Automated Product Data Extraction from Visual Media Production Documents
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Solution Overview
Problem
Viewers of visual media content often face difficulties in finding product information for items they are interested in while watching, as existing technologies struggle to efficiently identify and provide details on both visible and non-visible shoppable items within the content.
Innovation Solution
A system that utilizes object recognition, text recognition, and speech recognition to match user-provided data with a pre-indexed finite group of shoppable items from production data, including budgeting and inventory documents, to provide detailed information on desired items.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If automated extraction systems are implemented to identify shoppable items from visual media content, then the efficiency and accuracy of providing product information improve, but the system complexity and computational resources required increase
Solution Approach 1:
The system performs preliminary actions by pre-processing production data during content creation to extract and store product information in structured databases. This includes capturing item identifiers, pricing, inventory, and availability data before the content is consumed, so that when a viewer queries for product information, the system can quickly retrieve pre-extracted data rather than analyzing the entire content in real-time.
Solution Approach 2:
The system introduces an intermediary layer consisting of structured databases and product information repositories that mediate between the visual media content and the viewer's information needs. This intermediary structure stores extracted product data in organized formats, allowing efficient querying without requiring complex real-time analysis of the content itself.
2Loss of information
If comprehensive product information is provided for all visible and non-visible items, then the completeness of information available to viewers improves, but the data processing time and computational load increase
Solution Approach 1:
The system extracts and stores complete product information for all shoppable items during the content production phase, including both visible items shown on screen and non-visible items mentioned in dialogue or described in production documents. This pre-extraction ensures comprehensive information availability without requiring time-consuming processing during viewer interaction.
Solution Approach 2:
The system enables self-service by providing viewers with direct access to pre-organized product information databases through simple queries. Viewers can independently retrieve complete product details, pricing, inventory, and availability information without requiring complex real-time analysis or manual research, thus reducing their information-seeking time while maintaining data completeness.
3Measurement precision
If multiple recognition technologies are integrated to identify both visible and non-visible shoppable items, then the accuracy of item identification improves, but the device complexity and implementation difficulty increase
Solution Approach 1:
The system merges multiple recognition technologies including object recognition for visible items, text recognition for on-screen text, and speech recognition for dialogue-based item identification. These technologies are integrated into a unified system that processes different types of data (visual, textual, auditory) from the same content source to identify shoppable items, improving accuracy through multi-modal verification while consolidating implementation through a single integrated architecture.
Solution Approach 2:
The system achieves multi-functionality by using a single integrated platform that performs multiple recognition tasks (object, text, and speech recognition) and processes various types of production data (scripts, shot lists, inventory documents). This universal system handles both visible and non-visible shoppable items through the same technical infrastructure, reducing implementation difficulty compared to separate specialized systems.
Data Source
AI summary
In various example embodiments, a system and method for automated extraction of product data from production data of visual media content are presented. Production documents are received from a publisher of visual media content. The production documents contain information related to inventory items used in the production of the visual media content. Data, which represents shoppable items, is extracted from the production documents. The extracted data is used to create an index of shoppable items associated with the visual media content. The index includes metadata associated with the shoppable items. User provided data indicating a desired shoppable item from the visual media content is received. The desired shoppable item is identified and item information for the desired shoppable item is produced.