Messaging Search System Using Work Graphs for Message Prioritization
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Solution Overview
Problem
Current internet messaging systems lack effective mechanisms for searching and managing messages across users, channels, and topics, leading to inefficiencies in retrieving relevant information and prioritizing messages.
Innovation Solution
The Messaging Search and Management (MSM) system generates metadata for messages, utilizes work graphs to capture relationships between users, channels, and topics, and employs machine learning structures to rank messages, people, and channels, enabling advanced search and management functionalities.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional messaging systems are used, then message storage and transmission are simple, but message retrieval and management efficiency deteriorate
Solution Approach 1:
The system performs preliminary actions by generating metadata for messages in advance and organizing them into work graphs that capture relationships between users, channels, and topics. This preprocessing enables efficient retrieval without complex real-time processing, resolving the contradiction between retrieval efficiency and system complexity
Solution Approach 2:
The patent introduces machine learning structures as intermediaries between the message data and user queries. These ML models rank messages, people, and channels based on relevance, acting as a mediator that simplifies the retrieval process while improving efficiency, thus resolving the technical contradiction
2Ease of operation
If advanced search functionalities are added, then information accessibility improves, but system complexity increases
Solution Approach 1:
The system implements self-service by enabling users to perform advanced searches and manage their own messages efficiently. The work graphs and metadata automatically organize information, allowing users to access relevant content without complex manual management, thus improving ease of operation without proportionally increasing system complexity
Solution Approach 2:
The patent changes parameters by transforming raw message data into structured metadata with various attributes. This parameter transformation enables advanced search capabilities while maintaining manageable system complexity through systematic data organization and machine learning-based ranking
Data Source
AI summary
The Messaging Search and Management Apparatuses, Methods and Systems (“MSM”) transforms message, ranking request inputs via MSM components into work graphs, ML structure input data, ML structure, ranking response outputs. A work graph generation request that includes group level access control data may be obtained. A set of metadata access control carrying messages, a set of users, a set of channels, and a set of topics with access control data corresponding to the group level access control data may be determined. A user priority score for each of the other users, a channel priority score for each of the channels, and a topic priority score for each of the topics, from the perspective of each user, may be calculated. A work graph data structure may be generated that includes, for each user, data regarding the calculated user priority scores, channel priority scores, and topic priority scores.