Guided Feedback Loop for High-Confidence Information Categorization
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Solution Overview
Problem
Existing electronic transaction record categorization methods are time-consuming, inefficient, and prone to errors due to limited information availability and low accuracy in automated categorization techniques.
Innovation Solution
A guided feedback loop combining a machine learning model with a decision tree is used to dynamically select and request relevant information from users, ensuring high confidence scores before categorizing transactions, thereby improving accuracy and efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If automated categorization techniques are used, then productivity is improved, but manufacturing precision deteriorates due to low accuracy and errors
Solution Approach 1:
The patent implements a feedback mechanism where the system evaluates its own categorization confidence scores and dynamically adjusts its behavior. When confidence is low, it requests additional information from users, and this user feedback is used to re-evaluate and improve categorization accuracy. The system continuously learns from user corrections and feedback to enhance precision while maintaining automated processing.
Solution Approach 2:
The system dynamically adapts its categorization process based on real-time confidence assessments. It transitions between fully automated mode (high confidence) and user interaction mode (low confidence), adjusting the level of automation dynamically. This allows the system to maintain high productivity for clear cases while ensuring accuracy for ambiguous cases through selective user engagement.
2Manufacturing precision
If manual categorization is used, then manufacturing precision is improved, but productivity deteriorates due to time consumption
Solution Approach 1:
Instead of requiring complete manual review of all transactions, the system applies partial automation where only transactions with low confidence scores require user attention. The majority of clear, unambiguous transactions are categorized automatically without user intervention, achieving high productivity while maintaining accuracy for the subset of cases that need human review.
3Manufacturing precision
If more user input is requested, then manufacturing precision is improved, but device complexity increases
Solution Approach 1:
The system segments the user input requirements based on transaction complexity. Simple, clear transactions require no user input, while only ambiguous or complex transactions trigger targeted information requests. This segmentation allows the system to gather necessary information for improving accuracy without unnecessarily complicating the interaction for straightforward cases.
4Productivity
If automated processing is used, then productivity is improved, but loss of information increases due to limited information availability
Solution Approach 1:
The system performs preliminary automated categorization using available information to establish an initial classification. This preliminary action enables rapid processing of transactions with sufficient information, while simultaneously identifying cases where additional information is needed. The system then selectively requests补充 information only for cases where the preliminary categorization confidence is low, maintaining productivity while reducing information loss.
Data Source
AI summary
Aspects of the present disclosure provide techniques for automated categorization of electronic information. Embodiments include providing inputs to a machine learning model based on attributes of an electronic data item. Embodiments include receiving one or more first outputs from the machine learning model based on the inputs. Embodiments include selecting, based on the one or more first outputs, a question from a plurality of questions. Embodiments include providing the question for display via a user interface. Embodiments include receiving an answer to the question via the user interface. Embodiments include providing updated inputs to the machine learning model based on the answer. Embodiments include receiving one or more second outputs from the machine learning model based on the updated inputs. Embodiments include determining a category for the electronic data item based on the one or more second outputs.