Detailed information about the 100 most recent patent applications.
| Application Number | Title | Filing Date | Disposal Date | Disposition | Time (months) | Office Actions | Restrictions | Interview | Appeal |
|---|---|---|---|---|---|---|---|---|---|
| 17104003 | INFORMATION PROCESSING APPARATUS, NEURAL NETWORK COMPUTATION PROGRAM, AND NEURAL NETWORK COMPUTATION METHOD | November 2020 | August 2024 | Abandon | 45 | 1 | 0 | No | No |
| 17102722 | INFORMATION PROCESSING APPARATUS AND INFORMATION PROCESSING METHOD | November 2020 | April 2024 | Abandon | 40 | 1 | 0 | No | No |
| 16987021 | IOT GATEWAY FOR CONTROLLING DATA REPORTING INTERVAL OF IOT TERMINAL BASED ON DATA PREDICTION ACCURACY AND OPERATING METHOD THEREOF | August 2020 | April 2024 | Abandon | 44 | 1 | 0 | No | No |
| 16935323 | SYSTEM FOR COGNITIVE RESOURCE IDENTIFICATION USING SWARM INTELLIGENCE | July 2020 | April 2024 | Abandon | 44 | 3 | 0 | No | No |
| 16932043 | DEVICE AND METHOD FOR PROCESSING A DIGITAL DATA STREAM | July 2020 | November 2023 | Abandon | 40 | 2 | 0 | No | No |
| 16627293 | CALCULATION METHOD AND CALCULATION DEVICE FOR SPARSE NEURAL NETWORK, ELECTRONIC DEVICE, COMPUTER READABLE STORAGE MEDIUM, AND COMPUTER PROGRAM PRODUCT | December 2019 | September 2021 | Abandon | 20 | 2 | 0 | No | No |
| 16366841 | USER CLASSIFICATION USING A DEEP FOREST NETWORK | March 2019 | December 2019 | Allow | 9 | 1 | 0 | Yes | No |
| 16362186 | DATA PROCESSING METHOD AND DATA PROCESSING DEVICE | March 2019 | November 2019 | Abandon | 8 | 0 | 0 | No | No |
| 16262985 | METHOD FOR OPTIMIZING HYPERPARAMETERS OF AUTO-LABELING DEVICE WHICH AUTO-LABELS TRAINING IMAGES FOR USE IN DEEP LEARNING NETWORK TO ANALYZE IMAGES WITH HIGH PRECISION, AND OPTIMIZING DEVICE USING THE SAME | January 2019 | April 2020 | Allow | 15 | 2 | 0 | Yes | No |
| 16163375 | DATA ANALYTICS PLATFORM | October 2018 | June 2020 | Abandon | 20 | 0 | 0 | No | No |
| 15916951 | TIME SERIES DATA ANALYSIS DEVICE, TIME SERIES DATA ANALYSIS METHOD, AND COMPUTER PROGRAM | March 2018 | February 2019 | Abandon | 11 | 0 | 0 | No | No |
| 15835261 | NEURAL NETWORK COMBINED IMAGE AND TEXT EVALUATOR AND CLASSIFIER | December 2017 | March 2022 | Abandon | 51 | 4 | 0 | Yes | No |
| 15579240 | METHOD AND DEVICE OF CONSTRUCTING DECISION MODEL, COMPUTER DEVICE AND STORAGE APPARATUS | December 2017 | August 2020 | Abandon | 32 | 2 | 0 | No | No |
| 15820906 | SYSTEMS AND METHODS FOR TRAINING NEURAL NETWORKS | November 2017 | November 2018 | Abandon | 12 | 0 | 0 | No | No |
| 15633681 | GRINDING MACHINE | June 2017 | June 2019 | Allow | 24 | 0 | 0 | No | No |
| 15617795 | IDENTIFICATION OF DECISION BIAS WITH ARTIFICIAL INTELLIGENCE PROGRAM | June 2017 | November 2021 | Abandon | 53 | 1 | 0 | No | No |
| 15489234 | MULTIFACTOR VENUE LOCALIZATION USING CENTRALIZED LEARNING | April 2017 | July 2020 | Abandon | 39 | 1 | 0 | Yes | No |
| 15483859 | GENERATION AND USE OF MODEL PARAMETERS IN COLD-START SCENARIOS | April 2017 | March 2020 | Allow | 35 | 1 | 0 | Yes | No |
| 15455259 | System and Method for Training Parameter Set in Neural Network | March 2017 | March 2018 | Abandon | 12 | 0 | 0 | No | No |
| 15451553 | SYSTEMS AND METHODS FOR IMPROVING ACCURACY OF CLASSIFICATION-BASED TEXT DATA PROCESSING | March 2017 | November 2020 | Abandon | 44 | 1 | 0 | No | No |
| 15452449 | NEURAL NETWORK COMPRESSION VIA WEAK SUPERVISION | March 2017 | September 2020 | Abandon | 43 | 1 | 0 | No | No |
| 15403107 | SYSTEMS AND METHODS FOR CAPTIONING CONTENT | January 2017 | March 2020 | Abandon | 39 | 1 | 0 | No | No |
| 15389755 | A Method for Effluent Total Nitrogen-based on a Recurrent Self-organizing RBF Neural Network | December 2016 | January 2020 | Allow | 36 | 1 | 0 | No | No |
| 15388899 | SYSTEM AND METHOD FOR DEPLOYING CUSTOMIZED MACHINE LEARNING SERVICES | December 2016 | February 2020 | Abandon | 38 | 1 | 0 | No | No |
| 15386996 | LEARNING METHOD FOR SYNAPSES OF A NEUROMORPHIC DEVICE | December 2016 | December 2020 | Abandon | 47 | 2 | 0 | No | No |
| 15363519 | SYSTEM AND METHOD FOR AUTOMATICALLY UPDATING INFERENCE MODELS | November 2016 | March 2020 | Abandon | 40 | 2 | 0 | No | No |
| 15352318 | OPTIMIZED MACHINE LEARNING SYSTEM | November 2016 | December 2020 | Abandon | 49 | 2 | 0 | No | No |
| 15347638 | LEARNING SYSTEM, LEARNING PROGRAM, AND LEARNING METHOD | November 2016 | November 2019 | Abandon | 36 | 1 | 0 | No | No |
| 15343184 | SYSTEM AND METHOD OF WRITING COMPUTER PROGRAMS | November 2016 | February 2018 | Abandon | 16 | 0 | 0 | No | No |
| 15254958 | Modeling of Geospatial Location Over Time | September 2016 | July 2019 | Abandon | 34 | 1 | 0 | No | No |
| 15248497 | RECOMMENDING METHOD AND ELECTRONIC DEVICE | August 2016 | August 2017 | Abandon | 12 | 0 | 0 | No | No |
| 15225545 | TARGET VARIABLE DISTRIBUTION-BASED ACCEPTANCE OF MACHINE LEARNING TEST DATA SETS | August 2016 | March 2020 | Allow | 43 | 2 | 0 | No | No |
| 15211422 | OPERATIONAL PARAMETER VALUE LEARNING DEVICE, OPERATIONAL PARAMETER VALUE LEARNING METHOD, AND CONTROLLER FOR LEARNING DEVICE | July 2016 | February 2019 | Abandon | 31 | 0 | 0 | No | No |
| 15204984 | Method of Adaptively Predicting Blood-Glucose Level by Collecting Biometric and Activity Data with A User Portable Device | July 2016 | September 2019 | Abandon | 38 | 1 | 0 | No | No |
| 14999338 | Method for a system that discovers, locates, indexes, ranks, and clusters multimedia service providers using hierarchical communication topologies | April 2016 | March 2018 | Abandon | 58 | 2 | 0 | Yes | No |
| 14984216 | SYSTEMS AND METHODS FOR EFFICIENTLY CLASSIFYING DATA OBJECTS | December 2015 | March 2020 | Allow | 50 | 2 | 0 | Yes | No |
| 14971769 | APPARATUS AND METHOD FOR HIGH PERFORMANCE DATA ANALYSIS | December 2015 | June 2016 | Abandon | 6 | 0 | 0 | No | No |
| 14969755 | METHOD OF CONDITIONALLY PROMPTING WEARABLE SENSOR USERS FOR ACTIVITY CONTEXT IN THE PRESENCE OF SENSOR ANOMALIES | December 2015 | September 2019 | Abandon | 45 | 1 | 0 | No | No |
| 14970279 | DRIVING BEHAVIOR EVALUATING METHOD AND DEVICE | December 2015 | May 2018 | Abandon | 29 | 0 | 0 | No | No |
| 14540683 | CONTEXT REASONING APPARATUS, CONTEXT RECOGNITION SYSTEM AND CONTEXT REASONING METHOD | November 2014 | March 2016 | Abandon | 16 | 0 | 0 | No | No |
| 14495094 | FACILITATING DYNAMIC AFFECT-BASED ADAPTIVE REPRESENTATION AND REASONING OF USER BEHAVIOR ON COMPUTING DEVICES | September 2014 | February 2020 | Abandon | 60 | 4 | 0 | Yes | No |
| 14332968 | METHOD AND SYSTEM FOR PREDICTING POWER CONSUMPTION | July 2014 | February 2016 | Abandon | 19 | 0 | 0 | No | No |
| 13924209 | CORRECTING INFERRED KNOWLEDGE FOR EXPIRED EXPLICIT KNOWLEDGE | June 2013 | February 2016 | Abandon | 32 | 1 | 0 | No | No |
| 13815960 | Monitoring I/O, prompts, and collaborations of data, content, and correlations for evaluating, predicting, and ascertaining metrics for creativity, novelty, utility, consumption, reliability, success, values, rights, IP, and ROI | March 2013 | March 2014 | Abandon | 12 | 0 | 0 | No | No |
| 13534493 | COMPUTER HARDWARE AND SOFTWARE DIAGNOSTIC AND REPORT SYSTEM | June 2012 | October 2018 | Abandon | 60 | 6 | 0 | Yes | No |
| 13293983 | SYSTEM, METHOD AND COMPUTER PROGRAM PRODUCT FOR DYNAMICALLY MEASURING PROPERTIES OF OBJECTS RENDERED AND/OR REFERENCED BY AN APPLICATION EXECUTING ON A COMPUTING DEVICE | November 2011 | July 2013 | Abandon | 20 | 1 | 0 | No | No |
| 13254925 | LEARNING APPARATUS, IDENTIFYING APPARATUS AND METHOD THEREFOR | October 2011 | May 2014 | Abandon | 33 | 1 | 0 | No | No |
| 13188122 | Knowledge Reasoning Method of Boolean Satisfiability (SAT) | July 2011 | May 2014 | Abandon | 34 | 1 | 0 | No | No |
| 13036151 | DIGITAL WEIGHT LOSS AID | February 2011 | May 2014 | Abandon | 39 | 1 | 0 | No | No |
| 12960923 | Electronic Communications Triage | December 2010 | June 2012 | Abandon | 19 | 0 | 0 | No | No |
| 12525551 | STIMULI BASED INTELLIGENT ELECTRONIC SYSTEM | December 2009 | July 2013 | Abandon | 47 | 1 | 0 | No | No |
| 12587174 | System and method for the graphical presentation of the content of radiologic image study reports | October 2009 | November 2012 | Abandon | 38 | 1 | 0 | No | No |
| 12350418 | POWER SUPPLY CONTROL SYSTEM, POWER SUPPLY CONTROL DEVICE, CONTROL TERMINAL, POWER SUPPLY CONTROL METHOD, AND PROGRAM | January 2009 | November 2011 | Abandon | 34 | 1 | 0 | No | No |
| 12260511 | SENSOR UNIT FOR ENVIRONMENT OBSERVATION COMPRISING A NEURAL PROCESSOR | October 2008 | April 2012 | Abandon | 42 | 1 | 0 | No | No |
| 09771482 | METHOD FOR REMOVING DEPENDENT STORE-LOAD PAIR FROM CRITICAL PATH | January 2001 | July 2002 | Allow | 18 | 1 | 0 | No | No |
No appeal data available for this record. This may indicate that no appeals have been filed or decided for applications in this dataset.
Examiner CHAKI, KAKALI works in Art Unit 2122 and has examined 55 patent applications in our dataset. With an allowance rate of 14.5%, this examiner allows applications at a lower rate than most examiners at the USPTO. Applications typically reach final disposition in approximately 36 months.
Examiner CHAKI, KAKALI's allowance rate of 14.5% places them in the 2% percentile among all USPTO examiners. This examiner is less likely to allow applications than most examiners at the USPTO.
On average, applications examined by CHAKI, KAKALI receive 1.15 office actions before reaching final disposition. This places the examiner in the 11% percentile for office actions issued. This examiner issues significantly fewer office actions than most examiners.
The median time to disposition (half-life) for applications examined by CHAKI, KAKALI is 36 months. This places the examiner in the 35% percentile for prosecution speed. Prosecution timelines are slightly slower than average with this examiner.
Conducting an examiner interview provides a +35.7% benefit to allowance rate for applications examined by CHAKI, KAKALI. This interview benefit is in the 83% 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, 14.3% of applications are subsequently allowed. This success rate is in the 12% 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 25.0% of cases where such amendments are filed. This entry rate is in the 35% percentile among all examiners. Strategic Recommendation: This examiner shows below-average receptiveness to after-final amendments. You may need to file an RCE or appeal rather than relying on after-final amendment entry.
When applicants file petitions regarding this examiner's actions, 60.0% are granted (fully or in part). This grant rate is in the 61% percentile among all examiners. Strategic Note: Petitions show above-average success regarding this examiner's actions. Petitionable matters include restriction requirements (MPEP § 1002.02(c)(2)) and various procedural issues.
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 25.0% of allowed cases (in the 96% percentile). Per MPEP § 714.14, a Quayle action indicates that all claims are allowable but formal matters remain. This examiner frequently uses Quayle actions compared to other examiners, which is a positive indicator that once substantive issues are resolved, allowance follows quickly.
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.