Distributed IP Database Segmentation for Search Efficiency
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Solution Overview
Problem
Searching and processing large electronic databases for technical documents require significant network and computational resources, posing security risks and efficiency challenges, especially when dealing with millions of data entries.
Innovation Solution
A computer-implemented method that involves obtaining quality index models to label IP products with quality indices independently, using vectorized text data and machine learning techniques to reduce resource requirements and enhance security.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If electronic database is stored remotely on a database server, then network connectivity is required for searching, but this exposes security risks and requires continuous high-speed network connection
Solution Approach 1:
The patent segments the database system into distributed local copies across multiple computing devices rather than a centralized remote server. Each device stores a portion of the database locally, enabling independent searching without continuous network connectivity while maintaining data availability through the distributed architecture.
Solution Approach 2:
The database is pre-distributed to local computing devices before searching is needed. This preliminary action of copying data to local storage eliminates the need for real-time network connectivity during search operations, thereby reducing security risks associated with continuous network exposure.
2Object-affected harmful factors
If electronic database is stored locally on computing devices, then network security is improved and offline access is enabled, but this requires significant hard disk space
Solution Approach 1:
The database is divided into multiple segments distributed across different computing devices. Each device stores only a portion of the overall database, reducing the storage burden on individual devices while maintaining collective access to the complete dataset through the distributed network when needed.
Solution Approach 2:
Different computing devices store different portions of the database based on their local storage capacity and search requirements. This local quality approach allows each device to optimize its storage allocation while contributing to the overall distributed database system.
3Measurement precision
If search is performed over electronic databases containing millions of data entries, then comprehensive search results are obtained, but this requires large amounts of computer resources including RAM and processing power
Solution Approach 1:
The search process is segmented and distributed across multiple computing devices. Each device performs searching on its local database portion independently, dividing the overall computational workload. This segmentation reduces the processing power and RAM requirements for each individual device while maintaining comprehensive search coverage across the entire distributed database.
Data Source
AI summary
One aspect is a computer-implemented method for producing an indicator. The method includes:a. obtaining at least one quality index model of the first kind;b. providing at least one IP product;c. labelling the at least one IP product, using the at least one quality index model of the first kind, with at least one quality index of the first kind;d. obtaining the indicator using the at least one quality index of the first kind;wherein the at least one IP product is labelled with the quality index of the first kind independently of at least one other IP product.