Disclosed herein are example embodiments of an improved narrative generation system where an analysis service that executes data analysis logic that supports story generation is segregated from an authoring service that executes authoring logic for story generation through an interface. Accordingly, when the authoring service needs analysis from the analysis service, it can invoke the analysis service through the interface. By exposing the analysis service to the authoring service through the shared interface, the details of the logic underlying the analysis service are shielded from the authoring service (and vice versa where the details of the authoring service are shielded from the analysis service). Through parameterization of operating variables, the analysis service can thus be designed as a generalized data analysis service that can operate in a number of different content verticals with respect to a variety of different story types.

A data processing system for discovering a semantic meaning of a field included in one or more data sets is configured to identify a field included in one or more data sets, with the field having an identifier. For that field, the system profiles data values of the field to generate a data profile, accesses a plurality of label proposal tests, and generates a set of label proposals by applying the plurality of label proposal tests to the data profile. The system determines a similarity among the label proposals and selects a classification. The system identifies one of the label proposals as identifying the semantic meaning. The system stores the identifier of the field with the identified one of the label proposals that identifies the semantic meaning.

A data processing system for discovering a semantic meaning of a field included in one or more data sets is configured to identify a field included in one or more data sets, with the field having an identifier. For that field, the system profiles data values of the field to generate a data profile, accesses a plurality of label proposal tests, and generates a set of label proposals by applying the plurality of label proposal tests to the data profile. The system determines a similarity among the label proposals and selects a classification. The system identifies one of the label proposals as identifying the semantic meaning. The system stores the identifier of the field with the identified one of the label proposals that identifies the semantic meaning.

Contextual data may be generated from assets in asset portfolios using metadata enrichment services. A recommendation engine may generate a set of recommended assets for presentation in a content stream based on the contextual data. Brand safety may be implemented using a brand safety policy that uses the contextual data as indicators of potentially offensive content. Advertisements included in the content stream may also be targeted based on the contextual data.

Contextual data may be generated from assets in asset portfolios using metadata enrichment services. A recommendation engine may generate a set of recommended assets for presentation in a content stream based on the contextual data. Brand safety may be implemented using a brand safety policy that uses the contextual data as indicators of potentially offensive content. Advertisements included in the content stream may also be targeted based on the contextual data.

A machine learning-informed method executing automated workflows for digital file handling includes computing, by file classification machine learning models, a machine learning classification inference for each of a plurality of distinct digital files of a corpus of digital files; curating a plurality of distinct sub-corpora of digital files based on the machine learning classification inference associated with each of the plurality of distinct digital files, wherein the at least one machine learning classification inference comprises a digital file type classification inference of a plurality of distinct digital file type classification inferences; and selectiyely executing an automated digital file handling workflow for at least one sub-corpus based on the digital file type classification inference, wherein the automated digital file handling workflow includes a sequence of computer-executable tasks that, when executed, operates to modify one or more of a residency, permissions, and file metadata associated with each of the distinct digital files.

A machine learning-informed method executing automated workflows for digital file handling includes computing, by file classification machine learning models, a machine learning classification inference for each of a plurality of distinct digital files of a corpus of digital files; curating a plurality of distinct sub-corpora of digital files based on the machine learning classification inference associated with each of the plurality of distinct digital files, wherein the at least one machine learning classification inference comprises a digital file type classification inference of a plurality of distinct digital file type classification inferences; and selectiyely executing an automated digital file handling workflow for at least one sub-corpus based on the digital file type classification inference, wherein the automated digital file handling workflow includes a sequence of computer-executable tasks that, when executed, operates to modify one or more of a residency, permissions, and file metadata associated with each of the distinct digital files.

Techniques and systems are described that leverage computer vision as part of search to expand functionality of a computing device available to a user and increase operational computational efficiency as well as efficiency in user interaction. In a first example, user interaction with items of digital content is monitored. Computer vision techniques are used to identify digital images in the digital content, objects within the digital images, and characteristics of those objects. This information is used to assign a user to a user segment of a user population which is then used to control output of subsequent digital content to the user, e.g., recommendations, digital marketing content, and so forth.

One or more highlights of a video stream may be identified. The highlights may be segments of a video stream, such as a broadcast of a sporting event, that are of particular interest to one or more users. According to one method, at least a portion of the video stream may be stored. The portion of the video stream may be compared with templates of a template database to identify the one or more highlights. Each highlight may be a subset of the video stream that is deemed likely to match the one or more templates. The highlights, an identifier that identifies each of the highlights within the video stream, and/or metadata pertaining particularly to the one or more highlights may be stored to facilitate playback of the highlights for the users.

One or more highlights of a video stream may be identified. The highlights may be segments of a video stream, such as a broadcast of a sporting event, that are of particular interest to one or more users. According to one method, at least a portion of the video stream may be stored. The portion of the video stream may be compared with templates of a template database to identify the one or more highlights. Each highlight may be a subset of the video stream that is deemed likely to match the one or more templates. The highlights, an identifier that identifies each of the highlights within the video stream, and/or metadata pertaining particularly to the one or more highlights may be stored to facilitate playback of the highlights for the users.