NAS Search Personalization via Error Association
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Solution Overview
Problem
Current search technologies for network attached storage (NAS) devices lack an efficient method to utilize user errors and personal associations to improve search results, relying solely on conventional keyword searches that do not leverage unique user experiences and associations.
Innovation Solution
The implementation of an association-based search system that processes computer-perceptible user inputs, including errors and personal associations, to enhance search efficiency by storing and weighting seemingly unrelated search terms, using probabilistic learning algorithms and neural networks to reinforce associations between user inputs and digital items, allowing for personalized search results.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional keyword search is used, then search simplicity is maintained, but search accuracy and personalization are insufficient
Solution Approach 1:
The system pre-processes and stores user search inputs, errors, and associations in an association database before searches are performed. This preliminary action enables the system to quickly retrieve and utilize personalization data during actual searches, improving accuracy without adding complexity to the search execution process
Solution Approach 2:
An association database acts as an intermediary between user inputs and search results. This mediator stores and processes the complex relationships between search terms, errors, and digital items, allowing the search system to leverage personalized data without requiring complex real-time processing during searches
2Adaptability or versatility
If user errors and personal associations are leveraged, then search personalization is improved, but data processing complexity increases
Solution Approach 1:
The system automatically captures, processes, and stores user search inputs, errors, and corrections without requiring manual configuration or intervention. This self-service approach enables continuous personalization while minimizing the processing burden on users and simplifying the overall system architecture
Solution Approach 2:
The system implements feedback loops where user search behaviors, errors, and corrections are continuously monitored and used to update the association database. This feedback mechanism enables the system to adapt and improve personalization over time while maintaining a manageable processing complexity through iterative learning
3Productivity
If association database is implemented, then search efficiency is improved, but storage requirements increase
Solution Approach 1:
The system extracts and stores only the essential elements needed for personalization: search inputs, errors, corrections, and associations. By taking out only the critical data elements rather than storing complete search histories or all possible metadata, the system achieves effective personalization with minimized storage requirements
Solution Approach 2:
The association database is segmented into distinct components: search inputs, errors, corrections, and associations. This segmentation allows the system to store and process only the specific data elements needed for each aspect of personalization, improving search efficiency while managing storage requirements through organized, modular data structures
Data Source
AI summary
A computer-perceptible search input, whether typed, spoken, based upon machine vision, detection and/or interpretation of gestures, for example, may be received by a computing device from a single user. The received input by the single user may be matched with one or more stored digital items based upon prior inputs by the single user that previously led the single user to access the digital item(s). That is, it may be determined whether the received input is the same or similar to a previous input or inputs that led the computing device to search for, select and present digital items that were subsequently accessed (e.g., opened) by the user, which action signifies a successful search.