Self-Correcting Bot for Aberrant Output Detection
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Solution Overview
Problem
Automated bots often malfunction by misinterpreting user inputs, leading to irrelevant or aberrant outputs, making it challenging to determine the cause of the malfunction and rectify the issue.
Innovation Solution
An artificial intelligence method that autonomously diagnoses and remediates bot malfunctions by monitoring outputs, detecting aberrant patterns, adjusting processing parameters, and using machine learning algorithms to simulate inputs and analyze responses, thereby recalibrating the bot to improve its performance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If automated bots are deployed to handle user interactions, then productivity and service availability are improved, but malfunctions occur leading to aberrant outputs and loss of reliability
Solution Approach 1:
The patent implements a feedback mechanism where the bot's outputs are continuously monitored and evaluated against expected patterns. When aberrant outputs are detected, the system generates corrective inputs fed back to the bot to adjust its behavior. This closed-loop feedback system resolves the contradiction by maintaining reliability through continuous monitoring and self-correction while preserving productivity through automated operation.
Solution Approach 2:
The bot performs self-diagnosis and self-correction by autonomously detecting its own malfunctions and generating corrective inputs without human intervention. This self-service capability resolves the contradiction by enabling the bot to maintain its own reliability, ensuring accurate outputs while continuing to operate productively without requiring external maintenance.
2Ease of operation
If the bot autonomously processes user inputs, then ease of operation is improved, but difficulty in detecting and measuring malfunctions increases
Solution Approach 1:
The patent introduces an intermediary monitoring system that sits between the autonomous bot and user interactions. This intermediary continuously observes bot outputs, compares them against expected patterns, and detects malfunctions. By adding this intermediary layer, the system maintains ease of autonomous operation while solving the detection problem through automated surveillance and anomaly identification.
Solution Approach 2:
The monitoring system provides continuous feedback about bot performance by comparing actual outputs against expected patterns. This feedback mechanism makes malfunctions detectable and measurable by translating complex autonomous behavior into observable performance metrics, resolving the contradiction between autonomous operation and malfunction detectability.
3Adaptability or versatility
If the bot is designed to handle diverse user inputs, then adaptability is improved, but manufacturing precision of responses deteriorates
Solution Approach 1:
The patent dynamically changes the bot's processing parameters based on the type of user input received. The system adjusts its interpretation and response strategies according to input characteristics while maintaining output quality through continuous monitoring. This parameter adaptation resolves the contradiction by allowing flexible handling of diverse inputs while preserving manufacturing precision through context-aware parameter adjustment and output validation.
Data Source
AI summary
Bots are typically programmed to automate tasks and provide statistically expected results. However, a bot may malfunction and generate aberrant outputs. It is technically challenging to detect the aberrant outputs and determine whether the outputs are due to an error in how the bot processes inputs or because the bot has received unusual or unexpected input data. Apparatus and methods are provided for auto-determining why a bot has generated output outside expected results. An auto-correct bot will detect problems and auto-identify potential solutions, simulate those solutions and apply those solutions to remediate the malfunctioning bot.