Cross-Platform Query Interface Intent Extraction
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Solution Overview
Problem
Conventional client device query systems are inflexible, inaccurate, and inefficient due to platform-siloed querying, keyword-based ambiguity, and resource wastage in processing multiple software applications and user interactions.
Innovation Solution
A cross-platform search system utilizing a cross-platform language processing model and platform-specific configurations to generate contextually-based responses by extracting intents from digital text queries and generating platform-specific requests across multiple software platforms.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If conventional systems use platform-siloed querying, then each platform can provide specialized responses, but the system loses flexibility and cannot generate appropriate responses for cross-platform queries
Solution Approach 1:
The patent implements a universal query processing system that can handle queries from multiple different platforms through a single unified architecture. The system uses a common user interface that accepts queries from any platform and routes them to appropriate platform-specific services, eliminating the need for separate dedicated interfaces for each platform while maintaining platform-specific response capabilities.
Solution Approach 2:
The patent introduces an intermediary query routing layer that sits between the user interface and platform-specific services. This intermediary component receives queries from any platform, determines the appropriate target platform, and routes the query accordingly. This mediator approach allows the system to maintain flexibility across platforms without requiring direct integration between each platform and the user interface.
2Measurement precision
If conventional systems use keyword-based query processing, then the system can process natural language queries, but it introduces ambiguity and ignores contextual meanings leading to inaccurate responses
Solution Approach 1:
The patent applies local quality by detecting the specific platform context within a query and tailoring the query processing and response generation to match that platform's specific terminology, conventions, and data structures. Instead of using a uniform keyword-based approach, the system adapts its processing behavior based on the detected platform context, providing platform-appropriate responses that account for local semantic nuances.
3Productivity
If conventional systems process queries on a platform-by-platform basis with separate user interfaces, then each platform can be optimized for its specific queries, but it wastes significant computing resources through parallel software applications and repetitive processing
Solution Approach 1:
The patent merges multiple platform-specific query processing operations into a single unified processing pipeline. Instead of running separate software applications for each platform, the system combines query reception, platform detection, routing, and response generation into one integrated process. This consolidation eliminates redundant computing operations while maintaining the ability to provide platform-specific optimized responses through a single user interface.
Data Source
AI summary
The present disclosure relates to systems, non-transitory computer-readable media, and methods that generate a dynamic cross-platform ask interface and utilize a cross-platform language processing model to provide platform-specific, contextually based responses to natural language digital text queries. In particular, in one or more embodiments, the disclosed systems utilize machine learning models to extract registered intents from digital text queries to identify platform-specific configurations associated with the registered intents. Utilizing the platform-specific configurations, the disclosed systems can generate tailored platform-specific requests for information, as well as customized end-user search results that cause client devices to efficiently, accurately, and flexibly render platform-specific search results.