Neural Network Time Series Encoder for Natural Language Explanations
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Solution Overview
Problem
Current data analytics software generates unreadable results for time series data, hindering wider adoption of industrial analytics, as they lack natural language explanations that human operators can understand, despite the value of multi-modal data containing time series and human-written comments.
Innovation Solution
A neural network model is trained with text-annotated time series data, comprising a time series encoder and a text generator, allowing human operators to select segments for generating readable explanatory texts, utilizing a sequence of hidden state vectors and transformer-based mechanisms for diverse interpretations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If machine learning models are used to detect and classify time series patterns, then analysis accuracy is improved, but readability and comprehensibility for non-expert operators deteriorates
Solution Approach 1:
The patent introduces a natural language generation system as an intermediary between the machine learning analysis engine and the human operator. This mediator translates complex analysis results into readable natural language comments that explain detected patterns, anomalies, and predictions in human-understandable terms, thereby resolving the contradiction between maintaining high analysis accuracy and improving readability for non-expert users
Solution Approach 2:
The system changes the output parameter format from structured technical data to natural language text. By transforming the representation of analysis results from machine-oriented formats to human-oriented linguistic expressions, the system maintains the precision of underlying analyses while significantly improving comprehensibility for operators without specialized training
2Loss of information
If multi-modal data with human-written comments is collected, then data value and domain-specific correlations are improved, but data acquisition cost and time consumption increases
Solution Approach 1:
The system enables automated generation of natural language comments from time series data using trained machine learning models, allowing the system to serve itself by generating explanatory texts without requiring manual human annotation. This self-service capability captures the value of multi-modal data while eliminating the high time and labor costs of manual comment acquisition
Solution Approach 2:
The system performs preliminary training with available text-annotated data to build predictive models that can automatically generate comments for new time series segments. By preparing the models in advance with representative training data, the system captures domain-specific correlations while avoiding the need for time-consuming manual annotation of every new data segment
3Ease of operation
If natural language generation system is implemented, then operator understanding and decision-making are improved, but system complexity increases
Solution Approach 1:
The system is segmented into distinct functional modules: a time series analysis component that detects patterns and anomalies, a natural language generation component that formulates explanations, and an integration layer that connects them. This modular segmentation manages system complexity by allowing each component to be developed, trained, and maintained independently while working together to improve operator understanding
Data Source
AI summary
A method for explaining sensor time series data in natural language is presented. The method includes training a neural network model with text-annotated time series data, the neural network model including a time series encoder and a text generator, allowing a human operator to select a time series segment from the text-annotated time series data, the time series segment processed by the time series encoder, outputting, from the time series encoder, a sequence of hidden state vectors, one for each timestep, and generating readable explanatory texts for the human operator based on the selected time series segment, the readable explanatory texts being a set of comment texts explaining and interpreting the selected time series segment in a plurality of different ways.