Frame-slot architecture for context-dependent data conversion
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Solution Overview
Problem
Machine-based conversion tools face difficulties in handling context-dependent conversions, as they struggle to understand and apply context cues effectively, leading to ambiguities and inefficiencies in data conversion processes.
Innovation Solution
The implementation of a frame-slot architecture that utilizes context-dependent conversion rules, allowing machines to identify and apply contextual cues by recognizing frames and slots within data, enabling more accurate and flexible conversions without the need for deep parses or rigid classification structures.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If machine-based conversion tools use traditional rigid classification structures and deep parses to handle context-dependent conversions, then conversion accuracy may improve, but device complexity and start-up efforts increase significantly
Solution Approach 1:
The patent segments the complex classification structure into reusable frame templates and slot definitions. Each frame represents a contextual category (e.g., product, service, event) with predefined slots for specific attributes. This segmentation allows the system to handle context-dependent conversions without requiring a single monolithic classification structure, reducing overall complexity while maintaining accuracy.
Solution Approach 2:
The patent performs preliminary action by pre-defining frame templates and slot constraints before actual data conversion occurs. Contextual rules and validation constraints are established in advance for each frame type, allowing the system to quickly match incoming data to appropriate frames without performing deep parses during conversion. This preliminary structuring reduces both complexity and processing time.
2Adaptability or versatility
If machine-based conversion tools develop conversion rules through detailed analysis of new data (bottom-up evolution), then adaptation to specific contexts improves, but start-up efforts and costs increase
Solution Approach 1:
The patent creates universal frame templates that can be reused across multiple conversion scenarios and contexts. A single frame template (e.g., for product descriptions) can be applied to convert data from various sources and formats by simply instantiating the frame with context-specific slot values. This universality allows the system to adapt to new contexts quickly without developing new rules from scratch, reducing start-up effort while maintaining versatility.
Solution Approach 2:
The patent enables copying of existing frame templates and slot definitions to create new conversion rules for different contexts. Once a frame is defined for a particular context, it can be copied and adapted for similar contexts by modifying slot constraints and validation rules. This copying mechanism accelerates the adaptation process significantly compared to bottom-up evolution, reducing start-up time while preserving context-specific accuracy.
3Measurement precision
If machine-based conversion tools apply context-dependent conversion rules without leveraging existing schema, then conversion accuracy improves, but reusability of conversion rules across different environments decreases
Solution Approach 1:
The patent uses parameter changes by allowing slot constraints and validation rules within frame templates to be modified based on context without changing the overall frame structure. Each slot can have context-specific constraints (e.g., data types, value ranges, format patterns) that are adjusted according to the target environment while reusing the same frame template. This enables the system to maintain high conversion accuracy through context-dependent parameters while preserving rule reusability across different environments.
Data Source
AI summary
A machine based tool and associated logic and methodology used in converting data from an input form to a target form using context dependent conversion rules. A frame-slot architecture is utilized where a frame represents an intersection between a contextual cue recognized by the machine tool, associated content and related constraint information to specific to that conversion environment. A slot represents an included chunk of information. An exemplary conversion system (400) includes a parser (402) for use in parsing and converting an input stream (403) to provide an output stream (411) in a form for use by a target system (412). The parser (402) uses information from a public schema (406), a private schema (408) and a grammar (410). The public schema (406), private schema (408) and grammar (410) may include conversion rules applicable to less than the whole of a subject matter area including the input stream (403).