Machine-Interpretable Query Transliteration Using Lookup Tables
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Solution Overview
Problem
Existing machine interpretable language (MILP) translation processes are inefficient, inaccurate, and time-consuming, often failing to tolerate errors and lacking real-time processing capabilities compared to natural language processing (NLP).
Innovation Solution
A computing platform with a processor, communication interface, and memory uses query keys to translate queries by selecting a compaction method, removing non-essential parameters, and replacing them with variables, storing these in a lookup table for efficient translation between different formats.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual conversion between machine interpretable languages is performed, then translation accuracy can be maintained, but the process becomes time-consuming and expensive
Solution Approach 1:
The patent creates a lookup table that stores pre-computed query key mappings between different machine interpretable languages. Instead of manually converting queries, the system copies pre-established translation patterns from the lookup table, enabling fast and accurate translation without manual intervention.
Solution Approach 2:
The system performs preliminary action by pre-computing and storing query key mappings in the lookup table before actual translation is needed. This allows the translation process to simply retrieve pre-computed results rather than performing complex manual conversion in real-time.
2Productivity
If machine interpretable language processing is used, then real-time processing is enabled, but the system cannot tolerate errors in translation
Solution Approach 1:
The patent replaces manual mechanical translation processes with automated machine-based translation using query keys and lookup tables. This substitution enables real-time processing while maintaining reliability through systematic, rule-based translation that eliminates human error.
Solution Approach 2:
The system changes the parameters of translation by using standardized query keys as intermediaries. Instead of direct translation between languages, the system transforms queries into standardized query keys and then into target language queries, ensuring consistent and error-free translation while maintaining real-time performance.
3Adaptability or versatility
If multiple compaction methods are used to produce query keys, then translation flexibility is improved, but the complexity of the translation process increases
Solution Approach 1:
The patent segments the translation process into distinct stages: query parsing, compaction method selection, query key generation, and lookup table retrieval. This segmentation allows multiple compaction methods to be applied systematically without increasing overall complexity, as each stage is handled independently and efficiently.
Solution Approach 2:
The lookup table serves as a universal translation reference that can handle multiple compaction methods and language pairs. Instead of maintaining separate translation systems for each method, the universal lookup table consolidates all translation rules, reducing complexity while preserving flexibility.
Data Source
AI summary
Aspects of the disclosure relate to transliteration of machine interpretable languages. A computing platform may generate a plurality of query keys, configured for use in translating queries from a first format to a second format, which may include, for each input query corresponding to the plurality of query keys: selecting, based on features of the input query, a compaction method, and applying the selected compaction method to the input query to produce a corresponding query key. The computing platform may store the plurality of query keys in a lookup table. The computing platform may receive a first query, corresponding to the input queries, formatted in the first format. The computing platform may translate, by identifying a query key corresponding to the first query in the lookup table, the first query to produce a second query, formatted in the second format. The computing platform may execute the second query.