Theater Screening Table Generation Using Local Audience Prediction

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Solution Overview

Problem

Existing methods for organizing movie content in theaters require significant manual effort and intuition, leading to inconsistent audience engagement and low reservation rates due to diverse audience characteristics and surrounding commercial district influences.

Innovation Solution

A system and method using an AI model to generate a customized contents screening table by analyzing theater-specific data from various sources, including social networks, commercial district statistics, and OTT servers, to predict audience preferences and optimize content selection.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Ease of operation

If manual organization of movie contents is performed by a movie organizing team, then content selection can be made based on experience and intuition, but a lot of time and efforts are required and manpower recruitment is inconvenient

Engineering Contradiction:

Improvemanual content organizationVSAvoidtime and efforts required

Core Design Contradiction:

Ease of operationVSLoss of time

Solution Approach 1:

The patent replaces the manual mechanical process of content organization with an automated AI-based system. The AI model processes data from multiple sources (theater data, commercial district data, social network data) to automatically generate customized content screening tables, eliminating the need for manual analysis and decision-making by organizing teams.

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

Solution Approach 2:

The system enables self-service content organization by automatically analyzing various data sources and generating appropriate content schedules without human intervention. The AI model independently processes information, identifies patterns, and creates customized screening tables for each theater based on their specific characteristics and audience profiles.

Inventive Principle:

Principle #25Self-service

2Reliability

If manual organization of movie contents is performed, then content can be selected based on team expertise, but the characteristics of diverse audiences and surrounding commercial districts cannot be fully captured, leading to low reservation rates

Engineering Contradiction:

Improvecontent selection accuracyVSAvoidadaptation to diverse audience characteristics

Core Design Contradiction:

ReliabilityVSAdaptability or versatility

Solution Approach 1:

The patent applies local quality by customizing content screening tables for each individual theater based on its unique characteristics. The system analyzes theater-specific data including location, surrounding commercial districts, and audience demographics to create tailored content schedules that match the local population's preferences, rather than using a one-size-fits-all approach.

Inventive Principle:

Principle #3Local quality

Solution Approach 2:

The AI model acts as an intermediary between raw data from multiple sources and final content selection decisions. It processes and analyzes data from theaters, commercial districts, and social networks, extracting meaningful patterns and insights that guide automated content scheduling, thereby bridging the gap between data collection and practical application.

Inventive Principle:

Principle #24Intermediary (Mediator)

3Productivity

If data from multiple sources is collected and analyzed using AI models, then customized content screening tables can be generated for each theater, but the system complexity increases

Engineering Contradiction:

Improvecontent screening table generation efficiencyVSAvoidsystem complexity

Core Design Contradiction:

ProductivityVSDevice complexity

Solution Approach 1:

The patent implements multi-functionality by designing a comprehensive system that handles multiple tasks within a unified framework. The same AI model processes diverse data types (theater information, commercial district statistics, social network data) and performs multiple functions including data cleaning, analysis, pattern recognition, and content scheduling, thereby reducing overall system complexity despite the expanded capability.

Inventive Principle:

Principle #6Universality (Multi-functionality)

Solution Approach 2:

The system segments the content organization process into distinct modular components: data collection from various sources, data processing and cleaning, AI-based analysis, pattern recognition, and final screening table generation. This segmentation allows each component to be optimized independently and simplifies maintenance while improving overall productivity.

Inventive Principle:

Principle #1Segmentation

Data Source

PatentUS20250371035A1Method for generating customized contents screening table for each theater and device therefor

Publication Date: 2025.12.04 CJ OLIVENETWORKS

AI summary

The present invention relates to a method of generating a contents screening table customized for each theater and a system for the same. More specifically, the present invention relates to a method and system for deriving, when a contents screening table generation server acquires data on commercial districts around each theater, data on contents, and data on social networks from an analysis resource data providing server, prediction result values (e.g., number of audiences, main age group, etc.) by learning the acquired data using a prediction modeling algorithm, and generating a contents screening table customized for each theater on the basis of the prediction result values.