Beauty Domain Embedding Model for Query Accuracy
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Solution Overview
Problem
Existing systems for handling customer inquiries in the beauty and cosmetic industry are inadequate for addressing complex and beauty-specific queries, lacking the sophistication needed to provide accurate and relevant responses.
Innovation Solution
A computing platform that utilizes a machine-learning logic model to process natural language-based queries by converting them into embedding formatted queries. The model is trained using a multi-dimensional learned-embedding that includes semantically similar terms, and is specifically tailored to the beauty and cosmetic industry, allowing for accurate determination of query responses.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If general natural language processing is used to handle customer inquiries, then the system can process basic queries, but it fails to accurately handle complex beauty-specific queries
Solution Approach 1:
The patent applies local quality by creating a domain-specific embedding space tailored to the beauty industry. Instead of using general-purpose NLP models, the system constructs specialized embedding layers that capture beauty-specific terminology, product names, and contextual relationships. This allows the model to process complex beauty queries with higher accuracy while maintaining the ability to handle various types of customer inquiries.
Solution Approach 2:
The patent changes the parameters of the NLP system by using pre-trained language models (such as BERT, GPT) and fine-tuning them on beauty-specific datasets. The embedding dimensions, model architecture, and training parameters are optimized for the beauty domain, enabling the system to accurately understand and respond to complex beauty-related queries that general NLP models would mishandle.
2Measurement precision
If a specialized machine-learning model for beauty industry is trained, then complex beauty-specific queries can be handled accurately, but the system complexity increases
Solution Approach 1:
The patent applies preliminary action by pre-training language models on large corpora and then fine-tuning them on beauty-specific datasets before deployment. The embedding spaces are pre-computed and stored, allowing the system to quickly process queries without performing complex computations in real-time. This reduces the complexity of the inference process while maintaining high accuracy for beauty-specific queries.
3Measurement precision
If embedding technology with semantically similar terms is used, then query understanding improves, but the computational processing time increases
Solution Approach 1:
The patent pre-computes and stores embedding representations of beauty-related terms and phrases in advance. These pre-computed embeddings are stored in lookup tables or vector databases, allowing the system to quickly retrieve and compare semantic representations during query processing. This eliminates the need for real-time embedding computation, significantly reducing processing time while maintaining high semantic understanding accuracy.
Data Source
AI summary
An example method includes receiving by a server and from a computing device a natural language-based query, converting the natural language-based query into an embedding formatted query having a vector format, inputting the embedding formatted query into a machine-learning logic model, and determining by the machine-learning logic model a query response to the embedding formatted query that is predicted by the machine-learning logic model to be in context of beauty or cosmetic industry. The machine-learning logic model is trained using a multi-dimensional learned-embedding that includes semantically similar terms in proximity in an embedding space and the embedding space is limited to salient terms associated with the beauty or cosmetic industry. The method also includes communicating by the machine-learning logic model the query response to the computing device.