Implicit Quality Signals for Bot Recommendation
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Solution Overview
Problem
Users face difficulties in efficiently finding and accessing bots with sought-after capabilities due to the increasing number of available bots, leading to complications in identifying suitable services.
Innovation Solution
A technique that provides implicit quality signals based on user and bot interactions, allowing a search engine or recommendation engine to suggest alternative bots when a user is likely to abandon their current bot, leveraging user-behavior, bot-behavior, and transaction-summary implicit signals without explicit feedback.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If the number of available bots increases to provide broader services, then service variety is improved, but user difficulty in finding and accessing appropriate bots increases
Solution Approach 1:
The system implements feedback loops where user interactions with bots generate implicit quality signals (success rates, abandonment rates, task completion metrics). These signals are continuously fed back to the recommendation engine, which adjusts bot recommendations to improve matching accuracy. This resolves the contradiction by enabling the system to handle increased bot variety through data-driven adaptation, maintaining ease of access despite growing service variety.
Solution Approach 2:
The recommendation engine operates autonomously to match users with appropriate bots based on implicit quality signals and user behavior patterns. Instead of requiring manual curation or user effort to evaluate multiple bots, the system self-adjusts recommendations based on accumulated interaction data. This resolves the contradiction by enabling automatic adaptation to bot variety without increasing user burden for finding suitable services.
2Measurement precision
If explicit feedback mechanisms are implemented to gauge user satisfaction, then measurement accuracy is improved, but user burden and interaction complexity increase
Solution Approach 1:
The system employs implicit quality signals that are automatically generated from user interaction data without requiring explicit user feedback. Metrics such as task completion rates, session duration, and abandonment patterns are collected passively during normal bot interactions. This resolves the contradiction by enabling accurate satisfaction measurement through automated data collection, eliminating the need for additional user effort while maintaining measurement precision.
Solution Approach 2:
The patent replaces explicit feedback mechanisms (manual ratings, surveys) with implicit signal generation based on observed user behavior patterns. Machine learning models analyze interaction sequences, response times, and task outcomes to infer satisfaction levels. This substitution resolves the contradiction by achieving measurement precision through automated behavioral analysis rather than requiring direct user input, thereby maintaining interaction simplicity.
3Measurement precision
If implicit quality signals are collected and processed to generate recommendations, then recommendation accuracy is improved, but system complexity increases
Solution Approach 1:
The recommendation system is segmented into specialized components: signal generation modules that collect implicit quality data, signal processing modules that aggregate and analyze signals, and recommendation generation modules that produce bot suggestions. Each component handles specific aspects of the complex signal processing task. This segmentation resolves the contradiction by distributing system complexity across modular components, enabling accurate recommendation generation through coordinated specialized processing rather than monolithic complexity.
Data Source
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AI summary
A technique is described herein for providing implicit quality signals over a span of time that reflect quality of service provided by a collection of BOTs to a group of users. The technique can then leverage these implicit quality signals in various application-phase uses. In one use, an abandonment-determination component can leverage the implicit quality signals to provide an output result which indicates whether a current user has abandoned use of a current BOT with which he or she has been interacting, or is about to abandon use of that current BOT. In another use, a search engine or a recommendation engine can use the implicit quality signals to help identify an appropriate BOT for use by the current user. The implicit quality signals can include: one of more user-behavior implicit signals; one of more BOT-behavior implicit signals; and/or one of more transaction-summary implicit signals.