Estimation Model for Incident Prediction Using Onomatopoeia Time Series
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Solution Overview
Problem
Existing methods do not estimate the occurrence frequency and rate of incidents in society based on psychological-state/sensibility expressing words, such as onomatopoeia, which limits the prediction of future events.
Innovation Solution
A learning apparatus and estimation apparatus that utilize a storage unit to store psychological-state/sensibility expressing words and incident occurrence quantitative values, learning a model to estimate future incident occurrence quantitative values using time series data of these words, allowing for the prediction of future events in a region.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a model is used to estimate impression conveyed by onomatopoeia from phonological factors, then the impression estimation is achieved, but the occurrence frequency and rate of incidents cannot be estimated
Solution Approach 1:
The patent extends the onomatopoeia analysis model to serve multiple functions: it can estimate both the impression conveyed by onomatopoeia and the occurrence frequency/rate of incidents. The learning unit processes onomatopoeia data to generate multiple types of estimates, making the system versatile rather than limited to a single function.
Solution Approach 2:
The patent changes the output parameters of the onomatopoeia analysis system. Instead of only outputting impression estimates, the system now outputs both impression estimates and incident occurrence quantitative values (frequency and rate). The learning unit transforms the same input data to produce different types of predictive outputs by adjusting the estimation parameters.
2Device complexity
If only phonological factors are used for onomatopoeia analysis, then the analysis is simple, but incident occurrence quantitative values cannot be predicted
Solution Approach 1:
The patent performs preliminary learning to build a model that connects onomatopoeia characteristics to incident occurrence patterns. The learning unit pre-processes and analyzes the relationship between onomatopoeia data and incident statistics, storing this knowledge for future predictions. This preliminary action enables reliable incident prediction without adding complexity to the real-time analysis process.
Data Source
AI summary
An estimation apparatus includes an estimation unit that estimates a future incident occurrence quantitative value in a region on the basis of at least two or more inputted psychological-state/sensibility expressing words emitted in a predetermined region and the input order of the two or more psychological-state/sensibility expressing words, using an estimation model for estimating an incident occurrence quantitative value that is a quantitative value of an occurrence of a predetermined event in the region after a certain time, with an input being at least a time series of two or more psychological-state/sensibility expressing words emitted in the predetermined region before the certain time.