Dynamic Resource Allocation Using Contextual Data
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing resource allocation methods in enterprises lack contextual information, leading to inefficiencies in scheduling resources like 3D printers and meeting rooms, as they do not account for real-time changes, geographical distances, and other external factors.
Innovation Solution
A computing device identifies optimal resource allocation periods based on schedule data, user inputs, and data from remote applications, such as building access systems and weather systems, to select resources that are geographically convenient and available to users, using a scoring system that considers historical availability and real-time changes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional scheduling methods are used to allocate resources, then resource allocation can be performed with simple scheduling tools, but the allocation accuracy and efficiency deteriorate due to lack of contextual information
Solution Approach 1:
The patent combines multiple data sources including calendar applications, building access systems, weather systems, and transportation systems into a unified resource allocation system. This integration allows the system to access contextual information from various sources to improve allocation accuracy while managing complexity through centralized processing.
Solution Approach 2:
The computing device acts as an intermediary that receives and processes data from multiple remote applications (calendar, building access, weather, transportation). It generates scores based on contextual information and presents optimized resource allocation options to users, mediating between raw data and final allocation decisions.
2Adaptability or versatility
If static scheduling is used to allocate resources, then allocation can be made with fixed time blocks, but the system cannot adapt to real-time changes and external factors
Solution Approach 1:
The patent implements dynamic resource allocation by continuously monitoring contextual data from multiple sources and updating allocation recommendations in real-time. The system generates dynamic scores based on current conditions such as weather, transportation status, and user availability, allowing it to adapt to changing circumstances rather than relying on static schedules.
Solution Approach 2:
The system incorporates feedback mechanisms by monitoring user responses to allocation suggestions and actual resource usage patterns. This feedback is used to refine scoring algorithms and improve future allocation recommendations, enabling the system to learn and adapt over time based on real-world outcomes.
3Ease of operation
If geographical location is not considered in resource allocation, then scheduling can be simplified, but user convenience and accessibility deteriorate
Solution Approach 1:
The patent applies local quality by considering the specific geographical location and context of each user when generating resource allocation recommendations. The system calculates scores based on individual user locations, anticipated movements, and proximity to resources, tailoring allocations to local conditions rather than applying uniform scheduling rules.
Solution Approach 2:
The system dynamically changes allocation parameters based on geographical data. It incorporates user location, anticipated location changes, and resource location into the scoring mechanism, adjusting the weight and relevance of these parameters to optimize both user convenience and system efficiency.
Data Source
AI summary
Methods and systems for resource allocation using data from a variety of systems are described herein. A plurality of periods of time for using one or more resources may be identified based on a user request. Data indicative of locations of one or more users over one or more time periods may be received. Such data may be from an application other than a calendar application. Anticipated locations of the one or more users may be determined based on the data received. A resource of the one or more resources may be selected based on a distance between the resource and the anticipated locations of the one or more users. Based on detecting a change to the data, a new resource may be selected.