Constrained Topic Model for Automated Media Genome Assignment
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Solution Overview
Problem
Existing media program genome systems rely on manual and subjective processes for assigning words or phrases to reflect characteristics, which can lead to inconsistencies and inefficiencies in categorizing media programs based on their characteristics.
Innovation Solution
A method and system that utilize a constrained topic model to determine a probability distribution of terms for topics in media programs, allowing for the automatic assignment of genomes to media programs based on textual information, enabling accurate and objective characterization of media programs.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual processes are used to assign genome words or phrases to media programs, then the process allows for human judgment and interpretation, but it leads to subjectivity and inconsistency in genome assignment
Solution Approach 1:
The patent replaces the manual mechanical process of genome assignment with an automated computational system. A machine learning model processes media program metadata and automatically generates genome assignments, eliminating human subjectivity while maintaining or improving accuracy through consistent algorithmic application.
Solution Approach 2:
The system enables self-service genome generation where the media program data itself provides the information needed for genome assignment. The model learns from the data patterns and automatically performs the classification task without requiring external manual intervention, making the system self-sufficient and scalable.
2Productivity
If manual genome assignment processes are used, then flexibility in interpretation is maintained, but productivity and efficiency are reduced
Solution Approach 1:
The patent creates a universal genome generation system that can handle multiple media program types and genres through a single automated model. The system is designed to process diverse inputs and generate appropriate genome assignments across different domains, making it highly productive and broadly applicable without requiring separate manual processes for each case.
Solution Approach 2:
The system uses parameter changes in the form of learning thresholds and probability cutoffs to control the automation process. By adjusting these parameters, the system can balance between automation level and assignment confidence, managing complexity while maintaining high productivity through configurable operational characteristics.
3Reliability
If automated topic modeling is used to generate genomes, then objectivity and consistency are improved, but the complexity of the system increases
Solution Approach 1:
The patent applies preliminary action by pre-training the topic modeling system on large corpora of media program data before deployment. The model learns patterns, relationships, and genre characteristics in advance, so that when actual genome assignment is needed, the complex learning process has already been completed, ensuring consistency without adding complexity to the operational phase.
Solution Approach 2:
The patent introduces an intermediary layer in the form of topic models that mediate between raw media program data and final genome assignments. This intermediary processing layer translates complex data patterns into standardized genome terms, ensuring objectivity and consistency while managing system complexity through structured intermediate representation.
Data Source
AI summary
In one embodiment, a method defines information for a set of genomes where the set of genomes describe characteristics of media programs. The method also defines which genomes in the set of genomes correspond to which topics in a set of topics. Textual information for a plurality of media programs and the information are input into a model and the model is trained to determine a probability distribution of terms for the set of topics based on analyzing the textual information and the genome information. The method then outputs the trained model. The probability distribution of terms is usable to determine genomes for each of the plurality of media programs where a genome corresponds to a topic and is associated with a media program based on terms found in the textual information for the media program and the probability distribution of terms for the topic correspond to the genome.