Predictive Analytics & Operational Models for Artificial Intelligence & Machine Learning
Analytics Patents & Technologies for AI/ML
Predictive Analytics & Operational Models for Artificial Intelligence & Machine Learning
Requirements for Artificial Intelligence
Data Scoring
Real-Time Aggregation of Two (2) or More Data Streams
Real-Time Aggregation of Two (2) or More Data Streams
The automated Data Scoring used by the platform allows for the calibration, validation, and even updating of the Model Data File (MDF). The primary output of model building is the scoring code to be used for predictions. The secondary output is the data used for model validation.
Real-Time Aggregation of Two (2) or More Data Streams
Real-Time Aggregation of Two (2) or More Data Streams
Real-Time Aggregation of Two (2) or More Data Streams
To perform analytics through the platform that is building a model data file that includes normalized data values; the system has to utilize two or more data streams that have been joined and aggregated across one or more periods of time; scoring, in real-time & dynamically updating.
Automated Data Modeling
Real-Time Aggregation of Two (2) or More Data Streams
Automated Data Modeling
The Dynamic Model Data Facility includes flexible access to additional data sources & more timely access & manipulation of that data. New relationships may be discovered on the fly, new data elements can be evaluated on the fly, & target markets can be uncovered.
Data Predictions
Model Test, Validation & Re-Validation
Automated Data Modeling
Analytics provides the applications, techniques, and technologies for understanding performance drivers in an organization and for implementing fact-based, model-driven decision-making. Analytics focuses on extrapolating trends and making predictions and enabling actionable insights that can drive decision-making.
Model Test, Validation & Re-Validation
Model Test, Validation & Re-Validation
Model Test, Validation & Re-Validation
A platform model and method automatically tests & validates an operational model. The first expert system is coupled to the second expert system. The first expert system may correspond to a system in which models are built & deployed while the second expert system may correspond to a system in which models are made operational & continually tested and / or verified.
Licensed by Top Companies
Model Test, Validation & Re-Validation
Model Test, Validation & Re-Validation
Over 225+ technology companies have Licensed the Aha Analytics Technology Patents including leading companies from the following sectors:
- Cloud Engineering & Services
- Database Management Systems
- Internet Search & Services
- Programming Languages
- Computing Platforms
- E-Commerce
- Artificial & Machine Intelligence
- Healthcare
- Banking & Investments
- Media & Entertainment
- And many more.....
Analytics Systems & Methods
Analytics Platform Example
Model Data Files (MDF) & On-the-Fly Calibration
Model Data Files (MDF) & On-the-Fly Calibration
As outlined above, the analytics platform may include one or more analytics engines. The analytics engines may include, but are not limited to, expressions language, simulation and forecasting components, scoring, statistical regression, predictive streams, simulation, third-party models, & more engines.
Model Data Files (MDF) & On-the-Fly Calibration
Model Data Files (MDF) & On-the-Fly Calibration
Model Data Files (MDF) & On-the-Fly Calibration
A (MDF) or Model Data File may be generated on a periodic basis or on the fly. As an MDF is used in daily/and or periodic processing, the file can be updated to account for changes detected in the system. For example, as data is being scored in real-time, the MDF may be caused to recalibrate and/or rebuild based on how close the score is to predicted values of constancy. In other words, the automated scoring used by the system may allow for the calibration, validation, and even updating of the MDF.