Cohort Analysis Mechanism for Question Answering Systems
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Solution Overview
Problem
Existing question answering systems, such as Watson, are unable to provide confident answers when the corpus lacks sufficient knowledge to address a question, as they are not designed to extrapolate information from non-existent data.
Innovation Solution
A cohort analysis mechanism that identifies cohorts with similar attributes to an entity in a question, extracts relevant data, combines and ranks answers from these cohorts, and uses the gathered evidence to answer the question, thereby addressing the knowledge gap.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If a traditional question answering system is used, then it can provide answers when knowledge exists in the corpus, but it cannot provide confident answers when the corpus lacks sufficient knowledge
Solution Approach 1:
The patent transitions from searching for exact entity matches to searching for similar entity cohorts, adding a dimension of similarity-based aggregation. Instead of asking 'does this exact entity exist in the corpus?', the system asks 'are there similar entities whose collective data can answer the question?', enabling answers even when specific knowledge is absent.
Solution Approach 2:
The patent merges data from multiple similar entities (coorts) to answer a single entity's question. By combining information from cohorts of similar patients, products, or entities, the system generates answers that leverage collective knowledge, transforming individual data scarcity into群体 data abundance.
2Measurement precision
If the system searches for exact matches in the corpus, then it ensures precision, but it fails to find answers when exact knowledge is unavailable
Solution Approach 1:
The patent changes the matching parameter from exact entity identity to entity similarity based on shared attributes. Instead of requiring precise entity matches, the system identifies cohorts of entities with similar characteristics, allowing flexible adaptation to unknown topics while maintaining answer reliability through attribute-based similarity measurement.
Solution Approach 2:
The patent introduces dynamic cohort identification that adapts to different query contexts. The system dynamically determines which entities form relevant cohorts based on the question's attributes, enabling versatile handling of various unknown topics while maintaining precision through context-aware similarity grouping.
3Reliability
If the system aggregates data from multiple sources, then it improves answer reliability, but it increases system complexity
Solution Approach 1:
The patent segments the complex aggregation process into distinct modules: cohort identification, data extraction, answer synthesis, and confidence scoring. This segmentation manages complexity by breaking down the overall task into manageable, independent components that can be developed and maintained separately while collectively achieving high answer reliability.
Solution Approach 2:
The patent introduces cohort-based intermediaries as a bridging layer between the query and the corpus. Instead of directly querying individual entities, the system uses cohorts as intermediate aggregation units, simplifying the architecture by providing a structured intermediary that organizes similar entities and facilitates systematic data extraction and synthesis.
Data Source
AI summary
A cohort analysis mechanism analyzes cohorts with similar attributes to extrapolate additional knowledge and answer a question in a question answering system. The cohort analysis mechanism identifies cohorts for an entity of the question and extracts relevant data concerning the cohorts. The cohort analysis mechanism aggregates the relevant information for evidence scoring and answer scoring to answer a question posed to the question answering system. The aggregating of the data includes combining and ranking answers from the cohorts, gathering evidence and then answering the question with the gathered evidence.