Semantic Vector Translation Layer for Cross-Engine Search Compatibility
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Solution Overview
Problem
Current search engines face challenges in interpreting user intent due to differences in algorithms and data index compatibility across platforms, leading to difficulties in expressing search queries and scalability issues, as well as a lack of convenience in managing data records.
Innovation Solution
A machine-trained model converts user input into a natural language expression and semantic vector, allowing for the transformation of structured data records into a format that preserves intent and facilitates compatibility across different data stores, enabling easier query processing and data management.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If different search engines use different algorithms to process queries, then each search engine can optimize its search results, but the index generated by one search engine becomes incompatible with another search engine's index
Solution Approach 1:
The patent introduces a translation layer that acts as an intermediary between different search engines. This layer converts queries from one search engine's algorithm into another search engine's algorithm, enabling compatibility without requiring changes to the underlying search engines' optimized algorithms. The translation layer mediates the incompatibility by providing a universal interface that respects each search engine's proprietary processing methods.
2Reliability
If search engines use engine-specific algorithms to interpret queries, then search results can be optimized for each engine, but users have difficulties expressing search intent in the expected format
Solution Approach 1:
The patent employs a translation layer as an intermediary that sits between the user and the search engine. This layer translates user-friendly query expressions into the specific format required by each search engine's algorithm. Users can express their intent in natural language or familiar formats, and the translation layer automatically converts this into the engine-specific query structure, eliminating the need for users to learn different query formats for different search engines.
Solution Approach 2:
The patent changes the parameter representation of queries by translating them into semantic vectors. This transformation preserves the meaning and intent of the original query while representing it in a standardized format that can be universally processed. The semantic vector representation allows the same query to be interpreted consistently across different search engines without losing any semantic information.
3Reliability
If traditional search engines process queries using fixed algorithms, then search functionality is maintained, but the system is not readily scalable and difficult to modify
Solution Approach 1:
The patent segments the search system into independent functional modules: a query translation module, a semantic vector generation module, and search execution modules for different engines. This segmentation allows each module to be developed, tested, and modified independently. The translation layer can be updated to support new search engines without affecting existing ones, enabling scalable addition of new search capabilities while maintaining stability of individual components.
4Productivity
If traditional data stores maintain separate indexes, then data can be stored efficiently for each provider, but adding and removing data records becomes inconvenient and complex
Solution Approach 1:
The patent creates a universal data record format that can be used across multiple providers and search engines. Each data record is transformed into a standardized representation with semantic vectors that can be understood by any search engine in the system. This universal format allows a single data store to serve multiple providers efficiently, and adding or removing records can be done through a consistent interface without needing to understand the internal structures of individual provider indexes.
Data Source
AI summary
A computer-implemented technique uses a machine-trained model to convert input information provided by a user into a natural language expression and a semantic vector. The semantic vector provides a distributed representation of the natural language expression. The technique then uses the generated semantic vector to find a target data record having a target semantic vector that is within a prescribed distance to the generated semantic vector. The technique then outputs at least a target natural language expression that is associated with the target data record. The target data record is produced in an offline process that converts one or more attribute values in an existing structured data record into the target semantic vector and the target natural language expression. Also described herein is a technique for training the machine-trained model and for updating existing collections of data records using the machine-trained model.