Learning Model for Multidimensional Content Relevance Mapping
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Solution Overview
Problem
Existing technologies are limited to generating only text-based content and cannot effectively express various kinds of creative content that humans can create, requiring a method to represent components of diverse content as multidimensional vectors.
Innovation Solution
A learning model is developed to generate, present, and evaluate content by acquiring and decomposing it into components, measuring inter-concept distances, and generating new content based on relevance indices using a learning model that outputs index values indicating the relevance of components.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If text-based language models are used to generate content, then sentence generation is automated, but the system cannot express or generate diverse creative content beyond text
Solution Approach 1:
The patent segments content into discrete components (subjects, predicates, objects, modifiers) that can be independently represented as vectors. This segmentation allows the system to handle diverse content types by composing vectors from standardized component vectors, resolving the contradiction between versatility and complexity.
Solution Approach 2:
The patent transitions from text-only representation to multidimensional vector representation that encompasses various content dimensions (semantic, syntactic, stylistic). By embedding content components in a high-dimensional vector space, the system can represent and generate diverse content types while maintaining a unified model structure.
2Adaptability or versatility
If components are expressed as multidimensional vectors, then creative content generation is enabled, but measurement of relevance between components becomes complex
Solution Approach 1:
The patent changes the parameter representation from raw text to standardized vector embeddings with specific dimensions. By transforming content components into vectors with consistent dimensional properties, the system enables precise relevance measurement through vector operations while maintaining versatile content representation capability.
3Productivity
If a learning model outputs index values for component relevance, then content evaluation becomes automated, but the system cannot provide creative recommendations
Solution Approach 1:
The patent implements a feedback mechanism where the learning model evaluates component relevance and uses this information to generate creative recommendations. The system feeds evaluation results back into the content generation process, enabling automated evaluation while maintaining creative recommendation capability through iterative refinement.
Data Source
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AI summary
The present disclosure relates to a learning model generation method, a presentation method, an evaluation method, and a generation method which are capable of providing more creative content. A plurality of components constituting each of a plurality of different pieces of content is acquired, and a learning model that outputs an index value indicating relevance of a first component with respect to a second component among the plurality of components is generated. The technique according to the present disclosure can be applied to a system related to creation support for a creator, for example.