Bot Error Context Aggregation for Privacy-Aware Issue Resolution
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Solution Overview
Problem
Existing bot systems fail to effectively identify and correct errors due to a lack of real-time error observation and contextual data, leading to inefficiencies and frustration in user interactions, as critical error contexts are often not collected or shared without user consent.
Innovation Solution
Implementing insight collectors and bot monitors to gather and aggregate contextual data, including user consent mechanisms, to provide error notifications with detailed insights for support teams, enabling improved error resolution and model refinement.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If contextual information is collected for error analysis, then error identification capability is improved, but user privacy protection is worsened
Solution Approach 1:
The patent extracts only the necessary contextual information required for error analysis while leaving out sensitive personal data. The system collects error context such as bot behavior, interaction flow, and error type, but deliberately excludes or anonymizes personally identifiable information, thus achieving error identification without compromising user privacy
Solution Approach 2:
The patent introduces an intermediary processing layer that acts as a mediator between error collection and analysis. This intermediary component filters, aggregates, and anonymizes contextual data before making it available for error analysis, thereby enabling precise error identification while protecting user privacy through the intermediary filtering mechanism
2Loss of time
If real-time error data is collected and shared, then error resolution speed is improved, but data security is worsened
Solution Approach 1:
The patent applies preliminary action by pre-anonymizing and securing error context data before it is collected or shared. Data protection measures such as masking sensitive information and establishing security protocols are implemented in advance, enabling real-time error data sharing without compromising data security
Solution Approach 2:
The patent implements feedback mechanisms that allow error data to be shared and analyzed in real-time while continuously monitoring and enforcing security protocols. The system provides feedback loops that ensure data security constraints are maintained throughout the error resolution process, enabling fast resolution without security breaches
3Productivity
If comprehensive error context is aggregated, then model training effectiveness is improved, but system complexity is worsened
Solution Approach 1:
The patent segments the error context aggregation process into distinct modular components: error detection module, context collection module, data processing module, and model training module. Each segment handles specific tasks independently, reducing overall system complexity while maintaining comprehensive error context aggregation for effective model training
Solution Approach 2:
The patent applies parameter changes by transforming raw error context data into standardized formats and features suitable for model training. Through parameter transformation, aggregation, and normalization processes, the system handles comprehensive error context efficiently, improving model training effectiveness while managing system complexity through standardized data parameters
Data Source
AI summary
In one embodiment, an illustrative method herein may comprise: obtaining, by a device, a plurality of indications of errors experienced by a bot performing tasks, wherein each of the plurality of indications includes contextual information of a corresponding error; determining, by the device, correlated errors among the errors experienced by the bot; aggregating, by the device, contextual information of each of the correlated errors into aggregated contextual data; and providing, by the device, the aggregated contextual data with an error notification for a particular correlated error.