Probabilistic modeling for anonymized data integration and bayesian survey measurement of sparse and weakly-labeled datasets
US11568215B2 · kind B2 · utility
Assignee
Inventors
Key dates
| Filing date | May 28, 2020 |
| Grant date | Jan 31, 2023 |
| Priority date | — |
| Expiry date | Mar 9, 2041 |
Classification
- Technology area (CPC G)Physics
- CPC primaryG06N7/01
- WIPO fieldAudio-visual technology
- WIPO sectorElectrical engineering
Abstract
Example methods, apparatus, systems and articles of manufacture (e.g., physical storage media) to perform probabilistic modeling for anonymized data integration and measurement of sparse and weakly-labeled datasets are disclosed. An apparatus includes a training controller to train a neural network to produce a trained neural network to output model parameters of a probability model, a model evaluator to execute the trained neural network on input data specifying a time of day, a media source, and at least one feature different from the time of day and the media source to determine one or more first model parameters of the probability model, and a ratings metric generator to evaluate the probability model based on input census data to determine a ratings metric corresponding to the time of day, the media source, and the at least one feature, the probability model configured with the one or more first model parameters.
Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.