Motion Description NLG System Using Event Schemas
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current Natural Language Generation (NLG) systems are inadequate in describing motion events and states from spatio-temporal data in a linguistically coherent and efficient manner, failing to provide a proper framework for expressing motion in a linguistic format suitable for user consumption.
Innovation Solution
A method and apparatus that receive spatio-temporal data to generate a document plan, which is then used to create a linguistic representation of motion events and states, utilizing a processor to organize and describe the data in a natural language format, suitable for various domains such as weather or traffic.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If current NLG systems are used to describe motion events from spatio-temporal data, then basic data processing can be performed, but the linguistic coherence and efficiency of motion description is insufficient
Solution Approach 1:
The patent segments motion description into distinct event types (motion events, state events, transition events) and state types (location, orientation, shape). This segmentation allows the system to handle different aspects of motion separately, improving linguistic coherence without requiring a monolithic complex framework. Each event type can be processed and described using specialized templates and rules.
Solution Approach 2:
The patent introduces an intermediary representation layer between raw spatio-temporal data and natural language output. This intermediary structure (event schemas with properties like agent, patient, location, orientation) acts as a mediator that transforms complex data into structured motion events, which are then converted to linguistically coherent descriptions using domain-specific rules and templates.
2Reliability
If a proper framework for expressing motion in linguistic format is created, then linguistic coherence improves, but system complexity increases
Solution Approach 1:
The patent changes the parameters of motion description by introducing domain-specific event schemas with defined properties (agent, patient, location, orientation, shape). Instead of using generic natural language generation parameters, the system uses motion-specific parameters that ensure linguistic correctness. The framework transforms spatial and temporal parameters into linguistically appropriate descriptions using domain-specific rules.
Solution Approach 2:
The patent performs preliminary action by pre-defining motion event schemas and linguistic templates before processing actual data. The system establishes a framework with predefined event types, state properties, and linguistic patterns in advance. This preliminary structuring ensures that when motion data is processed, it can be reliably transformed into linguistically correct descriptions without requiring complex real-time decision-making.
3Loss of information
If spatio-temporal data is processed to generate coherent motion descriptions, then user understanding improves, but processing time increases
Solution Approach 1:
The patent implements self-service by enabling the motion description framework to automatically select and apply appropriate linguistic templates based on the type of motion event detected. The system self-organizes the description process by matching event schemas to corresponding linguistic patterns without requiring manual intervention or complex external processing. This automation improves processing efficiency while maintaining clarity.
Solution Approach 2:
The patent applies local quality by using domain-specific linguistic patterns and templates tailored to different types of motion events. Instead of using a single generic description approach, the system applies localized linguistic rules appropriate to each event type (e.g., different templates for translation vs. rotation vs. deformation). This localized approach improves clarity by using precisely appropriate language while reducing processing time through specialized rather than general-purpose rules.
Data Source
AI summary
A method, apparatus, and computer program product for describing motion. The method may include receiving a set of eventualities (114). The set of eventualities (114) may describe at least one of a domain event and a domain state. The at least one of the domain event and the domain state may be derived from a set of spatio-temporal data (102) and the set of eventualities (114) may be associated with a particular region and a particular time period. The method may include organizing the set of eventualities to generate a document plan. The method may further include generating, using a processor, a linguistic representation of the set of eventualities using the document plan.