Abstract: A computer-implemented method for determining an attribute for an online user of a candidate computing device is provided. The method implemented uses a host computing device. The method includes identifying a first set of model data including device data from a plurality of model computing devices including location data and access data, and a plurality of categories for an attribute of a population segment including an online user. Each category defines a segment of the attribute. The method further includes training a classification model by the host computing device with at least the first set of model data and the plurality of categories. The method also includes identifying device data associated with the candidate computing device. The method further includes applying the device data of the candidate computing device to the classification model to determine a category of the plurality of categories for the online user.

Abstract: Systems and methods for content selection with precision controls include receiving a content selection parameter value and a degree of precision specified by a content provider. A content selection parameter value for a device identifier may be predicted using a predictive model. A precision factor may be associated with the predicted content selection parameter value. Content from the provider may be selected based on a comparison between the predicted selection parameter value and precision factor for the device identifier and the selection parameter value and degree of precision specified by the content provider.

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for evaluating statistical significance using placebo actions. One method includes receiving exposure data and control data describing exposed and control subjects and determining a test statistic for each of a plurality of subject actions including a desired action and a plurality of placebo actions. The test statistic for an action corresponds to an estimated effect that exposure to the treatment has on a likelihood that a subject will take the action. The method further includes comparing the test statistic for the desired action to the test statistics for the plurality of placebo actions, and determining whether the test statistic for the desired action is statistically significant according to the comparison. Another method includes automatically identifying placebo actions using a taxonomy that classifies subject actions or treatments, and determining test statistics for the placebo actions.

Abstract: An electronic device with self-learning function includes an input unit, a state detecting unit, a state recording unit, a state analysis unit, and a processing unit. The input unit generates input signals according to operations of a user. The state detecting unit detects real-time values of the state parameters. The state recording unit obtains the real-time values of predetermined state parameters when the input signals are received. The state analysis unit analyzes whether the times of an operation under a same state parameter value or in same state parameter value range has reached or equal to a predetermined number of times within a predetermined time period. If yes, the state analysis unit determines the operation is a habit and the state parameter value corresponding to the operation is the state parameter value of the habit. The processing unit executes a task corresponding to the habit.

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for evaluating statistical significance using placebo actions. One method includes receiving exposure data and control data describing exposed and control subjects and determining a test statistic for each of a plurality of subject actions including a desired action and a plurality of placebo actions. The test statistic for an action corresponds to an estimated effect that exposure to the treatment has on a likelihood that a subject will take the action. The method further includes comparing the test statistic for the desired action to the test statistics for the plurality of placebo actions, and determining whether the test statistic for the desired action is statistically significant according to the comparison. Another method includes automatically identifying placebo actions using a taxonomy that classifies subject actions or treatments, and determining test statistics for the placebo actions.

Abstract: An automatic menu display, service request, order entry and management system used in restaurant comprises of a central server, Electronic Assistant Terminals (EAT) and service location terminals. The central server synchronizes the menu display, dispatches service requests and order entries to appropriate service locations, including kitchen, cashier, service desk and bar. All the information related to sales and requests are stored in a database located on the central server. The Electronic Assistant Terminals display image or text menu, transmit service requests and order entries as well as display summarized customers' order statement and bill statements. The EATs are placed on each table adjacent to each seat, talking to the center control server through wireless connection. The service location terminals include service desk terminals, kitchen terminals, cashier terminals and office terminals. Service desk terminals prompt the request contents, such as drinks, foods, special services, and their origins.