Semantic Message Clustering for Scalable Audience Responses
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Solution Overview
Problem
Large-scale, one-to-many communication in electronic messaging platforms, particularly between influencers and their fans, is challenging due to limitations in wireless networks and existing technologies, making it difficult to manage, organize, and respond to a large number of messages effectively.
Innovation Solution
A messaging platform utilizing semantic clustering and machine learning to group and analyze messages based on semantic meaning, allowing for intelligent response generation and targeted communication with large audiences.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If one-to-many communication is implemented at large scale in messaging platforms, then engagement rates between clients and users are improved, but the ability to manage and respond to large numbers of messages deteriorates
Solution Approach 1:
The patent segments incoming messages from multiple users into distinct groups or clusters based on semantic similarity. Messages with similar content, intent, or topics are grouped together, allowing the client to respond to clusters rather than individual messages. This segmentation reduces the cognitive load and operational complexity of managing thousands of messages while maintaining high engagement rates.
Solution Approach 2:
The patent introduces an intermediary processing layer (the messaging platform's server system) that automatically clusters and organizes messages before presenting them to the client. This intermediary performs semantic analysis, grouping, and filtering, acting as a mediator between the large volume of incoming messages and the client's response capabilities. The intermediary handles the complexity of message management, freeing the client to focus on meaningful interactions.
2Quantity of substance
If messages from millions of users are received and processed, then audience reach is improved, but the complexity of organizing and understanding messages increases
Solution Approach 1:
The system segments the vast influx of messages from millions of users into manageable clusters based on semantic content. By dividing the message stream into topic-based groups, the system maintains organization and understandability despite handling large quantities of messages. Each cluster represents a coherent topic or intent, making it feasible to process and respond to messages at scale.
Solution Approach 2:
The patent replaces manual message organization mechanisms with automated semantic clustering algorithms. Instead of relying on manual sorting, filtering, or categorization, the system uses machine learning and natural language processing to automatically group messages. This substitution of mechanical organization methods with intelligent automation reduces the complexity of managing large-scale message volumes.
3Adaptability or versatility
If individual responses are generated for each user message, then personalization is improved, but the time and resources required for response generation increase
Solution Approach 1:
The patent merges multiple similar user messages into single clusters, allowing one personalized response to serve multiple users with similar concerns or questions. By combining messages with identical or similar semantic content, the system maintains personalization for each user while reducing the total number of response generation operations. The personalized response is then distributed to all members of the cluster.
Solution Approach 2:
The system performs preliminary semantic analysis and clustering of messages before response generation. By pre-organizing messages into clusters based on content similarity, the system prepares the groundwork for efficient response generation. This preliminary action identifies which messages can share responses, reducing redundant work and accelerating the overall response process while maintaining personalization.
Data Source
AI summary
Example systems, methods, and computer-readable media are disclosed. In an example method, a first outbound text message is transmitted via a message broker of a messaging platform from a client to a plurality of recipients. In response to the first outbound message, a plurality of inbound text messages is received, via the message broker, from the plurality of recipients. A first grouping of the plurality of inbound text messages is determined, the first grouping associated with one or more recipients of the plurality of recipients. The first grouping is presented to the client. A second outbound text message is transmitted, via the message broker, from the client to the one or more recipients of the plurality of recipients. The second outbound text message is generated based on the first grouping. The message broker is in communication with a first messaging service and a second messaging service different from the first messaging service. The first outbound text message is transmitted via the first messaging service. A first inbound text message of the plurality of inbound text messages is received via the second messaging service. Each inbound text message of the plurality of inbound text message is addressed to a long-code telephone number generated by the messaging platform and uniquely associated with the client by the messaging platform.