Detailed information about the 100 most recent patent applications.
| Application Number | Title | Filing Date | Disposal Date | Disposition | Time (months) | Office Actions | Restrictions | Interview | Appeal |
|---|---|---|---|---|---|---|---|---|---|
| 19236733 | ADAPTIVELY TRAINING OF NEURAL NETWORKS VIA AN INTELLIGENT LEARNING MANAGEMENT SYSTEM | June 2025 | November 2025 | Allow | 5 | 1 | 0 | No | No |
| 19009560 | ACCELERATED TRAINING OF NEURAL NETWORKS WITH REGULARIZATION LINKS | January 2025 | March 2025 | Allow | 2 | 0 | 0 | No | No |
| 18612881 | ACCELERATING NEURAL NETWORKS IN HARDWARE USING INTERCONNECTED CROSSBARS | March 2024 | January 2026 | Allow | 22 | 2 | 0 | Yes | No |
| 18587242 | TARGETED INCREMENTAL GROWTH WITH CONTINUAL LEARNING IN DEEP NEURAL NETWORKS | February 2024 | September 2024 | Allow | 7 | 1 | 0 | No | No |
| 18362508 | LARGE LANGUAGE MODEL REGULATION SYSTEMS AND METHODS | July 2023 | January 2025 | Abandon | 18 | 3 | 0 | Yes | No |
| 18327527 | IMPROVING A DEEP NEURAL NETWORK WITH NODE-TO-NODE RELATIONSHIP REGULARIZATION | June 2023 | February 2024 | Allow | 8 | 1 | 0 | No | No |
| 18196963 | AUTOMATIC MACHINE LEARNING MODEL GENERATION | May 2023 | December 2025 | Abandon | 31 | 4 | 0 | Yes | No |
| 17713412 | TIME SERIES RETRIEVAL WITH CODE UPDATES | April 2022 | September 2025 | Abandon | 41 | 1 | 0 | No | No |
| 17704176 | SYSTEMS AND METHODS FOR RESOURCE-AWARE MODEL RECALIBRATION | March 2022 | December 2025 | Allow | 44 | 1 | 0 | Yes | No |
| 17613773 | SINGLE-STAGE MODEL TRAINING FOR NEURAL ARCHITECTURE SEARCH | November 2021 | November 2025 | Allow | 48 | 2 | 0 | No | No |
| 17372701 | ELECTRONIC DEVICE AND LEARNING METHOD FOR LEARNING OF LOW COMPLEXITY ARTIFICIAL INTELLIGENCE MODEL BASED ON SELECTING DYNAMIC PREDICTION CONFIDENCE THRESHOLD | July 2021 | March 2025 | Allow | 44 | 1 | 0 | No | No |
| 17260956 | ANOMALY DETECTION APPARATUS, ANOMALY DETECTION METHOD, AND PROGRAM | January 2021 | August 2024 | Allow | 43 | 1 | 0 | Yes | No |
| 17133222 | TIME SERIES ANOMALY DETECTION | December 2020 | September 2025 | Abandon | 57 | 4 | 0 | Yes | No |
| 17106293 | SYSTEM AND METHOD FOR PROVIDING UNSUPERVISED MODEL HEALTH MONITORING | November 2020 | February 2026 | Abandon | 60 | 6 | 0 | Yes | No |
| 17103921 | Systems and Methods for Simulating Sense Data and Creating Perceptions | November 2020 | August 2022 | Allow | 21 | 4 | 0 | Yes | No |
| 16951110 | Machine Learning Engine Providing Trained Request Approval Decisions | November 2020 | February 2025 | Abandon | 51 | 1 | 0 | No | No |
| 17089583 | Method and Apparatus for training an object recognition model | November 2020 | July 2025 | Abandon | 56 | 3 | 1 | No | No |
| 17086114 | FINE-GRAINED PER-VECTOR SCALING FOR NEURAL NETWORK QUANTIZATION | October 2020 | July 2025 | Allow | 57 | 3 | 0 | Yes | Yes |
| 17077709 | HORIZONTAL AND VERTICAL ASSERTIONS FOR VALIDATION OF NEUROMORPHIC HARDWARE | October 2020 | November 2025 | Allow | 60 | 3 | 0 | Yes | Yes |
| 17048539 | DEVICE, METHOD, AND SYSTEM FOR ANALYZING ASPECTS OF OBSERVATION DATA BY A NEURAL NETWORK | October 2020 | August 2025 | Abandon | 58 | 4 | 0 | Yes | No |
| 17066522 | INFORMATION PROCESSING SYSTEM AND METHOD FOR CONTROLLING INFORMATION PROCESSING SYSTEM | October 2020 | October 2024 | Abandon | 48 | 1 | 0 | No | No |
| 17024421 | PARKING LOT FREE PARKING SPACE PREDICTING METHOD, APPARATUS, ELECTRONIC DEVICE AND STORAGE MEDIUM | September 2020 | October 2024 | Abandon | 49 | 2 | 0 | No | No |
| 16999118 | ENHANCED GENERATIVE ADVERSARIAL NETWORK AND TARGET SAMPLE RECOGNITION METHOD | August 2020 | October 2024 | Allow | 49 | 2 | 0 | No | No |
| 16921944 | MACHINE LEARNING METHOD AND MACHINE LEARNING DEVICE | July 2020 | January 2024 | Abandon | 42 | 2 | 0 | No | No |
| 16890682 | METHODS AND SYSTEMS FOR HORIZONTAL FEDERATED LEARNING USING NON-IID DATA | June 2020 | March 2023 | Allow | 33 | 2 | 0 | No | No |
| 16763989 | SYSTEM, METHOD AND COMPUTER READABLE MEDIUM FOR DETERMINING VALIDITY OF A MACHINE LEARNING MODEL | May 2020 | September 2025 | Abandon | 60 | 4 | 0 | Yes | No |
| 16757283 | PCM-BASED NEURAL NETWORK DEVICE | April 2020 | April 2024 | Allow | 48 | 1 | 0 | Yes | No |
| 16850570 | Systems and Methods for Determining Graph Similarity | April 2020 | June 2023 | Allow | 38 | 0 | 0 | No | No |
| 16829076 | Learning Parameter Sampling Configuration for Automated Machine Learning | March 2020 | July 2024 | Allow | 51 | 2 | 0 | Yes | No |
| 16718607 | NEURAL NETWORK SYSTEM WITH TEMPORAL FEEDBACK FOR DENOISING OF RENDERED SEQUENCES | December 2019 | December 2022 | Allow | 36 | 1 | 0 | No | No |
| 16678038 | TRAINING ADAPTABLE NEURAL NETWORKS BASED ON EVOLVABILITY SEARCH | November 2019 | November 2025 | Allow | 60 | 5 | 0 | Yes | Yes |
| 16668947 | Augmenting End-to-End Transaction Visibility Using Artificial Intelligence | October 2019 | November 2025 | Abandon | 60 | 6 | 0 | Yes | No |
| 16577897 | CANDIDATE ANSWERS FOR SPECULATIVE QUESTIONS IN A DEEP QUESTION ANSWERING SYSTEM | September 2019 | June 2025 | Abandon | 60 | 7 | 0 | Yes | No |
| 16554745 | ENGAGEMENT PREDICTION USING MACHINE LEARNING IN DIGITAL WORKPLACE | August 2019 | November 2024 | Abandon | 60 | 4 | 0 | Yes | No |
| 16516009 | EXTRACTION OF ENTITIES HAVING DEFINED LENGTHS OF TEXT SPANS | July 2019 | May 2025 | Abandon | 60 | 5 | 0 | Yes | No |
| 16502625 | UNARY RELATION EXTRACTION USING DISTANT SUPERVISION | July 2019 | February 2023 | Allow | 43 | 1 | 0 | Yes | No |
| 16441494 | SYSTEMS AND METHODS FOR LIGHTWEIGHT CLOUD-BASED MACHINE LEARNING MODEL SERVICE | June 2019 | July 2024 | Allow | 60 | 3 | 0 | Yes | No |
| 16439891 | METHOD AND APPARATUS FOR ARTIFICIAL NEURAL NETWORK LEARNING FOR DATA PREDICTION | June 2019 | February 2023 | Abandon | 44 | 1 | 0 | No | No |
| 16396717 | IDENTIFYING DATA DRIFTS | April 2019 | November 2025 | Abandon | 60 | 6 | 0 | Yes | No |
| 16332961 | CONTROL POLICIES FOR ROBOTIC AGENTS | March 2019 | September 2023 | Allow | 54 | 3 | 0 | No | No |
| 16296513 | SOLUTION SEARCHING DEVICE | March 2019 | January 2024 | Abandon | 58 | 2 | 0 | No | No |
| 16290413 | LEXICOGRAPHIC DEEP REINFORCEMENT LEARNING USING STATE CONSTRAINTS AND CONDITIONAL POLICIES | March 2019 | May 2022 | Allow | 39 | 2 | 0 | Yes | No |
| 16289575 | METHODS AND SYSTEMS FOR USING MACHINE-LEARNING EXTRACTS AND SEMANTIC GRAPHS TO CREATE STRUCTURED DATA TO DRIVE SEARCH, RECOMMENDATION, AND DISCOVERY | February 2019 | November 2024 | Abandon | 60 | 4 | 0 | Yes | No |
| 16289573 | METHODS AND SYSTEMS FOR USING MACHINE-LEARNING EXTRACTS AND SEMANTIC GRAPHS TO CREATE STRUCTURED DATA TO DRIVE SEARCH, RECOMMENDATION, AND DISCOVERY | February 2019 | November 2024 | Abandon | 60 | 4 | 0 | Yes | No |
| 16286133 | METHOD FOR CONTROLLING SCENE DETECTION USING AN APPARATUS | February 2019 | May 2023 | Allow | 51 | 7 | 0 | Yes | Yes |
| 16281582 | GPU-BASED ARTIFICIAL INTELLIGENCE SYSTEM USING CHANNEL-LEVEL ARCHITECTURE SEARCH FOR DEEP NEURAL NETWORK | February 2019 | March 2023 | Allow | 48 | 4 | 0 | Yes | No |
| 16237617 | METHOD AND APPARATUS FOR DESIGNING FLEXIBLE DATAFLOW PROCESSOR FOR ARTIFICIAL INTELLIGENT DEVICES | December 2018 | July 2024 | Abandon | 60 | 4 | 0 | No | No |
| 16194862 | APPLICATION PREDICTION METHOD, APPLICATION PRELOADING METHOD AND APPLICATION PRELOADING APPARATUS | November 2018 | June 2023 | Abandon | 55 | 2 | 0 | No | No |
| 16192159 | METHODS AND SYSTEMS FOR DETECTING CHECK WORTHY CLAIMS FOR FACT CHECKING | November 2018 | April 2025 | Abandon | 60 | 6 | 0 | Yes | No |
| 16188835 | TRAINING A NEURAL NETWORK MODEL | November 2018 | September 2023 | Abandon | 58 | 2 | 0 | No | No |
| 16172399 | PREDICTION MODEL TRAINING MANAGEMENT SYSTEM, METHOD OF THE SAME, MASTER APPARATUS AND SLAVE APPARATUS FOR THE SAME | October 2018 | September 2023 | Allow | 58 | 3 | 0 | Yes | No |
| 16132015 | SYSTEM AND METHOD FOR COMPRESSING KERNELS | September 2018 | November 2023 | Allow | 60 | 4 | 0 | Yes | No |
| 16122487 | PROVISION OF COMPUTER RESOURCES BASED ON LOCATION HISTORY | September 2018 | May 2022 | Allow | 45 | 3 | 0 | No | No |
| 16109404 | SYSTEM AND METHOD OF MEASURING THE ROBUSTNESS OF A DEEP NEURAL NETWORK | August 2018 | January 2024 | Abandon | 60 | 4 | 0 | Yes | No |
| 16059578 | Accelerating Neural Networks in Hardware Using Interconnected Crossbars | August 2018 | December 2023 | Allow | 60 | 2 | 0 | Yes | Yes |
| 16041502 | TEMPORALLY STABLE DATA RECONSTRUCTION WITH AN EXTERNAL RECURRENT NEURAL NETWORK | July 2018 | September 2023 | Abandon | 60 | 1 | 0 | Yes | No |
| 16031568 | PLANT CONTROL SUPPORTING APPARATUS, PLANT CONTROL SUPPORTING METHOD, AND RECORDING MEDIUM | July 2018 | May 2022 | Allow | 46 | 2 | 0 | No | No |
| 16029377 | NEURAL NETWORK CONSENSUS USING BLOCKCHAIN | July 2018 | July 2022 | Abandon | 48 | 1 | 0 | No | No |
| 16002614 | ELECTRONIC APPARATUS AND METHOD FOR GENERATING TRAINED MODEL | June 2018 | February 2023 | Allow | 56 | 2 | 0 | Yes | No |
| 15987944 | CACHE CONFIGURATION PERFORMANCE ESTIMATION | May 2018 | January 2023 | Allow | 56 | 1 | 0 | No | No |
| 15948304 | Content-Specific Neural Network Distribution | April 2018 | January 2023 | Allow | 57 | 1 | 0 | No | No |
| 15866970 | REDUCING MACHINE-LEARNING MODEL COMPLEXITY WHILE MAINTAINING ACCURACY TO IMPROVE PROCESSING SPEED | January 2018 | March 2023 | Abandon | 60 | 3 | 0 | Yes | No |
| 15867252 | DYNAMICALLY GENERATING AN ADAPTED RECIPE BASED ON A DETERMINED CHARACTERISTIC OF A USER | January 2018 | March 2024 | Abandon | 60 | 6 | 0 | Yes | No |
| 15786434 | STATIC BLOCK SCHEDULING IN MASSIVELY PARALLEL SOFTWARE DEFINED HARDWARE SYSTEMS | October 2017 | April 2024 | Allow | 60 | 7 | 0 | Yes | No |
This analysis examines appeal outcomes and the strategic value of filing appeals for examiner ALGHAZZY, SHAMCY.
With a 100.0% 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, 60.0% of applications that filed an appeal were subsequently allowed. This appeal filing benefit rate is in the top 25% across the USPTO, indicating that filing appeals is particularly effective here. The act of filing often prompts favorable reconsideration during the mandatory appeal conference.
✓ 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 ALGHAZZY, SHAMCY works in Art Unit 2128 and has examined 55 patent applications in our dataset. With an allowance rate of 50.9%, this examiner allows applications at a lower rate than most examiners at the USPTO. Applications typically reach final disposition in approximately 57 months.
Examiner ALGHAZZY, SHAMCY's allowance rate of 50.9% places them in the 13% percentile among all USPTO examiners. This examiner is less likely to allow applications than most examiners at the USPTO.
On average, applications examined by ALGHAZZY, SHAMCY receive 3.04 office actions before reaching final disposition. This places the examiner in the 87% 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 ALGHAZZY, SHAMCY is 57 months. This places the examiner in the 1% percentile for prosecution speed. Applications take longer to reach final disposition with this examiner compared to most others.
Conducting an examiner interview provides a +1.5% benefit to allowance rate for applications examined by ALGHAZZY, SHAMCY. 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, 13.8% of applications are subsequently allowed. This success rate is in the 9% 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 14.3% of cases where such amendments are filed. This entry rate is in the 15% 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, 100.0% result in withdrawal of the rejection or reopening of prosecution. This success rate is in the 71% 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 80.0% of appeals filed. This is in the 72% percentile among all examiners. Of these withdrawals, 25.0% occur early in the appeal process (after Notice of Appeal but before Appeal Brief). Strategic Insight: This examiner shows above-average willingness to reconsider rejections during appeals. The mandatory appeal conference (MPEP § 1207.01) provides an opportunity for reconsideration.
When applicants file petitions regarding this examiner's actions, 33.3% are granted (fully or in part). This grant rate is in the 20% 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.