Abstract: A computer implemented method comprising: receiving a report on a condition of a human or animal subject, composed by a user based on a scan of the subject; inputting the current report and the scan into a trained machine learning model; and based on the report and the scan, the machine learning model generating one or more suggestions for updating the text of the report. The method further comprises causing a user interface to display to the user one or more suggestions for updating the text of the report, with each respective suggestion visually linked in the user interface to a corresponding subregion within at least one image of the scan based upon which the respective suggestion was generated.

Abstract: A tool for off-line experimentation with auction parameters for auctions for an ad space. The tool computes, using historical bid information, values per click to advertisers competing for the ad space. The tool applies a homotopy algorithm, using these values, to predict equilibrium bids based on the values to the advertisers. In computing the equilibrium bids, the bid of each advertiser is assumed to stay fixed over multiple auction cycles, but each advertiser is assumed to face uncertainty about quality scores used by the advertising platform in selecting bids and the rank of their bid with respect to others. The quality scores also are assumed to vary between auctions.

Abstract: A scalable clustering system is described. In an embodiment the clustering system is operable for extremely large scale applications where millions of items having tens of millions of features are clustered. In an embodiment the clustering system uses a probabilistic cluster model which models uncertainty in the data set where the data set may be for example, advertisements which are subscribed to keywords, text documents containing text keywords, images having associated features or other items. In an embodiment the clustering system is used to generate additional features for associating with a given item. For example, additional keywords are suggested which an advertiser may like to subscribe to. The additional features that are generated have associated probability values which may be used to rank those features in some embodiments. User feedback about the generated features is received and used to revise the feature generation process in some examples.

Abstract: A tool for off-line experimentation with auction parameters for auctions for an ad space. The tool computes, using historical bid information, values per click to advertisers competing for the ad space. The tool applies a homotopy algorithm, using these values, to predict equilibrium bids based on the values to the advertisers. In computing the equilibrium bids, the bid of each advertiser is assumed to stay fixed over multiple auction cycles, but each advertiser is assumed to face uncertainty about quality scores used by the advertising platform in selecting bids and the rank of their bid with respect to others. The quality scores also are assumed to vary between auctions.

Abstract: A scalable clustering system is described. In an embodiment the clustering system is operable for extremely large scale applications where millions of items having tens of millions of features are clustered. In an embodiment the clustering system uses a probabilistic cluster model which models uncertainty in the data set where the data set may be for example, advertisements which are subscribed to keywords, text documents containing text keywords, images having associated features or other items. In an embodiment the clustering system is used to generate additional features for associating with a given item. For example, additional keywords are suggested which an advertiser may like to subscribe to. The additional features that are generated have associated probability values which may be used to rank those features in some embodiments. User feedback about the generated features is received and used to revise the feature generation process in some examples.