SCREENPLAY CONTENT ANALYSIS ENGINE AND METHOD

• US 20170300748A1
• Filed: 03/31/2016
• Published: 10/19/2017
• Est. Priority Date: 04/02/2015
• Status: Abandoned Application

• Current Assignee: Snug Plug, LLC
• Original Assignee: Snug Plug, LLC
• Inventors: AUSTIN, BRIAN, FOSTER, SCOTT
• Application Number:
  US15/088,103
• Publication Number:
  US 20170300748A1
• Time in Patent Office: Days
• CPC Class Codes: G06F 16/345 Summarisation for human users
• CPC Class Codes: G06F 40/284 Lexical analysis, e.g. tokenisation or collocates
• CPC Class Codes: G06F 40/295 Named entity recognition
• CPC Class Codes: G06V 30/416 Extracting the logical structure, e.g. chapters, sections or page numbers; Identifying elements of the document, e.g. authors

Abstract

Embodiments of the inventive concept provide a screenplay content analysis engine and associated method for automatically analyzing a screenplay document. The screenplay content analysis engine can include logic sections for interpreting and analyzing the screenplay document. The logic sections can include a screenplay preconditioner logic section, an initial pass interpreter logic section, a second pass interpreter logic section, a deep interpreter logic section, and a screenplay analysis logic section. The method can include preconditioning the screenplay document, performing an initial interpretive pass of nodes associated with the screenplay document, performing a second interpretive pass, performing a deep interpretive pass, performing a screenplay analysis based on the interpretive passes, and displaying summarized information about the screenplay document. The nodes associated with the screenplay document can include left position information, which are grouped together and compared with predefined left positions, so that the various body section types can be detected and stored.

Claims

1. A screenplay content analysis engine, comprising:

a microprocessor;

one or more storage devices coupled to the microprocessor;

a user interface coupled to the microprocessor and configured to interface with one or more users;

a receiver configured to receive a screenplay document; and

one or more logic sections coupled to the microprocessor, the one or more logic sections being configured to receive the screenplay document from the receiver,

wherein the one or more logic sections and the microprocessor are configured to interpret and analyze the screenplay document, and to produce summary information about the screenplay document,

wherein the one or more storage devices are configured to store the summary information about the screenplay document, and

wherein the user interface is configured to display the summary information about the screenplay document on a display device for the one or more users.

2. The screenplay content analysis engine of claim 1, wherein:

the one or more logic sections includes a screenplay preconditioner logic section configured to receive the screenplay document from the receiver, and to extract and group textual information within the screenplay document,

the screenplay preconditioner logic section includes a text grouper logic section configured to pull the textual information from the screenplay document, and to organize the textual information into a plurality of relational blocks,

the screenplay preconditioner logic section further includes a position parser logic section configured to determine position and dimension information of the textual information, and

the text grouper logic section and the position parser logic section are configured to work in tandem to produce an ordered list of screenplay evaluation nodes based on the relational blocks and the position and dimension information.

3. The screenplay content analysis engine of claim 2, wherein:

the screenplay preconditioner logic section further comprises a non-standard text filter logic section configured to clean any non-standard text from the relational blocks.

4. The screenplay content analysis engine of claim 2, wherein:

each of the relational blocks includes a page number of the screenplay document on which it appears,

each of the screenplay evaluation nodes includes a corresponding relational block from among the plurality of relational blocks, and

each of the screenplay evaluation nodes further includes metadata information including the page number and the position and dimension information.

5. The screenplay content analysis engine of claim 4, wherein:

the one or more logic sections includes an initial pass interpreter logic section configured to receive the ordered list of screenplay evaluation nodes from the screenplay preconditioner logic section,

the initial pass interpreter logic section is configured to build a most common left position list of predefined common left positions, and

the initial pass interpreter logic section is configured to perform an initial interpretive pass of the ordered list of screenplay evaluation nodes to collect left positions of each of the screenplay evaluation nodes of the screenplay document.

6. The screenplay content analysis engine of claim 5, wherein:

the initial pass interpreter logic section further includes a position correlator logic section configured to group the left positions of each of the screenplay evaluation nodes into a plurality of groups based on left position commonality,

the position correlator logic section is further configured to compare left positions of each of the plurality of groups to the most common left position list of predefined common left positions, and

the position correlator logic section is further configured to store a plurality of matched positions as correlated section types.

7. The screenplay content analysis engine of claim 6, wherein:

a first matched position from among the plurality of matched positions corresponds to a character in dialogue body section dialogue type,

a second matched position from among the plurality of matched positions corresponds to a dialogue body section type,

a third matched position from among the plurality of matched positions corresponds to a slug line body section type,

a fourth matched position from among the plurality of matched positions corresponds to an action or description body section type,

a fifth matched position from among the plurality of matched positions corresponds to a parenthetical body section type,

a sixth matched position from among the plurality of matched positions corresponds to a camera direction body section type,

a seventh matched position from among the plurality of matched positions corresponds to an ancillary body section text type,

an eighth matched position from among the plurality of matched positions corresponds to a dual character in dialogue body section type, and

a ninth matched position from among the plurality of matched positions corresponds to a dual dialogue body section type.

8. The screenplay content analysis engine of claim 5, wherein:

the one or more logic sections further includes a second pass interpreter logic section configured to perform a second interpretive pass of the ordered list of screenplay evaluation nodes,

the second pass interpreter logic section includes a type correlator logic section, and

the type correlator logic section is configured to confirm whether the matched positions of the correlated section types are accurate, and to confirm which body section type of the screenplay document each of the nodes belongs, based at least on a width of each of the screenplay evaluation nodes being wider than a threshold width for each corresponding body section text type.

9. The screenplay content analysis engine of claim 8, wherein:

the second pass interpreter logic section further includes a character name extractor logic section, and

the character name extractor logic section is configured to capture character names and associated character dialogue in a character name and associated dialogue array.

10. The screenplay content analysis engine of claim 9, wherein:

the second pass interpreter logic section further includes an array examination logic section, and

the array examination logic section is configured to detect and merge any duplicate entries in the character name and associated dialogue array, and to count a number of words and a number of lines of dialogue for each of the character names

11. The screenplay content analysis engine of claim 8, wherein:

the one or more logic sections further includes a deep interpreter logic section configured to perform a deep interpretive pass of the ordered list of screenplay evaluation nodes,

the deep interpreter logic section is further configured to isolate text that is pertinent to each of a plurality of characters, and

the deep interpreter logic section is further configured to store the isolated text that is pertinent to each of the plurality of characters.

12. The screenplay content analysis engine of claim 11, wherein:

the deep interpreter logic section is further configured to search the isolated text for age-identifying text, and to associate any found age-identifying text to a particular character from among the plurality of characters,

the deep interpreter logic section is further configured to search the isolated text for gender-identifying text, and to associate any found gender-identifying text to a particular character from among the plurality of characters, and

the deep interpreter logic section is further configured to search the isolated text for character attributes including at least one of race, nationality, or physical attributes, and to associate any found character attributes to a particular character from among the plurality of characters.

13. The screenplay content analysis engine of claim 11, wherein:

the deep interpreter logic section is further configured to detect a plurality of story elements, wherein each story element corresponds to at least one of (a) an overarching theme of a story associated with the screenplay document, (b) an object of a story associated with the screenplay document, or (c) an action-based component of a story associated with the screenplay document, and

the deep interpreter logic section is further configured to apply a weight to each of the story elements based at least on a number of matching terms associated with each of the story elements.

14. The screenplay content analysis engine of claim 13, wherein:

the deep interpreter logic section is further configured to generate a final list of story elements including only those story elements having a weight that exceeds a predefined threshold weight.

15. The screenplay content analysis engine of claim 14, wherein:

the one or more logic sections further includes a screenplay analysis logic section configured to analyze the story elements from among the final list of story elements, and

the screenplay analysis logic section is further configured to determine a genre for the screenplay document based at least on the story elements from among the final list of story elements.

16. The screenplay content analysis engine of claim 15, wherein:

the screenplay analysis logic section is further configured to determine data points,

wherein the data points include at least one of (a) a line count, (b) a page count, (c) a scene count, (d) an all word count, (e) an all unique word count, (f) an all advanced word count, (g) an action word count, (h) an action unique word count, (i) an action advanced word count, (j) a dialogue word count, (k) a dialogue unique word count, (l) a dialogue advanced word count, or (m) a number of characters.

17. The screenplay content analysis engine of claim 15, wherein:

the screenplay analysis logic section is further configured to predict a content rating based on at least one of (a) the final list of story elements, (b) explicit dialogue, or (c) story elements not included in the final list of story elements.

18. The screenplay content analysis engine of claim 1, wherein the user interface is further configured to display on the display device at least one of (a) one or more story elements associated with the screenplay document, (b) a logline associated with the screenplay document, (c) a synopsis associated with the screenplay document, (d) a genre associated with the screenplay document, (e) characters associated with the screenplay document, (f) a budget level associated with the screenplay document, (g) a predicted content rating associated with the screenplay document, or (h) a dialogue to action ratio graph associated with the screenplay document.

19. A method for analyzing a screenplay document, the method comprising:

preconditioning, by a screenplay preconditioner logic section, the screenplay document by extracting and grouping textual information within the screenplay document;

generating, by the screenplay preconditioner logic section, an ordered list of screenplay evaluation nodes;

receiving, by an initial pass interpreter logic section, the ordered list of screenplay evaluation nodes;

building, by the initial pass interpreter logic section, a most common left position list of predefined common left positions;

performing, by the initial pass interpreter logic section, an initial interpretive pass of the ordered list of screenplay evaluation nodes to collect left positions of each of the screenplay evaluation nodes of the screenplay document;

grouping, by the initial pass interpreter logic section, the left positions of each of the screenplay evaluation nodes into a plurality of groups based on left position commonality;

comparing, by the initial pass interpreter logic section, left positions of each of the plurality of groups to the most common left position list of predefined common left positions; and

storing, by the initial pass interpreter logic section, a plurality of matched positions as correlated section types.

20. The method of claim 19, further comprising:

confirming, by a second pass interpreter logic section, whether the matched positions of the correlated section types are accurate;

confirming, by the second pass interpreter logic section, which of a plurality of body section types of the screenplay document each of the nodes belongs, based at least on a width of each of the screenplay evaluation nodes being wider than a threshold width for each corresponding body section text type;

isolating, by a deep interpretive logic section, text that is pertinent to each of a plurality of characters;

storing, by the deep interpretive logic section, the isolated text that is pertinent to each of the plurality of characters;

searching, by the deep interpretive logic section, the isolated text for character attributes associated with the plurality of characters;

generating, by the deep interpretive logic section, a list of story elements, wherein each of the story elements in the list has associated therewith a weight that exceeds a predefined threshold weight; and

analyzing, by a screenplay analysis logic section, the story elements from among the list of story elements.