Dynamic Recipient Filtering in Chat Messaging
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Solution Overview
Problem
Existing chat room messaging systems fail to efficiently filter and update recipient lists based on contextual information, leading to irrelevant messages being sent to participants who are not relevant to the conversation, resulting in unnecessary communication and potential exclusion from relevant discussions.
Innovation Solution
A system and method that utilize a natural language understanding processor and machine learning to dynamically update the message recipient list by removing participants who are not relevant to the contextual information within the message, using contextual parameters such as geographical location, calendar information, and organizational hierarchy, and allowing participants to opt in or out of specific discussions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If all participants in a chat room receive every message, then no participant is excluded from any discussion, but irrelevant messages are sent to participants who are not relevant to the conversation
Solution Approach 1:
The system extracts and removes participants who are not relevant to the message content from the recipient list. By analyzing contextual information parameters in the message and comparing them with participant profiles, the system identifies and excludes irrelevant participants, ensuring messages are sent only to relevant recipients and eliminating unnecessary communication overhead.
Solution Approach 2:
The system implements feedback by continuously monitoring message content and dynamically updating the recipient list based on relevance analysis. The system provides feedback to the messaging process by adjusting who receives each message in real-time, ensuring that communication is optimized for relevance while preventing exclusion of potentially interested participants through opt-in mechanisms.
2Reliability
If the recipient list is dynamically updated based on contextual information, then message relevance is improved, but system complexity increases
Solution Approach 1:
The messaging server is designed with multi-functionality, handling both traditional broadcast messaging and intelligent recipient filtering within a single system. The system universally processes different types of messages (personal, group, broadcast) using the same contextual analysis framework, reducing the need for separate complex subsystems while maintaining dynamic recipient update capabilities.
Solution Approach 2:
The system performs self-service by automatically analyzing message contextual information and determining relevant recipients without requiring manual intervention. The messaging server autonomously updates recipient lists based on contextual parameters extracted from messages, reducing operational complexity while improving recipient relevance.
3Measurement precision
If contextual information is analyzed to filter recipients, then message targeting accuracy improves, but processing time increases
Solution Approach 1:
The system performs preliminary action by pre-processing and indexing participant profiles and their associated contextual information (interests, expertise, location, availability) before messages are sent. This pre-organization of data enables rapid matching when messages arrive, improving recipient identification accuracy without significantly increasing processing time during actual message delivery.
Solution Approach 2:
The system applies partial action by analyzing only the most relevant contextual information parameters for each message type, rather than processing all possible participant attributes. This selective analysis approach maintains high recipient identification accuracy while reducing processing overhead by focusing on the most impactful filtering criteria for each messaging scenario.
Data Source
AI summary
In one embodiment, a system and method for targeted messaging is described. A text-based communication session among at least three participants is monitored. At least one contextual information parameter of a first message in a message input field of a client device associated with a first participant of the at least three participants is detected in in the text-based communication session. It is determined if a second participant of the at least three participants is relevant to the first message, based on the contextual information parameter. A message recipient list for the first message is dynamically updates by removing the second participant from the message recipient list and yielding an updated message recipient list. Related methods, systems, and apparatus are also described.