AI Support Query Assistance From Messaging Platform Histories
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Support agents face challenges in quickly locating accurate information to resolve user issues due to inefficient search processes in messaging platforms and databases, leading to resource wastage and prolonged issue resolution times, especially in systems with large numbers of support agents.
Innovation Solution
A machine learning model trained on historical communications from a messaging platform provides real-time answers and generates practice queries to assist support agents, reducing manual searches and enhancing training efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If support agents manually search messaging platforms and databases for information, then they can locate relevant information to assist users, but the process is time-consuming and inefficient
Solution Approach 1:
The patent introduces an intermediary system comprising a machine learning model and search interface that mediates between support agents and the messaging platform database. This intermediary automatically processes search queries, retrieves relevant historical communications, and presents synthesized results to agents, eliminating manual searching while maintaining high information retrieval accuracy.
Solution Approach 2:
The patent replaces the mechanical manual searching process with an automated machine learning-based information retrieval system. The system uses natural language processing and machine learning models to automatically query, filter, and retrieve relevant information from the messaging platform, substituting human manual effort with automated computational processes.
2Productivity
If large numbers of support agents access support resources simultaneously, then more users can be assisted, but resource consumption and system latency increase
Solution Approach 1:
The patent implements preliminary action by pre-processing and indexing historical messaging platform communications during off-peak periods. The machine learning model is pre-trained on historical data, and common queries are pre-computed and cached. This allows the system to handle multiple simultaneous agent requests efficiently without requiring proportional increases in real-time computing resources.
Solution Approach 2:
The patent employs caching mechanisms that discard temporary search results and computational intermediates after use, while recovering and reusing common search patterns and frequently accessed information. This reduces redundant computations and minimizes resource consumption when multiple agents access similar support information.
3Ease of operation
If manual searching processes are used, then support agents can access information, but the process is tedious and reduces overall efficiency
Solution Approach 1:
The patent implements self-service by enabling the system to automatically perform information retrieval tasks without requiring manual intervention from support agents. The machine learning model autonomously processes queries, retrieves relevant information, and presents results, allowing agents to simply input questions and receive answers without engaging in tedious manual searching processes.
Data Source
AI summary
Artificial intelligence trained with messaging communications can be used to provide support for software services. In an example, a computing system can receive, via a graphical user interface (GUI), a first query associated with a first request from a client with respect to an issue with a service. The first query can be provided in a natural language format to a machine learning model that is trained on historical communication logs from a messaging platform. The machine learning model can generate a first output indicating a first answer to the first query. The machine learning model can also generate a second output indicating a second query associated with a second answer provided as a second input. The first answer and the second query can be presented in the natural language format on the user device via the GUI for use in resolving the issue with the service.