Entity Group Matching Using Probabilistic Latent Semantic Analysis
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Solution Overview
Problem
Current methods lack the ability to effectively determine the appropriateness of a group of entities for fulfilling multiple mission goals represented by unstructured and structured data, with existing Latent Semantic Analysis (LSA) and Probabilistic Latent Semantic Analysis (PLSA) primarily focused on individual entity-mission matches rather than group capabilities.
Innovation Solution
A computer-based system and method utilizing Probabilistic Latent Semantic Analysis (PLSA) or Latent Dirichlet Allocation (LDA) to compare and maximize the appropriateness of a group of entities by creating topic models from unstructured and structured data, determining similarities between entities and missions, and optimizing group formations to meet mission requirements while minimizing redundancy and maximizing diversity.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If LSA or PLSA is used to match a single entity to a mission, then individual entity-mission matching accuracy is improved, but the ability to determine appropriateness of a group of entities for multiple mission goals deteriorates
Solution Approach 1:
The patent segments the assessment process into two distinct similarity measurements: (1) similarity between the mission and the group of entities as a whole, and (2) similarity between individual entities and the mission. This segmentation allows the system to evaluate both collective group capability and individual entity contributions, resolving the contradiction between individual matching accuracy and group assessment capability.
Solution Approach 2:
The patent adds a new dimension to the assessment by introducing group-level similarity measurement in addition to individual entity-mission matching. This dimensional expansion transforms the problem from single-entity matching to multi-level assessment, enabling simultaneous evaluation of both individual and collective appropriateness for mission fulfillment.
2Reliability
If more entities are included in a group to fulfill mission requirements, then mission capability is improved, but redundancy increases and diversity decreases
Solution Approach 1:
The patent applies partial action by determining the minimal sufficient subset of entities needed to fulfill mission requirements. Rather than including all available entities or using arbitrary thresholds, the system identifies the precise minimum set required, eliminating redundancy while maintaining adequate mission capability. This resolves the contradiction by avoiding excessive entity inclusion.
Solution Approach 2:
The patent changes the evaluation parameter from simple entity count to optimized group composition based on similarity measurements. By using similarity thresholds and optimization algorithms, the system transforms the group formation process from quantity-based to quality-based selection, achieving mission capability with minimal redundant entities and maximized diversity.
3Productivity
If traditional matching methods are used, then processing speed is maintained, but accuracy in determining group appropriateness for multiple missions deteriorates
Solution Approach 1:
The patent applies preliminary action by pre-computing similarity measurements between entities and missions before final group optimization. This preprocessing step creates a foundation of pre-analyzed data that accelerates the subsequent optimization process, maintaining processing speed while enabling accurate multi-mission group appropriateness determination through structured similarity comparisons.
Data Source
AI summary
The present invention relates in general to methods and systems for comparing and maximizing the appropriateness 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, this invention provides an effective and accurate method and system to compare and maximize the appropriateness between the requirements of a task and the second set's capabilities, while these capabilities and requirements are contained, even if only latently, in data objects such as written documents, electronic databases or other sources of data and information. In one embodiment, topic modeling techniques are utilized to compare the data objects.