Systems and methods for automatic generation of media promotions
AssigneeMusx
InventorJohn Reardon
Life of this patent
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Abstract
Disclosed embodiments provide a framework for automatically generating media promotions according to real-time media analytics and that can be presented to different users of a peer-to-peer music recommendation service. In response to a user query to generate a promotion for a song, the service converts the query into a set of embeddings. Using user profile data and historical music data corresponding to the song and obtained based on the set of embeddings, the service generates music analytics corresponding to the song and recommendations for different music promotions associated with the song. The service can implement these music promotions and track the efficacy of these music promotions as users engage with the music promotions and the song.
Claims21
A computer-implemented method comprising:
- receiving a user query to generate a music promotion corresponding to a song, wherein the user query is received during an ongoing communications session;
- dynamically converting the user query into a set of embeddings, wherein the set of embeddings are obtained through language processing of the user query;
- obtaining user profile data and historical music data corresponding to the song and based on the set of embeddings, wherein the user profile data is associated with a set of different users, and wherein the historical music data corresponds to different user interactions with the song;
- processing the user profile data and the historical music data through a trained machine learning algorithm to dynamically generate a set of music analytics corresponding to the song and a set of recommendations for different music promotions, wherein the trained machine learning algorithm is trained using a dataset of sample music data and sample promotions;
- generating a response to the user query, wherein the response includes the set of music analytics and the set of recommendations;
- and
- updating the trained machine learning algorithm based on feedback associated with the set of music analytics and the set of recommendations, wherein the feedback is obtained through the ongoing communications session.
A system, comprising:
- one or more processors;
- and
- memory storing thereon instructions that, as a result of being executed by the one or more processors, cause the system to:
- receive a user query to generate a music promotion corresponding to a song, wherein the user query is received during an ongoing communications session;
- dynamically convert the user query into a set of embeddings, wherein the set of embeddings are obtained through language processing of the user query;
- obtain user profile data and historical music data corresponding to the song and based on the set of embeddings, wherein the user profile data is associated with a set of different users, and wherein the historical music data corresponds to different user interactions with the song;
- process the user profile data and the historical music data through a trained machine learning algorithm to dynamically generate a set of music analytics corresponding to the song and a set of recommendations for different music promotions, wherein the trained machine learning algorithm is trained using a dataset of sample music data and sample promotions;
- generate a response to the user query, wherein the response includes the set of music analytics and the set of recommendations;
- and
- update the trained machine learning algorithm based on feedback associated with the set of music analytics and the set of recommendations, wherein the feedback is obtained through the ongoing communications session.
A non-transitory computer-readable storage medium storing thereon executable instructions that, as a result of being executed by one or more processors of a computer system, cause the computer system to:
- receive a user query to generate a music promotion corresponding to a song, wherein the user query is received during an ongoing communications session;
- dynamically convert the user query into a set of embeddings, wherein the set of embeddings are obtained through language processing of the user query;
- obtain user profile data and historical music data corresponding to the song and based on the set of embeddings, wherein the user profile data is associated with a set of different users, and wherein the historical music data corresponds to different user interactions with the song;
- process the user profile data and the historical music data through a trained machine learning algorithm to dynamically generate a set of music analytics corresponding to the song and a set of recommendations for different music promotions, wherein the trained machine learning algorithm is trained using a dataset of sample music data and sample promotions;
- generate a response to the user query, wherein the response includes the set of music analytics and the set of recommendations;
- and
- update the trained machine learning algorithm based on feedback associated with the set of music analytics and the set of recommendations, wherein the feedback is obtained through the ongoing communications session.
Dependencies are inferred from the claim text.