Secure Search Engine Using Neural Networks for Resource Matching
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Solution Overview
Problem
Conventional search engines fail to provide adequately customized search results for users, as they do not account for unique user characteristics beyond search query terms, leading to inadequate matching of physical resources with potential users.
Innovation Solution
The system employs neural networks to securely search for physical resources by matching user attributes with resource attributes, adjusting search match scores based on temporal usage conflicts, and ranking search results for display.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If conventional search engines use indexing systems to identify content based on search query terms, then search functionality is provided, but the search results are not adequately customized to user characteristics
Solution Approach 1:
The patent segments user information into multiple attribute dimensions (demographic attributes, behavioral attributes, preference attributes) and processes each segment through dedicated neural network layers. This segmentation allows the system to capture comprehensive user characteristics while maintaining organized processing pipelines for each attribute type.
Solution Approach 2:
The patent introduces encrypted communication channels and secure processing intermediaries between user devices and the search engine. User characteristics are transmitted through encrypted communications and processed through secure intermediaries (learning engines with encrypted weights) that prevent unauthorized access while enabling personalized search customization.
2Adaptability or versatility
If user attributes are collected and processed for personalized search, then search result customization improves, but system complexity increases
Solution Approach 1:
The patent replaces traditional mechanical search indexing systems with neural network-based learning engines. Instead of rule-based matching and manual indexing, the system uses encrypted neural networks with learnable weights that automatically adapt to user characteristics, reducing the need for complex manual system configuration and maintenance.
Solution Approach 2:
The patent dynamically adjusts neural network parameters (weights and biases) based on user attributes and search patterns. The learning engine modifies its internal parameters through training on user interaction data, enabling adaptive personalization without requiring changes to the overall system architecture or manual intervention.
3Reliability
If encrypted communications are used to protect user attributes, then security is improved, but processing time increases
Solution Approach 1:
The patent performs encryption of user attributes and weights during the data preparation and model training phases before deployment. Once trained, the encrypted model can perform inference with reduced computational overhead, as the heavy encryption/decryption operations have already been completed during the preliminary training stage.
Solution Approach 2:
The patent uses encrypted copies of user attributes and model weights that can be processed without requiring continuous decryption. The system maintains encrypted representations of data that preserve the necessary information for processing while avoiding the computational cost of repeated decryption operations during search queries.
Data Source
AI summary
Methods and systems are disclosed for securely searching for physical resources. Attributes of a plurality of shared physical resources are accessed. An encrypted communication is received and decrypted that provides attributes for a first user. A search is performed, using a first neural network, for physical resources corresponding to attributes of the user to identity a first set of physical resources using decrypted attributes of the user and attributes of the plurality of physical resources. Search match scores are generated for the first set of physical resources. A subset of physical resources that at least one other user has access to is identified. A second neural network identifies users associated with the subset of physical resources that have a temporal usage conflict likelihood with the user. Search match scores may be adjusted. The search results may be ranked using the adjusted search match scores, and the ranked search results may be displayed.