Compatibility Scoring System for Contact Data Matching
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Solution Overview
Problem
Traditional methods are inefficient in identifying compatible and productive matches between complementary data items from large and diverse databases, especially when data items are seemingly unrelated, leading to unproductive actions.
Innovation Solution
A computer system extracts and analyzes interaction data and metrics to generate compatibility factors between groups and contacts, using weighted averages and time weighting to prioritize resource allocation for productive interactions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional database systems are used to store and query data, then data storage capacity is sufficient, but the efficiency of identifying compatible and productive matches between complementary data items deteriorates
Solution Approach 1:
The system performs preliminary actions by pre-calculating and storing compatibility factors between data items in the database. When a query is made, the system retrieves pre-computed compatibility information rather than performing complex real-time analysis, significantly reducing query time and improving match identification efficiency.
Solution Approach 2:
The patent introduces an intermediary mechanism - a compatibility scoring system that acts as a mediator between raw data items and query results. This intermediary layer pre-processes data relationships and stores them in structured formats, enabling efficient retrieval and comparison without requiring complex real-time computations.
2Loss of information
If comprehensive data from multiple sources is collected, then data completeness improves, but the difficulty of making sense of and using the data meaningfully increases
Solution Approach 1:
The system transforms raw, unstructured data into structured information by applying parameter changes - converting diverse data types into standardized formats with defined schemas. This transformation process organizes comprehensive data from multiple sources into a consistent structure that can be efficiently queried and analyzed, reducing the apparent complexity while maintaining data completeness.
Solution Approach 2:
The patent segments comprehensive data into distinct, manageable components with specific schemas. By dividing large datasets into structured segments with defined relationships, the system makes complex data more tractable and easier to analyze while preserving the complete information from multiple sources.
3Loss of information
If data items from different sources are matched, then the value of complementary data items increases, but the difficulty of finding matches between seemingly unrelated items increases
Solution Approach 1:
The compatibility scoring system serves as an intermediary that detects and measures relationships between seemingly unrelated data items. This mediator layer analyzes data from different sources and assigns compatibility scores based on predefined criteria, making hidden correlations detectable and measurable without requiring direct inspection of raw data relationships.
Solution Approach 2:
The patent replaces manual or simple mechanical data matching approaches with an automated computational system. The compatibility scoring mechanism uses algorithmic processing to detect and measure correlations between data items, substituting complex manual analysis with efficient computational methods that can handle diverse data sources.
Data Source
AI summary
A computer extracts from contact records that each include a contact identifier, a group identifier for each group with which the contact has had an interaction, and interaction information that indicates a number of interactions and a timing of a most recent interaction. The contact data records are processed to generate a contact profile record for each contact including group metric values and a corresponding value for each group metric value based on an interaction history of groups the contact has interacted with. An interaction analytics databases stores a set of contact profile records and group profile records for groups that include metric values associated with the group and an interaction history. They are processed with at least thousands of the contact profile records to determine group-contact compatibility factors. A compatibility parameter is generated and communicated for each of at least thousands of contacts based on the group-contact compatibility parameters.