Learning Engine Resource Suitability Mapping
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Solution Overview
Problem
Conventional search engines fail to provide adequately customized search results for physical resources and do not effectively match users with resources based on complex user and resource attributes, particularly in scenarios involving time-displaced sharing of resources.
Innovation Solution
A system utilizing learning engines to evaluate data from multiple databases, generate suitability indicators for geographical areas and resources, and render these indicators on maps, enabling actions based on threshold satisfaction, thereby matching users with suitable physical resources in a time-displaced manner.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional search engines use simple indexing systems to identify content, then the system complexity is low, but the search result customization and relevance are insufficient
Solution Approach 1:
The system segments the search process into multiple evaluation stages: area suitability evaluation by the first learning engine, resource suitability evaluation by the second learning engine, and temporal availability checking. This segmentation allows complex evaluation to be broken down into manageable components, improving search result relevance while maintaining system organization.
Solution Approach 2:
The system performs preliminary actions by pre-evaluating area suitability and resource suitability before final matching. The learning engines pre-process and store suitability indicators, which are then quickly retrieved and combined with real-time query information, improving response accuracy without proportionally increasing real-time computational complexity.
2Measurement precision
If the system uses learning engines to evaluate complex attributes and generate suitability indicators, then the matching accuracy improves, but the computational time and processing complexity increase
Solution Approach 1:
The system performs suitability evaluations in advance using learning engines that process historical and static data offline. The generated suitability indicators are stored and reused for multiple queries, significantly reducing real-time computational time while maintaining high matching accuracy.
Solution Approach 2:
The system extracts and separates time-invariant attributes (area characteristics, resource properties) from time-variant attributes (availability, usage patterns). Static attributes are evaluated once by learning engines and stored as suitability indicators, while only dynamic attributes require real-time processing, reducing overall computational time.
3Loss of information
If the system provides detailed multilayer maps with contextual data, then the information completeness improves, but the data processing and storage requirements increase
Solution Approach 1:
The system extracts only the essential suitability indicators from large volumes of contextual data using learning engines. Instead of storing and processing all raw data, the system derives condensed suitability scores that capture the most important information, reducing data volume while maintaining information completeness for decision-making.
Solution Approach 2:
The system provides different levels of information detail at different locations in the search results. Multilayer maps display contextual data selectively based on user needs and query context, showing detailed suitability indicators where relevant and summarized information elsewhere, optimizing the balance between information completeness and data efficiency.
4Productivity
If the system implements time-displaced resource sharing evaluation, then the resource allocation efficiency improves, but the evaluation complexity increases
Solution Approach 1:
The system segments temporal resource sharing into discrete time periods and evaluates suitability for each period separately. The learning engines assess area and resource suitability independently of temporal constraints, then the system combines these with time-specific availability data, simplifying the overall evaluation of time-displaced sharing scenarios.
Solution Approach 2:
The suitability indicators generated by the learning engines serve multiple functions: they evaluate both spatial suitability (area and resource characteristics) and temporal suitability (when resources are available). This universal evaluation framework handles various time-displaced sharing scenarios without requiring separate evaluation mechanisms, reducing overall system complexity.
Data Source
AI summary
Methods and systems are disclosed for determining resource suitability based at least in part on physical geographic mapping data. An artificial intelligence/learning engine may be trained to determine such resource suitability using training data. The trained artificial intelligence/learning engine may then me used to generate suitability indicators. The suitability indicators may be rendered in association with a map comprising the resource. The resource may be configurable to be shared amongst a plurality of physical resource users in a time displaced manner.