Systems and methods for entity detection using artificial intelligence, including: a deep learning model service configured to: select and analyze a set of frames from a media item to determine a set of candidate brand-probability pairs; a voting engine configured to: determining that a first brand-probability pair of a set of candidate brand-probability pairs based on at least one obtained hyperparameter value does not meet a threshold for determining whether candidate brand-probability pairs are to be included in a result set; excluding the first brand-probability pair from the result set based on the determination; sorting the result set; and selecting at least one final brand-probability pair from the result set; and an offline transcoding service configured to: store the final brand-probability pair in a repository with a relation to an identifier of the media item.

Certain aspects provide a computer-implemented method for evaluating social media marketing campaigns using artificial intelligence (AI). The method comprises using a large language model (LLM) to generate a plurality of AI personas. Each AI persona represents a different segment of a target audience of a marketing campaign. The method uses a transformer model, a decision tree-based model, and a natural language processing (NLP) model to predict a response, a rating, and a feedback to the marketing campaign for each AI persona that represents a different segment of the target audience. The predicted responses, ratings, and feedback for the AI personas that represent different segments of the target audience are aggregated to form an evaluation of the marketing campaign for each segment of the target audience. The method sends the evaluation of the marketing campaign to a user.

A system and method for analyzing screen-recorded personalized digital content using on-device computer vision and generative AI. A user captures content from a recommender system interface, such as screen activity or browser-rendered content, optionally with concurrent voice commentary. On-device processing generates intermediate representations using CLIP-style embeddings, OCR text, and transcribed audio tokens, flagging advertisements, changes in content, diversity in content, and similarities in content. A compact generative AI model produces metadata summaries, which users may annotate with tags or comments. A composite metadata package is transmitted to a cloud system, while raw media is deleted. The invention enables privacy-preserving, bandwidth-efficient insight into recommender-based media and user feedback.

A system and method for promoting content items in a content platform, including: a computer processor, a content promoter service executing on the computer processor and including functionality to identify an impression budget and a pacing parameter for impressions of an unvetted content item and utilize the impression budget and the pacing parameter to control availability of the unvetted content item for artificial promotion, a content recommender service including functionality to receive a request for content for a container, identify a set of vetted content items based on historical performance, artificially promote the unvetted content item by injecting it into the set of vetted content items and providing the final set of content in response to the request, and a content cold-start service configured to select the unvetted content item as a candidate for injection based on similarity to a surrogate content item in the set of vetted content items.

Systems and methods for entity detection using artificial intelligence, including: a deep learning model service configured to: select and analyze a set of frames from a media item to determine a set of candidate brand-probability pairs; a voting engine configured to: determining that a first brand-probability pair of a set of candidate brand-probability pairs based on at least one obtained hyperparameter value does not meet a threshold for determining whether candidate brand-probability pairs are to be included in a result set; excluding the first brand-probability pair from the result set based on the determination; sorting the result set; and selecting at least one final brand-probability pair from the result set; and an offline transcoding service configured to: store the final brand-probability pair in a repository with a relation to an identifier of the media item.

This disclosure provides systems, methods, services, and platforms for automatically prompting a user of a mobile device to send feedback after a triggering event. This disclosure enables Mobile Network Operators to immediately solicit timely feedback from a mobile user after an interaction with the user. When a business, group, or other entity has an active survey campaign, a survey message comprising one or more response options is automatically sent to a mobile device after a triggering event occurs. Based on the user's selected response option, subsequent, follow-up survey messages can be automatically sent to the mobile device.

A method of providing information is disclosed. The method includes collecting, by a server, information about a business that seeks an advertisement for a service including a time restriction, determining an advertisement-target vehicle based on an occupant condition and a time condition provided in the collected information, and transferring the advertisement to the determined advertisement-target vehicle.