AI-Assisted Search with Correlated Metadata for Cloud Data Retrieval
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Solution Overview
Problem
The proliferation of cloud services and IoT devices has led to data fragmentation, making it difficult for users to quickly retrieve specific data across multiple platforms, and existing search technologies fail to efficiently utilize metadata for comprehensive data retrieval.
Innovation Solution
A system utilizing artificial intelligence to analyze semi-private metadata and correlated metadata to provide both a primary and expanded search response, incorporating image recognition and integration with digital assistants for enhanced data retrieval.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If users manually search for data across multiple cloud services and IoT platforms, then they can find specific data, but the time required for search increases significantly
Solution Approach 1:
The system performs preliminary actions by automatically generating and executing search queries across multiple cloud services and IoT platforms before the user needs the data. The search engine proactively retrieves data from various sources, indexes it, and makes it readily available, eliminating the need for users to manually search through each platform when they need information.
Solution Approach 2:
The patent introduces a search engine as an intermediary component that sits between the user and multiple cloud services/IoT platforms. This intermediary automatically manages search operations across diverse data sources, translating user needs into coordinated searches across platforms like Dropbox, Evernote, Facebook, and others, thereby reducing user effort and search time while maintaining comprehensive coverage.
2Reliability
If the system searches across all cloud services and IoT platforms, then data retrieval completeness improves, but system complexity increases
Solution Approach 1:
The system segments the complex task of searching across multiple platforms by creating separate interface modules for each cloud service and IoT platform (Dropbox interface, Evernote interface, Facebook interface, etc.). Each interface handles specific platform communications independently, allowing the system to manage complexity through modular design while maintaining comprehensive data retrieval capabilities across all platforms.
3Adaptability or versatility
If the system integrates with multiple cloud services and IoT platforms, then data accessibility improves, but security risks increase
Solution Approach 1:
The search engine acts as a secure intermediary that manages all communications with external cloud services and IoT platforms. It handles authentication, authorization, and data retrieval through controlled interfaces, reducing security risks by centralizing security management rather than requiring direct user interactions with multiple potentially vulnerable platform APIs.
Data Source
AI summary
A system for assisted expanded search can have a server, receiving from a user, a user search request to access semi-private data, and a controlled access non-transient memory storing at least the semi-private data. An expanded search engine can implement at least one algorithm to analyze semi-private metadata and semi-private correlated metadata related to the semi-private data to determine a primary response and an expanded response to the user search request. The system can also include a display providing the user with the primary response and the expanded response.