Processor-Based Entity Group Matching via Topic Modeling
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Solution Overview
Problem
Current methods lack the ability to accurately determine the appropriateness of a group of entities for fulfilling the goals of a single mission or multiple missions, represented by unstructured and structured data, with existing techniques only applicable to matching individual entities to job descriptions rather than evaluating group capabilities.
Innovation Solution
A processor-based method utilizing Probabilistic Latent Semantic Analysis (PLSA) and Latent Dirichlet Allocation (LDA) to create topic models, apply interaction measures, and determine similarity and appropriateness between entities and mission requirements, enabling the optimal selection of groups for specific tasks by analyzing teamwork skills and interaction attributes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional matching methods are used to evaluate entities, then individual entity-to-job matching can be achieved, but the ability to evaluate group capabilities for mission fulfillment is insufficient
Solution Approach 1:
The patent extends the matching methodology from individual entity-to-job evaluation to multi-functional group-to-mission evaluation. The system simultaneously assesses both individual entity capabilities and group dynamics, enabling universal application across individual and collective evaluation scenarios while maintaining measurement precision through consistent analytical frameworks.
Solution Approach 2:
The evaluation process is segmented into distinct analytical components: individual entity attribute analysis, interaction measure calculation, group formation evaluation, and mission fulfillment assessment. This segmentation allows the system to maintain precise measurement of individual contributions while simultaneously evaluating emergent group properties that arise from entity interactions.
2Adaptability or versatility
If group evaluation capabilities are added to the system, then the ability to assess team appropriateness improves, but the complexity of the evaluation process increases
Solution Approach 1:
The patent introduces interaction measures as intermediary metrics that bridge individual entity attributes and group-level evaluation. These interaction measures serve as mediators that quantify entity-entity relationships, enabling the system to assess group dynamics without requiring a complete redesign of the evaluation architecture. The interaction measures translate complex group behaviors into comparable quantitative metrics.
Solution Approach 2:
The system adds a new dimensional layer to the evaluation process by incorporating interaction measures that operate alongside traditional individual attribute assessments. This dimensional expansion allows the system to evaluate both individual capabilities and group dynamics simultaneously, transforming the evaluation from a single-dimensional individual assessment to a multi-dimensional framework that captures emergent group properties.
3Reliability
If interaction measures and teamwork skills are analyzed, then the appropriateness assessment becomes more comprehensive, but the computational requirements and processing time increase
Solution Approach 1:
The patent applies preliminary action by pre-calculating and storing interaction measures for entity pairs before group formation evaluation. By computing interaction metrics in advance and caching results, the system reduces redundant calculations during the group appropriateness assessment phase, thereby maintaining comprehensive evaluation while reducing processing time for subsequent group formation tasks.
Data Source
AI summary
The present invention relates in general to methods and systems for comparing and maximizing the optimal selection of a first set of one or more data objects to a set of second data objects. In one embodiment, the first set of data objects represent one or more tasks to be fulfilled by a set of capabilities represented by the second data objects. In one embodiment, methods and systems are provided that apply topic modeling and similarity metrics to determine the optimal selection. In one embodiment, methods and systems are provided to determine the appropriateness of a set of second data objects to satisfy the requirements of a first data object given interaction attributes. Embodiments may be used to compare mission requirements with potential team members to determine the appropriateness of team members and teams for a given mission based on interaction attributes of the team members and teams.