Microsoft technology licensing, llc (20240265250). SYSTEM AND METHOD FOR SPATIAL SALIENCY EXPLANATION FOR TIME SERIES MODELS simplified abstract

SYSTEM AND METHOD FOR SPATIAL SALIENCY EXPLANATION FOR TIME SERIES MODELS

Organization Name

microsoft technology licensing, llc

Inventor(s)

Pranay Kumar Lohia of Bengaluru (IN)

Badri Narayana Patro of Bengaluru (IN)

Naveen Panwar of Bengaluru (IN)

SYSTEM AND METHOD FOR SPATIAL SALIENCY EXPLANATION FOR TIME SERIES MODELS - A simplified explanation of the abstract

This abstract first appeared for US patent application 20240265250 titled 'SYSTEM AND METHOD FOR SPATIAL SALIENCY EXPLANATION FOR TIME SERIES MODELS

Simplified Explanation: The patent application discusses techniques for explaining spatial saliency in time series machine learning models using token-based importance methods.

• Identifying important tokens for machine learning inferences.
• Generating frequency distribution and quantile information based on token importance.
• Calculating spatial saliency information using frequency distribution and quantile data.
• Presenting spatial saliency information through a graphical user interface.

Key Features and Innovation: - Token-based importance methods for machine learning inferences. - Frequency distribution and quantile information generation. - Spatial saliency calculation for time series data. - User-friendly presentation of spatial saliency information.

Potential Applications: - Enhancing interpretability of machine learning models. - Improving decision-making processes in various industries. - Optimizing data visualization techniques for complex datasets.

Problems Solved: - Lack of transparency in machine learning models. - Difficulty in understanding the importance of features in time series data. - Limited interpretability of spatial saliency inferences.

Benefits: - Increased trust in machine learning predictions. - Enhanced insights into time series data patterns. - Improved user experience through graphical representations.

Commercial Applications: Potential commercial applications include: - Financial forecasting models. - Healthcare data analysis tools. - Environmental monitoring systems.

Prior Art: Readers can explore prior research on spatial saliency in machine learning models and time series data analysis to understand the existing knowledge in this field.

Frequently Updated Research: Stay updated on advancements in spatial saliency explanation techniques for machine learning models to leverage the latest innovations in the field.

Questions about Spatial Saliency Explanation in Time Series Machine Learning Models: 1. How does spatial saliency information improve the interpretability of machine learning models? 2. What are the key challenges in implementing token-based importance methods for time series data analysis?

Original Abstract Submitted

example aspects include techniques for spatial saliency explanation for time series machine learning models. these techniques may include identifying, based on a token-based importance method, a plurality of tokens of a predefined importance to a machine learning (ml) inference. in addition, the techniques may generating frequency distribution information based on the plurality of tokens of the predefined importance, and generating, based on the frequency distribution information, quantile information for the plurality of tokens of a predefined importance. further, the techniques may include calculating spatial saliency information based on the frequency distribution information and quantile information, the spatial saliency information including a spatial saliency value for a quantile of the quantile information, and presenting the spatial saliency information via a graphical user interface.