Abstract: In a digital image annotation and retrieval system, a machine learning model identifies an image feature in an image and generates a plurality of question prompts for the feature. For a particular feature, a feature annotation is generated, which can include capturing a narrative, determining a plurality of narrative units, and mapping a particular narrative unit to the identified image feature. An enriched image is generated using the generated feature annotation. The enriched image includes searchable metadata comprising the feature annotation and the plurality of question prompts.

Abstract: Example systems, methods, and non-transitory computer readable media are directed to an AI-assisted interview system that provides real-time analysis and suggestions during video and audio interviews. The system can analyze questions, response text, audio, and video in real-time and suggest follow-up topics and questions. Suggested topics and questions can optimize for breadth by sampling under-sampled semantic regions and/or depth by identifying related questions. The system can provide clarification suggestions for ambiguous responses and measure quality metrics for questions and answers.

Abstract: In a digital image annotation and retrieval system, a machine learning model identifies an image feature in an image and generates a plurality of question prompts for the feature. For a particular feature, a feature annotation is generated, which can include capturing a narrative, determining a plurality of narrative units, and mapping a particular narrative unit to the identified image feature. An enriched image is generated using the generated feature annotation. The enriched image includes searchable metadata comprising the feature annotation and the plurality of question prompts.

Abstract: In a digital image annotation and retrieval system, a machine learning model identifies an image feature in an image and generates a plurality of question prompts for the feature. For a particular feature, a feature annotation is generated, which can include capturing a narrative, determining a plurality of narrative units, and mapping a particular narrative unit to the identified image feature. An enriched image is generated using the generated feature annotation. The enriched image includes searchable metadata comprising the feature annotation and the plurality of question prompts.

Abstract: According to one embodiment, methods and systems are disclosed for converting multi-media content to multiple deliverable formats using a conversion workflow. For example, a single source content file can be captured and a plurality of deliverable content files can be derived from the single source content file. Each of the plurality of deliverable content files can be in a format specific to a different distribution channel. That is, a single source media file can be used to automatically build one or more distribution files for a particular channel or channels. Furthermore, the distribution files can be built including logos, branding, open/close graphics, ratings, promo material, etc. The inclusion of these elements can be based on a common set of business rules defining, for example, what elements should be included in different types of distribution files.

Abstract: According to one embodiment, methods and systems are disclosed for converting multi-media content to multiple deliverable formats using a conversion workflow. For example, a single source content file can be captured and a plurality of deliverable content files can be derived from the single source content file. Each of the plurality of deliverable content files can be in a format specific to a different distribution channel. That is, a single source media file can be used to automatically build one or more distribution files for a particular channel or channels. Furthermore, the distribution files can be built including logos, branding, open/close graphics, ratings, promo material, etc. The inclusion of these elements can be based on a common set of business rules defining, for example, what elements should be included in different types of distribution files.

Abstract: According to one embodiment, methods and systems are disclosed for converting multi-media content to multiple deliverable formats using a conversion workflow. For example, a single source content file can be captured and a plurality of deliverable content files can be derived from the single source content file. Each of the plurality of deliverable content files can be in a format specific to a different distribution channel. That is, a single source media file can be used to automatically build one or more distribution files for a particular channel or channels. Furthermore, the distribution files can be built including logos, branding, open/close graphics, ratings, promo material, etc. The inclusion of these elements can be based on a common set of business rules defining, for example, what elements should be included in different types of distribution files.