Data processing systems and methods, according to various embodiments, are adapted for determining a categorization for each tracking tool that executes on a particular webpage based on a variety of criteria, such as the purpose of the tracking tool and its source script. The system may compare the characteristics of tracking tools on a webpage to a database of known tracking tools to determine the appropriate categorization. When a user visits the webpage, the system analyzes these categories and determines whether the tracking tool should be permitted to run based on the categories and/or other criteria, such as whether the user has consented to the use of that type of tracking tool.

Index values of monitored objects are stored. Sets of index values, collected at time points within a time period and each including a first number of index values corresponding to a respective monitored object, are received. For each set, a second number of index values is selected from the first number to generate sets of reduced index values for storage. The second number to be used for reduction of subsequent index values is updated at least once, the updating comprising: generating a first list of monitored objects based on the sets of index values, generating a second list of monitored objects based on the sets of reduced index values, and updating the second number based on a comparison between the first list and the second list. The amount of monitoring data stored is reduced while enabling accurate searching for top-ranked monitored objects.

Index values of monitored objects are stored. Sets of index values, collected at time points within a time period and each including a first number of index values corresponding to a respective monitored object, are received. For each set, a second number of index values is selected from the first number to generate sets of reduced index values for storage. The second number to be used for reduction of subsequent index values is updated at least once, the updating comprising: generating a first list of monitored objects based on the sets of index values, generating a second list of monitored objects based on the sets of reduced index values, and updating the second number based on a comparison between the first list and the second list. The amount of monitoring data stored is reduced while enabling accurate searching for top-ranked monitored objects.

The exemplary embodiments described herein are related to techniques for automatically generating narratives about data based on communication goal data structures that are associated with configurable content blocks. The use of such communication goal data structures facilitates modes of operation whereby narratives can be generated in real-time and/or interactive manners.

The exemplary embodiments described herein are related to techniques for automatically generating narratives about data based on communication goal data structures that are associated with configurable content blocks. The use of such communication goal data structures facilitates modes of operation whereby narratives can be generated in real-time and/or interactive manners.

In one embodiment, the present invention includes a computer-implemented method comprising storing data in an application using an application custom data type and application custom data structure. The data is stored in a database using the application custom data type and the application custom data structure. In one embodiment, a request is sent to access the data from the application to the database. The data is retrieved from the database in response to the request in the application custom data type and the application custom data structure. In one embodiment, the data is sent from the database to a shared memory in the application custom data type and the application custom data structure and the data is retrieved by the application from the shared memory in the application custom data type and the application custom data structure.

In one embodiment, the present invention includes a computer-implemented method comprising storing data in an application using an application custom data type and application custom data structure. The data is stored in a database using the application custom data type and the application custom data structure. In one embodiment, a request is sent to access the data from the application to the database. The data is retrieved from the database in response to the request in the application custom data type and the application custom data structure. In one embodiment, the data is sent from the database to a shared memory in the application custom data type and the application custom data structure and the data is retrieved by the application from the shared memory in the application custom data type and the application custom data structure.

The present disclosure provides systems and methods for distributed training of machine learning models. In one example, a computer-implemented method is provided for training machine-learned models. The method includes obtaining, by one or more computing devices, a plurality of regions based at least in part on temporal availability of user devices; selecting a plurality of available user devices within a region; and providing a current version of a machine-learned model associated with the region to the plurality of selected user devices within the region. The method includes obtaining, from the plurality of selected user devices, updated machine-learned model data generated by the plurality of selected user devices through training of the current version of the machine-learned model associated with the region using data local to each of the plurality of selected user devices and generating an updated machine-learned model associated with the region based on the updated machine-learned model data.

The present disclosure provides systems and methods for distributed training of machine learning models. In one example, a computer-implemented method is provided for training machine-learned models. The method includes obtaining, by one or more computing devices, a plurality of regions based at least in part on temporal availability of user devices; selecting a plurality of available user devices within a region; and providing a current version of a machine-learned model associated with the region to the plurality of selected user devices within the region. The method includes obtaining, from the plurality of selected user devices, updated machine-learned model data generated by the plurality of selected user devices through training of the current version of the machine-learned model associated with the region using data local to each of the plurality of selected user devices and generating an updated machine-learned model associated with the region based on the updated machine-learned model data.

An ultrasound point-of-care system receives a worksheet from a worksheet server. The worksheet is encoded as HTML that is configured to be rendered by a browser component of system software. One or more predefined JavaScript functions in the HTML provide information that is entered by a user into the worksheet and meta-data about the worksheet. System software on the medical device uses an intermediate component or code insertion to execute the JavaScript functions to retrieve the entered information and the meta-data. The system software stores the information locally and packages the retrieved information and meta-data as a DICOM file for transmission to the worksheet server when a computer communication connection to the worksheet server becomes available.