Alphonso Inc.

Sartaki Sinha Roy

Susmita Ghose

A computer program product is provided for predicting ad spend for a specific media program aired or streamed on a specific network at a specific date and time using a database of media program data that includes known ad spend for a subset of media programs, and viewership data for each of the media programs, including total viewership and viewership ratings. Each of the media programs is identified by its respective network, and date and time of airing or streaming. A random forest model is trained to predict ad spend using variables that are identified as being correlated with ad spend. The trained random forest model is then used to predict ad spend for a specific media program that is aired or streamed on a specific network at a specific date and time, and which has an unknown ad spend.

Computer program product that implements a machine learning process using a random forest model for predicting advertisement spending

A method is provided to identify whether video content, which includes a plurality of image frames, is likely to include an advertisement. The video content is split into a plurality of segments, each segment having a pre-specified duration. Subtitle text information is extracted from each segment and is passed through a natural language processing (NLP) language model to extract an embedding representing the subtitle text information for each of the segments, wherein the NLP language model is previously trained to differentiate between subtitle text information from video content items that were each previously identified as being an advertisement in comparison to subtitle text information from video content items that were each previously identified as not being an advertisement. The embedding representing the subtitle text information for each of the segments is passed through a classifier to obtain a probability regarding whether each segment is an advertisement or not.

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West Bengal, India

Sartaki Sinha Roy