 2022 | Allow | 57 | 4 | 0 | Yes | No |
| 15536783 | LEARNING APPARATUS, LEARNING METHOD, AND RECORDING MEDIUM | June 2017 | December 2022 | Allow | 60 | 5 | 0 | No | No |
| 15620116 | AUTOMATIC DETECTION OF INFORMATION FIELD RELIABILITY FOR A NEW DATA SOURCE | June 2017 | August 2022 | Abandon | 60 | 4 | 0 | Yes | No |
| 15618906 | CONVOLUTIONAL NEURAL NETWORK ON ANALOG NEURAL NETWORK CHIP | June 2017 | May 2022 | Abandon | 59 | 4 | 0 | Yes | No |
| 15610310 | MONITORING CONSTRUCTION OF A STRUCTURE | May 2017 | August 2022 | Allow | 60 | 3 | 0 | Yes | No |
| 15597242 | TRAINING A MACHINE LEARNING MODEL IN A DISTRIBUTED PRIVACY-PRESERVING ENVIRONMENT | May 2017 | June 2022 | Allow | 60 | 4 | 0 | Yes | No |
This analysis examines appeal outcomes and the strategic value of filing appeals for examiner NGUYEN, HENRY K.
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, 35.3% 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 NGUYEN, HENRY K works in Art Unit 2121 and has examined 148 patent applications in our dataset. With an allowance rate of 56.1%, this examiner allows applications at a lower rate than most examiners at the USPTO. Applications typically reach final disposition in approximately 54 months.
Examiner NGUYEN, HENRY K's allowance rate of 56.1% places them in the 17% percentile among all USPTO examiners. This examiner is less likely to allow applications than most examiners at the USPTO.
On average, applications examined by NGUYEN, HENRY K receive 3.63 office actions before reaching final disposition. This places the examiner in the 95% 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 NGUYEN, HENRY K is 54 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 +32.9% benefit to allowance rate for applications examined by NGUYEN, HENRY K. This interview benefit is in the 81% 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, 17.1% of applications are subsequently allowed. This success rate is in the 15% 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 8.6% of cases where such amendments are filed. This entry rate is in the 8% 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 83.3% of appeals filed. This is in the 76% percentile among all examiners. Of these withdrawals, 10.0% 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, 11.1% are granted (fully or in part). This grant rate is in the 8% 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 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.