Semantic Graph Text Recommendation for Cross-App Data Entry
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Solution Overview
Problem
Users face tediousness in managing and navigating between multiple digital applications, requiring manual data transfer and entry across different platforms, which lacks efficient computerized assistance for seamless data management.
Innovation Solution
A method utilizing semantic graph analysis to identify and recommend required textual data by comparing user-generated data with reference data from various sources, enabling automated correction and completion of data entry through a human-machine interface.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If manual data transfer and entry across multiple applications is performed, then data accuracy can be maintained, but user productivity and ease of operation deteriorate due to tediousness
Solution Approach 1:
The patent introduces an intermediary system (the information-processing device with semantic graph analysis) that mediates between multiple applications and data sources. This intermediary automatically matches required data with available data by comparing semantic graphs, eliminating the need for manual data transfer while maintaining accuracy across applications.
Solution Approach 2:
The system enables self-service by automatically detecting when data is needed across applications and autonomously retrieving, matching, and suggesting appropriate data without user intervention. The semantic graph comparison and recommendation generation occur automatically, allowing the system to serve itself rather than requiring manual user operations.
2Productivity
If automated data recommendation system is implemented, then productivity and ease of operation improve, but device complexity increases
Solution Approach 1:
The patent applies universality by designing a multi-functional information-processing device that can operate in multiple modes: it can analyze semantic graphs for data matching, generate recommendations, detect errors, and facilitate navigation between applications. This single system performs multiple functions that would otherwise require separate tools, managing complexity through consolidation.
3Manufacturing precision
If semantic graph analysis is performed to ensure data accuracy, then manufacturing precision (data correctness) improves, but processing time and energy consumption increase
Solution Approach 1:
The patent applies partial action by performing semantic graph analysis selectively rather than comprehensively. The system compares semantic graphs only when data transfer or navigation between applications is detected, and only analyzes portions of data that are actually needed for the current task, rather than processing all available data continuously, thus reducing energy consumption while maintaining precision.
Data Source
AI summary
An information-processing device and a method for providing assistance in acquiring required textual data. The device: establishes a first semantic graph based at least on said required textual data; obtains textual data from data delivered by at least one source, and establishes at least a second semantic graph based on the obtained textual data; performs a search for an at least partial similarity between the first and second semantic graphs in order to identify at least part of the required textual data among the obtained textual data; and issues a recommendation on the basis of the identified textual data.