TENOKONDA Trademark
TENOKONDA is a USPTO trademark filed by Tenokonda, Inc.. Status: Registered.
Trademark Facts
| Mark | TENOKONDA |
|---|---|
| Serial Number | 90841169 |
| Registration Number | 6850024 |
| Status | Registered |
| Filing Date | 2021-07-21 |
| Registration Date | 2022-09-20 |
| Mark Type | Word |
| Nice Classes | 009 (Electronics & Software) |
| Owner | Tenokonda, Inc. |
| Attorney of Record | Jubin DANA |
| Prosecution Events | 16 |
| Latest Event | R.PR on 2022-09-20 |
Goods & Services
Downloadable computer simulation software for modeling scenario generation, scenario analysis, sensitivity analysis, risk estimation, probabilistic graph modeling, Bayesian network modeling, Bayesian inference, anomaly detection, data imputation, Monte Carlo simulation, probability distribution sampling, stochastic processes simulation, graph theory, probabilistic model calibration, graph structure learning, random numbers generation, low discrepancy sequences, distribution fitting; Downloadable computer software that provides real-time, integrated business management intelligence by combining information from various databases and presenting it in an easy-to-understand user interface | Business consulting services | Consulting in the field of engineering; Providing temporary use of online non-downloadable simulation software for modeling scenario generation, scenario analysis, sensitivity analysis, risk estimation, probabilistic graph modeling, Bayesian network modeling, Bayesian inference, anomaly detection, data imputation, Monte Carlo simulation, probability distribution sampling, stochastic processes simulation, graph theory, probabilistic model calibration, graph structure learning, random numbers generation, low discrepancy sequences, distribution fitting.; Technology consultation in the field of scenario generation, scenario analysis, sensitivity analysis, risk estimation, probabilistic graph modeling, Bayesian network modeling, Bayesian inference, anomaly detection, data imputation, Monte Carlo simulation, probability distribution sampling, stochastic processes simulation, graph theory, probabilistic model calibration, graph structure learning, random numbers generation, low discrepancy sequences, distribution fitting