Crowd-Sourced Consensus Map for Information Categorization
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Solution Overview
Problem
Current systems for ideation and innovation are often closed, leading to hidden ideas and counterproductive results, while open software and crowd sourcing have faults, and existing information management tools struggle with deep classification, relationship editing, and harmonization of categorizations across different sources.
Innovation Solution
A system and method for crowd sourced consensus building, providing a Common Mental Map (CMM) for navigation, using topic categorization services and online community services by topics, with a CMMDB populated by automated consolidation and maintained through crowd source collaboration, enabling visualization and editing interfaces for consensus on categorization accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If crowd sourcing is used to populate and maintain the CMMDB, then the quantity and diversity of information increases, but the complexity of managing consensus and categorization accuracy increases
Solution Approach 1:
The patent introduces automated tools and algorithms as intermediaries between crowd contributors and the final CMMDB content. These intermediaries include: (1) automated categorization algorithms that process crowd-submitted concepts and relationships, (2) consensus-building mechanisms that aggregate multiple user inputs, and (3) validation systems that ensure data quality. This mediator layer reduces the direct complexity burden on human users while maintaining the benefits of crowd-sourced quantity and diversity.
Solution Approach 2:
The system enables self-service through automated population and maintenance of the CMMDB. The database can autonomously: (1) ingest data from multiple sources, (2) apply categorization rules and algorithms, (3) resolve conflicts through predefined consensus mechanisms, and (4) update relationships without requiring manual intervention for each operation. This self-service capability reduces operational complexity while maintaining information quantity.
2Measurement precision
If deep classification and sub-categorization are implemented, then the precision of information organization improves, but the complexity of the categorization structure increases
Solution Approach 1:
The patent applies segmentation by dividing the categorization structure into hierarchical levels and modular components. The CMMDB organizes information into: (1) top-level domains, (2) sub-domains, (3) specific categories, and (4) detailed sub-categories. This segmented approach allows precise organization at each level while managing overall complexity through modular design, where each segment can be independently maintained and validated.
Solution Approach 2:
The system manages categorization complexity by adding dimensional organization beyond simple hierarchy. It implements: (1) multi-dimensional tagging systems where concepts can belong to multiple categories simultaneously, (2) relationship types that add contextual dimensions (e.g., precursor, competitor, application), and (3) dynamic categorization that adapts based on query context. This dimensional approach enables precise organization without linearly increasing structural complexity.
3Productivity
If automated consolidation of existing indices and tools is used to populate the CMMDB, then the productivity of information gathering increases, but the difficulty of harmonizing categorizations across different sources increases
Solution Approach 1:
The patent replaces manual mechanical harmonization processes with automated computational systems. These include: (1) algorithmic mapping that automatically aligns concepts from different sources to the CMMDB taxonomy, (2) conflict detection algorithms that identify categorization inconsistencies, and (3) automated resolution mechanisms that apply harmonization rules. This substitution maintains high productivity while systematically managing the difficulty of harmonization through computational rather than manual processes.
Solution Approach 2:
The system manages harmonization difficulty by dynamically adjusting parameters such as: (1) matching thresholds for concept similarity, (2) weightings for different data sources, (3) flexibility of category assignment, and (4) confidence levels for automated decisions. These parameter changes allow the system to adapt to different source characteristics while maintaining productivity, effectively tuning the harmonization process to balance automation efficiency with accuracy requirements.
Data Source
AI summary
The invention provides a system and method for providing ttx-based categorization services and a categorized commonplace of shared information. Currency of the contents is improved by a process called conjuring/concretizing wherein users' thoughts are rapidly infused into the Map from user entry or movement of ttxs; or pre-entry of fxxts of text, transform relationships, associations among ttxs, commonalities, or ttxs. A Map is generated based upon use of selected sets of relationships between ttxs, allowing for construction of subjective, compartmentalized, or objective depictions and organizations. The Map provides for modeling where ttx nodes are objects. The Map ttxs also serve as binding points to other modeling paradigms similar to how equations are bound to spreadsheet cells, but using the extended information of a graph rather than a rectangle of cells. As a new idea is sought, a goal is created for a search. After the goal idea is found, a ttx is concretized and categorized. The needs met by such a Map are prior art searching, competitive environmental scanning, competitive analysis study repository management and reuse, innovation gap analysis indication, novelty checking, technology value prediction, investment area indication and planning, and product technology comparison and feature planning.