Matrix-Connected Address Book System With Connective Recognition Logic
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Solution Overview
Problem
Conventional computer-based and Internet-based address books lack advanced features for intelligent querying, relationship management, and time-based access, limiting their ability to provide refined and relevant contact information.
Innovation Solution
An online address book system with a matrix of resources that includes server software for storing and accessing contact information based on contact, relationship, and time frame inputs, utilizing connective recognition logic and deductive reasoning to infer relationships and prioritize responses, along with features like spell checking, category creation, and historical context to enhance query results.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If conventional address books store contact information in traditional databases, then data storage is simple, but query capability and relationship management are limited
Solution Approach 1:
The patent introduces a matrix structure that adds multiple dimensions to traditional address book data storage. Instead of simple key-value pairs, contacts are organized in a matrix with rows representing contacts and columns representing attributes (name, phone, email, relationship, time frame). This dimensional expansion enables complex queries across multiple attributes simultaneously while maintaining structured data organization.
Solution Approach 2:
The system implements nested data structures where relationship information and time frame data are embedded within the contact matrix. Relationships are stored as connections between contacts in the matrix, and time frame information is layered within the same structure. This nesting allows the system to manage complex relationship networks and temporal data without requiring separate database tables for each aspect.
2Adaptability or versatility
If the system provides comprehensive relationship management features, then relationship inference capability improves, but processing time and computational resources increase
Solution Approach 1:
The system performs preliminary processing by pre-calculating and storing relationship information and time frame data within the contact matrix during data entry and updates. Relationship inference is performed in advance when contact information is added or modified, and the results are stored for rapid retrieval during queries. This preliminary action reduces the computational burden during actual query operations.
Solution Approach 2:
The system incorporates feedback mechanisms where query results and user interactions are used to refine future queries and recommendations. The matrix structure allows the system to learn from usage patterns and adjust relationship inferences and time frame assignments accordingly. This feedback loop continuously improves the accuracy and relevance of relationship management features while optimizing query response times.
3Productivity
If the system stores detailed contact information and relationships in a matrix structure, then data organization and retrieval improve, but data entry complexity increases
Solution Approach 1:
The system implements self-service features that automatically populate matrix fields based on available information. When contact information is entered, the system automatically infers relationships, assigns time frames, and organizes data within the matrix structure without requiring manual configuration. The matrix structure itself provides automatic sorting and indexing, eliminating the need for users to manually organize data while maintaining efficient retrieval capabilities.
4Measurement precision
If the system implements intelligent query features like spell checking and response prioritization, then query accuracy improves, but system complexity and resource requirements increase
Solution Approach 1:
The patent implements a universal query processing mechanism that handles multiple functions within the same matrix structure. The same matrix that stores contact information also serves as the basis for spell checking, relationship inference, time frame filtering, and response prioritization. This multi-functional approach eliminates the need for separate systems for each feature, reducing overall complexity while maintaining high query accuracy through integrated processing.
Data Source
AI summary
An online address book system having sufficient hardware and software to operate an address book user interface and to perform intelligent interpretations of inputs from users. The system includes at least one server software module that includes software to perform a plurality of functions. These include the ability to receive input data and separate user queries, wherein the software can arrange the data so as to create a data base that includes at least three access dimensions, including contact access, contact-relationship access and contact-time frame access, and so as to create a connectivity matrix based on a plurality of contact pair relationships applying connective recognition logic. The system provides a user interface that permits access to address book stored data based on user input selected from the group consisting of contact, a contact-relationship pair, a contact-time frame pair, and combinations thereof.