Probabilistic Classifier for Contact Entity Identification
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Solution Overview
Problem
In multi-tenant database systems, accurately identifying entities from character strings is challenging due to inconsistencies and inaccuracies in contact information, especially when new or updated information is received as character strings, making it difficult to maintain up-to-date and accurate data for customers.
Innovation Solution
A probabilistic statistical classifier using advanced features such as Bayes classifier, last word feature, digit-blurring feature, words feature, full string feature, and address line feature is employed to compute posterior probabilities and accurately identify entities within character strings, thereby updating contact information effectively.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional database approaches are used to store contact information, then data can be accessed and stored using user computing systems, but data accuracy and consistency deteriorate due to individual maintenance errors
Solution Approach 1:
The patent introduces a multi-tenant database system as an intermediary between users and the contact information data. This central化的数据库系统 serves as a mediator that receives, validates, stores, and distributes contact information to all users, eliminating the need for individual users to maintain their own copies of contact data. This intermediary structure ensures data accuracy and consistency across the organization while providing centralized control over data quality.
2Reliability
If multi-tenant database systems are used to share contact information centrally, then data consistency and accuracy improve, but the complexity of identifying entities from character strings increases
Solution Approach 1:
The patent implements feedback mechanisms where the multi-tenant database system continuously receives contact information updates, validates them against existing data, and distributes corrected information back to users. The system monitors data quality metrics and uses this feedback to improve entity identification algorithms over time, adapting to new patterns and formats of contact information while maintaining data consistency across the organization.
3Reliability
If manual methods are used to update contact information in multi-tenant systems, then implementation is simple, but data currency and completeness deteriorate
Solution Approach 1:
The patent enables the multi-tenant database system to automatically perform data validation, entity identification, and distribution of contact information updates without requiring manual intervention for each update cycle. The system self-manages the complexity of entity identification from character strings using automated algorithms, allowing rapid processing and distribution of contact information updates to all users while maintaining data currency and completeness.
Data Source
AI summary
A system and method for associating a character string with one or more defined entities of a contact record. An input character string is received. The string is first evaluated to see if the structure of the string is recognized. If not, then the string is compared to entries in a look up table. If the string format is not recognized, and the string is not found in the look up table, then a posterior probability is calculated for a set of defined entities over a limited set of string processing features. The result of probabilistic scoring determines which of the defined entities to associate with the character string.