Graph-Based Media Content Evaluation

Number: 20190026315
Published: 2019-01-24
Filed: 2017-07-19
Assignee: Disney Enterprises, Inc.
Inventors: Accardo; Anthony M. et al.
CPC: G06Q10/10; G06F16/532; G06F16/438; G06F3/048; G06F16/48
Verdict: Set aside graph-based media content evaluation, business analytics
Source: Google Patents · FreePatentsOnline

Abstract

According to one implementation, a system for performing graph-based media content evaluation includes a computing platform having a hardware processor, and a system memory storing a media content evaluation software code and a graph database. The hardware processor is configured to execute the media content evaluation software code to receive a query from a system user, and to identify one or more media content evaluation metrics corresponding to the query. In addition, the hardware processor is configured to execute the media content evaluation software code to search the graph database for a media content data relevant to the one or more media content evaluation metrics, and to retrieve the media content data from the graph database. The hardware processor is further configured to execute the media content evaluation software code to generate a report responsive to the query using the media content data.

Background

BACKGROUND

The popularity of video as an entertainment medium has resulted in a wide variety of video based media content being produced and made available to consumers. Traditionally, the creative development of such media content has been a largely subjective, instinct driven, process. For example, a creative decision maker may view programming, or read a screenplay or script, and determine that a work is too violent, or insufficiently romantic.

Despite the subjectivity with which creative decisions surrounding the development of media content have historically been made, media experts and screenwriters agree that there do exist core structural elements common to the types engaging storylines that underlie successful movies and television programming. Unfortunately, however, the conventional art lacks a structured and quantitative process for objectively evaluating or comparing distinct works, or to predict the likelihood that a particular work will be a commercial success.SUMMARY

There are provided systems and methods for graph-based media content evaluation, substantially as shown in and/or described in connection with at least one of the figures, and as set forth more completely in the claims.

Claims

1. A system comprising: a computing platform including a hardware processor and a system memory; the system memory storing a media content evaluation software code and a graph database; the hardware processor configured to execute the media content evaluation software code to: receive a query from a system user; identify at least one media content evaluation metric corresponding to the query; search the graph database for a media content data relevant to the at least one media content evaluation metric; retrieve the media content data from the graph database; and generate a report responsive to the query using the media content data. 8. A method for use by a system including a computing platform having a hardware processor and a system memory storing a media content evaluation software code and a graph database, the method comprising: receiving, using the hardware processor, a query from a system user; identifying, using the hardware processor, at least one media content evaluation metric corresponding to the query; searching, using the hardware processor, the graph database for a media content data relevant to the at least one media content evaluation metric; retrieving, using the hardware processor, the media content data from the graph database; and generating, using the hardware processor, a report responsive to the query using the media content data. 15. A computer-readable non-transitory medium having stored thereon instructions, which when executed by a hardware processor, instantiate a method comprising: receiving a query from a system user; identifying at least one media content evaluation metric corresponding to the query; searching a graph database for a media content data relevant to the at least one media content evaluation metric; retrieving the media content data from the graph database; and generating a report responsive to the query using the media content data.