Customized Contact Dataset Generation via Pattern Recognition
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Solution Overview
Problem
Conventional systems for generating contact datasets in business contexts are inefficient, labor-intensive, and prone to error, especially when creating focused datasets for sales planning or customer service. Additionally, the strategies and insights behind successful datasets are often not recorded or utilized effectively.
Innovation Solution
The system mines contacts from multiple enterprise members, applies pattern recognition to align contact attributes with success objectives, and uses machine learning to generate customized contact datasets and action steps datasets in response to user requests, events, or notifications.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual methods are used to build contact datasets, then the user can apply custom search criteria and objectives, but the process becomes labor-intensive and prone to error
Solution Approach 1:
The system automatically mines contacts from multiple enterprise members and generates customized contact datasets without requiring manual evaluation of each contact. The machine learning process autonomously applies pattern recognition to identify contacts aligned with success objectives, eliminating the need for users to manually build datasets while maintaining accuracy through automated pattern matching.
Solution Approach 2:
The patent replaces manual mechanical processes of contact dataset creation with automated machine learning and pattern recognition systems. The machine learning process substitutes human effort in evaluating and selecting contacts, using automated algorithms to identify patterns and generate focused datasets efficiently and accurately.
2Adaptability or versatility
If conventional filtering is applied to customer databases, then contacts can be segmented by existing account information, but the filtering is limited to already-associated data and order history
Solution Approach 1:
The system performs preliminary data mining and pattern recognition on enterprise contact data before users need to create datasets. By pre-processing and storing mined contact attributes and patterns in advance, the system enables versatile querying and filtering based on discovered patterns rather than just existing account data, while preserving strategic insights through stored pattern information.
Solution Approach 2:
The patent segments contacts into customized datasets based on multiple criteria including mined attributes, pattern recognition results, and user-defined objectives. This segmentation goes beyond conventional filtering by creating focused datasets tailored to specific sales planning needs, marketing campaigns, or customer service initiatives using both existing and discovered contact attributes.
3Measurement precision
If focused datasets are created for specific sales planning or customer service objectives, then the dataset relevance improves, but the process becomes more complex and time-consuming
Solution Approach 1:
The system performs preliminary mining of contacts and pattern recognition in advance, storing processed contact attributes and identified patterns in a ready-to-query format. When users need focused datasets for specific objectives, the system quickly retrieves and filters pre-processed data rather than starting from raw data, significantly reducing generation time while maintaining high relevance through objective-aligned filtering.
Solution Approach 2:
The machine learning process autonomously generates focused contact datasets by automatically applying pattern recognition and filtering based on success objectives. The system self-services the dataset creation process without requiring manual intervention to evaluate each contact, rapidly producing relevant datasets for sales planning, marketing, or customer service while preserving strategic insights through automated pattern capture.
4Reliability
If manual contact dataset creation is performed, then user expertise and insights can be applied, but the strategic elements are not recorded or utilized by other agents
Solution Approach 1:
The system captures and stores pattern recognition results, mined contact attributes, and successful dataset generation parameters as feedback for future use. When users create focused datasets aligned with success objectives, the system records the strategic elements and patterns discovered, making them available for training the machine learning process and assisting other agents, thereby institutionalizing expertise and improving future dataset quality.
Solution Approach 2:
The patent creates a universal system that serves multiple functions: generating customized contact datasets for individual users while simultaneously capturing and storing strategic insights for enterprise-wide use. The machine learning process and pattern recognition system function universally across different users and objectives, preserving and applying strategic elements across the entire enterprise rather than isolating them to individual manual processes.
Data Source
AI summary
This disclosure provides systems for generating customized datasets. An example dataset generator mines contacts from multiple members of an enterprise, applies a pattern recognition process to determine attributes of the contacts aligned with objectives of the enterprise, stores the contacts and associated attributes in a relational data structure, and through a machine learning process, generates a customized contact dataset of the contacts with attributes relevant to a user request, an event, or a notification received. The example dataset generator may also generate an action steps dataset for the user. The dataset generator may aggregate contacts from calls, chats, emails, SMS messages, and video feeds of an enterprise, apply a pattern recognition process to determine attributes of the contacts, and through a machine learning process, partition and distribute the contacts and associated action steps on respective dashboards of multiple customer service representatives of the enterprise.