Concurrent Entity Detection in Messaging Server Payloads
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Solution Overview
Problem
Businesses face latency and complexity in processing messages from dedicated messaging applications due to the need for additional API calls for natural language processing (NLP), which hinders efficient message routing, categorization, and automated responses.
Innovation Solution
A messaging server system that concurrently provides businesses with received messages and detected entities using NLP, where the business specifies NLP types, generates a configuration token, and sends the message payload to an NLP server for processing, allowing for enriched payload delivery and streamlined response generation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If additional API calls are made for NLP processing after message receipt, then entity detection and message processing capability are improved, but latency and workflow complexity increase
Solution Approach 1:
The NLP processing is initiated concurrently with message receipt rather than sequentially after receipt. The messaging server sends the message to the NLP server at the same time it notifies the business, so that entity detection is already complete when the business needs the information, eliminating the additional latency of sequential API calls
Solution Approach 2:
The NLP server acts as an intermediary service that the messaging server can call. Instead of the business directly making API calls to perform NLP processing, the messaging server mediates by forwarding messages to the NLP server and relaying results back to the business, streamlining the workflow
2Productivity
If additional API calls are made for NLP processing, then message processing capability is improved, but workflow complexity increases
Solution Approach 1:
The messaging server combines multiple functions into a single coordinated process: it receives messages, concurrently notifies businesses and forwards to NLP server, and consolidates results. This merging of message delivery and NLP processing coordination into one service reduces the number of separate API calls the business must make and simplifies the overall workflow
3Measurement precision
If NLP processing is performed after message receipt, then entity detection accuracy is improved, but response time deteriorates
Solution Approach 1:
NLP processing is performed in advance and concurrently with message delivery notification, so that entity detection is completed before the business needs to process the message. This preliminary action ensures both high detection accuracy and fast response time by eliminating sequential processing delays
Data Source
AI summary
A messaging server that concurrently provides a business or other recipient with a message sent to the recipient and the set of entities detected within the message using natural language processing (NLP). A business interacts with the messaging server to specify the types of NLP to perform on messages received by the business. The messaging server creates a configuration token describing the specified NLP configuration. The messaging server receives a message sent from a client to the business and generates a payload describing the message and including the configuration token. The messaging server then sends the payload to a NLP server. The NLP server performs NLP on the message in the payload according to the configuration specified by the token to detect a set of entities. The messaging server forms an enriched payload including the message and detected entities and forwards the enriched payload to the business.