- Number
- 12450499
- Published
- 2025-10-21
- Filed
- 2020-09-28
- Assignee
- Disney Enterprises, Inc.
- Inventors
- Huck; Lori L. et al.
- CPC
- G06Q10/06375; G06F40/205; G06F40/279; G06F40/289; G06F40/30; G06Q10/101; G06Q10/06395; G06N5/04; G06N20/00
- Verdict
- Set aside script quality analytics, business
- Source
- Google Patents · FreePatentsOnline
Abstract
Embodiments provide for evaluation of scripts. A script for producing media content is received, and a plurality of tags related to content of the script is determined. A quality score is generated for the script by processing the plurality of tags using a first model, and one or more modifications for the script are generated based on the quality score and the plurality of tags.
Background
BACKGROUND (1) Studios, networks, and streaming services expend significant resources in acquiring, developing, and/or rewriting scripts before actually producing media (e.g., filming and editing video) based on the “finished” script. A variety of techniques can be applied to attempt to ensure the resulting media will be successful. For example, media producers often consider factors including distribution plans and marketing for the media, as well as script-specific factors like what genres or actors are popular. However, these considerations are inherently subjective and imprecise, and existing approaches do not provide any cohesive and objective way to predict success (or failure) at early stages, especially at the script-level on which the media is based. SUMMARY (2) According to one embodiment of the present disclosure, a method is provided. The method comprises: receiving a script for producing media content; determining a plurality of tags related to content of the script; generating a quality score for the script by processing the plurality of tags using a first model; and generating, based on the quality score and the plurality of tags, one or more modifications for the script. (3) According to one embodiment of the present disclosure, a non-transitory computer-readable medium containing computer program code is provided. The computer code, when executed by operation of one or more computer processors, performs an operation comprises: receiving a script for producing
Claims
1. A computer-implemented method, comprising: receiving, by a processor: a script for producing media content, a plurality of tags related to content of the script, wherein the plurality of tags are generated using a first machine learning model trained to generate tags based on script elements, and wherein the script elements comprise plot devices, character types, or dialogue counts or distributions, and a quality score for the script generated by a second machine learning model configured to process the plurality of tags, wherein a selection of the second machine learning model is based at least in part on a genre of the script; outputting, by the processor and via a graphical user interface, one or more proposed modifications for the script based on the quality score and the plurality of tags; automatically applying the one or more proposed modifications to the script; and automatically refining the second machine learning model based on feedback relating to the quality of the script. ||
12. A non-transitory computer-readable medium containing computer program code that, when executed by operation of one or more computer processors, performs an operation comprising: receiving a script for producing media content; automatically generating a plurality of tags related to content of the script, wherein the plurality of tags are generated using a first machine learning model trained to generate tags based on script elements, and wherein the script elements comprise plot devices, character types, or dialogue counts or distributions; selecting a second machine learning model, wherein the second machine learning model is selected from a plurality of models based at least in part on a genre of the script; generating a quality score for the script by processing the plurality of tags using the selected second machine learning model; generating, based on the quality score and the plurality of tags, one or more modifications for the script; outputting, via a graphical user interface, the quality score and the one or more modifications for the script; automatically applying the one or more modifications to the script; and automatically refining the second machine learning model based on; a determination that the quality score is above or below a threshold, a popularity of the media content based on the modified script, a delivery mode of the media content, and adjusting one or more predefined thresholds used by the second machine learning model to generate the quality score. ||
17. A system, comprising: one or more computer processors; and a memory containing a program which when executed by the one or more computer processors performs an operation, the operation comprising: receiving a script for producing media content; automatically generating a plurality of tags related to content of the script, wherein the plurality of tags are generated using a first machine learning model trained to generate tags based on script elements, and wherein the script elements comprise plot devices, character types, or dialogue counts or distributions; selecting a second machine learning model, wherein the second machine learning model is selected from a plurality of models based at least in part on a genre of the script; generating a quality score for the script by processing the plurality of tags using the selected second machine learning model; generating, based on the quality score and the plurality of tags, one or more modifications for the script; outputting, via a graphical user interface, the quality score and the one or more modifications for the script; automatically applying the one or more modifications to the script; and automatically refining the second machine learning model based on: a determination that the quality score is above or below a threshold, a popularity of the media content based on the modified script, a delivery mode of the media content, and adjusting one or more predefined thresholds used by the second machine learning model to generate the quality score.