Natural Language Event Creation via ML Template Selection
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Solution Overview
Problem
Conventional methods for creating computer-based events, such as sourcing products in a supply chain, require manual data entry, which is time-consuming and prone to errors, necessitating a more efficient and accurate process.
Innovation Solution
The integration of natural language processing and machine learning to parse user-generated input, select an appropriate template, and partially populate it, allowing for the automation of the event creation process through various communication mediums like voice, email, or chatbots, using models like extreme gradient boosting.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual data entry is used to create sourcing events, then users can accurately input information, but the process becomes time-consuming and lengthy
Solution Approach 1:
The system performs preliminary actions by automatically populating form fields with relevant data before the user submits the sourcing event. Machine learning models pre-process the input text to extract and fill in commonly requested information, so the user only needs to review and confirm rather than manually enter every field.
Solution Approach 2:
The system enables self-service by allowing the sourcing event creation process to automatically extract and populate data from the user's natural language input without requiring manual field-by-field entry. The machine learning models autonomously identify and fill in relevant information, reducing human intervention to verification only.
2Ease of operation
If manual data entry is used to populate form fields, then users have control over information accuracy, but the process is prone to human error
Solution Approach 1:
The system implements feedback by presenting the auto-populated fields to the user for review and confirmation. The user can see what data has been automatically extracted and make corrections if needed, combining machine accuracy with human oversight to ensure both reliability and user control.
3Productivity
If fixed data entry fields are used, then information can be systematically collected, but the process becomes rigid and requires template selection
Solution Approach 1:
The system achieves universality by using a single natural language input interface that can handle multiple types of sourcing events without requiring users to select different templates. The machine learning models automatically adapt to the specific event type based on the input content, making the system multi-functional through a unified interface.
Solution Approach 2:
The system applies inversion by reversing the conventional approach: instead of having users select a template first and then fill in fields, the system allows users to provide natural language input first, and the machine learning models automatically determine the appropriate template and populate the corresponding fields.
4Manufacturing precision
If traditional form-based interfaces are used, then data can be structured and validated, but the user experience becomes cumbersome across different devices
Solution Approach 1:
The system substitutes the mechanical interaction of filling out structured forms with natural language processing. Users communicate through conversational text or speech that the machine learning models convert into structured data, maintaining data quality while dramatically improving ease of operation across smartphones, tablets, and other devices.
Data Source
AI summary
User-generated input is received that includes a sequence of words associated with initiation of a computer-implemented event. Thereafter, such input is parsed using at least one natural language processing (NLP) model. This parsed input is then used by a machine learning model to determine a suggested template having a plurality of fields for initiating the event. The template can then be presented in a graphical user interface. Related apparatus, systems, techniques and articles are also described.