Industrial Language Model for Contextual Natural Language Recommendations
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Solution Overview
Problem
Users interacting with domain-specific applications often lack the necessary domain-specific natural language knowledge, leading to contextually irrelevant outputs and inefficient knowledge transfer, which can result in errors and increased computing resources required for processing.
Innovation Solution
An industrial language model is employed to receive natural language inputs, process them, and generate domain-specific natural language outputs, including auto-suggestions and corrections, using a predictive model trained through machine learning to ensure contextual relevance and accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If users provide inputs without domain-specific natural language knowledge, then the application can process the inputs, but the outputs are not contextually relevant and knowledge transfer is reduced
Solution Approach 1:
The patent introduces an industrial language model as an intermediary component between the user input and the application processing. This model translates user inputs into domain-specific natural language representations, enabling contextually relevant outputs without requiring users to possess domain expertise. The model acts as a mediator that bridges the gap between general user language and specialized domain language.
Solution Approach 2:
The system performs preliminary action by pre-training the industrial language model on domain-specific corpora before actual application use. This preliminary training enables the model to understand and generate domain-specific natural language, so that when users provide inputs, the contextual relevance is already established through the pre-trained model's knowledge base.
2Productivity
If the application processes inputs without domain-specific language understanding, then processing can occur, but communication quality and task completion are reduced
Solution Approach 1:
The patent changes the parameter of language representation from general natural language to domain-specific natural language through the industrial language model. This parameter change enables the system to process inputs with proper domain context, improving task completion efficiency while preserving domain-specific knowledge that would otherwise be lost in generic processing.
3Reliability
If machine learning is used to train an industrial language model, then domain-specific natural language generation is improved, but computing resources and processing time are increased
Solution Approach 1:
The patent applies preliminary action by performing the computationally intensive machine learning training process in advance, before the industrial language model is deployed for actual use. This allows the model to learn domain-specific language patterns during an initial training phase, after which it can generate accurate domain-specific natural language without requiring additional training time during operational use.
Data Source
AI summary
Systems, methods, and a computer readable medium are provided for generating natural language recommendations based on an industrial language model. Input data, including a plurality of natural language input units is received from a first computing device and transmitted to a second computing device. The second computing device can determine an output including a plurality of natural language outputs using a predictive model trained to generate the output corresponding to an energy exploration lexicon. The second computing device can provide the output to the first computing device for display.