Abstract: A method and system for one or more application command recommendations may include receiving a search query, the search query being an in-application assistance query, accessing contextual data associated with the search query, providing at least one of the search query and the contextual data as input to a multilingual machine-learning (ML) model to identify one or more application command recommendations, obtaining the one or more application command recommendations as an output from the multilingual ML model, and providing data about the output to the application for display. The multilingual ML model uses a multilingual encoder to provide command recommendations for a plurality of languages.

Abstract: Systems and methods for classifying content of an electronic file. One system includes an electronic processor configured to determine a content type associated with a portion of content included in the electronic file using a classification model developed using machine learning. The electronic processor is also configured to determine a suggested modification for the portion of content based on the determined content type. The suggested modification is a modification to a format property of the portion of content. The electronic processor is also configured to provide a notification of the suggested modification to a user for acceptance of the suggested modification. In response to the user accepting the suggested modification, the electronic processor is configured to modify the format property of the portion of content in accordance with the suggested modification.

Abstract: Systems and methods directed to providing recommended charts are provided. More specifically, a selection of data arranged in a plurality of data series may be received and classified into series data types. Based on the series data type for each data series of the plurality of data series, a plurality of recommended charts visually describing the data may be automatically provided to a user interface, wherein each chart of the plurality of recommended charts is a different chart type visually describing the data. To provide the plurality of recommended charts, best practices and/or one or more machine learning models may be utilized. In some instances, the charts provided in the user interface may automatically change or otherwise updated based on a different selection of data and/or an assignment of a different data series type to a data series.

Abstract: Various embodiments pertain to techniques for forecasting and providing guaranteed delivery of ad impression opportunities using real-time bidded platform inventories. In various embodiments, bid landscape data, such as information regarding a number of impression requests by a particular supply unit, a time of the impression requests, and an amount of a winning bid for the impression requests, is recorded for use in forecasting an available impression volume as a function of cost. The forecasted available impression volume represents a number of ad impression requests likely to be obtained through real-time bidding channels. In various embodiments, an advertiser can place an order for a number of ad impressions at a particular price, and the ad impressions ordered can be specifically allocated for that advertiser. The number of ad impressions ordered by an advertiser that are guaranteed vary depending on the price and the available volume forecasted at that price.

Abstract: Computer-readable media for determining whether to accept a candidate order from a content provider, or advertiser, to display a particular number of advertisements within a specified time segment are provided. Initially, the content provider may include placement criteria that, among other things, identify a leaf node at which impressions of the advertisement are expected to be rendered. Generally, the leaf node refers to a location within a topic graph that describes inventory that is permissible to allocate to satisfy the candidate order. To perform the determination, the inventory of impressions available for accommodating the candidate order and a log of booked orders scheduled to be placed within the time segment are identified. Linear programs are then utilized to determine whether the estimated inventory that satisfies the placement criteria is available by predictively placing the booked orders at the estimated inventory. If estimated inventory remains available, the candidate order is accepted.

Abstract: Various embodiments provide techniques for advertisement inventory. In at least some embodiments, a scaled number of impressions can be matched to orders that have scaled impression goals. Impressions can be randomly selected from an offline traffic model and allocated to orders according to a matching algorithm until a number of impression defined by a scale factor is reached. This can occur by sampling the traffic model directly using the scale factor and/or by creating a scaled data set to which the matching algorithm can be applied. The matching algorithm can be configured to identify an order that is farthest away from being complete and then match the randomly selected impression to the identified order. If the scaled orders in the data set can be fulfilled using the scaled number of impressions, a conclusion is made that the original set of orders can be fulfilled using the original impressions.

Abstract: Computer-readable media, computer systems, and computing devices for responding to advertising inventory queries that specify frequency caps limiting the number of impressions that are shown to a unique user. Impression logs are retrieved from logging servers and processed to generate a unique user distribution that provides information regarding the numbers of unique users that receive ad impressions at a specified location and during a specified time period. Estimates of frequency-capped capacity to fulfill a booking request and estimates of frequency-capped availability of inventory are computed using the unique user distribution.

Abstract: Computer-readable media for determining whether to accept a candidate order from a content provider, or advertiser, to display a particular number of advertisements within a specified time segment are provided. Initially, the content provider may include placement criteria that, among other things, identify a leaf node at which impressions of the advertisement are expected to be rendered. Generally, the leaf node refers to a location within a topic graph that describes inventory that is permissible to allocate to satisfy the candidate order. To perform the determination, the inventory of impressions available for accommodating the candidate order and a log of booked orders scheduled to be placed within the time segment are identified. Linear programs are then utilized to determine whether the estimated inventory that satisfies the placement criteria is available by predictively placing the booked orders at the estimated inventory. If estimated inventory remains available, the candidate order is accepted.