Sergey A Prigogin

7519566, Apr 14, 2009

Nov 2, 2004

10/980421

Sergey A. Prigogin - Foster City CA, US
Michel Adar - Palo Alto CA, US

Oracle International Corporation - Redwood Shores CA

G06E 1/00

706 21, 707100

Disclosed are methods and apparatus for updating at least one prediction model for use by at least one interactive server. Each interactive server performs a plurality of actions in the context of a plurality of input attributes values of an input dataset wherein the actions are selected based on each prediction model. contextual data are automatically and continually obtained from the interactive server as it performs the plurality of actions. A learning model is automatically and continually updated based on all of the obtained contextual data. An updated prediction model and a prediction of a probability of an outcome using the updated prediction model are generated. A prediction is generated and an action of the plurality of actions based on the prediction is selected. The selection action is performed.

20050165596, Jul 28, 2005

Nov 30, 2004

11/000570

Michel Adar - Palo Alto CA, US
Sergey Prigogin - Foster City CA, US
Nicolas Bonnet - Palo Alto CA, US

G06F009/45

703022000

Disclosed are methods and apparatus for predicting an expected value associated with an end event of an event sequence. In one embodiment, the following operations are performed: (a) providing a current set of input attributes and contextual data collected during performance of previous event sequences; (b) predicting a plurality of expected values for going from a first event of a known event sequence to each of a plurality of subsequent events of the known event sequence based at least on the current set of input attributes and the collected contextual data; and (c) predicting an expected value for going from a first event of an unknown event sequence to an end event of such unknown event sequence based on at least two of the expected values predicted for the known event sequence and based at least on the current set of input attributes and the collected contextual data. The expected value for reaching the end goal of the unknown event sequence cannot be determined with a degree of certainty that is higher than a predetermined value and wherein the expected value for reaching the end goal of the known event sequence can be determined with a degree of certainty that is higher than the predetermined value.

20050169452, Aug 4, 2005

Jan 21, 2005

11/040644

Sergey Prigogin - Foster City CA, US
Michel Adar - Palo Alto CA, US
Nicolas Bonnet - Palo Alto CA, US

G10L015/00

379265010

Disclosed are methods and apparatus for evaluating a certainty characteristic of a predictive model. When a decision needs to be implemented, the predictive model is utilized unless the certainty characteristic of such model indicates that the predictive model results are unacceptably uncertain and should not be used. Otherwise, the predictive model is used to reach a decision. In a further embodiment, randomization is introduced into the results of the predictive model (when utilized for a decision). The amount of randomization is tied to the amount of uncertainty of results of the model to thereby balance exploitation and exploration goals.

20050197889, Sep 8, 2005

Jan 28, 2005

11/045665

Sergey Prigogin - Foster City CA, US
Michel Adar - Palo Alto CA, US
Nicolas Bonnet - Palo Alto CA, US

G06F017/60

705010000

Disclosed are methods and apparatus for reporting significant data mining changes. In general, embodiments of the present invention address the shortcomings of the prior art through comparison over time of prediction model characteristics, such as inferences. Embodiments of the present invention detect trends in the model itself by detecting changes in levels of correlation (or any other model aspect) between individual elements of input data and targets of predictions. In this specific embodiment, users of the model are preferably alerted when an input characteristic or other model aspect, which was not important before, becomes important and when an input characteristic, which was important, loses its importance.