Intent-Based Bot Recommendation System
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Solution Overview
Problem
Existing messaging systems lack efficient methods for users to interact with network-accessible services within a messaging context, particularly in recommending relevant messaging bots based on user intents and preferences, and fail to effectively manage multi-user interactions for service requests.
Innovation Solution
A system that includes a bot-service system with a bot application interface, client communication, and interaction processing components to receive user service prompts in natural language, match them against bot capability catalogs, and provide relevant messaging bots, while also handling multi-user interfaces and service requests across multiple client devices.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If the system recommends messaging bots based on user intents and preferences, then the relevance and usefulness of bot recommendations is improved, but the complexity of the recommendation system increases
Solution Approach 1:
The patent introduces a bot-service system as an intermediary layer between users and messaging bots. This mediator receives user service prompts, matches them against bot capability catalogs using natural language processing, and returns relevant bot recommendations. This intermediary approach improves recommendation relevance while managing system complexity by centralizing the matching logic in a dedicated service layer rather than distributing it across multiple components.
Solution Approach 2:
The patent replaces traditional keyword-based or rule-based bot recommendation mechanisms with natural language processing and machine learning models. The interaction processing component uses NLP to understand user intents from service prompts and matches them semantically against bot capability catalogs, enabling more accurate and context-aware recommendations while automating the complex matching process.
2Adaptability or versatility
If the system manages multi-user interactions for service requests, then the capability to handle collaborative services is improved, but the complexity of managing multiple client devices and interactions increases
Solution Approach 1:
The bot-service system is designed as a universal platform that can handle multiple user interactions, different types of service requests, and various messaging bots through a single unified interface. The system processes service prompts from multiple users, manages bot capability catalogs, and coordinates interactions across different client devices, enabling multi-user collaborative services without requiring separate systems for each function.
Solution Approach 2:
The bot-service system acts as a central mediator that coordinates interactions between multiple users and their respective bots. It receives service prompts from various client devices, processes them through the interaction processing component, and manages the matching between user intents and bot capabilities. This intermediary approach simplifies multi-device management by centralizing the coordination logic in the bot-service system rather than requiring direct peer-to-peer communication between multiple clients.
3Measurement precision
If natural language processing is used to match user prompts against bot capabilities, then the accuracy of bot matching is improved, but the processing time and computational resources increase
Solution Approach 1:
The system performs preliminary processing by maintaining pre-processed bot capability catalogs that are ready for matching. The bot application interface component stores bot capabilities in structured formats, and the interaction processing component has pre-loaded reference data for common service intents. This preliminary preparation reduces the computational burden during actual user interactions, allowing the system to perform accurate natural language matching without excessive processing delays.
Solution Approach 2:
The patent employs natural language processing and machine learning models to automatically understand and match user intents with bot capabilities. Instead of requiring manual configuration or exact keyword matching, the NLP system semantically analyzes service prompts and compares them against bot capability descriptions, achieving high matching accuracy while the automated processing reduces manual intervention time.
Data Source
AI summary
Techniques for intent-based search for bots are described. In one embodiment, an apparatus may comprise a bot application interface component operative to receive a plurality of bot capability catalogs for a plurality of bots at a bot-service system, wherein each of the plurality of bot capability catalogs expresses bot capability in natural language for an associated bot; a client communication component operative to receive a user service prompt from a user client device, the user service prompt expressed in natural language; and identify one or more selected bots to the user client device in response to receiving the user service prompt from the user client device; and an interaction processing component operative to determine the one or more selected bots of the plurality of bots by matching the user service prompt against the plurality of bot capability catalogs. Other embodiments are described and claimed.