Probability Contextualization via Similar Event Comparison
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Solution Overview
Problem
Current systems for calculating probabilities fail to provide intuitive explanations, making it difficult for users to understand and contextualize probability values, leading to misconceptions when results are not properly contextualized.
Innovation Solution
A computer-implemented method and system that parses natural language queries, determines relevant probability events, and fetches similar or well-known probability values to display them in a contextualized manner, using a contextualization program that includes a natural language query parser and probabilities fetcher, enabling users to better understand probability values through comparisons.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If probability values are calculated using current systems, then numerical accuracy is achieved, but user understanding and contextualization are insufficient
Solution Approach 1:
The patent introduces an intermediary contextualization layer that translates raw probability values into meaningful comparisons. The system fetches similar real-world events with known probabilities and uses them as mediators to bridge the gap between abstract numerical probabilities and user comprehension. This intermediary process adds contextual information without altering the underlying probability calculations.
2Productivity
If probability results are presented without contextualization, then calculation speed is maintained, but misconceptions arise
Solution Approach 1:
The system performs preliminary actions by pre-fetching and storing probabilities of similar real-world events before they are needed for contextualization. When a user queries a probability, the system has already prepared comparable events that can be quickly retrieved and presented, maintaining fast response times while ensuring accurate contextual interpretation.
3Ease of operation
If contextualization is added to probability results, then user comprehension improves, but system complexity increases
Solution Approach 1:
The patent implements a universal contextualization framework that can handle multiple types of probability queries through a single multi-functional system. The contextualization engine serves multiple purposes: fetching similar events, retrieving their probabilities, and presenting comparisons. This universal approach avoids the need for separate specialized systems for different probability contexts, managing complexity through consolidation.
Data Source
AI summary
In an approach to explaining probabilistic answers through contextualization, one or more computer processors receive a query associated with a probability value of a first event from a user. One or more computer processors parse the query into one or more constituent parts. Based on the one or more constituent parts, one or more computer processors determine the first event. One or more computer processors query a probability value of the first event, where the second event is similar to the first event. One or more computer processors determine the probability value of the first event and the probability value of the second event are known. One or more computer processors fetch the probability value of the first event and the probability value of the second event. One or more computer processors display the probability value of the first event and the probability value of the second event.