AI Narrative Generation and Semantic Newsfeed Matching
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Solution Overview
Problem
Conventional newsfeed generation systems face limitations in inferring the context of ambiguous terminology, references, and user interests, leading to suboptimal content serving decisions.
Innovation Solution
Integrate natural language generation (NLG) systems with newsfeed generation to leverage semantic source models, ontologies, and user data for better content decision-making, including user matching, calendar integration, and out-of-date story identification.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional NLP/NLU operations are applied to pre-written stories, then content coverage determination can be performed, but the accuracy of inferring ambiguous terminology and user interests is limited
Solution Approach 1:
The patent introduces semantic source models as an intermediary layer between the story and the NLP/NLU processing. These models contain pre-defined ontologies, entities, and relationships that mediate the interpretation of ambiguous terms. Instead of directly analyzing story text, the system queries the semantic source model which provides structured context about entities, their relationships, and domain-specific meanings, thereby resolving ambiguities without losing contextual information.
2Reliability
If NLG systems are integrated with newsfeed generation, then decision-making accuracy improves, but system complexity increases
Solution Approach 1:
The patent segments the newsfeed generation system into distinct functional modules: (1) story generation module that creates content, (2) semantic source model module that stores structured knowledge, (3) matching module that compares story entities with user profiles, and (4) newsfeed assembly module that compiles final output. Each module operates independently with well-defined interfaces, allowing the complex integrated system to maintain reliability through modular design while managing complexity through clear separation of concerns.
Solution Approach 2:
The semantic source model serves as an intermediary that bridges the NLG system and the newsfeed generation system. Rather than directly integrating all components, the semantic source model acts as a standardized interface that translates between the narrative structure of stories and the structured data requirements of user matching and recommendation algorithms, thereby reducing overall system complexity while maintaining decision accuracy.
3Measurement precision
If semantic source models are used for story generation, then user matching accuracy improves, but processing time increases
Solution Approach 1:
The patent applies preliminary action by pre-populating semantic source models with extensive ontologies, entity definitions, and relationship structures before runtime operations. User profiles are also pre-processed to extract and structure their interests, followed entities, and preferences in advance. When a story needs to be matched with users, the system queries these pre-prepared structures rather than performing full-text analysis, dramatically reducing processing time while maintaining high matching accuracy.
Data Source
AI summary
Systems and methods are disclosed for integrating NLG-based natural language narrative story generation with story sharing. A processor can (1) generate a plurality of natural language narrative stories based on a plurality of semantic source models for the natural language narrative stories, (2) analyze the semantic source models to determine a plurality of users to whom the natural language narrative stories that are generated from the analyzed semantic source models are to be shared, and (3) share the generated natural language narrative stories with their determined users. In this fashion, stories can be posted to user-customized newsfeeds in a manner that can more reliably capture stories that are of interest to the users.