Cloud Messaging Bot Server for Multi-Platform User Engagement
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Solution Overview
Problem
Enterprise mobile applications face low user engagement and retention due to the lack of effective behavioral analytics and interactive messaging systems that can scale and integrate with multiple messaging platforms.
Innovation Solution
A bot server system that uses natural language processing and a virtual database to analyze user interactions, providing scalable and multi-platform messaging capabilities, along with a system for managing event data and incremental analytics calculations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If enterprise applications use traditional messaging platforms, then user engagement is improved, but the system complexity increases due to integration requirements
Solution Approach 1:
The messaging system is designed to support multiple messaging platforms (WhatsApp, Facebook Messenger, WeChat, Telegram, Skype) through a unified architecture. The cloud-hosted server can receive messages from any of these platforms and route them to the appropriate mobile application, eliminating the need for separate integration systems for each platform while maintaining high user engagement across diverse channels
Solution Approach 2:
A cloud-hosted messaging server acts as an intermediary between multiple messaging platforms and mobile applications. This intermediary component handles the complexity of platform-specific protocols and data formats, allowing enterprise applications to engage users across different platforms without directly managing the underlying system complexities of each platform
2Ease of operation
If the system stores all event data in local database, then data accessibility is improved, but data retention capability deteriorates due to storage limitations
Solution Approach 1:
The database system is segmented into two distinct components: a local database for frequently accessed recent event data and a remote database for long-term historical data storage. The local database maintains data for a defined retention period (e.g., 7 days) to ensure fast accessibility for recent interactions, while the remote database preserves historical data beyond this period. This segmentation resolves the contradiction by assigning different storage locations to different data retention requirements
Solution Approach 2:
The system dynamically manages data distribution between local and remote databases based on time-based retention policies. Event data is automatically moved from the local database to the remote database after the local retention period expires, allowing the system to adapt its storage architecture dynamically rather than statically, thus maintaining both fast access to recent data and comprehensive historical data retention
3Measurement precision
If the system queries databases for every analytics calculation, then calculation accuracy is improved, but processing time increases
Solution Approach 1:
The system pre-calculates and stores summary analytics data in a dedicated analytics database table, including aggregated metrics such as total users, active users, and engagement statistics. This preliminary computation of analytics data allows the system to retrieve pre-aggregated results directly from the database rather than performing time-consuming full database queries during each analytics request, thus reducing processing time while maintaining calculation accuracy through proper aggregation functions
Solution Approach 2:
The system creates a separate analytics database table that contains copies of pre-computed summary statistics derived from the event data. This analytics table serves as a simplified copy of the full event data structure, containing only the aggregated metrics needed for common analytics queries. By querying this pre-computed copy rather than the full event data table, the system maintains measurement precision for analytics calculations while dramatically reducing processing time
Data Source
AI summary
Provided are methods, systems, and computer-program products for responding to a natural language communication, sending a response to request additional information from a user, and exposing an invocable method for accessing a virtual database. Some examples relate to a bot server that can respond to natural-language messages (e.g., questions or comments) through a messaging application using natural-language messages. Other examples relate to storage of event data associated with a web page or a mobile application. Event data can describe one or more actions performed in relation to the web page and/or the mobile application. Other examples relate to behavioral analytics of the event data.