A computer-implemented method comprising: outputting questions to a user via one or more user devices, and receiving back responses to some of the questions from the user via one or more user devices; over time, controlling the outputting of the questions so as to output the questions under circumstances of different values for each of one or more items of metadata, wherein the one or more items of metadata comprise at least a time and/or a location at which a question was output to the user via the one or more user devices; monitoring whether or not the user responds when the question is output with the different metadata values; training the machine learning algorithm to learn a value of each of the items of metadata which optimizes a reward function, and based thereon selecting a time and/or location at which to output subsequent questions.

A restaurant service system for assessing the accuracy of estimated delivery time provided by a restaurant includes an order server, a restaurant server, a service server, and an assessment server. Each of the servers includes a server software application. The order server software application collects a set of orders from a set of diner devices. The restaurant server software application retrieves an estimated delivery time for each order in the set. The service server software application determines an order actual delivery time for at least one order in the set. The assessment server software application determines an accuracy measure of estimated delivery time for the restaurant.

Methods, apparatuses, and computer program products are described herein that are configured to be embodied as a benchmarking service. In an example, an apparatus is configured to access input data, wherein the input data is representative of a current project; parse the input data to generate one or more input project units; extract one or more features from the one or more input project units, wherein the features are representative of at least one of project statistics, project bugs, project releases, project documentations, and organization data; receive a benchmarking model, wherein the benchmarking model was derived using a historical data set; and generate an output based on the benchmarking model and the one or more features, wherein the output is configured to provide an evaluation of the current project in the form at least one of a score and one or more recommendations.

A machine-learning ecosystem includes a correlation module for building at least one prediction model based on at least one data input including at least one input parameter and at least one output parameter, the prediction model relating the output parameter to the input parameter. The correlation module performs at least one threshold check on the prediction model to assess the robustness of the prediction model. The ecosystem further includes a decision module communicatively coupled to the correlation module and receiving the prediction model from the correlation module. Based on a verification check at the decision module, a confirmation, a deferral, or a rejection of the prediction model is sent from the decision module to the correlation module.

A machine-learning ecosystem includes a correlation module for building at least one prediction model based on at least one data input including at least one input parameter and at least one output parameter, the prediction model relating the output parameter to the input parameter. The correlation module performs at least one threshold check on the prediction model to assess the robustness of the prediction model. The ecosystem further includes a decision module communicatively coupled to the correlation module and receiving the prediction model from the correlation module. Based on a verification check at the decision module, a confirmation, a deferral, or a rejection of the prediction model is sent from the decision module to the correlation module.

Systems and methods for aggregating and storing different types of data, and generating interactive user interfaces for analyzing the stored data. In some embodiments, entity data is received for a plurality of entities from one or more data sources, and used to determine attribute values for the entities for one or more given time periods. The plurality of entities may be categorized into one or more entity groups, and aggregate attribute values may be generated based upon the entity groups. A first interactive user interface is generated displaying the one or more entity groups in association with the aggregated attribute values associated with the entity group. In response to a received indication of a user selection of an entity group, a second interactive user interface is generated displaying the one or more entities associated with the selected entity group, each entity displayed in association with the attribute values associated with the entity.

Systems and methods to improve the optical character recognition of records, and in particular health records, are provided. An image of a medical record is received, and an initial optical image recognition (OCR) on the image is performed to identify text information. The OCR signal quality may be measured, and areas of insufficient OCR signal quality may be isolated. The signal quality is determined by a weighted average of semantic analysis of the resulting text, and/or OCR accuracy measures. The OCR process may be repeated on the isolated regions of lower signal quality, each time using a different OCR transform, until all regions are completed with a desired degree of signal quality (accuracy). All the regions of the document may then be recompiled into a single document for outputting.

Method and system for determining a status of a mobile device of a user are disclosed. For example, the method includes receiving first sensor data at a first time from an application installed on a mobile device of a user, determining a first location of the mobile device, immediately subsequent to receiving the first sensor data at the first time, receiving second sensor data at a second time from the application, the second time following the first time by a time interval, determining a second location of the mobile device, determining a distance between the first location and the second location, determining whether the distance corresponding to the time interval exceeds a predetermined threshold, and determining whether a trip log indicative of at least one trip during the time interval is received from the application.

Systems and methods to automatically generate classifiers are provided. A labeled dataset is initially received. The dataset may be for a positive class, or may be a negative for a class, or a false positive class. N features that are predictive for the class (or false positive or the negative class) are identified. These features are combined within a classifier dictionary. Medical records received may be processed in order to be machine readable. Features within the medical records are identified and are compared against the dictionary of classifiers. Matches indicate classes within the medical record. The classifier dictionary may be periodically updated in response to insufficient classification accuracy, or when new data becomes available.

In operational methodology and a software package or other computer enabled business method, which enables users to apply the method and practice. A wide variety of means for identifying, evaluating and mitigating risk and performance factors within an organization are also provided.