Automated Software Setup Using Entity Descriptor Matching
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Solution Overview
Problem
The complexity of software setup processes for large entities is time-consuming and error-prone due to the need for extensive manual selection and review of numerous software functionality requirements, often resulting in either underinclusive or overinclusive configurations that waste resources and hinder user productivity.
Innovation Solution
An automated software setup method that uses entity descriptors to determine similar entities and statistically pre-select software functionality requirements based on the configurations of these entities, presenting them to users for review and configuration, thereby reducing the number of manual selections and improving efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual selection and review of software functionality requirements is performed, then software setup accuracy is improved, but setup time and complexity increase significantly
Solution Approach 1:
The system performs preliminary analysis of entity descriptors and automatically pre-selects software functionality requirements before user review, based on historical data from similar entities. This preliminary action reduces the time required for manual selection while maintaining accuracy through user verification of pre-selected options.
Solution Approach 2:
The system copies configuration patterns from similar entities (entities with matching descriptors) and applies them to the current entity being configured. By copying proven configurations from comparable entities, the system reduces setup time while maintaining accuracy through statistical validation of the copied configurations.
2Measurement precision
If extensive manual review of software functionality requirements is performed, then configuration accuracy is improved, but device complexity and operational difficulty increase
Solution Approach 1:
The system performs self-service by automatically analyzing entity descriptors, comparing them against historical data, and pre-selecting appropriate software functionality requirements without requiring extensive manual intervention. Users only need to review and confirm the pre-selected options, significantly reducing the complexity of the setup process while maintaining configuration accuracy.
Solution Approach 2:
The system replaces the mechanical process of manual review and selection with an automated computational process that analyzes entity descriptors, queries historical configurations, and generates pre-selected requirements. This substitution reduces setup process complexity while maintaining or improving configuration accuracy through systematic automated analysis.
3Adaptability or versatility
If comprehensive software functionality requirements are selected, then software versatility is improved, but resource consumption and system size increase
Solution Approach 1:
The system applies local quality by customizing software functionality selection based on the specific characteristics of each entity's descriptors. Rather than applying a uniform comprehensive configuration to all entities, the system tailors the selected functionality to match the specific needs and characteristics of each entity, improving versatility while reducing resource consumption by excluding unnecessary features.
Solution Approach 2:
The system changes parameters by dynamically adjusting the selected software functionality based on entity descriptors and historical configuration data. By analyzing specific entity characteristics and comparing them with similar entities, the system optimizes the configuration parameters to include only the necessary functionality, achieving versatility without excessive resource consumption.
4Productivity
If automated pre-selection of software functionality requirements is performed, then setup time is reduced, but setup accuracy may deteriorate
Solution Approach 1:
The system implements feedback by allowing users to review, modify, and provide feedback on the pre-selected software functionality requirements. This feedback mechanism ensures that automated pre-selection maintains high accuracy by incorporating user expertise and specific entity requirements, while still achieving significant time savings through the automated initial selection process.
Data Source
AI summary
The present disclosure pertains to automated software setup. In one embodiment, a first set of entity descriptors associated with a first entity is obtained. Similar entities to the first entity are determined by comparing the first set of entity descriptors to each of a plurality of sets of entity descriptors, each associated with a particular entity that previously setup the software. The percentage of the similar entities that selected a particular software functionality requirement is determined and a set of software functionality requirements for the first entity is determined based on the percentages. A user interface is provided for selecting the plurality of possible software functionality requirements and the first set of software functionality requirements are pre-selected in the user interface. Accordingly, software setup for a new entity is automated based on the software setup selections of similar entities.