AI Classification via Speech-to-Text Feature Segmentation
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Solution Overview
Problem
Automated classification of text messages and speech is challenging due to their unstructured and unorganized nature, making it difficult to derive insights and render accurate classifications, especially in contexts like insurance quoting where numerous business classifications are involved.
Innovation Solution
An automatic recognition system using biometrics that translates speech or text into feature groups processed by deep-learning and machine-learning models, simulating human dialogue to learn user preferences and provide suggestions, with evolutionary models and natural language processing to refine classifications and generate accurate business classifications.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If automated classification systems are used to process text messages and speech, then productivity is improved, but measurement precision deteriorates due to the unstructured nature of the input data
Solution Approach 1:
The system segments unstructured text and speech data into structured feature groups using natural language processing. The input data is divided into manageable components (features) that can be systematically processed by machine learning models, enabling both high-speed automated classification and maintained precision through structured analysis of individual features.
Solution Approach 2:
The patent introduces an intermediary layer of feature extraction and natural language processing between the raw unstructured input and the classification engine. This intermediary transforms difficult-to-classify text and speech into structured feature groups that serve as a bridge, allowing automated systems to process data efficiently while maintaining classification accuracy through the organized representation of input features.
2Measurement precision
If deep-learning models are used to process unstructured data, then measurement precision is improved, but device complexity increases
Solution Approach 1:
The complex deep-learning system is segmented into distinct functional modules: feature extraction layer, natural language processing layer, and classification layer. This segmentation allows the system to maintain high measurement precision through sophisticated modeling while managing device complexity by organizing functions into separate, manageable components that can be independently optimized and maintained.
3Measurement precision
If iterative training with confidence scoring is implemented, then measurement precision is improved, but loss of time increases due to multiple processing passes
Solution Approach 1:
The system performs preliminary feature extraction and natural language processing before the main classification passes. By preparing structured feature groups in advance, the iterative training and confidence scoring processes work with pre-organized data, reducing the time cost of multiple processing passes while maintaining or improving measurement precision through confident, well-prepared input features.
Data Source
AI summary
A system and method determine a classification by simulating a human user. The system and method translate an input segment such as speech into an output segment such as text and represents the frequency of words and phrases in the textual segment as an input vector. The system and method process the input vector and generate a plurality of intents and a plurality of sub-entities. The processing of multiple intents and sub-entities generates a second multiple of intents and sub-entities that represent a species classification. The system and method select an instance of an evolutionary model as a result of the recognition of one or more predefined semantically relevant words and phrases detected in the input vector.