ML Document Assistance via Co-occurrence Prediction
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Solution Overview
Problem
The process of creating electronic documents, such as invoices, is often time-consuming and inefficient due to repetitive tasks, especially for businesses that frequently generate similar documents.
Innovation Solution
A dynamic, iterative machine learning process is employed to predict and recommend items for inclusion in documents based on historical data, using a machine learning model that considers document creation time, customer information, and item co-occurrence probabilities, thereby reducing manual input and improving accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If conventional techniques are used for creating electronic documents, then users can manually enter information, but the process becomes time-consuming and inefficient
Solution Approach 1:
The system performs preliminary actions by automatically populating document fields with relevant information before the user completes the document. The machine learning model predicts and suggests items, customer details, and line items in advance, allowing users to review and approve rather than manually enter each piece of information from scratch.
Solution Approach 2:
The system enables self-service by allowing the machine learning model to autonomously gather information from multiple sources (emails, calendars, CRM systems), predict appropriate document contents, and generate draft documents without requiring continuous manual intervention. The system serves itself by automatically learning from historical data and improving its predictions over time.
2Reliability
If manual data entry is required for each document, then users have control over accuracy, but repetitive tasks increase effort and potential for errors
Solution Approach 1:
The system implements feedback mechanisms where user corrections and approvals of predicted items are fed back into the machine learning model. This allows the system to learn from user preferences and correction patterns, continuously improving the accuracy of its predictions while reducing the manual effort required. The feedback loop ensures that the system adapts to specific user needs and organizational patterns.
3Productivity
If a machine learning model is used to predict items automatically, then document creation time is reduced, but the system complexity increases
Solution Approach 1:
The system segments the document creation process into distinct components: information gathering from multiple sources, machine learning prediction, item co-occurrence analysis, and user approval. Each component handles a specific aspect of the task, making the overall complex system manageable through modular design. The segmentation allows each part to be optimized independently while working together to reduce overall document creation time.
4Measurement precision
If historical data is analyzed to predict item co-occurrence, then recommendation accuracy is improved, but data processing requirements increase
Solution Approach 1:
The system applies partial action by focusing computational resources on analyzing only the most relevant historical data and item co-occurrences rather than processing all possible data. The machine learning model prioritizes analyzing patterns that are most likely to influence current document predictions, performing sufficient analysis to achieve high accuracy without the excessive computational overhead of exhaustive data processing.
Data Source
AI summary
Aspects of the present disclosure relate to electronic document creation assistance. Embodiments include determining a current time related to creation of a document by a user and providing inputs to a machine learning model based on the current time. Embodiments include receiving output from the machine learning model based on the inputs and selecting, based on the output, a first recommended item from a plurality of items for inclusion in the document. Embodiments include determining a likelihood of each additional item of the plurality of items co-occurring with the first recommended item based on historical item co-occurrence data. Embodiments include selecting, based on the output and the likelihood of each additional item of the plurality of items co-occurring with the first recommended item, a second recommended item for inclusion in the document and providing, via a user interface, the first recommended item and the second recommended item to the user.