Detailed information about the 100 most recent patent applications.
| Application Number | Title | Filing Date | Disposal Date | Disposition | Time (months) | Office Actions | Restrictions | Interview | Appeal |
|---|---|---|---|---|---|---|---|---|---|
| 18923458 | METHOD AND SYSTEM FOR LOCAL COMPRESSION OF ARTIFICIAL INTELLIGENCE MODEL | October 2024 | March 2025 | Allow | 5 | 1 | 0 | Yes | No |
| 18632180 | HIGH-DENSITY NEUROMORPHIC COMPUTING ELEMENT | April 2024 | February 2025 | Allow | 10 | 1 | 0 | No | No |
| 18431472 | Method, Device and Equipment for Selecting Key Geological Parameters of a To-Be-Prospected Block | February 2024 | October 2024 | Abandon | 8 | 2 | 0 | Yes | No |
| 18394205 | SYSTEMS AND METHODS FOR BLIND MULTIMODAL LEARNING | December 2023 | February 2025 | Allow | 14 | 1 | 0 | No | No |
| 18495902 | MACHINE LEARNING FOR NUTRIENT QUANTITY ESTIMATION IN SCORE-BASED DIETS AND METHODS OF USE THEREOF | October 2023 | April 2025 | Abandon | 18 | 1 | 0 | No | No |
| 18378649 | METHOD FOR GENERATING PROGRAMMABLE ACTIVATION FUNCTION AND APPARATUS USING THE SAME | October 2023 | March 2025 | Allow | 17 | 1 | 0 | No | No |
| 18461473 | FLEET AND ASSET MANAGEMENT FOR EDGE COMPUTING OF MACHINE LEARNING AND ARTIFICIAL INTELLIGENCE WORKLOADS DEPLOYED FROM CLOUD TO EDGE | September 2023 | June 2024 | Allow | 9 | 2 | 1 | Yes | No |
| 18226858 | System, Method, and Computer Program Product for Implementing a Hybrid Deep Neural Network Model to Determine a Market Strategy | July 2023 | May 2024 | Allow | 10 | 1 | 0 | No | No |
| 18209188 | TRADE PLATFORM WITH REINFORCEMENT LEARNING | June 2023 | August 2024 | Allow | 15 | 1 | 0 | No | No |
| 18207043 | PROCESSING MACHINE LEARNING ATTRIBUTES | June 2023 | April 2025 | Allow | 22 | 4 | 0 | Yes | No |
| 18329418 | TRANSPOSING NEURAL NETWORK MATRICES IN HARDWARE | June 2023 | September 2024 | Allow | 15 | 1 | 0 | No | No |
| 18324453 | DETERMINING JOURNALIST RISK OF A DATASET USING POPULATION EQUIVALENCE CLASS DISTRIBUTION ESTIMATION | May 2023 | April 2025 | Abandon | 23 | 3 | 0 | Yes | No |
| 18310658 | AUTOMATED SYSTEMS FOR MACHINE LEARNING MODEL DEVELOPMENT, ANALYSIS, AND REFINEMENT | May 2023 | October 2023 | Allow | 5 | 2 | 0 | Yes | No |
| 18308784 | METHOD AND SYSTEM FOR DETERMINING CONVERTER TAPPING QUANTITY | April 2023 | January 2024 | Allow | 9 | 2 | 0 | No | No |
| 18309106 | PARTITIONING SENSOR BASED DATA TO GENERATE DRIVING PATTERN MAP | April 2023 | May 2024 | Allow | 12 | 1 | 0 | No | No |
| 18303134 | NEURAL NETWORK FOR PROCESSING GRAPH DATA | April 2023 | May 2024 | Allow | 13 | 1 | 0 | No | No |
| 18133800 | SYSTEM AND METHOD FOR DEVICE IDENTIFICATION AND UNIQUENESS | April 2023 | March 2024 | Allow | 11 | 1 | 0 | No | No |
| 18111471 | HIGH-DENSITY NEUROMORPHIC COMPUTING ELEMENT | February 2023 | January 2024 | Allow | 11 | 0 | 0 | Yes | No |
| 18162204 | PROVISION OF COMPUTER RESOURCES BASED ON LOCATION HISTORY | January 2023 | April 2024 | Allow | 15 | 1 | 0 | No | No |
| 18103483 | EVALUATING MACHINE LEARNING MODEL PERFORMANCE BY LEVERAGING SYSTEM FAILURES | January 2023 | July 2023 | Allow | 6 | 1 | 0 | Yes | No |
| 18154551 | COMPUTERIZED ASSISTANCE USING ARTIFICIAL INTELLIGENCE KNOWLEDGE BASE | January 2023 | April 2024 | Allow | 15 | 1 | 0 | Yes | No |
| 18081721 | Controller training based on historical data | December 2022 | March 2024 | Allow | 15 | 1 | 0 | Yes | No |
| 18061697 | GRAPH CONVOLUTIONAL NETWORKS WITH MOTIF-BASED ATTENTION | December 2022 | September 2024 | Allow | 22 | 2 | 0 | Yes | No |
| 17981243 | SCALABLE SYSTEMS AND METHODS FOR CURATING USER EXPERIENCE TEST RESULTS | November 2022 | July 2023 | Allow | 9 | 2 | 0 | Yes | No |
| 17976679 | LEAN PARSING: A NATURAL LANGUAGE PROCESSING SYSTEM AND METHOD FOR PARSING DOMAIN-SPECIFIC LANGUAGES | October 2022 | March 2024 | Allow | 17 | 1 | 0 | Yes | No |
| 17975198 | CONTROL METHOD BASED ON ADAPTIVE NEURAL NETWORK MODEL FOR DISSOLVED OXYGEN OF AERATION SYSTEM | October 2022 | May 2023 | Allow | 7 | 2 | 0 | No | No |
| 18046906 | COMPUTER-IMPLEMENTED OR HARDWARE-IMPLEMENTED METHOD OF ENTITY IDENTIFICATION, A COMPUTER PROGRAM PRODUCT AND AN APPARATUS FOR ENTITY IDENTIFICATION | October 2022 | April 2023 | Allow | 6 | 1 | 0 | No | No |
| 17953517 | METHOD FOR MULTI-TASK-BASED PREDICTING MASSIVEUSER LOADS BASED ON MULTI-CHANNEL CONVOLUTIONAL NEURAL NETWORK | September 2022 | June 2023 | Abandon | 9 | 1 | 0 | No | No |
| 17940798 | MACHINE LEARNING FOR NUTRIENT QUANTITY ESTIMATION IN SCORE-BASED DIETS AND METHODS OF USE THEREOF | September 2022 | June 2023 | Allow | 9 | 2 | 0 | Yes | No |
| 17939351 | SYSTEMS AND METHODS FOR BLIND MULTIMODAL LEARNING | September 2022 | August 2023 | Allow | 11 | 1 | 0 | No | No |
| 17821910 | TRANSFERRING INFORMATION THROUGH KNOWLEDGE GRAPH EMBEDDINGS | August 2022 | June 2025 | Allow | 34 | 0 | 0 | No | No |
| 17795233 | NEURAL HASHING FOR SIMILARITY SEARCH | July 2022 | June 2023 | Allow | 10 | 1 | 0 | No | No |
| 17785429 | SYSTEM, METHOD, AND COMPUTER PROGRAM PRODUCT FOR TIME-BASED ENSEMBLE LEARNING USING SUPERVISED AND UNSUPERVISED MACHINE LEARNING MODELS | June 2022 | March 2024 | Allow | 21 | 2 | 0 | Yes | No |
| 17750568 | METHOD FOR GENERATING PROGRAMMABLE ACTIVATION FUNCTION AND APPARATUS USING THE SAME | May 2022 | July 2023 | Allow | 14 | 2 | 0 | No | No |
| 17744601 | NON-UNIFORM QUANTIZATION OF PRE-TRAINED DEEP NEURAL NETWORK | May 2022 | March 2023 | Allow | 10 | 1 | 0 | No | No |
| 17659015 | HARMONIC DENSELY CONNECTING METHOD OF BLOCK OF CONVOLUTIONAL NEURAL NETWORK MODEL AND SYSTEM THEREOF, AND NON-TRANSITORY TANGIBLE COMPUTER READABLE RECORDING MEDIUM | April 2022 | May 2025 | Allow | 37 | 0 | 0 | No | No |
| 17719314 | INTELLIGENT TOPIC SEGMENTATION WITHIN A COMMUNICATION SESSION | April 2022 | January 2025 | Allow | 33 | 2 | 0 | Yes | No |
| 17716945 | HIGH PERFORAMANCE MACHINE LEARNING INFERENCE FRAMEWORK FOR EDGE DEVICES | April 2022 | February 2023 | Allow | 11 | 1 | 0 | No | No |
| 17712876 | Localized Temporal Model Forecasting | April 2022 | April 2024 | Allow | 25 | 3 | 0 | Yes | No |
| 17706586 | ASYNCHRONOUS NEURAL NETWORK TRAINING | March 2022 | May 2024 | Allow | 26 | 2 | 0 | No | No |
| 17703935 | MULTI-CHANNEL PROTEIN VOXELIZATION TO PREDICT VARIANT PATHOGENICITY USING DEEP CONVOLUTIONAL NEURAL NETWORKS | March 2022 | May 2025 | Allow | 38 | 1 | 0 | No | No |
| 17681480 | Processing Machine Learning Attributes | February 2022 | March 2023 | Allow | 12 | 1 | 0 | No | No |
| 17677556 | HYPERBOLIC FUNCTIONS FOR MACHINE LEARNING ACCELERATION | February 2022 | April 2023 | Allow | 13 | 1 | 0 | Yes | No |
| 17635210 | A COMPUTER-IMPLEMENTED OR HARDWARE-IMPLEMENTED METHOD OF ENTITY IDENTIFICATION, A COMPUTER PROGRAM PRODUCT AND AN APPARATUS FOR ENTITY IDENTIFICATION | February 2022 | July 2022 | Allow | 5 | 0 | 0 | No | No |
| 17591161 | CORRECTING CONTENT GENERATED BY DEEP LEARNING | February 2022 | October 2024 | Allow | 33 | 1 | 0 | Yes | No |
| 17588887 | MULTIMODAL ASSISTANT UNDERSTANDING USING ON-SCREEN AND DEVICE CONTEXT | January 2022 | June 2025 | Allow | 41 | 5 | 0 | Yes | No |
| 17577006 | PTF-BASED METHOD FOR PREDICTING TARGET SOIL PROPERTY AND CONTENT | January 2022 | January 2023 | Allow | 12 | 2 | 0 | Yes | No |
| 17572867 | METHOD FOR LOADING MULTIPLE NEURAL NETWORK MODELS AND ELECTRONIC DEVICE | January 2022 | April 2025 | Allow | 39 | 0 | 0 | No | No |
| 17570927 | Message Suggestions | January 2022 | February 2024 | Allow | 25 | 3 | 0 | Yes | No |
| 17560350 | DETERMINING JOURNALIST RISK OF A DATASET USING POPULATION EQUIVALENCE CLASS DISTRIBUTION ESTIMATION | December 2021 | January 2023 | Allow | 13 | 1 | 0 | Yes | No |
| 17549231 | Method for Processing Information by Intelligent Agent and Intelligent Agent | December 2021 | March 2024 | Allow | 27 | 3 | 0 | Yes | No |
| 17543262 | SYSTEM AND METHOD FOR AUTOMATIC LEARNING OF FUNCTIONS | December 2021 | February 2023 | Allow | 15 | 1 | 0 | No | No |
| 17534976 | SYSTEM AND METHOD FOR BALANCING SPARSITY IN WEIGHTS FOR ACCELERATING DEEP NEURAL NETWORKS | November 2021 | March 2025 | Allow | 40 | 0 | 0 | No | No |
| 17534145 | SELECTIVELY IMPLEMENTING ROLE CHANGE REQUESTS FOR AUXILIARY DEVICES THAT FACILITATE ASSISTANT INTERACTIONS | November 2021 | September 2024 | Allow | 34 | 1 | 0 | No | No |
| 17456302 | SYSTEM AND METHOD FOR REPRESENTING QUERY ELEMENTS IN AN ARTIFICIAL NEURAL NETWORK | November 2021 | January 2023 | Allow | 14 | 1 | 0 | No | No |
| 17529690 | SYSTEMS AND METHODS FOR ADAPTIVE TRAINING NEURAL NETWORKS | November 2021 | June 2025 | Abandon | 43 | 1 | 0 | No | No |
| 17453983 | SEMANTIC SEGMENTATION NETWORK MODEL UNCERTAINTY QUANTIFICATION METHOD BASED ON EVIDENCE INFERENCE | November 2021 | March 2025 | Allow | 41 | 1 | 0 | No | No |
| 17519285 | ELECTRONIC APPARATUS FOR DECOMPRESSING A COMPRESSED ARTIFICIAL INTELLIGENCE MODEL AND CONTROL METHOD THEREFOR | November 2021 | February 2025 | Allow | 40 | 0 | 0 | No | No |
| 17514760 | MACHINE LEARNING SYSTEM | October 2021 | March 2023 | Allow | 16 | 1 | 0 | Yes | No |
| 17607390 | VOICE WAKEUP METHOD AND DEVICE | October 2021 | July 2024 | Allow | 32 | 0 | 0 | No | No |
| 17469144 | TARGETED GRADIENT DESCENT FOR CONVOLUTIONAL NEURAL NETWORKS FINE-TUNING AND ONLINE-LEARNING | September 2021 | June 2025 | Abandon | 46 | 1 | 0 | No | No |
| 17464116 | Partitioning Sensor Based Data to Generate Driving Pattern Map | September 2021 | January 2023 | Allow | 17 | 1 | 0 | No | No |
| 17462339 | SEARCH SYSTEM AND CORRESPONDING METHOD | August 2021 | September 2024 | Abandon | 37 | 4 | 0 | Yes | Yes |
| 17410622 | NEURAL NETWORK FOR PROCESSING GRAPH DATA | August 2021 | January 2023 | Allow | 17 | 1 | 0 | No | No |
| 17389661 | Electronic Meeting Intelligence | July 2021 | August 2023 | Allow | 25 | 2 | 0 | Yes | No |
| 17378634 | INTERRUPT FOR NOISE-CANCELLING AUDIO DEVICES | July 2021 | November 2024 | Allow | 40 | 2 | 0 | No | No |
| 17370043 | ARTIFICIAL INTELLIGENCE MODEL AND DATA COLLECTION/DEVELOPMENT PLATFORM | July 2021 | February 2025 | Allow | 44 | 1 | 0 | No | No |
| 17370825 | EMPIRICAL GAME THEORETIC SYSTEM AND METHOD FOR ADVERSARIAL DECISION ANALYSIS | July 2021 | November 2024 | Allow | 40 | 0 | 0 | No | No |
| 17330465 | CONTROLLER TRAINING BASED ON HISTORICAL DATA | May 2021 | October 2022 | Allow | 17 | 1 | 0 | No | No |
| 17288969 | CODE SEQUENCE BASED INTELLIGENT KEY CODE IDENTIFICATION METHOD AND RECORDING MEDIUM AND DEVICE FOR PERFORMING THE SAME | April 2021 | February 2025 | Allow | 46 | 1 | 0 | No | No |
| 17241572 | METHOD AND DEVICE FOR DEEP NEURAL NETWORK COMPRESSION | April 2021 | March 2025 | Allow | 47 | 1 | 0 | No | No |
| 17288848 | ACOUSTIC MODEL LEARNING APPARATUS, MODEL LEARNING APPARATUS, METHOD AND PROGRAM FOR THE SAME | April 2021 | March 2025 | Allow | 46 | 1 | 0 | Yes | No |
| 17278726 | RISK CLASSIFICATION OF INFORMATION TECHNOLOGY CHANGE REQUESTS | March 2021 | April 2024 | Allow | 37 | 0 | 0 | No | No |
| 17272226 | A RECONFIGURABLE BIOLOGICAL COMPUTER BASED ON COUPLED TRAINABLE NEURONAL GATES | February 2021 | April 2024 | Allow | 37 | 0 | 0 | No | No |
| 17271875 | ARITHMETIC OPERATION CIRCUIT AND NEUROMORPHIC DEVICE | February 2021 | July 2024 | Allow | 40 | 0 | 1 | No | No |
| 17269685 | METHOD, COMPUTER SYSTEM, AND PROGRAM FOR PREDICTING CHARACTERISTICS OF TARGET COMPOUND | February 2021 | October 2024 | Allow | 44 | 1 | 0 | Yes | No |
| 17176530 | Systems and Methods for Dividing Filters in Neural Networks for Private Data Computations | February 2021 | September 2023 | Allow | 31 | 2 | 0 | Yes | No |
| 17175889 | ADAPTIVE QUANTUM SIGNAL PROCESSOR | February 2021 | November 2024 | Allow | 45 | 1 | 0 | No | No |
| 17267890 | PATTERN RECOGNITION DEVICE AND LEARNED MODEL | February 2021 | October 2024 | Allow | 44 | 1 | 1 | Yes | No |
| 17171234 | AUTOMATICALLY VALIDATING DECISION TABLES | February 2021 | November 2024 | Allow | 45 | 2 | 0 | Yes | No |
| 17266781 | ELECTRONIC DEVICE FOR CONTROLLING DATA PROCESSING OF MODULARIZED NEURAL NETWORK, AND METHOD FOR CONTROLLING SAME | February 2021 | March 2025 | Abandon | 50 | 2 | 1 | No | No |
| 17167001 | MULTI-AGENT PLANNING AND AUTONOMY | February 2021 | March 2024 | Allow | 38 | 1 | 0 | No | No |
| 17164486 | DETERMINING METRICS CHARACTERIZING NUMBERS OF UNIQUE MEMBERS OF MEDIA AUDIENCES | February 2021 | January 2023 | Allow | 24 | 1 | 0 | No | No |
| 17161845 | TRAINING A POLICY MODEL FOR A ROBOTIC TASK, USING REINFORCEMENT LEARNING AND UTILIZING DATA THAT IS BASED ON EPISODES, OF THE ROBOTIC TASK, GUIDED BY AN ENGINEERED POLICY | January 2021 | September 2024 | Allow | 44 | 1 | 0 | No | No |
| 17162745 | TRANSPOSING NEURAL NETWORK MATRICES IN HARDWARE | January 2021 | February 2023 | Allow | 25 | 1 | 0 | No | No |
| 17159217 | NETWORK MODEL QUANTIZATION METHOD AND ELECTRONIC APPARATUS | January 2021 | March 2025 | Abandon | 50 | 2 | 0 | No | No |
| 17159312 | Hierarchical Hybrid Network on Chip Architecture for Compute-in-memory Probabilistic Machine Learning Accelerator | January 2021 | November 2024 | Allow | 45 | 2 | 0 | No | No |
| 17258617 | ELECTRONIC DEVICE AND CONTROL METHOD THEREOF | January 2021 | December 2023 | Abandon | 35 | 2 | 0 | Yes | No |
| 17257314 | NEURAL NETWORK BATCH NORMALIZATION OPTIMIZATION METHOD AND APPARATUS | December 2020 | December 2021 | Allow | 11 | 2 | 1 | No | No |
| 17257326 | FRAMEWORK MANAGEMENT METHOD AND APPARATUS | December 2020 | October 2021 | Allow | 10 | 1 | 0 | No | No |
| 17136780 | MULTI-DIMENSIONAL TIME SERIES EVENT PREDICTION VIA CONVOLUTIONAL NEURAL NETWORK(S) | December 2020 | August 2024 | Allow | 44 | 5 | 0 | Yes | No |
| 17136509 | TRAINING MULTIPLE NEURAL NETWORKS WITH DIFFERENT ACCURACY | December 2020 | September 2022 | Allow | 21 | 0 | 0 | No | No |
| 17132486 | INTELLIGENT DATA OBJECT GENERATION AND ASSIGNMENT USING ARTIFICIAL INTELLIGENCE TECHNIQUES | December 2020 | June 2024 | Abandon | 42 | 1 | 0 | No | No |
| 17129521 | AUTOMATIC MULTI-OBJECTIVE HARDWARE OPTIMIZATION FOR PROCESSING OF DEEP LEARNING NETWORKS | December 2020 | May 2024 | Allow | 41 | 1 | 0 | No | No |
| 17129038 | ISA-BASED COMPRESSION IN DISTRIBUTED TRAINING OF NEURAL NETWORKS | December 2020 | March 2024 | Allow | 39 | 1 | 0 | No | No |
| 17114819 | Dynamic Gradient Deception Against Adversarial Examples in Machine Learning Models | December 2020 | March 2024 | Allow | 40 | 1 | 0 | Yes | No |
| 17114529 | Heuristic Inference of Topological Representation of Metric Relationships | December 2020 | June 2024 | Abandon | 42 | 1 | 0 | No | No |
| 17114041 | METHODS AND APPARATUS TO FACILITATE DYNAMIC CLASSIFICATION FOR MARKET RESEARCH | December 2020 | October 2023 | Abandon | 34 | 2 | 0 | Yes | No |
| 17112628 | Constraining neural networks for robustness through alternative encoding | December 2020 | November 2023 | Allow | 35 | 3 | 0 | No | No |
| 17107973 | METHOD AND DEVICE FOR PRUNING CONVOLUTIONAL LAYER IN NEURAL NETWORK | December 2020 | April 2024 | Abandon | 41 | 4 | 0 | Yes | No |
This analysis examines appeal outcomes and the strategic value of filing appeals for examiner LEE, TSU-CHANG.
With a 60.0% 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, 34.5% 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 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 LEE, TSU-CHANG works in Art Unit 2128 and has examined 251 patent applications in our dataset. With an allowance rate of 85.3%, this examiner has an above-average tendency to allow applications. Applications typically reach final disposition in approximately 40 months.
Examiner LEE, TSU-CHANG's allowance rate of 85.3% places them in the 56% percentile among all USPTO examiners. This examiner has an above-average tendency to allow applications.
On average, applications examined by LEE, TSU-CHANG receive 1.98 office actions before reaching final disposition. This places the examiner in the 64% 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 LEE, TSU-CHANG is 40 months. This places the examiner in the 8% percentile for prosecution speed. Applications take longer to reach final disposition with this examiner compared to most others.
Conducting an examiner interview provides a +2.5% benefit to allowance rate for applications examined by LEE, TSU-CHANG. This interview benefit is in the 20% 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, 32.5% of applications are subsequently allowed. This success rate is in the 61% percentile among all examiners. Strategic Insight: RCEs show above-average effectiveness with this examiner. Consider whether your amendments or new arguments are strong enough to warrant an RCE versus filing a continuation.
This examiner enters after-final amendments leading to allowance in 20.8% of cases where such amendments are filed. This entry rate is in the 18% 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, 72.7% result in withdrawal of the rejection or reopening of prosecution. This success rate is in the 57% 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 64.3% of appeals filed. This is in the 39% percentile among all examiners. Of these withdrawals, 27.8% 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, 34.1% are granted (fully or in part). This grant rate is in the 28% 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 8% 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 1.9% of allowed cases (in the 64% 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.