Detailed information about the 100 most recent patent applications.
| Application Number | Title | Filing Date | Disposal Date | Disposition | Time (months) | Office Actions | Restrictions | Interview | Appeal |
|---|---|---|---|---|---|---|---|---|---|
| 18799810 | ARCHITECTURES, SYSTEMS AND METHODS FOR PROGRAM DEFINED TRANSACTION SYSTEM AND DECENTRALIZED CRYPTOCURRENCY SYSTEM | August 2024 | October 2024 | Allow | 2 | 0 | 0 | Yes | No |
| 18639596 | Systems and Methods for Mitigating Hindsight Bias Related to Training and Using Artificial Intelligence Models for Outlier Events By Applying Model Constraints to a Synthetic Data Generator Model | April 2024 | May 2025 | Abandon | 13 | 2 | 0 | Yes | No |
| 18543804 | ITERATED TRAINING OF MACHINE MODELS WITH DEDUPLICATION | December 2023 | October 2024 | Allow | 10 | 0 | 0 | Yes | No |
| 18518136 | SYSTEM AND METHOD FOR PREDICTING THE PRESENCE OF AN ENTITY AT CERTAIN LOCATIONS | November 2023 | February 2025 | Abandon | 15 | 2 | 0 | Yes | No |
| 18478557 | SYSTEMS AND METHODS FOR MONITORING COMPLIANCE OF ARTIFICIAL INTELLIGENCE MODELS USING AN OBSERVER MODEL | September 2023 | April 2025 | Abandon | 19 | 2 | 0 | Yes | Yes |
| 18459320 | TRAINING ENSEMBLE MODELS TO IMPROVE PERFORMANCE IN THE PRESENCE OF UNRELIABLE BASE CLASSIFIERS | August 2023 | March 2025 | Abandon | 18 | 1 | 0 | Yes | No |
| 18345998 | DEVICE AND METHOD FOR PROVIDING BENCHMARK RESULT OF ARTIFICIAL INTELLIGENCE BASED MODEL | June 2023 | May 2025 | Abandon | 23 | 4 | 0 | Yes | No |
| 18203481 | QUANTUM REINFORCEMENT LEARNING FOR TARGET QUANTUM SYSTEM CONTROL | May 2023 | December 2024 | Abandon | 18 | 3 | 0 | Yes | No |
| 18134787 | USING ARTIFICIAL INTELLIGENCE TO DESIGN A PRODUCT | April 2023 | March 2025 | Abandon | 23 | 3 | 0 | Yes | No |
| 18102662 | SYNTHETIC DATA CREATION USING COUNTERFACTUALS | January 2023 | January 2025 | Abandon | 24 | 4 | 0 | Yes | No |
| 17330073 | MACHINE LEARNING FEATURE RECOMMENDATION | May 2021 | May 2025 | Abandon | 48 | 2 | 0 | Yes | No |
| 17063762 | ANSWER MANAGEMENT IN A QUESTION-ANSWERING ENVIRONMENT | October 2020 | November 2024 | Abandon | 50 | 4 | 0 | Yes | No |
| 17023582 | NEXT ACTION RECOMMENDATION SYSTEM | September 2020 | May 2025 | Abandon | 55 | 2 | 0 | No | No |
| 17016543 | ENTITY MODIFICATION OF MODELS | September 2020 | May 2025 | Abandon | 56 | 2 | 0 | No | No |
| 16931906 | MACHINE LEARNING FEATURE RECOMMENDATION | July 2020 | May 2025 | Abandon | 58 | 6 | 0 | Yes | No |
| 16960266 | VISUAL INTERPRETATION METHOD AND DEVICE FOR LOGISTIC REGRESSION MODEL | July 2020 | January 2025 | Abandon | 54 | 4 | 0 | Yes | No |
| 16813510 | AUTOMATIC DETECTION AND ASSOCIATION OF NEW ATTRIBUTES WITH ENTITIES IN KNOWLEDGE BASES | March 2020 | May 2025 | Abandon | 60 | 6 | 0 | Yes | No |
| 16507465 | DYNAMIC GENERATION OF RULE AND LOGIC STATEMENTS | July 2019 | November 2024 | Abandon | 60 | 6 | 0 | Yes | No |
| 16411767 | Homeostatic Capacity Evaluation of Artificial Intelligence Systems | May 2019 | April 2025 | Abandon | 60 | 7 | 0 | Yes | No |
| 16201232 | DYNAMIC RULE EXECUTION ORDER | November 2018 | December 2024 | Abandon | 60 | 4 | 0 | Yes | Yes |
| 16159486 | Cohort Event Prediction in a Digital Medium Environment using Regularization | October 2018 | November 2024 | Abandon | 60 | 3 | 0 | Yes | Yes |
| 15659904 | Forecasting Run Rate Revenue with Limited and Volatile Historical Data Using Self-Learning Blended Time Series Techniques | July 2017 | May 2025 | Abandon | 60 | 6 | 0 | No | Yes |
| 15604226 | SYSTEM AND METHOD FOR GENERATING MACHINE-CURATED SCENES | May 2017 | April 2019 | Allow | 23 | 3 | 0 | Yes | No |
| 15341147 | GENERATION APPARATUS, GENERATION METHOD, AND PROGRAM | November 2016 | December 2018 | Allow | 26 | 4 | 0 | Yes | No |
| 15042651 | COMPOSITE PROPENSITY PROFILE DETECTOR | February 2016 | March 2020 | Allow | 49 | 1 | 0 | Yes | No |
| 14956513 | SIGNIFICANCE OF RELATIONSHIPS DISCOVERED IN A CORPUS | December 2015 | December 2017 | Allow | 25 | 4 | 0 | Yes | No |
| 14920304 | ANNEALED DROPOUT TRAINING OF NEURAL NETWORKS | October 2015 | March 2019 | Allow | 41 | 0 | 0 | Yes | No |
| 14868442 | GENERATION APPARATUS, GENERATION METHOD, AND PROGRAM | September 2015 | December 2018 | Allow | 39 | 4 | 0 | Yes | No |
| 14867103 | PROBABILISTIC INFERENCE ENGINE BASED ON SYNTHETIC EVENTS FROM MEASURED DATA | September 2015 | February 2019 | Allow | 41 | 2 | 0 | No | No |
| 14857151 | HIERARCHICAL BUSINESS RULE MODEL | September 2015 | September 2019 | Allow | 48 | 2 | 0 | Yes | No |
| 14842348 | ANNEALED DROPOUT TRAINING OF NEURAL NETWORKS | September 2015 | March 2019 | Allow | 43 | 2 | 0 | Yes | No |
| 14814693 | ESTIMATING THE TIME UNTIL A REPLY EMAIL WILL BE RECEIVED USING A RECIPIENT BEHAVIOR MODEL | July 2015 | June 2016 | Allow | 10 | 1 | 0 | No | No |
| 14806879 | PREDICTING CAPACITY BASED UPON DATABASE ELEMENTS | July 2015 | September 2019 | Allow | 49 | 4 | 0 | Yes | No |
| 14650125 | MONITORING CONTROL APPARATUS AND MONITORING CONTROL METHOD | June 2015 | January 2019 | Allow | 43 | 1 | 0 | No | No |
| 14634203 | Deep Convolutional Neural Networks for Automated Scoring of Constructed Responses | February 2015 | March 2019 | Allow | 49 | 2 | 0 | Yes | No |
| 14332573 | EXPANDING AN ANSWER KEY TO VERIFY A QUESTION AND ANSWER SYSTEM | July 2014 | October 2019 | Allow | 60 | 3 | 0 | No | Yes |
| 14313906 | JOINT MODELING OF USER BEHAVIOR | June 2014 | March 2019 | Allow | 57 | 5 | 0 | Yes | No |
| 14266959 | Predicting and Enhancing Document Ingestion Time | May 2014 | September 2016 | Allow | 29 | 1 | 0 | No | No |
| 14146219 | PRODUCTION RULE ENGINE | January 2014 | June 2017 | Allow | 41 | 3 | 0 | Yes | No |
| 14135077 | SYSTEM RECOMMENDATIONS BASED ON INCIDENT ANALYSIS | December 2013 | April 2016 | Allow | 28 | 1 | 0 | Yes | No |
| 14109626 | EXPANDING AN ANSWER KEY TO VERIFY A QUESTION AND ANSWER SYSTEM | December 2013 | April 2016 | Allow | 28 | 2 | 0 | No | No |
| 14070679 | PIECEWISE LINEAR NEURON MODELING | November 2013 | August 2016 | Allow | 34 | 1 | 0 | No | No |
| 14057142 | STATISTICAL ESTIMATION OF ORIGIN AND DESTINATION POINTS OF TRIP USING PLURALITY OF TYPES OF DATA SOURCES | October 2013 | November 2015 | Allow | 25 | 1 | 0 | No | No |
| 14030708 | TRANSDUCTIVE FEATURE SELECTION WITH MAXIMUM-RELEVANCY AND MINIMUM-REDUNDANCY CRITERIA | September 2013 | June 2016 | Allow | 33 | 1 | 0 | No | No |
| 13971402 | COMPOSITE PROPENSITY PROFILE DETECTOR | August 2013 | January 2016 | Allow | 29 | 2 | 0 | No | No |
| 13958024 | INFORMATION PROCESSING DEVICE AND INFORMATION PROCESSING METHOD | August 2013 | January 2016 | Allow | 29 | 1 | 0 | Yes | No |
| 13949995 | SYNAPSE MAINTENANCE IN THE DEVELOPMENTAL NETWORKS | July 2013 | April 2016 | Allow | 33 | 2 | 0 | No | No |
| 13884814 | SENSOR DETECTION DEVICE, CORRESPONDING DETECTION METHOD AND COMPUTER PROGRAM | May 2013 | September 2015 | Allow | 28 | 1 | 0 | Yes | No |
| 13849446 | METHOD AND SYSTEM FOR PROCESSING INCOMPATIBLE NUI DATA IN A MEANINGFUL AND PRODUCTIVE WAY | March 2013 | September 2015 | Allow | 30 | 1 | 0 | No | No |
| 13764010 | MEASURING SENSITIVITY OF A FACTOR IN A DECISION | February 2013 | January 2014 | Allow | 12 | 1 | 0 | No | No |
| 13747538 | DFA COMPRESSION AND EXECUTION | January 2013 | October 2015 | Allow | 32 | 1 | 0 | No | No |
| 13745930 | TRANSDUCTIVE FEATURE SELECTION WITH MAXIMUM-RELEVANCY AND MINIMUM-REDUNDANCY CRITERIA | January 2013 | February 2016 | Allow | 37 | 3 | 0 | No | No |
| 13810815 | PSEUDO MESSAGE RECOGNITION BASED ON ONTOLOGY REASONING | January 2013 | January 2016 | Allow | 36 | 2 | 0 | No | No |
| 13725463 | TIME-DIVISION MULTIPLEXED NEUROSYNAPTIC MODULE WITH IMPLICIT MEMORY ADDRESSING FOR IMPLEMENTING A UNIVERSAL SUBSTRATE OF ADAPTATION | December 2012 | February 2016 | Allow | 38 | 3 | 0 | No | No |
| 13722003 | ESTIMATING THE TIME UNTIL A REPLY EMAIL WILL BE RECEIVED USING A RECIPIENT BEHAVIOR MODEL | December 2012 | May 2016 | Allow | 41 | 1 | 0 | No | No |
| 13710708 | METHOD OF ANSWERING QUESTIONS AND SCORING ANSWERS USING STRUCTURED KNOWLEDGE MINED FROM A CORPUS OF DATA | December 2012 | November 2015 | Allow | 36 | 2 | 0 | No | No |
| 13652087 | TWO-STAGE MULTIPLE KERNEL LEARNING METHOD | October 2012 | June 2014 | Allow | 20 | 0 | 0 | No | No |
| 13627075 | ESTIMATING THE TIME UNTIL A REPLY EMAIL WILL BE RECEIVED USING A RECIPIENT BEHAVIOR MODEL | September 2012 | May 2015 | Allow | 32 | 1 | 0 | Yes | No |
| 13597264 | SELF ORGANIZING MAPS FOR VISUALIZING AN OBJECTIVE SPACE | August 2012 | March 2015 | Allow | 31 | 1 | 0 | No | No |
| 13596502 | SELECTING SOLUTION FOR CARBON EMISSION PREDICTION | August 2012 | October 2015 | Allow | 37 | 0 | 0 | No | No |
| 13559099 | INFORMATION PROCESSING DEVICE, INFORMATION PROCESSING METHOD, AND PROGRAM | July 2012 | September 2014 | Allow | 26 | 1 | 0 | No | No |
| 13531117 | SYSTEMS AND METHODS FOR SEMANTIC DATA INTEGRATION | June 2012 | March 2016 | Allow | 45 | 3 | 0 | Yes | No |
| 13489280 | APPARATUS AND METHODS FOR REINFORCEMENT LEARNING IN ARTIFICIAL NEURAL NETWORKS | June 2012 | October 2014 | Allow | 29 | 1 | 0 | No | No |
| 13483868 | DOCUMENT CLASSIFICATION WITH WEIGHTED SUPERVISED N-GRAM EMBEDDING | May 2012 | October 2014 | Allow | 28 | 1 | 0 | Yes | No |
| 13474083 | SYSTEMS AND METHODS FOR SELF-ADAPTIVE EPISODE MINING UNDER THE THRESHOLD USING DELAY ESTIMATION AND TEMPORAL DIVISION | May 2012 | September 2014 | Allow | 28 | 1 | 0 | No | No |
| 13456284 | SELECTING SOLUTION FOR CARBON EMISSION PREDICTION | April 2012 | April 2014 | Allow | 23 | 1 | 0 | No | No |
| 13266101 | SYSTEM AND METHOD FOR DETECTING ABNORMAL AUDIO EVENTS | March 2012 | September 2014 | Allow | 35 | 1 | 0 | No | No |
| 13417432 | METHODS FOR GENERATING MISSING RULES MATCHING A MINIMAL SET OF OBJECTS | March 2012 | January 2016 | Allow | 46 | 1 | 0 | No | No |
| 13402751 | AUTOMATICALLY TRIGGERING PREDICTIONS IN RECOMMENDATION SYSTEMS BASED ON AN ACTIVITY-PROBABILITY THRESHOLD | February 2012 | December 2013 | Allow | 22 | 0 | 0 | No | No |
| 13370811 | IDENTIFYING AND GENERATING BIOMETRIC COHORTS BASED ON BIOMETRIC SENSOR INPUT | February 2012 | June 2015 | Allow | 40 | 2 | 0 | No | Yes |
| 13322626 | Forecasting Hotspots using Predictive Visual Analytics Approach | January 2012 | August 2014 | Allow | 33 | 2 | 0 | No | No |
| 13351243 | PREDICTING DIAGNOSIS OF A PATIENT | January 2012 | November 2014 | Allow | 34 | 2 | 0 | Yes | No |
| 13305741 | EXPLOITING SPARSENESS IN TRAINING DEEP NEURAL NETWORKS | November 2011 | November 2013 | Allow | 24 | 1 | 0 | No | No |
| 13373129 | VERTICAL CURVE SYSTEM FOR SURFACE GRADING | November 2011 | March 2014 | Allow | 28 | 1 | 0 | No | No |
| 13289909 | INFERRING DEMOGRAPHICS FOR WEBSITE MEMBERS | November 2011 | May 2013 | Allow | 19 | 1 | 0 | No | No |
| 13265480 | OPTIMIZATION TECHNIQUE USING EVOLUTIONARY ALGORITHMS | October 2011 | July 2013 | Allow | 21 | 0 | 0 | No | No |
| 13141944 | System, Method and Computer Program for Pattern Based Intelligent Control, Monitoring and Automation | September 2011 | April 2016 | Allow | 58 | 5 | 0 | Yes | No |
| 13123626 | Method And Apparatus For Creating State Estimation Models In Machine Condition Monitoring | April 2011 | October 2013 | Allow | 30 | 1 | 0 | No | No |
| 13078984 | SYSTEM AND METHODS FOR FINDING HIDDEN TOPICS OF DOCUMENTS AND PREFERENCE RANKING DOCUMENTS | April 2011 | May 2013 | Allow | 25 | 0 | 0 | No | No |
| 13020203 | SYSTEMS AND METHODS FOR GENERATING MISSING RULES MATCHING A MINIMAL SET OF OBJECTS | February 2011 | April 2013 | Allow | 26 | 1 | 0 | Yes | No |
| 12960131 | GROUP VARIABLE SELECTION IN SPATIOTEMPORAL MODELING | December 2010 | August 2013 | Allow | 33 | 2 | 0 | Yes | No |
| 12879781 | INFORMATION PROCESSING APPARATUS AND INFORMATION PROCESSING METHOD | September 2010 | July 2014 | Allow | 46 | 4 | 0 | No | No |
| 12853760 | SYSTEMS AND METHODS FOR GENERATING LEADS IN A NETWORK BY PREDICTING PROPERTIES OF EXTERNAL NODES | August 2010 | June 2013 | Allow | 34 | 1 | 0 | No | No |
| 12851474 | METHOD OF GENERATING AN INTEGRATED FUZZY-BASED GUIDANCE LAW USING TABU SEARCH | August 2010 | August 2012 | Allow | 25 | 0 | 0 | No | No |
| 12792853 | LEARNING CONTROL SYSTEM AND LEARNING CONTROL METHOD | June 2010 | February 2013 | Allow | 33 | 1 | 0 | No | No |
| 12646289 | MEASURING SENSITIVITY OF A FACTOR IN A DECISION | December 2009 | December 2012 | Allow | 36 | 1 | 0 | No | No |
| 12646312 | MEASURING CHANGE DISTANCE OF A FACTOR IN A DECISION | December 2009 | December 2012 | Allow | 36 | 1 | 0 | No | No |
| 12645317 | PROCESS OF DIALOGUE AND DISCUSSION | December 2009 | February 2013 | Allow | 38 | 1 | 0 | No | No |
| 12559921 | MACHINE LEARNING USING RELATIONAL DATABASES | September 2009 | September 2012 | Allow | 36 | 1 | 0 | Yes | No |
| 12302886 | PATTERN MATCHING | July 2009 | December 2012 | Allow | 48 | 1 | 0 | No | No |
| 12488881 | SYSTEM AND ASSOCIATED METHOD FOR DETERMINING AND APPLYING SOCIOCULTURAL CHARACTERISTICS | June 2009 | November 2012 | Allow | 41 | 2 | 0 | No | Yes |
| 12489211 | RULE CREATION METHOD AND RULE CREATING APPARATUS | June 2009 | April 2013 | Allow | 45 | 2 | 0 | No | No |
| 12480831 | FEATURE VECTOR CLUSTERING | June 2009 | February 2013 | Allow | 44 | 3 | 0 | No | No |
| 12478140 | METHOD AND SYSTEM OF INTERACTION WITHIN BOTH REAL AND VIRTUAL WORLDS | June 2009 | December 2012 | Allow | 43 | 3 | 0 | No | No |
| 12477145 | CONTEXT-BASED FAILURE REPORTING FOR A CONSTRAINT SATISFACTION PROBLEM | June 2009 | December 2012 | Allow | 43 | 2 | 0 | Yes | No |
| 12476017 | METHODS AND SYSTEMS FOR CREATING, ACCESSING, AND COMMUNICATING CONTENT | June 2009 | September 2013 | Allow | 52 | 4 | 0 | No | No |
| 12312317 | METHOD OF DOWNLOADING USAGE PARAMETERS INTO AN APPARATUS, AND APPARATUS FOR IMPLEMENTING THE INVENTION | May 2009 | October 2013 | Allow | 53 | 5 | 0 | No | No |
| 12306563 | CYBERPERSONALITIES IN ARTIFICIAL REALITY | February 2009 | November 2013 | Allow | 59 | 3 | 0 | Yes | No |
| 12279785 | DECISION MAKING UNIT FOR AUTONOMOUS PLATFORM | January 2009 | February 2012 | Allow | 42 | 1 | 0 | No | No |
| 12344093 | LEARNING LATENT SEMANTIC SPACE FOR RANKING | December 2008 | January 2012 | Allow | 37 | 1 | 0 | Yes | No |
This analysis examines appeal outcomes and the strategic value of filing appeals for examiner SECK, ABABACAR.
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, 27.3% of applications that filed an appeal were subsequently allowed. This appeal filing benefit rate is below the USPTO average, suggesting that filing an appeal has limited effectiveness in prompting favorable reconsideration.
⚠ Appeals to PTAB face challenges. Ensure your case has strong merit before committing to full Board review.
⚠ Filing a Notice of Appeal shows limited benefit. Consider other strategies like interviews or amendments before appealing.
Examiner SECK, ABABACAR works in Art Unit 2147 and has examined 102 patent applications in our dataset. With an allowance rate of 81.4%, this examiner has a below-average tendency to allow applications. Applications typically reach final disposition in approximately 36 months.
Examiner SECK, ABABACAR's allowance rate of 81.4% places them in the 46% percentile among all USPTO examiners. This examiner has a below-average tendency to allow applications.
On average, applications examined by SECK, ABABACAR receive 2.13 office actions before reaching final disposition. This places the examiner in the 71% 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 SECK, ABABACAR is 36 months. This places the examiner in the 16% percentile for prosecution speed. Applications take longer to reach final disposition with this examiner compared to most others.
Conducting an examiner interview provides a -31.2% benefit to allowance rate for applications examined by SECK, ABABACAR. This interview benefit is in the 0% 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, 20.7% 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 43.6% of cases where such amendments are filed. This entry rate is in the 60% percentile among all examiners. Strategic Recommendation: This examiner shows above-average receptiveness to after-final amendments. If your amendments clearly overcome the rejections and do not raise new issues, consider filing after-final amendments before resorting to an RCE.
When applicants request a pre-appeal conference (PAC) with this examiner, 100.0% result in withdrawal of the rejection or reopening of prosecution. This success rate is in the 69% percentile among all examiners. Strategic Recommendation: Pre-appeal conferences show above-average effectiveness with this examiner. If you have strong arguments, a PAC request may result in favorable reconsideration.
This examiner withdraws rejections or reopens prosecution in 63.6% of appeals filed. This is in the 37% percentile among all examiners. Of these withdrawals, 57.1% 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, 18.2% are granted (fully or in part). This grant rate is in the 9% 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 1.0% of allowed cases (in the 68% 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 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.