Machine Learning Model for Automatic Domain-Specific Language Parameter Updates
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Solution Overview
Problem
Existing machine learning systems face challenges in efficiently updating domain-specific language (DSL) parameters, particularly when dealing with natural language inputs, as they require complex instructions and repetitive processing, limiting flexibility and increasing computing resource usage.
Innovation Solution
A system that utilizes a machine learning model to analyze natural language inputs, such as audio or text data, to identify keywords indicative of DSL parameters. This system provides an indication of the entity and the keywords to the machine learning model, which outputs updates for the DSL parameters, allowing for automatic updating without manual programmer intervention.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If complex instructions and repetitive processing are used to update DSL parameters, then processing accuracy is improved, but device complexity and computing resource usage increase
Solution Approach 1:
The patent replaces manual programming and complex processing instructions with a machine learning model that automatically updates DSL parameters. The ML model learns from examples and autonomously determines parameter updates, eliminating the need for repetitive manual processing while maintaining accuracy.
Solution Approach 2:
The system enables self-service by allowing the machine learning model to automatically update DSL parameters without requiring manual intervention or complex processing instructions. The model serves itself by learning from input data and autonomously generating appropriate parameter updates.
2Measurement precision
If manual programming is used to update DSL parameters, then control precision is improved, but loss of time increases
Solution Approach 1:
The patent substitutes manual programming with a machine learning model that automatically processes inputs and generates parameter updates. This replacement dramatically reduces the time required for updates while maintaining the precision needed for accurate parameter control.
Solution Approach 2:
The machine learning model is pre-trained on examples of correct parameter updates, allowing it to quickly and accurately generate updates for new inputs without requiring time-consuming manual programming each time.
3Reliability
If repetitive processing is used to handle different data formats, then processing reliability is improved, but use of energy increases
Solution Approach 1:
The machine learning model serves as a universal processor that can handle multiple data formats and update scenarios through a single unified approach. Instead of requiring separate processing routines for different formats, the model learns to process all inputs through its trained parameters, improving efficiency and reducing energy consumption.
Data Source
AI summary
In some implementations, a device may obtain an input indicating information associated with one or more attributes of an entity. The device may analyze the input to identify one or more words or phrases from the input. The device may detect one or more domain specific language (DSL) keywords, from the one or more words or phrases, indicative of one or more brand parameters associated with the entity. The device may provide, to a machine learning model, an indication of the entity and the one or more words or phrases. The device may obtain, via an output of the machine learning model and based on providing the indication of the entity and the one or more words or phrases, an indication of updates to be made for the one or more brand parameters. The device may perform an action based on the output of the machine learning model.